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Training TF1 Detection Model SSDLite Mobilenet V3
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training-tf1-detection-model-ssdlite-mobilenet-v3.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Training TF1 Detection Model SSDLite Mobilenet V3",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"machine_shape": "hm",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/NobuoTsukamoto/ba7028bd4e77c1431f4ff560ad61e8d7/training-tf1-detection-model-ssdlite-mobilenet-v3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SEv4Nu2G8OFa"
},
"source": [
"# Training with TensorFlow 1 Detection Model\n",
"\n",
"This notebook provides steps for fine-tuning with the TensorFlow 1 Object detection API. Since the release of the TensorFlow 2 Object detection API, training with the TF 1 Object detection API has been unclear. Also, fine-tuning is not working with the latest commits([8454ded](https://github.com/tensorflow/models/commit/8454dedc4c0dc5eeea0f67c443fb9da32fcb7733)) as of 2021.05.02. \n",
"Work around the bug and use the Oxford-IIIT Pet Dataset to fine-tune the SSDLite MobileNet V3 Large. Finally, export the model and TF-Lite model.\n",
"\n",
"Reference:\n",
"- [Object Detection API with TensorFlow 1](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1.md)\n",
"- [Training and Evaluation with TensorFlow 1](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_training_and_evaluation.md)\n",
"- [Running on mobile with TensorFlow Lite](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_mobile_tensorflowlite.md)\n",
"- [cant load pre-train models #8695](https://github.com/tensorflow/models/issues/8695)\n",
"- [Transfer Learning with the MobileDet checkpoints #9641](https://github.com/tensorflow/models/issues/9641)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9TBgUmdwF8zw"
},
"source": [
"MIT License\n",
"\n",
"Copyright (c) 2019 Nobuo Tsukamoto\n",
"\n",
"Permission is hereby granted, free of charge, to any person obtaining a copy\n",
"of this software and associated documentation files (the \"Software\"), to deal\n",
"in the Software without restriction, including without limitation the rights\n",
"to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
"copies of the Software, and to permit persons to whom the Software is\n",
"furnished to do so, subject to the following conditions:\n",
"\n",
"The above copyright notice and this permission notice shall be included in all\n",
"copies or substantial portions of the Software.\n",
"\n",
"THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
"IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
"FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
"AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
"LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
"OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
"SOFTWARE."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "t67NpfjyBTnF"
},
"source": [
"# Install TensorFlow 1.x"
]
},
{
"cell_type": "code",
"metadata": {
"id": "45JZXAHmjdnf",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f415c41a-9a19-4079-a41d-522fc3f77600"
},
"source": [
"%tensorflow_version 1.x"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"TensorFlow 1.x selected.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6QB2TaR4AKve"
},
"source": [
"# Check TensorFlow and NVIDIA CUDA version."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"id": "7zCUA1hiPlWV",
"outputId": "121c0ad7-a568-47b3-d843-72d0d2bfc58d"
},
"source": [
"import tensorflow as tf\n",
"tf.__version__"
],
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'1.15.2'"
]
},
"metadata": {
"tags": []
},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "CnhOFVQTzoWD",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "62dfd8c8-3a77-4e6a-9208-c09a8f2b594d"
},
"source": [
"!nvidia-smi"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Mon May 3 22:43:22 2021 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 465.19.01 Driver Version: 460.32.03 CUDA Version: 11.2 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla P100-PCIE... Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 37C P0 27W / 250W | 0MiB / 16280MiB | 0% Default |\n",
"| | | N/A |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=============================================================================|\n",
"| No running processes found |\n",
"+-----------------------------------------------------------------------------+\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "y_owSZjMHN4-"
},
"source": [
"# Install TensorFlow 1 Object detection API\n",
"\n",
"Note:\n",
"- It is not included in [Python Package Installation](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1.md) procedure, but you need to install the tensorflow-addons module.\n",
"- Modify model_lib.py to avoid a bug where fine_tune_checkpoint is not enabled. \n",
"See the comment on bug [#8695](https://github.com/tensorflow/models/issues/8695#issuecomment-775456367) for more details.\n",
"- If you specify `--use-feature=2020-resolver` during installation, you will get an error."
]
},
{
"cell_type": "code",
"metadata": {
"id": "KIue85E-uRe8",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8d583b79-a38e-48b2-b55b-6c118616769c"
},
"source": [
"%%bash\n",
"\n",
"# Install tensorflow-addons.\n",
"# Note: tensorflow-addons have dependencies, \n",
"# but they are not installed by object_detection/packages/tf1/setup.py.\n",
"pip install tensorflow-addons\n",
"\n",
"# Clone tensorflow/models repository.\n",
"git clone https://github.com/tensorflow/models.git\n",
"cd models\n",
"git show"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting tensorflow-addons\n",
" Downloading https://files.pythonhosted.org/packages/74/e3/56d2fe76f0bb7c88ed9b2a6a557e25e83e252aec08f13de34369cd850a0b/tensorflow_addons-0.12.1-cp37-cp37m-manylinux2010_x86_64.whl (703kB)\n",
"Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow-addons) (2.7.1)\n",
"Installing collected packages: tensorflow-addons\n",
"Successfully installed tensorflow-addons-0.12.1\n",
"commit d7a784e6a85528292abf960be3a7cc643fa2b02c\n",
"Author: Yu-hui Chen <[email protected]>\n",
"Date: Mon May 3 14:54:11 2021 -0700\n",
"\n",
" Fixed the bug in the model builder of not setting the default values properly\n",
" for the object center head parameters in CenterNet.\n",
" \n",
" PiperOrigin-RevId: 371783912\n",
"\n",
"diff --git a/research/object_detection/builders/model_builder.py b/research/object_detection/builders/model_builder.py\n",
"index 949618f8..08c65e07 100644\n",
"--- a/research/object_detection/builders/model_builder.py\n",
"+++ b/research/object_detection/builders/model_builder.py\n",
"@@ -946,7 +946,7 @@ def object_center_proto_to_params(oc_config):\n",
" if oc_config.keypoint_weights_for_center:\n",
" keypoint_weights_for_center = list(oc_config.keypoint_weights_for_center)\n",
" \n",
"- if oc_config.center_head_params:\n",
"+ if oc_config.HasField('center_head_params'):\n",
" center_head_num_filters = list(oc_config.center_head_params.num_filters)\n",
" center_head_kernel_sizes = list(oc_config.center_head_params.kernel_sizes)\n",
" else:\n",
"diff --git a/research/object_detection/builders/model_builder_tf2_test.py b/research/object_detection/builders/model_builder_tf2_test.py\n",
"index 4bcc6dd6..9d386fdd 100644\n",
"--- a/research/object_detection/builders/model_builder_tf2_test.py\n",
"+++ b/research/object_detection/builders/model_builder_tf2_test.py\n",
"@@ -18,6 +18,7 @@\n",
" import os\n",
" import unittest\n",
" \n",
"+from absl.testing import parameterized\n",
" import tensorflow.compat.v1 as tf\n",
" \n",
" from google.protobuf import text_format\n",
"@@ -32,7 +33,8 @@ from object_detection.utils import tf_version\n",
" \n",
" \n",
" @unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')\n",
"-class ModelBuilderTF2Test(model_builder_test.ModelBuilderTest):\n",
"+class ModelBuilderTF2Test(\n",
"+ model_builder_test.ModelBuilderTest, parameterized.TestCase):\n",
" \n",
" def default_ssd_feature_extractor(self):\n",
" return 'ssd_resnet50_v1_fpn_keras'\n",
"@@ -79,7 +81,7 @@ class ModelBuilderTF2Test(model_builder_test.ModelBuilderTest):\n",
" f.write(keypoint_spec_text)\n",
" return keypoint_label_map_path\n",
" \n",
"- def get_fake_keypoint_proto(self):\n",
"+ def get_fake_keypoint_proto(self, customize_head_params=False):\n",
" task_proto_txt = \"\"\"\n",
" task_name: \"human_pose\"\n",
" task_loss_weight: 0.9\n",
"@@ -120,18 +122,27 @@ class ModelBuilderTF2Test(model_builder_test.ModelBuilderTest):\n",
" predict_depth: true\n",
" per_keypoint_depth: true\n",
" keypoint_depth_loss_weight: 0.3\n",
"+ \"\"\"\n",
"+ if customize_head_params:\n",
"+ task_proto_txt += \"\"\"\n",
" heatmap_head_params {\n",
" num_filters: 64\n",
" num_filters: 32\n",
" kernel_sizes: 5\n",
" kernel_sizes: 3\n",
" }\n",
"- \"\"\"\n",
"+ offset_head_params {\n",
"+ num_filters: 128\n",
"+ num_filters: 64\n",
"+ kernel_sizes: 5\n",
"+ kernel_sizes: 3\n",
"+ }\n",
"+ \"\"\"\n",
" config = text_format.Merge(task_proto_txt,\n",
" center_net_pb2.CenterNet.KeypointEstimation())\n",
" return config\n",
" \n",
"- def get_fake_object_center_proto(self):\n",
"+ def get_fake_object_center_proto(self, customize_head_params=False):\n",
" proto_txt = \"\"\"\n",
" object_center_loss_weight: 0.5\n",
" heatmap_bias_init: 3.14\n",
"@@ -143,13 +154,16 @@ class ModelBuilderTF2Test(model_builder_test.ModelBuilderTest):\n",
" beta: 4.0\n",
" }\n",
" }\n",
"+ \"\"\"\n",
"+ if customize_head_params:\n",
"+ proto_txt += \"\"\"\n",
" center_head_params {\n",
" num_filters: 64\n",
" num_filters: 32\n",
" kernel_sizes: 5\n",
" kernel_sizes: 3\n",
" }\n",
"- \"\"\"\n",
"+ \"\"\"\n",
" return text_format.Merge(proto_txt,\n",
" center_net_pb2.CenterNet.ObjectCenterParams())\n",
" \n",
"@@ -222,7 +236,11 @@ class ModelBuilderTF2Test(model_builder_test.ModelBuilderTest):\n",
" return text_format.Merge(proto_txt,\n",
" center_net_pb2.CenterNet.DensePoseEstimation())\n",
" \n",
"- def test_create_center_net_model(self):\n",
"+ @parameterized.parameters(\n",
"+ {'customize_head_params': True},\n",
"+ {'customize_head_params': False}\n",
"+ )\n",
"+ def test_create_center_net_model(self, customize_head_params):\n",
" \"\"\"Test building a CenterNet model from proto txt.\"\"\"\n",
" proto_txt = \"\"\"\n",
" center_net {\n",
"@@ -244,11 +262,13 @@ class ModelBuilderTF2Test(model_builder_test.ModelBuilderTest):\n",
" # Set up the configuration proto.\n",
" config = text_format.Merge(proto_txt, model_pb2.DetectionModel())\n",
" config.center_net.object_center_params.CopyFrom(\n",
"- self.get_fake_object_center_proto())\n",
"+ self.get_fake_object_center_proto(\n",
"+ customize_head_params=customize_head_params))\n",
" config.center_net.object_detection_task.CopyFrom(\n",
" self.get_fake_object_detection_proto())\n",
" config.center_net.keypoint_estimation_task.append(\n",
"- self.get_fake_keypoint_proto())\n",
"+ self.get_fake_keypoint_proto(\n",
"+ customize_head_params=customize_head_params))\n",
" config.center_net.keypoint_label_map_path = (\n",
" self.get_fake_label_map_file_path())\n",
" config.center_net.mask_estimation_task.CopyFrom(\n",
"@@ -269,8 +289,12 @@ class ModelBuilderTF2Test(model_builder_test.ModelBuilderTest):\n",
" self.assertAlmostEqual(\n",
" model._center_params.heatmap_bias_init, 3.14, places=4)\n",
" self.assertEqual(model._center_params.max_box_predictions, 15)\n",
"- self.assertEqual(model._center_params.center_head_num_filters, [64, 32])\n",
"- self.assertEqual(model._center_params.center_head_kernel_sizes, [5, 3])\n",
"+ if customize_head_params:\n",
"+ self.assertEqual(model._center_params.center_head_num_filters, [64, 32])\n",
"+ self.assertEqual(model._center_params.center_head_kernel_sizes, [5, 3])\n",
"+ else:\n",
"+ self.assertEqual(model._center_params.center_head_num_filters, [256])\n",
"+ self.assertEqual(model._center_params.center_head_kernel_sizes, [3])\n",
" \n",
" # Check object detection related parameters.\n",
" self.assertAlmostEqual(model._od_params.offset_loss_weight, 0.1)\n",
"@@ -305,12 +329,18 @@ class ModelBuilderTF2Test(model_builder_test.ModelBuilderTest):\n",
" self.assertEqual(kp_params.predict_depth, True)\n",
" self.assertEqual(kp_params.per_keypoint_depth, True)\n",
" self.assertAlmostEqual(kp_params.keypoint_depth_loss_weight, 0.3)\n",
"- # Set by the config.\n",
"- self.assertEqual(kp_params.heatmap_head_num_filters, [64, 32])\n",
"- self.assertEqual(kp_params.heatmap_head_kernel_sizes, [5, 3])\n",
"- # Default values:\n",
"- self.assertEqual(kp_params.offset_head_num_filters, [256])\n",
"- self.assertEqual(kp_params.offset_head_kernel_sizes, [3])\n",
"+ if customize_head_params:\n",
"+ # Set by the config.\n",
"+ self.assertEqual(kp_params.heatmap_head_num_filters, [64, 32])\n",
"+ self.assertEqual(kp_params.heatmap_head_kernel_sizes, [5, 3])\n",
"+ self.assertEqual(kp_params.offset_head_num_filters, [128, 64])\n",
"+ self.assertEqual(kp_params.offset_head_kernel_sizes, [5, 3])\n",
"+ else:\n",
"+ # Default values:\n",
"+ self.assertEqual(kp_params.heatmap_head_num_filters, [256])\n",
"+ self.assertEqual(kp_params.heatmap_head_kernel_sizes, [3])\n",
"+ self.assertEqual(kp_params.offset_head_num_filters, [256])\n",
"+ self.assertEqual(kp_params.offset_head_kernel_sizes, [3])\n",
" \n",
" # Check mask related parameters.\n",
" self.assertAlmostEqual(model._mask_params.task_loss_weight, 0.7)\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"Cloning into 'models'...\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "x8kQDJH55inl"
},
"source": [
"%%bash\n",
"\n",
"cat <<EOF > /content/model_lib.patch\n",
"diff --git a/research/object_detection/model_lib.py b/research/object_detection/model_lib.py\n",
"index 111be9cb..bd8ab197 100644\n",
"--- a/research/object_detection/model_lib.py\n",
"+++ b/research/object_detection/model_lib.py\n",
"@@ -497,7 +497,7 @@ def create_model_fn(detection_model_fn, configs, hparams=None, use_tpu=False,\n",
" features[fields.InputDataFields.true_image_shape]))\n",
" \n",
" if mode == tf.estimator.ModeKeys.TRAIN:\n",
"- load_pretrained = hparams.load_pretrained if hparams else False\n",
"+ load_pretrained = hparams.load_pretrained if hparams else True\n",
" if train_config.fine_tune_checkpoint and load_pretrained:\n",
" if not train_config.fine_tune_checkpoint_type:\n",
" # train_config.from_detection_checkpoint field is deprecated. For\n",
"@@ -507,6 +507,14 @@ def create_model_fn(detection_model_fn, configs, hparams=None, use_tpu=False,\n",
" train_config.fine_tune_checkpoint_type = 'detection'\n",
" else:\n",
" train_config.fine_tune_checkpoint_type = 'classification'\n",
"+\n",
"+ tf.logging.info('fine tuning parameter: fine_tune_checkpoint_type %s, '\n",
"+ 'from_detection_checkpoint %s fine_tune_checkpoint %s '\n",
"+ 'load_all_detection_checkpoint_vars %s',\n",
"+ train_config.fine_tune_checkpoint_type,\n",
"+ train_config.from_detection_checkpoint,\n",
"+ train_config.fine_tune_checkpoint,\n",
"+ train_config.load_all_detection_checkpoint_vars)\n",
" asg_map = detection_model.restore_map(\n",
" fine_tune_checkpoint_type=train_config.fine_tune_checkpoint_type,\n",
" load_all_detection_checkpoint_vars=(\n",
"EOF"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ueEgAw_s53dG",
"outputId": "73b077f1-a2c7-441e-bbd2-af5f4ed0d8af"
},
"source": [
"%%bash\n",
"\n",
"cd models\n",
"patch -p1 < /content/model_lib.patch"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"patching file research/object_detection/model_lib.py\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3d73ABl0hluK",
"outputId": "b37b6587-62f7-4692-f484-edcccad0e7f4"
},
"source": [
"%%bash\n",
"\n",
"cd models/research\n",
"# Compile protos.\n",
"protoc object_detection/protos/*.proto --python_out=.\n",
"\n",
"# Install TensorFlow Object Detection API.\n",
"cp object_detection/packages/tf1/setup.py .\n",
"python -m pip install ."
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"Processing /content/models/research\n",
"Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (7.1.2)\n",
"Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (4.2.6)\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (3.2.2)\n",
"Requirement already satisfied: Cython in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (0.29.22)\n",
"Requirement already satisfied: contextlib2 in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (0.5.5)\n",
"Collecting tf-slim\n",
" Downloading https://files.pythonhosted.org/packages/02/97/b0f4a64df018ca018cc035d44f2ef08f91e2e8aa67271f6f19633a015ff7/tf_slim-1.1.0-py2.py3-none-any.whl (352kB)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.15.0)\n",
"Requirement already satisfied: pycocotools in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (2.0.2)\n",
"Collecting lvis\n",
" Downloading https://files.pythonhosted.org/packages/72/b6/1992240ab48310b5360bfdd1d53163f43bb97d90dc5dc723c67d41c38e78/lvis-0.5.3-py3-none-any.whl\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.4.1)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.1.5)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->object-detection==0.1) (0.10.0)\n",
"Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib->object-detection==0.1) (1.19.5)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->object-detection==0.1) (1.3.1)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->object-detection==0.1) (2.4.7)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->object-detection==0.1) (2.8.1)\n",
"Requirement already satisfied: absl-py>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from tf-slim->object-detection==0.1) (0.12.0)\n",
"Requirement already satisfied: setuptools>=18.0 in /usr/local/lib/python3.7/dist-packages (from pycocotools->object-detection==0.1) (56.0.0)\n",
"Requirement already satisfied: opencv-python>=4.1.0.25 in /usr/local/lib/python3.7/dist-packages (from lvis->object-detection==0.1) (4.1.2.30)\n",
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->object-detection==0.1) (2018.9)\n",
"Building wheels for collected packages: object-detection\n",
" Building wheel for object-detection (setup.py): started\n",
" Building wheel for object-detection (setup.py): finished with status 'done'\n",
" Created wheel for object-detection: filename=object_detection-0.1-cp37-none-any.whl size=1647527 sha256=b63dc1d0f49f7e0b003b63c3d6429c8fe45af0a96d3a54a5255898f6db568f44\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-jo2lg33i/wheels/94/49/4b/39b051683087a22ef7e80ec52152a27249d1a644ccf4e442ea\n",
"Successfully built object-detection\n",
"Installing collected packages: tf-slim, lvis, object-detection\n",
"Successfully installed lvis-0.5.3 object-detection-0.1 tf-slim-1.1.0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "4F3kd4rEGIgo",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "90fc1deb-eb62-4b30-8595-397b4e8df4d9"
},
"source": [
"%cd /content/models/research"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"/content/models/research\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "XRfEVbHijRiq",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "20d5606d-4fa5-4848-b252-0d097ea73fc0"
},
"source": [
"import os\n",
"\n",
"os.environ['PYTHONPATH'] = '/content/models:' + os.environ['PYTHONPATH']\n",
"print(os.environ['PYTHONPATH'])"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"/content/models:/tensorflow-1.15.2/python3.7:/env/python\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Y_uBeLzBHHVH",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2a6a347e-2d50-4a09-b780-3ec8d9f388de"
},
"source": [
"!python object_detection/builders/model_builder_tf1_test.py"
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"Running tests under Python 3.7.10: /usr/bin/python3\n",
"[ RUN ] ModelBuilderTF1Test.test_create_context_rcnn_from_config_with_params0 (True)\n",
"[ OK ] ModelBuilderTF1Test.test_create_context_rcnn_from_config_with_params0 (True)\n",
"[ RUN ] ModelBuilderTF1Test.test_create_context_rcnn_from_config_with_params1 (False)\n",
"[ OK ] ModelBuilderTF1Test.test_create_context_rcnn_from_config_with_params1 (False)\n",
"[ RUN ] ModelBuilderTF1Test.test_create_experimental_model\n",
"[ OK ] ModelBuilderTF1Test.test_create_experimental_model\n",
"[ RUN ] ModelBuilderTF1Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n",
"[ OK ] ModelBuilderTF1Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n",
"[ RUN ] ModelBuilderTF1Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n",
"[ OK ] ModelBuilderTF1Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n",
"[ RUN ] ModelBuilderTF1Test.test_create_faster_rcnn_model_from_config_with_example_miner\n",
"[ OK ] ModelBuilderTF1Test.test_create_faster_rcnn_model_from_config_with_example_miner\n",
"[ RUN ] ModelBuilderTF1Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
"[ OK ] ModelBuilderTF1Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
"[ RUN ] ModelBuilderTF1Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
"[ OK ] ModelBuilderTF1Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
"[ RUN ] ModelBuilderTF1Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
"[ OK ] ModelBuilderTF1Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
"[ RUN ] ModelBuilderTF1Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
"[ OK ] ModelBuilderTF1Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
"[ RUN ] ModelBuilderTF1Test.test_create_rfcn_model_from_config\n",
"[ OK ] ModelBuilderTF1Test.test_create_rfcn_model_from_config\n",
"[ RUN ] ModelBuilderTF1Test.test_create_ssd_fpn_model_from_config\n",
"[ OK ] ModelBuilderTF1Test.test_create_ssd_fpn_model_from_config\n",
"[ RUN ] ModelBuilderTF1Test.test_create_ssd_models_from_config\n",
"[ OK ] ModelBuilderTF1Test.test_create_ssd_models_from_config\n",
"[ RUN ] ModelBuilderTF1Test.test_invalid_faster_rcnn_batchnorm_update\n",
"[ OK ] ModelBuilderTF1Test.test_invalid_faster_rcnn_batchnorm_update\n",
"[ RUN ] ModelBuilderTF1Test.test_invalid_first_stage_nms_iou_threshold\n",
"[ OK ] ModelBuilderTF1Test.test_invalid_first_stage_nms_iou_threshold\n",
"[ RUN ] ModelBuilderTF1Test.test_invalid_model_config_proto\n",
"[ OK ] ModelBuilderTF1Test.test_invalid_model_config_proto\n",
"[ RUN ] ModelBuilderTF1Test.test_invalid_second_stage_batch_size\n",
"[ OK ] ModelBuilderTF1Test.test_invalid_second_stage_batch_size\n",
"[ RUN ] ModelBuilderTF1Test.test_session\n",
"[ SKIPPED ] ModelBuilderTF1Test.test_session\n",
"[ RUN ] ModelBuilderTF1Test.test_unknown_faster_rcnn_feature_extractor\n",
"[ OK ] ModelBuilderTF1Test.test_unknown_faster_rcnn_feature_extractor\n",
"[ RUN ] ModelBuilderTF1Test.test_unknown_meta_architecture\n",
"[ OK ] ModelBuilderTF1Test.test_unknown_meta_architecture\n",
"[ RUN ] ModelBuilderTF1Test.test_unknown_ssd_feature_extractor\n",
"[ OK ] ModelBuilderTF1Test.test_unknown_ssd_feature_extractor\n",
"----------------------------------------------------------------------\n",
"Ran 21 tests in 0.214s\n",
"\n",
"OK (skipped=1)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iKdLuAihHhFA"
},
"source": [
"# Download Pet dataset and create tf-record"
]
},
{
"cell_type": "code",
"metadata": {
"id": "rZG8f4LsnpNl",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d2081229-049f-4b8e-ba02-0272ba4f0aea"
},
"source": [
"%set_env DATA_DIR=/content/data\n",
"%set_env TF_RECORD_DIR=/content/data/tf_record"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
"env: DATA_DIR=/content/data\n",
"env: TF_RECORD_DIR=/content/data/tf_record\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "8ANpO7QmHjbo",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "defe6d85-c385-4879-8f86-c706085b11eb"
},
"source": [
"# Download dataset and extrac tar files.\n",
"!mkdir ${DATA_DIR}\n",
"!mkdir ${TF_RECORD_DIR}\n",
"!wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz -P ${DATA_DIR}\n",
"!wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz -P ${DATA_DIR}\n",
"!tar xf ${DATA_DIR}/images.tar.gz -C ${DATA_DIR}\n",
"!tar xf ${DATA_DIR}/annotations.tar.gz -C ${DATA_DIR}\n",
"\n",
"# Create TF-Record.\n",
"!python object_detection/dataset_tools/create_pet_tf_record.py \\\n",
" --label_map_path=./object_detection/data/pet_label_map.pbtxt \\\n",
" --data_dir=\"${DATA_DIR}\" \\\n",
" --output_dir=\"${TF_RECORD_DIR}\"\n",
"\n",
"!ls ${TF_RECORD_DIR}"
],
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"text": [
"--2021-05-03 22:43:57-- http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\n",
"Resolving www.robots.ox.ac.uk (www.robots.ox.ac.uk)... 129.67.94.2\n",
"Connecting to www.robots.ox.ac.uk (www.robots.ox.ac.uk)|129.67.94.2|:80... connected.\n",
"HTTP request sent, awaiting response... 301 Moved Permanently\n",
"Location: https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz [following]\n",
"--2021-05-03 22:43:57-- https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz\n",
"Connecting to www.robots.ox.ac.uk (www.robots.ox.ac.uk)|129.67.94.2|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 791918971 (755M) [application/x-gzip]\n",
"Saving to: ‘/content/data/images.tar.gz’\n",
"\n",
"images.tar.gz 100%[===================>] 755.23M 68.9MB/s in 11s \n",
"\n",
"2021-05-03 22:44:08 (68.1 MB/s) - ‘/content/data/images.tar.gz’ saved [791918971/791918971]\n",
"\n",
"--2021-05-03 22:44:08-- http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\n",
"Resolving www.robots.ox.ac.uk (www.robots.ox.ac.uk)... 129.67.94.2\n",
"Connecting to www.robots.ox.ac.uk (www.robots.ox.ac.uk)|129.67.94.2|:80... connected.\n",
"HTTP request sent, awaiting response... 301 Moved Permanently\n",
"Location: https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz [following]\n",
"--2021-05-03 22:44:08-- https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz\n",
"Connecting to www.robots.ox.ac.uk (www.robots.ox.ac.uk)|129.67.94.2|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 19173078 (18M) [application/x-gzip]\n",
"Saving to: ‘/content/data/annotations.tar.gz’\n",
"\n",
"annotations.tar.gz 100%[===================>] 18.28M 58.9MB/s in 0.3s \n",
"\n",
"2021-05-03 22:44:09 (58.9 MB/s) - ‘/content/data/annotations.tar.gz’ saved [19173078/19173078]\n",
"\n",
"I0503 22:44:17.412478 139960508098432 create_pet_tf_record.py:273] Reading from Pet dataset.\n",
"I0503 22:44:17.422560 139960508098432 create_pet_tf_record.py:288] 2576 training and 1104 validation examples.\n",
"I0503 22:44:17.423127 139960508098432 create_pet_tf_record.py:241] On image 0 of 2576\n",
"/usr/local/lib/python3.7/dist-packages/object_detection/utils/dataset_util.py:83: FutureWarning: The behavior of this method will change in future versions. Use specific 'len(elem)' or 'elem is not None' test instead.\n",
" if not xml:\n",
"I0503 22:44:17.630365 139960508098432 create_pet_tf_record.py:241] On image 100 of 2576\n",
"I0503 22:44:17.820542 139960508098432 create_pet_tf_record.py:241] On image 200 of 2576\n",
"I0503 22:44:18.008630 139960508098432 create_pet_tf_record.py:241] On image 300 of 2576\n",
"I0503 22:44:18.212171 139960508098432 create_pet_tf_record.py:241] On image 400 of 2576\n",
"I0503 22:44:18.400474 139960508098432 create_pet_tf_record.py:241] On image 500 of 2576\n",
"W0503 22:44:18.538891 139960508098432 create_pet_tf_record.py:246] Could not find /content/data/annotations/xmls/Egyptian_Mau_14.xml, ignoring example.\n",
"W0503 22:44:18.570832 139960508098432 create_pet_tf_record.py:246] Could not find /content/data/annotations/xmls/saint_bernard_15.xml, ignoring example.\n",
"I0503 22:44:18.592516 139960508098432 create_pet_tf_record.py:241] On image 600 of 2576\n",
"W0503 22:44:18.600499 139960508098432 create_pet_tf_record.py:246] Could not find /content/data/annotations/xmls/Ragdoll_199.xml, ignoring example.\n",
"I0503 22:44:18.784520 139960508098432 create_pet_tf_record.py:241] On image 700 of 2576\n",
"I0503 22:44:18.980668 139960508098432 create_pet_tf_record.py:241] On image 800 of 2576\n",
"I0503 22:44:19.164678 139960508098432 create_pet_tf_record.py:241] On image 900 of 2576\n",
"I0503 22:44:19.353859 139960508098432 create_pet_tf_record.py:241] On image 1000 of 2576\n",
"I0503 22:44:19.542048 139960508098432 create_pet_tf_record.py:241] On image 1100 of 2576\n",
"I0503 22:44:19.754319 139960508098432 create_pet_tf_record.py:241] On image 1200 of 2576\n",
"I0503 22:44:19.959057 139960508098432 create_pet_tf_record.py:241] On image 1300 of 2576\n",
"I0503 22:44:20.199476 139960508098432 create_pet_tf_record.py:241] On image 1400 of 2576\n",
"W0503 22:44:20.300813 139960508098432 create_pet_tf_record.py:246] Could not find /content/data/annotations/xmls/Egyptian_Mau_186.xml, ignoring example.\n",
"I0503 22:44:20.392260 139960508098432 create_pet_tf_record.py:241] On image 1500 of 2576\n",
"W0503 22:44:20.521817 139960508098432 create_pet_tf_record.py:246] Could not find /content/data/annotations/xmls/Bengal_175.xml, ignoring example.\n",
"I0503 22:44:20.583704 139960508098432 create_pet_tf_record.py:241] On image 1600 of 2576\n",
"I0503 22:44:20.766173 139960508098432 create_pet_tf_record.py:241] On image 1700 of 2576\n",
"W0503 22:44:20.880429 139960508098432 create_pet_tf_record.py:246] Could not find /content/data/annotations/xmls/samoyed_10.xml, ignoring example.\n",
"I0503 22:44:20.946202 139960508098432 create_pet_tf_record.py:241] On image 1800 of 2576\n",
"I0503 22:44:21.145046 139960508098432 create_pet_tf_record.py:241] On image 1900 of 2576\n",
"I0503 22:44:21.332493 139960508098432 create_pet_tf_record.py:241] On image 2000 of 2576\n",
"I0503 22:44:21.528210 139960508098432 create_pet_tf_record.py:241] On image 2100 of 2576\n",
"W0503 22:44:21.596982 139960508098432 create_pet_tf_record.py:246] Could not find /content/data/annotations/xmls/Egyptian_Mau_156.xml, ignoring example.\n",
"I0503 22:44:21.728883 139960508098432 create_pet_tf_record.py:241] On image 2200 of 2576\n",
"I0503 22:44:21.926898 139960508098432 create_pet_tf_record.py:241] On image 2300 of 2576\n",
"I0503 22:44:22.108330 139960508098432 create_pet_tf_record.py:241] On image 2400 of 2576\n",
"I0503 22:44:22.298947 139960508098432 create_pet_tf_record.py:241] On image 2500 of 2576\n",
"W0503 22:44:22.395591 139960508098432 create_pet_tf_record.py:246] Could not find /content/data/annotations/xmls/Abyssinian_104.xml, ignoring example.\n",
"I0503 22:44:22.450354 139960508098432 create_pet_tf_record.py:241] On image 0 of 1104\n",
"I0503 22:44:22.641016 139960508098432 create_pet_tf_record.py:241] On image 100 of 1104\n",
"I0503 22:44:22.834721 139960508098432 create_pet_tf_record.py:241] On image 200 of 1104\n",
"I0503 22:44:23.016927 139960508098432 create_pet_tf_record.py:241] On image 300 of 1104\n",
"I0503 22:44:23.217114 139960508098432 create_pet_tf_record.py:241] On image 400 of 1104\n",
"I0503 22:44:23.400382 139960508098432 create_pet_tf_record.py:241] On image 500 of 1104\n",
"I0503 22:44:23.596589 139960508098432 create_pet_tf_record.py:241] On image 600 of 1104\n",
"I0503 22:44:23.786553 139960508098432 create_pet_tf_record.py:241] On image 700 of 1104\n",
"I0503 22:44:23.993257 139960508098432 create_pet_tf_record.py:241] On image 800 of 1104\n",
"W0503 22:44:24.078453 139960508098432 create_pet_tf_record.py:246] Could not find /content/data/annotations/xmls/Bengal_111.xml, ignoring example.\n",
"I0503 22:44:24.195823 139960508098432 create_pet_tf_record.py:241] On image 900 of 1104\n",
"I0503 22:44:24.379019 139960508098432 create_pet_tf_record.py:241] On image 1000 of 1104\n",
"I0503 22:44:24.605327 139960508098432 create_pet_tf_record.py:241] On image 1100 of 1104\n",
"pet_faces_train.record-00000-of-00010 pet_faces_val.record-00000-of-00010\n",
"pet_faces_train.record-00001-of-00010 pet_faces_val.record-00001-of-00010\n",
"pet_faces_train.record-00002-of-00010 pet_faces_val.record-00002-of-00010\n",
"pet_faces_train.record-00003-of-00010 pet_faces_val.record-00003-of-00010\n",
"pet_faces_train.record-00004-of-00010 pet_faces_val.record-00004-of-00010\n",
"pet_faces_train.record-00005-of-00010 pet_faces_val.record-00005-of-00010\n",
"pet_faces_train.record-00006-of-00010 pet_faces_val.record-00006-of-00010\n",
"pet_faces_train.record-00007-of-00010 pet_faces_val.record-00007-of-00010\n",
"pet_faces_train.record-00008-of-00010 pet_faces_val.record-00008-of-00010\n",
"pet_faces_train.record-00009-of-00010 pet_faces_val.record-00009-of-00010\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ndb6N895JwJ6"
},
"source": [
"# Get the pretrained model for transfer learning\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "at-wzrEXkDIe",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d3a1fb15-ede1-4240-b36e-bfbe1107bef1"
},
"source": [
"!wget http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v3_large_coco_2020_01_14.tar.gz -P ${DATA_DIR}\n",
"!tar xf ${DATA_DIR}/ssd_mobilenet_v3_large_coco_2020_01_14.tar.gz -C ${DATA_DIR}"
],
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": [
"--2021-05-03 22:44:25-- http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v3_large_coco_2020_01_14.tar.gz\n",
"Resolving download.tensorflow.org (download.tensorflow.org)... 173.194.79.128, 2a00:1450:4013:c00::80\n",
"Connecting to download.tensorflow.org (download.tensorflow.org)|173.194.79.128|:80... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 49318766 (47M) [application/gzip]\n",
"Saving to: ‘/content/data/ssd_mobilenet_v3_large_coco_2020_01_14.tar.gz’\n",
"\n",
"ssd_mobilenet_v3_la 100%[===================>] 47.03M 31.6MB/s in 1.5s \n",
"\n",
"2021-05-03 22:44:27 (31.6 MB/s) - ‘/content/data/ssd_mobilenet_v3_large_coco_2020_01_14.tar.gz’ saved [49318766/49318766]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KlCKFYLmBvI6"
},
"source": [
"# Create config file"
]
},
{
"cell_type": "code",
"metadata": {
"id": "pDwELNPC-Sqh"
},
"source": [
"!cp ./object_detection/samples/configs/ssdlite_mobilenet_v3_large_320x320_coco.config ${DATA_DIR}/ssdlite_mobilenet_v3_large_320x320_pet.config"
],
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "wpRIcKWn_NnN"
},
"source": [
"%%bash\n",
"\n",
"cat <<EOF > ${DATA_DIR}/config.patch\n",
"--- ./object_detection/samples/configs/ssdlite_mobilenet_v3_large_320x320_coco.config\t2021-05-03 08:34:03.934459490 +0000\n",
"+++ /content/data/ssdlite_mobilenet_v3_large_320x320_pet.config\t2021-05-03 09:01:56.235655642 +0000\n",
"@@ -11,7 +11,7 @@\n",
" ssd {\n",
" inplace_batchnorm_update: true\n",
" freeze_batchnorm: false\n",
"- num_classes: 90\n",
"+ num_classes: 37\n",
" box_coder {\n",
" faster_rcnn_box_coder {\n",
" y_scale: 10.0\n",
"@@ -147,11 +147,14 @@\n",
" }\n",
" \n",
" train_config: {\n",
"- batch_size: 512\n",
"+ batch_size: 128\n",
" sync_replicas: true\n",
" startup_delay_steps: 0\n",
" replicas_to_aggregate: 32\n",
" num_steps: 400000\n",
"+ fine_tune_checkpoint: \"/content/data/ssd_mobilenet_v3_large_coco_2020_01_14/model.ckpt\"\n",
"+ load_all_detection_checkpoint_vars: true\n",
"+ fine_tune_checkpoint_type: \"detection\"\n",
" data_augmentation_options {\n",
" random_horizontal_flip {\n",
" }\n",
"@@ -164,9 +167,9 @@\n",
" momentum_optimizer: {\n",
" learning_rate: {\n",
" cosine_decay_learning_rate {\n",
"- learning_rate_base: 0.4\n",
"+ learning_rate_base: 0.04\n",
" total_steps: 400000\n",
"- warmup_learning_rate: 0.13333\n",
"+ warmup_learning_rate: 0.013333\n",
" warmup_steps: 2000\n",
" }\n",
" }\n",
"@@ -180,20 +183,20 @@\n",
" \n",
" train_input_reader: {\n",
" tf_record_input_reader {\n",
"- input_path: \"PATH_TO_BE_CONFIGURED/mscoco_train.record-?????-of-00100\"\n",
"+ input_path: \"/content/data/tf_record/pet_faces_train.record-?????-of-00010\"\n",
" }\n",
"- label_map_path: \"PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt\"\n",
"+ label_map_path: \"/content/models/research/object_detection/data/pet_label_map.pbtxt\"\n",
" }\n",
" \n",
" eval_config: {\n",
"- num_examples: 8000\n",
"+ num_examples: 1104\n",
" }\n",
" \n",
" eval_input_reader: {\n",
" tf_record_input_reader {\n",
"- input_path: \"PATH_TO_BE_CONFIGURED/mscoco_val.record-?????-of-00010\"\n",
"+ input_path: \"/content/data/tf_record/pet_faces_train.record-?????-of-00010\"\n",
" }\n",
"- label_map_path: \"PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt\"\n",
"+ label_map_path: \"/content/models/research/object_detection/data/pet_label_map.pbtxt\"\n",
" shuffle: false\n",
" num_readers: 1\n",
" }\n",
"EOF"
],
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kmptYYT6-PyE",
"outputId": "45ee3bbe-4286-4e0d-bfb5-1b58b18a09fb"
},
"source": [
"!patch -u ${DATA_DIR}/ssdlite_mobilenet_v3_large_320x320_pet.config < ${DATA_DIR}/config.patch"
],
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"text": [
"patching file /content/data/ssdlite_mobilenet_v3_large_320x320_pet.config\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "SwbAVryqA5t7",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3f75d300-d25f-4bf8-d26a-cb9eccf9d6b6"
},
"source": [
"!cat ${DATA_DIR}/ssdlite_mobilenet_v3_large_320x320_pet.config"
],
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"text": [
"# SSDLite with Mobilenet v3 large feature extractor.\n",
"# Trained on COCO14, initialized from scratch.\n",
"# 3.22M parameters, 1.02B FLOPs\n",
"# TPU-compatible.\n",
"# Users should configure the fine_tune_checkpoint field in the train config as\n",
"# well as the label_map_path and input_path fields in the train_input_reader and\n",
"# eval_input_reader. Search for \"PATH_TO_BE_CONFIGURED\" to find the fields that\n",
"# should be configured.\n",
"\n",
"model {\n",
" ssd {\n",
" inplace_batchnorm_update: true\n",
" freeze_batchnorm: false\n",
" num_classes: 37\n",
" box_coder {\n",
" faster_rcnn_box_coder {\n",
" y_scale: 10.0\n",
" x_scale: 10.0\n",
" height_scale: 5.0\n",
" width_scale: 5.0\n",
" }\n",
" }\n",
" matcher {\n",
" argmax_matcher {\n",
" matched_threshold: 0.5\n",
" unmatched_threshold: 0.5\n",
" ignore_thresholds: false\n",
" negatives_lower_than_unmatched: true\n",
" force_match_for_each_row: true\n",
" use_matmul_gather: true\n",
" }\n",
" }\n",
" similarity_calculator {\n",
" iou_similarity {\n",
" }\n",
" }\n",
" encode_background_as_zeros: true\n",
" anchor_generator {\n",
" ssd_anchor_generator {\n",
" num_layers: 6\n",
" min_scale: 0.2\n",
" max_scale: 0.95\n",
" aspect_ratios: 1.0\n",
" aspect_ratios: 2.0\n",
" aspect_ratios: 0.5\n",
" aspect_ratios: 3.0\n",
" aspect_ratios: 0.3333\n",
" }\n",
" }\n",
" image_resizer {\n",
" fixed_shape_resizer {\n",
" height: 320\n",
" width: 320\n",
" }\n",
" }\n",
" box_predictor {\n",
" convolutional_box_predictor {\n",
" min_depth: 0\n",
" max_depth: 0\n",
" num_layers_before_predictor: 0\n",
" use_dropout: false\n",
" dropout_keep_probability: 0.8\n",
" kernel_size: 3\n",
" use_depthwise: true\n",
" box_code_size: 4\n",
" apply_sigmoid_to_scores: false\n",
" class_prediction_bias_init: -4.6\n",
" conv_hyperparams {\n",
" activation: RELU_6,\n",
" regularizer {\n",
" l2_regularizer {\n",
" weight: 0.00004\n",
" }\n",
" }\n",
" initializer {\n",
" random_normal_initializer {\n",
" stddev: 0.03\n",
" mean: 0.0\n",
" }\n",
" }\n",
" batch_norm {\n",
" train: true,\n",
" scale: true,\n",
" center: true,\n",
" decay: 0.97,\n",
" epsilon: 0.001,\n",
" }\n",
" }\n",
" }\n",
" }\n",
" feature_extractor {\n",
" type: 'ssd_mobilenet_v3_large'\n",
" min_depth: 16\n",
" depth_multiplier: 1.0\n",
" use_depthwise: true\n",
" conv_hyperparams {\n",
" activation: RELU_6,\n",
" regularizer {\n",
" l2_regularizer {\n",
" weight: 0.00004\n",
" }\n",
" }\n",
" initializer {\n",
" truncated_normal_initializer {\n",
" stddev: 0.03\n",
" mean: 0.0\n",
" }\n",
" }\n",
" batch_norm {\n",
" train: true,\n",
" scale: true,\n",
" center: true,\n",
" decay: 0.97,\n",
" epsilon: 0.001,\n",
" }\n",
" }\n",
" override_base_feature_extractor_hyperparams: true\n",
" }\n",
" loss {\n",
" classification_loss {\n",
" weighted_sigmoid_focal {\n",
" alpha: 0.75,\n",
" gamma: 2.0\n",
" }\n",
" }\n",
" localization_loss {\n",
" weighted_smooth_l1 {\n",
" delta: 1.0\n",
" }\n",
" }\n",
" classification_weight: 1.0\n",
" localization_weight: 1.0\n",
" }\n",
" normalize_loss_by_num_matches: true\n",
" normalize_loc_loss_by_codesize: true\n",
" post_processing {\n",
" batch_non_max_suppression {\n",
" score_threshold: 1e-8\n",
" iou_threshold: 0.6\n",
" max_detections_per_class: 100\n",
" max_total_detections: 100\n",
" use_static_shapes: true\n",
" }\n",
" score_converter: SIGMOID\n",
" }\n",
" }\n",
"}\n",
"\n",
"train_config: {\n",
" batch_size: 128\n",
" sync_replicas: true\n",
" startup_delay_steps: 0\n",
" replicas_to_aggregate: 32\n",
" num_steps: 400000\n",
" fine_tune_checkpoint: \"/content/data/ssd_mobilenet_v3_large_coco_2020_01_14/model.ckpt\"\n",
" load_all_detection_checkpoint_vars: true\n",
" fine_tune_checkpoint_type: \"detection\"\n",
" data_augmentation_options {\n",
" random_horizontal_flip {\n",
" }\n",
" }\n",
" data_augmentation_options {\n",
" ssd_random_crop {\n",
" }\n",
" }\n",
" optimizer {\n",
" momentum_optimizer: {\n",
" learning_rate: {\n",
" cosine_decay_learning_rate {\n",
" learning_rate_base: 0.04\n",
" total_steps: 400000\n",
" warmup_learning_rate: 0.013333\n",
" warmup_steps: 2000\n",
" }\n",
" }\n",
" momentum_optimizer_value: 0.9\n",
" }\n",
" use_moving_average: false\n",
" }\n",
" max_number_of_boxes: 100\n",
" unpad_groundtruth_tensors: false\n",
"}\n",
"\n",
"train_input_reader: {\n",
" tf_record_input_reader {\n",
" input_path: \"/content/data/tf_record/pet_faces_train.record-?????-of-00010\"\n",
" }\n",
" label_map_path: \"/content/models/research/object_detection/data/pet_label_map.pbtxt\"\n",
"}\n",
"\n",
"eval_config: {\n",
" num_examples: 1104\n",
"}\n",
"\n",
"eval_input_reader: {\n",
" tf_record_input_reader {\n",
" input_path: \"/content/data/tf_record/pet_faces_train.record-?????-of-00010\"\n",
" }\n",
" label_map_path: \"/content/models/research/object_detection/data/pet_label_map.pbtxt\"\n",
" shuffle: false\n",
" num_readers: 1\n",
"}\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rF7J2C_aUb0n"
},
"source": [
"# Train model"
]
},
{
"cell_type": "code",
"metadata": {
"id": "gs57oAxVUery",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d630b65c-d947-4fcd-dd20-849914a53466"
},
"source": [
"!python ./object_detection/model_main.py \\\n",
" --alsologtostderr \\\n",
" --pipeline_config_path=${DATA_DIR}/ssdlite_mobilenet_v3_large_320x320_pet.config \\\n",
" --num_train_steps=5000 \\\n",
" --sample_1_of_n_eval_examples=1 \\\n",
" --model_dir=${DATA_DIR}/train_ssdlite_mobilenet_v3_large_320x320_pet"
],
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:Forced number of epochs for all eval validations to be 1.\n",
"W0503 22:44:31.944674 140215059400576 model_lib.py:820] Forced number of epochs for all eval validations to be 1.\n",
"INFO:tensorflow:Maybe overwriting train_steps: 5000\n",
"I0503 22:44:31.944860 140215059400576 config_util.py:552] Maybe overwriting train_steps: 5000\n",
"INFO:tensorflow:Maybe overwriting use_bfloat16: False\n",
"I0503 22:44:31.944965 140215059400576 config_util.py:552] Maybe overwriting use_bfloat16: False\n",
"INFO:tensorflow:Maybe overwriting sample_1_of_n_eval_examples: 1\n",
"I0503 22:44:31.945029 140215059400576 config_util.py:552] Maybe overwriting sample_1_of_n_eval_examples: 1\n",
"INFO:tensorflow:Maybe overwriting eval_num_epochs: 1\n",
"I0503 22:44:31.945102 140215059400576 config_util.py:552] Maybe overwriting eval_num_epochs: 1\n",
"WARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.\n",
"W0503 22:44:31.945193 140215059400576 model_lib.py:836] Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.\n",
"INFO:tensorflow:create_estimator_and_inputs: use_tpu False, export_to_tpu None\n",
"I0503 22:44:31.945266 140215059400576 model_lib.py:873] create_estimator_and_inputs: use_tpu False, export_to_tpu None\n",
"INFO:tensorflow:Using config: {'_model_dir': '/content/data/train_ssdlite_mobilenet_v3_large_320x320_pet', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
"graph_options {\n",
" rewrite_options {\n",
" meta_optimizer_iterations: ONE\n",
" }\n",
"}\n",
", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f85c7012d90>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
"I0503 22:44:31.945604 140215059400576 estimator.py:212] Using config: {'_model_dir': '/content/data/train_ssdlite_mobilenet_v3_large_320x320_pet', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
"graph_options {\n",
" rewrite_options {\n",
" meta_optimizer_iterations: ONE\n",
" }\n",
"}\n",
", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f85c7012d90>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n",
"WARNING:tensorflow:Estimator's model_fn (<function create_model_fn.<locals>.model_fn at 0x7f85c7013a70>) includes params argument, but params are not passed to Estimator.\n",
"W0503 22:44:31.945781 140215059400576 model_fn.py:630] Estimator's model_fn (<function create_model_fn.<locals>.model_fn at 0x7f85c7013a70>) includes params argument, but params are not passed to Estimator.\n",
"INFO:tensorflow:Not using Distribute Coordinator.\n",
"I0503 22:44:31.946280 140215059400576 estimator_training.py:186] Not using Distribute Coordinator.\n",
"INFO:tensorflow:Running training and evaluation locally (non-distributed).\n",
"I0503 22:44:31.946429 140215059400576 training.py:612] Running training and evaluation locally (non-distributed).\n",
"INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps None or save_checkpoints_secs 600.\n",
"I0503 22:44:31.946604 140215059400576 training.py:700] Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps None or save_checkpoints_secs 600.\n",
"WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
"W0503 22:44:31.950980 140215059400576 deprecation.py:323] From /tensorflow-1.15.2/python3.7/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n",
"INFO:tensorflow:Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 22:44:31.978471 140215059400576 dataset_builder.py:163] Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 22:44:31.979441 140215059400576 dataset_builder.py:80] Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Number of filenames to read: 10\n",
"I0503 22:44:31.979539 140215059400576 dataset_builder.py:81] Number of filenames to read: 10\n",
"WARNING:tensorflow:num_readers has been reduced to 10 to match input file shards.\n",
"W0503 22:44:31.979594 140215059400576 dataset_builder.py:88] num_readers has been reduced to 10 to match input file shards.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_determinstic`.\n",
"W0503 22:44:31.984447 140215059400576 deprecation.py:323] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_determinstic`.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use `tf.data.Dataset.map()\n",
"W0503 22:44:32.002814 140215059400576 deprecation.py:323] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use `tf.data.Dataset.map()\n",
"WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f85c7027dd0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"W0503 22:44:32.031176 140215059400576 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f85c7027dd0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"WARNING:tensorflow:Entity <function train_input.<locals>.transform_and_pad_input_data_fn at 0x7f85c7013e60> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"W0503 22:44:32.213216 140215059400576 ag_logging.py:146] Entity <function train_input.<locals>.transform_and_pad_input_data_fn at 0x7f85c7013e60> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/inputs.py:111: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
"W0503 22:44:32.214441 140215059400576 deprecation.py:323] From /usr/local/lib/python3.7/dist-packages/object_detection/inputs.py:111: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/inputs.py:97: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
"W0503 22:44:32.223712 140215059400576 deprecation.py:323] From /usr/local/lib/python3.7/dist-packages/object_detection/inputs.py:97: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/core/preprocessor.py:199: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n",
"W0503 22:44:32.318740 140215059400576 deprecation.py:323] From /usr/local/lib/python3.7/dist-packages/object_detection/core/preprocessor.py:199: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/inputs.py:284: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use `tf.cast` instead.\n",
"W0503 22:44:32.987949 140215059400576 deprecation.py:323] From /usr/local/lib/python3.7/dist-packages/object_detection/inputs.py:284: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use `tf.cast` instead.\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0503 22:44:33.405897 140215059400576 estimator.py:1148] Calling model_fn.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tf_slim/layers/layers.py:1089: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use `layer.__call__` method instead.\n",
"W0503 22:44:33.543333 140215059400576 deprecation.py:323] From /usr/local/lib/python3.7/dist-packages/tf_slim/layers/layers.py:1089: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Please use `layer.__call__` method instead.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 22:44:35.805704 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 22:44:35.882828 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 22:44:35.962100 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 22:44:36.040409 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 22:44:36.118508 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 22:44:36.195902 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:fine tuning parameter: fine_tune_checkpoint_type detection, from_detection_checkpoint False fine_tune_checkpoint /content/data/ssd_mobilenet_v3_large_coco_2020_01_14/model.ckpt load_all_detection_checkpoint_vars True\n",
"I0503 22:44:36.389824 140215059400576 model_lib.py:517] fine tuning parameter: fine_tune_checkpoint_type detection, from_detection_checkpoint False fine_tune_checkpoint /content/data/ssd_mobilenet_v3_large_coco_2020_01_14/model.ckpt load_all_detection_checkpoint_vars True\n",
"W0503 22:44:36.397223 140215059400576 variables_helper.py:153] Variable [BoxPredictor_0/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[273]], model variable shape: [[114]]. This variable will not be initialized from the checkpoint.\n",
"W0503 22:44:36.397351 140215059400576 variables_helper.py:153] Variable [BoxPredictor_0/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 672, 273]], model variable shape: [[1, 1, 672, 114]]. This variable will not be initialized from the checkpoint.\n",
"W0503 22:44:36.397506 140215059400576 variables_helper.py:153] Variable [BoxPredictor_1/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[228]]. This variable will not be initialized from the checkpoint.\n",
"W0503 22:44:36.397562 140215059400576 variables_helper.py:153] Variable [BoxPredictor_1/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 480, 546]], model variable shape: [[1, 1, 480, 228]]. This variable will not be initialized from the checkpoint.\n",
"W0503 22:44:36.397706 140215059400576 variables_helper.py:153] Variable [BoxPredictor_2/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[228]]. This variable will not be initialized from the checkpoint.\n",
"W0503 22:44:36.397794 140215059400576 variables_helper.py:153] Variable [BoxPredictor_2/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 512, 546]], model variable shape: [[1, 1, 512, 228]]. This variable will not be initialized from the checkpoint.\n",
"W0503 22:44:36.397950 140215059400576 variables_helper.py:153] Variable [BoxPredictor_3/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[228]]. This variable will not be initialized from the checkpoint.\n",
"W0503 22:44:36.398005 140215059400576 variables_helper.py:153] Variable [BoxPredictor_3/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 228]]. This variable will not be initialized from the checkpoint.\n",
"W0503 22:44:36.398206 140215059400576 variables_helper.py:153] Variable [BoxPredictor_4/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[228]]. This variable will not be initialized from the checkpoint.\n",
"W0503 22:44:36.398305 140215059400576 variables_helper.py:153] Variable [BoxPredictor_4/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 546]], model variable shape: [[1, 1, 256, 228]]. This variable will not be initialized from the checkpoint.\n",
"W0503 22:44:36.398443 140215059400576 variables_helper.py:153] Variable [BoxPredictor_5/ClassPredictor/biases] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[546]], model variable shape: [[228]]. This variable will not be initialized from the checkpoint.\n",
"W0503 22:44:36.398492 140215059400576 variables_helper.py:153] Variable [BoxPredictor_5/ClassPredictor/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 128, 546]], model variable shape: [[1, 1, 128, 228]]. This variable will not be initialized from the checkpoint.\n",
"W0503 22:44:36.400852 140215059400576 variables_helper.py:156] Variable [global_step] is not available in checkpoint\n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0503 22:44:57.526339 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"INFO:tensorflow:Create CheckpointSaverHook.\n",
"I0503 22:44:57.527533 140215059400576 basic_session_run_hooks.py:541] Create CheckpointSaverHook.\n",
"INFO:tensorflow:Graph was finalized.\n",
"I0503 22:45:02.287679 140215059400576 monitored_session.py:240] Graph was finalized.\n",
"2021-05-03 22:45:02.288169: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX512F\n",
"2021-05-03 22:45:02.299423: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2000179999 Hz\n",
"2021-05-03 22:45:02.299789: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x555b93d53800 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
"2021-05-03 22:45:02.299819: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version\n",
"2021-05-03 22:45:02.304706: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
"2021-05-03 22:45:02.464597: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 22:45:02.465331: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x555b93d53480 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
"2021-05-03 22:45:02.465366: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n",
"2021-05-03 22:45:02.466466: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 22:45:02.467000: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-03 22:45:02.481921: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-03 22:45:02.649101: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-03 22:45:02.748976: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-03 22:45:02.786580: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-03 22:45:02.973976: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-03 22:45:02.992988: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-03 22:45:03.339491: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-03 22:45:03.339677: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 22:45:03.340388: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 22:45:03.340903: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-03 22:45:03.344743: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-03 22:45:03.346960: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-03 22:45:03.346999: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-03 22:45:03.347007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-03 22:45:03.347882: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 22:45:03.348510: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 22:45:03.349029: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
"2021-05-03 22:45:03.349062: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Running local_init_op.\n",
"I0503 22:45:10.319791 140215059400576 session_manager.py:500] Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"I0503 22:45:10.910343 140215059400576 session_manager.py:502] Done running local_init_op.\n",
"INFO:tensorflow:Saving checkpoints for 0 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"I0503 22:45:25.765369 140215059400576 basic_session_run_hooks.py:606] Saving checkpoints for 0 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"2021-05-03 22:45:50.528150: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"Corrupt JPEG data: premature end of data segment\n",
"2021-05-03 22:45:57.377577: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"INFO:tensorflow:loss = 535.09656, step = 0\n",
"I0503 22:46:02.159381 140215059400576 basic_session_run_hooks.py:262] loss = 535.09656, step = 0\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.654843\n",
"I0503 22:48:34.867180 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.654843\n",
"INFO:tensorflow:loss = 1.5590442, step = 100 (152.709 sec)\n",
"I0503 22:48:34.868344 140215059400576 basic_session_run_hooks.py:260] loss = 1.5590442, step = 100 (152.709 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.740994\n",
"I0503 22:50:49.821016 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.740994\n",
"INFO:tensorflow:loss = 1.3520994, step = 200 (134.954 sec)\n",
"I0503 22:50:49.822021 140215059400576 basic_session_run_hooks.py:260] loss = 1.3520994, step = 200 (134.954 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.734331\n",
"I0503 22:53:05.999416 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.734331\n",
"INFO:tensorflow:loss = 1.2393731, step = 300 (136.178 sec)\n",
"I0503 22:53:06.000502 140215059400576 basic_session_run_hooks.py:260] loss = 1.2393731, step = 300 (136.178 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.737303\n",
"I0503 22:55:21.628954 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.737303\n",
"INFO:tensorflow:loss = 1.1287594, step = 400 (135.629 sec)\n",
"I0503 22:55:21.629845 140215059400576 basic_session_run_hooks.py:260] loss = 1.1287594, step = 400 (135.629 sec)\n",
"INFO:tensorflow:Saving checkpoints for 407 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"I0503 22:55:29.701190 140215059400576 basic_session_run_hooks.py:606] Saving checkpoints for 407 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"INFO:tensorflow:Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 22:55:31.790008 140215059400576 dataset_builder.py:163] Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 22:55:31.790931 140215059400576 dataset_builder.py:80] Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Number of filenames to read: 10\n",
"I0503 22:55:31.791066 140215059400576 dataset_builder.py:81] Number of filenames to read: 10\n",
"WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f85ac22f090>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"W0503 22:55:31.832900 140215059400576 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f85ac22f090>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"WARNING:tensorflow:Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f85ba63f710> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"W0503 22:55:31.993654 140215059400576 ag_logging.py:146] Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f85ba63f710> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0503 22:55:32.463019 140215059400576 estimator.py:1148] Calling model_fn.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 22:55:34.454580 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 22:55:34.524233 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 22:55:34.592488 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 22:55:34.662521 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 22:55:34.730876 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 22:55:34.799815 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/eval_util.py:929: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use `tf.cast` instead.\n",
"W0503 22:55:35.840647 140215059400576 deprecation.py:323] From /usr/local/lib/python3.7/dist-packages/object_detection/eval_util.py:929: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use `tf.cast` instead.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/utils/visualization_utils.py:618: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"tf.py_func is deprecated in TF V2. Instead, there are two\n",
" options available in V2.\n",
" - tf.py_function takes a python function which manipulates tf eager\n",
" tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
" an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
" means `tf.py_function`s can use accelerators such as GPUs as well as\n",
" being differentiable using a gradient tape.\n",
" - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n",
" (it is not differentiable, and manipulates numpy arrays). It drops the\n",
" stateful argument making all functions stateful.\n",
" \n",
