From 98f771fc028979c53a9cd23db6823d6d7a23bd14 Mon Sep 17 00:00:00 2001 From: Idan Ben Ami <109598548+Idan-BenAmi@users.noreply.github.com> Date: Wed, 8 May 2024 20:38:37 +0300 Subject: [PATCH] removing quickstart project from mct (#1054) * Remove quickstart project from mct --- README.md | 3 - model_compression_toolkit/gptq/README.md | 2 +- tutorials/notebooks/IMX500_notebooks.md | 68 +- tutorials/notebooks/MCT_notebooks.md | 10 - .../example_quick_start_torchvision.ipynb | 214 ------ tutorials/quick_start/README.md | 256 ------- tutorials/quick_start/__init__.py | 14 - tutorials/quick_start/common/__init__.py | 14 - tutorials/quick_start/common/constants.py | 42 -- .../quick_start/common/library_mapping.py | 55 -- tutorials/quick_start/common/model_lib.py | 122 ---- tutorials/quick_start/common/results.py | 159 ----- tutorials/quick_start/common/tpc_info.py | 53 -- tutorials/quick_start/common/utils.py | 106 --- tutorials/quick_start/keras_fw/__init__.py | 14 - .../keras_fw/keras_applications/LICENSE | 33 - .../keras_fw/keras_applications/__init__.py | 14 - .../keras_fw/keras_applications/model_lib.py | 141 ---- tutorials/quick_start/keras_fw/quant.py | 141 ---- tutorials/quick_start/keras_fw/utils.py | 122 ---- tutorials/quick_start/main.py | 168 ----- tutorials/quick_start/pytorch_fw/__init__.py | 14 - tutorials/quick_start/pytorch_fw/quant.py | 149 ---- .../quick_start/pytorch_fw/timm_lib/LICENSE | 201 ------ .../pytorch_fw/timm_lib/__init__.py | 14 - .../pytorch_fw/timm_lib/model_lib.py | 105 --- .../pytorch_fw/torchvision_lib/LICENSE | 29 - .../pytorch_fw/torchvision_lib/__init__.py | 14 - .../pytorch_fw/torchvision_lib/model_lib.py | 105 --- .../pytorch_fw/ultralytics_lib/LICENSE | 661 ------------------ .../pytorch_fw/ultralytics_lib/README.md | 51 -- .../pytorch_fw/ultralytics_lib/__init__.py | 14 - .../ultralytics_lib/common_replacers.py | 167 ----- .../ultralytics_lib/detect_replacers.py | 155 ---- .../pytorch_fw/ultralytics_lib/model_lib.py | 124 ---- .../ultralytics_lib/pose_replacers.py | 97 --- .../ultralytics_lib/replace_module.py | 25 - .../ultralytics_lib/segment_replacers.py | 90 --- tutorials/quick_start/pytorch_fw/utils.py | 64 -- .../results/model_quantization_results.csv | 15 - 40 files changed, 63 insertions(+), 3782 deletions(-) delete mode 100644 tutorials/notebooks/pytorch/example_quick_start_torchvision.ipynb delete mode 100644 tutorials/quick_start/README.md delete mode 100644 tutorials/quick_start/__init__.py delete mode 100644 tutorials/quick_start/common/__init__.py delete mode 100644 tutorials/quick_start/common/constants.py delete mode 100644 tutorials/quick_start/common/library_mapping.py delete mode 100644 tutorials/quick_start/common/model_lib.py delete mode 100644 tutorials/quick_start/common/results.py delete mode 100644 tutorials/quick_start/common/tpc_info.py delete mode 100644 tutorials/quick_start/common/utils.py delete mode 100644 tutorials/quick_start/keras_fw/__init__.py delete mode 100644 tutorials/quick_start/keras_fw/keras_applications/LICENSE delete mode 100644 tutorials/quick_start/keras_fw/keras_applications/__init__.py delete mode 100644 tutorials/quick_start/keras_fw/keras_applications/model_lib.py delete mode 100644 tutorials/quick_start/keras_fw/quant.py delete mode 100644 tutorials/quick_start/keras_fw/utils.py delete mode 100644 tutorials/quick_start/main.py delete mode 100644 tutorials/quick_start/pytorch_fw/__init__.py delete mode 100644 tutorials/quick_start/pytorch_fw/quant.py delete mode 100644 tutorials/quick_start/pytorch_fw/timm_lib/LICENSE delete mode 100644 tutorials/quick_start/pytorch_fw/timm_lib/__init__.py delete mode 100644 tutorials/quick_start/pytorch_fw/timm_lib/model_lib.py delete mode 100644 tutorials/quick_start/pytorch_fw/torchvision_lib/LICENSE delete mode 100644 tutorials/quick_start/pytorch_fw/torchvision_lib/__init__.py delete mode 100644 tutorials/quick_start/pytorch_fw/torchvision_lib/model_lib.py delete mode 100644 tutorials/quick_start/pytorch_fw/ultralytics_lib/LICENSE delete mode 100644 tutorials/quick_start/pytorch_fw/ultralytics_lib/README.md delete mode 100644 tutorials/quick_start/pytorch_fw/ultralytics_lib/__init__.py delete mode 100644 tutorials/quick_start/pytorch_fw/ultralytics_lib/common_replacers.py delete mode 100644 tutorials/quick_start/pytorch_fw/ultralytics_lib/detect_replacers.py delete mode 100644 tutorials/quick_start/pytorch_fw/ultralytics_lib/model_lib.py delete mode 100644 tutorials/quick_start/pytorch_fw/ultralytics_lib/pose_replacers.py delete mode 100644 tutorials/quick_start/pytorch_fw/ultralytics_lib/replace_module.py delete mode 100644 tutorials/quick_start/pytorch_fw/ultralytics_lib/segment_replacers.py delete mode 100644 tutorials/quick_start/pytorch_fw/utils.py delete mode 100644 tutorials/quick_start/results/model_quantization_results.csv diff --git a/README.md b/README.md index a5f1b8df0..7492a1e28 100644 --- a/README.md +++ b/README.md @@ -45,9 +45,6 @@ for hands-on learning. For example: * [Post training quantization with PyTorch](tutorials/notebooks/pytorch/ptq/example_pytorch_quantization_mnist.ipynb) * [Data Generation for ResNet18 with PyTorch](tutorials/notebooks/pytorch/data_generation/example_pytorch_data_generation.ipynb). -Additionally, for quick quantization of a variety of models from well-known collections, -visit the [quick-start page](tutorials/quick_start/README.md) and the -[results CSV](tutorials/quick_start/results/model_quantization_results.csv). ### Supported Versions diff --git a/model_compression_toolkit/gptq/README.md b/model_compression_toolkit/gptq/README.md index 338014eda..8a60c7ff4 100644 --- a/model_compression_toolkit/gptq/README.md +++ b/model_compression_toolkit/gptq/README.md @@ -17,7 +17,7 @@ The Hessian information is approximated in a label-free manner, thus, does not r ## GPTQ Usage -For detailed examples and tutorials on using GPTQ in MCT with TensorFlow or PyTorch across various models and tasks, please refer to the [tutorials package](../../tutorials). You will find comprehensive explanations, notebook examples, and a [quick-start guide](../../tutorials/quick_start/README.md) for straightforward execution. +For detailed examples and tutorials on using GPTQ in MCT with TensorFlow or PyTorch across various models and tasks, please refer to the [tutorials package](../../tutorials). You will find comprehensive explanations and notebook examples for straightforward execution. ## References diff --git a/tutorials/notebooks/IMX500_notebooks.md b/tutorials/notebooks/IMX500_notebooks.md index ad914988c..57d3ac8ba 100644 --- a/tutorials/notebooks/IMX500_notebooks.md +++ b/tutorials/notebooks/IMX500_notebooks.md @@ -3,10 +3,66 @@ Here we provide examples on quantizing pre-trained models for deployment on Sony-IMX500 processing platform. We will cover various tasks and demonstrate the necessary steps to achieve efficient quantization for optimal deployment performance. - - | Task | Model | Source Repository | Notebook | - |-----------------------------------------------------------------|----------------|---------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------| - | Classification | MobileNetV2 | [Keras Applications](https://keras.io/api/applications/) | [Keras notebook](keras/ptq/example_keras_imagenet.ipynb) | - | Object Detection | YOLOv8n | [Ultralytics](https://github.com/ultralytics/ultralytics) | [Keras notebook](keras/ptq/keras_yolov8n_for_imx500.ipynb) | - | Semantic Segmentation | DeepLabV3-Plus | [bonlime's repo](https://github.com/bonlime/keras-deeplab-v3-plus) | [Keras notebook](keras/ptq/keras_deeplabv3plus_for_imx500.ipynb) | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TaskModel NameFrameworkDataset Name
Classification MobilenetV2KerasImageNet
MobileVitPyTorchImageNet
Object Detection YOLOv8nKerasCOCO
YOLOv8nPyTorchCOCO
NanoDet-Plus-m-416KerasCOCO
EfficientDet-lite0KerasCOCO
Semantic Segmentation Deeplabv3plusKerasPASCAL VOC
Instance SegmentationYOLOv8n-segPyTorchCOCO
Pose EstimationYOLOv8n-posePyTorchCOCO
diff --git a/tutorials/notebooks/MCT_notebooks.md b/tutorials/notebooks/MCT_notebooks.md index 251194ad8..06350e446 100644 --- a/tutorials/notebooks/MCT_notebooks.md +++ b/tutorials/notebooks/MCT_notebooks.md @@ -69,16 +69,6 @@ These techniques are essential for further optimizing models and achieving super ### Pytorch Tutorials -
- Quick-Start with Torchvision - - | Tutorial | - |-----------------------------------------------------------------------------------------------------------------| - | [Quick Start - Torchvision Pretrained Model](pytorch/example_quick_start_torchvision.ipynb) | - -
- -
Post-Training Quantization (PTQ) diff --git a/tutorials/notebooks/pytorch/example_quick_start_torchvision.ipynb b/tutorials/notebooks/pytorch/example_quick_start_torchvision.ipynb deleted file mode 100644 index fb675d727..000000000 --- a/tutorials/notebooks/pytorch/example_quick_start_torchvision.ipynb +++ /dev/null @@ -1,214 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "c9e7b10d2bfe67d4", - "metadata": { - "collapsed": false, - "id": "c9e7b10d2bfe67d4" - }, - "source": [ - "# Quick Start\n", - "\n", - "[Run this tutorial in Google Colab](https://colab.research.google.com/github/sony/model_optimization/blob/main/tutorials/notebooks/pytorch/example_quick_start_torchvision.ipynb)" - ] - }, - { - "cell_type": "markdown", - "id": "9bc664bf", - "metadata": {}, - "source": [ - "## Overview\n", - "This tutorial shows how to use \"quick-start\" with a pre-trained model from the torchvision library.\n", - "\n", - "The following steps will be covered:\n", - "\n", - "* **Setup the environment**: install MCT and add tutorials to PYTHONPATH\n", - "* **Download and organize the imagenet dataset**\n", - "* **Run quick_start on your model**\n", - "\n", - "**Note**: The following code will run faster on a GPU." - ] - }, - { - "cell_type": "markdown", - "id": "d0e81b09e6d30873", - "metadata": { - "collapsed": false, - "id": "d0e81b09e6d30873" - }, - "source": [ - "## Setup\n", - "\n", - "Install and import relevant packages" - ] - }, - { - "cell_type": "markdown", - "id": "eda6ab0d8f0b6b56", - "metadata": { - "collapsed": false, - "id": "eda6ab0d8f0b6b56" - }, - "source": [ - "In order to use quick-start you'll need the [MCT tutorial folder](https://github.com/sony/model_optimization/tree/main/tutorials), so we'll clone the MCT repository to a local folder and use that code.\n", - "\n", - " **It's important to note that we use the most up-to-date MCT code available.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ca9a743c0e7ba067", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ca9a743c0e7ba067", - "outputId": "10c7246b-8b31-4c44-9a45-04d4d3a2958b" - }, - "outputs": [], - "source": [ - "!git clone https://github.com/sony/model_optimization.git local_mct\n", - "!pip install -r ./local_mct/requirements.txt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "PfJ3_AyieBL0", - "metadata": { - "id": "PfJ3_AyieBL0" - }, - "outputs": [], - "source": [ - "import os\n", - "os.environ['PYTHONPATH'] = './local_mct/'" - ] - }, - { - "cell_type": "markdown", - "id": "ufTwrlH-eCyB", - "metadata": { - "id": "ufTwrlH-eCyB" - }, - "source": [ - "## Dataset\n", - "Download the imagenet validation dataset.