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ZackAkil/automl-edge-tflite-metadata.ipynb

Created November 7, 2022 12:22
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automl-edge-tflite-metadata.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyN5C7ksmPxMRl+1Zjtfz39p",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ZackAkil/d1a7987b764c8f4cbcb2715db712bfb9/automl-edge-tflite-metadata.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"!wget https://storage.googleapis.com/automl-demo-misc/model-5586077621008990208/tflite/2022-11-05T12%3A56%3A46.449246Z/model.tflite"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "96Q74FQLz1gY",
"outputId": "c60f2215-84ac-4cf0-dee3-da2ac4629a4f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2022-11-07 11:18:21-- https://storage.googleapis.com/automl-demo-misc/model-5586077621008990208/tflite/2022-11-05T12%3A56%3A46.449246Z/model.tflite\n",
"Resolving storage.googleapis.com (storage.googleapis.com)... 172.253.122.128, 172.253.63.128, 142.251.111.128, ...\n",
"Connecting to storage.googleapis.com (storage.googleapis.com)|172.253.122.128|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 5853661 (5.6M) [application/octet-stream]\n",
"Saving to: ‘model.tflite’\n",
"\n",
"model.tflite 100%[===================>] 5.58M --.-KB/s in 0.1s \n",
"\n",
"2022-11-07 11:18:21 (39.8 MB/s) - ‘model.tflite’ saved [5853661/5853661]\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip3 install --extra-index-url https://google-coral.github.io/py-repo/ tflite_runtime"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zjK_M37P0IAI",
"outputId": "29d5e3af-f434-4920-dcb5-ba67a9878b8f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/, https://google-coral.github.io/py-repo/\n",
"Collecting tflite_runtime\n",
" Downloading tflite_runtime-2.10.0-cp37-cp37m-manylinux2014_x86_64.whl (2.5 MB)\n",
"\u001b[K |████████████████████████████████| 2.5 MB 16.2 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.19.2 in /usr/local/lib/python3.7/dist-packages (from tflite_runtime) (1.21.6)\n",
"Installing collected packages: tflite-runtime\n",
"Successfully installed tflite-runtime-2.10.0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import tflite_runtime.interpreter as tflite"
],
"metadata": {
"id": "fQXcwoCxz-6N"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TFAVJsevzxkq"
},
"outputs": [],
"source": [
"model_path = 'model.tflite'\n",
"interpreter = tflite.Interpreter(model_path)"
]
},
{
"cell_type": "code",
"source": [
"interpreter"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "97QWoCve0M8C",
"outputId": "28663fe9-9bdf-4a88-ec73-7b39bc74e521"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tflite_runtime.interpreter.Interpreter at 0x7f32152901d0>"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [
"interpreter.get_input_details()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "H5zfwxf50P3x",
"outputId": "ac1ec0d9-cf8f-418d-fc84-b2baea7eee76"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'name': 'image',\n",
" 'index': 0,\n",
" 'shape': array([ 1, 224, 224, 3], dtype=int32),\n",
" 'shape_signature': array([ 1, 224, 224, 3], dtype=int32),\n",
" 'dtype': numpy.uint8,\n",
" 'quantization': (0.007874015718698502, 128),\n",
" 'quantization_parameters': {'scales': array([0.00787402], dtype=float32),\n",
" 'zero_points': array([128], dtype=int32),\n",
" 'quantized_dimension': 0},\n",
" 'sparsity_parameters': {}}]"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"source": [
"interpreter.get_output_details()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xjO8JHRP0SnG",
"outputId": "0e8e096e-6aa3-4ae8-aaa7-f79e8da3057e"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'name': 'scores',\n",
" 'index': 172,\n",
" 'shape': array([1, 3], dtype=int32),\n",