"W0503 22:55:36.018670 140215059400576 deprecation.py:323] From /usr/local/lib/python3.7/dist-packages/object_detection/utils/visualization_utils.py:618: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"tf.py_func is deprecated in TF V2. Instead, there are two\n",
" options available in V2.\n",
" - tf.py_function takes a python function which manipulates tf eager\n",
" tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
" an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
" means `tf.py_function`s can use accelerators such as GPUs as well as\n",
" being differentiable using a gradient tape.\n",
" - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n",
" (it is not differentiable, and manipulates numpy arrays). It drops the\n",
" stateful argument making all functions stateful.\n",
" \n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0503 22:55:36.534003 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2021-05-03T22:55:36Z\n",
"I0503 22:55:36.548862 140215059400576 evaluation.py:255] Starting evaluation at 2021-05-03T22:55:36Z\n",
"INFO:tensorflow:Graph was finalized.\n",
"I0503 22:55:37.000953 140215059400576 monitored_session.py:240] Graph was finalized.\n",
"2021-05-03 22:55:37.002135: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 22:55:37.002428: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-03 22:55:37.002548: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-03 22:55:37.002585: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-03 22:55:37.002615: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-03 22:55:37.002646: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-03 22:55:37.002668: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-03 22:55:37.002691: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-03 22:55:37.002714: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-03 22:55:37.002806: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 22:55:37.003114: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 22:55:37.003346: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-03 22:55:37.003395: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-03 22:55:37.003411: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-03 22:55:37.003421: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-03 22:55:37.003530: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 22:55:37.003797: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 22:55:37.004029: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-407\n",
"I0503 22:55:37.004954 140215059400576 saver.py:1284] Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-407\n",
"INFO:tensorflow:Running local_init_op.\n",
"I0503 22:55:37.990526 140215059400576 session_manager.py:500] Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"I0503 22:55:38.125984 140215059400576 session_manager.py:502] Done running local_init_op.\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Performing evaluation on 2568 images.\n",
"I0503 22:56:40.951148 140210432521984 coco_evaluation.py:293] Performing evaluation on 2568 images.\n",
"creating index...\n",
"index created!\n",
"INFO:tensorflow:Loading and preparing annotation results...\n",
"I0503 22:56:40.959702 140210432521984 coco_tools.py:116] Loading and preparing annotation results...\n",
"INFO:tensorflow:DONE (t=0.19s)\n",
"I0503 22:56:41.151035 140210432521984 coco_tools.py:138] DONE (t=0.19s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=19.53s).\n",
"Accumulating evaluation results...\n",
"DONE (t=7.02s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.072\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.096\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.086\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.049\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.080\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.760\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.773\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.773\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.645\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.799\n",
"INFO:tensorflow:Finished evaluation at 2021-05-03-22:57:08\n",
"I0503 22:57:08.682882 140215059400576 evaluation.py:275] Finished evaluation at 2021-05-03-22:57:08\n",
"INFO:tensorflow:Saving dict for global step 407: DetectionBoxes_Precision/mAP = 0.07237052, DetectionBoxes_Precision/mAP (large) = 0.07999087, DetectionBoxes_Precision/mAP (medium) = 0.049325153, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/[email protected] = 0.09640334, DetectionBoxes_Precision/[email protected] = 0.08647537, DetectionBoxes_Recall/AR@1 = 0.75975764, DetectionBoxes_Recall/AR@10 = 0.77336717, DetectionBoxes_Recall/AR@100 = 0.77336717, DetectionBoxes_Recall/AR@100 (large) = 0.7987, DetectionBoxes_Recall/AR@100 (medium) = 0.6453445, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 0.8944688, Loss/localization_loss = 0.067470625, Loss/regularization_loss = 0.2306503, Loss/total_loss = 1.1925858, global_step = 407, learning_rate = 0.018759735, loss = 1.1925858\n",
"I0503 22:57:08.683235 140215059400576 estimator.py:2049] Saving dict for global step 407: DetectionBoxes_Precision/mAP = 0.07237052, DetectionBoxes_Precision/mAP (large) = 0.07999087, DetectionBoxes_Precision/mAP (medium) = 0.049325153, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/[email protected] = 0.09640334, DetectionBoxes_Precision/[email protected] = 0.08647537, DetectionBoxes_Recall/AR@1 = 0.75975764, DetectionBoxes_Recall/AR@10 = 0.77336717, DetectionBoxes_Recall/AR@100 = 0.77336717, DetectionBoxes_Recall/AR@100 (large) = 0.7987, DetectionBoxes_Recall/AR@100 (medium) = 0.6453445, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 0.8944688, Loss/localization_loss = 0.067470625, Loss/regularization_loss = 0.2306503, Loss/total_loss = 1.1925858, global_step = 407, learning_rate = 0.018759735, loss = 1.1925858\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 407: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-407\n",
"I0503 22:57:09.511198 140215059400576 estimator.py:2109] Saving 'checkpoint_path' summary for global step 407: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-407\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.42602\n",
"I0503 22:59:16.359457 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.42602\n",
"INFO:tensorflow:loss = 1.1076175, step = 500 (234.731 sec)\n",
"I0503 22:59:16.360794 140215059400576 basic_session_run_hooks.py:260] loss = 1.1076175, step = 500 (234.731 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.741202\n",
"I0503 23:01:31.275370 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.741202\n",
"INFO:tensorflow:loss = 1.0399542, step = 600 (134.916 sec)\n",
"I0503 23:01:31.276308 140215059400576 basic_session_run_hooks.py:260] loss = 1.0399542, step = 600 (134.916 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.738895\n",
"I0503 23:03:46.612538 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.738895\n",
"INFO:tensorflow:loss = 0.92504525, step = 700 (135.337 sec)\n",
"I0503 23:03:46.613550 140215059400576 basic_session_run_hooks.py:260] loss = 0.92504525, step = 700 (135.337 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Saving checkpoints for 778 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"I0503 23:05:30.540197 140215059400576 basic_session_run_hooks.py:606] Saving checkpoints for 778 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"INFO:tensorflow:Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 23:05:32.588105 140215059400576 dataset_builder.py:163] Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 23:05:32.589048 140215059400576 dataset_builder.py:80] Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Number of filenames to read: 10\n",
"I0503 23:05:32.589216 140215059400576 dataset_builder.py:81] Number of filenames to read: 10\n",
"WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f85ac180510>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"W0503 23:05:32.626588 140215059400576 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f85ac180510>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"WARNING:tensorflow:Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f805cd06d40> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"W0503 23:05:32.788226 140215059400576 ag_logging.py:146] Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f805cd06d40> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0503 23:05:33.322505 140215059400576 estimator.py:1148] Calling model_fn.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:05:35.335118 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:05:35.403026 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:05:35.469755 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:05:35.536586 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:05:35.605880 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:05:35.673256 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0503 23:05:37.381840 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2021-05-03T23:05:37Z\n",
"I0503 23:05:37.396681 140215059400576 evaluation.py:255] Starting evaluation at 2021-05-03T23:05:37Z\n",
"INFO:tensorflow:Graph was finalized.\n",
"I0503 23:05:37.850116 140215059400576 monitored_session.py:240] Graph was finalized.\n",
"2021-05-03 23:05:37.850790: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:05:37.851180: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-03 23:05:37.851323: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-03 23:05:37.851346: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-03 23:05:37.851362: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-03 23:05:37.851377: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-03 23:05:37.851392: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-03 23:05:37.851405: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-03 23:05:37.851423: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-03 23:05:37.851498: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:05:37.851760: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:05:37.851960: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-03 23:05:37.852001: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-03 23:05:37.852011: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-03 23:05:37.852018: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-03 23:05:37.852111: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:05:37.852376: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:05:37.852594: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-778\n",
"I0503 23:05:37.853563 140215059400576 saver.py:1284] Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-778\n",
"INFO:tensorflow:Running local_init_op.\n",
"I0503 23:05:38.816956 140215059400576 session_manager.py:500] Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"I0503 23:05:38.954433 140215059400576 session_manager.py:502] Done running local_init_op.\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Performing evaluation on 2568 images.\n",
"I0503 23:06:42.640245 140210432521984 coco_evaluation.py:293] Performing evaluation on 2568 images.\n",
"creating index...\n",
"index created!\n",
"INFO:tensorflow:Loading and preparing annotation results...\n",
"I0503 23:06:42.647192 140210432521984 coco_tools.py:116] Loading and preparing annotation results...\n",
"INFO:tensorflow:DONE (t=0.20s)\n",
"I0503 23:06:42.843179 140210432521984 coco_tools.py:138] DONE (t=0.20s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=20.69s).\n",
"Accumulating evaluation results...\n",
"DONE (t=6.94s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.281\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.354\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.339\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.107\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.315\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.804\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.809\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.809\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.700\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.829\n",
"INFO:tensorflow:Finished evaluation at 2021-05-03-23:07:11\n",
"I0503 23:07:11.459625 140215059400576 evaluation.py:275] Finished evaluation at 2021-05-03-23:07:11\n",
"INFO:tensorflow:Saving dict for global step 778: DetectionBoxes_Precision/mAP = 0.2810173, DetectionBoxes_Precision/mAP (large) = 0.3148433, DetectionBoxes_Precision/mAP (medium) = 0.10657664, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/[email protected] = 0.35377043, DetectionBoxes_Precision/[email protected] = 0.33944526, DetectionBoxes_Recall/AR@1 = 0.80354273, DetectionBoxes_Recall/AR@10 = 0.8086807, DetectionBoxes_Recall/AR@100 = 0.8086807, DetectionBoxes_Recall/AR@100 (large) = 0.8292294, DetectionBoxes_Recall/AR@100 (medium) = 0.69966906, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 0.63184404, Loss/localization_loss = 0.049853478, Loss/regularization_loss = 0.229921, Loss/total_loss = 0.9116245, global_step = 778, learning_rate = 0.023706462, loss = 0.9116245\n",
"I0503 23:07:11.459895 140215059400576 estimator.py:2049] Saving dict for global step 778: DetectionBoxes_Precision/mAP = 0.2810173, DetectionBoxes_Precision/mAP (large) = 0.3148433, DetectionBoxes_Precision/mAP (medium) = 0.10657664, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/[email protected] = 0.35377043, DetectionBoxes_Precision/[email protected] = 0.33944526, DetectionBoxes_Recall/AR@1 = 0.80354273, DetectionBoxes_Recall/AR@10 = 0.8086807, DetectionBoxes_Recall/AR@100 = 0.8086807, DetectionBoxes_Recall/AR@100 (large) = 0.8292294, DetectionBoxes_Recall/AR@100 (medium) = 0.69966906, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 0.63184404, Loss/localization_loss = 0.049853478, Loss/regularization_loss = 0.229921, Loss/total_loss = 0.9116245, global_step = 778, learning_rate = 0.023706462, loss = 0.9116245\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 778: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-778\n",
"I0503 23:07:11.463823 140215059400576 estimator.py:2109] Saving 'checkpoint_path' summary for global step 778: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-778\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.423704\n",
"I0503 23:07:42.626347 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.423704\n",
"INFO:tensorflow:loss = 0.8872799, step = 800 (236.014 sec)\n",
"I0503 23:07:42.627664 140215059400576 basic_session_run_hooks.py:260] loss = 0.8872799, step = 800 (236.014 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.741439\n",
"I0503 23:09:57.499171 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.741439\n",
"INFO:tensorflow:loss = 0.7907349, step = 900 (134.873 sec)\n",
"I0503 23:09:57.500277 140215059400576 basic_session_run_hooks.py:260] loss = 0.7907349, step = 900 (134.873 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.74041\n",
"I0503 23:12:12.559424 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.74041\n",
"INFO:tensorflow:loss = 0.7546335, step = 1000 (135.060 sec)\n",
"I0503 23:12:12.560322 140215059400576 basic_session_run_hooks.py:260] loss = 0.7546335, step = 1000 (135.060 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.740813\n",
"I0503 23:14:27.546211 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.740813\n",
"INFO:tensorflow:loss = 0.67965263, step = 1100 (134.987 sec)\n",
"I0503 23:14:27.547178 140215059400576 basic_session_run_hooks.py:260] loss = 0.67965263, step = 1100 (134.987 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Saving checkpoints for 1148 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"I0503 23:15:30.629634 140215059400576 basic_session_run_hooks.py:606] Saving checkpoints for 1148 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"INFO:tensorflow:Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 23:15:32.691810 140215059400576 dataset_builder.py:163] Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 23:15:32.692809 140215059400576 dataset_builder.py:80] Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Number of filenames to read: 10\n",
"I0503 23:15:32.692945 140215059400576 dataset_builder.py:81] Number of filenames to read: 10\n",
"WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f806463d610>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"W0503 23:15:32.730887 140215059400576 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f806463d610>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"WARNING:tensorflow:Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f8064a3a320> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"W0503 23:15:32.891924 140215059400576 ag_logging.py:146] Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f8064a3a320> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0503 23:15:33.377007 140215059400576 estimator.py:1148] Calling model_fn.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:15:35.388021 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:15:35.458101 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:15:35.525646 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:15:35.593148 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:15:35.662152 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:15:35.730339 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0503 23:15:37.442188 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2021-05-03T23:15:37Z\n",
"I0503 23:15:37.457147 140215059400576 evaluation.py:255] Starting evaluation at 2021-05-03T23:15:37Z\n",
"INFO:tensorflow:Graph was finalized.\n",
"I0503 23:15:37.906858 140215059400576 monitored_session.py:240] Graph was finalized.\n",
"2021-05-03 23:15:37.907585: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:15:37.907897: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-03 23:15:37.908022: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-03 23:15:37.908077: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-03 23:15:37.908126: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-03 23:15:37.908152: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-03 23:15:37.908168: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-03 23:15:37.908182: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-03 23:15:37.908204: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-03 23:15:37.908287: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:15:37.908553: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:15:37.908759: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-03 23:15:37.908828: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-03 23:15:37.908840: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-03 23:15:37.908847: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-03 23:15:37.908932: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:15:37.909208: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:15:37.909428: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-1148\n",
"I0503 23:15:37.910608 140215059400576 saver.py:1284] Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-1148\n",
"INFO:tensorflow:Running local_init_op.\n",
"I0503 23:15:38.892279 140215059400576 session_manager.py:500] Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"I0503 23:15:39.031018 140215059400576 session_manager.py:502] Done running local_init_op.\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Performing evaluation on 2568 images.\n",
"I0503 23:16:44.290100 140210424129280 coco_evaluation.py:293] Performing evaluation on 2568 images.\n",
"creating index...\n",
"index created!\n",
"INFO:tensorflow:Loading and preparing annotation results...\n",
"I0503 23:16:44.298785 140210424129280 coco_tools.py:116] Loading and preparing annotation results...\n",
"INFO:tensorflow:DONE (t=0.19s)\n",
"I0503 23:16:44.486017 140210424129280 coco_tools.py:138] DONE (t=0.19s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=19.51s).\n",
"Accumulating evaluation results...\n",
"DONE (t=7.22s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.490\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.601\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.587\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.145\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.547\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.815\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.819\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.819\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.703\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.842\n",
"INFO:tensorflow:Finished evaluation at 2021-05-03-23:17:12\n",
"I0503 23:17:12.198430 140215059400576 evaluation.py:275] Finished evaluation at 2021-05-03-23:17:12\n",
"INFO:tensorflow:Saving dict for global step 1148: DetectionBoxes_Precision/mAP = 0.48982677, DetectionBoxes_Precision/mAP (large) = 0.5474836, DetectionBoxes_Precision/mAP (medium) = 0.14508818, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/[email protected] = 0.6014761, DetectionBoxes_Precision/[email protected] = 0.58707434, DetectionBoxes_Recall/AR@1 = 0.81457376, DetectionBoxes_Recall/AR@10 = 0.819219, DetectionBoxes_Recall/AR@100 = 0.819219, DetectionBoxes_Recall/AR@100 (large) = 0.8421357, DetectionBoxes_Recall/AR@100 (medium) = 0.7030473, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 0.4837387, Loss/localization_loss = 0.045580048, Loss/regularization_loss = 0.22921352, Loss/total_loss = 0.7585366, global_step = 1148, learning_rate = 0.028639857, loss = 0.7585366\n",
"I0503 23:17:12.198681 140215059400576 estimator.py:2049] Saving dict for global step 1148: DetectionBoxes_Precision/mAP = 0.48982677, DetectionBoxes_Precision/mAP (large) = 0.5474836, DetectionBoxes_Precision/mAP (medium) = 0.14508818, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/[email protected] = 0.6014761, DetectionBoxes_Precision/[email protected] = 0.58707434, DetectionBoxes_Recall/AR@1 = 0.81457376, DetectionBoxes_Recall/AR@10 = 0.819219, DetectionBoxes_Recall/AR@100 = 0.819219, DetectionBoxes_Recall/AR@100 (large) = 0.8421357, DetectionBoxes_Recall/AR@100 (medium) = 0.7030473, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 0.4837387, Loss/localization_loss = 0.045580048, Loss/regularization_loss = 0.22921352, Loss/total_loss = 0.7585366, global_step = 1148, learning_rate = 0.028639857, loss = 0.7585366\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1148: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-1148\n",
"I0503 23:17:12.202373 140215059400576 estimator.py:2109] Saving 'checkpoint_path' summary for global step 1148: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-1148\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.422406\n",
"I0503 23:18:24.285328 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.422406\n",
"INFO:tensorflow:loss = 0.6914617, step = 1200 (236.739 sec)\n",
"I0503 23:18:24.286469 140215059400576 basic_session_run_hooks.py:260] loss = 0.6914617, step = 1200 (236.739 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.739784\n",
"I0503 23:20:39.459912 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.739784\n",
"INFO:tensorflow:loss = 0.6413725, step = 1300 (135.175 sec)\n",
"I0503 23:20:39.461007 140215059400576 basic_session_run_hooks.py:260] loss = 0.6413725, step = 1300 (135.175 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.741462\n",
"I0503 23:22:54.328642 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.741462\n",
"INFO:tensorflow:loss = 0.64289474, step = 1400 (134.869 sec)\n",
"I0503 23:22:54.329862 140215059400576 basic_session_run_hooks.py:260] loss = 0.64289474, step = 1400 (134.869 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.741569\n",
"I0503 23:25:09.177831 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.741569\n",
"INFO:tensorflow:loss = 0.6105086, step = 1500 (134.849 sec)\n",
"I0503 23:25:09.178897 140215059400576 basic_session_run_hooks.py:260] loss = 0.6105086, step = 1500 (134.849 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Saving checkpoints for 1517 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"I0503 23:25:30.690894 140215059400576 basic_session_run_hooks.py:606] Saving checkpoints for 1517 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"INFO:tensorflow:Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 23:25:32.766442 140215059400576 dataset_builder.py:163] Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 23:25:32.767466 140215059400576 dataset_builder.py:80] Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Number of filenames to read: 10\n",
"I0503 23:25:32.767613 140215059400576 dataset_builder.py:81] Number of filenames to read: 10\n",
"WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f852809bad0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"W0503 23:25:32.805515 140215059400576 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f852809bad0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"WARNING:tensorflow:Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f85ac1f1d40> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"W0503 23:25:32.967390 140215059400576 ag_logging.py:146] Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f85ac1f1d40> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0503 23:25:33.443732 140215059400576 estimator.py:1148] Calling model_fn.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:25:35.480487 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:25:35.550337 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:25:35.618407 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:25:35.687046 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:25:35.756820 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:25:35.826197 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0503 23:25:37.560714 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2021-05-03T23:25:37Z\n",
"I0503 23:25:37.575657 140215059400576 evaluation.py:255] Starting evaluation at 2021-05-03T23:25:37Z\n",
"INFO:tensorflow:Graph was finalized.\n",
"I0503 23:25:38.028902 140215059400576 monitored_session.py:240] Graph was finalized.\n",
"2021-05-03 23:25:38.029615: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:25:38.029984: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-03 23:25:38.030103: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-03 23:25:38.030129: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-03 23:25:38.030146: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-03 23:25:38.030161: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-03 23:25:38.030174: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-03 23:25:38.030188: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-03 23:25:38.030202: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-03 23:25:38.030274: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:25:38.030521: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:25:38.030711: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-03 23:25:38.030750: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-03 23:25:38.030769: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-03 23:25:38.030775: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-03 23:25:38.030852: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:25:38.031104: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:25:38.031307: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-1517\n",
"I0503 23:25:38.032435 140215059400576 saver.py:1284] Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-1517\n",
"INFO:tensorflow:Running local_init_op.\n",
"I0503 23:25:39.018480 140215059400576 session_manager.py:500] Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"I0503 23:25:39.154753 140215059400576 session_manager.py:502] Done running local_init_op.\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Performing evaluation on 2568 images.\n",
"I0503 23:26:44.479240 140210432521984 coco_evaluation.py:293] Performing evaluation on 2568 images.\n",
"creating index...\n",
"index created!\n",
"INFO:tensorflow:Loading and preparing annotation results...\n",
"I0503 23:26:44.485937 140210432521984 coco_tools.py:116] Loading and preparing annotation results...\n",
"INFO:tensorflow:DONE (t=0.18s)\n",
"I0503 23:26:44.670150 140210432521984 coco_tools.py:138] DONE (t=0.18s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=19.66s).\n",
"Accumulating evaluation results...\n",
"DONE (t=6.90s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.632\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.796\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.756\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.195\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.703\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.799\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.810\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.810\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.684\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.835\n",
"INFO:tensorflow:Finished evaluation at 2021-05-03-23:27:12\n",
"I0503 23:27:12.192707 140215059400576 evaluation.py:275] Finished evaluation at 2021-05-03-23:27:12\n",
"INFO:tensorflow:Saving dict for global step 1517: DetectionBoxes_Precision/mAP = 0.6323059, DetectionBoxes_Precision/mAP (large) = 0.70349455, DetectionBoxes_Precision/mAP (medium) = 0.19478361, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/[email protected] = 0.79594624, DetectionBoxes_Precision/[email protected] = 0.7563676, DetectionBoxes_Recall/AR@1 = 0.7993223, DetectionBoxes_Recall/AR@10 = 0.80954814, DetectionBoxes_Recall/AR@100 = 0.80954814, DetectionBoxes_Recall/AR@100 (large) = 0.83493155, DetectionBoxes_Recall/AR@100 (medium) = 0.6838918, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 0.39249372, Loss/localization_loss = 0.051634457, Loss/regularization_loss = 0.22847962, Loss/total_loss = 0.6726066, global_step = 1517, learning_rate = 0.03355992, loss = 0.6726066\n",
"I0503 23:27:12.192950 140215059400576 estimator.py:2049] Saving dict for global step 1517: DetectionBoxes_Precision/mAP = 0.6323059, DetectionBoxes_Precision/mAP (large) = 0.70349455, DetectionBoxes_Precision/mAP (medium) = 0.19478361, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/[email protected] = 0.79594624, DetectionBoxes_Precision/[email protected] = 0.7563676, DetectionBoxes_Recall/AR@1 = 0.7993223, DetectionBoxes_Recall/AR@10 = 0.80954814, DetectionBoxes_Recall/AR@100 = 0.80954814, DetectionBoxes_Recall/AR@100 (large) = 0.83493155, DetectionBoxes_Recall/AR@100 (medium) = 0.6838918, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 0.39249372, Loss/localization_loss = 0.051634457, Loss/regularization_loss = 0.22847962, Loss/total_loss = 0.6726066, global_step = 1517, learning_rate = 0.03355992, loss = 0.6726066\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1517: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-1517\n",
"I0503 23:27:12.196601 140215059400576 estimator.py:2109] Saving 'checkpoint_path' summary for global step 1517: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-1517\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.422346\n",
"I0503 23:29:05.950512 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.422346\n",
"INFO:tensorflow:loss = 0.5302361, step = 1600 (236.772 sec)\n",
"I0503 23:29:05.951356 140215059400576 basic_session_run_hooks.py:260] loss = 0.5302361, step = 1600 (236.772 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.741342\n",
"I0503 23:31:20.840980 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.741342\n",
"INFO:tensorflow:loss = 0.5171362, step = 1700 (134.891 sec)\n",
"I0503 23:31:20.841886 140215059400576 basic_session_run_hooks.py:260] loss = 0.5171362, step = 1700 (134.891 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.740093\n",
"I0503 23:33:35.959067 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.740093\n",
"INFO:tensorflow:loss = 0.50612265, step = 1800 (135.118 sec)\n",
"I0503 23:33:35.960165 140215059400576 basic_session_run_hooks.py:260] loss = 0.50612265, step = 1800 (135.118 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Saving checkpoints for 1887 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"I0503 23:35:31.814470 140215059400576 basic_session_run_hooks.py:606] Saving checkpoints for 1887 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/tensorflow_core/python/training/saver.py:963: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use standard file APIs to delete files with this prefix.\n",
"W0503 23:35:31.851176 140215059400576 deprecation.py:323] From /tensorflow-1.15.2/python3.7/tensorflow_core/python/training/saver.py:963: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use standard file APIs to delete files with this prefix.\n",
"INFO:tensorflow:Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 23:35:33.899321 140215059400576 dataset_builder.py:163] Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 23:35:33.900343 140215059400576 dataset_builder.py:80] Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Number of filenames to read: 10\n",
"I0503 23:35:33.900473 140215059400576 dataset_builder.py:81] Number of filenames to read: 10\n",
"WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f8061e9df10>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"W0503 23:35:33.938002 140215059400576 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f8061e9df10>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"WARNING:tensorflow:Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f85a0369830> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"W0503 23:35:34.098869 140215059400576 ag_logging.py:146] Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f85a0369830> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0503 23:35:34.580175 140215059400576 estimator.py:1148] Calling model_fn.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:35:36.596133 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:35:36.664330 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:35:36.732290 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:35:36.799439 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:35:36.868535 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:35:36.937592 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0503 23:35:38.644671 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2021-05-03T23:35:38Z\n",
"I0503 23:35:38.659392 140215059400576 evaluation.py:255] Starting evaluation at 2021-05-03T23:35:38Z\n",
"INFO:tensorflow:Graph was finalized.\n",
"I0503 23:35:39.110249 140215059400576 monitored_session.py:240] Graph was finalized.\n",
"2021-05-03 23:35:39.110821: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:35:39.111126: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-03 23:35:39.111218: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-03 23:35:39.111240: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-03 23:35:39.111255: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-03 23:35:39.111273: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-03 23:35:39.111313: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-03 23:35:39.111326: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-03 23:35:39.111341: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-03 23:35:39.111410: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:35:39.111657: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:35:39.111844: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-03 23:35:39.111918: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-03 23:35:39.111930: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-03 23:35:39.111936: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-03 23:35:39.112023: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:35:39.112268: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:35:39.112467: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-1887\n",
"I0503 23:35:39.113397 140215059400576 saver.py:1284] Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-1887\n",
"INFO:tensorflow:Running local_init_op.\n",
"I0503 23:35:40.097286 140215059400576 session_manager.py:500] Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"I0503 23:35:40.235534 140215059400576 session_manager.py:502] Done running local_init_op.\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Performing evaluation on 2568 images.\n",
"I0503 23:36:45.215229 140210415736576 coco_evaluation.py:293] Performing evaluation on 2568 images.\n",
"creating index...\n",
"index created!\n",
"INFO:tensorflow:Loading and preparing annotation results...\n",
"I0503 23:36:45.220566 140210415736576 coco_tools.py:116] Loading and preparing annotation results...\n",
"INFO:tensorflow:DONE (t=0.19s)\n",
"I0503 23:36:45.412241 140210415736576 coco_tools.py:138] DONE (t=0.19s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=19.16s).\n",
"Accumulating evaluation results...\n",
"DONE (t=7.61s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.672\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.826\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.799\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.189\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.747\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.822\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.829\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.829\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.714\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.852\n",
"INFO:tensorflow:Finished evaluation at 2021-05-03-23:37:13\n",
"I0503 23:37:13.162960 140215059400576 evaluation.py:275] Finished evaluation at 2021-05-03-23:37:13\n",
"INFO:tensorflow:Saving dict for global step 1887: DetectionBoxes_Precision/mAP = 0.67242706, DetectionBoxes_Precision/mAP (large) = 0.74694633, DetectionBoxes_Precision/mAP (medium) = 0.18869188, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/[email protected] = 0.82617134, DetectionBoxes_Precision/[email protected] = 0.79909503, DetectionBoxes_Recall/AR@1 = 0.82155824, DetectionBoxes_Recall/AR@10 = 0.82916605, DetectionBoxes_Recall/AR@100 = 0.8292433, DetectionBoxes_Recall/AR@100 (large) = 0.85227907, DetectionBoxes_Recall/AR@100 (medium) = 0.7142042, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 0.35495389, Loss/localization_loss = 0.044486213, Loss/regularization_loss = 0.22757879, Loss/total_loss = 0.62702036, global_step = 1887, learning_rate = 0.038493313, loss = 0.62702036\n",
"I0503 23:37:13.163253 140215059400576 estimator.py:2049] Saving dict for global step 1887: DetectionBoxes_Precision/mAP = 0.67242706, DetectionBoxes_Precision/mAP (large) = 0.74694633, DetectionBoxes_Precision/mAP (medium) = 0.18869188, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/[email protected] = 0.82617134, DetectionBoxes_Precision/[email protected] = 0.79909503, DetectionBoxes_Recall/AR@1 = 0.82155824, DetectionBoxes_Recall/AR@10 = 0.82916605, DetectionBoxes_Recall/AR@100 = 0.8292433, DetectionBoxes_Recall/AR@100 (large) = 0.85227907, DetectionBoxes_Recall/AR@100 (medium) = 0.7142042, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 0.35495389, Loss/localization_loss = 0.044486213, Loss/regularization_loss = 0.22757879, Loss/total_loss = 0.62702036, global_step = 1887, learning_rate = 0.038493313, loss = 0.62702036\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1887: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-1887\n",
"I0503 23:37:13.166974 140215059400576 estimator.py:2109] Saving 'checkpoint_path' summary for global step 1887: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-1887\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.42351\n",
"I0503 23:37:32.081185 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.42351\n",
"INFO:tensorflow:loss = 0.5166437, step = 1900 (236.122 sec)\n",
"I0503 23:37:32.082311 140215059400576 basic_session_run_hooks.py:260] loss = 0.5166437, step = 1900 (236.122 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.739704\n",
"I0503 23:39:47.270425 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.739704\n",
"INFO:tensorflow:loss = 0.50334144, step = 2000 (135.189 sec)\n",
"I0503 23:39:47.271392 140215059400576 basic_session_run_hooks.py:260] loss = 0.50334144, step = 2000 (135.189 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.738424\n",
"I0503 23:42:02.694048 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.738424\n",
"INFO:tensorflow:loss = 0.50477946, step = 2100 (135.424 sec)\n",
"I0503 23:42:02.695218 140215059400576 basic_session_run_hooks.py:260] loss = 0.50477946, step = 2100 (135.424 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.740113\n",
"I0503 23:44:17.808477 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.740113\n",
"INFO:tensorflow:loss = 0.47339472, step = 2200 (135.114 sec)\n",
"I0503 23:44:17.809370 140215059400576 basic_session_run_hooks.py:260] loss = 0.47339472, step = 2200 (135.114 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Saving checkpoints for 2256 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"I0503 23:45:32.084156 140215059400576 basic_session_run_hooks.py:606] Saving checkpoints for 2256 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"INFO:tensorflow:Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 23:45:34.181569 140215059400576 dataset_builder.py:163] Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 23:45:34.182606 140215059400576 dataset_builder.py:80] Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Number of filenames to read: 10\n",
"I0503 23:45:34.182740 140215059400576 dataset_builder.py:81] Number of filenames to read: 10\n",
"WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f8064e651d0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"W0503 23:45:34.220583 140215059400576 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f8064e651d0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"WARNING:tensorflow:Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f8063b79200> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"W0503 23:45:34.381741 140215059400576 ag_logging.py:146] Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f8063b79200> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0503 23:45:34.859106 140215059400576 estimator.py:1148] Calling model_fn.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:45:36.859054 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:45:36.928016 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:45:36.996630 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:45:37.065469 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:45:37.133673 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:45:37.203128 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0503 23:45:38.929836 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2021-05-03T23:45:38Z\n",
"I0503 23:45:38.944777 140215059400576 evaluation.py:255] Starting evaluation at 2021-05-03T23:45:38Z\n",
"INFO:tensorflow:Graph was finalized.\n",
"I0503 23:45:39.392005 140215059400576 monitored_session.py:240] Graph was finalized.\n",
"2021-05-03 23:45:39.392753: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:45:39.393127: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-03 23:45:39.393243: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-03 23:45:39.393275: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-03 23:45:39.393300: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-03 23:45:39.393325: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-03 23:45:39.393353: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-03 23:45:39.393376: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-03 23:45:39.393400: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-03 23:45:39.393493: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:45:39.393766: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:45:39.393977: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-03 23:45:39.394021: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-03 23:45:39.394036: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-03 23:45:39.394046: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-03 23:45:39.394161: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:45:39.394432: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:45:39.394647: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-2256\n",
"I0503 23:45:39.395557 140215059400576 saver.py:1284] Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-2256\n",
"INFO:tensorflow:Running local_init_op.\n",
"I0503 23:45:40.371191 140215059400576 session_manager.py:500] Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"I0503 23:45:40.509582 140215059400576 session_manager.py:502] Done running local_init_op.\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Performing evaluation on 2568 images.\n",
"I0503 23:46:45.338707 140210432521984 coco_evaluation.py:293] Performing evaluation on 2568 images.\n",
"creating index...\n",
"index created!\n",
"INFO:tensorflow:Loading and preparing annotation results...\n",
"I0503 23:46:45.344563 140210432521984 coco_tools.py:116] Loading and preparing annotation results...\n",
"INFO:tensorflow:DONE (t=0.19s)\n",
"I0503 23:46:45.535475 140210432521984 coco_tools.py:138] DONE (t=0.19s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=19.42s).\n",
"Accumulating evaluation results...\n",
"DONE (t=6.71s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.720\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.864\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.841\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.241\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.788\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.840\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.845\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.845\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.713\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.870\n",
"INFO:tensorflow:Finished evaluation at 2021-05-03-23:47:12\n",
"I0503 23:47:12.657825 140215059400576 evaluation.py:275] Finished evaluation at 2021-05-03-23:47:12\n",
"INFO:tensorflow:Saving dict for global step 2256: DetectionBoxes_Precision/mAP = 0.7203577, DetectionBoxes_Precision/mAP (large) = 0.78831357, DetectionBoxes_Precision/mAP (medium) = 0.2406104, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/[email protected] = 0.86428666, DetectionBoxes_Precision/[email protected] = 0.84071743, DetectionBoxes_Recall/AR@1 = 0.83993936, DetectionBoxes_Recall/AR@10 = 0.8446778, DetectionBoxes_Recall/AR@100 = 0.8446778, DetectionBoxes_Recall/AR@100 (large) = 0.87, DetectionBoxes_Recall/AR@100 (medium) = 0.71340513, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 0.31298685, Loss/localization_loss = 0.036284216, Loss/regularization_loss = 0.22650018, Loss/total_loss = 0.57576823, global_step = 2256, learning_rate = 0.039999958, loss = 0.57576823\n",
"I0503 23:47:12.658100 140215059400576 estimator.py:2049] Saving dict for global step 2256: DetectionBoxes_Precision/mAP = 0.7203577, DetectionBoxes_Precision/mAP (large) = 0.78831357, DetectionBoxes_Precision/mAP (medium) = 0.2406104, DetectionBoxes_Precision/mAP (small) = 0.0, DetectionBoxes_Precision/[email protected] = 0.86428666, DetectionBoxes_Precision/[email protected] = 0.84071743, DetectionBoxes_Recall/AR@1 = 0.83993936, DetectionBoxes_Recall/AR@10 = 0.8446778, DetectionBoxes_Recall/AR@100 = 0.8446778, DetectionBoxes_Recall/AR@100 (large) = 0.87, DetectionBoxes_Recall/AR@100 (medium) = 0.71340513, DetectionBoxes_Recall/AR@100 (small) = 0.0, Loss/classification_loss = 0.31298685, Loss/localization_loss = 0.036284216, Loss/regularization_loss = 0.22650018, Loss/total_loss = 0.57576823, global_step = 2256, learning_rate = 0.039999958, loss = 0.57576823\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2256: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-2256\n",
"I0503 23:47:12.661755 140215059400576 estimator.py:2109] Saving 'checkpoint_path' summary for global step 2256: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-2256\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.425083\n",
"I0503 23:48:13.056861 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.425083\n",
"INFO:tensorflow:loss = 0.48110726, step = 2300 (235.248 sec)\n",
"I0503 23:48:13.057757 140215059400576 basic_session_run_hooks.py:260] loss = 0.48110726, step = 2300 (235.248 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.740782\n",
"I0503 23:50:28.049423 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.740782\n",
"INFO:tensorflow:loss = 0.4774669, step = 2400 (134.993 sec)\n",
"I0503 23:50:28.050292 140215059400576 basic_session_run_hooks.py:260] loss = 0.4774669, step = 2400 (134.993 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.74331\n",
"I0503 23:52:42.582801 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.74331\n",
"INFO:tensorflow:loss = 0.51193935, step = 2500 (134.533 sec)\n",
"I0503 23:52:42.583755 140215059400576 basic_session_run_hooks.py:260] loss = 0.51193935, step = 2500 (134.533 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.741872\n",
"I0503 23:54:57.376957 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.741872\n",
"INFO:tensorflow:loss = 0.4634264, step = 2600 (134.794 sec)\n",
"I0503 23:54:57.377939 140215059400576 basic_session_run_hooks.py:260] loss = 0.4634264, step = 2600 (134.794 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Saving checkpoints for 2627 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"I0503 23:55:32.360968 140215059400576 basic_session_run_hooks.py:606] Saving checkpoints for 2627 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"INFO:tensorflow:Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 23:55:34.447839 140215059400576 dataset_builder.py:163] Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0503 23:55:34.448860 140215059400576 dataset_builder.py:80] Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Number of filenames to read: 10\n",
"I0503 23:55:34.448984 140215059400576 dataset_builder.py:81] Number of filenames to read: 10\n",
"WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f8536566e90>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"W0503 23:55:34.486161 140215059400576 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f8536566e90>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"WARNING:tensorflow:Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f805fc77170> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"W0503 23:55:34.647705 140215059400576 ag_logging.py:146] Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f805fc77170> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0503 23:55:35.134610 140215059400576 estimator.py:1148] Calling model_fn.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:55:37.151859 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:55:37.220672 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:55:37.288931 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:55:37.356766 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:55:37.427735 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0503 23:55:37.498246 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0503 23:55:39.250312 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2021-05-03T23:55:39Z\n",
"I0503 23:55:39.264970 140215059400576 evaluation.py:255] Starting evaluation at 2021-05-03T23:55:39Z\n",
"INFO:tensorflow:Graph was finalized.\n",
"I0503 23:55:39.714994 140215059400576 monitored_session.py:240] Graph was finalized.\n",
"2021-05-03 23:55:39.715629: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:55:39.715926: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-03 23:55:39.716017: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-03 23:55:39.716039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-03 23:55:39.716054: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-03 23:55:39.716071: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-03 23:55:39.716099: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-03 23:55:39.716118: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-03 23:55:39.716130: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-03 23:55:39.716193: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:55:39.716434: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:55:39.716623: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-03 23:55:39.716697: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-03 23:55:39.716709: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-03 23:55:39.716715: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-03 23:55:39.716795: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:55:39.717024: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-03 23:55:39.717234: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-2627\n",
"I0503 23:55:39.718320 140215059400576 saver.py:1284] Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-2627\n",
"INFO:tensorflow:Running local_init_op.\n",
"I0503 23:55:40.710749 140215059400576 session_manager.py:500] Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"I0503 23:55:40.846814 140215059400576 session_manager.py:502] Done running local_init_op.\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Performing evaluation on 2568 images.\n",
"I0503 23:56:46.608262 140210432521984 coco_evaluation.py:293] Performing evaluation on 2568 images.\n",
"creating index...\n",
"index created!\n",
"INFO:tensorflow:Loading and preparing annotation results...\n",
"I0503 23:56:46.615258 140210432521984 coco_tools.py:116] Loading and preparing annotation results...\n",
"INFO:tensorflow:DONE (t=0.19s)\n",
"I0503 23:56:46.803236 140210432521984 coco_tools.py:138] DONE (t=0.19s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=19.62s).\n",
"Accumulating evaluation results...\n",
"DONE (t=6.87s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.767\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.901\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.887\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.309\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.826\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.860\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.862\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.862\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.200\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.740\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.883\n",
"INFO:tensorflow:Finished evaluation at 2021-05-03-23:57:14\n",
"I0503 23:57:14.283967 140215059400576 evaluation.py:275] Finished evaluation at 2021-05-03-23:57:14\n",
"INFO:tensorflow:Saving dict for global step 2627: DetectionBoxes_Precision/mAP = 0.76652586, DetectionBoxes_Precision/mAP (large) = 0.82630664, DetectionBoxes_Precision/mAP (medium) = 0.30866796, DetectionBoxes_Precision/mAP (small) = 0.0033663367, DetectionBoxes_Precision/[email protected] = 0.9011963, DetectionBoxes_Precision/[email protected] = 0.88666576, DetectionBoxes_Recall/AR@1 = 0.85985476, DetectionBoxes_Recall/AR@10 = 0.8622456, DetectionBoxes_Recall/AR@100 = 0.8622456, DetectionBoxes_Recall/AR@100 (large) = 0.8833723, DetectionBoxes_Recall/AR@100 (medium) = 0.74026436, DetectionBoxes_Recall/AR@100 (small) = 0.2, Loss/classification_loss = 0.2612513, Loss/localization_loss = 0.031032389, Loss/regularization_loss = 0.22521982, Loss/total_loss = 0.5175049, global_step = 2627, learning_rate = 0.039999757, loss = 0.5175049\n",
"I0503 23:57:14.284255 140215059400576 estimator.py:2049] Saving dict for global step 2627: DetectionBoxes_Precision/mAP = 0.76652586, DetectionBoxes_Precision/mAP (large) = 0.82630664, DetectionBoxes_Precision/mAP (medium) = 0.30866796, DetectionBoxes_Precision/mAP (small) = 0.0033663367, DetectionBoxes_Precision/[email protected] = 0.9011963, DetectionBoxes_Precision/[email protected] = 0.88666576, DetectionBoxes_Recall/AR@1 = 0.85985476, DetectionBoxes_Recall/AR@10 = 0.8622456, DetectionBoxes_Recall/AR@100 = 0.8622456, DetectionBoxes_Recall/AR@100 (large) = 0.8833723, DetectionBoxes_Recall/AR@100 (medium) = 0.74026436, DetectionBoxes_Recall/AR@100 (small) = 0.2, Loss/classification_loss = 0.2612513, Loss/localization_loss = 0.031032389, Loss/regularization_loss = 0.22521982, Loss/total_loss = 0.5175049, global_step = 2627, learning_rate = 0.039999757, loss = 0.5175049\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2627: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-2627\n",
"I0503 23:57:14.287927 140215059400576 estimator.py:2109] Saving 'checkpoint_path' summary for global step 2627: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-2627\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.423392\n",
"I0503 23:58:53.564939 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.423392\n",
"INFO:tensorflow:loss = 0.44238347, step = 2700 (236.188 sec)\n",
"I0503 23:58:53.566258 140215059400576 basic_session_run_hooks.py:260] loss = 0.44238347, step = 2700 (236.188 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.738639\n",
"I0504 00:01:08.949173 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.738639\n",
"INFO:tensorflow:loss = 0.44358426, step = 2800 (135.384 sec)\n",
"I0504 00:01:08.950550 140215059400576 basic_session_run_hooks.py:260] loss = 0.44358426, step = 2800 (135.384 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.739956\n",
"I0504 00:03:24.092235 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.739956\n",
"INFO:tensorflow:loss = 0.4026372, step = 2900 (135.143 sec)\n",
"I0504 00:03:24.093274 140215059400576 basic_session_run_hooks.py:260] loss = 0.4026372, step = 2900 (135.143 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Saving checkpoints for 2996 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"I0504 00:05:32.992605 140215059400576 basic_session_run_hooks.py:606] Saving checkpoints for 2996 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"INFO:tensorflow:Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0504 00:05:35.185099 140215059400576 dataset_builder.py:163] Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0504 00:05:35.186123 140215059400576 dataset_builder.py:80] Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Number of filenames to read: 10\n",
"I0504 00:05:35.186289 140215059400576 dataset_builder.py:81] Number of filenames to read: 10\n",
"WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f85c66c8410>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"W0504 00:05:35.223627 140215059400576 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f85c66c8410>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"WARNING:tensorflow:Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f85ba63fa70> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"W0504 00:05:35.383057 140215059400576 ag_logging.py:146] Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f85ba63fa70> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0504 00:05:35.860261 140215059400576 estimator.py:1148] Calling model_fn.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:05:37.884728 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:05:37.954850 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:05:38.026676 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:05:38.096376 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:05:38.166676 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:05:38.238041 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0504 00:05:39.966716 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2021-05-04T00:05:39Z\n",
"I0504 00:05:39.981636 140215059400576 evaluation.py:255] Starting evaluation at 2021-05-04T00:05:39Z\n",
"INFO:tensorflow:Graph was finalized.\n",
"I0504 00:05:40.430765 140215059400576 monitored_session.py:240] Graph was finalized.\n",
"2021-05-04 00:05:40.431482: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:05:40.431842: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-04 00:05:40.431951: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-04 00:05:40.431975: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-04 00:05:40.431991: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-04 00:05:40.432008: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-04 00:05:40.432033: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-04 00:05:40.432046: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-04 00:05:40.432061: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-04 00:05:40.432152: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:05:40.432400: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:05:40.432589: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-04 00:05:40.432628: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-04 00:05:40.432639: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-04 00:05:40.432654: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-04 00:05:40.432742: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:05:40.432973: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:05:40.433184: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-2996\n",
