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model-maker-generated-saved_model-test.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/dusskapark/cdda3487ed913554247682b238bdebda/model-maker-generated-saved_model-test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"## Install TensorFlow2 Object Detection Dependencies"
],
"metadata": {
"id": "4tkgB2FIU1wX"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vhhdy8sg5X6v",
"outputId": "dd573fdd-015b-4dd8-c718-0a8c68580910"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Python 3.7.12\n"
]
}
],
"source": [
"import tensorflow\n",
"\n",
"#print(tensorflow.__version__)\n",
"!python3 --version"
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"import pathlib\n",
"\n",
"# Clone the tensorflow models repository if it doesn't already exist\n",
"if \"models\" in pathlib.Path.cwd().parts:\n",
" while \"models\" in pathlib.Path.cwd().parts:\n",
" os.chdir('..')\n",
"elif not pathlib.Path('models').exists():\n",
" !git clone --depth 1 https://github.com/tensorflow/models"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "A6mEkdlfUxl2",
"outputId": "2c2155a6-b41f-4ae5-b4e7-4861561aa0ea"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'models'...\n",
"remote: Enumerating objects: 3222, done.\u001b[K\n",
"remote: Counting objects: 100% (3222/3222), done.\u001b[K\n",
"remote: Compressing objects: 100% (2737/2737), done.\u001b[K\n",
"remote: Total 3222 (delta 865), reused 1348 (delta 441), pack-reused 0\u001b[K\n",
"Receiving objects: 100% (3222/3222), 33.42 MiB | 19.14 MiB/s, done.\n",
"Resolving deltas: 100% (865/865), done.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Install the Object Detection API\n",
"%%bash\n",
"cd models/research/\n",
"protoc object_detection/protos/*.proto --python_out=.\n",
"cp object_detection/packages/tf2/setup.py .\n",
"python -m pip install ."
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Fhae9nseU9C8",
"outputId": "e04eb6f4-1a44-4c27-e9e3-e55d5279ddc5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Processing /content/models/research\n",
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]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.\n",
" pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.\n",
"ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"multiprocess 0.70.12.2 requires dill>=0.3.4, but you have dill 0.3.1.1 which is incompatible.\n",
"google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.0 which is incompatible.\n",
"datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Set object detection mothods\n"
],
"metadata": {
"id": "rSMyQUh-VDh8"
}
},
{
"cell_type": "code",
"source": [
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import os\n",
"import random\n",
"import io\n",
"import imageio\n",
"import glob\n",
"import scipy.misc\n",
"import numpy as np\n",
"from six import BytesIO\n",
"from PIL import Image, ImageDraw, ImageFont\n",
"from IPython.display import display, Javascript\n",
"from IPython.display import Image as IPyImage\n",
"\n",
"import tensorflow as tf\n",
"\n",
"from object_detection.utils import label_map_util\n",
"from object_detection.utils import config_util\n",
"from object_detection.utils import visualization_utils as viz_utils\n",
"from object_detection.utils import colab_utils\n",
"from object_detection.builders import model_builder\n",
"\n",
"%matplotlib inline"
],
"metadata": {
"id": "A8siEJqdVL_6"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "m3zGDuA75X6x"
},
"source": [