\n", - "\n", - "Use torchvision.datasets.ImageNet to create the dataset in the correct structure.\n", - "\n", - "**Note**: We use validation for time convinience since the training split is too big. Typically, the validation samples should only be used for testing." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cj6WQGrLvkvx", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cj6WQGrLvkvx", - "outputId": "46efb6a0-abe3-48d9-ce89-6890a566e6de" - }, - "outputs": [], - "source": [ - "!mkdir imagenet\n", - "!wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_devkit_t12.tar.gz\n", - "!mv ILSVRC2012_devkit_t12.tar.gz imagenet/\n", - "!wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar\n", - "!mv ILSVRC2012_img_val.tar imagenet/" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "coxDAP4Av3ol", - "metadata": { - "id": "coxDAP4Av3ol" - }, - "outputs": [], - "source": [ - "import torchvision\n", - "ds = torchvision.datasets.ImageNet(root='./imagenet', split='val')" - ] - }, - { - "cell_type": "markdown", - "id": "gY2UsnzOfjtk", - "metadata": { - "id": "gY2UsnzOfjtk" - }, - "source": [ - "## Run Quick Start script on our model of choice\n", - "\n", - "Here we set the model name, model library, validation dataset path, and representative datasets path.\n", - " \n", - "The remaining arguments are left with their default settings. Please verify that the dataset paths are configured correctly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ZJ8CR1FQRwg2", - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ZJ8CR1FQRwg2", - "outputId": "607f95e7-8c80-408f-ebe8-ace70f999e3e" - }, - "outputs": [], - "source": [ - "!python ./local_mct/tutorials/quick_start/main.py --model_name mobilenet_v2 --model_library torchvision --validation_dataset_folder ./imagenet/val --representative_dataset_folder ./imagenet/val" - ] - }, - { - "cell_type": "markdown", - "id": "d5d80935", - "metadata": {}, - "source": [ - "\n", - "Copyright 2024 Sony Semiconductor Israel, Inc. All rights reserved.\n", - "\n", - "Licensed under the Apache License, Version 2.0 (the \"License\");\n", - "you may not use this file except in compliance with the License.\n", - "You may obtain a copy of the License at\n", - "\n", - " http://www.apache.org/licenses/LICENSE-2.0\n", - "\n", - "Unless required by applicable law or agreed to in writing, software\n", - "distributed under the License is distributed on an \"AS IS\" BASIS,\n", - "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", - "See the License for the specific language governing permissions and\n", - "limitations under the License.\n" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tutorials/quick_start/README.md b/tutorials/quick_start/README.md deleted file mode 100644 index 067be655e..000000000 --- a/tutorials/quick_start/README.md +++ /dev/null @@ -1,256 +0,0 @@ -# MCT Quick Start - -This example project demonstrates the capabilities of MCT (Model Compression Toolkit) and illustrates its interface -with various model collections libraries. By accessing a wide range of pre-trained models, this project allows users to -generate a quantized version of their chosen model with a single click. - -Currently, the project supports a selection of models from each library. However, our ongoing goal is to continually -expand the support, aiming to include more models -in the future. - -## Supported Features - - Quantize pretrained models from various model [libraries](#supported-libraries) - - Evaluate the performance of the floating point model and the quantized model. - - Use MCT's Post-Training Quantization (PTQ) scheme [example](#basic-model-quantization-example---post-training-quantization-ptq) - - Use MCT's advanced quantization techniques (such as GPTQ and Mixed Precision) [example](#advanced-model-quantization-example) - - -## Results - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
TaskModel SourceModel NameDataset NameFloatQuantized - W8A8
PerformanceSize [MB]PerformanceSize [MB]
Top-1 Accuracy
Classificationtorchvision mobilenet_v2ImageNet72.15%13.8871.88%3.47
regnet_y_400mfImageNet75.78%17.2475.42%4.31
shufflenet_v2_x0_5ImageNet60.55%5.4459.7%1.36
squeezenet1_0ImageNet58.1%4.9657.67%1.24
timmregnetx_002ImageNet68.76%10.6868.27%2.67
regnety_008ImageNet76.32%24.9275.98%6.23
resnet10tImageNet66.56%21.7266.43%5.43
resnet18ImageNet69.76%46.7269.61%11.68
efficientnet_esImageNet78.08%21.5677.74%5.39
keras_applicationsMobileNetV2ImageNet71.85%13.8871.57%3.47
EfficientNetV2B0ImageNet78.41%28.2477.44%7.06
ResNet50ImageNet74.22%10274.08%25.5
mAP
Object Detectionultralyticsyolov8nCOCO37.0412.636.173.15
yolov8mCOCO49.99103.649.425.9
Instance Segmentationyolov8n-segCOCO30.5113.630.183.4
Pose Estimationyolov8n-poseCOCO50.9213.1249.183.28
- -## Supported libraries -- torchvision: [https://pytorch.org/vision/stable/index.html](https://pytorch.org/vision/stable/index.html) -- timm: [https://github.com/huggingface/pytorch-image-models/tree/main/timm](https://github.com/huggingface/pytorch-image-models/tree/main/timm) -- ultralytics: [https://ultralytics.com](https://ultralytics.com) -- keras-applications: [https://keras.io/api/applications](https://keras.io/api/applications/) - - -## Getting Started -### Installation -Install one of the supported libraries above. - - -### Usage Examples -#### Basic model quantization example - Post training quantization (PTQ) -In the following example, we are evaluating the MCT on pre-trained mobilenet_v2, taken from torchvision models library -1. Install the required library using `pip`: -```bash -pip install torchvision - ``` -2. Run main.py script: -```python -python main.py --model_name mobilenet_v2 --model_library torchvision --validation_dataset_folder --representative_dataset_folder -``` -In this example, we are running the main.py script with the following parameters: -- `--model_name`: Specifies the name of the model to be used (mobilenet_v2 in this case). -- `--model_library`: Specifies the package of the pre-trained models from which the model name is taken (in this case, torchvision). -- `--validation_dataset_folder`: Specifies the path to the local copy of the dataset to be used for evaluation. -- `--representative_dataset_folder`: Specifies the path to the local copy of the dataset to be used for quantization. - -Please note that the above example assumes the command is run from the command line and that the [main.py](./main.py) script is in the current directory. - -Make sure to refer to the script itself to understand all available parameters and their usage. - -#### Advanced model quantization example -##### Mixed-precision -In this example, we use the MCT Mixed-Precision quantization workflow to further reduce the model's size, with minimal reduction in the quantized model accuracy. -We use the same pre-trained mobilenet_v2 model as our baseline, with the goal of attaining a model size -that is 1/5 of the original floating-point model weights size. This is equivalent to a size reduction factor of 5. -In contrast, the basic PTQ example illustrated earlier only manages to decrease the model size by a factor of 4. - -You can execute the following Python script to compress the model: -```python -python main.py --model_name mobilenet_v2 --model_library torchvision --mp_weights_compression 5 --validation_dataset_folder --representative_dataset_folder -``` - -##### Gradient-based post training quantization -The following example demontrates the use of MCT's Gradient-based Post-Training Quantization (GPTQ) workflow. -This approach is superior to the basic PTQ method as it refines the quantized model weights in order to regain performance. -The weights modification is done through a knowledge distillation technique sourced from the original floating-point model. - -To execute the model compression with this approach, run the following Python script: -```python -python main.py --model_name mobilenet_v2 --model_library torchvision --gptq --validation_dataset_folder --representative_dataset_folder -``` - -Please note that the Mixed-Precision and Gradient-based Post Training Quantization (GPTQ) strategies can be combined to achieve a more significant model compression while mitigating the impact on model performance. -#### More examples -More details and examples for using Ultrlytics models can be found in this [readme](./pytorch_fw/ultralytics_lib/README.md) - -## External Package Versions - -The following external packages were tested with this project: - -- torchvision: Version 0.14.0 -- timm: Version 0.6.13 -- ultralytics: Version 8.0.47 -- keras-applications: Version 2.9.0 - -## License -This project is licensed under [Apache License 2.0](../../LICENSE.md). -However, please note that different external packages have their own licenses. When using this project, you have the option to choose one of the following external packages: - -- torchvision: [license link](https://github.com/UiPath/torchvision/blob/master/LICENSE), [license copy](./pytorch_fw/torchvision_lib/LICENSE) -- timm: [license link](https://github.com/huggingface/pytorch-image-models/blob/main/LICENSE), [license copy](./pytorch_fw/timm_lib/LICENSE) -- ultralytics: [license link](https://github.com/ultralytics/ultralytics/blob/main/LICENSE), [license copy](./pytorch_fw/ultralytics_lib/LICENSE) -- keras-applications: [license link](https://github.com/keras-team/keras-applications/blob/master/LICENSE), [license copy](./keras_fw/keras_applications/LICENSE) diff --git a/tutorials/quick_start/__init__.py b/tutorials/quick_start/__init__.py deleted file mode 100644 index 2147ec284..000000000 --- a/tutorials/quick_start/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== diff --git a/tutorials/quick_start/common/__init__.py b/tutorials/quick_start/common/__init__.py deleted file mode 100644 index 2147ec284..000000000 --- a/tutorials/quick_start/common/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== diff --git a/tutorials/quick_start/common/constants.py b/tutorials/quick_start/common/constants.py deleted file mode 100644 index d0e44ac9a..000000000 --- a/tutorials/quick_start/common/constants.py +++ /dev/null @@ -1,42 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== - -MODEL_NAME = 'model_name' -MODEL_LIBRARY = 'model_library' -OUTPUT_RESULTS_FILE = 'output_results_file' -NUM_REPRESENTATIVE_IMAGES = 'num_representative_images' -BATCH_SIZE = 'batch_size' -REPRESENTATIVE_DATASET_FOLDER = 'representative_dataset_folder' -VALIDATION_DATASET_FOLDER = 'validation_dataset_folder' -TARGET_PLATFORM_NAME = 'target_platform_name' -TARGET_PLATFORM_VERSION = 'target_platform_version' -VALIDATION_SET_LIMIT = 'validation_set_limit' -MP_WEIGHTS_COMPRESSION = 'mp_weights_compression' -EDGE_TPU = 'edgetpu' -MODULE_REPLACER = 'module_replacer' -VALIDATOR = 'validator' -DETECT = 'detect' -SEGMENT = 'segment' -POSE = 'pose' -RECT = 'rect' -MODE = 'mode' -DEVICE = 'device' -VAL = 'val' -COCO_POSE = "coco-pose.yaml" - -IMAGENET_DATASET = 'ImageNet' -COCO_DATASET = 'COCO' - -BYTES_TO_FP32 = 4 \ No newline at end of file diff --git a/tutorials/quick_start/common/library_mapping.py b/tutorials/quick_start/common/library_mapping.py deleted file mode 100644 index 0bb1b721d..000000000 --- a/tutorials/quick_start/common/library_mapping.py +++ /dev/null @@ -1,55 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== -from typing import Tuple -import