"I0504 00:05:40.434189 140215059400576 saver.py:1284] Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-2996\n",
"INFO:tensorflow:Running local_init_op.\n",
"I0504 00:05:41.416208 140215059400576 session_manager.py:500] Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"I0504 00:05:41.550564 140215059400576 session_manager.py:502] Done running local_init_op.\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Performing evaluation on 2568 images.\n",
"I0504 00:06:46.620033 140210432521984 coco_evaluation.py:293] Performing evaluation on 2568 images.\n",
"creating index...\n",
"index created!\n",
"INFO:tensorflow:Loading and preparing annotation results...\n",
"I0504 00:06:46.625624 140210432521984 coco_tools.py:116] Loading and preparing annotation results...\n",
"INFO:tensorflow:DONE (t=0.21s)\n",
"I0504 00:06:46.832377 140210432521984 coco_tools.py:138] DONE (t=0.21s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=19.79s).\n",
"Accumulating evaluation results...\n",
"DONE (t=6.77s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.773\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.920\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.903\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.322\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.830\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.851\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.855\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.855\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.730\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.877\n",
"INFO:tensorflow:Finished evaluation at 2021-05-04-00:07:14\n",
"I0504 00:07:14.388152 140215059400576 evaluation.py:275] Finished evaluation at 2021-05-04-00:07:14\n",
"INFO:tensorflow:Saving dict for global step 2996: DetectionBoxes_Precision/mAP = 0.77297103, DetectionBoxes_Precision/mAP (large) = 0.82952887, DetectionBoxes_Precision/mAP (medium) = 0.3219404, DetectionBoxes_Precision/mAP (small) = 0.00050495047, DetectionBoxes_Precision/[email protected] = 0.9201254, DetectionBoxes_Precision/[email protected] = 0.903216, DetectionBoxes_Recall/AR@1 = 0.8508792, DetectionBoxes_Recall/AR@10 = 0.85464895, DetectionBoxes_Recall/AR@100 = 0.85472006, DetectionBoxes_Recall/AR@100 (large) = 0.8771587, DetectionBoxes_Recall/AR@100 (medium) = 0.7297572, DetectionBoxes_Recall/AR@100 (small) = 0.1, Loss/classification_loss = 0.2320339, Loss/localization_loss = 0.033271253, Loss/regularization_loss = 0.22389893, Loss/total_loss = 0.4892042, global_step = 2996, learning_rate = 0.03999938, loss = 0.4892042\n",
"I0504 00:07:14.388409 140215059400576 estimator.py:2049] Saving dict for global step 2996: DetectionBoxes_Precision/mAP = 0.77297103, DetectionBoxes_Precision/mAP (large) = 0.82952887, DetectionBoxes_Precision/mAP (medium) = 0.3219404, DetectionBoxes_Precision/mAP (small) = 0.00050495047, DetectionBoxes_Precision/[email protected] = 0.9201254, DetectionBoxes_Precision/[email protected] = 0.903216, DetectionBoxes_Recall/AR@1 = 0.8508792, DetectionBoxes_Recall/AR@10 = 0.85464895, DetectionBoxes_Recall/AR@100 = 0.85472006, DetectionBoxes_Recall/AR@100 (large) = 0.8771587, DetectionBoxes_Recall/AR@100 (medium) = 0.7297572, DetectionBoxes_Recall/AR@100 (small) = 0.1, Loss/classification_loss = 0.2320339, Loss/localization_loss = 0.033271253, Loss/regularization_loss = 0.22389893, Loss/total_loss = 0.4892042, global_step = 2996, learning_rate = 0.03999938, loss = 0.4892042\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2996: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-2996\n",
"I0504 00:07:14.392197 140215059400576 estimator.py:2109] Saving 'checkpoint_path' summary for global step 2996: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-2996\n",
"INFO:tensorflow:global_step/sec: 0.421709\n",
"I0504 00:07:21.222511 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.421709\n",
"INFO:tensorflow:loss = 0.40506738, step = 3000 (237.130 sec)\n",
"I0504 00:07:21.223703 140215059400576 basic_session_run_hooks.py:260] loss = 0.40506738, step = 3000 (237.130 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.738826\n",
"I0504 00:09:36.572391 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.738826\n",
"INFO:tensorflow:loss = 0.4035138, step = 3100 (135.350 sec)\n",
"I0504 00:09:36.573441 140215059400576 basic_session_run_hooks.py:260] loss = 0.4035138, step = 3100 (135.350 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.742076\n",
"I0504 00:11:51.329473 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.742076\n",
"INFO:tensorflow:loss = 0.3989345, step = 3200 (134.757 sec)\n",
"I0504 00:11:51.330450 140215059400576 basic_session_run_hooks.py:260] loss = 0.3989345, step = 3200 (134.757 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.740713\n",
"I0504 00:14:06.334585 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.740713\n",
"INFO:tensorflow:loss = 0.39785632, step = 3300 (135.005 sec)\n",
"I0504 00:14:06.335711 140215059400576 basic_session_run_hooks.py:260] loss = 0.39785632, step = 3300 (135.005 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Saving checkpoints for 3366 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"I0504 00:15:33.556672 140215059400576 basic_session_run_hooks.py:606] Saving checkpoints for 3366 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"INFO:tensorflow:Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0504 00:15:35.672658 140215059400576 dataset_builder.py:163] Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0504 00:15:35.673603 140215059400576 dataset_builder.py:80] Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Number of filenames to read: 10\n",
"I0504 00:15:35.673736 140215059400576 dataset_builder.py:81] Number of filenames to read: 10\n",
"WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f8061a0bf50>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"W0504 00:15:35.711191 140215059400576 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f8061a0bf50>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"WARNING:tensorflow:Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f805cd06d40> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"W0504 00:15:35.870846 140215059400576 ag_logging.py:146] Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f805cd06d40> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0504 00:15:36.724589 140215059400576 estimator.py:1148] Calling model_fn.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:15:38.753186 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:15:38.822665 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:15:38.899943 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:15:38.970214 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:15:39.044627 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:15:39.116199 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0504 00:15:40.858367 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2021-05-04T00:15:40Z\n",
"I0504 00:15:40.873203 140215059400576 evaluation.py:255] Starting evaluation at 2021-05-04T00:15:40Z\n",
"INFO:tensorflow:Graph was finalized.\n",
"I0504 00:15:41.351674 140215059400576 monitored_session.py:240] Graph was finalized.\n",
"2021-05-04 00:15:41.352276: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:15:41.352571: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-04 00:15:41.352665: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-04 00:15:41.352688: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-04 00:15:41.352702: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-04 00:15:41.352720: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-04 00:15:41.352739: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-04 00:15:41.352752: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-04 00:15:41.352767: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-04 00:15:41.352834: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:15:41.353077: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:15:41.353303: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-04 00:15:41.353392: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-04 00:15:41.353408: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-04 00:15:41.353417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-04 00:15:41.353534: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:15:41.353810: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:15:41.354072: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-3366\n",
"I0504 00:15:41.355482 140215059400576 saver.py:1284] Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-3366\n",
"INFO:tensorflow:Running local_init_op.\n",
"I0504 00:15:42.373521 140215059400576 session_manager.py:500] Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"I0504 00:15:42.529315 140215059400576 session_manager.py:502] Done running local_init_op.\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Performing evaluation on 2568 images.\n",
"I0504 00:16:48.795337 140210424129280 coco_evaluation.py:293] Performing evaluation on 2568 images.\n",
"creating index...\n",
"index created!\n",
"INFO:tensorflow:Loading and preparing annotation results...\n",
"I0504 00:16:48.813607 140210424129280 coco_tools.py:116] Loading and preparing annotation results...\n",
"INFO:tensorflow:DONE (t=0.21s)\n",
"I0504 00:16:49.026471 140210424129280 coco_tools.py:138] DONE (t=0.21s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=19.41s).\n",
"Accumulating evaluation results...\n",
"DONE (t=7.61s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.714\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.885\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.843\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.307\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.768\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.815\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.827\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.827\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.698\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.852\n",
"INFO:tensorflow:Finished evaluation at 2021-05-04-00:17:17\n",
"I0504 00:17:17.009499 140215059400576 evaluation.py:275] Finished evaluation at 2021-05-04-00:17:17\n",
"INFO:tensorflow:Saving dict for global step 3366: DetectionBoxes_Precision/mAP = 0.7135667, DetectionBoxes_Precision/mAP (large) = 0.7683116, DetectionBoxes_Precision/mAP (medium) = 0.30692887, DetectionBoxes_Precision/mAP (small) = 0.00068236556, DetectionBoxes_Precision/[email protected] = 0.8847008, DetectionBoxes_Precision/[email protected] = 0.8431751, DetectionBoxes_Recall/AR@1 = 0.8154388, DetectionBoxes_Recall/AR@10 = 0.8272542, DetectionBoxes_Recall/AR@100 = 0.8273293, DetectionBoxes_Recall/AR@100 (large) = 0.8517159, DetectionBoxes_Recall/AR@100 (medium) = 0.6981567, DetectionBoxes_Recall/AR@100 (small) = 0.1, Loss/classification_loss = 0.29136416, Loss/localization_loss = 0.047467694, Loss/regularization_loss = 0.2224056, Loss/total_loss = 0.561232, global_step = 3366, learning_rate = 0.039998837, loss = 0.561232\n",
"I0504 00:17:17.009752 140215059400576 estimator.py:2049] Saving dict for global step 3366: DetectionBoxes_Precision/mAP = 0.7135667, DetectionBoxes_Precision/mAP (large) = 0.7683116, DetectionBoxes_Precision/mAP (medium) = 0.30692887, DetectionBoxes_Precision/mAP (small) = 0.00068236556, DetectionBoxes_Precision/[email protected] = 0.8847008, DetectionBoxes_Precision/[email protected] = 0.8431751, DetectionBoxes_Recall/AR@1 = 0.8154388, DetectionBoxes_Recall/AR@10 = 0.8272542, DetectionBoxes_Recall/AR@100 = 0.8273293, DetectionBoxes_Recall/AR@100 (large) = 0.8517159, DetectionBoxes_Recall/AR@100 (medium) = 0.6981567, DetectionBoxes_Recall/AR@100 (small) = 0.1, Loss/classification_loss = 0.29136416, Loss/localization_loss = 0.047467694, Loss/regularization_loss = 0.2224056, Loss/total_loss = 0.561232, global_step = 3366, learning_rate = 0.039998837, loss = 0.561232\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3366: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-3366\n",
"I0504 00:17:17.013588 140215059400576 estimator.py:2109] Saving 'checkpoint_path' summary for global step 3366: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-3366\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.419808\n",
"I0504 00:18:04.538462 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.419808\n",
"INFO:tensorflow:loss = 0.37604678, step = 3400 (238.204 sec)\n",
"I0504 00:18:04.539533 140215059400576 basic_session_run_hooks.py:260] loss = 0.37604678, step = 3400 (238.204 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.736851\n",
"I0504 00:20:20.251132 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.736851\n",
"INFO:tensorflow:loss = 0.37406266, step = 3500 (135.713 sec)\n",
"I0504 00:20:20.252358 140215059400576 basic_session_run_hooks.py:260] loss = 0.37406266, step = 3500 (135.713 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.742703\n",
"I0504 00:22:34.894427 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.742703\n",
"INFO:tensorflow:loss = 0.39673847, step = 3600 (134.643 sec)\n",
"I0504 00:22:34.895339 140215059400576 basic_session_run_hooks.py:260] loss = 0.39673847, step = 3600 (134.643 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.741718\n",
"I0504 00:24:49.716670 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.741718\n",
"INFO:tensorflow:loss = 0.39229053, step = 3700 (134.822 sec)\n",
"I0504 00:24:49.717634 140215059400576 basic_session_run_hooks.py:260] loss = 0.39229053, step = 3700 (134.822 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Saving checkpoints for 3734 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"I0504 00:25:34.346060 140215059400576 basic_session_run_hooks.py:606] Saving checkpoints for 3734 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"INFO:tensorflow:Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0504 00:25:36.445817 140215059400576 dataset_builder.py:163] Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0504 00:25:36.446804 140215059400576 dataset_builder.py:80] Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Number of filenames to read: 10\n",
"I0504 00:25:36.446929 140215059400576 dataset_builder.py:81] Number of filenames to read: 10\n",
"WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f8047904f10>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"W0504 00:25:36.484265 140215059400576 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f8047904f10>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"WARNING:tensorflow:Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f85285340e0> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"W0504 00:25:36.645600 140215059400576 ag_logging.py:146] Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f85285340e0> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0504 00:25:37.124161 140215059400576 estimator.py:1148] Calling model_fn.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:25:39.123061 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:25:39.192206 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:25:39.262681 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:25:39.331016 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:25:39.399062 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:25:39.472804 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0504 00:25:41.188759 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2021-05-04T00:25:41Z\n",
"I0504 00:25:41.203554 140215059400576 evaluation.py:255] Starting evaluation at 2021-05-04T00:25:41Z\n",
"INFO:tensorflow:Graph was finalized.\n",
"I0504 00:25:41.659526 140215059400576 monitored_session.py:240] Graph was finalized.\n",
"2021-05-04 00:25:41.660275: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:25:41.660651: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-04 00:25:41.660769: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-04 00:25:41.660802: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-04 00:25:41.660827: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-04 00:25:41.660851: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-04 00:25:41.660874: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-04 00:25:41.660895: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-04 00:25:41.660917: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-04 00:25:41.661007: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:25:41.661342: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:25:41.661565: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-04 00:25:41.661610: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-04 00:25:41.661623: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-04 00:25:41.661633: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-04 00:25:41.661729: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:25:41.662015: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:25:41.662275: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-3734\n",
"I0504 00:25:41.663229 140215059400576 saver.py:1284] Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-3734\n",
"INFO:tensorflow:Running local_init_op.\n",
"I0504 00:25:42.661723 140215059400576 session_manager.py:500] Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"I0504 00:25:42.803506 140215059400576 session_manager.py:502] Done running local_init_op.\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Performing evaluation on 2568 images.\n",
"I0504 00:26:48.375295 140210415736576 coco_evaluation.py:293] Performing evaluation on 2568 images.\n",
"creating index...\n",
"index created!\n",
"INFO:tensorflow:Loading and preparing annotation results...\n",
"I0504 00:26:48.382606 140210415736576 coco_tools.py:116] Loading and preparing annotation results...\n",
"INFO:tensorflow:DONE (t=0.19s)\n",
"I0504 00:26:48.575759 140210415736576 coco_tools.py:138] DONE (t=0.19s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=19.74s).\n",
"Accumulating evaluation results...\n",
"DONE (t=6.98s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.834\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.957\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.947\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.006\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.457\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.874\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.885\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.889\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.889\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.400\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.782\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.907\n",
"INFO:tensorflow:Finished evaluation at 2021-05-04-00:27:16\n",
"I0504 00:27:16.270267 140215059400576 evaluation.py:275] Finished evaluation at 2021-05-04-00:27:16\n",
"INFO:tensorflow:Saving dict for global step 3734: DetectionBoxes_Precision/mAP = 0.83393395, DetectionBoxes_Precision/mAP (large) = 0.8735613, DetectionBoxes_Precision/mAP (medium) = 0.4568373, DetectionBoxes_Precision/mAP (small) = 0.006100533, DetectionBoxes_Precision/[email protected] = 0.9569519, DetectionBoxes_Precision/[email protected] = 0.9465632, DetectionBoxes_Recall/AR@1 = 0.8853066, DetectionBoxes_Recall/AR@10 = 0.8890051, DetectionBoxes_Recall/AR@100 = 0.8890051, DetectionBoxes_Recall/AR@100 (large) = 0.90670025, DetectionBoxes_Recall/AR@100 (medium) = 0.7820043, DetectionBoxes_Recall/AR@100 (small) = 0.4, Loss/classification_loss = 0.17915349, Loss/localization_loss = 0.023984391, Loss/regularization_loss = 0.22084594, Loss/total_loss = 0.42398, global_step = 3734, learning_rate = 0.039998125, loss = 0.42398\n",
"I0504 00:27:16.270514 140215059400576 estimator.py:2049] Saving dict for global step 3734: DetectionBoxes_Precision/mAP = 0.83393395, DetectionBoxes_Precision/mAP (large) = 0.8735613, DetectionBoxes_Precision/mAP (medium) = 0.4568373, DetectionBoxes_Precision/mAP (small) = 0.006100533, DetectionBoxes_Precision/[email protected] = 0.9569519, DetectionBoxes_Precision/[email protected] = 0.9465632, DetectionBoxes_Recall/AR@1 = 0.8853066, DetectionBoxes_Recall/AR@10 = 0.8890051, DetectionBoxes_Recall/AR@100 = 0.8890051, DetectionBoxes_Recall/AR@100 (large) = 0.90670025, DetectionBoxes_Recall/AR@100 (medium) = 0.7820043, DetectionBoxes_Recall/AR@100 (small) = 0.4, Loss/classification_loss = 0.17915349, Loss/localization_loss = 0.023984391, Loss/regularization_loss = 0.22084594, Loss/total_loss = 0.42398, global_step = 3734, learning_rate = 0.039998125, loss = 0.42398\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3734: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-3734\n",
"I0504 00:27:16.274342 140215059400576 estimator.py:2109] Saving 'checkpoint_path' summary for global step 3734: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-3734\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.421316\n",
"I0504 00:28:47.068329 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.421316\n",
"INFO:tensorflow:loss = 0.39515993, step = 3800 (237.352 sec)\n",
"I0504 00:28:47.069396 140215059400576 basic_session_run_hooks.py:260] loss = 0.39515993, step = 3800 (237.352 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.740153\n",
"I0504 00:31:02.175557 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.740153\n",
"INFO:tensorflow:loss = 0.37139446, step = 3900 (135.107 sec)\n",
"I0504 00:31:02.176748 140215059400576 basic_session_run_hooks.py:260] loss = 0.37139446, step = 3900 (135.107 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.740383\n",
"I0504 00:33:17.240847 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.740383\n",
"INFO:tensorflow:loss = 0.40161628, step = 4000 (135.065 sec)\n",
"I0504 00:33:17.241988 140215059400576 basic_session_run_hooks.py:260] loss = 0.40161628, step = 4000 (135.065 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.740292\n",
"I0504 00:35:32.322628 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.740292\n",
"INFO:tensorflow:loss = 0.383332, step = 4100 (135.082 sec)\n",
"I0504 00:35:32.323656 140215059400576 basic_session_run_hooks.py:260] loss = 0.383332, step = 4100 (135.082 sec)\n",
"INFO:tensorflow:Saving checkpoints for 4103 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"I0504 00:35:35.057940 140215059400576 basic_session_run_hooks.py:606] Saving checkpoints for 4103 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"INFO:tensorflow:Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0504 00:35:37.166390 140215059400576 dataset_builder.py:163] Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0504 00:35:37.167422 140215059400576 dataset_builder.py:80] Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Number of filenames to read: 10\n",
"I0504 00:35:37.167551 140215059400576 dataset_builder.py:81] Number of filenames to read: 10\n",
"WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f8063900f50>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"W0504 00:35:37.204989 140215059400576 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f8063900f50>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"WARNING:tensorflow:Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f80646d0170> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"W0504 00:35:37.365764 140215059400576 ag_logging.py:146] Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f80646d0170> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0504 00:35:37.850458 140215059400576 estimator.py:1148] Calling model_fn.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:35:39.881611 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:35:39.950798 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:35:40.020510 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:35:40.088593 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:35:40.159373 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:35:40.228047 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0504 00:35:41.970594 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2021-05-04T00:35:41Z\n",
"I0504 00:35:41.985589 140215059400576 evaluation.py:255] Starting evaluation at 2021-05-04T00:35:41Z\n",
"INFO:tensorflow:Graph was finalized.\n",
"I0504 00:35:42.442586 140215059400576 monitored_session.py:240] Graph was finalized.\n",
"2021-05-04 00:35:42.443217: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:35:42.443514: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-04 00:35:42.443611: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-04 00:35:42.443630: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-04 00:35:42.443643: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-04 00:35:42.443658: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-04 00:35:42.443672: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-04 00:35:42.443686: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-04 00:35:42.443700: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-04 00:35:42.443777: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:35:42.444014: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:35:42.444218: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-04 00:35:42.444297: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-04 00:35:42.444308: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-04 00:35:42.444315: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-04 00:35:42.444407: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:35:42.444642: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:35:42.444849: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-4103\n",
"I0504 00:35:42.448815 140215059400576 saver.py:1284] Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-4103\n",
"INFO:tensorflow:Running local_init_op.\n",
"I0504 00:35:43.439375 140215059400576 session_manager.py:500] Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"I0504 00:35:43.582256 140215059400576 session_manager.py:502] Done running local_init_op.\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Performing evaluation on 2568 images.\n",
"I0504 00:36:50.223706 140210440914688 coco_evaluation.py:293] Performing evaluation on 2568 images.\n",
"creating index...\n",
"index created!\n",
"INFO:tensorflow:Loading and preparing annotation results...\n",
"I0504 00:36:50.232547 140210440914688 coco_tools.py:116] Loading and preparing annotation results...\n",
"INFO:tensorflow:DONE (t=0.19s)\n",
"I0504 00:36:50.418372 140210440914688 coco_tools.py:138] DONE (t=0.19s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=19.73s).\n",
"Accumulating evaluation results...\n",
"DONE (t=6.79s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.832\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.962\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.948\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.034\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.484\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.871\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.881\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.884\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.884\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.100\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.758\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.906\n",
"INFO:tensorflow:Finished evaluation at 2021-05-04-00:37:17\n",
"I0504 00:37:17.920741 140215059400576 evaluation.py:275] Finished evaluation at 2021-05-04-00:37:17\n",
"INFO:tensorflow:Saving dict for global step 4103: DetectionBoxes_Precision/mAP = 0.8317826, DetectionBoxes_Precision/mAP (large) = 0.87101597, DetectionBoxes_Precision/mAP (medium) = 0.4841115, DetectionBoxes_Precision/mAP (small) = 0.033663366, DetectionBoxes_Precision/[email protected] = 0.96235496, DetectionBoxes_Precision/[email protected] = 0.94771767, DetectionBoxes_Recall/AR@1 = 0.88121945, DetectionBoxes_Recall/AR@10 = 0.88393545, DetectionBoxes_Recall/AR@100 = 0.88393545, DetectionBoxes_Recall/AR@100 (large) = 0.90570116, DetectionBoxes_Recall/AR@100 (medium) = 0.7580175, DetectionBoxes_Recall/AR@100 (small) = 0.1, Loss/classification_loss = 0.16863383, Loss/localization_loss = 0.025558911, Loss/regularization_loss = 0.21923961, Loss/total_loss = 0.4134348, global_step = 4103, learning_rate = 0.039997242, loss = 0.4134348\n",
"I0504 00:37:17.921006 140215059400576 estimator.py:2049] Saving dict for global step 4103: DetectionBoxes_Precision/mAP = 0.8317826, DetectionBoxes_Precision/mAP (large) = 0.87101597, DetectionBoxes_Precision/mAP (medium) = 0.4841115, DetectionBoxes_Precision/mAP (small) = 0.033663366, DetectionBoxes_Precision/[email protected] = 0.96235496, DetectionBoxes_Precision/[email protected] = 0.94771767, DetectionBoxes_Recall/AR@1 = 0.88121945, DetectionBoxes_Recall/AR@10 = 0.88393545, DetectionBoxes_Recall/AR@100 = 0.88393545, DetectionBoxes_Recall/AR@100 (large) = 0.90570116, DetectionBoxes_Recall/AR@100 (medium) = 0.7580175, DetectionBoxes_Recall/AR@100 (small) = 0.1, Loss/classification_loss = 0.16863383, Loss/localization_loss = 0.025558911, Loss/regularization_loss = 0.21923961, Loss/total_loss = 0.4134348, global_step = 4103, learning_rate = 0.039997242, loss = 0.4134348\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4103: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-4103\n",
"I0504 00:37:17.925047 140215059400576 estimator.py:2109] Saving 'checkpoint_path' summary for global step 4103: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-4103\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.418877\n",
"I0504 00:39:31.056219 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.418877\n",
"INFO:tensorflow:loss = 0.41872814, step = 4200 (238.734 sec)\n",
"I0504 00:39:31.057484 140215059400576 basic_session_run_hooks.py:260] loss = 0.41872814, step = 4200 (238.734 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.740372\n",
"I0504 00:41:46.123488 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.740372\n",
"INFO:tensorflow:loss = 0.38020697, step = 4300 (135.067 sec)\n",
"I0504 00:41:46.124709 140215059400576 basic_session_run_hooks.py:260] loss = 0.38020697, step = 4300 (135.067 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.738541\n",
"I0504 00:44:01.525508 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.738541\n",
"INFO:tensorflow:loss = 0.34623438, step = 4400 (135.402 sec)\n",
"I0504 00:44:01.526607 140215059400576 basic_session_run_hooks.py:260] loss = 0.34623438, step = 4400 (135.402 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Saving checkpoints for 4471 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"I0504 00:45:36.171920 140215059400576 basic_session_run_hooks.py:606] Saving checkpoints for 4471 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"INFO:tensorflow:Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0504 00:45:38.285873 140215059400576 dataset_builder.py:163] Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0504 00:45:38.286848 140215059400576 dataset_builder.py:80] Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Number of filenames to read: 10\n",
"I0504 00:45:38.286989 140215059400576 dataset_builder.py:81] Number of filenames to read: 10\n",
"WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f805d93c6d0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"W0504 00:45:38.325303 140215059400576 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f805d93c6d0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"WARNING:tensorflow:Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f805cd06d40> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"W0504 00:45:38.486684 140215059400576 ag_logging.py:146] Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f805cd06d40> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0504 00:45:38.964005 140215059400576 estimator.py:1148] Calling model_fn.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:45:40.985460 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:45:41.054814 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:45:41.123134 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:45:41.191340 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:45:41.262410 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:45:41.331383 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0504 00:45:43.070206 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2021-05-04T00:45:43Z\n",
"I0504 00:45:43.085019 140215059400576 evaluation.py:255] Starting evaluation at 2021-05-04T00:45:43Z\n",
"INFO:tensorflow:Graph was finalized.\n",
"I0504 00:45:43.531547 140215059400576 monitored_session.py:240] Graph was finalized.\n",
"2021-05-04 00:45:43.532221: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:45:43.532569: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-04 00:45:43.532679: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-04 00:45:43.532699: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-04 00:45:43.532717: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-04 00:45:43.532733: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-04 00:45:43.532747: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-04 00:45:43.532763: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-04 00:45:43.532781: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-04 00:45:43.532852: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:45:43.533145: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:45:43.533352: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-04 00:45:43.533390: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-04 00:45:43.533414: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-04 00:45:43.533420: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-04 00:45:43.533513: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:45:43.533737: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:45:43.533935: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-4471\n",
"I0504 00:45:43.534970 140215059400576 saver.py:1284] Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-4471\n",
"INFO:tensorflow:Running local_init_op.\n",
"I0504 00:45:44.609776 140215059400576 session_manager.py:500] Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"I0504 00:45:44.770944 140215059400576 session_manager.py:502] Done running local_init_op.\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Performing evaluation on 2568 images.\n",
"I0504 00:46:51.560856 140210424129280 coco_evaluation.py:293] Performing evaluation on 2568 images.\n",
"creating index...\n",
"index created!\n",
"INFO:tensorflow:Loading and preparing annotation results...\n",
"I0504 00:46:51.567034 140210424129280 coco_tools.py:116] Loading and preparing annotation results...\n",
"INFO:tensorflow:DONE (t=0.20s)\n",
"I0504 00:46:51.771404 140210424129280 coco_tools.py:138] DONE (t=0.20s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=19.81s).\n",
"Accumulating evaluation results...\n",
"DONE (t=6.68s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.857\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.980\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.969\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.017\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.599\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.886\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.892\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.894\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.894\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.200\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.792\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.912\n",
"INFO:tensorflow:Finished evaluation at 2021-05-04-00:47:19\n",
"I0504 00:47:19.259883 140215059400576 evaluation.py:275] Finished evaluation at 2021-05-04-00:47:19\n",
"INFO:tensorflow:Saving dict for global step 4471: DetectionBoxes_Precision/mAP = 0.85685056, DetectionBoxes_Precision/mAP (large) = 0.8855686, DetectionBoxes_Precision/mAP (medium) = 0.5988093, DetectionBoxes_Precision/mAP (small) = 0.016831683, DetectionBoxes_Precision/[email protected] = 0.9797586, DetectionBoxes_Precision/[email protected] = 0.96924186, DetectionBoxes_Recall/AR@1 = 0.8917457, DetectionBoxes_Recall/AR@10 = 0.8937107, DetectionBoxes_Recall/AR@100 = 0.8937107, DetectionBoxes_Recall/AR@100 (large) = 0.91176426, DetectionBoxes_Recall/AR@100 (medium) = 0.7915283, DetectionBoxes_Recall/AR@100 (small) = 0.2, Loss/classification_loss = 0.13611425, Loss/localization_loss = 0.02159942, Loss/regularization_loss = 0.21761687, Loss/total_loss = 0.37532955, global_step = 4471, learning_rate = 0.039996196, loss = 0.37532955\n",
"I0504 00:47:19.260176 140215059400576 estimator.py:2049] Saving dict for global step 4471: DetectionBoxes_Precision/mAP = 0.85685056, DetectionBoxes_Precision/mAP (large) = 0.8855686, DetectionBoxes_Precision/mAP (medium) = 0.5988093, DetectionBoxes_Precision/mAP (small) = 0.016831683, DetectionBoxes_Precision/[email protected] = 0.9797586, DetectionBoxes_Precision/[email protected] = 0.96924186, DetectionBoxes_Recall/AR@1 = 0.8917457, DetectionBoxes_Recall/AR@10 = 0.8937107, DetectionBoxes_Recall/AR@100 = 0.8937107, DetectionBoxes_Recall/AR@100 (large) = 0.91176426, DetectionBoxes_Recall/AR@100 (medium) = 0.7915283, DetectionBoxes_Recall/AR@100 (small) = 0.2, Loss/classification_loss = 0.13611425, Loss/localization_loss = 0.02159942, Loss/regularization_loss = 0.21761687, Loss/total_loss = 0.37532955, global_step = 4471, learning_rate = 0.039996196, loss = 0.37532955\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4471: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-4471\n",
"I0504 00:47:19.263885 140215059400576 estimator.py:2109] Saving 'checkpoint_path' summary for global step 4471: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-4471\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.419648\n",
"I0504 00:47:59.820444 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.419648\n",
"INFO:tensorflow:loss = 0.32304123, step = 4500 (238.295 sec)\n",
"I0504 00:47:59.821493 140215059400576 basic_session_run_hooks.py:260] loss = 0.32304123, step = 4500 (238.295 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.738003\n",
"I0504 00:50:15.321199 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.738003\n",
"INFO:tensorflow:loss = 0.38807705, step = 4600 (135.501 sec)\n",
"I0504 00:50:15.322352 140215059400576 basic_session_run_hooks.py:260] loss = 0.38807705, step = 4600 (135.501 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.739737\n",
"I0504 00:52:30.504318 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.739737\n",
"INFO:tensorflow:loss = 0.33238897, step = 4700 (135.183 sec)\n",
"I0504 00:52:30.505529 140215059400576 basic_session_run_hooks.py:260] loss = 0.33238897, step = 4700 (135.183 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.735586\n",
"I0504 00:54:46.450327 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.735586\n",
"INFO:tensorflow:loss = 0.39697814, step = 4800 (135.946 sec)\n",
"I0504 00:54:46.451421 140215059400576 basic_session_run_hooks.py:260] loss = 0.39697814, step = 4800 (135.946 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Saving checkpoints for 4838 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"I0504 00:55:36.339880 140215059400576 basic_session_run_hooks.py:606] Saving checkpoints for 4838 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"INFO:tensorflow:Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0504 00:55:38.447259 140215059400576 dataset_builder.py:163] Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0504 00:55:38.448234 140215059400576 dataset_builder.py:80] Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Number of filenames to read: 10\n",
"I0504 00:55:38.448386 140215059400576 dataset_builder.py:81] Number of filenames to read: 10\n",
"WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f8060f32f50>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"W0504 00:55:38.485988 140215059400576 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f8060f32f50>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"WARNING:tensorflow:Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f805d37d830> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"W0504 00:55:38.655124 140215059400576 ag_logging.py:146] Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f805d37d830> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0504 00:55:39.201994 140215059400576 estimator.py:1148] Calling model_fn.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:55:41.364953 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:55:41.434043 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:55:41.502981 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:55:41.571713 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:55:41.639482 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 00:55:41.711484 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0504 00:55:43.432304 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2021-05-04T00:55:43Z\n",
"I0504 00:55:43.447510 140215059400576 evaluation.py:255] Starting evaluation at 2021-05-04T00:55:43Z\n",
"INFO:tensorflow:Graph was finalized.\n",
"I0504 00:55:43.906028 140215059400576 monitored_session.py:240] Graph was finalized.\n",
"2021-05-04 00:55:43.906670: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:55:43.906977: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-04 00:55:43.907096: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-04 00:55:43.907132: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-04 00:55:43.907159: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-04 00:55:43.907183: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-04 00:55:43.907205: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-04 00:55:43.907226: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-04 00:55:43.907248: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-04 00:55:43.907335: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:55:43.907614: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:55:43.907829: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-04 00:55:43.907917: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-04 00:55:43.907932: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-04 00:55:43.907942: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-04 00:55:43.908043: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:55:43.908316: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 00:55:43.908537: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-4838\n",
"I0504 00:55:43.909493 140215059400576 saver.py:1284] Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-4838\n",
"INFO:tensorflow:Running local_init_op.\n",
"I0504 00:55:44.964654 140215059400576 session_manager.py:500] Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"I0504 00:55:45.104933 140215059400576 session_manager.py:502] Done running local_init_op.\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Performing evaluation on 2568 images.\n",
"I0504 00:56:52.510566 140210415736576 coco_evaluation.py:293] Performing evaluation on 2568 images.\n",
"creating index...\n",
"index created!\n",
"INFO:tensorflow:Loading and preparing annotation results...\n",
"I0504 00:56:52.518196 140210415736576 coco_tools.py:116] Loading and preparing annotation results...\n",
"INFO:tensorflow:DONE (t=0.19s)\n",
"I0504 00:56:52.708378 140210415736576 coco_tools.py:138] DONE (t=0.19s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=19.40s).\n",
"Accumulating evaluation results...\n",
"DONE (t=6.77s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.842\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.971\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.954\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.252\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.578\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.874\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.881\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.885\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.885\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.250\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.765\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.905\n",
"INFO:tensorflow:Finished evaluation at 2021-05-04-00:57:19\n",
"I0504 00:57:19.849217 140215059400576 evaluation.py:275] Finished evaluation at 2021-05-04-00:57:19\n",
"INFO:tensorflow:Saving dict for global step 4838: DetectionBoxes_Precision/mAP = 0.8417523, DetectionBoxes_Precision/mAP (large) = 0.87389356, DetectionBoxes_Precision/mAP (medium) = 0.5784151, DetectionBoxes_Precision/mAP (small) = 0.25247526, DetectionBoxes_Precision/[email protected] = 0.9710778, DetectionBoxes_Precision/[email protected] = 0.9540878, DetectionBoxes_Recall/AR@1 = 0.88101786, DetectionBoxes_Recall/AR@10 = 0.88519293, DetectionBoxes_Recall/AR@100 = 0.88519293, DetectionBoxes_Recall/AR@100 (large) = 0.9049079, DetectionBoxes_Recall/AR@100 (medium) = 0.7649073, DetectionBoxes_Recall/AR@100 (small) = 0.25, Loss/classification_loss = 0.1509938, Loss/localization_loss = 0.025918532, Loss/regularization_loss = 0.21599802, Loss/total_loss = 0.39290625, global_step = 4838, learning_rate = 0.03999498, loss = 0.39290625\n",
"I0504 00:57:19.849468 140215059400576 estimator.py:2049] Saving dict for global step 4838: DetectionBoxes_Precision/mAP = 0.8417523, DetectionBoxes_Precision/mAP (large) = 0.87389356, DetectionBoxes_Precision/mAP (medium) = 0.5784151, DetectionBoxes_Precision/mAP (small) = 0.25247526, DetectionBoxes_Precision/[email protected] = 0.9710778, DetectionBoxes_Precision/[email protected] = 0.9540878, DetectionBoxes_Recall/AR@1 = 0.88101786, DetectionBoxes_Recall/AR@10 = 0.88519293, DetectionBoxes_Recall/AR@100 = 0.88519293, DetectionBoxes_Recall/AR@100 (large) = 0.9049079, DetectionBoxes_Recall/AR@100 (medium) = 0.7649073, DetectionBoxes_Recall/AR@100 (small) = 0.25, Loss/classification_loss = 0.1509938, Loss/localization_loss = 0.025918532, Loss/regularization_loss = 0.21599802, Loss/total_loss = 0.39290625, global_step = 4838, learning_rate = 0.03999498, loss = 0.39290625\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4838: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-4838\n",
"I0504 00:57:19.853150 140215059400576 estimator.py:2109] Saving 'checkpoint_path' summary for global step 4838: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-4838\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:global_step/sec: 0.41944\n",
"I0504 00:58:44.863583 140215059400576 basic_session_run_hooks.py:692] global_step/sec: 0.41944\n",
"INFO:tensorflow:loss = 0.35798532, step = 4900 (238.413 sec)\n",
"I0504 00:58:44.864837 140215059400576 basic_session_run_hooks.py:260] loss = 0.35798532, step = 4900 (238.413 sec)\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Saving checkpoints for 5000 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"I0504 01:00:58.754750 140215059400576 basic_session_run_hooks.py:606] Saving checkpoints for 5000 into /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt.\n",
"INFO:tensorflow:Skip the current checkpoint eval due to throttle secs (600 secs).\n",
"I0504 01:01:00.836331 140215059400576 training.py:527] Skip the current checkpoint eval due to throttle secs (600 secs).\n",
"INFO:tensorflow:Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0504 01:01:00.861598 140215059400576 dataset_builder.py:163] Reading unweighted datasets: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"I0504 01:01:00.862543 140215059400576 dataset_builder.py:80] Reading record datasets for input file: ['/content/data/tf_record/pet_faces_train.record-?????-of-00010']\n",
"INFO:tensorflow:Number of filenames to read: 10\n",
"I0504 01:01:00.862663 140215059400576 dataset_builder.py:81] Number of filenames to read: 10\n",
"WARNING:tensorflow:Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f806346ff10>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"W0504 01:01:00.899821 140215059400576 ag_logging.py:146] Entity <bound method TfExampleDecoder.decode of <object_detection.data_decoders.tf_example_decoder.TfExampleDecoder object at 0x7f806346ff10>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
"WARNING:tensorflow:Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f852836dcb0> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"W0504 01:01:01.059637 140215059400576 ag_logging.py:146] Entity <function eval_input.<locals>.transform_and_pad_input_data_fn at 0x7f852836dcb0> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0504 01:01:01.534383 140215059400576 estimator.py:1148] Calling model_fn.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 01:01:03.650071 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 01:01:03.720171 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 01:01:03.787953 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 01:01:03.856566 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 01:01:03.924641 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 01:01:03.994073 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0504 01:01:06.120358 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"INFO:tensorflow:Starting evaluation at 2021-05-04T01:01:06Z\n",
"I0504 01:01:06.135504 140215059400576 evaluation.py:255] Starting evaluation at 2021-05-04T01:01:06Z\n",
"INFO:tensorflow:Graph was finalized.\n",
"I0504 01:01:06.595707 140215059400576 monitored_session.py:240] Graph was finalized.\n",
"2021-05-04 01:01:06.596335: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 01:01:06.596643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-04 01:01:06.596733: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-04 01:01:06.596753: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-04 01:01:06.596766: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-04 01:01:06.596781: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-04 01:01:06.596795: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-04 01:01:06.596810: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-04 01:01:06.596825: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-04 01:01:06.596890: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 01:01:06.597178: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 01:01:06.597396: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-04 01:01:06.597438: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-04 01:01:06.597449: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-04 01:01:06.597456: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-04 01:01:06.597543: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 01:01:06.597787: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 01:01:06.597999: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-5000\n",
"I0504 01:01:06.601448 140215059400576 saver.py:1284] Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-5000\n",
"INFO:tensorflow:Running local_init_op.\n",
"I0504 01:01:07.712863 140215059400576 session_manager.py:500] Running local_init_op.\n",
"INFO:tensorflow:Done running local_init_op.\n",
"I0504 01:01:07.899731 140215059400576 session_manager.py:502] Done running local_init_op.\n",
"Corrupt JPEG data: premature end of data segment\n",
"INFO:tensorflow:Performing evaluation on 2568 images.\n",
"I0504 01:02:14.895640 140210432521984 coco_evaluation.py:293] Performing evaluation on 2568 images.\n",
"creating index...\n",
"index created!\n",
"INFO:tensorflow:Loading and preparing annotation results...\n",
"I0504 01:02:14.906803 140210432521984 coco_tools.py:116] Loading and preparing annotation results...\n",
"INFO:tensorflow:DONE (t=0.21s)\n",
"I0504 01:02:15.115651 140210432521984 coco_tools.py:138] DONE (t=0.21s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=19.63s).\n",
"Accumulating evaluation results...\n",
"DONE (t=6.65s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.844\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.965\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.957\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.217\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.594\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.877\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.891\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.893\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.893\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.250\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.787\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.913\n",
"INFO:tensorflow:Finished evaluation at 2021-05-04-01:02:42\n",
"I0504 01:02:42.384744 140215059400576 evaluation.py:275] Finished evaluation at 2021-05-04-01:02:42\n",
"INFO:tensorflow:Saving dict for global step 5000: DetectionBoxes_Precision/mAP = 0.84409505, DetectionBoxes_Precision/mAP (large) = 0.87721324, DetectionBoxes_Precision/mAP (medium) = 0.59417856, DetectionBoxes_Precision/mAP (small) = 0.21749175, DetectionBoxes_Precision/[email protected] = 0.9647228, DetectionBoxes_Precision/[email protected] = 0.95705926, DetectionBoxes_Recall/AR@1 = 0.8907396, DetectionBoxes_Recall/AR@10 = 0.89296573, DetectionBoxes_Recall/AR@100 = 0.89296573, DetectionBoxes_Recall/AR@100 (large) = 0.91329783, DetectionBoxes_Recall/AR@100 (medium) = 0.7871724, DetectionBoxes_Recall/AR@100 (small) = 0.25, Loss/classification_loss = 0.14837511, Loss/localization_loss = 0.02272812, Loss/regularization_loss = 0.2152484, Loss/total_loss = 0.3863538, global_step = 5000, learning_rate = 0.039994393, loss = 0.3863538\n",
"I0504 01:02:42.385046 140215059400576 estimator.py:2049] Saving dict for global step 5000: DetectionBoxes_Precision/mAP = 0.84409505, DetectionBoxes_Precision/mAP (large) = 0.87721324, DetectionBoxes_Precision/mAP (medium) = 0.59417856, DetectionBoxes_Precision/mAP (small) = 0.21749175, DetectionBoxes_Precision/[email protected] = 0.9647228, DetectionBoxes_Precision/[email protected] = 0.95705926, DetectionBoxes_Recall/AR@1 = 0.8907396, DetectionBoxes_Recall/AR@10 = 0.89296573, DetectionBoxes_Recall/AR@100 = 0.89296573, DetectionBoxes_Recall/AR@100 (large) = 0.91329783, DetectionBoxes_Recall/AR@100 (medium) = 0.7871724, DetectionBoxes_Recall/AR@100 (small) = 0.25, Loss/classification_loss = 0.14837511, Loss/localization_loss = 0.02272812, Loss/regularization_loss = 0.2152484, Loss/total_loss = 0.3863538, global_step = 5000, learning_rate = 0.039994393, loss = 0.3863538\n",
"INFO:tensorflow:Saving 'checkpoint_path' summary for global step 5000: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-5000\n",
"I0504 01:02:42.388940 140215059400576 estimator.py:2109] Saving 'checkpoint_path' summary for global step 5000: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-5000\n",
"INFO:tensorflow:Performing the final export in the end of training.\n",
"I0504 01:02:42.389613 140215059400576 exporter.py:410] Performing the final export in the end of training.\n",
"INFO:tensorflow:Calling model_fn.\n",
"I0504 01:02:42.629983 140215059400576 estimator.py:1148] Calling model_fn.\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 01:02:44.649070 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 01:02:44.721361 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 01:02:44.788778 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 01:02:44.856194 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 01:02:44.928780 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:depth of additional conv before box predictor: 0\n",
"I0504 01:02:45.000893 140215059400576 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0\n",
"INFO:tensorflow:Done calling model_fn.\n",
"I0504 01:02:46.088902 140215059400576 estimator.py:1150] Done calling model_fn.\n",
"WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/tensorflow_core/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
"W0504 01:02:46.089168 140215059400576 deprecation.py:323] From /tensorflow-1.15.2/python3.7/tensorflow_core/python/saved_model/signature_def_utils_impl.py:201: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.\n",
"INFO:tensorflow:Signatures INCLUDED in export for Classify: None\n",
"I0504 01:02:46.089719 140215059400576 export_utils.py:170] Signatures INCLUDED in export for Classify: None\n",
"INFO:tensorflow:Signatures INCLUDED in export for Regress: None\n",
"I0504 01:02:46.089831 140215059400576 export_utils.py:170] Signatures INCLUDED in export for Regress: None\n",
"INFO:tensorflow:Signatures INCLUDED in export for Predict: ['tensorflow/serving/predict', 'serving_default']\n",
"I0504 01:02:46.089886 140215059400576 export_utils.py:170] Signatures INCLUDED in export for Predict: ['tensorflow/serving/predict', 'serving_default']\n",
"INFO:tensorflow:Signatures INCLUDED in export for Train: None\n",
"I0504 01:02:46.089935 140215059400576 export_utils.py:170] Signatures INCLUDED in export for Train: None\n",
"INFO:tensorflow:Signatures INCLUDED in export for Eval: None\n",
"I0504 01:02:46.089978 140215059400576 export_utils.py:170] Signatures INCLUDED in export for Eval: None\n",
"2021-05-04 01:02:46.090425: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 01:02:46.090723: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties: \n",
"name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
"pciBusID: 0000:00:04.0\n",
"2021-05-04 01:02:46.090814: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-05-04 01:02:46.090834: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
"2021-05-04 01:02:46.090849: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
"2021-05-04 01:02:46.090864: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
"2021-05-04 01:02:46.090878: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
"2021-05-04 01:02:46.090892: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
"2021-05-04 01:02:46.090906: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
"2021-05-04 01:02:46.090973: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 01:02:46.091218: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 01:02:46.091406: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0\n",
"2021-05-04 01:02:46.091445: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-05-04 01:02:46.091475: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186] 0 \n",
"2021-05-04 01:02:46.091481: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0: N \n",
"2021-05-04 01:02:46.091562: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 01:02:46.091784: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-05-04 01:02:46.091988: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15224 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
"INFO:tensorflow:Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-5000\n",
"I0504 01:02:46.094195 140215059400576 saver.py:1284] Restoring parameters from /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/model.ckpt-5000\n",
"INFO:tensorflow:Assets added to graph.\n",
"I0504 01:02:46.613681 140215059400576 builder_impl.py:665] Assets added to graph.\n",
"INFO:tensorflow:No assets to write.\n",
"I0504 01:02:46.613869 140215059400576 builder_impl.py:460] No assets to write.\n",
"INFO:tensorflow:SavedModel written to: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/export/Servo/temp-b'1620090162'/saved_model.pb\n",
"I0504 01:02:47.470509 140215059400576 builder_impl.py:425] SavedModel written to: /content/data/train_ssdlite_mobilenet_v3_large_320x320_pet/export/Servo/temp-b'1620090162'/saved_model.pb\n",
"INFO:tensorflow:Loss for final step: 0.3643322.\n",
"I0504 01:02:47.987567 140215059400576 estimator.py:371] Loss for final step: 0.3643322.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"resources": {
"http://localhost:6006/": {
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@license
Copyright 2016 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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limitations under the License.
--><meta charset="utf-8">
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<script>// Copyright 2014 Google Inc. All rights reserved.
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//     You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
//     See the License for the specific language governing permissions and
// limitations under the License.