"# Object Detection From TF2 Saved Model\n",
"\n",
"This demo will take you through the steps of running an \"Android figurine\" TensorFlow 2 detection model on a collection of images. More specifically, this example was generated by `TFlite-model-maker`and exported with `Saved Model Format`.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dX0HHkcS5X6z"
},
"source": [
"## Download images \n",
"\n",
"In this example, I used 2 Android figurine images that @khanhlvg uses in his Colab notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pRL6gLTF5X6z",
"outputId": "0e7eb49f-3a30-4b50-b96b-51d2398f858e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading data from http://download.tensorflow.org/example_images/android_figurine_sample.jpg\n",
"40960/38234 [================================] - 0s 0us/step\n",
"49152/38234 [======================================] - 0s 0us/step\n",
"Downloading data from http://download.tensorflow.org/example_images/android_figurine.jpg\n",
"1105920/1098205 [==============================] - 0s 0us/step\n",
"1114112/1098205 [==============================] - 0s 0us/step\n"
]
}
],
"source": [
"import os\n",
"os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1)\n",
"import pathlib\n",
"import tensorflow as tf\n",
"\n",
"tf.get_logger().setLevel('ERROR') # Suppress TensorFlow logging (2)\n",
"\n",
"# Enable GPU dynamic memory allocation\n",
"gpus = tf.config.experimental.list_physical_devices('GPU')\n",
"for gpu in gpus:\n",
" tf.config.experimental.set_memory_growth(gpu, True)\n",
"\n",
"def download_images():\n",
" base_url = 'http://download.tensorflow.org/example_images/'\n",
" filenames = ['android_figurine_sample.jpg', 'android_figurine.jpg']\n",
" image_paths = []\n",
" for filename in filenames:\n",
" image_path = tf.keras.utils.get_file(fname=filename,\n",
" origin=base_url + filename,\n",
" untar=False)\n",
" image_path = pathlib.Path(image_path)\n",
" image_paths.append(str(image_path))\n",
" return image_paths\n",
"\n",
"IMAGE_PATHS = download_images()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sytlUr3q5X60"
},
"source": [
"# Create model with Model-maker \n",
"\n",
"> This notebook comes from the [Model Maker Object Detection for Android Figurine](https://colab.research.google.com/github/khanhlvg/tflite_raspberry_pi/blob/main/object_detection/Train_custom_model_tutorial.ipynb#scrollTo=9ZsLQtJ1AlW_) of @khanhlvg.\n"
]
},
{
"cell_type": "code",
"source": [
"#@title Install the required packages\n",
"!pip install -q tflite-model-maker\n",
"!pip install -q tflite-support"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ULF6Gku0YVYK",
"outputId": "bacfae22-5ea6-474a-ba0e-5eef35878939"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[K |████████████████████████████████| 616 kB 20.4 MB/s \n",
"\u001b[K |████████████████████████████████| 3.4 MB 57.9 MB/s \n",
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"\u001b[K |████████████████████████████████| 120 kB 62.1 MB/s \n",
"\u001b[K |████████████████████████████████| 840 kB 53.0 MB/s \n",
"\u001b[K |████████████████████████████████| 6.4 MB 48.6 MB/s \n",
"\u001b[K |████████████████████████████████| 1.1 MB 53.9 MB/s \n",
"\u001b[K |████████████████████████████████| 87 kB 7.7 MB/s \n",
"\u001b[K |████████████████████████████████| 25.3 MB 1.5 MB/s \n",
"\u001b[K |████████████████████████████████| 210 kB 72.5 MB/s \n",