importlib - - -# Dictionary mapping library names to folder names -LIB2FOLDER_DICT = { - 'torchvision': 'torchvision_lib', # TorchVision library folder - 'timm': 'timm_lib', # TIMM library folder - 'ultralytics': 'ultralytics_lib', # Ultralytics folder - 'keras_applications': 'keras_applications' # Keras Applications library folder -} - - -def find_modules(lib: str) -> Tuple[str, str]: - """ - Finds the relevant module paths for a given pre-trained models library. - - Args: - lib (str): Name of the library. - - Returns: - Tuple of model_lib_module and quant_module, representing the module paths needed to be imported - for the given library. - - Raises: - Error: If the specified library is not found. - """ - - # Search in PyTorch libraries - folder_name = LIB2FOLDER_DICT[lib] - if importlib.util.find_spec('pytorch_fw.' + folder_name) is not None: - model_lib_module = 'pytorch_fw.' + folder_name + '.model_lib' - quant_module = 'pytorch_fw.quant' - - # Search in Keras libraries - elif importlib.util.find_spec('keras_fw.' + folder_name) is not None: - model_lib_module = 'keras_fw.' + folder_name + '.model_lib' - quant_module = 'keras_fw.quant' - else: - raise Exception(f'Error: model library {lib} is not supported') - return model_lib_module, quant_module diff --git a/tutorials/quick_start/common/model_lib.py b/tutorials/quick_start/common/model_lib.py deleted file mode 100644 index 7ed0884fc..000000000 --- a/tutorials/quick_start/common/model_lib.py +++ /dev/null @@ -1,122 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== -from abc import abstractmethod, ABC - - -class BaseModelLib(ABC): - """ - Abstract base class representing pre-trained model library. - - Args: - args: Arguments required for initializing the model library and selecting the relevant model - - Attributes: - args: The input arguments - """ - - def __init__(self, args): - self.args = args - - @abstractmethod - def get_model(self): - """ - Abstract method to return the floating-point model for quantization. - - Returns: - The floating-point model to be used for quantization. - """ - raise NotImplementedError - - @abstractmethod - def get_representative_dataset(self, representative_dataset_folder, n_iter, batch_size): - """ - Abstract method to return the representative dataset required by MCT for quantization. - - Args: - representative_dataset_folder: Path to the representative dataset folder. - n_iter: Number of calibration iterations to perform for quantization. It uses for creating the representative dataset. - batch_size: Batch size for processing the representative dataset. - - Returns: - The representative dataset generator to be used for quantization. - """ - raise NotImplementedError - - @abstractmethod - def evaluate(self, model): - """ - Abstract method to perform evaluation for a given model. - - Args: - model: The model to be evaluated. - - Returns: - The evaluation results of the model. - """ - raise NotImplementedError - - -class ModuleReplacer(ABC): - """ - Abstract base class for replacing modules in a given model. - - """ - - def __init__(self): - """ - Initializes the ModuleReplacer object. - """ - pass - - @abstractmethod - def get_new_module(self, config): - """ - Abstract method to implement and return the new module based on a given configuration. - - Args: - config: Configuration for the new module. - - Returns: - The new module based on the given configuration. - """ - raise NotImplementedError - - @abstractmethod - def get_config(self, old_module): - """ - Abstract method to return the configuration of the old module. - - Args: - old_module: The old module to retrieve the configuration from. - - Returns: - The configuration of the old module. - """ - raise NotImplementedError - - @abstractmethod - def replace(self, model): - """ - Abstract method to replace the old module with the new module in a given model. - - Args: - model: The model in which the module replacement should be performed. - - Returns: - The model with the replaced module. - """ - raise NotImplementedError - - diff --git a/tutorials/quick_start/common/results.py b/tutorials/quick_start/common/results.py deleted file mode 100644 index 4f50550f8..000000000 --- a/tutorials/quick_start/common/results.py +++ /dev/null @@ -1,159 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== -import csv -import logging -from os import path -from typing import List, Union - -from model_compression_toolkit.core.common.user_info import UserInformation -from common.constants import MODEL_NAME, MODEL_LIBRARY, VALIDATION_DATASET_FOLDER - -from model_compression_toolkit.target_platform_capabilities.target_platform import TargetPlatformCapabilities -from tutorials.quick_start.common.tpc_info import TPCInfo - - -class DatasetInfo: - """ - Holds information about the evaluation dataset. - """ - def __init__(self, dataset_name: str, n_images: int): - """ - Initializes a new instance of the DatasetInfo class. - - Args: - dataset_name (str): The name of the dataset. - n_images (int): The number of images in the dataset. - """ - self.dataset_name = dataset_name - self.n_images = n_images - - -class QuantInfo: - """ - Holds information about the quantization process. - """ - def __init__(self, user_info: UserInformation, - tpc_info: TPCInfo, - quantization_workflow: str, - mp_weights_compression: float = None - ): - """ - Initializes a new instance of the QuantInfo class. - - Args: - user_info (UserInformation): Quantization information returned from MCT - tpc_info (TPCInfo): The target platform capabilities information which is provided to the MCT. - quantization_workflow (str): String to describe the quantization workflow (PTQ, GPTQ etc.). - mp_weights_compression (float): Weights compression factor for mixed precision Resource Utilization constraint. - """ - self.user_info = user_info - self.tpc_info = tpc_info - self.quantization_workflow = quantization_workflow - self.mp_weights_compression = mp_weights_compression - - -def read_models_list(filename: str) -> csv.DictReader: - """ - Reads a models list and parameters from a CSV file and returns csv.DictReader. - - Args: - filename: Path to the CSV file containing the models list. - - Returns: - A dictionary read from the CSV file. - - """ - return csv.DictReader(open(path.join(filename))) - - -def write_results(filename: str, models_list: List[dict], fieldnames: List[str]): - """ - Writes the results of a models list to a CSV file. - - Args: - filename: Path to the CSV file to write the results. - models_list: List of model results, where each result is represented by a dictionary. - fieldnames: List of fieldnames to be written as headers in the CSV file. - - Returns: - None - - """ - writer = csv.DictWriter(open(filename, 'w'), fieldnames=fieldnames) - writer.writeheader() - for item in models_list: - writer.writerow(item) - - -def parse_results(params: dict, float_acc: float, quant_acc: float, quant_info: QuantInfo, dataset_info: DatasetInfo) -> dict: - """ - Parses the results of model evaluation and quantization into a dictionary format. - - Args: - params: Dictionary of parameters containing information about the model, dataset, etc. - float_acc: Floating-point accuracy of the model. - quant_acc: Quantized accuracy of the model. - quant_info: Quantization information object containing details of the quantized model. - - Returns: - A dictionary containing the parsed results. - - """ - a_bits = quant_info.tpc_info.activation_nbits - w_bits = quant_info.tpc_info.weights_nbits - bit_config = f'W{w_bits}A{a_bits}' - if quant_info.mp_weights_compression: - bit_config = f'{bit_config},MP-x{quant_info.mp_weights_compression}' - - res = {} - res['ModelName'] = params[MODEL_NAME] - res['ModelLibrary'] = params[MODEL_LIBRARY] - res['DatasetName'] = dataset_info.dataset_name - res['TotalImages'] = dataset_info.n_images - res['FloatAcc'] = round(float_acc, 4) - res['QuantAcc'] = round(quant_acc, 4) - res['Size[MB]'] = round(quant_info.user_info.final_resource_utilization.weights_memory / 1e6, 2) - res['BitsConfig'] = bit_config - res['QuantWorkflow'] = quant_info.quantization_workflow - res['TPC'] = quant_info.tpc_info.tp_model_name + '-' + quant_info.tpc_info.version - - return res - - -def plot_results(data: Union[dict, List[dict]], spacing=15): - """ - Prints the given dictionary or list of dictionaries as a table to the screen. - - Args: - data (Union[dict, List[dict]]): The dictionary or list of dictionaries to be printed as a table. - """ - # Handle a single dictionary - if isinstance(data, dict): - data = [data] - - # Extract the keys as the table header - header = list(data[0].keys()) - - # Print the header - header_title = [h + (spacing - len(h)) * " " for h in header] - logging.info("\t".join(header_title)) - - # Print the rows - for item in data: - values = [str(item.get(key, "")) for key in header] - values = [v + (spacing - len(v)) * " " for v in values] - logging.info("\t".join(values)) - - diff --git a/tutorials/quick_start/common/tpc_info.py b/tutorials/quick_start/common/tpc_info.py deleted file mode 100644 index 4bdc9fa6f..000000000 --- a/tutorials/quick_start/common/tpc_info.py +++ /dev/null @@ -1,53 +0,0 @@ -# Copyright 2024 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== - - -def get_tpc_info(tpc): - # Retrieve the number of bits used for activation functions within the TPC's default operation. - activation_nbits = tpc.get_default_op_qc().activation_n_bits - - # Retrieve the number of bits used for weights in the TPC's default weight configuration. - weights_nbits = tpc.get_default_op_qc().default_weight_attr_config.weights_n_bits - - # Extract the name of the tp model associated with the TPC. - tp_model_name = tpc.tp_model.name - - # Get the version of the TPC. - version = tpc.version - - return TPCInfo(activation_nbits=activation_nbits, - weights_nbits=weights_nbits, - tp_model_name=tp_model_name, - version=version) - -class TPCInfo: - def __init__(self, - activation_nbits: int, - weights_nbits: int, - tp_model_name: str, - version: str): - """ - Args: - activation_nbits: Number of bits used for activation functions. - weights_nbits: Number of bits used for weights. - tp_model_name: TP model's name. - version: TPC's version. - """ - self.activation_nbits = activation_nbits - self.weights_nbits = weights_nbits - self.tp_model_name = tp_model_name - self.version = version - - diff --git a/tutorials/quick_start/common/utils.py b/tutorials/quick_start/common/utils.py deleted file mode 100644 index 386f249c8..000000000 --- a/tutorials/quick_start/common/utils.py +++ /dev/null @@ -1,106 +0,0 @@ -# Copyright 2024 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== -import os -import numpy as np -from PIL import Image - -from typing import List, Callable -import logging - -FILETYPES = ['jpeg', 'jpg', 'bmp', 'png'] - - -class FolderImageLoader(object): - """ - - Class for images loading, processing and retrieving. - - """ - - def __init__(self, - folder: str, - preprocessing: List[Callable], - batch_size: int, - file_types: List[str] = FILETYPES): - - """ Initialize a FolderImageLoader object. - - Args: - folder: Path of folder with images to load. The path has to exist, and has to contain at - least one image. - preprocessing: List of functions to use when processing the images before retrieving them. - batch_size: Number of images to retrieve each sample. - file_types: Files types to scan in the folder. Default list is :data:`~model_compression_toolkit.core.common.data_loader.FILETYPES` - - Examples: - - Instantiate a FolderImageLoader using a directory of images, that returns 10 images randomly each time it is sampled: - - >>> image_data_loader = FolderImageLoader('path/to/images/directory', preprocessing=[], batch_size=10) - >>> images = image_data_loader.sample() - - To preprocess the images before retrieving them, a list of preprocessing methods can be passed: - - >>> image_data_loader = FolderImageLoader('path/to/images/directory', preprocessing=[lambda x: (x-127.5)/127.5], batch_size=10) - - For the FolderImageLoader to scan only specific files extensions, a list of extensions can be passed: - - >>> image_data_loader = FolderImageLoader('path/to/images/directory', preprocessing=[], batch_size=10, file_types=['png']) - - """ - - self.folder = folder - self.image_list = [] - logging.info(f"Starting Scanning Disk: {self.folder}") - for root, dirs, files in os.walk(self.folder): - for file in files: - file_type = file.split('.')