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(function() {
  'use strict';

  const MODULE_STYLE_LINK_SELECTOR = 'link[rel=import][type~=css]';
  const INCLUDE_ATTR = 'include';
  const SHADY_UNSCOPED_ATTR = 'shady-unscoped';

  function importModule(moduleId) {
    const /** Polymer.DomModule */ PolymerDomModule = customElements.get('dom-module');
    if (!PolymerDomModule) {
      return null;
    }
    return PolymerDomModule.import(moduleId);
  }

  function styleForImport(importDoc) {
    // NOTE: polyfill affordance.
    // under the HTMLImports polyfill, there will be no 'body',
    // but the import pseudo-doc can be used directly.
    let container = importDoc.body ? importDoc.body : importDoc;
    const importCss = Polymer.ResolveUrl.resolveCss(container.textContent,
      importDoc.baseURI);
    const style = document.createElement('style');
    style.textContent = importCss;
    return style;
  }

  /** @typedef {{assetpath: string}} */
  let templateWithAssetPath; // eslint-disable-line no-unused-vars

  /**
   * Module with utilities for collection CSS text from `<templates>`, external
   * stylesheets, and `dom-module`s.
   *
   * @namespace
   * @memberof Polymer
   * @summary Module with utilities for collection CSS text from various sources.
   */
  const StyleGather = {

    /**
     * Returns a list of <style> elements in a space-separated list of `dom-module`s.
     *
     * @memberof Polymer.StyleGather
     * @param {string} moduleIds List of dom-module id's within which to
     * search for css.
     * @return {!Array<!HTMLStyleElement>} Array of contained <style> elements
     * @this {StyleGather}
     */
     stylesFromModules(moduleIds) {
      const modules = moduleIds.trim().split(/\s+/);
      const styles = [];
      for (let i=0; i < modules.length; i++) {
        styles.push(...this.stylesFromModule(modules[i]));
      }
      return styles;
    },

    /**
     * Returns a list of <style> elements in a given `dom-module`.
     * Styles in a `dom-module` can come either from `<style>`s within the
     * first `<template>`, or else from one or more
     * `<link rel="import" type="css">` links outside the template.
     *
     * @memberof Polymer.StyleGather
     * @param {string} moduleId dom-module id to gather styles from
     * @return {!Array<!HTMLStyleElement>} Array of contained styles.
     * @this {StyleGather}
     */
    stylesFromModule(moduleId) {
      const m = importModule(moduleId);

      if (!m) {
        console.warn('Could not find style data in module named', moduleId);
        return [];
      }

      if (m._styles === undefined) {
        const styles = [];
        // module imports: <link rel="import" type="css">
        styles.push(...this._stylesFromModuleImports(m));
        // include css from the first template in the module
        const template = m.querySelector('template');
        if (template) {
          styles.push(...this.stylesFromTemplate(template,
            /** @type {templateWithAssetPath} */(m).assetpath));
        }

        m._styles = styles;
      }

      return m._styles;
    },

    /**
     * Returns the `<style>` elements within a given template.
     *
     * @memberof Polymer.StyleGather
     * @param {!HTMLTemplateElement} template Template to gather styles from
     * @param {string} baseURI baseURI for style content
     * @return {!Array<!HTMLStyleElement>} Array of styles
     * @this {StyleGather}
     */
    stylesFromTemplate(template, baseURI) {
      if (!template._styles) {
        const styles = [];
        // if element is a template, get content from its .content
        const e$ = template.content.querySelectorAll('style');
        for (let i=0; i < e$.length; i++) {
          let e = e$[i];
          // support style sharing by allowing styles to "include"
          // other dom-modules that contain styling
          let include = e.getAttribute(INCLUDE_ATTR);
          if (include) {
            styles.push(...this.stylesFromModules(include).filter(function(item, index, self) {
              return self.indexOf(item) === index;
            }));
          }
          if (baseURI) {
            e.textContent = Polymer.ResolveUrl.resolveCss(e.textContent, baseURI);
          }
          styles.push(e);
        }
        template._styles = styles;
      }
      return template._styles;
    },

    /**
     * Returns a list of <style> elements  from stylesheets loaded via `<link rel="import" type="css">` links within the specified `dom-module`.
     *
     * @memberof Polymer.StyleGather
     * @param {string} moduleId Id of `dom-module` to gather CSS from
     * @return {!Array<!HTMLStyleElement>} Array of contained styles.
     * @this {StyleGather}
     */
     stylesFromModuleImports(moduleId) {
      let m = importModule(moduleId);
      return m ? this._stylesFromModuleImports(m) : [];
    },

    /**
     * @memberof Polymer.StyleGather
     * @this {StyleGather}
     * @param {!HTMLElement} module dom-module element that could contain `<link rel="import" type="css">` styles
     * @return {!Array<!HTMLStyleElement>} Array of contained styles
     */
    _stylesFromModuleImports(module) {
      const styles = [];
      const p$ = module.querySelectorAll(MODULE_STYLE_LINK_SELECTOR);
      for (let i=0; i < p$.length; i++) {
        let p = p$[i];
        if (p.import) {
          const importDoc = p.import;
          const unscoped = p.hasAttribute(SHADY_UNSCOPED_ATTR);
          if (unscoped && !importDoc._unscopedStyle) {
            const style = styleForImport(importDoc);
            style.setAttribute(SHADY_UNSCOPED_ATTR, '');
            importDoc._unscopedStyle = style;
          } else if (!importDoc._style) {
            importDoc._style = styleForImport(importDoc);
          }
          styles.push(unscoped ? importDoc._unscopedStyle : importDoc._style);
        }
      }
      return styles;
    },

    /**
     *
     * Returns CSS text of styles in a space-separated list of `dom-module`s.
     * Note: This method is deprecated, use `stylesFromModules` instead.
     *
     * @deprecated
     * @memberof Polymer.StyleGather
     * @param {string} moduleIds List of dom-module id's within which to
     * search for css.
     * @return {string} Concatenated CSS content from specified `dom-module`s
     * @this {StyleGather}
     */
     cssFromModules(moduleIds) {
      let modules = moduleIds.trim().split(/\s+/);
      let cssText = '';
      for (let i=0; i < modules.length; i++) {
        cssText += this.cssFromModule(modules[i]);
      }
      return cssText;
    },

    /**
     * Returns CSS text of styles in a given `dom-module`.  CSS in a `dom-module`
     * can come either from `<style>`s within the first `<template>`, or else
     * from one or more `<link rel="import" type="css">` links outside the
     * template.
     *
     * Any `<styles>` processed are removed from their original location.
     * Note: This method is deprecated, use `styleFromModule` instead.
     *
     * @deprecated
     * @memberof Polymer.StyleGather
     * @param {string} moduleId dom-module id to gather styles from
     * @return {string} Concatenated CSS content from specified `dom-module`
     * @this {StyleGather}
     */
    cssFromModule(moduleId) {
      let m = importModule(moduleId);
      if (m && m._cssText === undefined) {
        // module imports: <link rel="import" type="css">
        let cssText = this._cssFromModuleImports(m);
        // include css from the first template in the module
        let t = m.querySelector('template');
        if (t) {
          cssText += this.cssFromTemplate(t,
            /** @type {templateWithAssetPath} */(m).assetpath);
        }
        m._cssText = cssText || null;
      }
      if (!m) {
        console.warn('Could not find style data in module named', moduleId);
      }
      return m && m._cssText || '';
    },

    /**
     * Returns CSS text of `<styles>` within a given template.
     *
     * Any `<styles>` processed are removed from their original location.
     * Note: This method is deprecated, use `styleFromTemplate` instead.
     *
     * @deprecated
     * @memberof Polymer.StyleGather
     * @param {!HTMLTemplateElement} template Template to gather styles from
     * @param {string} baseURI Base URI to resolve the URL against
     * @return {string} Concatenated CSS content from specified template
     * @this {StyleGather}
     */
    cssFromTemplate(template, baseURI) {
      let cssText = '';
      const e$ = this.stylesFromTemplate(template, baseURI);
      // if element is a template, get content from its .content
      for (let i=0; i < e$.length; i++) {
        let e = e$[i];
        if (e.parentNode) {
          e.parentNode.removeChild(e);
        }
        cssText += e.textContent;
      }
      return cssText;
    },

    /**
     * Returns CSS text from stylesheets loaded via `<link rel="import" type="css">`
     * links within the specified `dom-module`.
     *
     * Note: This method is deprecated, use `stylesFromModuleImports` instead.
     *
     * @deprecated
     *
     * @memberof Polymer.StyleGather
     * @param {string} moduleId Id of `dom-module` to gather CSS from
     * @return {string} Concatenated CSS content from links in specified `dom-module`
     * @this {StyleGather}
     */
    cssFromModuleImports(moduleId) {
      let m = importModule(moduleId);
      return m ? this._cssFromModuleImports(m) : '';
    },

    /**
     * @deprecated
     * @memberof Polymer.StyleGather
     * @this {StyleGather}
     * @param {!HTMLElement} module dom-module element that could contain `<link rel="import" type="css">` styles
     * @return {string} Concatenated CSS content from links in the dom-module
     */
     _cssFromModuleImports(module) {
      let cssText = '';
      let styles = this._stylesFromModuleImports(module);
      for (let i=0; i < styles.length; i++) {
        cssText += styles[i].textContent;
      }
      return cssText;
    }
  };

  Polymer.StyleGather = StyleGather;
})();
</script><script>
(function() {
  'use strict';

  let modules = {};
  let lcModules = {};
  function setModule(id, module) {
    // store id separate from lowercased id so that
    // in all cases mixedCase id will stored distinctly
    // and lowercase version is a fallback
    modules[id] = lcModules[id.toLowerCase()] = module;
  }
  function findModule(id) {
    return modules[id] || lcModules[id.toLowerCase()];
  }

  function styleOutsideTemplateCheck(inst) {
    if (inst.querySelector('style')) {
      console.warn('dom-module %s has style outside template', inst.id);
    }
  }

  /**
   * The `dom-module` element registers the dom it contains to the name given
   * by the module's id attribute. It provides a unified database of dom
   * accessible via its static `import` API.
   *
   * A key use case of `dom-module` is for providing custom element `<template>`s
   * via HTML imports that are parsed by the native HTML parser, that can be
   * relocated during a bundling pass and still looked up by `id`.
   *
   * Example:
   *
   *     <dom-module id="foo">
   *       <img src="stuff.png">
   *     </dom-module>
   *
   * Then in code in some other location that cannot access the dom-module above
   *
   *     let img = customElements.get('dom-module').import('foo', 'img');
   *
   * @customElement
   * @extends HTMLElement
   * @memberof Polymer
   * @summary Custom element that provides a registry of relocatable DOM content
   *   by `id` that is agnostic to bundling.
   * @unrestricted
   */
  class DomModule extends HTMLElement {

    static get observedAttributes() { return ['id']; }

    /**
     * Retrieves the element specified by the css `selector` in the module
     * registered by `id`. For example, this.import('foo', 'img');
     * @param {string} id The id of the dom-module in which to search.
     * @param {string=} selector The css selector by which to find the element.
     * @return {Element} Returns the element which matches `selector` in the
     * module registered at the specified `id`.
     */
    static import(id, selector) {
      if (id) {
        let m = findModule(id);
        if (m && selector) {
          return m.querySelector(selector);
        }
        return m;
      }
      return null;
    }

    /* eslint-disable no-unused-vars */
    /**
     * @param {string} name Name of attribute.
     * @param {?string} old Old value of attribute.
     * @param {?string} value Current value of attribute.
     * @param {?string} namespace Attribute namespace.
     * @return {void}
     */
    attributeChangedCallback(name, old, value, namespace) {
      if (old !== value) {
        this.register();
      }
    }
    /* eslint-enable no-unused-args */

    /**
     * The absolute URL of the original location of this `dom-module`.
     *
     * This value will differ from this element's `ownerDocument` in the
     * following ways:
     * - Takes into account any `assetpath` attribute added during bundling
     *   to indicate the original location relative to the bundled location
     * - Uses the HTMLImports polyfill's `importForElement` API to ensure
     *   the path is relative to the import document's location since
     *   `ownerDocument` is not currently polyfilled
     */
    get assetpath() {
      // Don't override existing assetpath.
      if (!this.__assetpath) {
        // note: assetpath set via an attribute must be relative to this
        // element's location; accomodate polyfilled HTMLImports
        const owner = window.HTMLImports && HTMLImports.importForElement ?
          HTMLImports.importForElement(this) || document : this.ownerDocument;
        const url = Polymer.ResolveUrl.resolveUrl(
          this.getAttribute('assetpath') || '', owner.baseURI);
        this.__assetpath = Polymer.ResolveUrl.pathFromUrl(url);
      }
      return this.__assetpath;
    }

    /**
     * Registers the dom-module at a given id. This method should only be called
     * when a dom-module is imperatively created. For
     * example, `document.createElement('dom-module').register('foo')`.
     * @param {string=} id The id at which to register the dom-module.
     * @return {void}
     */
    register(id) {
      id = id || this.id;
      if (id) {
        // Under strictTemplatePolicy, reject and null out any re-registered
        // dom-module since it is ambiguous whether first-in or last-in is trusted 
        if (Polymer.strictTemplatePolicy && findModule(id) !== undefined) {
          setModule(id, null);
          throw new Error(`strictTemplatePolicy: dom-module ${id} re-registered`);
        }
        this.id = id;
        setModule(id, this);
        styleOutsideTemplateCheck(this);
      }
    }
  }

  DomModule.prototype['modules'] = modules;

  customElements.define('dom-module', DomModule);

  /** @const */
  Polymer.DomModule = DomModule;

})();
</script><script>
(function() {
  'use strict';

  /**
   * Module with utilities for manipulating structured data path strings.
   *
   * @namespace
   * @memberof Polymer
   * @summary Module with utilities for manipulating structured data path strings.
   */
  const Path = {

    /**
     * Returns true if the given string is a structured data path (has dots).
     *
     * Example:
     *
     * ```
     * Polymer.Path.isPath('foo.bar.baz') // true
     * Polymer.Path.isPath('foo')         // false
     * ```
     *
     * @memberof Polymer.Path
     * @param {string} path Path string
     * @return {boolean} True if the string contained one or more dots
     */
    isPath: function(path) {
      return path.indexOf('.') >= 0;
    },

    /**
     * Returns the root property name for the given path.
     *
     * Example:
     *
     * ```
     * Polymer.Path.root('foo.bar.baz') // 'foo'
     * Polymer.Path.root('foo')         // 'foo'
     * ```
     *
     * @memberof Polymer.Path
     * @param {string} path Path string
     * @return {string} Root property name
     */
    root: function(path) {
      let dotIndex = path.indexOf('.');
      if (dotIndex === -1) {
        return path;
      }
      return path.slice(0, dotIndex);
    },

    /**
     * Given `base` is `foo.bar`, `foo` is an ancestor, `foo.bar` is not
     * Returns true if the given path is an ancestor of the base path.
     *
     * Example:
     *
     * ```
     * Polymer.Path.isAncestor('foo.bar', 'foo')         // true
     * Polymer.Path.isAncestor('foo.bar', 'foo.bar')     // false
     * Polymer.Path.isAncestor('foo.bar', 'foo.bar.baz') // false
     * ```
     *
     * @memberof Polymer.Path
     * @param {string} base Path string to test against.
     * @param {string} path Path string to test.
     * @return {boolean} True if `path` is an ancestor of `base`.
     */
    isAncestor: function(base, path) {
      //     base.startsWith(path + '.');
      return base.indexOf(path + '.') === 0;
    },

    /**
     * Given `base` is `foo.bar`, `foo.bar.baz` is an descendant
     *
     * Example:
     *
     * ```
     * Polymer.Path.isDescendant('foo.bar', 'foo.bar.baz') // true
     * Polymer.Path.isDescendant('foo.bar', 'foo.bar')     // false
     * Polymer.Path.isDescendant('foo.bar', 'foo')         // false
     * ```
     *
     * @memberof Polymer.Path
     * @param {string} base Path string to test against.
     * @param {string} path Path string to test.
     * @return {boolean} True if `path` is a descendant of `base`.
     */
    isDescendant: function(base, path) {
      //     path.startsWith(base + '.');
      return path.indexOf(base + '.') === 0;
    },

    /**
     * Replaces a previous base path with a new base path, preserving the
     * remainder of the path.
     *
     * User must ensure `path` has a prefix of `base`.
     *
     * Example:
     *
     * ```
     * Polymer.Path.translate('foo.bar', 'zot', 'foo.bar.baz') // 'zot.baz'
     * ```
     *
     * @memberof Polymer.Path
     * @param {string} base Current base string to remove
     * @param {string} newBase New base string to replace with
     * @param {string} path Path to translate
     * @return {string} Translated string
     */
    translate: function(base, newBase, path) {
      return newBase + path.slice(base.length);
    },

    /**
     * @param {string} base Path string to test against
     * @param {string} path Path string to test
     * @return {boolean} True if `path` is equal to `base`
     * @this {Path}
     */
    matches: function(base, path) {
      return (base === path) ||
             this.isAncestor(base, path) ||
             this.isDescendant(base, path);
    },

    /**
     * Converts array-based paths to flattened path.  String-based paths
     * are returned as-is.
     *
     * Example:
     *
     * ```
     * Polymer.Path.normalize(['foo.bar', 0, 'baz'])  // 'foo.bar.0.baz'
     * Polymer.Path.normalize('foo.bar.0.baz')        // 'foo.bar.0.baz'
     * ```
     *
     * @memberof Polymer.Path
     * @param {string | !Array<string|number>} path Input path
     * @return {string} Flattened path
     */
    normalize: function(path) {
      if (Array.isArray(path)) {
        let parts = [];
        for (let i=0; i<path.length; i++) {
          let args = path[i].toString().split('.');
          for (let j=0; j<args.length; j++) {
            parts.push(args[j]);
          }
        }
        return parts.join('.');
      } else {
        return path;
      }
    },

    /**
     * Splits a path into an array of property names. Accepts either arrays
     * of path parts or strings.
     *
     * Example:
     *
     * ```
     * Polymer.Path.split(['foo.bar', 0, 'baz'])  // ['foo', 'bar', '0', 'baz']
     * Polymer.Path.split('foo.bar.0.baz')        // ['foo', 'bar', '0', 'baz']
     * ```
     *
     * @memberof Polymer.Path
     * @param {string | !Array<string|number>} path Input path
     * @return {!Array<string>} Array of path parts
     * @this {Path}
     * @suppress {checkTypes}
     */
    split: function(path) {
      if (Array.isArray(path)) {
        return this.normalize(path).split('.');
      }
      return path.toString().split('.');
    },

    /**
     * Reads a value from a path.  If any sub-property in the path is `undefined`,
     * this method returns `undefined` (will never throw.
     *
     * @memberof Polymer.Path
     * @param {Object} root Object from which to dereference path from
     * @param {string | !Array<string|number>} path Path to read
     * @param {Object=} info If an object is provided to `info`, the normalized
     *  (flattened) path will be set to `info.path`.
     * @return {*} Value at path, or `undefined` if the path could not be
     *  fully dereferenced.
     * @this {Path}
     */
    get: function(root, path, info) {
      let prop = root;
      let parts = this.split(path);
      // Loop over path parts[0..n-1] and dereference
      for (let i=0; i<parts.length; i++) {
        if (!prop) {
          return;
        }
        let part = parts[i];
        prop = prop[part];
      }
      if (info) {
        info.path = parts.join('.');
      }
      return prop;
    },

    /**
     * Sets a value to a path.  If any sub-property in the path is `undefined`,
     * this method will no-op.
     *
     * @memberof Polymer.Path
     * @param {Object} root Object from which to dereference path from
     * @param {string | !Array<string|number>} path Path to set
     * @param {*} value Value to set to path
     * @return {string | undefined} The normalized version of the input path
     * @this {Path}
     */
    set: function(root, path, value) {
      let prop = root;
      let parts = this.split(path);
      let last = parts[parts.length-1];
      if (parts.length > 1) {
        // Loop over path parts[0..n-2] and dereference
        for (let i=0; i<parts.length-1; i++) {
          let part = parts[i];
          prop = prop[part];
          if (!prop) {
            return;
          }
        }
        // Set value to object at end of path
        prop[last] = value;
      } else {
        // Simple property set
        prop[path] = value;
      }
      return parts.join('.');
    }

  };

  /**
   * Returns true if the given string is a structured data path (has dots).
   *
   * This function is deprecated.  Use `Polymer.Path.isPath` instead.
   *
   * Example:
   *
   * ```
   * Polymer.Path.isDeep('foo.bar.baz') // true
   * Polymer.Path.isDeep('foo')         // false
   * ```
   *
   * @deprecated
   * @memberof Polymer.Path
   * @param {string} path Path string
   * @return {boolean} True if the string contained one or more dots
   */
  Path.isDeep = Path.isPath;

  Polymer.Path = Path;

})();
</script><script>
(function() {
  'use strict';

  const caseMap = {};
  const DASH_TO_CAMEL = /-[a-z]/g;
  const CAMEL_TO_DASH = /([A-Z])/g;

  /**
   * Module with utilities for converting between "dash-case" and "camelCase"
   * identifiers.
   *
   * @namespace
   * @memberof Polymer
   * @summary Module that provides utilities for converting between "dash-case"
   *   and "camelCase".
   */
  const CaseMap = {

    /**
     * Converts "dash-case" identifier (e.g. `foo-bar-baz`) to "camelCase"
     * (e.g. `fooBarBaz`).
     *
     * @memberof Polymer.CaseMap
     * @param {string} dash Dash-case identifier
     * @return {string} Camel-case representation of the identifier
     */
    dashToCamelCase(dash) {
      return caseMap[dash] || (
        caseMap[dash] = dash.indexOf('-') < 0 ? dash : dash.replace(DASH_TO_CAMEL,
          (m) => m[1].toUpperCase()
        )
      );
    },

    /**
     * Converts "camelCase" identifier (e.g. `fooBarBaz`) to "dash-case"
     * (e.g. `foo-bar-baz`).
     *
     * @memberof Polymer.CaseMap
     * @param {string} camel Camel-case identifier
     * @return {string} Dash-case representation of the identifier
     */
    camelToDashCase(camel) {
      return caseMap[camel] || (
        caseMap[camel] = camel.replace(CAMEL_TO_DASH, '-$1').toLowerCase()
      );
    }

  };

  Polymer.CaseMap = CaseMap;
})();
</script><script>
(function() {

  'use strict';

  // Microtask implemented using Mutation Observer
  let microtaskCurrHandle = 0;
  let microtaskLastHandle = 0;
  let microtaskCallbacks = [];
  let microtaskNodeContent = 0;
  let microtaskNode = document.createTextNode('');
  new window.MutationObserver(microtaskFlush).observe(microtaskNode, {characterData: true});

  function microtaskFlush() {
    const len = microtaskCallbacks.length;
    for (let i = 0; i < len; i++) {
      let cb = microtaskCallbacks[i];
      if (cb) {
        try {
          cb();
        } catch (e) {
          setTimeout(() => { throw e; });
        }
      }
    }
    microtaskCallbacks.splice(0, len);
    microtaskLastHandle += len;
  }

  /**
   * Module that provides a number of strategies for enqueuing asynchronous
   * tasks.  Each sub-module provides a standard `run(fn)` interface that returns a
   * handle, and a `cancel(handle)` interface for canceling async tasks before
   * they run.
   *
   * @namespace
   * @memberof Polymer
   * @summary Module that provides a number of strategies for enqueuing asynchronous
   * tasks.
   */
  Polymer.Async = {

    /**
     * Async interface wrapper around `setTimeout`.
     *
     * @namespace
     * @memberof Polymer.Async
     * @summary Async interface wrapper around `setTimeout`.
     */
    timeOut: {
      /**
       * Returns a sub-module with the async interface providing the provided
       * delay.
       *
       * @memberof Polymer.Async.timeOut
       * @param {number=} delay Time to wait before calling callbacks in ms
       * @return {!AsyncInterface} An async timeout interface
       */
      after(delay) {
        return {
          run(fn) { return window.setTimeout(fn, delay); },
          cancel(handle) {
            window.clearTimeout(handle);
          }
        };
      },
      /**
       * Enqueues a function called in the next task.
       *
       * @memberof Polymer.Async.timeOut
       * @param {!Function} fn Callback to run
       * @param {number=} delay Delay in milliseconds
       * @return {number} Handle used for canceling task
       */
      run(fn, delay) {
        return window.setTimeout(fn, delay);
      },
      /**
       * Cancels a previously enqueued `timeOut` callback.
       *
       * @memberof Polymer.Async.timeOut
       * @param {number} handle Handle returned from `run` of callback to cancel
       * @return {void}
       */
      cancel(handle) {
        window.clearTimeout(handle);
      }
    },

    /**
     * Async interface wrapper around `requestAnimationFrame`.
     *
     * @namespace
     * @memberof Polymer.Async
     * @summary Async interface wrapper around `requestAnimationFrame`.
     */
    animationFrame: {
      /**
       * Enqueues a function called at `requestAnimationFrame` timing.
       *
       * @memberof Polymer.Async.animationFrame
       * @param {function(number):void} fn Callback to run
       * @return {number} Handle used for canceling task
       */
      run(fn) {
        return window.requestAnimationFrame(fn);
      },
      /**
       * Cancels a previously enqueued `animationFrame` callback.
       *
       * @memberof Polymer.Async.animationFrame
       * @param {number} handle Handle returned from `run` of callback to cancel
       * @return {void}
       */
      cancel(handle) {
        window.cancelAnimationFrame(handle);
      }
    },

    /**
     * Async interface wrapper around `requestIdleCallback`.  Falls back to
     * `setTimeout` on browsers that do not support `requestIdleCallback`.
     *
     * @namespace
     * @memberof Polymer.Async
     * @summary Async interface wrapper around `requestIdleCallback`.
     */
    idlePeriod: {
      /**
       * Enqueues a function called at `requestIdleCallback` timing.
       *
       * @memberof Polymer.Async.idlePeriod
       * @param {function(!IdleDeadline):void} fn Callback to run
       * @return {number} Handle used for canceling task
       */
      run(fn) {
        return window.requestIdleCallback ?
          window.requestIdleCallback(fn) :
          window.setTimeout(fn, 16);
      },
      /**
       * Cancels a previously enqueued `idlePeriod` callback.
       *
       * @memberof Polymer.Async.idlePeriod
       * @param {number} handle Handle returned from `run` of callback to cancel
       * @return {void}
       */
      cancel(handle) {
        window.cancelIdleCallback ?
          window.cancelIdleCallback(handle) :
          window.clearTimeout(handle);
      }
    },

    /**
     * Async interface for enqueuing callbacks that run at microtask timing.
     *
     * Note that microtask timing is achieved via a single `MutationObserver`,
     * and thus callbacks enqueued with this API will all run in a single
     * batch, and not interleaved with other microtasks such as promises.
     * Promises are avoided as an implementation choice for the time being
     * due to Safari bugs that cause Promises to lack microtask guarantees.
     *
     * @namespace
     * @memberof Polymer.Async
     * @summary Async interface for enqueuing callbacks that run at microtask
     *   timing.
     */
    microTask: {

      /**
       * Enqueues a function called at microtask timing.
       *
       * @memberof Polymer.Async.microTask
       * @param {!Function=} callback Callback to run
       * @return {number} Handle used for canceling task
       */
      run(callback) {
        microtaskNode.textContent = microtaskNodeContent++;
        microtaskCallbacks.push(callback);
        return microtaskCurrHandle++;
      },

      /**
       * Cancels a previously enqueued `microTask` callback.
       *
       * @memberof Polymer.Async.microTask
       * @param {number} handle Handle returned from `run` of callback to cancel
       * @return {void}
       */
      cancel(handle) {
        const idx = handle - microtaskLastHandle;
        if (idx >= 0) {
          if (!microtaskCallbacks[idx]) {
            throw new Error('invalid async handle: ' + handle);
          }
          microtaskCallbacks[idx] = null;
        }
      }

    }
  };

})();
</script><script>
  (function () {

    'use strict';

    /** @const {!AsyncInterface} */
    const microtask = Polymer.Async.microTask;

    /**
     * Element class mixin that provides basic meta-programming for creating one
     * or more property accessors (getter/setter pair) that enqueue an async
     * (batched) `_propertiesChanged` callback.
     *
     * For basic usage of this mixin, call `MyClass.createProperties(props)`
     * once at class definition time to create property accessors for properties
     * named in props, implement `_propertiesChanged` to react as desired to
     * property changes, and implement `static get observedAttributes()` and
     * include lowercase versions of any property names that should be set from
     * attributes. Last, call `this._enableProperties()` in the element's
     * `connectedCallback` to enable the accessors.
     *
     * @mixinFunction
     * @polymer
     * @memberof Polymer
     * @summary Element class mixin for reacting to property changes from
     *   generated property accessors.
     */
    Polymer.PropertiesChanged = Polymer.dedupingMixin(superClass => {

      /**
       * @polymer
       * @mixinClass
       * @extends {superClass}
       * @implements {Polymer_PropertiesChanged}
       * @unrestricted
       */
      class PropertiesChanged extends superClass {

        /**
         * Creates property accessors for the given property names.
         * @param {!Object} props Object whose keys are names of accessors.
         * @return {void}
         * @protected
         */
        static createProperties(props) {
          const proto = this.prototype;
          for (let prop in props) {
            // don't stomp an existing accessor
            if (!(prop in proto)) {
              proto._createPropertyAccessor(prop);
            }
          }
        }

        /**
         * Returns an attribute name that corresponds to the given property.
         * The attribute name is the lowercased property name. Override to
         * customize this mapping.
         * @param {string} property Property to convert
         * @return {string} Attribute name corresponding to the given property.
         *
         * @protected
         */
        static attributeNameForProperty(property) {
          return property.toLowerCase();
        }

        /**
         * Override point to provide a type to which to deserialize a value to
         * a given property.
         * @param {string} name Name of property
         *
         * @protected
         */
        static typeForProperty(name) { } //eslint-disable-line no-unused-vars

        /**
         * Creates a setter/getter pair for the named property with its own
         * local storage.  The getter returns the value in the local storage,
         * and the setter calls `_setProperty`, which updates the local storage
         * for the property and enqueues a `_propertiesChanged` callback.
         *
         * This method may be called on a prototype or an instance.  Calling
         * this method may overwrite a property value that already exists on
         * the prototype/instance by creating the accessor.
         *
         * @param {string} property Name of the property
         * @param {boolean=} readOnly When true, no setter is created; the
         *   protected `_setProperty` function must be used to set the property
         * @return {void}
         * @protected
         */
        _createPropertyAccessor(property, readOnly) {
          this._addPropertyToAttributeMap(property);
          if (!this.hasOwnProperty('__dataHasAccessor')) {
            this.__dataHasAccessor = Object.assign({}, this.__dataHasAccessor);
          }
          if (!this.__dataHasAccessor[property]) {
            this.__dataHasAccessor[property] = true;
            this._definePropertyAccessor(property, readOnly);
          }
        }

        /**
         * Adds the given `property` to a map matching attribute names
         * to property names, using `attributeNameForProperty`. This map is
         * used when deserializing attribute values to properties.
         *
         * @param {string} property Name of the property
         */
        _addPropertyToAttributeMap(property) {
          if (!this.hasOwnProperty('__dataAttributes')) {
            this.__dataAttributes = Object.assign({}, this.__dataAttributes);
          }
          if (!this.__dataAttributes[property]) {
            const attr = this.constructor.attributeNameForProperty(property);
            this.__dataAttributes[attr] = property;
          }
        }

        /**
         * Defines a property accessor for the given property.
         * @param {string} property Name of the property
         * @param {boolean=} readOnly When true, no setter is created
         * @return {void}
         */
         _definePropertyAccessor(property, readOnly) {
          Object.defineProperty(this, property, {
            /* eslint-disable valid-jsdoc */
            /** @this {PropertiesChanged} */
            get() {
              return this._getProperty(property);
            },
            /** @this {PropertiesChanged} */
            set: readOnly ? function () {} : function (value) {
              this._setProperty(property, value);
            }
            /* eslint-enable */
          });
        }

        constructor() {
          super();
          this.__dataEnabled = false;
          this.__dataReady = false;
          this.__dataInvalid = false;
          this.__data = {};
          this.__dataPending = null;
          this.__dataOld = null;
          this.__dataInstanceProps = null;
          this.__serializing = false;
          this._initializeProperties();
        }

        /**
         * Lifecycle callback called when properties are enabled via
         * `_enableProperties`.
         *
         * Users may override this function to implement behavior that is
         * dependent on the element having its property data initialized, e.g.
         * from defaults (initialized from `constructor`, `_initializeProperties`),
         * `attributeChangedCallback`, or values propagated from host e.g. via
         * bindings.  `super.ready()` must be called to ensure the data system
         * becomes enabled.
         *
         * @return {void}
         * @public
         */
        ready() {
          this.__dataReady = true;
          this._flushProperties();
        }

        /**
         * Initializes the local storage for property accessors.
         *
         * Provided as an override point for performing any setup work prior
         * to initializing the property accessor system.
         *
         * @return {void}
         * @protected
         */
        _initializeProperties() {
          // Capture instance properties; these will be set into accessors
          // during first flush. Don't set them here, since we want
          // these to overwrite defaults/constructor assignments
          for (let p in this.__dataHasAccessor) {
            if (this.hasOwnProperty(p)) {
              this.__dataInstanceProps = this.__dataInstanceProps || {};
              this.__dataInstanceProps[p] = this[p];
              delete this[p];
            }
          }
        }

        /**
         * Called at ready time with bag of instance properties that overwrote
         * accessors when the element upgraded.
         *
         * The default implementation sets these properties back into the
         * setter at ready time.  This method is provided as an override
         * point for customizing or providing more efficient initialization.
         *
         * @param {Object} props Bag of property values that were overwritten
         *   when creating property accessors.
         * @return {void}
         * @protected
         */
        _initializeInstanceProperties(props) {
          Object.assign(this, props);
        }

        /**
         * Updates the local storage for a property (via `_setPendingProperty`)
         * and enqueues a `_proeprtiesChanged` callback.
         *
         * @param {string} property Name of the property
         * @param {*} value Value to set
         * @return {void}
         * @protected
         */
        _setProperty(property, value) {
          if (this._setPendingProperty(property, value)) {
            this._invalidateProperties();
          }
        }

        /**
         * Returns the value for the given property.
         * @param {string} property Name of property
         * @return {*} Value for the given property
         * @protected
         */
        _getProperty(property) {
          return this.__data[property];
        }

        /* eslint-disable no-unused-vars */
        /**
         * Updates the local storage for a property, records the previous value,
         * and adds it to the set of "pending changes" that will be passed to the
         * `_propertiesChanged` callback.  This method does not enqueue the
         * `_propertiesChanged` callback.
         *
         * @param {string} property Name of the property
         * @param {*} value Value to set
         * @param {boolean=} ext Not used here; affordance for closure
         * @return {boolean} Returns true if the property changed
         * @protected
         */
        _setPendingProperty(property, value, ext) {
          let old = this.__data[property];
          let changed = this._shouldPropertyChange(property, value, old);
          if (changed) {
            if (!this.__dataPending) {
              this.__dataPending = {};
              this.__dataOld = {};
            }
            // Ensure old is captured from the last turn
            if (this.__dataOld && !(property in this.__dataOld)) {
              this.__dataOld[property] = old;
            }
            this.__data[property] = value;
            this.__dataPending[property] = value;
          }
          return changed;
        }
        /* eslint-enable */

        /**
         * Marks the properties as invalid, and enqueues an async
         * `_propertiesChanged` callback.
         *
         * @return {void}
         * @protected
         */
        _invalidateProperties() {
          if (!this.__dataInvalid && this.__dataReady) {
            this.__dataInvalid = true;
            microtask.run(() => {
              if (this.__dataInvalid) {
                this.__dataInvalid = false;
                this._flushProperties();
              }
            });
          }
        }

        /**
         * Call to enable property accessor processing. Before this method is
         * called accessor values will be set but side effects are
         * queued. When called, any pending side effects occur immediately.
         * For elements, generally `connectedCallback` is a normal spot to do so.
         * It is safe to call this method multiple times as it only turns on
         * property accessors once.
         *
         * @return {void}
         * @protected
         */
        _enableProperties() {
          if (!this.__dataEnabled) {
            this.__dataEnabled = true;
            if (this.__dataInstanceProps) {
              this._initializeInstanceProperties(this.__dataInstanceProps);
              this.__dataInstanceProps = null;
            }
            this.ready();
          }
        }

        /**
         * Calls the `_propertiesChanged` callback with the current set of
         * pending changes (and old values recorded when pending changes were
         * set), and resets the pending set of changes. Generally, this method
         * should not be called in user code.
         *
         * @return {void}
         * @protected
         */
        _flushProperties() {
          const props = this.__data;
          const changedProps = this.__dataPending;
          const old = this.__dataOld;
          if (this._shouldPropertiesChange(props, changedProps, old)) {
            this.__dataPending = null;
            this.__dataOld = null;
            this._propertiesChanged(props, changedProps, old);
          }
        }

        /**
         * Called in `_flushProperties` to determine if `_propertiesChanged`
         * should be called. The default implementation returns true if
         * properties are pending. Override to customize when
         * `_propertiesChanged` is called.
         * @param {!Object} currentProps Bag of all current accessor values
         * @param {!Object} changedProps Bag of properties changed since the last
         *   call to `_propertiesChanged`
         * @param {!Object} oldProps Bag of previous values for each property
         *   in `changedProps`
         * @return {boolean} true if changedProps is truthy
         */
        _shouldPropertiesChange(currentProps, changedProps, oldProps) { // eslint-disable-line no-unused-vars
          return Boolean(changedProps);
        }

        /**
         * Callback called when any properties with accessors created via
         * `_createPropertyAccessor` have been set.
         *
         * @param {!Object} currentProps Bag of all current accessor values
         * @param {!Object} changedProps Bag of properties changed since the last
         *   call to `_propertiesChanged`
         * @param {!Object} oldProps Bag of previous values for each property
         *   in `changedProps`
         * @return {void}
         * @protected
         */
        _propertiesChanged(currentProps, changedProps, oldProps) { // eslint-disable-line no-unused-vars
        }