"\u001b[?25h Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"object-detection 0.1 requires tf-models-official>=2.5.1, but you have tf-models-official 2.3.0 which is incompatible.\u001b[0m\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#@title Import the required packages.\n",
"import numpy as np\n",
"import os\n",
"\n",
"from tflite_model_maker.config import ExportFormat, QuantizationConfig\n",
"from tflite_model_maker import model_spec\n",
"from tflite_model_maker import object_detector\n",
"\n",
"from tflite_support import metadata\n",
"\n",
"import tensorflow as tf\n",
"assert tf.__version__.startswith('2')\n",
"\n",
"tf.get_logger().setLevel('ERROR')\n",
"from absl import logging\n",
"logging.set_verbosity(logging.ERROR)"
],
"metadata": {
"id": "5Ehr5PXZYfgd"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@title downloading the dataset\n",
"!wget https://storage.googleapis.com/download.tensorflow.org/data/android_figurine.zip\n",
"!unzip -q android_figurine.zip\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nqJgpf79Yu2D",
"outputId": "50756fa9-776d-451a-a7f3-b4cab6bd65e6"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2022-01-04 04:40:23-- https://storage.googleapis.com/download.tensorflow.org/data/android_figurine.zip\n",
"Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.31.128, 142.251.111.128, 142.251.16.128, ...\n",
"Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.31.128|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 14333895 (14M) [application/zip]\n",
"Saving to: ‘android_figurine.zip’\n",
"\n",
"android_figurine.zi 100%[===================>] 13.67M 73.8MB/s in 0.2s \n",
"\n",
"2022-01-04 04:40:23 (73.8 MB/s) - ‘android_figurine.zip’ saved [14333895/14333895]\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#@title Train the object detection model\n",
"#@markdown Step 1: Load the dataset\n",
"\n",
"train_data = object_detector.DataLoader.from_pascal_voc(\n",
" 'android_figurine/train',\n",
" 'android_figurine/train',\n",
" ['android', 'pig_android']\n",
")\n",
"\n",
"val_data = object_detector.DataLoader.from_pascal_voc(\n",
" 'android_figurine/validate',\n",
" 'android_figurine/validate',\n",
" ['android', 'pig_android']\n",
")\n"
],
"metadata": {
"id": "aJDMCeo6Y8A3"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@markdown Step 2: Select a model architecture\n",
"\n",
"spec = model_spec.get('efficientdet_lite0')\n"
],
"metadata": {
"id": "4Yftc3wrZInw"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#@markdown Step 3: Train the TensorFlow model with the training data.\n",
"\n",
"model = object_detector.create(train_data, model_spec=spec, batch_size=4, train_whole_model=True, epochs=20, validation_data=val_data)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "sKHSVM9IZWgP",
"outputId": "d915c984-de6e-4679-cbd3-1c437ec07fb5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/20\n",
"15/15 [==============================] - 72s 2s/step - det_loss: 1.6625 - cls_loss: 1.0859 - box_loss: 0.0115 - reg_l2_loss: 0.0630 - loss: 1.7255 - learning_rate: 0.0065 - gradient_norm: 2.1645 - val_det_loss: 1.4981 - val_cls_loss: 1.0361 - val_box_loss: 0.0092 - val_reg_l2_loss: 0.0630 - val_loss: 1.5611\n",
"Epoch 2/20\n",
"15/15 [==============================] - 32s 2s/step - det_loss: 1.4214 - cls_loss: 0.9565 - box_loss: 0.0093 - reg_l2_loss: 0.0630 - loss: 1.4844 - learning_rate: 0.0049 - gradient_norm: 2.2217 - val_det_loss: 1.1747 - val_cls_loss: 0.7725 - val_box_loss: 0.0080 - val_reg_l2_loss: 0.0630 - val_loss: 1.2377\n",
"Epoch 3/20\n",