[-1].lower() - if file_type in file_types: - self.image_list.append(os.path.join(root, file)) - self.n_files = len(self.image_list) - if self.n_files == 0: - logging.critical( - f"Expected files of type {FILETYPES}. No files of type {FILETYPES} were found.") # pragma: no cover - logging.info(f"Finished Disk Scanning: Found {self.n_files} files") - self.preprocessing = preprocessing - self.batch_size = batch_size - - def _sample(self): - """ - Read batch_size random images from the image_list the FolderImageLoader holds. - Process them using the preprocessing list that was passed at initialization, and - prepare it for retrieving. - """ - - index = np.random.randint(0, self.n_files, self.batch_size) - image_list = [] - for i in index: - file = self.image_list[i] - img = np.uint8(np.array(Image.open(file).convert('RGB'))) - for p in self.preprocessing: # preprocess images - img = p(img) - image_list.append(img) - self.next_batch_data = np.stack(image_list, axis=0) - - def sample(self): - """ - - Returns: A sample of batch_size images from the folder the FolderImageLoader scanned. - - """ - - self._sample() - data = self.next_batch_data # get current data - return data diff --git a/tutorials/quick_start/keras_fw/__init__.py b/tutorials/quick_start/keras_fw/__init__.py deleted file mode 100644 index 2147ec284..000000000 --- a/tutorials/quick_start/keras_fw/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== diff --git a/tutorials/quick_start/keras_fw/keras_applications/LICENSE b/tutorials/quick_start/keras_fw/keras_applications/LICENSE deleted file mode 100644 index 77a89428b..000000000 --- a/tutorials/quick_start/keras_fw/keras_applications/LICENSE +++ /dev/null @@ -1,33 +0,0 @@ -COPYRIGHT - -Copyright (c) 2016 - 2018, the respective contributors. -All rights reserved. - -Each contributor holds copyright over their respective contributions. -The project versioning (Git) records all such contribution source information. -The initial code of this repository came from https://github.com/keras-team/keras -(the Keras repository), hence, for author information regarding commits -that occured earlier than the first commit in the present repository, -please see the original Keras repository. - -LICENSE - -The MIT License (MIT) - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/tutorials/quick_start/keras_fw/keras_applications/__init__.py b/tutorials/quick_start/keras_fw/keras_applications/__init__.py deleted file mode 100644 index 2147ec284..000000000 --- a/tutorials/quick_start/keras_fw/keras_applications/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== diff --git a/tutorials/quick_start/keras_fw/keras_applications/model_lib.py b/tutorials/quick_start/keras_fw/keras_applications/model_lib.py deleted file mode 100644 index 27688b704..000000000 --- a/tutorials/quick_start/keras_fw/keras_applications/model_lib.py +++ /dev/null @@ -1,141 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== - -from typing import Dict -import tensorflow as tf -import keras - -from tutorials.quick_start.common.model_lib import BaseModelLib -from tutorials.quick_start.keras_fw.utils import classification_eval, get_representative_dataset, separate_preprocess_model -from tutorials.quick_start.common.constants import MODEL_NAME, BATCH_SIZE, VALIDATION_SET_LIMIT, VALIDATION_DATASET_FOLDER, IMAGENET_DATASET - -from tutorials.quick_start.common.results import DatasetInfo - - -class ModelLib(BaseModelLib): - """ - A class for quantizing pre-trained models from the tensorflow.keras.applications library - """ - def __init__(self, args: Dict): - """ - Init the ModelLib with user arguments - Args: - args (dict): user arguments - """ - model_fn, self.model_package = self.get_keras_apps_model(args[MODEL_NAME]) - # create model - self.model = model_fn() - self.preprocess = self.generate_preprocess() - self.dataset_name = IMAGENET_DATASET - # Extract image input size and verify it's not dynamic (equals None) - self.input_size = self.model.layers[0].input_shape[0][1:3] - if None in self.input_size: - raise Exception(f'model {args[MODEL_NAME]} has a dynamic input size, which is not supported by the MCT') - super().__init__(args) - - def get_keras_apps_model(self, model_name: str): - """ - Extracts the model package and model name from the input string: . - The model package class contains the 'preprocess_input' method for preprocessing input images - One exception is the MobileNetV3 models that are located in the parent model (tf.keras.applications) - - Args: - model_name (str): A string containing the model to quantize in the following format: . (e.g. mobilenet_v2.MobileNetV2) - - Returns: - model package class - model class - - """ - if hasattr(tf.keras.applications, model_name): - model_class = getattr(tf.keras.applications, model_name) - model_package = eval(model_class.__module__) - else: - raise Exception(f'Unknown Keras Applications model class {model_name}') - - return model_class, model_package - - @staticmethod - def get_keras_apps_weights(model_name): - return None - - def generate_preprocess(self, truncate_preprocess: bool = False): - """ - Generates the preprocess function for evaluation and quantization (representative dataset) - Some models in this library contain the normalization of the preprocess in the beginning of the model. They can be removed. - Args: - truncate_preprocess (bool): Removes Normalization & Rescaling layers from the beginning of the model - - Returns: - - """ - pp_model = None - if truncate_preprocess: - self.model, pp_model = separate_preprocess_model(self.model) - - def _preprocess(x, l): - x = self.model_package.preprocess_input(x) - if pp_model is not None: - x = pp_model(x) - return x, l - - return _preprocess - - def get_model(self): - return self.model - - def get_representative_dataset(self, representative_dataset_folder: str, n_iter: int, batch_size: int): - """ - Create a representative dataset generator - Args: - representative_dataset_folder: Dataset location folder, in the format: representative_dataset_folder// - n_iter: number batches to run in the generator - batch_size: number of images in each batch - - Returns: - A generator for the representative dataset, as the MCT expects - - """ - dl = tf.keras.utils.image_dataset_from_directory(representative_dataset_folder, - batch_size=batch_size, - image_size=self.input_size, - crop_to_aspect_ratio=True).map(self.preprocess) - - return get_representative_dataset(dl, n_iter) - - def evaluate(self, model): - """ - Evaluate a model - Args: - model: A keras model to evaluate - - Returns: - model accuracy - dataset info (dataset name, number of images in dataset) - - """ - batch_size = int(self.args[BATCH_SIZE]) - validation_dataset_folder = self.args[VALIDATION_DATASET_FOLDER] - testloader = tf.keras.utils.image_dataset_from_directory(validation_dataset_folder, - batch_size=batch_size, - image_size=self.input_size, - shuffle=False, - crop_to_aspect_ratio=True) - - testloader = testloader.map(self.preprocess) - - acc, total = classification_eval(model, testloader, self.args[VALIDATION_SET_LIMIT]) - dataset_info = DatasetInfo(self.dataset_name, total) - return acc, dataset_info diff --git a/tutorials/quick_start/keras_fw/quant.py b/tutorials/quick_start/keras_fw/quant.py deleted file mode 100644 index fe6be8b9d..000000000 --- a/tutorials/quick_start/keras_fw/quant.py +++ /dev/null @@ -1,141 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== - -import math -import tensorflow as tf -from keras.optimizers import Adam - -import model_compression_toolkit as mct -import logging -from tutorials.quick_start.common.constants import NUM_REPRESENTATIVE_IMAGES, BATCH_SIZE, \ - REPRESENTATIVE_DATASET_FOLDER, TARGET_PLATFORM_NAME, TARGET_PLATFORM_VERSION, BYTES_TO_FP32, MP_WEIGHTS_COMPRESSION - - -from model_compression_toolkit.core import MixedPrecisionQuantizationConfig, CoreConfig, ResourceUtilization -from model_compression_toolkit.target_platform_capabilities.target_platform import TargetPlatformCapabilities -from tutorials.quick_start.common.results import QuantInfo -from tutorials.quick_start.common.tpc_info import get_tpc_info - - -def get_tpc(target_platform_name: str, target_platform_version: str) -> TargetPlatformCapabilities: - """ - Returns the target platform capabilities according to the given platform name and version. - - Args: - target_platform_name: Name of the target platform - target_platform_version: Version of the target platform - - Returns: - The target platform capabilities. - - """ - return mct.get_target_platform_capabilities('tensorflow', target_platform_name, target_platform_version) - - -def get_target_resource_utilization(model, weights_compression, representative_data_gen, core_config, tpc): - """ - Calculates the model's required size according to the given weights compression rate, to provide as a constraint for mixed precision search. - - Args: - model: The model to calculate the target resource utilization for. - weights_compression: The required weights compression ratio. - representative_data_gen: Callable function to generate the representative dataset. - core_config (CoreConfig): CoreConfig containing parameters for quantization and mixed precision. - tpc (TargetPlatformCapabilities): TargetPlatformCapabilities to optimize the TensorFlow model according to. - - Returns: - A ResourceUtilization object computed from MCT and contains info about the target model size. - - """ - ru_data = mct.core.keras_resource_utilization_data(model, representative_data_gen, core_config=core_config, - target_platform_capabilities=tpc) - weights_ru = BYTES_TO_FP32 * ru_data.weights_memory / weights_compression # (4 bytes for fp32) * weights memory(in Bytes) / compression rate - return ResourceUtilization(weights_memory=weights_ru) - - -def quantize(model: tf.keras.Model, - get_representative_dataset: callable, - tpc: TargetPlatformCapabilities, - args: dict): - """ - Returns a quantized model and a quantization info from MCT for the given TensorFlow floating-point model. - - Args: - model: TensorFlow floating-point model to be quantized. - get_representative_dataset: Callable function to generate the representative