        /**
         * Method called to determine whether a property value should be
         * considered as a change and cause the `_propertiesChanged` callback
         * to be enqueued.
         *
         * The default implementation returns `true` if a strict equality
         * check fails. The method always returns false for `NaN`.
         *
         * Override this method to e.g. provide stricter checking for
         * Objects/Arrays when using immutable patterns.
         *
         * @param {string} property Property name
         * @param {*} value New property value
         * @param {*} old Previous property value
         * @return {boolean} Whether the property should be considered a change
         *   and enqueue a `_proeprtiesChanged` callback
         * @protected
         */
        _shouldPropertyChange(property, value, old) {
          return (
            // Strict equality check
            (old !== value &&
              // This ensures (old==NaN, value==NaN) always returns false
              (old === old || value === value))
          );
        }

        /**
         * Implements native Custom Elements `attributeChangedCallback` to
         * set an attribute value to a property via `_attributeToProperty`.
         *
         * @param {string} name Name of attribute that changed
         * @param {?string} old Old attribute value
         * @param {?string} value New attribute value
         * @param {?string} namespace Attribute namespace.
         * @return {void}
         * @suppress {missingProperties} Super may or may not implement the callback
         */
        attributeChangedCallback(name, old, value, namespace) {
          if (old !== value) {
            this._attributeToProperty(name, value);
          }
          if (super.attributeChangedCallback) {
            super.attributeChangedCallback(name, old, value, namespace);
          }
        }

        /**
         * Deserializes an attribute to its associated property.
         *
         * This method calls the `_deserializeValue` method to convert the string to
         * a typed value.
         *
         * @param {string} attribute Name of attribute to deserialize.
         * @param {?string} value of the attribute.
         * @param {*=} type type to deserialize to, defaults to the value
         * returned from `typeForProperty`
         * @return {void}
         */
        _attributeToProperty(attribute, value, type) {
          if (!this.__serializing) {
            const map = this.__dataAttributes;
            const property = map && map[attribute] || attribute;
            this[property] = this._deserializeValue(value, type ||
              this.constructor.typeForProperty(property));
          }
        }

        /**
         * Serializes a property to its associated attribute.
         *
         * @suppress {invalidCasts} Closure can't figure out `this` is an element.
         *
         * @param {string} property Property name to reflect.
         * @param {string=} attribute Attribute name to reflect to.
         * @param {*=} value Property value to refect.
         * @return {void}
         */
        _propertyToAttribute(property, attribute, value) {
          this.__serializing = true;
          value = (arguments.length < 3) ? this[property] : value;
          this._valueToNodeAttribute(/** @type {!HTMLElement} */(this), value,
            attribute || this.constructor.attributeNameForProperty(property));
          this.__serializing = false;
        }

        /**
         * Sets a typed value to an HTML attribute on a node.
         *
         * This method calls the `_serializeValue` method to convert the typed
         * value to a string.  If the `_serializeValue` method returns `undefined`,
         * the attribute will be removed (this is the default for boolean
         * type `false`).
         *
         * @param {Element} node Element to set attribute to.
         * @param {*} value Value to serialize.
         * @param {string} attribute Attribute name to serialize to.
         * @return {void}
         */
        _valueToNodeAttribute(node, value, attribute) {
          const str = this._serializeValue(value);
          if (str === undefined) {
            node.removeAttribute(attribute);
          } else {
            node.setAttribute(attribute, str);
          }
        }

        /**
         * Converts a typed JavaScript value to a string.
         *
         * This method is called when setting JS property values to
         * HTML attributes.  Users may override this method to provide
         * serialization for custom types.
         *
         * @param {*} value Property value to serialize.
         * @return {string | undefined} String serialized from the provided
         * property  value.
         */
        _serializeValue(value) {
          switch (typeof value) {
            case 'boolean':
              return value ? '' : undefined;
            default:
              return value != null ? value.toString() : undefined;
          }
        }

        /**
         * Converts a string to a typed JavaScript value.
         *
         * This method is called when reading HTML attribute values to
         * JS properties.  Users may override this method to provide
         * deserialization for custom `type`s. Types for `Boolean`, `String`,
         * and `Number` convert attributes to the expected types.
         *
         * @param {?string} value Value to deserialize.
         * @param {*=} type Type to deserialize the string to.
         * @return {*} Typed value deserialized from the provided string.
         */
        _deserializeValue(value, type) {
          switch (type) {
            case Boolean:
              return (value !== null);
            case Number:
              return Number(value);
            default:
              return value;
          }
        }

      }

      return PropertiesChanged;
    });


  })();

</script><script>
(function() {

  'use strict';

  let caseMap = Polymer.CaseMap;

  // Save map of native properties; this forms a blacklist or properties
  // that won't have their values "saved" by `saveAccessorValue`, since
  // reading from an HTMLElement accessor from the context of a prototype throws
  const nativeProperties = {};
  let proto = HTMLElement.prototype;
  while (proto) {
    let props = Object.getOwnPropertyNames(proto);
    for (let i=0; i<props.length; i++) {
      nativeProperties[props[i]] = true;
    }
    proto = Object.getPrototypeOf(proto);
  }

  /**
   * Used to save the value of a property that will be overridden with
   * an accessor. If the `model` is a prototype, the values will be saved
   * in `__dataProto`, and it's up to the user (or downstream mixin) to
   * decide how/when to set these values back into the accessors.
   * If `model` is already an instance (it has a `__data` property), then
   * the value will be set as a pending property, meaning the user should
   * call `_invalidateProperties` or `_flushProperties` to take effect
   *
   * @param {Object} model Prototype or instance
   * @param {string} property Name of property
   * @return {void}
   * @private
   */
  function saveAccessorValue(model, property) {
    // Don't read/store value for any native properties since they could throw
    if (!nativeProperties[property]) {
      let value = model[property];
      if (value !== undefined) {
        if (model.__data) {
          // Adding accessor to instance; update the property
          // It is the user's responsibility to call _flushProperties
          model._setPendingProperty(property, value);
        } else {
          // Adding accessor to proto; save proto's value for instance-time use
          if (!model.__dataProto) {
            model.__dataProto = {};
          } else if (!model.hasOwnProperty(JSCompiler_renameProperty('__dataProto', model))) {
            model.__dataProto = Object.create(model.__dataProto);
          }
          model.__dataProto[property] = value;
        }
      }
    }
  }

  /**
   * Element class mixin that provides basic meta-programming for creating one
   * or more property accessors (getter/setter pair) that enqueue an async
   * (batched) `_propertiesChanged` callback.
   *
   * For basic usage of this mixin:
   * 
   * -   Declare attributes to observe via the standard `static get observedAttributes()`. Use
   *     `dash-case` attribute names to represent `camelCase` property names. 
   * -   Implement the `_propertiesChanged` callback on the class.
   * -   Call `MyClass.createPropertiesForAttributes()` **once** on the class to generate 
   *     property accessors for each observed attribute. This must be called before the first 
   *     instance is created, for example, by calling it before calling `customElements.define`.
   *     It can also be called lazily from the element's `constructor`, as long as it's guarded so
   *     that the call is only made once, when the first instance is created.
   * -   Call `this._enableProperties()` in the element's `connectedCallback` to enable 
   *     the accessors.
   *
   * Any `observedAttributes` will automatically be
   * deserialized via `attributeChangedCallback` and set to the associated
   * property using `dash-case`-to-`camelCase` convention.
   *
   * @mixinFunction
   * @polymer
   * @appliesMixin Polymer.PropertiesChanged
   * @memberof Polymer
   * @summary Element class mixin for reacting to property changes from
   *   generated property accessors.
   */
  Polymer.PropertyAccessors = Polymer.dedupingMixin(superClass => {

    /**
     * @constructor
     * @extends {superClass}
     * @implements {Polymer_PropertiesChanged}
     * @unrestricted
     * @private
     */
     const base = Polymer.PropertiesChanged(superClass);

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_PropertyAccessors}
     * @extends {base}
     * @unrestricted
     */
    class PropertyAccessors extends base {

      /**
       * Generates property accessors for all attributes in the standard
       * static `observedAttributes` array.
       *
       * Attribute names are mapped to property names using the `dash-case` to
       * `camelCase` convention
       *
       * @return {void}
       */
      static createPropertiesForAttributes() {
        let a$ = this.observedAttributes;
        for (let i=0; i < a$.length; i++) {
          this.prototype._createPropertyAccessor(caseMap.dashToCamelCase(a$[i]));
        }
      }

      /**
       * Returns an attribute name that corresponds to the given property.
       * By default, converts camel to dash case, e.g. `fooBar` to `foo-bar`.
       * @param {string} property Property to convert
       * @return {string} Attribute name corresponding to the given property.
       *
       * @protected
       */
      static attributeNameForProperty(property) {
        return caseMap.camelToDashCase(property);
      }

      /**
       * Overrides PropertiesChanged implementation to initialize values for
       * accessors created for values that already existed on the element
       * prototype.
       *
       * @return {void}
       * @protected
       */
      _initializeProperties() {
        if (this.__dataProto) {
          this._initializeProtoProperties(this.__dataProto);
          this.__dataProto = null;
        }
        super._initializeProperties();
      }

      /**
       * Called at instance time with bag of properties that were overwritten
       * by accessors on the prototype when accessors were created.
       *
       * The default implementation sets these properties back into the
       * setter at instance time.  This method is provided as an override
       * point for customizing or providing more efficient initialization.
       *
       * @param {Object} props Bag of property values that were overwritten
       *   when creating property accessors.
       * @return {void}
       * @protected
       */
      _initializeProtoProperties(props) {
        for (let p in props) {
          this._setProperty(p, props[p]);
        }
      }

      /**
       * Ensures the element has the given attribute. If it does not,
       * assigns the given value to the attribute.
       *
       * @suppress {invalidCasts} Closure can't figure out `this` is infact an element
       *
       * @param {string} attribute Name of attribute to ensure is set.
       * @param {string} value of the attribute.
       * @return {void}
       */
      _ensureAttribute(attribute, value) {
        const el = /** @type {!HTMLElement} */(this);
        if (!el.hasAttribute(attribute)) {
          this._valueToNodeAttribute(el, value, attribute);
        }
      }

      /**
       * Overrides PropertiesChanged implemention to serialize objects as JSON.
       *
       * @param {*} value Property value to serialize.
       * @return {string | undefined} String serialized from the provided property value.
       */
      _serializeValue(value) {
        /* eslint-disable no-fallthrough */
        switch (typeof value) {
          case 'object':
            if (value instanceof Date) {
              return value.toString();
            } else if (value) {
              try {
                return JSON.stringify(value);
              } catch(x) {
                return '';
              }
            }

          default:
            return super._serializeValue(value);
        }
      }

      /**
       * Converts a string to a typed JavaScript value.
       *
       * This method is called by Polymer when reading HTML attribute values to
       * JS properties.  Users may override this method on Polymer element
       * prototypes to provide deserialization for custom `type`s.  Note,
       * the `type` argument is the value of the `type` field provided in the
       * `properties` configuration object for a given property, and is
       * by convention the constructor for the type to deserialize.
       *
       *
       * @param {?string} value Attribute value to deserialize.
       * @param {*=} type Type to deserialize the string to.
       * @return {*} Typed value deserialized from the provided string.
       */
      _deserializeValue(value, type) {
        /**
         * @type {*}
         */
        let outValue;
        switch (type) {
          case Object:
            try {
              outValue = JSON.parse(/** @type {string} */(value));
            } catch(x) {
              // allow non-JSON literals like Strings and Numbers
              outValue = value;
            }
            break;
          case Array:
            try {
              outValue = JSON.parse(/** @type {string} */(value));
            } catch(x) {
              outValue = null;
              console.warn(`Polymer::Attributes: couldn't decode Array as JSON: ${value}`);
            }
            break;
          case Date:
            outValue = isNaN(value) ? String(value) : Number(value);
            outValue = new Date(outValue);
            break;
          default:
            outValue = super._deserializeValue(value, type);
            break;
        }
        return outValue;
      }
      /* eslint-enable no-fallthrough */

      /**
       * Overrides PropertiesChanged implementation to save existing prototype
       * property value so that it can be reset.
       * @param {string} property Name of the property
       * @param {boolean=} readOnly When true, no setter is created
       *
       * When calling on a prototype, any overwritten values are saved in
       * `__dataProto`, and it is up to the subclasser to decide how/when
       * to set those properties back into the accessor.  When calling on an
       * instance, the overwritten value is set via `_setPendingProperty`,
       * and the user should call `_invalidateProperties` or `_flushProperties`
       * for the values to take effect.
       * @protected
       * @return {void}
       */
      _definePropertyAccessor(property, readOnly) {
        saveAccessorValue(this, property);
        super._definePropertyAccessor(property, readOnly);
      }

      /**
       * Returns true if this library created an accessor for the given property.
       *
       * @param {string} property Property name
       * @return {boolean} True if an accessor was created
       */
      _hasAccessor(property) {
        return this.__dataHasAccessor && this.__dataHasAccessor[property];
      }

      /**
       * Returns true if the specified property has a pending change.
       *
       * @param {string} prop Property name
       * @return {boolean} True if property has a pending change
       * @protected
       */
      _isPropertyPending(prop) {
        return Boolean(this.__dataPending && (prop in this.__dataPending));
      }

    }

    return PropertyAccessors;

  });

})();
</script><script>
(function() {

  'use strict';

  const walker = document.createTreeWalker(document, NodeFilter.SHOW_ALL,
      null, false);

  // 1.x backwards-compatible auto-wrapper for template type extensions
  // This is a clear layering violation and gives favored-nation status to
  // dom-if and dom-repeat templates.  This is a conceit we're choosing to keep
  // a.) to ease 1.x backwards-compatibility due to loss of `is`, and
  // b.) to maintain if/repeat capability in parser-constrained elements
  //     (e.g. table, select) in lieu of native CE type extensions without
  //     massive new invention in this space (e.g. directive system)
  const templateExtensions = {
    'dom-if': true,
    'dom-repeat': true
  };
  function wrapTemplateExtension(node) {
    let is = node.getAttribute('is');
    if (is && templateExtensions[is]) {
      let t = node;
      t.removeAttribute('is');
      node = t.ownerDocument.createElement(is);
      t.parentNode.replaceChild(node, t);
      node.appendChild(t);
      while(t.attributes.length) {
        node.setAttribute(t.attributes[0].name, t.attributes[0].value);
        t.removeAttribute(t.attributes[0].name);
      }
    }
    return node;
  }

  function findTemplateNode(root, nodeInfo) {
    // recursively ascend tree until we hit root
    let parent = nodeInfo.parentInfo && findTemplateNode(root, nodeInfo.parentInfo);
    // unwind the stack, returning the indexed node at each level
    if (parent) {
      // note: marginally faster than indexing via childNodes
      // (http://jsperf.com/childnodes-lookup)
      walker.currentNode = parent;
      for (let n=walker.firstChild(), i=0; n; n=walker.nextSibling()) {
        if (nodeInfo.parentIndex === i++) {
          return n;
        }
      }
    } else {
      return root;
    }
  }

  // construct `$` map (from id annotations)
  function applyIdToMap(inst, map, node, nodeInfo) {
    if (nodeInfo.id) {
      map[nodeInfo.id] = node;
    }
  }

  // install event listeners (from event annotations)
  function applyEventListener(inst, node, nodeInfo) {
    if (nodeInfo.events && nodeInfo.events.length) {
      for (let j=0, e$=nodeInfo.events, e; (j<e$.length) && (e=e$[j]); j++) {
        inst._addMethodEventListenerToNode(node, e.name, e.value, inst);
      }
    }
  }

  // push configuration references at configure time
  function applyTemplateContent(inst, node, nodeInfo) {
    if (nodeInfo.templateInfo) {
      node._templateInfo = nodeInfo.templateInfo;
    }
  }

  function createNodeEventHandler(context, eventName, methodName) {
    // Instances can optionally have a _methodHost which allows redirecting where
    // to find methods. Currently used by `templatize`.
    context = context._methodHost || context;
    let handler = function(e) {
      if (context[methodName]) {
        context[methodName](e, e.detail);
      } else {
        console.warn('listener method `' + methodName + '` not defined');
      }
    };
    return handler;
  }

  /**
   * Element mixin that provides basic template parsing and stamping, including
   * the following template-related features for stamped templates:
   *
   * - Declarative event listeners (`on-eventname="listener"`)
   * - Map of node id's to stamped node instances (`this.$.id`)
   * - Nested template content caching/removal and re-installation (performance
   *   optimization)
   *
   * @mixinFunction
   * @polymer
   * @memberof Polymer
   * @summary Element class mixin that provides basic template parsing and stamping
   */
  Polymer.TemplateStamp = Polymer.dedupingMixin(superClass => {

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_TemplateStamp}
     */
    class TemplateStamp extends superClass {

      /**
       * Scans a template to produce template metadata.
       *
       * Template-specific metadata are stored in the object returned, and node-
       * specific metadata are stored in objects in its flattened `nodeInfoList`
       * array.  Only nodes in the template that were parsed as nodes of
       * interest contain an object in `nodeInfoList`.  Each `nodeInfo` object
       * contains an `index` (`childNodes` index in parent) and optionally
       * `parent`, which points to node info of its parent (including its index).
       *
       * The template metadata object returned from this method has the following
       * structure (many fields optional):
       *
       * ```js
       *   {
       *     // Flattened list of node metadata (for nodes that generated metadata)
       *     nodeInfoList: [
       *       {
       *         // `id` attribute for any nodes with id's for generating `$` map
       *         id: {string},
       *         // `on-event="handler"` metadata
       *         events: [
       *           {
       *             name: {string},   // event name
       *             value: {string},  // handler method name
       *           }, ...
       *         ],
       *         // Notes when the template contained a `<slot>` for shady DOM
       *         // optimization purposes
       *         hasInsertionPoint: {boolean},
       *         // For nested `<template>`` nodes, nested template metadata
       *         templateInfo: {object}, // nested template metadata
       *         // Metadata to allow efficient retrieval of instanced node
       *         // corresponding to this metadata
       *         parentInfo: {number},   // reference to parent nodeInfo>
       *         parentIndex: {number},  // index in parent's `childNodes` collection
       *         infoIndex: {number},    // index of this `nodeInfo` in `templateInfo.nodeInfoList`
       *       },
       *       ...
       *     ],
       *     // When true, the template had the `strip-whitespace` attribute
       *     // or was nested in a template with that setting
       *     stripWhitespace: {boolean},
       *     // For nested templates, nested template content is moved into
       *     // a document fragment stored here; this is an optimization to
       *     // avoid the cost of nested template cloning
       *     content: {DocumentFragment}
       *   }
       * ```
       *
       * This method kicks off a recursive treewalk as follows:
       *
       * ```
       *    _parseTemplate <---------------------+
       *      _parseTemplateContent              |
       *        _parseTemplateNode  <------------|--+
       *          _parseTemplateNestedTemplate --+  |
       *          _parseTemplateChildNodes ---------+
       *          _parseTemplateNodeAttributes
       *            _parseTemplateNodeAttribute
       *
       * ```
       *
       * These methods may be overridden to add custom metadata about templates
       * to either `templateInfo` or `nodeInfo`.
       *
       * Note that this method may be destructive to the template, in that
       * e.g. event annotations may be removed after being noted in the
       * template metadata.
       *
       * @param {!HTMLTemplateElement} template Template to parse
       * @param {TemplateInfo=} outerTemplateInfo Template metadata from the outer
       *   template, for parsing nested templates
       * @return {!TemplateInfo} Parsed template metadata
       */
      static _parseTemplate(template, outerTemplateInfo) {
        // since a template may be re-used, memo-ize metadata
        if (!template._templateInfo) {
          let templateInfo = template._templateInfo = {};
          templateInfo.nodeInfoList = [];
          templateInfo.stripWhiteSpace = Polymer.legacyOptimizations ||
            (outerTemplateInfo && outerTemplateInfo.stripWhiteSpace) ||
            template.hasAttribute('strip-whitespace');
          this._parseTemplateContent(template, templateInfo, {parent: null});
        }
        return template._templateInfo;
      }

      static _parseTemplateContent(template, templateInfo, nodeInfo) {
        return this._parseTemplateNode(template.content, templateInfo, nodeInfo);
      }

      /**
       * Parses template node and adds template and node metadata based on
       * the current node, and its `childNodes` and `attributes`.
       *
       * This method may be overridden to add custom node or template specific
       * metadata based on this node.
       *
       * @param {Node} node Node to parse
       * @param {!TemplateInfo} templateInfo Template metadata for current template
       * @param {!NodeInfo} nodeInfo Node metadata for current template.
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       */
      static _parseTemplateNode(node, templateInfo, nodeInfo) {
        let noted;
        let element = /** @type {Element} */(node);
        if (element.localName == 'template' && !element.hasAttribute('preserve-content')) {
          noted = this._parseTemplateNestedTemplate(element, templateInfo, nodeInfo) || noted;
        } else if (element.localName === 'slot') {
          // For ShadyDom optimization, indicating there is an insertion point
          templateInfo.hasInsertionPoint = true;
        }
        walker.currentNode = element;
        if (walker.firstChild()) {
          noted = this._parseTemplateChildNodes(element, templateInfo, nodeInfo) || noted;
        }
        if (element.hasAttributes && element.hasAttributes()) {
          noted = this._parseTemplateNodeAttributes(element, templateInfo, nodeInfo) || noted;
        }
        return noted;
      }

      /**
       * Parses template child nodes for the given root node.
       *
       * This method also wraps whitelisted legacy template extensions
       * (`is="dom-if"` and `is="dom-repeat"`) with their equivalent element
       * wrappers, collapses text nodes, and strips whitespace from the template
       * if the `templateInfo.stripWhitespace` setting was provided.
       *
       * @param {Node} root Root node whose `childNodes` will be parsed
       * @param {!TemplateInfo} templateInfo Template metadata for current template
       * @param {!NodeInfo} nodeInfo Node metadata for current template.
       * @return {void}
       */
      static _parseTemplateChildNodes(root, templateInfo, nodeInfo) {
        if (root.localName === 'script' || root.localName === 'style') {
          return;
        }
        walker.currentNode = root;
        for (let node=walker.firstChild(), parentIndex=0, next; node; node=next) {
          // Wrap templates
          if (node.localName == 'template') {
            node = wrapTemplateExtension(node);
          }
          // collapse adjacent textNodes: fixes an IE issue that can cause
          // text nodes to be inexplicably split =(
          // note that root.normalize() should work but does not so we do this
          // manually.
          walker.currentNode = node;
          next = walker.nextSibling();
          if (node.nodeType === Node.TEXT_NODE) {
            let /** Node */ n = next;
            while (n && (n.nodeType === Node.TEXT_NODE)) {
              node.textContent += n.textContent;
              next = walker.nextSibling();
              root.removeChild(n);
              n = next;
            }
            // optionally strip whitespace
            if (templateInfo.stripWhiteSpace && !node.textContent.trim()) {
              root.removeChild(node);
              continue;
            }
          }
          let childInfo = { parentIndex, parentInfo: nodeInfo };
          if (this._parseTemplateNode(node, templateInfo, childInfo)) {
            childInfo.infoIndex = templateInfo.nodeInfoList.push(/** @type {!NodeInfo} */(childInfo)) - 1;
          }
          // Increment if not removed
          walker.currentNode = node;
          if (walker.parentNode()) {
            parentIndex++;
          }
        }
      }

      /**
       * Parses template content for the given nested `<template>`.
       *
       * Nested template info is stored as `templateInfo` in the current node's
       * `nodeInfo`. `template.content` is removed and stored in `templateInfo`.
       * It will then be the responsibility of the host to set it back to the
       * template and for users stamping nested templates to use the
       * `_contentForTemplate` method to retrieve the content for this template
       * (an optimization to avoid the cost of cloning nested template content).
       *
       * @param {HTMLTemplateElement} node Node to parse (a <template>)
       * @param {TemplateInfo} outerTemplateInfo Template metadata for current template
       *   that includes the template `node`
       * @param {!NodeInfo} nodeInfo Node metadata for current template.
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       */
      static _parseTemplateNestedTemplate(node, outerTemplateInfo, nodeInfo) {
        let templateInfo = this._parseTemplate(node, outerTemplateInfo);
        let content = templateInfo.content =
          node.content.ownerDocument.createDocumentFragment();
        content.appendChild(node.content);
        nodeInfo.templateInfo = templateInfo;
        return true;
      }

      /**
       * Parses template node attributes and adds node metadata to `nodeInfo`
       * for nodes of interest.
       *
       * @param {Element} node Node to parse
       * @param {TemplateInfo} templateInfo Template metadata for current template
       * @param {NodeInfo} nodeInfo Node metadata for current template.
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       */
      static _parseTemplateNodeAttributes(node, templateInfo, nodeInfo) {
        // Make copy of original attribute list, since the order may change
        // as attributes are added and removed
        let noted = false;
        let attrs = Array.from(node.attributes);
        for (let i=attrs.length-1, a; (a=attrs[i]); i--) {
          noted = this._parseTemplateNodeAttribute(node, templateInfo, nodeInfo, a.name, a.value) || noted;
        }
        return noted;
      }

      /**
       * Parses a single template node attribute and adds node metadata to
       * `nodeInfo` for attributes of interest.
       *
       * This implementation adds metadata for `on-event="handler"` attributes
       * and `id` attributes.
       *
       * @param {Element} node Node to parse
       * @param {!TemplateInfo} templateInfo Template metadata for current template
       * @param {!NodeInfo} nodeInfo Node metadata for current template.
       * @param {string} name Attribute name
       * @param {string} value Attribute value
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       */
      static _parseTemplateNodeAttribute(node, templateInfo, nodeInfo, name, value) {
        // events (on-*)
        if (name.slice(0, 3) === 'on-') {
          node.removeAttribute(name);
          nodeInfo.events = nodeInfo.events || [];
          nodeInfo.events.push({
            name: name.slice(3),
            value
          });
          return true;
        }
        // static id
        else if (name === 'id') {
          nodeInfo.id = value;
          return true;
        }
        return false;
      }

      /**
       * Returns the `content` document fragment for a given template.
       *
       * For nested templates, Polymer performs an optimization to cache nested
       * template content to avoid the cost of cloning deeply nested templates.
       * This method retrieves the cached content for a given template.
       *
       * @param {HTMLTemplateElement} template Template to retrieve `content` for
       * @return {DocumentFragment} Content fragment
       */
      static _contentForTemplate(template) {
        let templateInfo = /** @type {HTMLTemplateElementWithInfo} */ (template)._templateInfo;
        return (templateInfo && templateInfo.content) || template.content;
      }

      /**
       * Clones the provided template content and returns a document fragment
       * containing the cloned dom.
       *
       * The template is parsed (once and memoized) using this library's
       * template parsing features, and provides the following value-added
       * features:
       * * Adds declarative event listeners for `on-event="handler"` attributes
       * * Generates an "id map" for all nodes with id's under `$` on returned
       *   document fragment
       * * Passes template info including `content` back to templates as
       *   `_templateInfo` (a performance optimization to avoid deep template
       *   cloning)
       *
       * Note that the memoized template parsing process is destructive to the
       * template: attributes for bindings and declarative event listeners are
       * removed after being noted in notes, and any nested `<template>.content`
       * is removed and stored in notes as well.
       *
       * @param {!HTMLTemplateElement} template Template to stamp
       * @return {!StampedTemplate} Cloned template content
       */
      _stampTemplate(template) {
        // Polyfill support: bootstrap the template if it has not already been
        if (template && !template.content &&
            window.HTMLTemplateElement && HTMLTemplateElement.decorate) {
          HTMLTemplateElement.decorate(template);
        }
        let templateInfo = this.constructor._parseTemplate(template);
        let nodeInfo = templateInfo.nodeInfoList;
        let content = templateInfo.content || template.content;
        let dom = /** @type {DocumentFragment} */ (document.importNode(content, true));
        // NOTE: ShadyDom optimization indicating there is an insertion point
        dom.__noInsertionPoint = !templateInfo.hasInsertionPoint;
        let nodes = dom.nodeList = new Array(nodeInfo.length);
        dom.$ = {};
        for (let i=0, l=nodeInfo.length, info; (i<l) && (info=nodeInfo[i]); i++) {
          let node = nodes[i] = findTemplateNode(dom, info);
          applyIdToMap(this, dom.$, node, info);
          applyTemplateContent(this, node, info);
          applyEventListener(this, node, info);
        }
        dom = /** @type {!StampedTemplate} */(dom); // eslint-disable-line no-self-assign
        return dom;
      }

      /**
       * Adds an event listener by method name for the event provided.
       *
       * This method generates a handler function that looks up the method
       * name at handling time.
       *
       * @param {!Node} node Node to add listener on
       * @param {string} eventName Name of event
       * @param {string} methodName Name of method
       * @param {*=} context Context the method will be called on (defaults
       *   to `node`)
       * @return {Function} Generated handler function
       */
      _addMethodEventListenerToNode(node, eventName, methodName, context) {
        context = context || node;
        let handler = createNodeEventHandler(context, eventName, methodName);
        this._addEventListenerToNode(node, eventName, handler);
        return handler;
      }

      /**
       * Override point for adding custom or simulated event handling.
       *
       * @param {!Node} node Node to add event listener to
       * @param {string} eventName Name of event
       * @param {function(!Event):void} handler Listener function to add
       * @return {void}
       */
      _addEventListenerToNode(node, eventName, handler) {
        node.addEventListener(eventName, handler);
      }

      /**
       * Override point for adding custom or simulated event handling.
       *
       * @param {!Node} node Node to remove event listener from
       * @param {string} eventName Name of event
       * @param {function(!Event):void} handler Listener function to remove
       * @return {void}
       */
      _removeEventListenerFromNode(node, eventName, handler) {
        node.removeEventListener(eventName, handler);
      }

    }

    return TemplateStamp;

  });

})();
</script><script>
(function() {

  'use strict';

  /** @const {Object} */
  const CaseMap = Polymer.CaseMap;

  // Monotonically increasing unique ID used for de-duping effects triggered
  // from multiple properties in the same turn
  let dedupeId = 0;

  /**
   * Property effect types; effects are stored on the prototype using these keys
   * @enum {string}
   */
  const TYPES = {
    COMPUTE: '__computeEffects',
    REFLECT: '__reflectEffects',
    NOTIFY: '__notifyEffects',
    PROPAGATE: '__propagateEffects',
    OBSERVE: '__observeEffects',
    READ_ONLY: '__readOnly'
  };

  /** @const {RegExp} */
  const capitalAttributeRegex = /[A-Z]/;

  /**
   * @typedef {{
   * name: (string | undefined),
   * structured: (boolean | undefined),
   * wildcard: (boolean | undefined)
   * }}
   */
  let DataTrigger; //eslint-disable-line no-unused-vars

  /**
   * @typedef {{
   * info: ?,
   * trigger: (!DataTrigger | undefined),
   * fn: (!Function | undefined)
   * }}
   */
  let DataEffect; //eslint-disable-line no-unused-vars

  let PropertyEffectsType; //eslint-disable-line no-unused-vars

  /**
   * Ensures that the model has an own-property map of effects for the given type.
   * The model may be a prototype or an instance.
   *
   * Property effects are stored as arrays of effects by property in a map,
   * by named type on the model. e.g.
   *
   *   __computeEffects: {
   *     foo: [ ... ],
   *     bar: [ ... ]
   *   }
   *
   * If the model does not yet have an effect map for the type, one is created
   * and returned.  If it does, but it is not an own property (i.e. the
   * prototype had effects), the the map is deeply cloned and the copy is
   * set on the model and returned, ready for new effects to be added.
   *
   * @param {Object} model Prototype or instance
   * @param {string} type Property effect type
   * @return {Object} The own-property map of effects for the given type
   * @private
   */
  function ensureOwnEffectMap(model, type) {
    let effects = model[type];
    if (!effects) {
      effects = model[type] = {};
    } else if (!model.hasOwnProperty(type)) {
      effects = model[type] = Object.create(model[type]);
      for (let p in effects) {
        let protoFx = effects[p];
        let instFx = effects[p] = Array(protoFx.length);
        for (let i=0; i<protoFx.length; i++) {
          instFx[i] = protoFx[i];
        }
      }
    }
    return effects;
  }

  // -- effects ----------------------------------------------

  /**
   * Runs all effects of a given type for the given set of property changes
   * on an instance.
   *
   * @param {!PropertyEffectsType} inst The instance with effects to run
   * @param {Object} effects Object map of property-to-Array of effects
   * @param {Object} props Bag of current property changes
   * @param {Object=} oldProps Bag of previous values for changed properties
   * @param {boolean=} hasPaths True with `props` contains one or more paths
   * @param {*=} extraArgs Additional metadata to pass to effect function
   * @return {boolean} True if an effect ran for this property
   * @private
   */
  function runEffects(inst, effects, props, oldProps, hasPaths, extraArgs) {
    if (effects) {
      let ran = false;
      let id = dedupeId++;
      for (let prop in props) {
        if (runEffectsForProperty(inst, effects, id, prop, props, oldProps, hasPaths, extraArgs)) {
          ran = true;
        }
      }
      return ran;
    }
    return false;
  }

  /**
   * Runs a list of effects for a given property.
   *
   * @param {!PropertyEffectsType} inst The instance with effects to run
   * @param {Object} effects Object map of property-to-Array of effects
   * @param {number} dedupeId Counter used for de-duping effects
   * @param {string} prop Name of changed property
   * @param {*} props Changed properties
   * @param {*} oldProps Old properties
   * @param {boolean=} hasPaths True with `props` contains one or more paths
   * @param {*=} extraArgs Additional metadata to pass to effect function
   * @return {boolean} True if an effect ran for this property
   * @private
   */
  function runEffectsForProperty(inst, effects, dedupeId, prop, props, oldProps, hasPaths, extraArgs) {
    let ran = false;
    let rootProperty = hasPaths ? Polymer.Path.root(prop) : prop;
    let fxs = effects[rootProperty];
    if (fxs) {
      for (let i=0, l=fxs.length, fx; (i<l) && (fx=fxs[i]); i++) {
        if ((!fx.info || fx.info.lastRun !== dedupeId) &&
            (!hasPaths || pathMatchesTrigger(prop, fx.trigger))) {
          if (fx.info) {
            fx.info.lastRun = dedupeId;
          }
          fx.fn(inst, prop, props, oldProps, fx.info, hasPaths, extraArgs);
          ran = true;
        }
      }
    }
    return ran;
  }

  /**
   * Determines whether a property/path that has changed matches the trigger
   * criteria for an effect.  A trigger is a descriptor with the following
   * structure, which matches the descriptors returned from `parseArg`.
   * e.g. for `foo.bar.*`:
   * ```
   * trigger: {
   *   name: 'a.b',
   *   structured: true,
   *   wildcard: true
   * }
   * ```
   * If no trigger is given, the path is deemed to match.
   *
   * @param {string} path Path or property that changed
   * @param {DataTrigger} trigger Descriptor
   * @return {boolean} Whether the path matched the trigger
   */
  function pathMatchesTrigger(path, trigger) {
    if (trigger) {
      let triggerPath = trigger.name;
      return (triggerPath == path) ||
        (trigger.structured && Polymer.Path.isAncestor(triggerPath, path)) ||
        (trigger.wildcard && Polymer.Path.isDescendant(triggerPath, path));
    } else {
      return true;
    }
  }

  /**
   * Implements the "observer" effect.
   *
   * Calls the method with `info.methodName` on the instance, passing the
   * new and old values.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} property Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @return {void}
   * @private
   */
  function runObserverEffect(inst, property, props, oldProps, info) {
    let fn = typeof info.method === "string" ? inst[info.method] : info.method;
    let changedProp = info.property;
    if (fn) {
      fn.call(inst, inst.__data[changedProp], oldProps[changedProp]);
    } else if (!info.dynamicFn) {
      console.warn('observer method `' + info.method + '` not defined');
    }
  }

  /**
   * Runs "notify" effects for a set of changed properties.
   *
   * This method differs from the generic `runEffects` method in that it
   * will dispatch path notification events in the case that the property
   * changed was a path and the root property for that path didn't have a
   * "notify" effect.  This is to maintain 1.0 behavior that did not require
   * `notify: true` to ensure object sub-property notifications were
   * sent.
   *
   * @param {!PropertyEffectsType} inst The instance with effects to run
   * @param {Object} notifyProps Bag of properties to notify
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {boolean} hasPaths True with `props` contains one or more paths
   * @return {void}
   * @private
   */
  function runNotifyEffects(inst, notifyProps, props, oldProps, hasPaths) {
    // Notify
    let fxs = inst[TYPES.NOTIFY];
    let notified;
    let id = dedupeId++;
    // Try normal notify effects; if none, fall back to try path notification
    for (let prop in notifyProps) {
      if (notifyProps[prop]) {
        if (fxs && runEffectsForProperty(inst, fxs, id, prop, props, oldProps, hasPaths)) {
          notified = true;
        } else if (hasPaths && notifyPath(inst, prop, props)) {
          notified = true;
        }
      }
    }
    // Flush host if we actually notified and host was batching
    // And the host has already initialized clients; this prevents
    // an issue with a host observing data changes before clients are ready.
    let host;
    if (notified && (host = inst.__dataHost) && host._invalidateProperties) {
      host._invalidateProperties();
    }
  }

  /**
   * Dispatches {property}-changed events with path information in the detail
   * object to indicate a sub-path of the property was changed.
   *
   * @param {!PropertyEffectsType} inst The element from which to fire the event
   * @param {string} path The path that was changed
   * @param {Object} props Bag of current property changes
   * @return {boolean} Returns true if the path was notified
   * @private
   */
  function notifyPath(inst, path, props) {
    let rootProperty = Polymer.Path.root(path);
    if (rootProperty !== path) {
      let eventName = Polymer.CaseMap.camelToDashCase(rootProperty) + '-changed';
      dispatchNotifyEvent(inst, eventName, props[path], path);
      return true;
    }
    return false;
  }

  /**
   * Dispatches {property}-changed events to indicate a property (or path)
   * changed.
   *
   * @param {!PropertyEffectsType} inst The element from which to fire the event
   * @param {string} eventName The name of the event to send ('{property}-changed')
   * @param {*} value The value of the changed property
   * @param {string | null | undefined} path If a sub-path of this property changed, the path
   *   that changed (optional).
   * @return {void}
   * @private
   * @suppress {invalidCasts}
   */
  function dispatchNotifyEvent(inst, eventName, value, path) {
    let detail = {
      value: value,
      queueProperty: true
    };
    if (path) {
      detail.path = path;
    }
    /** @type {!HTMLElement} */(inst).dispatchEvent(new CustomEvent(eventName, { detail }));
  }

  /**
   * Implements the "notify" effect.
   *
   * Dispatches a non-bubbling event named `info.eventName` on the instance
   * with a detail object containing the new `value`.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} property Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @param {boolean} hasPaths True with `props` contains one or more paths
   * @return {void}
   * @private
   */
  function runNotifyEffect(inst, property, props, oldProps, info, hasPaths) {
    let rootProperty = hasPaths ? Polymer.Path.root(property) : property;
    let path = rootProperty != property ? property : null;
    let value = path ? Polymer.Path.get(inst, path) : inst.__data[property];
    if (path && value === undefined) {
      value = props[property];  // specifically for .splices
    }
    dispatchNotifyEvent(inst, info.eventName, value, path);
  }

  /**
   * Handler function for 2-way notification events. Receives context
   * information captured in the `addNotifyListener` closure from the
   * `__notifyListeners` metadata.
   *
   * Sets the value of the notified property to the host property or path.  If
   * the event contained path information, translate that path to the host
   * scope's name for that path first.
   *
   * @param {CustomEvent} event Notification event (e.g. '<property>-changed')
   * @param {!PropertyEffectsType} inst Host element instance handling the notification event
   * @param {string} fromProp Child element property that was bound
   * @param {string} toPath Host property/path that was bound
   * @param {boolean} negate Whether the binding was negated
   * @return {void}
   * @private
   */
  function handleNotification(event, inst, fromProp, toPath, negate) {
    let value;
    let detail = /** @type {Object} */(event.detail);
    let fromPath = detail && detail.path;
    if (fromPath) {
      toPath = Polymer.Path.translate(fromProp, toPath, fromPath);
      value = detail && detail.value;
    } else {
      value = event.currentTarget[fromProp];
    }
    value = negate ? !value : value;
    if (!inst[TYPES.READ_ONLY] || !inst[TYPES.READ_ONLY][toPath]) {
      if (inst._setPendingPropertyOrPath(toPath, value, true, Boolean(fromPath))
        && (!detail || !detail.queueProperty)) {
        inst._invalidateProperties();
      }
    }
  }

  /**
   * Implements the "reflect" effect.
   *
   * Sets the attribute named `info.attrName` to the given property value.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} property Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @return {void}
   * @private
   */
  function runReflectEffect(inst, property, props, oldProps, info) {
    let value = inst.__data[property];
    if (Polymer.sanitizeDOMValue) {
      value = Polymer.sanitizeDOMValue(value, info.attrName, 'attribute', /** @type {Node} */(inst));
    }
    inst._propertyToAttribute(property, info.attrName, value);
  }

  /**
   * Runs "computed" effects for a set of changed properties.
   *
   * This method differs from the generic `runEffects` method in that it
   * continues to run computed effects based on the output of each pass until
   * there are no more newly computed properties.  This ensures that all
   * properties that will be computed by the initial set of changes are
   * computed before other effects (binding propagation, observers, and notify)
   * run.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {!Object} changedProps Bag of changed properties
   * @param {!Object} oldProps Bag of previous values for changed properties
   * @param {boolean} hasPaths True with `props` contains one or more paths
   * @return {void}
   * @private
   */
  function runComputedEffects(inst, changedProps, oldProps, hasPaths) {
    let computeEffects = inst[TYPES.COMPUTE];
    if (computeEffects) {
      let inputProps = changedProps;
      while (runEffects(inst, computeEffects, inputProps, oldProps, hasPaths)) {
        Object.assign(oldProps, inst.__dataOld);
        Object.assign(changedProps, inst.__dataPending);
        inputProps = inst.__dataPending;
        inst.__dataPending = null;
      }
    }
  }

  /**
   * Implements the "computed property" effect by running the method with the
   * values of the arguments specified in the `info` object and setting the
   * return value to the computed property specified.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} property Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @return {void}
   * @private
   */
  function runComputedEffect(inst, property, props, oldProps, info) {
    let result = runMethodEffect(inst, property, props, oldProps, info);
    let computedProp = info.methodInfo;
    if (inst.__dataHasAccessor && inst.__dataHasAccessor[computedProp]) {
      inst._setPendingProperty(computedProp, result, true);
    } else {
      inst[computedProp] = result;
    }
  }

  /**
   * Computes path changes based on path links set up using the `linkPaths`
   * API.
   *
   * @param {!PropertyEffectsType} inst The instance whose props are changing
   * @param {string | !Array<(string|number)>} path Path that has changed
   * @param {*} value Value of changed path
   * @return {void}
   * @private
   */
  function computeLinkedPaths(inst, path, value) {
    let links = inst.__dataLinkedPaths;
    if (links) {
      let link;
      for (let a in links) {
        let b = links[a];
        if (Polymer.Path.isDescendant(a, path)) {
          link = Polymer.Path.translate(a, b, path);
          inst._setPendingPropertyOrPath(link, value, true, true);
        } else if (Polymer.Path.isDescendant(b, path)) {
          link = Polymer.Path.translate(b, a, path);
          inst._setPendingPropertyOrPath(link, value, true, true);
        }
      }
    }
  }

  // -- bindings ----------------------------------------------

  /**
   * Adds binding metadata to the current `nodeInfo`, and binding effects
   * for all part dependencies to `templateInfo`.
   *
   * @param {Function} constructor Class that `_parseTemplate` is currently
   *   running on
   * @param {TemplateInfo} templateInfo Template metadata for current template
   * @param {NodeInfo} nodeInfo Node metadata for current template node
   * @param {string} kind Binding kind, either 'property', 'attribute', or 'text'
   * @param {string} target Target property name
   * @param {!Array<!BindingPart>} parts Array of binding part metadata
   * @param {string=} literal Literal text surrounding binding parts (specified
   *   only for 'property' bindings, since these must be initialized as part
   *   of boot-up)
   * @return {void}
   * @private
   */
  function addBinding(constructor, templateInfo, nodeInfo, kind, target, parts, literal) {
    // Create binding metadata and add to nodeInfo
    nodeInfo.bindings = nodeInfo.bindings || [];
    let /** Binding */ binding = { kind, target, parts, literal, isCompound: (parts.length !== 1) };
    nodeInfo.bindings.push(binding);
    // Add listener info to binding metadata
    if (shouldAddListener(binding)) {
      let {event, negate} = binding.parts[0];
      binding.listenerEvent = event || (CaseMap.camelToDashCase(target) + '-changed');
      binding.listenerNegate = negate;
    }
    // Add "propagate" property effects to templateInfo
    let index = templateInfo.nodeInfoList.length;
    for (let i=0; i<binding.parts.length; i++) {
      let part = binding.parts[i];
      part.compoundIndex = i;
      addEffectForBindingPart(constructor, templateInfo, binding, part, index);
    }
  }

  /**
   * Adds property effects to the given `templateInfo` for the given binding
   * part.
   *
   * @param {Function} constructor Class that `_parseTemplate` is currently
   *   running on
   * @param {TemplateInfo} templateInfo Template metadata for current template
   * @param {!Binding} binding Binding metadata
   * @param {!BindingPart} part Binding part metadata
   * @param {number} index Index into `nodeInfoList` for this node
   * @return {void}
   */
  function addEffectForBindingPart(constructor, templateInfo, binding, part, index) {
    if (!part.literal) {
      if (binding.kind === 'attribute' && binding.target[0] === '-') {
        console.warn('Cannot set attribute ' + binding.target +
          ' because "-" is not a valid attribute starting character');
      } else {
        let dependencies = part.dependencies;
        let info = { index, binding, part, evaluator: constructor };
        for (let j=0; j<dependencies.length; j++) {
          let trigger = dependencies[j];
          if (typeof trigger == 'string') {
            trigger = parseArg(trigger);
            trigger.wildcard = true;
          }
          constructor._addTemplatePropertyEffect(templateInfo, trigger.rootProperty, {
            fn: runBindingEffect,
            info, trigger
          });
        }
      }
    }
  }

  /**
   * Implements the "binding" (property/path binding) effect.
   *
   * Note that binding syntax is overridable via `_parseBindings` and
   * `_evaluateBinding`.  This method will call `_evaluateBinding` for any
   * non-literal parts returned from `_parseBindings`.  However,
   * there is no support for _path_ bindings via custom binding parts,
   * as this is specific to Polymer's path binding syntax.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} path Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @param {boolean} hasPaths True with `props` contains one or more paths
   * @param {Array} nodeList List of nodes associated with `nodeInfoList` template
   *   metadata
   * @return {void}
   * @private
   */
  function runBindingEffect(inst, path, props, oldProps, info, hasPaths, nodeList) {
    let node = nodeList[info.index];
    let binding = info.binding;
    let part = info.part;
    // Subpath notification: transform path and set to client
    // e.g.: foo="{{obj.sub}}", path: 'obj.sub.prop', set 'foo.prop'=obj.sub.prop
    if (hasPaths && part.source && (path.length > part.source.length) &&
        (binding.kind == 'property') && !binding.isCompound &&
        node.__isPropertyEffectsClient &&
        node.__dataHasAccessor && node.__dataHasAccessor[binding.target]) {
      let value = props[path];
      path = Polymer.Path.translate(part.source, binding.target, path);
      if (node._setPendingPropertyOrPath(path, value, false, true)) {
        inst._enqueueClient(node);
      }
    } else {
      let value = info.evaluator._evaluateBinding(inst, part, path, props, oldProps, hasPaths);
      // Propagate value to child
      applyBindingValue(inst, node, binding, part, value);
    }
  }

  /**
   * Sets the value for an "binding" (binding) effect to a node,
   * either as a property or attribute.
   *
   * @param {!PropertyEffectsType} inst The instance owning the binding effect
   * @param {Node} node Target node for binding
   * @param {!Binding} binding Binding metadata
   * @param {!BindingPart} part Binding part metadata
   * @param {*} value Value to set
   * @return {void}
   * @private
   */
  function applyBindingValue(inst, node, binding, part, value) {
    value = computeBindingValue(node, value, binding, part);
    if (Polymer.sanitizeDOMValue) {
      value = Polymer.sanitizeDOMValue(value, binding.target, binding.kind, node);
    }
    if (binding.kind == 'attribute') {
      // Attribute binding
      inst._valueToNodeAttribute(/** @type {Element} */(node), value, binding.target);
    } else {
      // Property binding
      let prop = binding.target;
      if (node.__isPropertyEffectsClient &&
          node.__dataHasAccessor && node.__dataHasAccessor[prop]) {
        if (!node[TYPES.READ_ONLY] || !node[TYPES.READ_ONLY][prop]) {
          if (node._setPendingProperty(prop, value)) {
            inst._enqueueClient(node);
          }
        }
      } else  {
        inst._setUnmanagedPropertyToNode(node, prop, value);
      }
    }
  }