" 'shape_signature': array([1, 3], dtype=int32),\n",
" 'dtype': numpy.uint8,\n",
" 'quantization': (0.00390625, 0),\n",
" 'quantization_parameters': {'scales': array([0.00390625], dtype=float32),\n",
" 'zero_points': array([0], dtype=int32),\n",
" 'quantized_dimension': 0},\n",
" 'sparsity_parameters': {}}]"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"!pip install tflite-support"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 680
},
"id": "aIJj-WTq1syN",
"outputId": "50315b93-0d65-42b5-acf2-7fadc0055c17"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting tflite-support\n",
" Downloading tflite_support-0.4.3-cp37-cp37m-manylinux2014_x86_64.whl (60.9 MB)\n",
"\u001b[K |████████████████████████████████| 60.9 MB 1.2 MB/s \n",
"\u001b[?25hCollecting protobuf<4,>=3.18.0\n",
" Downloading protobuf-3.20.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB)\n",
"\u001b[K |████████████████████████████████| 1.0 MB 50.8 MB/s \n",
"\u001b[?25hRequirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tflite-support) (1.3.0)\n",
"Collecting pybind11>=2.6.0\n",
" Downloading pybind11-2.10.1-py3-none-any.whl (216 kB)\n",
"\u001b[K |████████████████████████████████| 216 kB 48.7 MB/s \n",
"\u001b[?25hRequirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.7/dist-packages (from tflite-support) (1.21.6)\n",
"Collecting flatbuffers>=2.0\n",
" Downloading flatbuffers-22.10.26-py2.py3-none-any.whl (26 kB)\n",
"Collecting sounddevice>=0.4.4\n",
" Downloading sounddevice-0.4.5-py3-none-any.whl (31 kB)\n",
"Requirement already satisfied: CFFI>=1.0 in /usr/local/lib/python3.7/dist-packages (from sounddevice>=0.4.4->tflite-support) (1.15.1)\n",
"Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from CFFI>=1.0->sounddevice>=0.4.4->tflite-support) (2.21)\n",
"Installing collected packages: sounddevice, pybind11, protobuf, flatbuffers, tflite-support\n",
" Attempting uninstall: protobuf\n",
" Found existing installation: protobuf 3.17.3\n",
" Uninstalling protobuf-3.17.3:\n",
" Successfully uninstalled protobuf-3.17.3\n",
" Attempting uninstall: flatbuffers\n",
" Found existing installation: flatbuffers 1.12\n",
" Uninstalling flatbuffers-1.12:\n",
" Successfully uninstalled flatbuffers-1.12\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",
"tensorflow 2.9.2 requires flatbuffers<2,>=1.12, but you have flatbuffers 22.10.26 which is incompatible.\n",
"tensorflow 2.9.2 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.3 which is incompatible.\n",
"tensorboard 2.9.1 requires protobuf<3.20,>=3.9.2, but you have protobuf 3.20.3 which is incompatible.\u001b[0m\n",
"Successfully installed flatbuffers-22.10.26 protobuf-3.20.3 pybind11-2.10.1 sounddevice-0.4.5 tflite-support-0.4.3\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"google"
]
}
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from tflite_support import metadata"
],
"metadata": {
"id": "ik8mwF320U5W"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"displayer = metadata.MetadataDisplayer.with_model_file(model_path)"
],
"metadata": {
"id": "iLFH50q31_QV"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(displayer.get_metadata_json())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HHILNeU02BvM",
"outputId": "56237d30-fd02-48d4-a78f-059ab3cab8c5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{\n",
" \"name\": \"Image Classification Model\",\n",
" \"description\": \"Model built using AutoML Vision\",\n",
" \"subgraph_metadata\": [\n",
" {\n",
" \"input_tensor_metadata\": [\n",
" {\n",
" \"name\": \"image\",\n",
" \"description\": \"Input image to be classified. The expected image is 224 x 224, with three channels (red, blue, and green) per pixel. Each value in the tensor is a single byte between 0 and 255.\",\n",
" \"content\": {\n",