"15/15 [==============================] - 37s 3s/step - det_loss: 1.0357 - cls_loss: 0.6933 - box_loss: 0.0068 - reg_l2_loss: 0.0630 - loss: 1.0987 - learning_rate: 0.0048 - gradient_norm: 2.3619 - val_det_loss: 0.9099 - val_cls_loss: 0.5911 - val_box_loss: 0.0064 - val_reg_l2_loss: 0.0630 - val_loss: 0.9729\n",
"Epoch 4/20\n",
"15/15 [==============================] - 32s 2s/step - det_loss: 0.8590 - cls_loss: 0.5653 - box_loss: 0.0059 - reg_l2_loss: 0.0630 - loss: 0.9220 - learning_rate: 0.0046 - gradient_norm: 2.7841 - val_det_loss: 0.8002 - val_cls_loss: 0.5099 - val_box_loss: 0.0058 - val_reg_l2_loss: 0.0630 - val_loss: 0.8632\n",
"Epoch 5/20\n",
"15/15 [==============================] - 43s 3s/step - det_loss: 0.7599 - cls_loss: 0.4689 - box_loss: 0.0058 - reg_l2_loss: 0.0630 - loss: 0.8230 - learning_rate: 0.0043 - gradient_norm: 2.7318 - val_det_loss: 0.6555 - val_cls_loss: 0.4129 - val_box_loss: 0.0049 - val_reg_l2_loss: 0.0630 - val_loss: 0.7185\n",
"Epoch 6/20\n",
"15/15 [==============================] - 38s 3s/step - det_loss: 0.6445 - cls_loss: 0.4079 - box_loss: 0.0047 - reg_l2_loss: 0.0630 - loss: 0.7075 - learning_rate: 0.0040 - gradient_norm: 2.9677 - val_det_loss: 0.5936 - val_cls_loss: 0.3788 - val_box_loss: 0.0043 - val_reg_l2_loss: 0.0630 - val_loss: 0.6566\n",
"Epoch 7/20\n",
"15/15 [==============================] - 32s 2s/step - det_loss: 0.5586 - cls_loss: 0.3521 - box_loss: 0.0041 - reg_l2_loss: 0.0630 - loss: 0.6216 - learning_rate: 0.0037 - gradient_norm: 2.8052 - val_det_loss: 0.5450 - val_cls_loss: 0.3584 - val_box_loss: 0.0037 - val_reg_l2_loss: 0.0630 - val_loss: 0.6081\n",
"Epoch 8/20\n",
"15/15 [==============================] - 33s 2s/step - det_loss: 0.5463 - cls_loss: 0.3632 - box_loss: 0.0037 - reg_l2_loss: 0.0630 - loss: 0.6094 - learning_rate: 0.0033 - gradient_norm: 3.6594 - val_det_loss: 0.5208 - val_cls_loss: 0.3453 - val_box_loss: 0.0035 - val_reg_l2_loss: 0.0630 - val_loss: 0.5838\n",
"Epoch 9/20\n",
"15/15 [==============================] - 39s 3s/step - det_loss: 0.4618 - cls_loss: 0.2935 - box_loss: 0.0034 - reg_l2_loss: 0.0630 - loss: 0.5249 - learning_rate: 0.0029 - gradient_norm: 3.0050 - val_det_loss: 0.4686 - val_cls_loss: 0.3070 - val_box_loss: 0.0032 - val_reg_l2_loss: 0.0630 - val_loss: 0.5317\n",
"Epoch 10/20\n",
"15/15 [==============================] - 40s 3s/step - det_loss: 0.4616 - cls_loss: 0.2958 - box_loss: 0.0033 - reg_l2_loss: 0.0630 - loss: 0.5246 - learning_rate: 0.0025 - gradient_norm: 2.6188 - val_det_loss: 0.4542 - val_cls_loss: 0.3073 - val_box_loss: 0.0029 - val_reg_l2_loss: 0.0630 - val_loss: 0.5172\n",
"Epoch 11/20\n",
"15/15 [==============================] - 36s 2s/step - det_loss: 0.4015 - cls_loss: 0.2673 - box_loss: 0.0027 - reg_l2_loss: 0.0630 - loss: 0.4646 - learning_rate: 0.0021 - gradient_norm: 2.5937 - val_det_loss: 0.4039 - val_cls_loss: 0.2786 - val_box_loss: 0.0025 - val_reg_l2_loss: 0.0630 - val_loss: 0.4670\n",
"Epoch 12/20\n",
"15/15 [==============================] - 35s 2s/step - det_loss: 0.4026 - cls_loss: 0.2647 - box_loss: 0.0028 - reg_l2_loss: 0.0631 - loss: 0.4657 - learning_rate: 0.0017 - gradient_norm: 2.4295 - val_det_loss: 0.3910 - val_cls_loss: 0.2594 - val_box_loss: 0.0026 - val_reg_l2_loss: 0.0631 - val_loss: 0.4540\n",
"Epoch 13/20\n",
"15/15 [==============================] - 34s 2s/step - det_loss: 0.4019 - cls_loss: 0.2542 - box_loss: 0.0030 - reg_l2_loss: 0.0631 - loss: 0.4650 - learning_rate: 0.0013 - gradient_norm: 2.6056 - val_det_loss: 0.3534 - val_cls_loss: 0.2480 - val_box_loss: 0.0021 - val_reg_l2_loss: 0.0631 - val_loss: 0.4164\n",