  /**
   * Transforms an "binding" effect value based on compound & negation
   * effect metadata, as well as handling for special-case properties
   *
   * @param {Node} node Node the value will be set to
   * @param {*} value Value to set
   * @param {!Binding} binding Binding metadata
   * @param {!BindingPart} part Binding part metadata
   * @return {*} Transformed value to set
   * @private
   */
  function computeBindingValue(node, value, binding, part) {
    if (binding.isCompound) {
      let storage = node.__dataCompoundStorage[binding.target];
      storage[part.compoundIndex] = value;
      value = storage.join('');
    }
    if (binding.kind !== 'attribute') {
      // Some browsers serialize `undefined` to `"undefined"`
      if (binding.target === 'textContent' ||
          (binding.target === 'value' &&
            (node.localName === 'input' || node.localName === 'textarea'))) {
        value = value == undefined ? '' : value;
      }
    }
    return value;
  }

  /**
   * Returns true if a binding's metadata meets all the requirements to allow
   * 2-way binding, and therefore a `<property>-changed` event listener should be
   * added:
   * - used curly braces
   * - is a property (not attribute) binding
   * - is not a textContent binding
   * - is not compound
   *
   * @param {!Binding} binding Binding metadata
   * @return {boolean} True if 2-way listener should be added
   * @private
   */
  function shouldAddListener(binding) {
    return Boolean(binding.target) &&
           binding.kind != 'attribute' &&
           binding.kind != 'text' &&
           !binding.isCompound &&
           binding.parts[0].mode === '{';
  }

  /**
   * Setup compound binding storage structures, notify listeners, and dataHost
   * references onto the bound nodeList.
   *
   * @param {!PropertyEffectsType} inst Instance that bas been previously bound
   * @param {TemplateInfo} templateInfo Template metadata
   * @return {void}
   * @private
   */
  function setupBindings(inst, templateInfo) {
    // Setup compound storage, dataHost, and notify listeners
    let {nodeList, nodeInfoList} = templateInfo;
    if (nodeInfoList.length) {
      for (let i=0; i < nodeInfoList.length; i++) {
        let info = nodeInfoList[i];
        let node = nodeList[i];
        let bindings = info.bindings;
        if (bindings) {
          for (let i=0; i<bindings.length; i++) {
            let binding = bindings[i];
            setupCompoundStorage(node, binding);
            addNotifyListener(node, inst, binding);
          }
        }
        node.__dataHost = inst;
      }
    }
  }

  /**
   * Initializes `__dataCompoundStorage` local storage on a bound node with
   * initial literal data for compound bindings, and sets the joined
   * literal parts to the bound property.
   *
   * When changes to compound parts occur, they are first set into the compound
   * storage array for that property, and then the array is joined to result in
   * the final value set to the property/attribute.
   *
   * @param {Node} node Bound node to initialize
   * @param {Binding} binding Binding metadata
   * @return {void}
   * @private
   */
  function setupCompoundStorage(node, binding) {
    if (binding.isCompound) {
      // Create compound storage map
      let storage = node.__dataCompoundStorage ||
        (node.__dataCompoundStorage = {});
      let parts = binding.parts;
      // Copy literals from parts into storage for this binding
      let literals = new Array(parts.length);
      for (let j=0; j<parts.length; j++) {
        literals[j] = parts[j].literal;
      }
      let target = binding.target;
      storage[target] = literals;
      // Configure properties with their literal parts
      if (binding.literal && binding.kind == 'property') {
        node[target] = binding.literal;
      }
    }
  }

  /**
   * Adds a 2-way binding notification event listener to the node specified
   *
   * @param {Object} node Child element to add listener to
   * @param {!PropertyEffectsType} inst Host element instance to handle notification event
   * @param {Binding} binding Binding metadata
   * @return {void}
   * @private
   */
  function addNotifyListener(node, inst, binding) {
    if (binding.listenerEvent) {
      let part = binding.parts[0];
      node.addEventListener(binding.listenerEvent, function(e) {
        handleNotification(e, inst, binding.target, part.source, part.negate);
      });
    }
  }

  // -- for method-based effects (complexObserver & computed) --------------

  /**
   * Adds property effects for each argument in the method signature (and
   * optionally, for the method name if `dynamic` is true) that calls the
   * provided effect function.
   *
   * @param {Element | Object} model Prototype or instance
   * @param {!MethodSignature} sig Method signature metadata
   * @param {string} type Type of property effect to add
   * @param {Function} effectFn Function to run when arguments change
   * @param {*=} methodInfo Effect-specific information to be included in
   *   method effect metadata
   * @param {boolean|Object=} dynamicFn Boolean or object map indicating whether
   *   method names should be included as a dependency to the effect. Note,
   *   defaults to true if the signature is static (sig.static is true).
   * @return {void}
   * @private
   */
  function createMethodEffect(model, sig, type, effectFn, methodInfo, dynamicFn) {
    dynamicFn = sig.static || (dynamicFn &&
      (typeof dynamicFn !== 'object' || dynamicFn[sig.methodName]));
    let info = {
      methodName: sig.methodName,
      args: sig.args,
      methodInfo,
      dynamicFn
    };
    for (let i=0, arg; (i<sig.args.length) && (arg=sig.args[i]); i++) {
      if (!arg.literal) {
        model._addPropertyEffect(arg.rootProperty, type, {
          fn: effectFn, info: info, trigger: arg
        });
      }
    }
    if (dynamicFn) {
      model._addPropertyEffect(sig.methodName, type, {
        fn: effectFn, info: info
      });
    }
  }

  /**
   * Calls a method with arguments marshaled from properties on the instance
   * based on the method signature contained in the effect metadata.
   *
   * Multi-property observers, computed properties, and inline computing
   * functions call this function to invoke the method, then use the return
   * value accordingly.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} property Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @return {*} Returns the return value from the method invocation
   * @private
   */
  function runMethodEffect(inst, property, props, oldProps, info) {
    // Instances can optionally have a _methodHost which allows redirecting where
    // to find methods. Currently used by `templatize`.
    let context = inst._methodHost || inst;
    let fn = context[info.methodName];
    if (fn) {
      let args = inst._marshalArgs(info.args, property, props);
      return fn.apply(context, args);
    } else if (!info.dynamicFn) {
      console.warn('method `' + info.methodName + '` not defined');
    }
  }

  const emptyArray = [];

  // Regular expressions used for binding
  const IDENT  = '(?:' + '[a-zA-Z_$][\\w.:$\\-*]*' + ')';
  const NUMBER = '(?:' + '[-+]?[0-9]*\\.?[0-9]+(?:[eE][-+]?[0-9]+)?' + ')';
  const SQUOTE_STRING = '(?:' + '\'(?:[^\'\\\\]|\\\\.)*\'' + ')';
  const DQUOTE_STRING = '(?:' + '"(?:[^"\\\\]|\\\\.)*"' + ')';
  const STRING = '(?:' + SQUOTE_STRING + '|' + DQUOTE_STRING + ')';
  const ARGUMENT = '(?:(' + IDENT + '|' + NUMBER + '|' +  STRING + ')\\s*' + ')';
  const ARGUMENTS = '(?:' + ARGUMENT + '(?:,\\s*' + ARGUMENT + ')*' + ')';
  const ARGUMENT_LIST = '(?:' + '\\(\\s*' +
                                '(?:' + ARGUMENTS + '?' + ')' +
                              '\\)\\s*' + ')';
  const BINDING = '(' + IDENT + '\\s*' + ARGUMENT_LIST + '?' + ')'; // Group 3
  const OPEN_BRACKET = '(\\[\\[|{{)' + '\\s*';
  const CLOSE_BRACKET = '(?:]]|}})';
  const NEGATE = '(?:(!)\\s*)?'; // Group 2
  const EXPRESSION = OPEN_BRACKET + NEGATE + BINDING + CLOSE_BRACKET;
  const bindingRegex = new RegExp(EXPRESSION, "g");

  /**
   * Create a string from binding parts of all the literal parts
   *
   * @param {!Array<BindingPart>} parts All parts to stringify
   * @return {string} String made from the literal parts
   */
  function literalFromParts(parts) {
    let s = '';
    for (let i=0; i<parts.length; i++) {
      let literal = parts[i].literal;
      s += literal || '';
    }
    return s;
  }

  /**
   * Parses an expression string for a method signature, and returns a metadata
   * describing the method in terms of `methodName`, `static` (whether all the
   * arguments are literals), and an array of `args`
   *
   * @param {string} expression The expression to parse
   * @return {?MethodSignature} The method metadata object if a method expression was
   *   found, otherwise `undefined`
   * @private
   */
  function parseMethod(expression) {
    // tries to match valid javascript property names
    let m = expression.match(/([^\s]+?)\(([\s\S]*)\)/);
    if (m) {
      let methodName = m[1];
      let sig = { methodName, static: true, args: emptyArray };
      if (m[2].trim()) {
        // replace escaped commas with comma entity, split on un-escaped commas
        let args = m[2].replace(/\\,/g, '&comma;').split(',');
        return parseArgs(args, sig);
      } else {
        return sig;
      }
    }
    return null;
  }

  /**
   * Parses an array of arguments and sets the `args` property of the supplied
   * signature metadata object. Sets the `static` property to false if any
   * argument is a non-literal.
   *
   * @param {!Array<string>} argList Array of argument names
   * @param {!MethodSignature} sig Method signature metadata object
   * @return {!MethodSignature} The updated signature metadata object
   * @private
   */
  function parseArgs(argList, sig) {
    sig.args = argList.map(function(rawArg) {
      let arg = parseArg(rawArg);
      if (!arg.literal) {
        sig.static = false;
      }
      return arg;
    }, this);
    return sig;
  }

  /**
   * Parses an individual argument, and returns an argument metadata object
   * with the following fields:
   *
   *   {
   *     value: 'prop',        // property/path or literal value
   *     literal: false,       // whether argument is a literal
   *     structured: false,    // whether the property is a path
   *     rootProperty: 'prop', // the root property of the path
   *     wildcard: false       // whether the argument was a wildcard '.*' path
   *   }
   *
   * @param {string} rawArg The string value of the argument
   * @return {!MethodArg} Argument metadata object
   * @private
   */
  function parseArg(rawArg) {
    // clean up whitespace
    let arg = rawArg.trim()
      // replace comma entity with comma
      .replace(/&comma;/g, ',')
      // repair extra escape sequences; note only commas strictly need
      // escaping, but we allow any other char to be escaped since its
      // likely users will do this
      .replace(/\\(.)/g, '\$1')
      ;
    // basic argument descriptor
    let a = {
      name: arg,
      value: '',
      literal: false
    };
    // detect literal value (must be String or Number)
    let fc = arg[0];
    if (fc === '-') {
      fc = arg[1];
    }
    if (fc >= '0' && fc <= '9') {
      fc = '#';
    }
    switch(fc) {
      case "'":
      case '"':
        a.value = arg.slice(1, -1);
        a.literal = true;
        break;
      case '#':
        a.value = Number(arg);
        a.literal = true;
        break;
    }
    // if not literal, look for structured path
    if (!a.literal) {
      a.rootProperty = Polymer.Path.root(arg);
      // detect structured path (has dots)
      a.structured = Polymer.Path.isPath(arg);
      if (a.structured) {
        a.wildcard = (arg.slice(-2) == '.*');
        if (a.wildcard) {
          a.name = arg.slice(0, -2);
        }
      }
    }
    return a;
  }

  // data api

  /**
   * Sends array splice notifications (`.splices` and `.length`)
   *
   * Note: this implementation only accepts normalized paths
   *
   * @param {!PropertyEffectsType} inst Instance to send notifications to
   * @param {Array} array The array the mutations occurred on
   * @param {string} path The path to the array that was mutated
   * @param {Array} splices Array of splice records
   * @return {void}
   * @private
   */
  function notifySplices(inst, array, path, splices) {
    let splicesPath = path + '.splices';
    inst.notifyPath(splicesPath, { indexSplices: splices });
    inst.notifyPath(path + '.length', array.length);
    // Null here to allow potentially large splice records to be GC'ed.
    inst.__data[splicesPath] = {indexSplices: null};
  }

  /**
   * Creates a splice record and sends an array splice notification for
   * the described mutation
   *
   * Note: this implementation only accepts normalized paths
   *
   * @param {!PropertyEffectsType} inst Instance to send notifications to
   * @param {Array} array The array the mutations occurred on
   * @param {string} path The path to the array that was mutated
   * @param {number} index Index at which the array mutation occurred
   * @param {number} addedCount Number of added items
   * @param {Array} removed Array of removed items
   * @return {void}
   * @private
   */
  function notifySplice(inst, array, path, index, addedCount, removed) {
    notifySplices(inst, array, path, [{
      index: index,
      addedCount: addedCount,
      removed: removed,
      object: array,
      type: 'splice'
    }]);
  }

  /**
   * Returns an upper-cased version of the string.
   *
   * @param {string} name String to uppercase
   * @return {string} Uppercased string
   * @private
   */
  function upper(name) {
    return name[0].toUpperCase() + name.substring(1);
  }

  /**
   * Element class mixin that provides meta-programming for Polymer's template
   * binding and data observation (collectively, "property effects") system.
   *
   * This mixin uses provides the following key static methods for adding
   * property effects to an element class:
   * - `addPropertyEffect`
   * - `createPropertyObserver`
   * - `createMethodObserver`
   * - `createNotifyingProperty`
   * - `createReadOnlyProperty`
   * - `createReflectedProperty`
   * - `createComputedProperty`
   * - `bindTemplate`
   *
   * Each method creates one or more property accessors, along with metadata
   * used by this mixin's implementation of `_propertiesChanged` to perform
   * the property effects.
   *
   * Underscored versions of the above methods also exist on the element
   * prototype for adding property effects on instances at runtime.
   *
   * Note that this mixin overrides several `PropertyAccessors` methods, in
   * many cases to maintain guarantees provided by the Polymer 1.x features;
   * notably it changes property accessors to be synchronous by default
   * whereas the default when using `PropertyAccessors` standalone is to be
   * async by default.
   *
   * @mixinFunction
   * @polymer
   * @appliesMixin Polymer.TemplateStamp
   * @appliesMixin Polymer.PropertyAccessors
   * @memberof Polymer
   * @summary Element class mixin that provides meta-programming for Polymer's
   * template binding and data observation system.
   */
  Polymer.PropertyEffects = Polymer.dedupingMixin(superClass => {

    /**
     * @constructor
     * @extends {superClass}
     * @implements {Polymer_PropertyAccessors}
     * @implements {Polymer_TemplateStamp}
     * @unrestricted
     * @private
     */
    const propertyEffectsBase = Polymer.TemplateStamp(Polymer.PropertyAccessors(superClass));

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_PropertyEffects}
     * @extends {propertyEffectsBase}
     * @unrestricted
     */
    class PropertyEffects extends propertyEffectsBase {

      constructor() {
        super();
        /** @type {boolean} */
        // Used to identify users of this mixin, ala instanceof
        this.__isPropertyEffectsClient = true;
        /** @type {number} */
        // NOTE: used to track re-entrant calls to `_flushProperties`
        // path changes dirty check against `__dataTemp` only during one "turn"
        // and are cleared when `__dataCounter` returns to 0.
        this.__dataCounter = 0;
        /** @type {boolean} */
        this.__dataClientsReady;
        /** @type {Array} */
        this.__dataPendingClients;
        /** @type {Object} */
        this.__dataToNotify;
        /** @type {Object} */
        this.__dataLinkedPaths;
        /** @type {boolean} */
        this.__dataHasPaths;
        /** @type {Object} */
        this.__dataCompoundStorage;
        /** @type {Polymer_PropertyEffects} */
        this.__dataHost;
        /** @type {!Object} */
        this.__dataTemp;
        /** @type {boolean} */
        this.__dataClientsInitialized;
        /** @type {!Object} */
        this.__data;
        /** @type {!Object} */
        this.__dataPending;
        /** @type {!Object} */
        this.__dataOld;
        /** @type {Object} */
        this.__computeEffects;
        /** @type {Object} */
        this.__reflectEffects;
        /** @type {Object} */
        this.__notifyEffects;
        /** @type {Object} */
        this.__propagateEffects;
        /** @type {Object} */
        this.__observeEffects;
        /** @type {Object} */
        this.__readOnly;
        /** @type {!TemplateInfo} */
        this.__templateInfo;
      }

      get PROPERTY_EFFECT_TYPES() {
        return TYPES;
      }

      /**
       * @return {void}
       */
      _initializeProperties() {
        super._initializeProperties();
        hostStack.registerHost(this);
        this.__dataClientsReady = false;
        this.__dataPendingClients = null;
        this.__dataToNotify = null;
        this.__dataLinkedPaths = null;
        this.__dataHasPaths = false;
        // May be set on instance prior to upgrade
        this.__dataCompoundStorage = this.__dataCompoundStorage || null;
        this.__dataHost = this.__dataHost || null;
        this.__dataTemp = {};
        this.__dataClientsInitialized = false;
      }

      /**
       * Overrides `Polymer.PropertyAccessors` implementation to provide a
       * more efficient implementation of initializing properties from
       * the prototype on the instance.
       *
       * @override
       * @param {Object} props Properties to initialize on the prototype
       * @return {void}
       */
      _initializeProtoProperties(props) {
        this.__data = Object.create(props);
        this.__dataPending = Object.create(props);
        this.__dataOld = {};
      }

      /**
       * Overrides `Polymer.PropertyAccessors` implementation to avoid setting
       * `_setProperty`'s `shouldNotify: true`.
       *
       * @override
       * @param {Object} props Properties to initialize on the instance
       * @return {void}
       */
      _initializeInstanceProperties(props) {
        let readOnly = this[TYPES.READ_ONLY];
        for (let prop in props) {
          if (!readOnly || !readOnly[prop]) {
            this.__dataPending = this.__dataPending || {};
            this.__dataOld = this.__dataOld || {};
            this.__data[prop] = this.__dataPending[prop] = props[prop];
          }
        }
      }

      // Prototype setup ----------------------------------------

      /**
       * Equivalent to static `addPropertyEffect` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Property that should trigger the effect
       * @param {string} type Effect type, from this.PROPERTY_EFFECT_TYPES
       * @param {Object=} effect Effect metadata object
       * @return {void}
       * @protected
       */
      _addPropertyEffect(property, type, effect) {
        this._createPropertyAccessor(property, type == TYPES.READ_ONLY);
        // effects are accumulated into arrays per property based on type
        let effects = ensureOwnEffectMap(this, type)[property];
        if (!effects) {
          effects = this[type][property] = [];
        }
        effects.push(effect);
      }

      /**
       * Removes the given property effect.
       *
       * @param {string} property Property the effect was associated with
       * @param {string} type Effect type, from this.PROPERTY_EFFECT_TYPES
       * @param {Object=} effect Effect metadata object to remove
       * @return {void}
       */
      _removePropertyEffect(property, type, effect) {
        let effects = ensureOwnEffectMap(this, type)[property];
        let idx = effects.indexOf(effect);
        if (idx >= 0) {
          effects.splice(idx, 1);
        }
      }

      /**
       * Returns whether the current prototype/instance has a property effect
       * of a certain type.
       *
       * @param {string} property Property name
       * @param {string=} type Effect type, from this.PROPERTY_EFFECT_TYPES
       * @return {boolean} True if the prototype/instance has an effect of this type
       * @protected
       */
      _hasPropertyEffect(property, type) {
        let effects = this[type];
        return Boolean(effects && effects[property]);
      }

      /**
       * Returns whether the current prototype/instance has a "read only"
       * accessor for the given property.
       *
       * @param {string} property Property name
       * @return {boolean} True if the prototype/instance has an effect of this type
       * @protected
       */
      _hasReadOnlyEffect(property) {
        return this._hasPropertyEffect(property, TYPES.READ_ONLY);
      }

      /**
       * Returns whether the current prototype/instance has a "notify"
       * property effect for the given property.
       *
       * @param {string} property Property name
       * @return {boolean} True if the prototype/instance has an effect of this type
       * @protected
       */
      _hasNotifyEffect(property) {
        return this._hasPropertyEffect(property, TYPES.NOTIFY);
      }

      /**
       * Returns whether the current prototype/instance has a "reflect to attribute"
       * property effect for the given property.
       *
       * @param {string} property Property name
       * @return {boolean} True if the prototype/instance has an effect of this type
       * @protected
       */
      _hasReflectEffect(property) {
        return this._hasPropertyEffect(property, TYPES.REFLECT);
      }

      /**
       * Returns whether the current prototype/instance has a "computed"
       * property effect for the given property.
       *
       * @param {string} property Property name
       * @return {boolean} True if the prototype/instance has an effect of this type
       * @protected
       */
      _hasComputedEffect(property) {
        return this._hasPropertyEffect(property, TYPES.COMPUTE);
      }

      // Runtime ----------------------------------------

      /**
       * Sets a pending property or path.  If the root property of the path in
       * question had no accessor, the path is set, otherwise it is enqueued
       * via `_setPendingProperty`.
       *
       * This function isolates relatively expensive functionality necessary
       * for the public API (`set`, `setProperties`, `notifyPath`, and property
       * change listeners via {{...}} bindings), such that it is only done
       * when paths enter the system, and not at every propagation step.  It
       * also sets a `__dataHasPaths` flag on the instance which is used to
       * fast-path slower path-matching code in the property effects host paths.
       *
       * `path` can be a path string or array of path parts as accepted by the
       * public API.
       *
       * @param {string | !Array<number|string>} path Path to set
       * @param {*} value Value to set
       * @param {boolean=} shouldNotify Set to true if this change should
       *  cause a property notification event dispatch
       * @param {boolean=} isPathNotification If the path being set is a path
       *   notification of an already changed value, as opposed to a request
       *   to set and notify the change.  In the latter `false` case, a dirty
       *   check is performed and then the value is set to the path before
       *   enqueuing the pending property change.
       * @return {boolean} Returns true if the property/path was enqueued in
       *   the pending changes bag.
       * @protected
       */
      _setPendingPropertyOrPath(path, value, shouldNotify, isPathNotification) {
        if (isPathNotification ||
            Polymer.Path.root(Array.isArray(path) ? path[0] : path) !== path) {
          // Dirty check changes being set to a path against the actual object,
          // since this is the entry point for paths into the system; from here
          // the only dirty checks are against the `__dataTemp` cache to prevent
          // duplicate work in the same turn only. Note, if this was a notification
          // of a change already set to a path (isPathNotification: true),
          // we always let the change through and skip the `set` since it was
          // already dirty checked at the point of entry and the underlying
          // object has already been updated
          if (!isPathNotification) {
            let old = Polymer.Path.get(this, path);
            path = /** @type {string} */ (Polymer.Path.set(this, path, value));
            // Use property-accessor's simpler dirty check
            if (!path || !super._shouldPropertyChange(path, value, old)) {
              return false;
            }
          }
          this.__dataHasPaths = true;
          if (this._setPendingProperty(/**@type{string}*/(path), value, shouldNotify)) {
            computeLinkedPaths(this, path, value);
            return true;
          }
        } else {
          if (this.__dataHasAccessor && this.__dataHasAccessor[path]) {
            return this._setPendingProperty(/**@type{string}*/(path), value, shouldNotify);
          } else {
            this[path] = value;
          }
        }
        return false;
      }

      /**
       * Applies a value to a non-Polymer element/node's property.
       *
       * The implementation makes a best-effort at binding interop:
       * Some native element properties have side-effects when
       * re-setting the same value (e.g. setting `<input>.value` resets the
       * cursor position), so we do a dirty-check before setting the value.
       * However, for better interop with non-Polymer custom elements that
       * accept objects, we explicitly re-set object changes coming from the
       * Polymer world (which may include deep object changes without the
       * top reference changing), erring on the side of providing more
       * information.
       *
       * Users may override this method to provide alternate approaches.
       *
       * @param {!Node} node The node to set a property on
       * @param {string} prop The property to set
       * @param {*} value The value to set
       * @return {void}
       * @protected
       */
      _setUnmanagedPropertyToNode(node, prop, value) {
        // It is a judgment call that resetting primitives is
        // "bad" and resettings objects is also "good"; alternatively we could
        // implement a whitelist of tag & property values that should never
        // be reset (e.g. <input>.value && <select>.value)
        if (value !== node[prop] || typeof value == 'object') {
          node[prop] = value;
        }
      }

      /**
       * Overrides the `PropertiesChanged` implementation to introduce special
       * dirty check logic depending on the property & value being set:
       *
       * 1. Any value set to a path (e.g. 'obj.prop': 42 or 'obj.prop': {...})
       *    Stored in `__dataTemp`, dirty checked against `__dataTemp`
       * 2. Object set to simple property (e.g. 'prop': {...})
       *    Stored in `__dataTemp` and `__data`, dirty checked against
       *    `__dataTemp` by default implementation of `_shouldPropertyChange`
       * 3. Primitive value set to simple property (e.g. 'prop': 42)
       *    Stored in `__data`, dirty checked against `__data`
       *
       * The dirty-check is important to prevent cycles due to two-way
       * notification, but paths and objects are only dirty checked against any
       * previous value set during this turn via a "temporary cache" that is
       * cleared when the last `_propertiesChanged` exits. This is so:
       * a. any cached array paths (e.g. 'array.3.prop') may be invalidated
       *    due to array mutations like shift/unshift/splice; this is fine
       *    since path changes are dirty-checked at user entry points like `set`
       * b. dirty-checking for objects only lasts one turn to allow the user
       *    to mutate the object in-place and re-set it with the same identity
       *    and have all sub-properties re-propagated in a subsequent turn.
       *
       * The temp cache is not necessarily sufficient to prevent invalid array
       * paths, since a splice can happen during the same turn (with pathological
       * user code); we could introduce a "fixup" for temporarily cached array
       * paths if needed: https://github.com/Polymer/polymer/issues/4227
       *
       * @override
       * @param {string} property Name of the property
       * @param {*} value Value to set
       * @param {boolean=} shouldNotify True if property should fire notification
       *   event (applies only for `notify: true` properties)
       * @return {boolean} Returns true if the property changed
       */
      _setPendingProperty(property, value, shouldNotify) {
        let isPath = this.__dataHasPaths && Polymer.Path.isPath(property);
        let prevProps = isPath ? this.__dataTemp : this.__data;
        if (this._shouldPropertyChange(property, value, prevProps[property])) {
          if (!this.__dataPending) {
            this.__dataPending = {};
            this.__dataOld = {};
          }
          // Ensure old is captured from the last turn
          if (!(property in this.__dataOld)) {
            this.__dataOld[property] = this.__data[property];
          }
          // Paths are stored in temporary cache (cleared at end of turn),
          // which is used for dirty-checking, all others stored in __data
          if (isPath) {
            this.__dataTemp[property] = value;
          } else {
            this.__data[property] = value;
          }
          // All changes go into pending property bag, passed to _propertiesChanged
          this.__dataPending[property] = value;
          // Track properties that should notify separately
          if (isPath || (this[TYPES.NOTIFY] && this[TYPES.NOTIFY][property])) {
            this.__dataToNotify = this.__dataToNotify || {};
            this.__dataToNotify[property] = shouldNotify;
          }
          return true;
        }
        return false;
      }

      /**
       * Overrides base implementation to ensure all accessors set `shouldNotify`
       * to true, for per-property notification tracking.
       *
       * @override
       * @param {string} property Name of the property
       * @param {*} value Value to set
       * @return {void}
       */
      _setProperty(property, value) {
        if (this._setPendingProperty(property, value, true)) {
          this._invalidateProperties();
        }
      }

      /**
       * Overrides `PropertyAccessor`'s default async queuing of
       * `_propertiesChanged`: if `__dataReady` is false (has not yet been
       * manually flushed), the function no-ops; otherwise flushes
       * `_propertiesChanged` synchronously.
       *
       * @override
       * @return {void}
       */
      _invalidateProperties() {
        if (this.__dataReady) {
          this._flushProperties();
        }
      }

      /**
       * Enqueues the given client on a list of pending clients, whose
       * pending property changes can later be flushed via a call to
       * `_flushClients`.
       *
       * @param {Object} client PropertyEffects client to enqueue
       * @return {void}
       * @protected
       */
      _enqueueClient(client) {
        this.__dataPendingClients = this.__dataPendingClients || [];
        if (client !== this) {
          this.__dataPendingClients.push(client);
        }
      }

      /**
       * Overrides superclass implementation.
       *
       * @return {void}
       * @protected
       */
      _flushProperties() {
        this.__dataCounter++;
        super._flushProperties();
        this.__dataCounter--;
      }

      /**
       * Flushes any clients previously enqueued via `_enqueueClient`, causing
       * their `_flushProperties` method to run.
       *
       * @return {void}
       * @protected
       */
      _flushClients() {
        if (!this.__dataClientsReady) {
          this.__dataClientsReady = true;
          this._readyClients();
          // Override point where accessors are turned on; importantly,
          // this is after clients have fully readied, providing a guarantee
          // that any property effects occur only after all clients are ready.
          this.__dataReady = true;
        } else {
          this.__enableOrFlushClients();
        }
      }

      // NOTE: We ensure clients either enable or flush as appropriate. This
      // handles two corner cases:
      // (1) clients flush properly when connected/enabled before the host
      // enables; e.g.
      //   (a) Templatize stamps with no properties and does not flush and
      //   (b) the instance is inserted into dom and
      //   (c) then the instance flushes.
      // (2) clients enable properly when not connected/enabled when the host
      // flushes; e.g.
      //   (a) a template is runtime stamped and not yet connected/enabled
      //   (b) a host sets a property, causing stamped dom to flush
      //   (c) the stamped dom enables.
      __enableOrFlushClients() {
        let clients = this.__dataPendingClients;
        if (clients) {
          this.__dataPendingClients = null;
          for (let i=0; i < clients.length; i++) {
            let client = clients[i];
            if (!client.__dataEnabled) {
              client._enableProperties();
            } else if (client.__dataPending) {
              client._flushProperties();
            }
          }
        }
      }

      /**
       * Perform any initial setup on client dom. Called before the first
       * `_flushProperties` call on client dom and before any element
       * observers are called.
       *
       * @return {void}
       * @protected
       */
      _readyClients() {
        this.__enableOrFlushClients();
      }

      /**
       * Sets a bag of property changes to this instance, and
       * synchronously processes all effects of the properties as a batch.
       *
       * Property names must be simple properties, not paths.  Batched
       * path propagation is not supported.
       *
       * @param {Object} props Bag of one or more key-value pairs whose key is
       *   a property and value is the new value to set for that property.
       * @param {boolean=} setReadOnly When true, any private values set in
       *   `props` will be set. By default, `setProperties` will not set
       *   `readOnly: true` root properties.
       * @return {void}
       * @public
       */
      setProperties(props, setReadOnly) {
        for (let path in props) {
          if (setReadOnly || !this[TYPES.READ_ONLY] || !this[TYPES.READ_ONLY][path]) {
            //TODO(kschaaf): explicitly disallow paths in setProperty?
            // wildcard observers currently only pass the first changed path
            // in the `info` object, and you could do some odd things batching
            // paths, e.g. {'foo.bar': {...}, 'foo': null}
            this._setPendingPropertyOrPath(path, props[path], true);
          }
        }
        this._invalidateProperties();
      }

      /**
       * Overrides `PropertyAccessors` so that property accessor
       * side effects are not enabled until after client dom is fully ready.
       * Also calls `_flushClients` callback to ensure client dom is enabled
       * that was not enabled as a result of flushing properties.
       *
       * @override
       * @return {void}
       */
      ready() {
        // It is important that `super.ready()` is not called here as it
        // immediately turns on accessors. Instead, we wait until `readyClients`
        // to enable accessors to provide a guarantee that clients are ready
        // before processing any accessors side effects.
        this._flushProperties();
        // If no data was pending, `_flushProperties` will not `flushClients`
        // so ensure this is done.
        if (!this.__dataClientsReady) {
          this._flushClients();
        }
        // Before ready, client notifications do not trigger _flushProperties.
        // Therefore a flush is necessary here if data has been set.
        if (this.__dataPending) {
          this._flushProperties();
        }
      }

      /**
       * Implements `PropertyAccessors`'s properties changed callback.
       *
       * Runs each class of effects for the batch of changed properties in
       * a specific order (compute, propagate, reflect, observe, notify).
       *
       * @param {!Object} currentProps Bag of all current accessor values
       * @param {!Object} changedProps Bag of properties changed since the last
       *   call to `_propertiesChanged`
       * @param {!Object} oldProps Bag of previous values for each property
       *   in `changedProps`
       * @return {void}
       */
      _propertiesChanged(currentProps, changedProps, oldProps) {
        // ----------------------------
        // let c = Object.getOwnPropertyNames(changedProps || {});
        // window.debug && console.group(this.localName + '#' + this.id + ': ' + c);
        // if (window.debug) { debugger; }
        // ----------------------------
        let hasPaths = this.__dataHasPaths;
        this.__dataHasPaths = false;
        // Compute properties
        runComputedEffects(this, changedProps, oldProps, hasPaths);
        // Clear notify properties prior to possible reentry (propagate, observe),
        // but after computing effects have a chance to add to them
        let notifyProps = this.__dataToNotify;
        this.__dataToNotify = null;
        // Propagate properties to clients
        this._propagatePropertyChanges(changedProps, oldProps, hasPaths);
        // Flush clients
        this._flushClients();
        // Reflect properties
        runEffects(this, this[TYPES.REFLECT], changedProps, oldProps, hasPaths);
        // Observe properties
        runEffects(this, this[TYPES.OBSERVE], changedProps, oldProps, hasPaths);
        // Notify properties to host
        if (notifyProps) {
          runNotifyEffects(this, notifyProps, changedProps, oldProps, hasPaths);
        }
        // Clear temporary cache at end of turn
        if (this.__dataCounter == 1) {
          this.__dataTemp = {};
        }
        // ----------------------------
        // window.debug && console.groupEnd(this.localName + '#' + this.id + ': ' + c);
        // ----------------------------
      }

      /**
       * Called to propagate any property changes to stamped template nodes
       * managed by this element.
       *
       * @param {Object} changedProps Bag of changed properties
       * @param {Object} oldProps Bag of previous values for changed properties
       * @param {boolean} hasPaths True with `props` contains one or more paths
       * @return {void}
       * @protected
       */
      _propagatePropertyChanges(changedProps, oldProps, hasPaths) {
        if (this[TYPES.PROPAGATE]) {
          runEffects(this, this[TYPES.PROPAGATE], changedProps, oldProps, hasPaths);
        }
        let templateInfo = this.__templateInfo;
        while (templateInfo) {
          runEffects(this, templateInfo.propertyEffects, changedProps, oldProps,
            hasPaths, templateInfo.nodeList);
          templateInfo = templateInfo.nextTemplateInfo;
        }
      }

      /**
       * Aliases one data path as another, such that path notifications from one
       * are routed to the other.
       *
       * @param {string | !Array<string|number>} to Target path to link.
       * @param {string | !Array<string|number>} from Source path to link.
       * @return {void}
       * @public
       */
      linkPaths(to, from) {
        to = Polymer.Path.normalize(to);
        from = Polymer.Path.normalize(from);
        this.__dataLinkedPaths = this.__dataLinkedPaths || {};
        this.__dataLinkedPaths[to] = from;
      }

      /**
       * Removes a data path alias previously established with `_linkPaths`.
       *
       * Note, the path to unlink should be the target (`to`) used when
       * linking the paths.
       *
       * @param {string | !Array<string|number>} path Target path to unlink.
       * @return {void}
       * @public
       */
      unlinkPaths(path) {
        path = Polymer.Path.normalize(path);
        if (this.__dataLinkedPaths) {
          delete this.__dataLinkedPaths[path];
        }
      }

      /**
       * Notify that an array has changed.
       *
       * Example:
       *
       *     this.items = [ {name: 'Jim'}, {name: 'Todd'}, {name: 'Bill'} ];
       *     ...
       *     this.items.splice(1, 1, {name: 'Sam'});
       *     this.items.push({name: 'Bob'});
       *     this.notifySplices('items', [
       *       { index: 1, removed: [{name: 'Todd'}], addedCount: 1, object: this.items, type: 'splice' },
       *       { index: 3, removed: [], addedCount: 1, object: this.items, type: 'splice'}
       *     ]);
       *
       * @param {string} path Path that should be notified.
       * @param {Array} splices Array of splice records indicating ordered
       *   changes that occurred to the array. Each record should have the
       *   following fields:
       *    * index: index at which the change occurred
       *    * removed: array of items that were removed from this index
       *    * addedCount: number of new items added at this index
       *    * object: a reference to the array in question
       *    * type: the string literal 'splice'
       *
       *   Note that splice records _must_ be normalized such that they are
       *   reported in index order (raw results from `Object.observe` are not
       *   ordered and must be normalized/merged before notifying).
       * @return {void}
       * @public
      */
      notifySplices(path, splices) {
        let info = {path: ''};
        let array = /** @type {Array} */(Polymer.Path.get(this, path, info));
        notifySplices(this, array, info.path, splices);
      }

      /**
       * Convenience method for reading a value from a path.
       *
       * Note, if any part in the path is undefined, this method returns
       * `undefined` (this method does not throw when dereferencing undefined
       * paths).
       *
       * @param {(string|!Array<(string|number)>)} path Path to the value
       *   to read.  The path may be specified as a string (e.g. `foo.bar.baz`)
       *   or an array of path parts (e.g. `['foo.bar', 'baz']`).  Note that
       *   bracketed expressions are not supported; string-based path parts
       *   *must* be separated by dots.  Note that when dereferencing array
       *   indices, the index may be used as a dotted part directly
       *   (e.g. `users.12.name` or `['users', 12, 'name']`).
       * @param {Object=} root Root object from which the path is evaluated.
       * @return {*} Value at the path, or `undefined` if any part of the path
       *   is undefined.
       * @public
       */
      get(path, root) {
        return Polymer.Path.get(root || this, path);
      }

      /**
       * Convenience method for setting a value to a path and notifying any
       * elements bound to the same path.
       *
       * Note, if any part in the path except for the last is undefined,
       * this method does nothing (this method does not throw when
       * dereferencing undefined paths).
       *
       * @param {(string|!Array<(string|number)>)} path Path to the value
       *   to write.  The path may be specified as a string (e.g. `'foo.bar.baz'`)
       *   or an array of path parts (e.g. `['foo.bar', 'baz']`).  Note that
       *   bracketed expressions are not supported; string-based path parts
       *   *must* be separated by dots.  Note that when dereferencing array
       *   indices, the index may be used as a dotted part directly
       *   (e.g. `'users.12.name'` or `['users', 12, 'name']`).
       * @param {*} value Value to set at the specified path.
       * @param {Object=} root Root object from which the path is evaluated.
       *   When specified, no notification will occur.
       * @return {void}
       * @public
      */
      set(path, value, root) {
        if (root) {
          Polymer.Path.set(root, path, value);
        } else {
          if (!this[TYPES.READ_ONLY] || !this[TYPES.READ_ONLY][/** @type {string} */(path)]) {
            if (this._setPendingPropertyOrPath(path, value, true)) {
              this._invalidateProperties();
            }
          }
        }
      }

      /**
       * Adds items onto the end of the array at the path specified.
       *
       * The arguments after `path` and return value match that of
       * `Array.prototype.push`.
       *
       * This method notifies other paths to the same array that a
       * splice occurred to the array.
       *
       * @param {string | !Array<string|number>} path Path to array.
       * @param {...*} items Items to push onto array
       * @return {number} New length of the array.
       * @public
       */
      push(path, ...items) {
        let info = {path: ''};
        let array = /** @type {Array}*/(Polymer.Path.get(this, path, info));
        let len = array.length;
        let ret = array.push(...items);
        if (items.length) {
          notifySplice(this, array, info.path, len, items.length, []);
        }
        return ret;
      }

      /**
       * Removes an item from the end of array at the path specified.
       *
       * The arguments after `path` and return value match that of
       * `Array.prototype.pop`.
       *
       * This method notifies other paths to the same array that a
       * splice occurred to the array.
       *
       * @param {string | !Array<string|number>} path Path to array.
       * @return {*} Item that was removed.
       * @public
       */
      pop(path) {
        let info = {path: ''};
        let array = /** @type {Array} */(Polymer.Path.get(this, path, info));
        let hadLength = Boolean(array.length);
        let ret = array.pop();
        if (hadLength) {
          notifySplice(this, array, info.path, array.length, 0, [ret]);
        }
        return ret;
      }

      /**
       * Starting from the start index specified, removes 0 or more items
       * from the array and inserts 0 or more new items in their place.
       *
       * The arguments after `path` and return value match that of
       * `Array.prototype.splice`.
       *
       * This method notifies other paths to the same array that a
       * splice occurred to the array.
       *
       * @param {string | !Array<string|number>} path Path to array.
       * @param {number} start Index from which to start removing/inserting.
       * @param {number} deleteCount Number of items to remove.
       * @param {...*} items Items to insert into array.
       * @return {Array} Array of removed items.
       * @public
       */
      splice(path, start, deleteCount, ...items) {
        let info = {path : ''};
        let array = /** @type {Array} */(Polymer.Path.get(this, path, info));
        // Normalize fancy native splice handling of crazy start values
        if (start < 0) {
          start = array.length - Math.floor(-start);
        } else if (start) {
          start = Math.floor(start);
        }
        // array.splice does different things based on the number of arguments
        // you pass in. Therefore, array.splice(0) and array.splice(0, undefined)
        // do different things. In the former, the whole array is cleared. In the
        // latter, no items are removed.
        // This means that we need to detect whether 1. one of the arguments
        // is actually passed in and then 2. determine how many arguments
        // we should pass on to the native array.splice
        //
        let ret;
        // Omit any additional arguments if they were not passed in
        if (arguments.length === 2) {
          ret = array.splice(start);
        // Either start was undefined and the others were defined, but in this
        // case we can safely pass on all arguments
        //
        // Note: this includes the case where none of the arguments were passed in,
        // e.g. this.splice('array'). However, if both start and deleteCount
        // are undefined, array.splice will not modify the array (as expected)
        } else {
          ret = array.splice(start, deleteCount, ...items);
        }
        // At the end, check whether any items were passed in (e.g. insertions)
        // or if the return array contains items (e.g. deletions).
        // Only notify if items were added or deleted.
        if (items.length || ret.length) {
          notifySplice(this, array, info.path, start, items.length, ret);
        }
        return ret;
      }

      /**
       * Removes an item from the beginning of array at the path specified.
       *
       * The arguments after `path` and return value match that of
       * `Array.prototype.pop`.
       *
       * This method notifies other paths to the same array that a
       * splice occurred to the array.
       *
       * @param {string | !Array<string|number>} path Path to array.
       * @return {*} Item that was removed.
       * @public
       */
      shift(path) {
        let info = {path: ''};
        let array = /** @type {Array} */(Polymer.Path.get(this, path, info));
        let hadLength = Boolean(array.length);
        let ret = array.shift();
        if (hadLength) {
          notifySplice(this, array, info.path, 0, 0, [ret]);
        }
        return ret;
      }

      /**
       * Adds items onto the beginning of the array at the path specified.
       *
       * The arguments after `path` and return value match that of
       * `Array.prototype.push`.
       *
       * This method notifies other paths to the same array that a
       * splice occurred to the array.
       *
       * @param {string | !Array<string|number>} path Path to array.
       * @param {...*} items Items to insert info array
       * @return {number} New length of the array.
       * @public
       */
      unshift(path, ...items) {
        let info = {path: ''};
        let array = /** @type {Array} */(Polymer.Path.get(this, path, info));
        let ret = array.unshift(...items);
        if (items.length) {
          notifySplice(this, array, info.path, 0, items.length, []);
        }
        return ret;
      }

      /**
       * Notify that a path has changed.
       *
       * Example:
       *
       *     this.item.user.name = 'Bob';
       *     this.notifyPath('item.user.name');
       *
       * @param {string} path Path that should be notified.
       * @param {*=} value Value at the path (optional).
       * @return {void}
       * @public
      */
      notifyPath(path, value) {
        /** @type {string} */
        let propPath;
        if (arguments.length == 1) {
          // Get value if not supplied
          let info = {path: ''};
          value = Polymer.Path.get(this, path, info);
          propPath = info.path;
        } else if (Array.isArray(path)) {
          // Normalize path if needed
          propPath = Polymer.Path.normalize(path);
        } else {
          propPath = /** @type{string} */(path);
        }
        if (this._setPendingPropertyOrPath(propPath, value, true, true)) {
          this._invalidateProperties();
        }
      }

      /**
       * Equivalent to static `createReadOnlyProperty` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Property name
       * @param {boolean=} protectedSetter Creates a custom protected setter
       *   when `true`.
       * @return {void}
       * @protected
       */
      _createReadOnlyProperty(property, protectedSetter) {
        this._addPropertyEffect(property, TYPES.READ_ONLY);
        if (protectedSetter) {
          this['_set' + upper(property)] = /** @this {PropertyEffects} */function(value) {
            this._setProperty(property, value);
          };
        }
      }

      /**
       * Equivalent to static `createPropertyObserver` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Property name
       * @param {string|function(*,*)} method Function or name of observer method to call
       * @param {boolean=} dynamicFn Whether the method name should be included as
       *   a dependency to the effect.
       * @return {void}
       * @protected
       */
      _createPropertyObserver(property, method, dynamicFn) {
        let info = { property, method, dynamicFn: Boolean(dynamicFn) };
        this._addPropertyEffect(property, TYPES.OBSERVE, {
          fn: runObserverEffect, info, trigger: {name: property}
        });
        if (dynamicFn) {
          this._addPropertyEffect(/** @type {string} */(method), TYPES.OBSERVE, {
            fn: runObserverEffect, info, trigger: {name: method}
          });
        }
      }

      /**
       * Equivalent to static `createMethodObserver` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} expression Method expression
       * @param {boolean|Object=} dynamicFn Boolean or object map indicating
       *   whether method names should be included as a dependency to the effect.
       * @return {void}
       * @protected
       */
      _createMethodObserver(expression, dynamicFn) {
        let sig = parseMethod(expression);
        if (!sig) {
          throw new Error("Malformed observer expression '" + expression + "'");
        }
        createMethodEffect(this, sig, TYPES.OBSERVE, runMethodEffect, null, dynamicFn);
      }

      /**
       * Equivalent to static `createNotifyingProperty` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Property name
       * @return {void}
       * @protected
       */
      _createNotifyingProperty(property) {
        this._addPropertyEffect(property, TYPES.NOTIFY, {
          fn: runNotifyEffect,
          info: {
            eventName: CaseMap.camelToDashCase(property) + '-changed',
            property: property
          }
        });
      }

      /**
       * Equivalent to static `createReflectedProperty` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Property name
       * @return {void}
       * @protected
       */
      _createReflectedProperty(property) {
        let attr = this.constructor.attributeNameForProperty(property);
        if (attr[0] === '-') {
          console.warn('Property ' + property + ' cannot be reflected to attribute ' +
            attr + ' because "-" is not a valid starting attribute name. Use a lowercase first letter for the property instead.');
        } else {
          this._addPropertyEffect(property, TYPES.REFLECT, {
            fn: runReflectEffect,
            info: {
              attrName: attr
            }
          });
        }
      }

      /**
       * Equivalent to static `createComputedProperty` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Name of computed property to set
       * @param {string} expression Method expression
       * @param {boolean|Object=} dynamicFn Boolean or object map indicating
       *   whether method names should be included as a dependency to the effect.
       * @return {void}
       * @protected
       */
      _createComputedProperty(property, expression, dynamicFn) {
        let sig = parseMethod(expression);
        if (!sig) {
          throw new Error("Malformed computed expression '" + expression + "'");
        }
        createMethodEffect(this, sig, TYPES.COMPUTE, runComputedEffect, property, dynamicFn);
      }