" \"content_properties_type\": \"ImageProperties\",\n",
" \"content_properties\": {\n",
" \"color_space\": \"RGB\"\n",
" }\n",
" },\n",
" \"process_units\": [\n",
" {\n",
" \"options_type\": \"NormalizationOptions\",\n",
" \"options\": {\n",
" \"mean\": [\n",
" 127.5\n",
" ],\n",
" \"std\": [\n",
" 127.5\n",
" ]\n",
" }\n",
" }\n",
" ],\n",
" \"stats\": {\n",
" \"max\": [\n",
" 255.0\n",
" ],\n",
" \"min\": [\n",
" 0.0\n",
" ]\n",
" }\n",
" }\n",
" ],\n",
" \"output_tensor_metadata\": [\n",
" {\n",
" \"name\": \"scores\",\n",
" \"description\": \"Probabilities of the labels.\",\n",
" \"content\": {\n",
" \"content_properties_type\": \"FeatureProperties\"\n",
" },\n",
" \"stats\": {\n",
" \"max\": [\n",
" 1.0\n",
" ],\n",
" \"min\": [\n",
" 0.0\n",
" ]\n",
" },\n",
" \"associated_files\": [\n",
" {\n",
" \"name\": \"dict.txt\",\n",
" \"description\": \"Labels for objects that the model can recognize.\",\n",
" \"type\": \"TENSOR_AXIS_LABELS\"\n",
" }\n",
" ]\n",
" }\n",
" ]\n",
" }\n",
" ],\n",
" \"min_parser_version\": \"1.0.0\"\n",
"}\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from tflite_support import metadata\n",
"import zipfile"
],
"metadata": {
"id": "LAc-95Lb2MVi"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model_file = 'model.tflite'\n",
"\n",
"def get_labels_from_tflite(model_file_name, format=0):\n",
" '''\n",
" Unpack the metadata from a tflite model and return the first associated file \n",
" which is assumed to be the label file within AutoML edge models\n",
" input : str (model file name), int (formate 0=list, 1=dict)\n",
" output : str (text from labels file)\n",
" '''\n",
"\n",
" displayer = metadata.MetadataDisplayer.with_model_file(model_file_name)\n",
" associate_files= displayer.get_packed_associated_file_list()\n",
" resource_file = associate_files[0]\n",
" archive = zipfile.ZipFile(model_file_name, 'r')\n",
" labels = archive.read(resource_file).decode().split('\\n')\n",
"\n",
" if format == 1:\n",
" labels = {i:label for (i, label) in enumerate(labels)}\n",
"\n",
" return labels\n",
"\n",
"print(get_labels_from_tflite(model_file))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4ctlXDcc0HN0",
"outputId": "ccfc1443-43db-41c8-a778-817615eaed07"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"cirrus\n",
"cumulus\n",
"cumulonimbus\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Gus' metadata reader"
],
"metadata": {
"id": "XIbd_7Ec0Q1C"
}
},
{
"cell_type": "code",
"source": [
"!wget https://raw.githubusercontent.com/gustheman/metadata_viewer/master/metadata_viewer.py"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "T1MxE-8B0Trb",
"outputId": "42609e31-15fe-44a3-a1e3-f7219f5115f7"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"--2022-11-07 11:19:53-- https://raw.githubusercontent.com/gustheman/metadata_viewer/master/metadata_viewer.py\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.108.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 2131 (2.1K) [text/plain]\n",
"Saving to: ‘metadata_viewer.py’\n",
"\n",
"\rmetadata_viewer.py 0%[ ] 0 --.-KB/s \rmetadata_viewer.py 100%[===================>] 2.08K --.-KB/s in 0s \n",
"\n",
"2022-11-07 11:19:53 (28.8 MB/s) - ‘metadata_viewer.py’ saved [2131/2131]\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!python metadata_viewer.py --model_file=model.tflite --appended_resource_id=0"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IuWFUpwT0XZy",
"outputId": "a0e390be-dd7c-415c-a29e-833467de74b5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2022-11-07 11:22:14.010709: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
"cirrus\n",
"cumulus\n",
"cumulonimbus\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "hMLg-9kq0g-s"
},
"execution_count": null,
"outputs": []
}
]
}
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