"Epoch 14/20\n",
"15/15 [==============================] - 31s 2s/step - det_loss: 0.3663 - cls_loss: 0.2509 - box_loss: 0.0023 - reg_l2_loss: 0.0631 - loss: 0.4294 - learning_rate: 9.6772e-04 - gradient_norm: 2.5868 - val_det_loss: 0.3507 - val_cls_loss: 0.2319 - val_box_loss: 0.0024 - val_reg_l2_loss: 0.0631 - val_loss: 0.4137\n",
"Epoch 15/20\n",
"15/15 [==============================] - 43s 3s/step - det_loss: 0.3671 - cls_loss: 0.2369 - box_loss: 0.0026 - reg_l2_loss: 0.0631 - loss: 0.4301 - learning_rate: 6.6413e-04 - gradient_norm: 1.8747 - val_det_loss: 0.3293 - val_cls_loss: 0.2237 - val_box_loss: 0.0021 - val_reg_l2_loss: 0.0631 - val_loss: 0.3923\n",
"Epoch 16/20\n",
"15/15 [==============================] - 34s 2s/step - det_loss: 0.3719 - cls_loss: 0.2478 - box_loss: 0.0025 - reg_l2_loss: 0.0631 - loss: 0.4349 - learning_rate: 4.1061e-04 - gradient_norm: 2.3121 - val_det_loss: 0.3185 - val_cls_loss: 0.2195 - val_box_loss: 0.0020 - val_reg_l2_loss: 0.0631 - val_loss: 0.3815\n",
"Epoch 17/20\n",
"15/15 [==============================] - 33s 2s/step - det_loss: 0.3738 - cls_loss: 0.2416 - box_loss: 0.0026 - reg_l2_loss: 0.0631 - loss: 0.4369 - learning_rate: 2.1409e-04 - gradient_norm: 2.5710 - val_det_loss: 0.3213 - val_cls_loss: 0.2169 - val_box_loss: 0.0021 - val_reg_l2_loss: 0.0631 - val_loss: 0.3844\n",
"Epoch 18/20\n",
"15/15 [==============================] - 38s 3s/step - det_loss: 0.3335 - cls_loss: 0.2279 - box_loss: 0.0021 - reg_l2_loss: 0.0631 - loss: 0.3965 - learning_rate: 7.9920e-05 - gradient_norm: 2.3260 - val_det_loss: 0.3209 - val_cls_loss: 0.2145 - val_box_loss: 0.0021 - val_reg_l2_loss: 0.0631 - val_loss: 0.3839\n",
"Epoch 19/20\n",
"15/15 [==============================] - 33s 2s/step - det_loss: 0.3517 - cls_loss: 0.2451 - box_loss: 0.0021 - reg_l2_loss: 0.0631 - loss: 0.4148 - learning_rate: 1.1764e-05 - gradient_norm: 3.1505 - val_det_loss: 0.3199 - val_cls_loss: 0.2131 - val_box_loss: 0.0021 - val_reg_l2_loss: 0.0631 - val_loss: 0.3830\n",
"Epoch 20/20\n",
"15/15 [==============================] - 42s 3s/step - det_loss: 0.3602 - cls_loss: 0.2349 - box_loss: 0.0025 - reg_l2_loss: 0.0631 - loss: 0.4232 - learning_rate: 1.1480e-05 - gradient_norm: 2.1088 - val_det_loss: 0.3212 - val_cls_loss: 0.2131 - val_box_loss: 0.0022 - val_reg_l2_loss: 0.0631 - val_loss: 0.3843\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#@markdown Step 4. Evaluate the model with the validation data.\n",
"\n",
"model.evaluate(val_data)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "m17zvqkXZk2r",
"outputId": "f073a473-ef3d-4257-b698-ad0be94bf51e"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\r1/1 [==============================] - 8s 8s/step\n",
"\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'AP': 0.7415968,\n",
" 'AP50': 1.0,\n",
" 'AP75': 1.0,\n",
" 'AP_/android': 0.7218075,\n",
" 'AP_/pig_android': 0.76138616,\n",
" 'APl': 0.7415968,\n",
" 'APm': -1.0,\n",
" 'APs': -1.0,\n",
" 'ARl': 0.77569443,\n",
" 'ARm': -1.0,\n",
" 'ARmax1': 0.74583334,\n",
" 'ARmax10': 0.77569443,\n",
" 'ARmax100': 0.77569443,\n",
" 'ARs': -1.0}"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"#Step 5: Export as a TensorFlow Lite model.\n",
"model.export(export_dir=\"./js_export/\", export_format=[ExportFormat.SAVED_MODEL])\n"
],
"metadata": {
"id": "Z3XhSR0yZseq"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "yyuKbX6q5X61"
},
"source": [
"# Test the SAVED MODEL\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zkInf8gu5X62",