dataset for quantization. - tpc: Target platform capabilities. - args: Dictionary containing the necessary configurations for quantization. - - Returns: - Tuple containing the quantized model and quantization info. - - """ - - # PTQ - general configurations - n_iter = math.ceil(int(args[NUM_REPRESENTATIVE_IMAGES]) // int(args[BATCH_SIZE])) # Number of batches - logging.info(f"Running MCT... number of representative images: {args[REPRESENTATIVE_DATASET_FOLDER]}, number of calibration iters: {n_iter}") - - representative_data_gen = get_representative_dataset( - representative_dataset_folder=args[REPRESENTATIVE_DATASET_FOLDER], - n_iter=n_iter, - batch_size=int(args[BATCH_SIZE]) - ) - - # Mixed-precision configurations - mp_wcr = args.get(MP_WEIGHTS_COMPRESSION, None) - if mp_wcr: - mp_conf = MixedPrecisionQuantizationConfig() - core_conf = CoreConfig(quantization_config=mct.core.QuantizationConfig( - shift_negative_activation_correction=True), - mixed_precision_config=mp_conf) - target_resource_utilization = get_target_resource_utilization(model, mp_wcr, representative_data_gen, core_conf, tpc) - else: - core_conf = CoreConfig(quantization_config=mct.core.QuantizationConfig( - shift_negative_activation_correction=True)) - target_resource_utilization = None - - # Quantize model - if args.get('gptq', False): - - workflow = 'GPTQ' - n_epochs = args.get('gptq_num_calibration_iter') // n_iter - logging.info( - f"MCT Gradient-based Post Training Quantization is enabled. Number of epochs: {n_epochs}") - - gptq_conf = mct.gptq.get_keras_gptq_config(n_epochs=n_epochs, optimizer=Adam(learning_rate=args['gptq_lr'])) - - quantized_model, quantization_info = \ - mct.gptq.keras_gradient_post_training_quantization(model, - representative_data_gen=representative_data_gen, - target_resource_utilization=target_resource_utilization, - core_config=core_conf, - gptq_config=gptq_conf, - gptq_representative_data_gen=representative_data_gen, - target_platform_capabilities=tpc) - - - else: - workflow = 'PTQ' - quantized_model, quantization_info = \ - mct.ptq.keras_post_training_quantization(model, - representative_data_gen=representative_data_gen, - target_resource_utilization=target_resource_utilization, - core_config=core_conf, - target_platform_capabilities=tpc) - - return quantized_model, QuantInfo(user_info=quantization_info, - tpc_info=get_tpc_info(tpc=tpc), - quantization_workflow=workflow, - mp_weights_compression=mp_wcr) \ No newline at end of file diff --git a/tutorials/quick_start/keras_fw/utils.py b/tutorials/quick_start/keras_fw/utils.py deleted file mode 100644 index b89813978..000000000 --- a/tutorials/quick_start/keras_fw/utils.py +++ /dev/null @@ -1,122 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== - -import tensorflow as tf -import logging -from tqdm import tqdm - - -def classification_eval(model: tf.keras.Model, data_loader: tf.data.Dataset, limit=None): - """ - Evaluate a classification model - - Args: - model: a model to evaluate - data_loader: tensorflow evaluation dataset - limit (int): optionally evaluate the model over less images than in the dataset - - Returns: - model accuracy - total number of images evaluated - - """ - logging.info(f'Start classification evaluation') - acc = tf.keras.metrics.Accuracy() - total = 0 - for data in tqdm(data_loader, desc="Classification evaluation"): - images, labels = data - # calculate outputs by running images through the network - outputs = model(images) - # the class with the highest energy is what we choose as prediction - predicted = tf.argmax(outputs, 1) - acc.update_state(labels, predicted) - total += labels.shape[0] - if limit and total >= int(limit): - break - - logging.info(f'Num of images: {total}, Accuracy: {round(100 * acc.result().numpy(), 2)} %') - - return acc.result().numpy(), total - - -def get_representative_dataset(data_loader: tf.data.Dataset, n_iters: int, data_loader_key: int = 0, preprocess=None): - """ - A function to generate a representative dataset generator. The class can iterate the rep. dataset over and - over if it contains fewer images than required iterations. - Args: - data_loader (tf.data.Dataset): a tensorflow dataset - n_iters (int): number of iterations - data_loader_key (int): index of images in data_loader output (usually the output is a tuple: [image, label]) - preprocess (callable): a function to preprocess a batch of dataset outputs: tuple of (images, labels) - - Returns: - A representative dataset generator - - """ - - class RepresentativeDataset: - def __init__(self, in_data_loader): - self.dl = in_data_loader - self.iter = iter(self.dl) - - def __call__(self): - for _ in range(n_iters): - try: - x = next(self.iter)[data_loader_key] - except StopIteration: - self.iter = iter(self.dl) - x = next(self.iter)[data_loader_key] - if preprocess is not None: - x = preprocess(x, None)[0] - yield [x.numpy()] - - def __len__(self): - return n_iters - - return RepresentativeDataset(data_loader) - - -def separate_preprocess_model(model: tf.keras.Model): - """ - Separate the first layers of a model if they are considered as preprocess layers. - Args: - model (tf.keras.Model): input model - - Returns: - The model without preprocessing layers - A model of the preprocessing layers to use in the preprocess of the representative dataset and evaluation - - """ - pp_model = None - - preprocess_layers = [] - layer, last_layer = None, None - for layer in model.layers[1:]: - if isinstance(layer, (tf.keras.layers.Normalization, - tf.keras.layers.Rescaling)): - # Collect layers predefined as preprocess (normalization & Rescaling) at the beginning of the model - preprocess_layers.append(layer.__class__.from_config(layer.get_config())) - else: - break - last_layer = layer - - if preprocess_layers and not isinstance(layer, tf.keras.Sequential): - # Separate the model into 2 models: preprocess-model and model without preprocess - pp_model = tf.keras.Model(inputs=model.input, outputs=last_layer.output) - trunc_model = tf.keras.Model(inputs=layer.input, outputs=model.output) - else: - trunc_model = model - - return trunc_model, pp_model diff --git a/tutorials/quick_start/main.py b/tutorials/quick_start/main.py deleted file mode 100644 index 163425051..000000000 --- a/tutorials/quick_start/main.py +++ /dev/null @@ -1,168 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== -import argparse -import importlib -import logging -import sys -from typing import Dict, Tuple - -from common.results import write_results, read_models_list, parse_results, QuantInfo, plot_results, DatasetInfo -from common.library_mapping import find_modules -from common.constants import MODEL_NAME, MODEL_LIBRARY, OUTPUT_RESULTS_FILE, TARGET_PLATFORM_NAME, \ - TARGET_PLATFORM_VERSION - - -# Script to Evaluate and Compress Pre-trained Neural Network Model(s) using MCT (Model Compression Toolkit) - -def argument_handler(): - parser = argparse.ArgumentParser() - - parser.add_argument('--model_name', '-m', type=str, required=False, - help='The name of the pre-trained model to run') - parser.add_argument('--model_library', type=str, required=False, - help='The library that contains the pre-trained model') - parser.add_argument('--validation_dataset_folder', type=str, required=False, - help='Path to the validation dataset') - parser.add_argument('--representative_dataset_folder', type=str, required=False, - help='Path to the representative dataset used for quantization') - parser.add_argument('--num_representative_images', type=int, default=200, - help='Number of images for representative dataset') - parser.add_argument('--target_platform_name', type=str, default='default', - help='Specifies the name of the target platform capabilities (TPC) to select from the available TPCs provided by MCT') - parser.add_argument('--target_platform_version', type=str, default='latest', - help='Specifies the version of the target platform capabilities (TPC) to select from the available TPCs provided by MCT') - parser.add_argument('--batch_size', type=int, default=10, - help='Batch size for accuracy evaluation') - parser.add_argument('--models_list_csv', type=str, default=None, - help='Run according to a list of models and parameters taken from a csv file') - parser.add_argument('--output_results_file', type=str, default='model_quantization_results.csv', - help='Run according to a list of models and parameters taken from a csv file') - parser.add_argument('--validation_set_limit', type=int, default=None, - help='Limits the number of images taken for evaluation') - parser.add_argument('--export_model', action="store_true", - help='Whether to export the model after quantization') - parser.add_argument('--gptq', action="store_true", - help='Enables Gradient-based Post Training Quantization (GPTQ)') - parser.add_argument('--gptq_num_calibration_iter', type=int, default=5000, - help='The number of iterations on the representative dataset') - parser.add_argument('--gptq_lr', type=float, default=3e-2, - help='The number of iterations on the representative dataset') - parser.add_argument("--mp_weights_compression", type=float, default=None, - help='Enables mixed-precision quantization for a given weights compression rate. ' - 'The compression rate is relative to 32 bits fp precision, i.e. For a given' - ' compression-rate of C, the average bits per parameter = 32/C for the compressed model') - - args = parser.parse_args() - - if args.models_list_csv is None: - # If models_list_csv is not provided, then the following arguments are required - required_args = ['model_name', 'model_library', 'validation_dataset_folder', 'representative_dataset_folder'] - missing_args = [arg for arg in required_args if getattr(args, arg) is None] - - if missing_args: - parser.error(f"Missing required arguments: {', '.join(missing_args)}") - sys.exit(1) - return args - - -def quantization_flow(config: Dict) -> Tuple[float, float, QuantInfo, DatasetInfo]: - """ - This function implements the typical workflow when using MCT. - It begins by evaluating the performance of the floating-point model. - Next, the model is compressed using MCT quantization techniques. - Finally, the evaluation process is repeated on the compressed model to assess its performance. - - Args: - config (Dict): Configurations dictionary that contains the settings for the quantization flow - - Returns: - float_results (float): The accuracy of the floating point model - quant_results (float): The accuracy of the quantized model - quantization_info (QuantInfo): Information of the model optimization process from MCT - dataset_info (DatasetInfo): Information about the evaluated dataset - """ - - # Find and import the required modules for the models collection library ("model_library") - model_lib_module, quant_module = find_modules(config[MODEL_LIBRARY]) - model_lib = importlib.import_module(model_lib_module) - quant = importlib.import_module(quant_module) - - # Create a ModelLibrary object that combines the selected pre-trained model with the corresponding performance - # evaluation functionality - ml = model_lib.ModelLib(config) - - # Get the floating point model - float_model = ml.get_model() - - # Evaluate the float model - float_results, _ = ml.evaluate(float_model) - - # Select the target platform capabilities (https://github.com/sony/model_optimization/blob/main/model_compression_toolkit/target_platform_capabilities/README.md) - target_platform_cap = quant.get_tpc(config[TARGET_PLATFORM_NAME], config[TARGET_PLATFORM_VERSION]) - - # Run model compression toolkit - quantized_model, quantization_info = quant.quantize(float_model, - ml.get_representative_dataset, - target_platform_cap, - config) - - # Evaluate quantized model - quant_results, dataset_info = ml.evaluate(quantized_model) - - return float_results, quant_results, quantization_info, dataset_info - - -if __name__ == '__main__': - - # Set arguments and parameters - args = argument_handler() - - # Set logger level - logging.getLogger().setLevel(logging.INFO) - - # Create configuration dictionary - config = dict(args._get_kwargs()) - - if args.models_list_csv is None: - - # Run quantization flow and add results to the table - float_acc, quant_acc, quant_info, dataset_info = quantization_flow(config) - - # Parse the results and print to screen - res = parse_results(config, float_acc, quant_acc, quant_info, dataset_info) - plot_results(res) - - else: - models_list = read_models_list(args.models_list_csv) - results_table = [] - for p in models_list: - - # Get next model and parameters from the list - logging.info(f"Testing model: {p[MODEL_NAME]} from library: {p[MODEL_LIBRARY]}") - config.update(p) - - # Run quantization flow and add results to the table - float_acc, quant_acc, quant_info, dataset_info = quantization_flow(config) - - # Add results to the table - res = parse_results(config, float_acc, quant_acc, quant_info, dataset_info) - results_table.append(res) - - # Store results table - write_results(config[OUTPUT_RESULTS_FILE], results_table, res.keys()) - - # Print results to screen - plot_results(results_table) - diff --git a/tutorials/quick_start/pytorch_fw/__init__.py b/tutorials/quick_start/pytorch_fw/__init__.py deleted file mode 100644 index 2147ec284..000000000 --- a/tutorials/quick_start/pytorch_fw/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== diff --git a/tutorials/quick_start/pytorch_fw/quant.py b/tutorials/quick_start/pytorch_fw/quant.py deleted file mode 100644 index 53a9520d0..000000000 --- a/tutorials/quick_start/pytorch_fw/quant.py +++ /dev/null @@ -1,149 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== -import math -from torch import nn, Tensor -from torch.optim import Adam - -import tempfile - -import model_compression_toolkit as mct -import logging -from common.constants import NUM_REPRESENTATIVE_IMAGES, BATCH_SIZE, REPRESENTATIVE_DATASET_FOLDER, \ - TARGET_PLATFORM_NAME, TARGET_PLATFORM_VERSION - -from model_compression_toolkit.core import MixedPrecisionQuantizationConfig, CoreConfig, ResourceUtilization -from model_compression_toolkit.target_platform_capabilities.target_platform import TargetPlatformCapabilities -from tutorials.quick_start.common.constants import BYTES_TO_FP32, MP_WEIGHTS_COMPRESSION -from tutorials.quick_start.common.results import QuantInfo -from tutorials.quick_start.common.tpc_info import get_tpc_info - - -def get_tpc(target_platform_name: str, target_platform_version: str) -> TargetPlatformCapabilities: - """ - Returns the target platform capabilities according to the given platform name and version. - - Args: - target_platform_name: Name of the target platform - target_platform_version: Version of the target platform - - Returns: - The target platform capabilities. - - """ - return mct.get_target_platform_capabilities('pytorch', target_platform_name, target_platform_version) - - -def get_target_resource_utilization(model, weights_compression, representative_data_gen, core_config, tpc): - """ - Calculates the model's required size according to the given weights compression rate, to provide as a constraint for mixed precision search. - - Args: - model: The model to calculate the required size. - weights_compression: The required weights compression ratio. - representative_data_gen: Callable function to generate the representative dataset. - core_config (CoreConfig): CoreConfig containing parameters for quantization and mixed precision. - tpc (TargetPlatformCapabilities): TargetPlatformCapabilities to optimize the PyTorch model according to. - - Returns: - A ResourceUtilization object computed from MCT and contains info about the target model size. - - """ - ru_data = mct.core.pytorch_resource_utilization_data(model, representative_data_gen, core_config=core_config, - target_platform_capabilities=tpc) - weights_ru = BYTES_TO_FP32 * ru_data.weights_memory / weights_compression # (4 bytes for fp32) * weights memory(in Bytes) / compression rate - return ResourceUtilization(weights_memory=weights_ru) - - -def quantize(model: nn.Module, - get_representative_dataset: callable, - tpc: TargetPlatformCapabilities, - args: dict): - """ - Returns a quantized model and a quantization info from MCT for the given PyTorch floating-point model. - - Args: - model: PyTorch floating-point model to be quantized. - get_representative_dataset: Callable function to generate the representative dataset for quantization. - tpc: Target platform capabilities. - args: Dictionary containing the necessary configurations for quantization. - - Returns: - Tuple containing the quantized model and quantization info. - - """ - - # PTQ - general configurations - n_iter = math.ceil(int(args[NUM_REPRESENTATIVE_IMAGES]) // int(args[BATCH_SIZE])) # Number of batches - logging.info(f"Running MCT... number of representative images: {args[REPRESENTATIVE_DATASET_FOLDER]}, number of calibration iters: {n_iter}") - - representative_data_gen = get_representative_dataset( - representative_dataset_folder=args[REPRESENTATIVE_DATASET_FOLDER], - n_iter=n_iter, - batch_size=int(args[BATCH_SIZE]) - ) - - # Mixed-precision configurations - mp_wcr = args.get(MP_WEIGHTS_COMPRESSION, None) - if mp_wcr: - mp_conf = MixedPrecisionQuantizationConfig() - core_conf = CoreConfig(quantization_config=mct.core.QuantizationConfig( - shift_negative_activation_correction=True), - mixed_precision_config=mp_conf) - target_resource_utilization = get_target_resource_utilization(model, mp_wcr, representative_data_gen, core_conf, tpc) - else: - core_conf = CoreConfig(quantization_config=mct.core.QuantizationConfig( - shift_negative_activation_correction=True)) - target_resource_utilization = None - - # Quantize model - if args.get('gptq', False): - - workflow = 'GPTQ' - n_epochs = args.get('gptq_num_calibration_iter') // n_iter - logging.info( - f"MCT Gradient-based Post Training Quantization is enabled. Number of epochs: {n_epochs}") - - gptq_conf = mct.gptq.get_pytorch_gptq_config(n_epochs=n_epochs, optimizer=Adam([Tensor([])], lr=args['gptq_lr'])) - - quantized_model, quantization_info = \ - mct.gptq.pytorch_gradient_post_training_quantization(model, - representative_data_gen=representative_data_gen, - target_resource_utilization=target_resource_utilization, - core_config=core_conf, - gptq_config=gptq_conf, - gptq_representative_data_gen=representative_data_gen, - target_platform_capabilities=tpc) - - - else: - workflow = 'PTQ' - quantized_model, quantization_info = \ - mct.ptq.pytorch_post_training_quantization(model, - representative_data_gen=representative_data_gen, - core_config=core_conf, - target_platform_capabilities=tpc) - - - # Export quantized model to ONNX - if args.get('export_model',False): - _, onnx_file_path = tempfile.mkstemp('.onnx') # Path of exported model - mct.exporter.pytorch_export_model(model=quantized_model, - save_model_path=onnx_file_path, - repr_dataset=representative_data_gen) - - return quantized_model, QuantInfo(user_info=quantization_info, - tpc_info=get_tpc_info(tpc=tpc), - quantization_workflow=workflow, - mp_weights_compression=mp_wcr) \ No newline at end of file diff --git a/tutorials/quick_start/pytorch_fw/timm_lib/LICENSE b/tutorials/quick_start/pytorch_fw/timm_lib/LICENSE deleted file mode 100644 index b4e9438bd..000000000 --- a/tutorials/quick_start/pytorch_fw/timm_lib/LICENSE +++ /dev/null @@ -1,201 +0,0 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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We also recommend that a - file or class name and description of purpose be included on the - same "printed page" as the copyright notice for easier - identification within third-party archives. - - Copyright 2019 Ross Wightman - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - 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. \ No newline at end of file diff --git a/tutorials/quick_start/pytorch_fw/timm_lib/__init__.py b/tutorials/quick_start/pytorch_fw/timm_lib/__init__.py deleted file mode 100644 index 2147ec284..000000000 --- a/tutorials/quick_start/pytorch_fw/timm_lib/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== diff --git a/tutorials/quick_start/pytorch_fw/timm_lib/model_lib.py b/tutorials/quick_start/pytorch_fw/timm_lib/model_lib.py deleted file mode 100644 index a80f1bd4a..000000000 --- a/tutorials/quick_start/pytorch_fw/timm_lib/model_lib.py +++ /dev/null @@ -1,105 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== -import timm -from timm.data import create_dataset, create_loader, resolve_data_config - -from tutorials.quick_start.common.model_lib import BaseModelLib -from tutorials.quick_start.pytorch_fw.utils import classification_eval, get_representative_dataset -from tutorials.quick_start.common.constants import MODEL_NAME, BATCH_SIZE, VALIDATION_SET_LIMIT, \ - VALIDATION_DATASET_FOLDER, IMAGENET_DATASET -from tutorials.quick_start.common.results import DatasetInfo - - -class ModelLib(BaseModelLib): - """ - A class representing timm model library (https://github.com/huggingface/pytorch-image-models/tree/main). - """ - - def __init__(self, args): - """ - Init the ModelLib with user arguments - Args: - args (dict): user arguments - """ - avialable_models = timm.list_models('') - model_name = args[MODEL_NAME] - if model_name in avialable_models: - self.model = timm.create_model(args[MODEL_NAME], pretrained=True) - self.data_config = resolve_data_config([], model=self.model) # include the pre-processing - self.dataset_name = IMAGENET_DATASET - super().