      /**
       * Gather the argument values for a method specified in the provided array
       * of argument metadata.
       *
       * The `path` and `value` arguments are used to fill in wildcard descriptor
       * when the method is being called as a result of a path notification.
       *
       * @param {!Array<!MethodArg>} args Array of argument metadata
       * @param {string} path Property/path name that triggered the method effect
       * @param {Object} props Bag of current property changes
       * @return {Array<*>} Array of argument values
       * @private
       */
      _marshalArgs(args, path, props) {
        const data = this.__data;
        let values = [];
        for (let i=0, l=args.length; i<l; i++) {
          let arg = args[i];
          let name = arg.name;
          let v;
          if (arg.literal) {
            v = arg.value;
          } else {
            if (arg.structured) {
              v = Polymer.Path.get(data, name);
              // when data is not stored e.g. `splices`
              if (v === undefined) {
                v = props[name];
              }
            } else {
              v = data[name];
            }
          }
          if (arg.wildcard) {
            // Only send the actual path changed info if the change that
            // caused the observer to run matched the wildcard
            let baseChanged = (name.indexOf(path + '.') === 0);
            let matches = (path.indexOf(name) === 0 && !baseChanged);
            values[i] = {
              path: matches ? path : name,
              value: matches ? props[path] : v,
              base: v
            };
          } else {
            values[i] = v;
          }
        }
        return values;
      }

      // -- static class methods ------------

      /**
       * Ensures an accessor exists for the specified property, and adds
       * to a list of "property effects" that will run when the accessor for
       * the specified property is set.  Effects are grouped by "type", which
       * roughly corresponds to a phase in effect processing.  The effect
       * metadata should be in the following form:
       *
       *     {
       *       fn: effectFunction, // Reference to function to call to perform effect
       *       info: { ... }       // Effect metadata passed to function
       *       trigger: {          // Optional triggering metadata; if not provided
       *         name: string      // the property is treated as a wildcard
       *         structured: boolean
       *         wildcard: boolean
       *       }
       *     }
       *
       * Effects are called from `_propertiesChanged` in the following order by
       * type:
       *
       * 1. COMPUTE
       * 2. PROPAGATE
       * 3. REFLECT
       * 4. OBSERVE
       * 5. NOTIFY
       *
       * Effect functions are called with the following signature:
       *
       *     effectFunction(inst, path, props, oldProps, info, hasPaths)
       *
       * @param {string} property Property that should trigger the effect
       * @param {string} type Effect type, from this.PROPERTY_EFFECT_TYPES
       * @param {Object=} effect Effect metadata object
       * @return {void}
       * @protected
       */
      static addPropertyEffect(property, type, effect) {
        this.prototype._addPropertyEffect(property, type, effect);
      }

      /**
       * Creates a single-property observer for the given property.
       *
       * @param {string} property Property name
       * @param {string|function(*,*)} method Function or name of observer method to call
       * @param {boolean=} dynamicFn Whether the method name should be included as
       *   a dependency to the effect.
       * @return {void}
       * @protected
       */
      static createPropertyObserver(property, method, dynamicFn) {
        this.prototype._createPropertyObserver(property, method, dynamicFn);
      }

      /**
       * Creates a multi-property "method observer" based on the provided
       * expression, which should be a string in the form of a normal JavaScript
       * function signature: `'methodName(arg1, [..., argn])'`.  Each argument
       * should correspond to a property or path in the context of this
       * prototype (or instance), or may be a literal string or number.
       *
       * @param {string} expression Method expression
       * @param {boolean|Object=} dynamicFn Boolean or object map indicating
       * @return {void}
       *   whether method names should be included as a dependency to the effect.
       * @protected
       */
      static createMethodObserver(expression, dynamicFn) {
        this.prototype._createMethodObserver(expression, dynamicFn);
      }

      /**
       * Causes the setter for the given property to dispatch `<property>-changed`
       * events to notify of changes to the property.
       *
       * @param {string} property Property name
       * @return {void}
       * @protected
       */
      static createNotifyingProperty(property) {
        this.prototype._createNotifyingProperty(property);
      }

      /**
       * Creates a read-only accessor for the given property.
       *
       * To set the property, use the protected `_setProperty` API.
       * To create a custom protected setter (e.g. `_setMyProp()` for
       * property `myProp`), pass `true` for `protectedSetter`.
       *
       * Note, if the property will have other property effects, this method
       * should be called first, before adding other effects.
       *
       * @param {string} property Property name
       * @param {boolean=} protectedSetter Creates a custom protected setter
       *   when `true`.
       * @return {void}
       * @protected
       */
      static createReadOnlyProperty(property, protectedSetter) {
        this.prototype._createReadOnlyProperty(property, protectedSetter);
      }

      /**
       * Causes the setter for the given property to reflect the property value
       * to a (dash-cased) attribute of the same name.
       *
       * @param {string} property Property name
       * @return {void}
       * @protected
       */
      static createReflectedProperty(property) {
        this.prototype._createReflectedProperty(property);
      }

      /**
       * Creates a computed property whose value is set to the result of the
       * method described by the given `expression` each time one or more
       * arguments to the method changes.  The expression should be a string
       * in the form of a normal JavaScript function signature:
       * `'methodName(arg1, [..., argn])'`
       *
       * @param {string} property Name of computed property to set
       * @param {string} expression Method expression
       * @param {boolean|Object=} dynamicFn Boolean or object map indicating whether
       *   method names should be included as a dependency to the effect.
       * @return {void}
       * @protected
       */
      static createComputedProperty(property, expression, dynamicFn) {
        this.prototype._createComputedProperty(property, expression, dynamicFn);
      }

      /**
       * Parses the provided template to ensure binding effects are created
       * for them, and then ensures property accessors are created for any
       * dependent properties in the template.  Binding effects for bound
       * templates are stored in a linked list on the instance so that
       * templates can be efficiently stamped and unstamped.
       *
       * @param {!HTMLTemplateElement} template Template containing binding
       *   bindings
       * @return {!TemplateInfo} Template metadata object
       * @protected
       */
      static bindTemplate(template) {
        return this.prototype._bindTemplate(template);
      }

      // -- binding ----------------------------------------------

      /**
       * Equivalent to static `bindTemplate` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * This method may be called on the prototype (for prototypical template
       * binding, to avoid creating accessors every instance) once per prototype,
       * and will be called with `runtimeBinding: true` by `_stampTemplate` to
       * create and link an instance of the template metadata associated with a
       * particular stamping.
       *
       * @param {!HTMLTemplateElement} template Template containing binding
       *   bindings
       * @param {boolean=} instanceBinding When false (default), performs
       *   "prototypical" binding of the template and overwrites any previously
       *   bound template for the class. When true (as passed from
       *   `_stampTemplate`), the template info is instanced and linked into
       *   the list of bound templates.
       * @return {!TemplateInfo} Template metadata object; for `runtimeBinding`,
       *   this is an instance of the prototypical template info
       * @protected
       */
      _bindTemplate(template, instanceBinding) {
        let templateInfo = this.constructor._parseTemplate(template);
        let wasPreBound = this.__templateInfo == templateInfo;
        // Optimization: since this is called twice for proto-bound templates,
        // don't attempt to recreate accessors if this template was pre-bound
        if (!wasPreBound) {
          for (let prop in templateInfo.propertyEffects) {
            this._createPropertyAccessor(prop);
          }
        }
        if (instanceBinding) {
          // For instance-time binding, create instance of template metadata
          // and link into list of templates if necessary
          templateInfo = /** @type {!TemplateInfo} */(Object.create(templateInfo));
          templateInfo.wasPreBound = wasPreBound;
          if (!wasPreBound && this.__templateInfo) {
            let last = this.__templateInfoLast || this.__templateInfo;
            this.__templateInfoLast = last.nextTemplateInfo = templateInfo;
            templateInfo.previousTemplateInfo = last;
            return templateInfo;
          }
        }
        return this.__templateInfo = templateInfo;
      }

      /**
       * Adds a property effect to the given template metadata, which is run
       * at the "propagate" stage of `_propertiesChanged` when the template
       * has been bound to the element via `_bindTemplate`.
       *
       * The `effect` object should match the format in `_addPropertyEffect`.
       *
       * @param {Object} templateInfo Template metadata to add effect to
       * @param {string} prop Property that should trigger the effect
       * @param {Object=} effect Effect metadata object
       * @return {void}
       * @protected
       */
      static _addTemplatePropertyEffect(templateInfo, prop, effect) {
        let hostProps = templateInfo.hostProps = templateInfo.hostProps || {};
        hostProps[prop] = true;
        let effects = templateInfo.propertyEffects = templateInfo.propertyEffects || {};
        let propEffects = effects[prop] = effects[prop] || [];
        propEffects.push(effect);
      }

      /**
       * Stamps the provided template and performs instance-time setup for
       * Polymer template features, including data bindings, declarative event
       * listeners, and the `this.$` map of `id`'s to nodes.  A document fragment
       * is returned containing the stamped DOM, ready for insertion into the
       * DOM.
       *
       * This method may be called more than once; however note that due to
       * `shadycss` polyfill limitations, only styles from templates prepared
       * using `ShadyCSS.prepareTemplate` will be correctly polyfilled (scoped
       * to the shadow root and support CSS custom properties), and note that
       * `ShadyCSS.prepareTemplate` may only be called once per element. As such,
       * any styles required by in runtime-stamped templates must be included
       * in the main element template.
       *
       * @param {!HTMLTemplateElement} template Template to stamp
       * @return {!StampedTemplate} Cloned template content
       * @override
       * @protected
       */
      _stampTemplate(template) {
        // Ensures that created dom is `_enqueueClient`'d to this element so
        // that it can be flushed on next call to `_flushProperties`
        hostStack.beginHosting(this);
        let dom = super._stampTemplate(template);
        hostStack.endHosting(this);
        let templateInfo = /** @type {!TemplateInfo} */(this._bindTemplate(template, true));
        // Add template-instance-specific data to instanced templateInfo
        templateInfo.nodeList = dom.nodeList;
        // Capture child nodes to allow unstamping of non-prototypical templates
        if (!templateInfo.wasPreBound) {
          let nodes = templateInfo.childNodes = [];
          for (let n=dom.firstChild; n; n=n.nextSibling) {
            nodes.push(n);
          }
        }
        dom.templateInfo = templateInfo;
        // Setup compound storage, 2-way listeners, and dataHost for bindings
        setupBindings(this, templateInfo);
        // Flush properties into template nodes if already booted
        if (this.__dataReady) {
          runEffects(this, templateInfo.propertyEffects, this.__data, null,
            false, templateInfo.nodeList);
        }
        return dom;
      }

      /**
       * Removes and unbinds the nodes previously contained in the provided
       * DocumentFragment returned from `_stampTemplate`.
       *
       * @param {!StampedTemplate} dom DocumentFragment previously returned
       *   from `_stampTemplate` associated with the nodes to be removed
       * @return {void}
       * @protected
       */
      _removeBoundDom(dom) {
        // Unlink template info
        let templateInfo = dom.templateInfo;
        if (templateInfo.previousTemplateInfo) {
          templateInfo.previousTemplateInfo.nextTemplateInfo =
            templateInfo.nextTemplateInfo;
        }
        if (templateInfo.nextTemplateInfo) {
          templateInfo.nextTemplateInfo.previousTemplateInfo =
            templateInfo.previousTemplateInfo;
        }
        if (this.__templateInfoLast == templateInfo) {
          this.__templateInfoLast = templateInfo.previousTemplateInfo;
        }
        templateInfo.previousTemplateInfo = templateInfo.nextTemplateInfo = null;
        // Remove stamped nodes
        let nodes = templateInfo.childNodes;
        for (let i=0; i<nodes.length; i++) {
          let node = nodes[i];
          node.parentNode.removeChild(node);
        }
      }

      /**
       * Overrides default `TemplateStamp` implementation to add support for
       * parsing bindings from `TextNode`'s' `textContent`.  A `bindings`
       * array is added to `nodeInfo` and populated with binding metadata
       * with information capturing the binding target, and a `parts` array
       * with one or more metadata objects capturing the source(s) of the
       * binding.
       *
       * @override
       * @param {Node} node Node to parse
       * @param {TemplateInfo} templateInfo Template metadata for current template
       * @param {NodeInfo} nodeInfo Node metadata for current template node
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       * @protected
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static _parseTemplateNode(node, templateInfo, nodeInfo) {
        let noted = super._parseTemplateNode(node, templateInfo, nodeInfo);
        if (node.nodeType === Node.TEXT_NODE) {
          let parts = this._parseBindings(node.textContent, templateInfo);
          if (parts) {
            // Initialize the textContent with any literal parts
            // NOTE: default to a space here so the textNode remains; some browsers
            // (IE) omit an empty textNode following cloneNode/importNode.
            node.textContent = literalFromParts(parts) || ' ';
            addBinding(this, templateInfo, nodeInfo, 'text', 'textContent', parts);
            noted = true;
          }
        }
        return noted;
      }

      /**
       * Overrides default `TemplateStamp` implementation to add support for
       * parsing bindings from attributes.  A `bindings`
       * array is added to `nodeInfo` and populated with binding metadata
       * with information capturing the binding target, and a `parts` array
       * with one or more metadata objects capturing the source(s) of the
       * binding.
       *
       * @override
       * @param {Element} node Node to parse
       * @param {TemplateInfo} templateInfo Template metadata for current template
       * @param {NodeInfo} nodeInfo Node metadata for current template node
       * @param {string} name Attribute name
       * @param {string} value Attribute value
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       * @protected
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static _parseTemplateNodeAttribute(node, templateInfo, nodeInfo, name, value) {
        let parts = this._parseBindings(value, templateInfo);
        if (parts) {
          // Attribute or property
          let origName = name;
          let kind = 'property';
          // The only way we see a capital letter here is if the attr has
          // a capital letter in it per spec. In this case, to make sure
          // this binding works, we go ahead and make the binding to the attribute.
          if (capitalAttributeRegex.test(name)) {
            kind = 'attribute';
          } else if (name[name.length-1] == '$') {
            name = name.slice(0, -1);
            kind = 'attribute';
          }
          // Initialize attribute bindings with any literal parts
          let literal = literalFromParts(parts);
          if (literal && kind == 'attribute') {
            // Ensure a ShadyCSS template scoped style is not removed
            // when a class$ binding's initial literal value is set.
            if (name == 'class' && node.hasAttribute('class')) {
              literal += ' ' + node.getAttribute(name);
            }
            node.setAttribute(name, literal);
          }
          // Clear attribute before removing, since IE won't allow removing
          // `value` attribute if it previously had a value (can't
          // unconditionally set '' before removing since attributes with `$`
          // can't be set using setAttribute)
          if (node.localName === 'input' && origName === 'value') {
            node.setAttribute(origName, '');
          }
          // Remove annotation
          node.removeAttribute(origName);
          // Case hackery: attributes are lower-case, but bind targets
          // (properties) are case sensitive. Gambit is to map dash-case to
          // camel-case: `foo-bar` becomes `fooBar`.
          // Attribute bindings are excepted.
          if (kind === 'property') {
            name = Polymer.CaseMap.dashToCamelCase(name);
          }
          addBinding(this, templateInfo, nodeInfo, kind, name, parts, literal);
          return true;
        } else {
          return super._parseTemplateNodeAttribute(node, templateInfo, nodeInfo, name, value);
        }
      }

      /**
       * Overrides default `TemplateStamp` implementation to add support for
       * binding the properties that a nested template depends on to the template
       * as `_host_<property>`.
       *
       * @override
       * @param {Node} node Node to parse
       * @param {TemplateInfo} templateInfo Template metadata for current template
       * @param {NodeInfo} nodeInfo Node metadata for current template node
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       * @protected
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static _parseTemplateNestedTemplate(node, templateInfo, nodeInfo) {
        let noted = super._parseTemplateNestedTemplate(node, templateInfo, nodeInfo);
        // Merge host props into outer template and add bindings
        let hostProps = nodeInfo.templateInfo.hostProps;
        let mode = '{';
        for (let source in hostProps) {
          let parts = [{ mode, source, dependencies: [source] }];
          addBinding(this, templateInfo, nodeInfo, 'property', '_host_' + source, parts);
        }
        return noted;
      }

      /**
       * Called to parse text in a template (either attribute values or
       * textContent) into binding metadata.
       *
       * Any overrides of this method should return an array of binding part
       * metadata  representing one or more bindings found in the provided text
       * and any "literal" text in between.  Any non-literal parts will be passed
       * to `_evaluateBinding` when any dependencies change.  The only required
       * fields of each "part" in the returned array are as follows:
       *
       * - `dependencies` - Array containing trigger metadata for each property
       *   that should trigger the binding to update
       * - `literal` - String containing text if the part represents a literal;
       *   in this case no `dependencies` are needed
       *
       * Additional metadata for use by `_evaluateBinding` may be provided in
       * each part object as needed.
       *
       * The default implementation handles the following types of bindings
       * (one or more may be intermixed with literal strings):
       * - Property binding: `[[prop]]`
       * - Path binding: `[[object.prop]]`
       * - Negated property or path bindings: `[[!prop]]` or `[[!object.prop]]`
       * - Two-way property or path bindings (supports negation):
       *   `{{prop}}`, `{{object.prop}}`, `{{!prop}}` or `{{!object.prop}}`
       * - Inline computed method (supports negation):
       *   `[[compute(a, 'literal', b)]]`, `[[!compute(a, 'literal', b)]]`
       *
       * The default implementation uses a regular expression for best
       * performance. However, the regular expression uses a white-list of
       * allowed characters in a data-binding, which causes problems for
       * data-bindings that do use characters not in this white-list.
       *
       * Instead of updating the white-list with all allowed characters,
       * there is a StrictBindingParser (see lib/mixins/strict-binding-parser)
       * that uses a state machine instead. This state machine is able to handle
       * all characters. However, it is slightly less performant, therefore we
       * extracted it into a separate optional mixin.
       *
       * @param {string} text Text to parse from attribute or textContent
       * @param {Object} templateInfo Current template metadata
       * @return {Array<!BindingPart>} Array of binding part metadata
       * @protected
       */
      static _parseBindings(text, templateInfo) {
        let parts = [];
        let lastIndex = 0;
        let m;
        // Example: "literal1{{prop}}literal2[[!compute(foo,bar)]]final"
        // Regex matches:
        //        Iteration 1:  Iteration 2:
        // m[1]: '{{'          '[['
        // m[2]: ''            '!'
        // m[3]: 'prop'        'compute(foo,bar)'
        while ((m = bindingRegex.exec(text)) !== null) {
          // Add literal part
          if (m.index > lastIndex) {
            parts.push({literal: text.slice(lastIndex, m.index)});
          }
          // Add binding part
          let mode = m[1][0];
          let negate = Boolean(m[2]);
          let source = m[3].trim();
          let customEvent = false, notifyEvent = '', colon = -1;
          if (mode == '{' && (colon = source.indexOf('::')) > 0) {
            notifyEvent = source.substring(colon + 2);
            source = source.substring(0, colon);
            customEvent = true;
          }
          let signature = parseMethod(source);
          let dependencies = [];
          if (signature) {
            // Inline computed function
            let {args, methodName} = signature;
            for (let i=0; i<args.length; i++) {
              let arg = args[i];
              if (!arg.literal) {
                dependencies.push(arg);
              }
            }
            let dynamicFns = templateInfo.dynamicFns;
            if (dynamicFns && dynamicFns[methodName] || signature.static) {
              dependencies.push(methodName);
              signature.dynamicFn = true;
            }
          } else {
            // Property or path
            dependencies.push(source);
          }
          parts.push({
            source, mode, negate, customEvent, signature, dependencies,
            event: notifyEvent
          });
          lastIndex = bindingRegex.lastIndex;
        }
        // Add a final literal part
        if (lastIndex && lastIndex < text.length) {
          let literal = text.substring(lastIndex);
          if (literal) {
            parts.push({
              literal: literal
            });
          }
        }
        if (parts.length) {
          return parts;
        } else {
          return null;
        }
      }

      /**
       * Called to evaluate a previously parsed binding part based on a set of
       * one or more changed dependencies.
       *
       * @param {this} inst Element that should be used as scope for
       *   binding dependencies
       * @param {BindingPart} part Binding part metadata
       * @param {string} path Property/path that triggered this effect
       * @param {Object} props Bag of current property changes
       * @param {Object} oldProps Bag of previous values for changed properties
       * @param {boolean} hasPaths True with `props` contains one or more paths
       * @return {*} Value the binding part evaluated to
       * @protected
       */
      static _evaluateBinding(inst, part, path, props, oldProps, hasPaths) {
        let value;
        if (part.signature) {
          value = runMethodEffect(inst, path, props, oldProps, part.signature);
        } else if (path != part.source) {
          value = Polymer.Path.get(inst, part.source);
        } else {
          if (hasPaths && Polymer.Path.isPath(path)) {
            value = Polymer.Path.get(inst, path);
          } else {
            value = inst.__data[path];
          }
        }
        if (part.negate) {
          value = !value;
        }
        return value;
      }

    }

    // make a typing for closure :P
    PropertyEffectsType = PropertyEffects;

    return PropertyEffects;
  });

  /**
   * Helper api for enqueuing client dom created by a host element.
   *
   * By default elements are flushed via `_flushProperties` when
   * `connectedCallback` is called. Elements attach their client dom to
   * themselves at `ready` time which results from this first flush.
   * This provides an ordering guarantee that the client dom an element
   * creates is flushed before the element itself (i.e. client `ready`
   * fires before host `ready`).
   *
   * However, if `_flushProperties` is called *before* an element is connected,
   * as for example `Templatize` does, this ordering guarantee cannot be
   * satisfied because no elements are connected. (Note: Bound elements that
   * receive data do become enqueued clients and are properly ordered but
   * unbound elements are not.)
   *
   * To maintain the desired "client before host" ordering guarantee for this
   * case we rely on the "host stack. Client nodes registers themselves with
   * the creating host element when created. This ensures that all client dom
   * is readied in the proper order, maintaining the desired guarantee.
   *
   * @private
   */
  let hostStack = {

    stack: [],

    /**
     * @param {*} inst Instance to add to hostStack
     * @return {void}
     * @this {hostStack}
     */
    registerHost(inst) {
      if (this.stack.length) {
        let host = this.stack[this.stack.length-1];
        host._enqueueClient(inst);
      }
    },

    /**
     * @param {*} inst Instance to begin hosting
     * @return {void}
     * @this {hostStack}
     */
    beginHosting(inst) {
      this.stack.push(inst);
    },

    /**
     * @param {*} inst Instance to end hosting
     * @return {void}
     * @this {hostStack}
     */
    endHosting(inst) {
      let stackLen = this.stack.length;
      if (stackLen && this.stack[stackLen-1] == inst) {
        this.stack.pop();
      }
    }

  };

})();
</script><script>
(function() {
  'use strict';

  /**
   * Provides basic tracking of element definitions (registrations) and
   * instance counts.
   *
   * @namespace
   * @summary Provides basic tracking of element definitions (registrations) and
   * instance counts.
   */
  Polymer.telemetry = {
    /**
     * Total number of Polymer element instances created.
     * @type {number}
     */
    instanceCount: 0,
    /**
     * Array of Polymer element classes that have been finalized.
     * @type {Array<Polymer.Element>}
     */
    registrations: [],
    /**
     * @param {!PolymerElementConstructor} prototype Element prototype to log
     * @this {this}
     * @private
     */
    _regLog: function(prototype) {
      console.log('[' + prototype.is + ']: registered');
    },
    /**
     * Registers a class prototype for telemetry purposes.
     * @param {HTMLElement} prototype Element prototype to register
     * @this {this}
     * @protected
     */
    register: function(prototype) {
      this.registrations.push(prototype);
      Polymer.log && this._regLog(prototype);
    },
    /**
     * Logs all elements registered with an `is` to the console.
     * @public
     * @this {this}
     */
    dumpRegistrations: function() {
      this.registrations.forEach(this._regLog);
    }
  };

})();
</script><script>
(function() {
  'use strict';

  /**
   * Creates a copy of `props` with each property normalized such that
   * upgraded it is an object with at least a type property { type: Type}.
   *
   * @param {Object} props Properties to normalize
   * @return {Object} Copy of input `props` with normalized properties that
   * are in the form {type: Type}
   * @private
   */
  function normalizeProperties(props) {
    const output = {};
    for (let p in props) {
      const o = props[p];
      output[p] = (typeof o === 'function') ? {type: o} : o;
    }
    return output;
  }

  /**
   * Mixin that provides a minimal starting point to using the PropertiesChanged
   * mixin by providing a mechanism to declare properties in a static
   * getter (e.g. static get properties() { return { foo: String } }). Changes
   * are reported via the `_propertiesChanged` method.
   *
   * This mixin provides no specific support for rendering. Users are expected
   * to create a ShadowRoot and put content into it and update it in whatever
   * way makes sense. This can be done in reaction to properties changing by
   * implementing `_propertiesChanged`.
   *
   * @mixinFunction
   * @polymer
   * @appliesMixin Polymer.PropertiesChanged
   * @memberof Polymer
   * @summary Mixin that provides a minimal starting point for using
   * the PropertiesChanged mixin by providing a declarative `properties` object.
   */
   Polymer.PropertiesMixin = Polymer.dedupingMixin(superClass => {

    /**
     * @constructor
     * @extends {superClass}
     * @implements {Polymer_PropertiesChanged}
     * @private
     */
    const base = Polymer.PropertiesChanged(superClass);

    /**
     * Returns the super class constructor for the given class, if it is an
     * instance of the PropertiesMixin.
     *
     * @param {!PropertiesMixinConstructor} constructor PropertiesMixin constructor
     * @return {PropertiesMixinConstructor} Super class constructor
     */
    function superPropertiesClass(constructor) {
      const superCtor = Object.getPrototypeOf(constructor);

      // Note, the `PropertiesMixin` class below only refers to the class
      // generated by this call to the mixin; the instanceof test only works
      // because the mixin is deduped and guaranteed only to apply once, hence
      // all constructors in a proto chain will see the same `PropertiesMixin`
      return (superCtor.prototype instanceof PropertiesMixin) ?
        /** @type {PropertiesMixinConstructor} */ (superCtor) : null;
    }

    /**
     * Returns a memoized version of the `properties` object for the
     * given class. Properties not in object format are converted to at
     * least {type}.
     *
     * @param {PropertiesMixinConstructor} constructor PropertiesMixin constructor
     * @return {Object} Memoized properties object
     */
    function ownProperties(constructor) {
      if (!constructor.hasOwnProperty(JSCompiler_renameProperty('__ownProperties', constructor))) {
        let props = null;

        if (constructor.hasOwnProperty(JSCompiler_renameProperty('properties', constructor))) {
          const properties = constructor.properties;
          
          if (properties) {
            props = normalizeProperties(properties);
          }
        }

        constructor.__ownProperties = props;
      }
      return constructor.__ownProperties;
    }

    /**
     * @polymer
     * @mixinClass
     * @extends {base}
     * @implements {Polymer_PropertiesMixin}
     * @unrestricted
     */
    class PropertiesMixin extends base {

      /**
       * Implements standard custom elements getter to observes the attributes
       * listed in `properties`.
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static get observedAttributes() {
        if (!this.hasOwnProperty('__observedAttributes')) {
          Polymer.telemetry.register(this.prototype);
          const props = this._properties;
          this.__observedAttributes = props ? Object.keys(props).map(p => this.attributeNameForProperty(p)) : [];
        }
        return this.__observedAttributes;
      }

      /**
       * Finalizes an element definition, including ensuring any super classes
       * are also finalized. This includes ensuring property
       * accessors exist on the element prototype. This method calls
       * `_finalizeClass` to finalize each constructor in the prototype chain.
       * @return {void}
       */
      static finalize() {
        if (!this.hasOwnProperty(JSCompiler_renameProperty('__finalized', this))) {
          const superCtor = superPropertiesClass(/** @type {PropertiesMixinConstructor} */(this));
          if (superCtor) {
            superCtor.finalize();
          }
          this.__finalized = true;
          this._finalizeClass();
        }
      }

      /**
       * Finalize an element class. This includes ensuring property
       * accessors exist on the element prototype. This method is called by
       * `finalize` and finalizes the class constructor.
       *
       * @protected
       */
      static _finalizeClass() {
        const props = ownProperties(/** @type {PropertiesMixinConstructor} */(this));
        if (props) {
          this.createProperties(props);
        }
      }

      /**
       * Returns a memoized version of all properties, including those inherited
       * from super classes. Properties not in object format are converted to
       * at least {type}.
       *
       * @return {Object} Object containing properties for this class
       * @protected
       */
      static get _properties() {
        if (!this.hasOwnProperty(
          JSCompiler_renameProperty('__properties', this))) {
          const superCtor = superPropertiesClass(/** @type {PropertiesMixinConstructor} */(this));
          this.__properties = Object.assign({},
            superCtor && superCtor._properties,
            ownProperties(/** @type {PropertiesMixinConstructor} */(this)));
        }
        return this.__properties;
      }

      /**
       * Overrides `PropertiesChanged` method to return type specified in the
       * static `properties` object for the given property.
       * @param {string} name Name of property
       * @return {*} Type to which to deserialize attribute
       *
       * @protected
       */
      static typeForProperty(name) {
        const info = this._properties[name];
        return info && info.type;
      }

      /**
       * Overrides `PropertiesChanged` method and adds a call to
       * `finalize` which lazily configures the element's property accessors.
       * @override
       * @return {void}
       */
      _initializeProperties() {
        Polymer.telemetry.instanceCount++;
        this.constructor.finalize();
        super._initializeProperties();
      }

      /**
       * Called when the element is added to a document.
       * Calls `_enableProperties` to turn on property system from
       * `PropertiesChanged`.
       * @suppress {missingProperties} Super may or may not implement the callback
       * @return {void}
       */
      connectedCallback() {
        if (super.connectedCallback) {
          super.connectedCallback();
        }
        this._enableProperties();
      }

      /**
       * Called when the element is removed from a document
       * @suppress {missingProperties} Super may or may not implement the callback
       * @return {void}
       */
      disconnectedCallback() {
        if (super.disconnectedCallback) {
          super.disconnectedCallback();
        }
      }

    }

    return PropertiesMixin;

  });

})();

</script><script>
(function() {
  'use strict';

  const builtCSS = window.ShadyCSS && window.ShadyCSS['cssBuild'];

  /**
   * Element class mixin that provides the core API for Polymer's meta-programming
   * features including template stamping, data-binding, attribute deserialization,
   * and property change observation.
   *
   * Subclassers may provide the following static getters to return metadata
   * used to configure Polymer's features for the class:
   *
   * - `static get is()`: When the template is provided via a `dom-module`,
   *   users should return the `dom-module` id from a static `is` getter.  If
   *   no template is needed or the template is provided directly via the
   *   `template` getter, there is no need to define `is` for the element.
   *
   * - `static get template()`: Users may provide the template directly (as
   *   opposed to via `dom-module`) by implementing a static `template` getter.
   *   The getter may return an `HTMLTemplateElement` or a string, which will
   *   automatically be parsed into a template.
   *
   * - `static get properties()`: Should return an object describing
   *   property-related metadata used by Polymer features (key: property name
   *   value: object containing property metadata). Valid keys in per-property
   *   metadata include:
   *   - `type` (String|Number|Object|Array|...): Used by
   *     `attributeChangedCallback` to determine how string-based attributes
   *     are deserialized to JavaScript property values.
   *   - `notify` (boolean): Causes a change in the property to fire a
   *     non-bubbling event called `<property>-changed`. Elements that have
   *     enabled two-way binding to the property use this event to observe changes.
   *   - `readOnly` (boolean): Creates a getter for the property, but no setter.
   *     To set a read-only property, use the private setter method
   *     `_setProperty(property, value)`.
   *   - `observer` (string): Observer method name that will be called when
   *     the property changes. The arguments of the method are
   *     `(value, previousValue)`.
   *   - `computed` (string): String describing method and dependent properties
   *     for computing the value of this property (e.g. `'computeFoo(bar, zot)'`).
   *     Computed properties are read-only by default and can only be changed
   *     via the return value of the computing method.
   *
   * - `static get observers()`: Array of strings describing multi-property
   *   observer methods and their dependent properties (e.g.
   *   `'observeABC(a, b, c)'`).
   *
   * The base class provides default implementations for the following standard
   * custom element lifecycle callbacks; users may override these, but should
   * call the super method to ensure
   * - `constructor`: Run when the element is created or upgraded
   * - `connectedCallback`: Run each time the element is connected to the
   *   document
   * - `disconnectedCallback`: Run each time the element is disconnected from
   *   the document
   * - `attributeChangedCallback`: Run each time an attribute in
   *   `observedAttributes` is set or removed (note: this element's default
   *   `observedAttributes` implementation will automatically return an array
   *   of dash-cased attributes based on `properties`)
   *
   * @mixinFunction
   * @polymer
   * @appliesMixin Polymer.PropertyEffects
   * @appliesMixin Polymer.PropertiesMixin
   * @memberof Polymer
   * @property rootPath {string} Set to the value of `Polymer.rootPath`,
   *   which defaults to the main document path
   * @property importPath {string} Set to the value of the class's static
   *   `importPath` property, which defaults to the path of this element's
   *   `dom-module` (when `is` is used), but can be overridden for other
   *   import strategies.
   * @summary Element class mixin that provides the core API for Polymer's
   * meta-programming features.
   */
  Polymer.ElementMixin = Polymer.dedupingMixin(base => {

    /**
     * @constructor
     * @extends {base}
     * @implements {Polymer_PropertyEffects}
     * @implements {Polymer_PropertiesMixin}
     * @private
     */
    const polymerElementBase = Polymer.PropertiesMixin(Polymer.PropertyEffects(base));

    /**
     * Returns a list of properties with default values.
     * This list is created as an optimization since it is a subset of
     * the list returned from `_properties`.
     * This list is used in `_initializeProperties` to set property defaults.
     *
     * @param {PolymerElementConstructor} constructor Element class
     * @return {PolymerElementProperties} Flattened properties for this class
     *   that have default values
     * @private
     */
    function propertyDefaults(constructor) {
      if (!constructor.hasOwnProperty(
        JSCompiler_renameProperty('__propertyDefaults', constructor))) {
        constructor.__propertyDefaults = null;
        let props = constructor._properties;
        for (let p in props) {
          let info = props[p];
          if ('value' in info) {
            constructor.__propertyDefaults = constructor.__propertyDefaults || {};
            constructor.__propertyDefaults[p] = info;
          }
        }
      }
      return constructor.__propertyDefaults;
    }

    /**
     * Returns a memoized version of the `observers` array.
     * @param {PolymerElementConstructor} constructor Element class
     * @return {Array} Array containing own observers for the given class
     * @protected
     */
    function ownObservers(constructor) {
      if (!constructor.hasOwnProperty(
        JSCompiler_renameProperty('__ownObservers', constructor))) {
          constructor.__ownObservers =
          constructor.hasOwnProperty(JSCompiler_renameProperty('observers', constructor)) ?
          /** @type {PolymerElementConstructor} */ (constructor).observers : null;
      }
      return constructor.__ownObservers;
    }

    /**
     * Creates effects for a property.
     *
     * Note, once a property has been set to
     * `readOnly`, `computed`, `reflectToAttribute`, or `notify`
     * these values may not be changed. For example, a subclass cannot
     * alter these settings. However, additional `observers` may be added
     * by subclasses.
     *
     * The info object should contain property metadata as follows:
     *
     * * `type`: {function} type to which an attribute matching the property
     * is deserialized. Note the property is camel-cased from a dash-cased
     * attribute. For example, 'foo-bar' attribute is deserialized to a
     * property named 'fooBar'.
     *
     * * `readOnly`: {boolean} creates a readOnly property and
     * makes a private setter for the private of the form '_setFoo' for a
     * property 'foo',
     *
     * * `computed`: {string} creates a computed property. A computed property
     * is also automatically set to `readOnly: true`. The value is calculated
     * by running a method and arguments parsed from the given string. For
     * example 'compute(foo)' will compute a given property when the
     * 'foo' property changes by executing the 'compute' method. This method
     * must return the computed value.
     *
     * * `reflectToAttribute`: {boolean} If true, the property value is reflected
     * to an attribute of the same name. Note, the attribute is dash-cased
     * so a property named 'fooBar' is reflected as 'foo-bar'.
     *
     * * `notify`: {boolean} sends a non-bubbling notification event when
     * the property changes. For example, a property named 'foo' sends an
     * event named 'foo-changed' with `event.detail` set to the value of
     * the property.
     *
     * * observer: {string} name of a method that runs when the property
     * changes. The arguments of the method are (value, previousValue).
     *
     * Note: Users may want control over modifying property
     * effects via subclassing. For example, a user might want to make a
     * reflectToAttribute property not do so in a subclass. We've chosen to
     * disable this because it leads to additional complication.
     * For example, a readOnly effect generates a special setter. If a subclass
     * disables the effect, the setter would fail unexpectedly.
     * Based on feedback, we may want to try to make effects more malleable
     * and/or provide an advanced api for manipulating them.
     * Also consider adding warnings when an effect cannot be changed.
     *
     * @param {!PolymerElement} proto Element class prototype to add accessors
     *   and effects to
     * @param {string} name Name of the property.
     * @param {Object} info Info object from which to create property effects.
     * Supported keys:
     * @param {Object} allProps Flattened map of all properties defined in this
     *   element (including inherited properties)
     * @return {void}
     * @private
     */
    function createPropertyFromConfig(proto, name, info, allProps) {
      // computed forces readOnly...
      if (info.computed) {
        info.readOnly = true;
      }
      // Note, since all computed properties are readOnly, this prevents
      // adding additional computed property effects (which leads to a confusing
      // setup where multiple triggers for setting a property)
      // While we do have `hasComputedEffect` this is set on the property's
      // dependencies rather than itself.
      if (info.computed && !proto._hasReadOnlyEffect(name)) {
        proto._createComputedProperty(name, info.computed, allProps);
      }
      if (info.readOnly && !proto._hasReadOnlyEffect(name)) {
        proto._createReadOnlyProperty(name, !info.computed);
      }
      if (info.reflectToAttribute && !proto._hasReflectEffect(name)) {
        proto._createReflectedProperty(name);
      }
      if (info.notify && !proto._hasNotifyEffect(name)) {
        proto._createNotifyingProperty(name);
      }
      // always add observer
      if (info.observer) {
        proto._createPropertyObserver(name, info.observer, allProps[info.observer]);
      }
      // always create the mapping from attribute back to property for deserialization.
      proto._addPropertyToAttributeMap(name);
    }

    /**
     * Process all style elements in the element template. Styles with the
     * `include` attribute are processed such that any styles in
     * the associated "style modules" are included in the element template.
     * @param {PolymerElementConstructor} klass Element class
     * @param {!HTMLTemplateElement} template Template to process
     * @param {string} is Name of element
     * @param {string} baseURI Base URI for element
     * @private
     */
    function processElementStyles(klass, template, is, baseURI) {
      if (!builtCSS) {
        const templateStyles = template.content.querySelectorAll('style');
        const stylesWithImports = Polymer.StyleGather.stylesFromTemplate(template);
        // insert styles from <link rel="import" type="css"> at the top of the template
        const linkedStyles = Polymer.StyleGather.stylesFromModuleImports(is);
        const firstTemplateChild = template.content.firstElementChild;
        for (let idx = 0; idx < linkedStyles.length; idx++) {
          let s = linkedStyles[idx];
          s.textContent = klass._processStyleText(s.textContent, baseURI);
          template.content.insertBefore(s, firstTemplateChild);
        }
        // keep track of the last "concrete" style in the template we have encountered
        let templateStyleIndex = 0;
        // ensure all gathered styles are actually in this template.
        for (let i = 0; i < stylesWithImports.length; i++) {
          let s = stylesWithImports[i];
          let templateStyle = templateStyles[templateStyleIndex];
          // if the style is not in this template, it's been "included" and
          // we put a clone of it in the template before the style that included it
          if (templateStyle !== s) {
            s = s.cloneNode(true);
            templateStyle.parentNode.insertBefore(s, templateStyle);
          } else {
            templateStyleIndex++;
          }
          s.textContent = klass._processStyleText(s.textContent, baseURI);
        }
      }
      if (window.ShadyCSS) {
        window.ShadyCSS.prepareTemplate(template, is);
      }
    }

    /**
     * Look up template from dom-module for element
     *
     * @param {!string} is Element name to look up
     * @return {!HTMLTemplateElement} Template found in dom module, or
     *   undefined if not found
     * @protected
     */
    function getTemplateFromDomModule(is) {
      let template = null;
      if (is && Polymer.DomModule) {
        template = Polymer.DomModule.import(is, 'template');
        // Under strictTemplatePolicy, require any element with an `is`
        // specified to have a dom-module
        if (Polymer.strictTemplatePolicy && !template) {
          throw new Error(`strictTemplatePolicy: expecting dom-module or null template for ${is}`);
        }
      }
      return template;
    }

  /**
     * @polymer
     * @mixinClass
     * @unrestricted
     * @implements {Polymer_ElementMixin}
     */
    class PolymerElement extends polymerElementBase {

      /**
       * Override of PropertiesMixin _finalizeClass to create observers and
       * find the template.
       * @return {void}
       * @protected
       * @override
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static _finalizeClass() {
        super._finalizeClass();
        const observers = ownObservers(this);
        if (observers) {
          this.createObservers(observers, this._properties);
        }
        this._prepareTemplate();
      }

      static _prepareTemplate() {
        // note: create "working" template that is finalized at instance time
        let template = /** @type {PolymerElementConstructor} */ (this).template;
        if (template) {
          if (typeof template === 'string') {
            let t = document.createElement('template');
            t.innerHTML = template;
            template = t;
          } else if (!Polymer.legacyOptimizations) {
             template = template.cloneNode(true);
          }
        }

        this.prototype._template = template;
      }

      /**
       * Override of PropertiesChanged createProperties to create accessors
       * and property effects for all of the properties.
       * @return {void}
       * @protected
       * @override
       */
      static createProperties(props) {
        for (let p in props) {
          createPropertyFromConfig(this.prototype, p, props[p], props);
        }
      }

      /**
       * Creates observers for the given `observers` array.
       * Leverages `PropertyEffects` to create observers.
       * @param {Object} observers Array of observer descriptors for
       *   this class
       * @param {Object} dynamicFns Object containing keys for any properties
       *   that are functions and should trigger the effect when the function
       *   reference is changed
       * @return {void}
       * @protected
       */
      static createObservers(observers, dynamicFns) {
        const proto = this.prototype;
        for (let i=0; i < observers.length; i++) {
          proto._createMethodObserver(observers[i], dynamicFns);
        }
      }

      /**
       * Returns the template that will be stamped into this element's shadow root.
       *
       * If a `static get is()` getter is defined, the default implementation
       * will return the first `<template>` in a `dom-module` whose `id`
       * matches this element's `is`.
       *
       * Users may override this getter to return an arbitrary template
       * (in which case the `is` getter is unnecessary). The template returned
       * may be either an `HTMLTemplateElement` or a string that will be
       * automatically parsed into a template.
       *
       * Note that when subclassing, if the super class overrode the default
       * implementation and the subclass would like to provide an alternate
       * template via a `dom-module`, it should override this getter and
       * return `Polymer.DomModule.import(this.is, 'template')`.
       *
       * If a subclass would like to modify the super class template, it should
       * clone it rather than modify it in place.  If the getter does expensive
       * work such as cloning/modifying a template, it should memoize the
       * template for maximum performance:
       *
       *   let memoizedTemplate;
       *   class MySubClass extends MySuperClass {
       *     static get template() {
       *       if (!memoizedTemplate) {
       *         memoizedTemplate = MySuperClass.template.cloneNode(true);
       *         let subContent = document.createElement('div');
       *         subContent.textContent = 'This came from MySubClass';
       *         memoizedTemplate.content.appendChild(subContent);
       *       }
       *       return memoizedTemplate;
       *     }
       *   }
       *
       * @return {HTMLTemplateElement|string} Template to be stamped
       */
      static get template() {
        // Explanation of template-related properties:
        // - constructor.template (this getter): the template for the class.
        //     This can come from the prototype (for legacy elements), from a
        //     dom-module, or from the super class's template (or can be overridden
        //     altogether by the user)
        // - constructor._template: memoized version of constructor.template
        // - prototype._template: working template for the element, which will be
        //     parsed and modified in place. It is a cloned version of
        //     constructor.template, saved in _finalizeClass(). Note that before
        //     this getter is called, for legacy elements this could be from a
        //     _template field on the info object passed to Polymer(), a behavior,
        //     or set in registered(); once the static getter runs, a clone of it
        //     will overwrite it on the prototype as the working template.
        if (!this.hasOwnProperty(JSCompiler_renameProperty('_template', this))) {
          this._template =
            // If user has put template on prototype (e.g. in legacy via registered
            // callback or info object), prefer that first
            this.prototype.hasOwnProperty(JSCompiler_renameProperty('_template', this.prototype)) ?
            this.prototype._template :
            // Look in dom-module associated with this element's is
            (getTemplateFromDomModule(/** @type {PolymerElementConstructor}*/ (this).is) ||
            // Next look for superclass template (call the super impl this
            // way so that `this` points to the superclass)
            Object.getPrototypeOf(/** @type {PolymerElementConstructor}*/ (this).prototype).constructor.template);
        }
        return this._template;
      }

      /**
       * Set the template.
       *
       * @param {!HTMLTemplateElement|string} value Template to set.
       */
      static set template(value) {
        this._template = value;
      }

      /**
       * Path matching the url from which the element was imported.
       *
       * This path is used to resolve url's in template style cssText.
       * The `importPath` property is also set on element instances and can be
       * used to create bindings relative to the import path.
       *
       * For elements defined in ES modules, users should implement
       * `static get importMeta() { return import.meta; }`, and the default
       * implementation of `importPath` will  return `import.meta.url`'s path.
       * For elements defined in HTML imports, this getter will return the path
       * to the document containing a `dom-module` element matching this
       * element's static `is` property.
       *
       * Note, this path should contain a trailing `/`.
       *
       * @return {string} The import path for this element class
       * @suppress {missingProperties}
       */
      static get importPath() {
        if (!this.hasOwnProperty(JSCompiler_renameProperty('_importPath', this))) {
          const meta = this.importMeta;
          if (meta) {
            this._importPath = Polymer.ResolveUrl.pathFromUrl(meta.url);
          } else {
            const module = Polymer.DomModule && Polymer.DomModule.import(/** @type {PolymerElementConstructor} */ (this).is);
            this._importPath = (module && module.assetpath) ||
              Object.getPrototypeOf(/** @type {PolymerElementConstructor}*/ (this).prototype).constructor.importPath;
          }
        }
        return this._importPath;
      }

      constructor() {
        super();
        /** @type {HTMLTemplateElement} */
        this._template;
        /** @type {string} */
        this._importPath;
        /** @type {string} */
        this.rootPath;
        /** @type {string} */
        this.importPath;
        /** @type {StampedTemplate | HTMLElement | ShadowRoot} */
        this.root;
        /** @type {!Object<string, !Element>} */
        this.$;
      }

      /**
       * Overrides the default `Polymer.PropertyAccessors` to ensure class
       * metaprogramming related to property accessors and effects has
       * completed (calls `finalize`).
       *
       * It also initializes any property defaults provided via `value` in
       * `properties` metadata.
       *
       * @return {void}
       * @override
       * @suppress {invalidCasts}
       */
      _initializeProperties() {
        this.constructor.finalize();
        // note: finalize template when we have access to `localName` to
        // avoid dependence on `is` for polyfilling styling.
        this.constructor._finalizeTemplate(/** @type {!HTMLElement} */(this).localName);
        super._initializeProperties();
        // set path defaults
        this.rootPath = Polymer.rootPath;
        this.importPath = this.constructor.importPath;
        // apply property defaults...
        let p$ = propertyDefaults(this.constructor);
        if (!p$) {
          return;
        }
        for (let p in p$) {
          let info = p$[p];
          // Don't set default value if there is already an own property, which
          // happens when a `properties` property with default but no effects had
          // a property set (e.g. bound) by its host before upgrade
          if (!this.hasOwnProperty(p)) {
            let value = typeof info.value == 'function' ?
              info.value.call(this) :
              info.value;
            // Set via `_setProperty` if there is an accessor, to enable
            // initializing readOnly property defaults
            if (this._hasAccessor(p)) {
              this._setPendingProperty(p, value, true);
            } else {
              this[p] = value;
            }
          }
        }
      }