"outputId": "8c59b86d-71ef-4eb8-eb58-16d226c813b0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"item {\n",
" name: \"android\",\n",
" id: 1\n",
"}\n",
"\n",
"item {\n",
" name: \"pig_android\",\n",
" id: 2\n",
"}\n",
"\n",
"\n"
]
}
],
"source": [
"# Generate label_map.pbtxt\n",
"\n",
"import os\n",
"\n",
"obj = ['android', 'pig_android']\n",
"\n",
"pbtxt = \"\"\n",
"for i in range(len(obj)):\n",
" pbtxt += \"item {\\n name: \\\"\"+obj[i]+\"\\\",\\n id: \"+str(i+1)+\"\\n}\\n\"+\"\\n\"\n",
" \n",
"with open(\"label_map.pbtxt\", \"w\", encoding=\"utf-8\") as f:\n",
" f.write(pbtxt)\n",
"\n",
"print(pbtxt)\n",
"\n",
"PATH_TO_LABELS = './label_map.pbtxt'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "p4Fq2mCX5X63",
"outputId": "a4cf56e8-d5d4-4bcc-8dfd-6666618f153f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Loading model...Done! Took 22.472423315048218 seconds\n"
]
}
],
"source": [
"# Load the model\n",
"\n",
"import time\n",
"from object_detection.utils import label_map_util\n",
"from object_detection.utils import visualization_utils as viz_utils\n",
"\n",
"PATH_TO_SAVED_MODEL = \"./js_export/saved_model\"\n",
"\n",
"print('Loading model...', end='')\n",
"start_time = time.time()\n",
"\n",
"# Load saved model and build the detection function\n",
"detect_fn = tf.saved_model.load(PATH_TO_SAVED_MODEL)\n",
"\n",
"end_time = time.time()\n",
"elapsed_time = end_time - start_time\n",
"print('Done! Took {} seconds'.format(elapsed_time))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IlII0T575X63"
},
"source": [
"## Load label map data (for plotting)\n",
"Label maps correspond index numbers to category names, so that when our convolution network\n",
"predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility\n",
"functions, but anything that returns a dictionary mapping integers to appropriate string labels\n",
"would be fine.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2seNv1mn5X63"
},
"outputs": [],
"source": [
"category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,\n",
" use_display_name=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RBtfZ3aY5X64"
},
"source": [
"## Putting everything together\n",
"The code shown below loads an image, runs it through the detection model and visualizes the\n",
"detection results, including the keypoints.\n",
"\n",
"Note that this will take a long time (several minutes) the first time you run this code due to\n",
"tf.function's trace-compilation --- on subsequent runs (e.g. on new images), things will be\n",
"faster.\n",
"\n",
"Here are some simple things to try out if you are curious:\n",
"\n",
"* Modify some of the input images and see if detection still works. Some simple things to try out here (just uncomment the relevant portions of code) include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels).\n",
"* Print out `detections['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]).\n",
"* Set ``min_score_thresh`` to other values (between 0 and 1) to allow more detections in or to filter out more detections.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 253
},
"id": "ticEgeTB5X64",
"outputId": "c0419d41-21ac-4f13-996c-f35a90402cad"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Running inference for /root/.keras/datasets/android_figurine_sample.jpg... "
]
},
{
"output_type": "error",