__init__(args) - else: - raise Exception(f'Unknown timm model name {model_name}, Available models : {avialable_models}') - - def get_model(self): - """ - Returns the model instance. - """ - return self.model - - def get_representative_dataset(self, representative_dataset_folder, n_iter, batch_size): - """ - Create a representative dataset generator - Args: - representative_dataset_folder: Dataset location folder, in the format: representative_dataset_folder// - n_iter: number batches to run in the generator - batch_size: number of images in each batch - - Returns: - A generator for the representative dataset, as the MCT expects - - """ - train_dataset = create_dataset(name='', root=representative_dataset_folder, - is_training=False, batch_size=batch_size) - dl = create_loader( - train_dataset, - input_size=self.data_config['input_size'], - batch_size=batch_size, - interpolation=self.data_config['interpolation'], - mean=self.data_config['mean'], - std=self.data_config['std'], - crop_pct=self.data_config['crop_pct']) - return get_representative_dataset(dl, n_iter) - - def evaluate(self, model): - """ - Evaluates the model's performance. - - Args: - model: The model to evaluate. - - Returns: - acc: The accuracy of the model. - DatasetInfo: Information about the dataset used. - """ - batch_size = int(self.args[BATCH_SIZE]) - validation_dataset_folder = self.args[VALIDATION_DATASET_FOLDER] - val_dataset = create_dataset(name='', root=validation_dataset_folder, is_training=False, - batch_size=batch_size) - testloader = create_loader( - val_dataset, - input_size=self.data_config['input_size'], - batch_size=batch_size, - interpolation=self.data_config['interpolation'], - mean=self.data_config['mean'], - std=self.data_config['std'], - crop_pct=self.data_config['crop_pct']) - - acc, total = classification_eval(model, testloader, self.args[VALIDATION_SET_LIMIT]) - dataset_info = DatasetInfo(self.dataset_name, total) - return acc, dataset_info - - - diff --git a/tutorials/quick_start/pytorch_fw/torchvision_lib/LICENSE b/tutorials/quick_start/pytorch_fw/torchvision_lib/LICENSE deleted file mode 100644 index a95ba5204..000000000 --- a/tutorials/quick_start/pytorch_fw/torchvision_lib/LICENSE +++ /dev/null @@ -1,29 +0,0 @@ -BSD 3-Clause License - -Copyright (c) Soumith Chintala 2016, -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -* Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -* Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/tutorials/quick_start/pytorch_fw/torchvision_lib/__init__.py b/tutorials/quick_start/pytorch_fw/torchvision_lib/__init__.py deleted file mode 100644 index 2147ec284..000000000 --- a/tutorials/quick_start/pytorch_fw/torchvision_lib/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== diff --git a/tutorials/quick_start/pytorch_fw/torchvision_lib/model_lib.py b/tutorials/quick_start/pytorch_fw/torchvision_lib/model_lib.py deleted file mode 100644 index 85b623b33..000000000 --- a/tutorials/quick_start/pytorch_fw/torchvision_lib/model_lib.py +++ /dev/null @@ -1,105 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== -import torch -import torchvision -from torchvision.models import list_models, get_model, get_model_weights, get_weight -from torch.utils.data import Subset - -from tutorials.quick_start.common.model_lib import BaseModelLib -from tutorials.quick_start.pytorch_fw.utils import classification_eval, get_representative_dataset -from tutorials.quick_start.common.constants import MODEL_NAME, BATCH_SIZE, VALIDATION_SET_LIMIT, \ - VALIDATION_DATASET_FOLDER, IMAGENET_DATASET - -from tutorials.quick_start.common.results import DatasetInfo - - -class ModelLib(BaseModelLib): - """ - A class representing torchvision model library (https://pytorch.org/vision/stable/models.html) - """ - - @staticmethod - def get_torchvision_model(model_name): - """ - Retrieves a torchvision model by name. - """ - all_models = list_models() # List all torchvision models - if model_name in all_models: - return get_model(model_name, weights="DEFAULT") # Best available weights ("DEFAULT") - else: - raise Exception(f'Unknown torchvision model name {model_name}, Please check available models in https://pytorch.org/vision/stable/models.html') - - @staticmethod - def get_torchvision_weights(model_name): - """ - Returns the best available weights for the specified model. - """ - return get_model_weights(model_name).DEFAULT - - def __init__(self, args): - """ - Init the ModelLib with user arguments - Args: - args (dict): user arguments - """ - self.model = self.get_torchvision_model(args[MODEL_NAME]) - self.preprocess = self.get_torchvision_weights(args[MODEL_NAME]).transforms() - self.dataset_name = IMAGENET_DATASET - super().__init__(args) - - def get_model(self): - """ - Returns the model instance. - """ - return self.model - - def get_representative_dataset(self, representative_dataset_folder, n_iter, batch_size): - """ - Create a representative dataset generator - Args: - representative_dataset_folder: Dataset location folder, in the format: representative_dataset_folder// - n_iter: number batches to run in the generator - batch_size: number of images in each batch - - Returns: - A generator for the representative dataset, as the MCT expects - - """ - ds = torchvision.datasets.ImageFolder(representative_dataset_folder, transform=self.preprocess) - dl = torch.utils.data.DataLoader(ds, batch_size, shuffle=True) - return get_representative_dataset(dl, n_iter) - - def evaluate(self, model): - """ - Evaluates the model's performance. - - Args: - model: The model to evaluate. - - Returns: - acc: The accuracy of the model. - DatasetInfo: Information about the dataset used. - """ - batch_size = int(self.args[BATCH_SIZE]) - validation_dataset_folder = self.args[VALIDATION_DATASET_FOLDER] - testset = torchvision.datasets.ImageFolder(validation_dataset_folder, transform=self.preprocess) - testloader = torch.utils.data.DataLoader(testset, - batch_size=batch_size, - shuffle=False) - acc, total = classification_eval(model, testloader, self.args[VALIDATION_SET_LIMIT]) - dataset_info = DatasetInfo(self.dataset_name, total) - return acc, dataset_info - - diff --git a/tutorials/quick_start/pytorch_fw/ultralytics_lib/LICENSE b/tutorials/quick_start/pytorch_fw/ultralytics_lib/LICENSE deleted file mode 100644 index bae94e189..000000000 --- a/tutorials/quick_start/pytorch_fw/ultralytics_lib/LICENSE +++ /dev/null @@ -1,661 +0,0 @@ - GNU AFFERO GENERAL PUBLIC LICENSE - Version 3, 19 November 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU Affero General Public License is a free, copyleft license for -software and other kinds of works, specifically designed to ensure -cooperation with the community in the case of network server software. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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In this case, we use the 'train' split of the downloaded dataset provided by Ultralytics. -- `--num_representative_images`: Specifies the number of representative images to be used for quantization. -- `--batch_size`: Specifies the batch size to be used. - -Please note that during the first model evaluation, Ultralytics downloads the COCO dataset to the folder specified in [coco.yaml](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/coco.yaml) file. Therefore, the 'validation_dataset_folder' value is not required in this case. - -For the representative dataset, it is expected to follow the same format as the downloaded COCO dataset. For instance, you can use the 'train' split of the COCO dataset as the representative dataset or create a new split with the same format. - -For more information on the dataset format please refer the [ultralytics docs](https://docs.ultralytics.com/datasets/) - -## Model Replacers -During the process, we perform a few manipulations to achieve better quantization results: - -1. We replace certain modules with modules supported by `torch.fx`, which our project relies on. The `torch.fx` toolkit helps us acquire a static graph representation from PyTorch models, enabling model compression manipulations like batch norm folding. -2. We remove the last part of the detection head, responsible for bounding box decoding, and include it as part of the postprocessing. You can find the additional postprocessing in the new definition of the `postprocess` method under [detect_replacer.py](./detect_replacers.py). -3. In Segmentation and Pose-Estimation tasks, which use the detect head, we use the replaced detect head and also modify the postprocessing accordingly. You can find the additional postprocessing in the new definition of the `postprocess` method under [segment_replacers.py](./segment_replacers.py) and [pose_replacers.py](./pose_replacers.py). - - -## License -This project is licensed under [Apache License 2.0](../../../../LICENSE.md). -Ultralytics has the following license requirements: -- ultralytics: [license link](https://github.com/ultralytics/ultralytics/blob/main/LICENSE), [license copy](./LICENSE) diff --git a/tutorials/quick_start/pytorch_fw/ultralytics_lib/__init__.py b/tutorials/quick_start/pytorch_fw/ultralytics_lib/__init__.py deleted file mode 100644 index 2147ec284..000000000 --- a/tutorials/quick_start/pytorch_fw/ultralytics_lib/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== diff --git a/tutorials/quick_start/pytorch_fw/ultralytics_lib/common_replacers.py b/tutorials/quick_start/pytorch_fw/ultralytics_lib/common_replacers.py deleted file mode 100644 index 36f779f67..000000000 --- a/tutorials/quick_start/pytorch_fw/ultralytics_lib/common_replacers.py +++ /dev/null @@ -1,167 +0,0 @@ -# Copyright 2024 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== - -""" - Parts of this file were copied from https://github.com/ultralytics/ultralytics and modified for this project needs. - - The Licence of the ultralytics project is shown in: https://github.com/ultralytics/ultralytics/blob/main/LICENSE -""" -from typing import Any - -import torch - -from common.model_lib import ModuleReplacer -from ultralytics import YOLO -from ultralytics.nn.modules import C2f -from ultralytics.cfg import get_cfg -from ultralytics.utils import DEFAULT_CFG -from ultralytics.utils.checks import check_imgsz -from pathlib import Path - -from model_compression_toolkit.core.pytorch.back2framework.pytorch_model_builder import PytorchModel -from tutorials.quick_start.common.constants import MODULE_REPLACER, VALIDATOR, DETECT, SEGMENT, POSE, RECT, MODE, \ - DEVICE, VAL, COCO_POSE -from tutorials.quick_start.pytorch_fw.ultralytics_lib.detect_replacers import DetectionValidatorReplacer, \ - DetectModuleReplacer -from tutorials.quick_start.pytorch_fw.ultralytics_lib.pose_replacers import PoseValidatorReplacer, PoseModuleReplacer -from tutorials.quick_start.pytorch_fw.ultralytics_lib.replace_module import replace_2d_deg_module -from tutorials.quick_start.pytorch_fw.ultralytics_lib.segment_replacers import SegmentationValidatorReplacer, \ - SegmentModuleReplacer - -TASK_MAP = { - DETECT: { - MODULE_REPLACER: DetectModuleReplacer(), - VALIDATOR: DetectionValidatorReplacer}, - SEGMENT: { - MODULE_REPLACER: SegmentModuleReplacer(), - VALIDATOR: SegmentationValidatorReplacer}, - POSE: { - MODULE_REPLACER: PoseModuleReplacer(), - VALIDATOR: PoseValidatorReplacer} -} - - -# In this section we slightly modify C2f module and replace the "list" function which is not supported by torch.fx -class C2fReplacer(C2f): - """ - A new C2f module definition supported by torch.fx - """ - - def forward(self, x): - y1 = self.cv1(x).chunk(2, 1) - y = [y1[0], y1[1]] - y.extend(m(y[-1]) for m in self.m) - return self.cv2(torch.cat(y, 1)) - - -class C2fModuleReplacer(ModuleReplacer): - """ - A module replacer for C2f modules. - """ - - def get_new_module(self, config): - return C2fReplacer(*config) - - def get_config(self, c): - c1 = next(c.cv1.children()).in_channels - c2 = next(c.cv1.children()).out_channels - cc = c.c - n = int(next(c.cv2.children()).in_channels / cc - 2) - e = cc / c2 - g = next(next(next(c.m.children()).children()).children()).groups - shortcut = next(c.m.children()).add - return [c1, c2, n, shortcut, g, e] - - def replace(self, model): - return replace_2d_deg_module(model, C2f, self.get_new_module, self.get_config) - - -class YOLOReplacer(YOLO): - """ - Replaces the YOLO class to include the modified DetectionValidator - """ - - def val(self, data=None, **kwargs): - """ - Validate a model on a given dataset . - - Args: - data (str): The dataset to validate on. Accepts all formats accepted by yolo - **kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs - """ - overrides = self.overrides.copy() - overrides[RECT] = False # rect batches as default - overrides.update(kwargs) - overrides[MODE] = VAL - overrides[DEVICE] = torch.device("cuda" if torch.cuda.is_available() else "cpu") - args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides) - args.data = data or args.data - args.data = COCO_POSE if self.task == 'pose' else args.data - if 'task' in overrides: - self.task = args.task - else: - args.task = self.task - if args.imgsz == DEFAULT_CFG.imgsz and not isinstance(self.model, (str, Path)): - args.imgsz = self.model.args['imgsz'] # use trained imgsz unless custom value is passed - args.imgsz = check_imgsz(args.imgsz, max_dim=1) - - validator = TASK_MAP[self.task][VALIDATOR](args=args) - validator.model = self.model - validator(model=self.model) - self.metrics_data = validator.metrics - - return validator.metrics - - -def prepare_model_for_ultralytics_val(ultralytics_model: YOLOReplacer, model: Any, quantized=False): - """ - Prepares the model for Ultralytics validation by setting necessary attributes. - Post quantization, MCT returns a PytorchModel instance, which does not have some of the arguments required by - Ultralytics code. We replace the model with a custom model that has these attributes. - Args: - ultralytics_model: The Ultralytics model instance. - model: The model to validate. - quantized: Whether the model is quantized. - """ - - class CustomPytorchModel(PytorchModel): - def __init__(self, graph, append2output, return_float_outputs, wrapper, get_activation_quantizer_holder_fn): - super().