      /**
       * Gather style text for a style element in the template.
       *
       * @param {string} cssText Text containing styling to process
       * @param {string} baseURI Base URI to rebase CSS paths against
       * @return {string} The processed CSS text
       * @protected
       */
      static _processStyleText(cssText, baseURI) {
        return Polymer.ResolveUrl.resolveCss(cssText, baseURI);
      }

      /**
      * Configures an element `proto` to function with a given `template`.
      * The element name `is` and extends `ext` must be specified for ShadyCSS
      * style scoping.
      *
      * @param {string} is Tag name (or type extension name) for this element
      * @return {void}
      * @protected
      */
      static _finalizeTemplate(is) {
        /** @const {HTMLTemplateElement} */
        const template = this.prototype._template;
        if (template && !template.__polymerFinalized) {
          template.__polymerFinalized = true;
          const importPath = this.importPath;
          const baseURI = importPath ? Polymer.ResolveUrl.resolveUrl(importPath) : '';
          // e.g. support `include="module-name"`, and ShadyCSS
          processElementStyles(this, template, is, baseURI);
          this.prototype._bindTemplate(template);
        }
      }

      /**
       * Provides a default implementation of the standard Custom Elements
       * `connectedCallback`.
       *
       * The default implementation enables the property effects system and
       * flushes any pending properties, and updates shimmed CSS properties
       * when using the ShadyCSS scoping/custom properties polyfill.
       *
       * @suppress {missingProperties, invalidCasts} Super may or may not implement the callback
       * @return {void}
       */
      connectedCallback() {
        if (window.ShadyCSS && this._template) {
          window.ShadyCSS.styleElement(/** @type {!HTMLElement} */(this));
        }
        super.connectedCallback();
      }

      /**
       * Stamps the element template.
       *
       * @return {void}
       * @override
       */
      ready() {
        if (this._template) {
          this.root = this._stampTemplate(this._template);
          this.$ = this.root.$;
        }
        super.ready();
      }

      /**
       * Implements `PropertyEffects`'s `_readyClients` call. Attaches
       * element dom by calling `_attachDom` with the dom stamped from the
       * element's template via `_stampTemplate`. Note that this allows
       * client dom to be attached to the element prior to any observers
       * running.
       *
       * @return {void}
       * @override
       */
      _readyClients() {
        if (this._template) {
          this.root = this._attachDom(/** @type {StampedTemplate} */(this.root));
        }
        // The super._readyClients here sets the clients initialized flag.
        // We must wait to do this until after client dom is created/attached
        // so that this flag can be checked to prevent notifications fired
        // during this process from being handled before clients are ready.
        super._readyClients();
      }


      /**
       * Attaches an element's stamped dom to itself. By default,
       * this method creates a `shadowRoot` and adds the dom to it.
       * However, this method may be overridden to allow an element
       * to put its dom in another location.
       *
       * @throws {Error}
       * @suppress {missingReturn}
       * @param {StampedTemplate} dom to attach to the element.
       * @return {ShadowRoot} node to which the dom has been attached.
       */
      _attachDom(dom) {
        if (this.attachShadow) {
          if (dom) {
            if (!this.shadowRoot) {
              this.attachShadow({mode: 'open'});
            }
            this.shadowRoot.appendChild(dom);
            return this.shadowRoot;
          }
          return null;
        } else {
          throw new Error('ShadowDOM not available. ' +
            // TODO(sorvell): move to compile-time conditional when supported
          'Polymer.Element can create dom as children instead of in ' +
          'ShadowDOM by setting `this.root = this;\` before \`ready\`.');
        }
      }

      /**
       * When using the ShadyCSS scoping and custom property shim, causes all
       * shimmed styles in this element (and its subtree) to be updated
       * based on current custom property values.
       *
       * The optional parameter overrides inline custom property styles with an
       * object of properties where the keys are CSS properties, and the values
       * are strings.
       *
       * Example: `this.updateStyles({'--color': 'blue'})`
       *
       * These properties are retained unless a value of `null` is set.
       *
       * Note: This function does not support updating CSS mixins.
       * You can not dynamically change the value of an `@apply`.
       *
       * @param {Object=} properties Bag of custom property key/values to
       *   apply to this element.
       * @return {void}
       * @suppress {invalidCasts}
       */
      updateStyles(properties) {
        if (window.ShadyCSS) {
          window.ShadyCSS.styleSubtree(/** @type {!HTMLElement} */(this), properties);
        }
      }

      /**
       * Rewrites a given URL relative to a base URL. The base URL defaults to
       * the original location of the document containing the `dom-module` for
       * this element. This method will return the same URL before and after
       * bundling.
       *
       * Note that this function performs no resolution for URLs that start
       * with `/` (absolute URLs) or `#` (hash identifiers).  For general purpose
       * URL resolution, use `window.URL`.
       *
       * @param {string} url URL to resolve.
       * @param {string=} base Optional base URL to resolve against, defaults
       * to the element's `importPath`
       * @return {string} Rewritten URL relative to base
       */
      resolveUrl(url, base) {
        if (!base && this.importPath) {
          base = Polymer.ResolveUrl.resolveUrl(this.importPath);
        }
        return Polymer.ResolveUrl.resolveUrl(url, base);
      }

      /**
       * Overrides `PropertyAccessors` to add map of dynamic functions on
       * template info, for consumption by `PropertyEffects` template binding
       * code. This map determines which method templates should have accessors
       * created for them.
       *
       * @override
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static _parseTemplateContent(template, templateInfo, nodeInfo) {
        templateInfo.dynamicFns = templateInfo.dynamicFns || this._properties;
        return super._parseTemplateContent(template, templateInfo, nodeInfo);
      }

    }

    return PolymerElement;
  });

  /**
   * When using the ShadyCSS scoping and custom property shim, causes all
   * shimmed `styles` (via `custom-style`) in the document (and its subtree)
   * to be updated based on current custom property values.
   *
   * The optional parameter overrides inline custom property styles with an
   * object of properties where the keys are CSS properties, and the values
   * are strings.
   *
   * Example: `Polymer.updateStyles({'--color': 'blue'})`
   *
   * These properties are retained unless a value of `null` is set.
   *
   * @param {Object=} props Bag of custom property key/values to
   *   apply to the document.
   * @return {void}
   */
  Polymer.updateStyles = function(props) {
    if (window.ShadyCSS) {
      window.ShadyCSS.styleDocument(props);
    }
  };

})();
</script><script>
(function() {
  'use strict';

  /**
   * @summary Collapse multiple callbacks into one invocation after a timer.
   * @memberof Polymer
   */
  class Debouncer {
    constructor() {
      this._asyncModule = null;
      this._callback = null;
      this._timer = null;
    }
    /**
     * Sets the scheduler; that is, a module with the Async interface,
     * a callback and optional arguments to be passed to the run function
     * from the async module.
     *
     * @param {!AsyncInterface} asyncModule Object with Async interface.
     * @param {function()} callback Callback to run.
     * @return {void}
     */
    setConfig(asyncModule, callback) {
      this._asyncModule = asyncModule;
      this._callback = callback;
      this._timer = this._asyncModule.run(() => {
        this._timer = null;
        this._callback();
      });
    }
    /**
     * Cancels an active debouncer and returns a reference to itself.
     *
     * @return {void}
     */
    cancel() {
      if (this.isActive()) {
        this._asyncModule.cancel(this._timer);
        this._timer = null;
      }
    }
    /**
     * Flushes an active debouncer and returns a reference to itself.
     *
     * @return {void}
     */
    flush() {
      if (this.isActive()) {
        this.cancel();
        this._callback();
      }
    }
    /**
     * Returns true if the debouncer is active.
     *
     * @return {boolean} True if active.
     */
    isActive() {
      return this._timer != null;
    }
    /**
     * Creates a debouncer if no debouncer is passed as a parameter
     * or it cancels an active debouncer otherwise. The following
     * example shows how a debouncer can be called multiple times within a
     * microtask and "debounced" such that the provided callback function is
     * called once. Add this method to a custom element:
     *
     * _debounceWork() {
     *   this._debounceJob = Polymer.Debouncer.debounce(this._debounceJob,
     *       Polymer.Async.microTask, () => {
     *     this._doWork();
     *   });
     * }
     *
     * If the `_debounceWork` method is called multiple times within the same
     * microtask, the `_doWork` function will be called only once at the next
     * microtask checkpoint.
     *
     * Note: In testing it is often convenient to avoid asynchrony. To accomplish
     * this with a debouncer, you can use `Polymer.enqueueDebouncer` and
     * `Polymer.flush`. For example, extend the above example by adding
     * `Polymer.enqueueDebouncer(this._debounceJob)` at the end of the
     * `_debounceWork` method. Then in a test, call `Polymer.flush` to ensure
     * the debouncer has completed.
     *
     * @param {Debouncer?} debouncer Debouncer object.
     * @param {!AsyncInterface} asyncModule Object with Async interface
     * @param {function()} callback Callback to run.
     * @return {!Debouncer} Returns a debouncer object.
     */
    static debounce(debouncer, asyncModule, callback) {
      if (debouncer instanceof Debouncer) {
        debouncer.cancel();
      } else {
        debouncer = new Debouncer();
      }
      debouncer.setConfig(asyncModule, callback);
      return debouncer;
    }
  }

  /** @const */
  Polymer.Debouncer = Debouncer;
})();
</script><script>
(function() {

  'use strict';

  // detect native touch action support
  let HAS_NATIVE_TA = typeof document.head.style.touchAction === 'string';
  let GESTURE_KEY = '__polymerGestures';
  let HANDLED_OBJ = '__polymerGesturesHandled';
  let TOUCH_ACTION = '__polymerGesturesTouchAction';
  // radius for tap and track
  let TAP_DISTANCE = 25;
  let TRACK_DISTANCE = 5;
  // number of last N track positions to keep
  let TRACK_LENGTH = 2;

  // Disabling "mouse" handlers for 2500ms is enough
  let MOUSE_TIMEOUT = 2500;
  let MOUSE_EVENTS = ['mousedown', 'mousemove', 'mouseup', 'click'];
  // an array of bitmask values for mapping MouseEvent.which to MouseEvent.buttons
  let MOUSE_WHICH_TO_BUTTONS = [0, 1, 4, 2];
  let MOUSE_HAS_BUTTONS = (function() {
    try {
      return new MouseEvent('test', {buttons: 1}).buttons === 1;
    } catch (e) {
      return false;
    }
  })();

  /**
   * @param {string} name Possible mouse event name
   * @return {boolean} true if mouse event, false if not
   */
  function isMouseEvent(name) {
    return MOUSE_EVENTS.indexOf(name) > -1;
  }

  /* eslint no-empty: ["error", { "allowEmptyCatch": true }] */
  // check for passive event listeners
  let SUPPORTS_PASSIVE = false;
  (function() {
    try {
      let opts = Object.defineProperty({}, 'passive', {get() {SUPPORTS_PASSIVE = true;}});
      window.addEventListener('test', null, opts);
      window.removeEventListener('test', null, opts);
    } catch(e) {}
  })();

  /**
   * Generate settings for event listeners, dependant on `Polymer.passiveTouchGestures`
   *
   * @param {string} eventName Event name to determine if `{passive}` option is needed
   * @return {{passive: boolean} | undefined} Options to use for addEventListener and removeEventListener
   */
  function PASSIVE_TOUCH(eventName) {
    if (isMouseEvent(eventName) || eventName === 'touchend') {
      return;
    }
    if (HAS_NATIVE_TA && SUPPORTS_PASSIVE && Polymer.passiveTouchGestures) {
      return {passive: true};
    } else {
      return;
    }
  }

  // Check for touch-only devices
  let IS_TOUCH_ONLY = navigator.userAgent.match(/iP(?:[oa]d|hone)|Android/);

  let GestureRecognizer = function(){}; // eslint-disable-line no-unused-vars
  /** @type {function(): void} */
  GestureRecognizer.prototype.reset;
  /** @type {function(MouseEvent): void | undefined} */
  GestureRecognizer.prototype.mousedown;
  /** @type {(function(MouseEvent): void | undefined)} */
  GestureRecognizer.prototype.mousemove;
  /** @type {(function(MouseEvent): void | undefined)} */
  GestureRecognizer.prototype.mouseup;
  /** @type {(function(TouchEvent): void | undefined)} */
  GestureRecognizer.prototype.touchstart;
  /** @type {(function(TouchEvent): void | undefined)} */
  GestureRecognizer.prototype.touchmove;
  /** @type {(function(TouchEvent): void | undefined)} */
  GestureRecognizer.prototype.touchend;
  /** @type {(function(MouseEvent): void | undefined)} */
  GestureRecognizer.prototype.click;

  // keep track of any labels hit by the mouseCanceller
  /** @type {!Array<!HTMLLabelElement>} */
  const clickedLabels = [];

  /** @type {!Object<boolean>} */
  const labellable = {
    'button': true,
    'input': true,
    'keygen': true,
    'meter': true,
    'output': true,
    'textarea': true,
    'progress': true,
    'select': true
  };

  // Defined at https://html.spec.whatwg.org/multipage/form-control-infrastructure.html#enabling-and-disabling-form-controls:-the-disabled-attribute
  /** @type {!Object<boolean>} */
  const canBeDisabled = {
    'button': true,
    'command': true,
    'fieldset': true,
    'input': true,
    'keygen': true,
    'optgroup': true,
    'option': true,
    'select': true,
    'textarea': true
  };

  /**
   * @param {HTMLElement} el Element to check labelling status
   * @return {boolean} element can have labels
   */
  function canBeLabelled(el) {
    return labellable[el.localName] || false;
  }

  /**
   * @param {HTMLElement} el Element that may be labelled.
   * @return {!Array<!HTMLLabelElement>} Relevant label for `el`
   */
  function matchingLabels(el) {
    let labels = Array.from(/** @type {HTMLInputElement} */(el).labels || []);
    // IE doesn't have `labels` and Safari doesn't populate `labels`
    // if element is in a shadowroot.
    // In this instance, finding the non-ancestor labels is enough,
    // as the mouseCancellor code will handle ancstor labels
    if (!labels.length) {
      labels = [];
      let root = el.getRootNode();
      // if there is an id on `el`, check for all labels with a matching `for` attribute
      if (el.id) {
        let matching = root.querySelectorAll(`label[for = ${el.id}]`);
        for (let i = 0; i < matching.length; i++) {
          labels.push(/** @type {!HTMLLabelElement} */(matching[i]));
        }
      }
    }
    return labels;
  }

  // touch will make synthetic mouse events
  // `preventDefault` on touchend will cancel them,
  // but this breaks `<input>` focus and link clicks
  // disable mouse handlers for MOUSE_TIMEOUT ms after
  // a touchend to ignore synthetic mouse events
  let mouseCanceller = function(mouseEvent) {
    // Check for sourceCapabilities, used to distinguish synthetic events
    // if mouseEvent did not come from a device that fires touch events,
    // it was made by a real mouse and should be counted
    // http://wicg.github.io/InputDeviceCapabilities/#dom-inputdevicecapabilities-firestouchevents
    let sc = mouseEvent.sourceCapabilities;
    if (sc && !sc.firesTouchEvents) {
      return;
    }
    // skip synthetic mouse events
    mouseEvent[HANDLED_OBJ] = {skip: true};
    // disable "ghost clicks"
    if (mouseEvent.type === 'click') {
      let clickFromLabel = false;
      let path = mouseEvent.composedPath && mouseEvent.composedPath();
      if (path) {
        for (let i = 0; i < path.length; i++) {
          if (path[i].nodeType === Node.ELEMENT_NODE) {
            if (path[i].localName === 'label') {
              clickedLabels.push(path[i]);
            } else if (canBeLabelled(path[i])) {
              let ownerLabels = matchingLabels(path[i]);
              // check if one of the clicked labels is labelling this element
              for (let j = 0; j < ownerLabels.length; j++) {
                clickFromLabel = clickFromLabel || clickedLabels.indexOf(ownerLabels[j]) > -1;
              }
            }
          }
          if (path[i] === POINTERSTATE.mouse.target) {
            return;
          }
        }
      }
      // if one of the clicked labels was labelling the target element,
      // this is not a ghost click
      if (clickFromLabel) {
        return;
      }
      mouseEvent.preventDefault();
      mouseEvent.stopPropagation();
    }
  };

  /**
   * @param {boolean=} setup True to add, false to remove.
   * @return {void}
   */
  function setupTeardownMouseCanceller(setup) {
    let events = IS_TOUCH_ONLY ? ['click'] : MOUSE_EVENTS;
    for (let i = 0, en; i < events.length; i++) {
      en = events[i];
      if (setup) {
        // reset clickLabels array
        clickedLabels.length = 0;
        document.addEventListener(en, mouseCanceller, true);
      } else {
        document.removeEventListener(en, mouseCanceller, true);
      }
    }
  }

  function ignoreMouse(e) {
    if (!POINTERSTATE.mouse.mouseIgnoreJob) {
      setupTeardownMouseCanceller(true);
    }
    let unset = function() {
      setupTeardownMouseCanceller();
      POINTERSTATE.mouse.target = null;
      POINTERSTATE.mouse.mouseIgnoreJob = null;
    };
    POINTERSTATE.mouse.target = e.composedPath()[0];
    POINTERSTATE.mouse.mouseIgnoreJob = Polymer.Debouncer.debounce(
          POINTERSTATE.mouse.mouseIgnoreJob
        , Polymer.Async.timeOut.after(MOUSE_TIMEOUT)
        , unset);
  }

  /**
   * @param {MouseEvent} ev event to test for left mouse button down
   * @return {boolean} has left mouse button down
   */
  function hasLeftMouseButton(ev) {
    let type = ev.type;
    // exit early if the event is not a mouse event
    if (!isMouseEvent(type)) {
      return false;
    }
    // ev.button is not reliable for mousemove (0 is overloaded as both left button and no buttons)
    // instead we use ev.buttons (bitmask of buttons) or fall back to ev.which (deprecated, 0 for no buttons, 1 for left button)
    if (type === 'mousemove') {
      // allow undefined for testing events
      let buttons = ev.buttons === undefined ? 1 : ev.buttons;
      if ((ev instanceof window.MouseEvent) && !MOUSE_HAS_BUTTONS) {
        buttons = MOUSE_WHICH_TO_BUTTONS[ev.which] || 0;
      }
      // buttons is a bitmask, check that the left button bit is set (1)
      return Boolean(buttons & 1);
    } else {
      // allow undefined for testing events
      let button = ev.button === undefined ? 0 : ev.button;
      // ev.button is 0 in mousedown/mouseup/click for left button activation
      return button === 0;
    }
  }

  function isSyntheticClick(ev) {
    if (ev.type === 'click') {
      // ev.detail is 0 for HTMLElement.click in most browsers
      if (ev.detail === 0) {
        return true;
      }
      // in the worst case, check that the x/y position of the click is within
      // the bounding box of the target of the event
      // Thanks IE 10 >:(
      let t = Gestures._findOriginalTarget(ev);
      // make sure the target of the event is an element so we can use getBoundingClientRect,
      // if not, just assume it is a synthetic click
      if (!t.nodeType || /** @type {Element} */(t).nodeType !== Node.ELEMENT_NODE) {
        return true;
      }
      let bcr = /** @type {Element} */(t).getBoundingClientRect();
      // use page x/y to account for scrolling
      let x = ev.pageX, y = ev.pageY;
      // ev is a synthetic click if the position is outside the bounding box of the target
      return !((x >= bcr.left && x <= bcr.right) && (y >= bcr.top && y <= bcr.bottom));
    }
    return false;
  }

  let POINTERSTATE = {
    mouse: {
      target: null,
      mouseIgnoreJob: null
    },
    touch: {
      x: 0,
      y: 0,
      id: -1,
      scrollDecided: false
    }
  };

  function firstTouchAction(ev) {
    let ta = 'auto';
    let path = ev.composedPath && ev.composedPath();
    if (path) {
      for (let i = 0, n; i < path.length; i++) {
        n = path[i];
        if (n[TOUCH_ACTION]) {
          ta = n[TOUCH_ACTION];
          break;
        }
      }
    }
    return ta;
  }

  function trackDocument(stateObj, movefn, upfn) {
    stateObj.movefn = movefn;
    stateObj.upfn = upfn;
    document.addEventListener('mousemove', movefn);
    document.addEventListener('mouseup', upfn);
  }

  function untrackDocument(stateObj) {
    document.removeEventListener('mousemove', stateObj.movefn);
    document.removeEventListener('mouseup', stateObj.upfn);
    stateObj.movefn = null;
    stateObj.upfn = null;
  }

  // use a document-wide touchend listener to start the ghost-click prevention mechanism
  // Use passive event listeners, if supported, to not affect scrolling performance
  document.addEventListener('touchend', ignoreMouse, SUPPORTS_PASSIVE ? {passive: true} : false);

  /**
   * Module for adding listeners to a node for the following normalized
   * cross-platform "gesture" events:
   * - `down` - mouse or touch went down
   * - `up` - mouse or touch went up
   * - `tap` - mouse click or finger tap
   * - `track` - mouse drag or touch move
   *
   * @namespace
   * @memberof Polymer
   * @summary Module for adding cross-platform gesture event listeners.
   */
  const Gestures = {
    gestures: {},
    recognizers: [],

    /**
     * Finds the element rendered on the screen at the provided coordinates.
     *
     * Similar to `document.elementFromPoint`, but pierces through
     * shadow roots.
     *
     * @memberof Polymer.Gestures
     * @param {number} x Horizontal pixel coordinate
     * @param {number} y Vertical pixel coordinate
     * @return {Element} Returns the deepest shadowRoot inclusive element
     * found at the screen position given.
     */
    deepTargetFind: function(x, y) {
      let node = document.elementFromPoint(x, y);
      let next = node;
      // this code path is only taken when native ShadowDOM is used
      // if there is a shadowroot, it may have a node at x/y
      // if there is not a shadowroot, exit the loop
      while (next && next.shadowRoot && !window.ShadyDOM) {
        // if there is a node at x/y in the shadowroot, look deeper
        let oldNext = next;
        next = next.shadowRoot.elementFromPoint(x, y);
        // on Safari, elementFromPoint may return the shadowRoot host
        if (oldNext === next) {
          break;
        }
        if (next) {
          node = next;
        }
      }
      return node;
    },
    /**
     * a cheaper check than ev.composedPath()[0];
     *
     * @private
     * @param {Event} ev Event.
     * @return {EventTarget} Returns the event target.
     */
    _findOriginalTarget: function(ev) {
      // shadowdom
      if (ev.composedPath) {
        const targets = /** @type {!Array<!EventTarget>} */(ev.composedPath());
        // It shouldn't be, but sometimes targets is empty (window on Safari).
        return targets.length > 0 ? targets[0] : ev.target;
      }
      // shadydom
      return ev.target;
    },

    /**
     * @private
     * @param {Event} ev Event.
     * @return {void}
     */
    _handleNative: function(ev) {
      let handled;
      let type = ev.type;
      let node = ev.currentTarget;
      let gobj = node[GESTURE_KEY];
      if (!gobj) {
        return;
      }
      let gs = gobj[type];
      if (!gs) {
        return;
      }
      if (!ev[HANDLED_OBJ]) {
        ev[HANDLED_OBJ] = {};
        if (type.slice(0, 5) === 'touch') {
          ev = /** @type {TouchEvent} */(ev); // eslint-disable-line no-self-assign
          let t = ev.changedTouches[0];
          if (type === 'touchstart') {
            // only handle the first finger
            if (ev.touches.length === 1) {
              POINTERSTATE.touch.id = t.identifier;
            }
          }
          if (POINTERSTATE.touch.id !== t.identifier) {
            return;
          }
          if (!HAS_NATIVE_TA) {
            if (type === 'touchstart' || type === 'touchmove') {
              Gestures._handleTouchAction(ev);
            }
          }
        }
      }
      handled = ev[HANDLED_OBJ];
      // used to ignore synthetic mouse events
      if (handled.skip) {
        return;
      }
      // reset recognizer state
      for (let i = 0, r; i < Gestures.recognizers.length; i++) {
        r = Gestures.recognizers[i];
        if (gs[r.name] && !handled[r.name]) {
          if (r.flow && r.flow.start.indexOf(ev.type) > -1 && r.reset) {
            r.reset();
          }
        }
      }
      // enforce gesture recognizer order
      for (let i = 0, r; i < Gestures.recognizers.length; i++) {
        r = Gestures.recognizers[i];
        if (gs[r.name] && !handled[r.name]) {
          handled[r.name] = true;
          r[type](ev);
        }
      }
    },

    /**
     * @private
     * @param {TouchEvent} ev Event.
     * @return {void}
     */
    _handleTouchAction: function(ev) {
      let t = ev.changedTouches[0];
      let type = ev.type;
      if (type === 'touchstart') {
        POINTERSTATE.touch.x = t.clientX;
        POINTERSTATE.touch.y = t.clientY;
        POINTERSTATE.touch.scrollDecided = false;
      } else if (type === 'touchmove') {
        if (POINTERSTATE.touch.scrollDecided) {
          return;
        }
        POINTERSTATE.touch.scrollDecided = true;
        let ta = firstTouchAction(ev);
        let prevent = false;
        let dx = Math.abs(POINTERSTATE.touch.x - t.clientX);
        let dy = Math.abs(POINTERSTATE.touch.y - t.clientY);
        if (!ev.cancelable) {
          // scrolling is happening
        } else if (ta === 'none') {
          prevent = true;
        } else if (ta === 'pan-x') {
          prevent = dy > dx;
        } else if (ta === 'pan-y') {
          prevent = dx > dy;
        }
        if (prevent) {
          ev.preventDefault();
        } else {
          Gestures.prevent('track');
        }
      }
    },

    /**
     * Adds an event listener to a node for the given gesture type.
     *
     * @memberof Polymer.Gestures
     * @param {!Node} node Node to add listener on
     * @param {string} evType Gesture type: `down`, `up`, `track`, or `tap`
     * @param {!function(!Event):void} handler Event listener function to call
     * @return {boolean} Returns true if a gesture event listener was added.
     * @this {Gestures}
     */
    addListener: function(node, evType, handler) {
      if (this.gestures[evType]) {
        this._add(node, evType, handler);
        return true;
      }
      return false;
    },

    /**
     * Removes an event listener from a node for the given gesture type.
     *
     * @memberof Polymer.Gestures
     * @param {!Node} node Node to remove listener from
     * @param {string} evType Gesture type: `down`, `up`, `track`, or `tap`
     * @param {!function(!Event):void} handler Event listener function previously passed to
     *  `addListener`.
     * @return {boolean} Returns true if a gesture event listener was removed.
     * @this {Gestures}
     */
    removeListener: function(node, evType, handler) {
      if (this.gestures[evType]) {
        this._remove(node, evType, handler);
        return true;
      }
      return false;
    },

    /**
     * automate the event listeners for the native events
     *
     * @private
     * @param {!HTMLElement} node Node on which to add the event.
     * @param {string} evType Event type to add.
     * @param {function(!Event)} handler Event handler function.
     * @return {void}
     * @this {Gestures}
     */
    _add: function(node, evType, handler) {
      let recognizer = this.gestures[evType];
      let deps = recognizer.deps;
      let name = recognizer.name;
      let gobj = node[GESTURE_KEY];
      if (!gobj) {
        node[GESTURE_KEY] = gobj = {};
      }
      for (let i = 0, dep, gd; i < deps.length; i++) {
        dep = deps[i];
        // don't add mouse handlers on iOS because they cause gray selection overlays
        if (IS_TOUCH_ONLY && isMouseEvent(dep) && dep !== 'click') {
          continue;
        }
        gd = gobj[dep];
        if (!gd) {
          gobj[dep] = gd = {_count: 0};
        }
        if (gd._count === 0) {
          node.addEventListener(dep, this._handleNative, PASSIVE_TOUCH(dep));
        }
        gd[name] = (gd[name] || 0) + 1;
        gd._count = (gd._count || 0) + 1;
      }
      node.addEventListener(evType, handler);
      if (recognizer.touchAction) {
        this.setTouchAction(node, recognizer.touchAction);
      }
    },

    /**
     * automate event listener removal for native events
     *
     * @private
     * @param {!HTMLElement} node Node on which to remove the event.
     * @param {string} evType Event type to remove.
     * @param {function(Event?)} handler Event handler function.
     * @return {void}
     * @this {Gestures}
     */
    _remove: function(node, evType, handler) {
      let recognizer = this.gestures[evType];
      let deps = recognizer.deps;
      let name = recognizer.name;
      let gobj = node[GESTURE_KEY];
      if (gobj) {
        for (let i = 0, dep, gd; i < deps.length; i++) {
          dep = deps[i];
          gd = gobj[dep];
          if (gd && gd[name]) {
            gd[name] = (gd[name] || 1) - 1;
            gd._count = (gd._count || 1) - 1;
            if (gd._count === 0) {
              node.removeEventListener(dep, this._handleNative, PASSIVE_TOUCH(dep));
            }
          }
        }
      }
      node.removeEventListener(evType, handler);
    },

    /**
     * Registers a new gesture event recognizer for adding new custom
     * gesture event types.
     *
     * @memberof Polymer.Gestures
     * @param {!GestureRecognizer} recog Gesture recognizer descriptor
     * @return {void}
     * @this {Gestures}
     */
    register: function(recog) {
      this.recognizers.push(recog);
      for (let i = 0; i < recog.emits.length; i++) {
        this.gestures[recog.emits[i]] = recog;
      }
    },

    /**
     * @private
     * @param {string} evName Event name.
     * @return {Object} Returns the gesture for the given event name.
     * @this {Gestures}
     */
    _findRecognizerByEvent: function(evName) {
      for (let i = 0, r; i < this.recognizers.length; i++) {
        r = this.recognizers[i];
        for (let j = 0, n; j < r.emits.length; j++) {
          n = r.emits[j];
          if (n === evName) {
            return r;
          }
        }
      }
      return null;
    },

    /**
     * Sets scrolling direction on node.
     *
     * This value is checked on first move, thus it should be called prior to
     * adding event listeners.
     *
     * @memberof Polymer.Gestures
     * @param {!Element} node Node to set touch action setting on
     * @param {string} value Touch action value
     * @return {void}
     */
    setTouchAction: function(node, value) {
      if (HAS_NATIVE_TA) {
        // NOTE: add touchAction async so that events can be added in
        // custom element constructors. Otherwise we run afoul of custom
        // elements restriction against settings attributes (style) in the
        // constructor.
        Polymer.Async.microTask.run(() => {
          node.style.touchAction = value;
        });
      }
      node[TOUCH_ACTION] = value;
    },

    /**
     * Dispatches an event on the `target` element of `type` with the given
     * `detail`.
     * @private
     * @param {!EventTarget} target The element on which to fire an event.
     * @param {string} type The type of event to fire.
     * @param {!Object=} detail The detail object to populate on the event.
     * @return {void}
     */
    _fire: function(target, type, detail) {
      let ev = new Event(type, { bubbles: true, cancelable: true, composed: true });
      ev.detail = detail;
      target.dispatchEvent(ev);
      // forward `preventDefault` in a clean way
      if (ev.defaultPrevented) {
        let preventer = detail.preventer || detail.sourceEvent;
        if (preventer && preventer.preventDefault) {
          preventer.preventDefault();
        }
      }
    },

    /**
     * Prevents the dispatch and default action of the given event name.
     *
     * @memberof Polymer.Gestures
     * @param {string} evName Event name.
     * @return {void}
     * @this {Gestures}
     */
    prevent: function(evName) {
      let recognizer = this._findRecognizerByEvent(evName);
      if (recognizer.info) {
        recognizer.info.prevent = true;
      }
    },

    /**
     * Reset the 2500ms timeout on processing mouse input after detecting touch input.
     *
     * Touch inputs create synthesized mouse inputs anywhere from 0 to 2000ms after the touch.
     * This method should only be called during testing with simulated touch inputs.
     * Calling this method in production may cause duplicate taps or other Gestures.
     *
     * @memberof Polymer.Gestures
     * @return {void}
     */
    resetMouseCanceller: function() {
      if (POINTERSTATE.mouse.mouseIgnoreJob) {
        POINTERSTATE.mouse.mouseIgnoreJob.flush();
      }
    }
  };

  /* eslint-disable valid-jsdoc */

  Gestures.register({
    name: 'downup',
    deps: ['mousedown', 'touchstart', 'touchend'],
    flow: {
      start: ['mousedown', 'touchstart'],
      end: ['mouseup', 'touchend']
    },
    emits: ['down', 'up'],

    info: {
      movefn: null,
      upfn: null
    },

    /**
     * @this {GestureRecognizer}
     * @return {void}
     */
    reset: function() {
      untrackDocument(this.info);
    },

    /**
     * @this {GestureRecognizer}
     * @param {MouseEvent} e
     * @return {void}
     */
    mousedown: function(e) {
      if (!hasLeftMouseButton(e)) {
        return;
      }
      let t = Gestures._findOriginalTarget(e);
      let self = this;
      let movefn = function movefn(e) {
        if (!hasLeftMouseButton(e)) {
          self._fire('up', t, e);
          untrackDocument(self.info);
        }
      };
      let upfn = function upfn(e) {
        if (hasLeftMouseButton(e)) {
          self._fire('up', t, e);
        }
        untrackDocument(self.info);
      };
      trackDocument(this.info, movefn, upfn);
      this._fire('down', t, e);
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchstart: function(e) {
      this._fire('down', Gestures._findOriginalTarget(e), e.changedTouches[0], e);
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchend: function(e) {
      this._fire('up', Gestures._findOriginalTarget(e), e.changedTouches[0], e);
    },
    /**
     * @param {string} type
     * @param {!EventTarget} target
     * @param {Event} event
     * @param {Function} preventer
     * @return {void}
     */
    _fire: function(type, target, event, preventer) {
      Gestures._fire(target, type, {
        x: event.clientX,
        y: event.clientY,
        sourceEvent: event,
        preventer: preventer,
        prevent: function(e) {
          return Gestures.prevent(e);
        }
      });
    }
  });

  Gestures.register({
    name: 'track',
    touchAction: 'none',
    deps: ['mousedown', 'touchstart', 'touchmove', 'touchend'],
    flow: {
      start: ['mousedown', 'touchstart'],
      end: ['mouseup', 'touchend']
    },
    emits: ['track'],

    info: {
      x: 0,
      y: 0,
      state: 'start',
      started: false,
      moves: [],
      /** @this {GestureRecognizer} */
      addMove: function(move) {
        if (this.moves.length > TRACK_LENGTH) {
          this.moves.shift();
        }
        this.moves.push(move);
      },
      movefn: null,
      upfn: null,
      prevent: false
    },

    /**
     * @this {GestureRecognizer}
     * @return {void}
     */
    reset: function() {
      this.info.state = 'start';
      this.info.started = false;
      this.info.moves = [];
      this.info.x = 0;
      this.info.y = 0;
      this.info.prevent = false;
      untrackDocument(this.info);
    },

    /**
     * @this {GestureRecognizer}
     * @param {number} x
     * @param {number} y
     * @return {boolean}
     */
    hasMovedEnough: function(x, y) {
      if (this.info.prevent) {
        return false;
      }
      if (this.info.started) {
        return true;
      }
      let dx = Math.abs(this.info.x - x);
      let dy = Math.abs(this.info.y - y);
      return (dx >= TRACK_DISTANCE || dy >= TRACK_DISTANCE);
    },
    /**
     * @this {GestureRecognizer}
     * @param {MouseEvent} e
     * @return {void}
     */
    mousedown: function(e) {
      if (!hasLeftMouseButton(e)) {
        return;
      }
      let t = Gestures._findOriginalTarget(e);
      let self = this;
      let movefn = function movefn(e) {
        let x = e.clientX, y = e.clientY;
        if (self.hasMovedEnough(x, y)) {
          // first move is 'start', subsequent moves are 'move', mouseup is 'end'
          self.info.state = self.info.started ? (e.type === 'mouseup' ? 'end' : 'track') : 'start';
          if (self.info.state === 'start') {
            // if and only if tracking, always prevent tap
            Gestures.prevent('tap');
          }
          self.info.addMove({x: x, y: y});
          if (!hasLeftMouseButton(e)) {
            // always _fire "end"
            self.info.state = 'end';
            untrackDocument(self.info);
          }
          self._fire(t, e);
          self.info.started = true;
        }
      };
      let upfn = function upfn(e) {
        if (self.info.started) {
          movefn(e);
        }

        // remove the temporary listeners
        untrackDocument(self.info);
      };
      // add temporary document listeners as mouse retargets
      trackDocument(this.info, movefn, upfn);
      this.info.x = e.clientX;
      this.info.y = e.clientY;
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchstart: function(e) {
      let ct = e.changedTouches[0];
      this.info.x = ct.clientX;
      this.info.y = ct.clientY;
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchmove: function(e) {
      let t = Gestures._findOriginalTarget(e);
      let ct = e.changedTouches[0];
      let x = ct.clientX, y = ct.clientY;
      if (this.hasMovedEnough(x, y)) {
        if (this.info.state === 'start') {
          // if and only if tracking, always prevent tap
          Gestures.prevent('tap');
        }
        this.info.addMove({x: x, y: y});
        this._fire(t, ct);
        this.info.state = 'track';
        this.info.started = true;
      }
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchend: function(e) {
      let t = Gestures._findOriginalTarget(e);
      let ct = e.changedTouches[0];
      // only trackend if track was started and not aborted
      if (this.info.started) {
        // reset started state on up
        this.info.state = 'end';
        this.info.addMove({x: ct.clientX, y: ct.clientY});
        this._fire(t, ct, e);
      }
    },

    /**
     * @this {GestureRecognizer}
     * @param {!EventTarget} target
     * @param {Touch} touch
     * @return {void}
     */
    _fire: function(target, touch) {
      let secondlast = this.info.moves[this.info.moves.length - 2];
      let lastmove = this.info.moves[this.info.moves.length - 1];
      let dx = lastmove.x - this.info.x;
      let dy = lastmove.y - this.info.y;
      let ddx, ddy = 0;
      if (secondlast) {
        ddx = lastmove.x - secondlast.x;
        ddy = lastmove.y - secondlast.y;
      }
      Gestures._fire(target, 'track', {
        state: this.info.state,
        x: touch.clientX,
        y: touch.clientY,
        dx: dx,
        dy: dy,
        ddx: ddx,
        ddy: ddy,
        sourceEvent: touch,
        hover: function() {
          return Gestures.deepTargetFind(touch.clientX, touch.clientY);
        }
      });
    }

  });

  Gestures.register({
    name: 'tap',
    deps: ['mousedown', 'click', 'touchstart', 'touchend'],
    flow: {
      start: ['mousedown', 'touchstart'],
      end: ['click', 'touchend']
    },
    emits: ['tap'],
    info: {
      x: NaN,
      y: NaN,
      prevent: false
    },
    /**
     * @this {GestureRecognizer}
     * @return {void}
     */
    reset: function() {
      this.info.x = NaN;
      this.info.y = NaN;
      this.info.prevent = false;
    },
    /**
     * @this {GestureRecognizer}
     * @param {MouseEvent} e
     * @return {void}
     */
    save: function(e) {
      this.info.x = e.clientX;
      this.info.y = e.clientY;
    },
    /**
     * @this {GestureRecognizer}
     * @param {MouseEvent} e
     * @return {void}
     */
    mousedown: function(e) {
      if (hasLeftMouseButton(e)) {
        this.save(e);
      }
    },
    /**
     * @this {GestureRecognizer}
     * @param {MouseEvent} e
     * @return {void}
     */
    click: function(e) {
      if (hasLeftMouseButton(e)) {
        this.forward(e);
      }
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchstart: function(e) {
      this.save(e.changedTouches[0], e);
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchend: function(e) {
      this.forward(e.changedTouches[0], e);
    },
    /**
     * @this {GestureRecognizer}
     * @param {Event | Touch} e
     * @param {Event=} preventer
     * @return {void}
     */
    forward: function(e, preventer) {
      let dx = Math.abs(e.clientX - this.info.x);
      let dy = Math.abs(e.clientY - this.info.y);
      // find original target from `preventer` for TouchEvents, or `e` for MouseEvents
      let t = Gestures._findOriginalTarget(/** @type {Event} */(preventer || e));
      if (!t || (canBeDisabled[/** @type {!HTMLElement} */(t).localName] && t.hasAttribute('disabled'))) {
        return;
      }
      // dx,dy can be NaN if `click` has been simulated and there was no `down` for `start`
      if (isNaN(dx) || isNaN(dy) || (dx <= TAP_DISTANCE && dy <= TAP_DISTANCE) || isSyntheticClick(e)) {
        // prevent taps from being generated if an event has canceled them
        if (!this.info.prevent) {
          Gestures._fire(t, 'tap', {
            x: e.clientX,
            y: e.clientY,
            sourceEvent: e,
            preventer: preventer
          });
        }
      }
    }
  });

  /* eslint-enable valid-jsdoc */

  /** @deprecated */
  Gestures.findOriginalTarget = Gestures._findOriginalTarget;

  /** @deprecated */
  Gestures.add = Gestures.addListener;

  /** @deprecated */
  Gestures.remove = Gestures.removeListener;

  Polymer.Gestures = Gestures;

})();
</script><script>
(function() {

  'use strict';

  /**
   * @const {Polymer.Gestures}
   */
  const gestures = Polymer.Gestures;

  /**
   * Element class mixin that provides API for adding Polymer's cross-platform
   * gesture events to nodes.
   *
   * The API is designed to be compatible with override points implemented
   * in `Polymer.TemplateStamp` such that declarative event listeners in
   * templates will support gesture events when this mixin is applied along with
   * `Polymer.TemplateStamp`.
   *
   * @mixinFunction
   * @polymer
   * @memberof Polymer
   * @summary Element class mixin that provides API for adding Polymer's cross-platform
   * gesture events to nodes
   */
  Polymer.GestureEventListeners = Polymer.dedupingMixin(superClass => {

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_GestureEventListeners}
     */
    class GestureEventListeners extends superClass {

      /**
       * Add the event listener to the node if it is a gestures event.
       *
       * @param {!Node} node Node to add event listener to
       * @param {string} eventName Name of event
       * @param {function(!Event):void} handler Listener function to add
       * @return {void}
       */
      _addEventListenerToNode(node, eventName, handler) {
        if (!gestures.addListener(node, eventName, handler)) {
          super._addEventListenerToNode(node, eventName, handler);
        }
      }

      /**
       * Remove the event listener to the node if it is a gestures event.
       *
       * @param {!Node} node Node to remove event listener from
       * @param {string} eventName Name of event
       * @param {function(!Event):void} handler Listener function to remove
       * @return {void}
       */
      _removeEventListenerFromNode(node, eventName, handler) {
        if (!gestures.removeListener(node, eventName, handler)) {
          super._removeEventListenerFromNode(node, eventName, handler);
        }
      }

    }

    return GestureEventListeners;

  });

})();
</script><script>
  (function() {
    'use strict';

    const HOST_DIR = /:host\(:dir\((ltr|rtl)\)\)/g;
    const HOST_DIR_REPLACMENT = ':host([dir="$1"])';

    const EL_DIR = /([\s\w-#\.\[\]\*]*):dir\((ltr|rtl)\)/g;
    const EL_DIR_REPLACMENT = ':host([dir="$2"]) $1';

    const DIR_CHECK = /:dir\((?:ltr|rtl)\)/;
    
    const SHIM_SHADOW = Boolean(window['ShadyDOM'] && window['ShadyDOM']['inUse']);

    /**
     * @type {!Array<!Polymer_DirMixin>}
     */
    const DIR_INSTANCES = [];

    /** @type {MutationObserver} */
    let observer = null;

    let DOCUMENT_DIR = '';

    function getRTL() {
      DOCUMENT_DIR = document.documentElement.getAttribute('dir');
    }

    /**
     * @param {!Polymer_DirMixin} instance Instance to set RTL status on
     */
    function setRTL(instance) {
      if (!instance.__autoDirOptOut) {
        const el = /** @type {!HTMLElement} */(instance);
        el.setAttribute('dir', DOCUMENT_DIR);
      }
    }

    function updateDirection() {
      getRTL();
      DOCUMENT_DIR = document.documentElement.getAttribute('dir');
      for (let i = 0; i < DIR_INSTANCES.length; i++) {
        setRTL(DIR_INSTANCES[i]);
      }
    }

    function takeRecords() {
      if (observer && observer.takeRecords().length) {
        updateDirection();
      }
    }

    /**
     * Element class mixin that allows elements to use the `:dir` CSS Selector to have
     * text direction specific styling.
     *
     * With this mixin, any stylesheet provided in the template will transform `:dir` into
     * `:host([dir])` and sync direction with the page via the element's `dir` attribute.
     *
     * Elements can opt out of the global page text direction by setting the `dir` attribute
     * directly in `ready()` or in HTML.
     *
     * Caveats:
     * - Applications must set `<html dir="ltr">` or `<html dir="rtl">` to sync direction
     * - Automatic left-to-right or right-to-left styling is sync'd with the `<html>` element only.
     * - Changing `dir` at runtime is supported.
     * - Opting out of the global direction styling is permanent
     *
     * @mixinFunction
     * @polymer
     * @appliesMixin Polymer.PropertyAccessors
     * @memberof Polymer
     */
    Polymer.DirMixin = Polymer.dedupingMixin((base) => {

      if (!SHIM_SHADOW) {
        if (!observer) {
          getRTL();
          observer = new MutationObserver(updateDirection);
          observer.observe(document.documentElement, {attributes: true, attributeFilter: ['dir']});
        }
      }

      /**
       * @constructor
       * @extends {base}
       * @implements {Polymer_PropertyAccessors}
       * @private
       */
      const elementBase = Polymer.PropertyAccessors(base);

      /**
       * @polymer
       * @mixinClass
       * @implements {Polymer_DirMixin}
       */
      class Dir extends elementBase {

        /**
         * @override
         * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
         */
        static _processStyleText(cssText, baseURI) {
          cssText = super._processStyleText(cssText, baseURI);
          if (!SHIM_SHADOW && DIR_CHECK.test(cssText)) {
            cssText = this._replaceDirInCssText(cssText);
            this.__activateDir = true;
          }
          return cssText;
        }

        /**
         * Replace `:dir` in the given CSS text
         *
         * @param {string} text CSS text to replace DIR
         * @return {string} Modified CSS
         */
        static _replaceDirInCssText(text) {
          let replacedText = text;
          replacedText = replacedText.replace(HOST_DIR, HOST_DIR_REPLACMENT);
          replacedText = replacedText.replace(EL_DIR, EL_DIR_REPLACMENT);
          return replacedText;
        }

        constructor() {
          super();
          /** @type {boolean} */
          this.__autoDirOptOut = false;
        }

        /**
         * @suppress {invalidCasts} Closure doesn't understand that `this` is an HTMLElement
         * @return {void}
         */
        ready() {
          super.ready();
          this.__autoDirOptOut = /** @type {!HTMLElement} */(this).hasAttribute('dir');
        }

        /**
         * @suppress {missingProperties} If it exists on elementBase, it can be super'd
         * @return {void}
         */
        connectedCallback() {
          if (elementBase.prototype.connectedCallback) {
            super.connectedCallback();
          }
          if (this.constructor.__activateDir) {
            takeRecords();
            DIR_INSTANCES.push(this);
            setRTL(this);
          }
        }

        /**
         * @suppress {missingProperties} If it exists on elementBase, it can be super'd
         * @return {void}
         */
        disconnectedCallback() {
          if (elementBase.prototype.disconnectedCallback) {
            super.disconnectedCallback();
          }
          if (this.constructor.__activateDir) {
            const idx = DIR_INSTANCES.indexOf(this);
            if (idx > -1) {
              DIR_INSTANCES.splice(idx, 1);
            }
          }
        }
      }

      Dir.__activateDir = false;

      return Dir;
    });
  })();
</script><script>

(function() {

  'use strict';

  // run a callback when HTMLImports are ready or immediately if
  // this api is not available.
  function whenImportsReady(cb) {
    if (window.HTMLImports) {
      HTMLImports.whenReady(cb);
    } else {
      cb();
    }
  }

  /**
   * Convenience method for importing an HTML document imperatively.
   *
   * This method creates a new `<link rel="import">` element with
   * the provided URL and appends it to the document to start loading.
   * In the `onload` callback, the `import` property of the `link`
   * element will contain the imported document contents.
   *
   * @memberof Polymer
   * @param {string} href URL to document to load.
   * @param {?function(!Event):void=} onload Callback to notify when an import successfully
   *   loaded.
   * @param {?function(!Error
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