"ename": "AttributeError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-235f1e21b5e7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;31m# Convert to numpy arrays, and take index [0] to remove the batch dimension.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0;31m# We're only interested in the first num_detections.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m \u001b[0mnum_detections\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdetections\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'num_detections'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 49\u001b[0m detections = {key: value[0, :num_detections].numpy()\n\u001b[1;32m 50\u001b[0m for key, value in detections.items()}\n",
"\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'pop'"
]
}
],
"source": [
"import numpy as np\n",
"from PIL import Image\n",
"import matplotlib.pyplot as plt\n",
"import warnings\n",
"warnings.filterwarnings('ignore') # Suppress Matplotlib warnings\n",
"\n",
"def load_image_into_numpy_array(path):\n",
" \"\"\"Load an image from file into a numpy array.\n",
"\n",
" Puts image into numpy array to feed into tensorflow graph.\n",
" Note that by convention we put it into a numpy array with shape\n",
" (height, width, channels), where channels=3 for RGB.\n",
"\n",
" Args:\n",
" path: the file path to the image\n",
"\n",
" Returns:\n",
" uint8 numpy array with shape (img_height, img_width, 3)\n",
" \"\"\"\n",
" return np.array(Image.open(path))\n",
"\n",
"\n",
"for image_path in IMAGE_PATHS:\n",
"\n",
" print('Running inference for {}... '.format(image_path), end='')\n",
"\n",
" image_np = load_image_into_numpy_array(image_path)\n",
"\n",
" # Things to try:\n",
" # Flip horizontally\n",
" # image_np = np.fliplr(image_np).copy()\n",
"\n",
" # Convert image to grayscale\n",
" # image_np = np.tile(\n",
" # np.mean(image_np, 2, keepdims=True), (1, 1, 3)).astype(np.uint8)\n",
"\n",
" # The input needs to be a tensor, convert it using `tf.convert_to_tensor`.\n",
" input_tensor = tf.convert_to_tensor(image_np)\n",
" # The model expects a batch of images, so add an axis with `tf.newaxis`.\n",
" input_tensor = input_tensor[tf.newaxis, ...]\n",
"\n",
" # input_tensor = np.expand_dims(image_np, 0)\n",
" detections = detect_fn(input_tensor)\n",
"\n",
" # All outputs are batches tensors.\n",
" # Convert to numpy arrays, and take index [0] to remove the batch dimension.\n",
" # We're only interested in the first num_detections.\n",
" num_detections = int(detections.pop('num_detections'))\n",
" detections = {key: value[0, :num_detections].numpy()\n",
" for key, value in detections.items()}\n",
" detections['num_detections'] = num_detections\n",
"\n",
" # detection_classes should be ints.\n",
" detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n",
"\n",
" image_np_with_detections = image_np.copy()\n",
"\n",
" viz_utils.visualize_boxes_and_labels_on_image_array(\n",
" image_np_with_detections,\n",
" detections['detection_boxes'],\n",
" detections['detection_classes'],\n",
" detections['detection_scores'],\n",
" category_index,\n",
" use_normalized_coordinates=True,\n",
" max_boxes_to_draw=200,\n",
" min_score_thresh=.30,\n",
" agnostic_mode=False)\n",
"\n",
" plt.figure()\n",
" plt.imshow(image_np_with_detections)\n",
" print('Done')\n",
"plt.show()\n",
"\n",
"# sphinx_gallery_thumbnail_number = 2"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.5"
},
"colab": {
"name": "model maker generated saved_model test.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"include_colab_link": true
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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