__init__(graph, append2output, return_float_outputs, wrapper, get_activation_quantizer_holder_fn) - self.args = ultralytics_model.model.args - self.names = ultralytics_model.model.names - self.stride = ultralytics_model.model.stride - self.device = next(model.parameters()).device - - def forward(self, x, augment=False, visualize=False, embed=False): - return super().forward(x) - - def fuse(self, verbose=True): - return self - - if not hasattr(model, 'args'): - setattr(model, 'args', ultralytics_model.model.args) - setattr(model, 'names', ultralytics_model.model.names) - setattr(model, 'stride', ultralytics_model.model.stride) - setattr(model, "device", next(model.parameters()).device) - if quantized: # cast to custom pytorch model that accepts augment, visualize and embed - custom_model = CustomPytorchModel(graph=model.graph, append2output=model.append2output, - return_float_outputs=model.return_float_outputs, wrapper=model.wrapper, - get_activation_quantizer_holder_fn=model.get_activation_quantizer_holder) - for attr_name, attr_value in vars(model).items(): - setattr(custom_model, attr_name, attr_value) - - setattr(ultralytics_model, 'model', custom_model if quantized else model) - - return ultralytics_model diff --git a/tutorials/quick_start/pytorch_fw/ultralytics_lib/detect_replacers.py b/tutorials/quick_start/pytorch_fw/ultralytics_lib/detect_replacers.py deleted file mode 100644 index 36a683ed1..000000000 --- a/tutorials/quick_start/pytorch_fw/ultralytics_lib/detect_replacers.py +++ /dev/null @@ -1,155 +0,0 @@ -# Copyright 2024 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== - -""" - Parts of this file were copied from https://github.com/ultralytics/ultralytics and modified for this project needs. - - The Licence of the ultralytics project is shown in: https://github.com/ultralytics/ultralytics/blob/main/LICENSE -""" -from typing import Tuple - -import torch -from torch import Tensor -from ultralytics.models.yolo.detect import DetectionValidator -from ultralytics.nn import DetectionModel -from ultralytics.nn.modules import Detect -from ultralytics.utils.tal import make_anchors, dist2bbox - -from tutorials.quick_start.common.constants import EDGE_TPU -from tutorials.quick_start.common.model_lib import ModuleReplacer -from tutorials.quick_start.pytorch_fw.ultralytics_lib.replace_module import replace_2d_deg_module - - -# In this section we modify Detect module to exclude dynamic condition which is not supported by torch.fx -# In addition, we remove the last part of the detection head which is essential for improving the quantization -# This missing part will be added to the postprocessing implementation -class DetectReplacer(Detect): - """ - Replaces the Detect module with modifications to support torch.fx and removes the last part of the detection head. - Specifically, we remove the concatenation of the outputs and the dynamic condition - """ - - def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]: - shape = x[0].shape # BCHW - for i in range(self.nl): - x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1) - - if self.export and self.format == EDGE_TPU: # FlexSplitV ops issue - x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2) - box = x_cat[:, :self.reg_max * 4] - cls = x_cat[:, self.reg_max * 4:] - else: - box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split( - (self.reg_max * 4, self.nc), 1) - - y_cls = cls.sigmoid() - y_bb = self.dfl(box) - return y_bb, y_cls - - -class DetectModuleReplacer(ModuleReplacer): - """ - A module replacer for Detect modules. - """ - - def get_new_module(self, config: list) -> DetectReplacer: - return DetectReplacer(*config) - - def get_config(self, c: torch.nn.Module) -> list[int, tuple]: - nc = c.nc - ch = [next(next(x.children()).children()).in_channels for x in c.cv2.children()] - return [nc, ch] - - def replace(self, model: torch.nn.Module) -> torch.nn.Module: - return replace_2d_deg_module(model, Detect, self.get_new_module, self.get_config) - - -# In this section we modify the DetectionModel to exclude dynamic condition which is not supported by torch.fx -class DetectionModelReplacer(DetectionModel): - """ - Replaces the DetectionModel to exclude dynamic condition not supported by torch.fx. - """ - - # Original forward functions uses *args and **kwargs and these are not supported by torch.fx and therefore - # we need to overload the forward function - def forward(self, x, profile=False, visualize=False, augment=False, embed=False): - return self.predict(x, profile=False, visualize=False, augment=False, embed=False) - - # Original predict uses a dynamic condition (if augment) which is not supported by torch.fx and we remove it - def predict(self, x, profile=False, visualize=False, augment=False, embed=False): - return self._predict_once(x, profile, visualize) # single-scale inference, train - - # Original _predict_once uses a dynamic condition (if profile, if visualize, if embed) which are not supported by - # torch.fx and we remove them - def _predict_once(self, x, profile=False, visualize=False, embed=False): - y, dt, embeddings = [], [], [] # outputs - for m in self.model: - if m.f != -1: # if not from previous layer - x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in - m.f] # from earlier layers - - x = m(x) # run - y.append(x if m.i in self.save else None) # save output - - return x - - -class DetectionModelModuleReplacer(ModuleReplacer): - """ - A module replacer for DetectionModel modules. - """ - - def get_config(self, c: torch.nn.Module) -> list: - return [c.yaml] - - def get_new_module(self, config: list) -> DetectionModelReplacer: - return DetectionModelReplacer(*config) - - def replace(self, model: torch.nn.Module) -> torch.nn.Module: - return self.get_new_module(self.get_config(model)) - - -# In this section we modify the DetectionValidator (not part of the model) to include the missing functionality -# that was removed from the Detect module -class DetectionValidatorReplacer(DetectionValidator): - """ - Replaces the DetectionValidator to include missing functionality from the Detect module. - """ - - # Code modified from make_anchors function in /ultralytics/utils/tal.py since we remove this part from the forward - # function in the replaced Detect module - def create_anchors_strides(self): - # Post-processing additional part - exported from Detect module - strides = [8, 16, 32] - stride = torch.tensor(strides, dtype=torch.float32) - grid = (self.args.imgsz / stride).numpy().astype(int) - in_ch = 64 + self.nc # 144 - x_dummy = [torch.ones(1, in_ch, grid[0], grid[0]), torch.ones(1, in_ch, grid[1], grid[1]), - torch.ones(1, in_ch, grid[2], grid[2])] - anchors, strides = (x.transpose(0, 1) for x in make_anchors(x_dummy, stride, 0.5)) - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - a = anchors.to(device) - s = strides.to(device) - return a, s - - def postprocess(self, preds: tuple[Tensor, Tensor]) -> list[Tensor]: - a, s = self.create_anchors_strides() - dbox = dist2bbox(preds[0], a.unsqueeze(0), xywh=True, dim=1) * s - preds = torch.cat((dbox, preds[1]), 1) - - # Original post-processing part - preds = super().postprocess(preds) - - return preds diff --git a/tutorials/quick_start/pytorch_fw/ultralytics_lib/model_lib.py b/tutorials/quick_start/pytorch_fw/ultralytics_lib/model_lib.py deleted file mode 100644 index ed4cd4963..000000000 --- a/tutorials/quick_start/pytorch_fw/ultralytics_lib/model_lib.py +++ /dev/null @@ -1,124 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== - -""" - Parts of this file were copied from https://github.com/ultralytics/ultralytics and modified for this project needs. - - The Licence of the ultralytics project is shown in: https://github.com/ultralytics/ultralytics/blob/main/LICENSE -""" -import logging - -import torch -from ultralytics.utils.torch_utils import initialize_weights - -from tutorials.quick_start.pytorch_fw.ultralytics_lib.common_replacers import C2fModuleReplacer, \ - YOLOReplacer, TASK_MAP -from tutorials.quick_start.pytorch_fw.ultralytics_lib.detect_replacers import DetectionModelModuleReplacer -from tutorials.quick_start.pytorch_fw.ultralytics_lib.common_replacers import prepare_model_for_ultralytics_val -from tutorials.quick_start.common.model_lib import BaseModelLib -from tutorials.quick_start.common.constants import MODEL_NAME, BATCH_SIZE, COCO_DATASET, VALIDATION_DATASET_FOLDER, \ - MODULE_REPLACER -from tutorials.quick_start.common.results import DatasetInfo -from tutorials.mct_model_garden.models_keras.yolov8.yolov8_preprocess import yolov8_preprocess_chw_transpose -from tutorials.quick_start.common.utils import FolderImageLoader -from model_compression_toolkit.core.pytorch.back2framework.pytorch_model_builder import PytorchModel - - -class ModelLib(BaseModelLib): - """ - A class representing ultralytics model library (https://github.com/ultralytics/ultralytics). - """ - - def __init__(self, args): - """ - Init the ModelLib with user arguments - Args: - args (dict): user arguments - """ - # Load model from ultralytics - self.ultralytics_model = YOLOReplacer(args[MODEL_NAME]) - self.dataset_name = COCO_DATASET - self.preprocess = yolov8_preprocess_chw_transpose - model_weights = self.ultralytics_model.model.state_dict() - - # Replace few modules with quantization-friendly modules - self.model = self.ultralytics_model.model - self.model = DetectionModelModuleReplacer().replace(self.model) - self.model = C2fModuleReplacer().replace(self.model) - self.model = TASK_MAP[self.ultralytics_model.task][MODULE_REPLACER].replace(self.model) - - # load pre-trained weights - initialize_weights(self.model) - self.model.load_state_dict(model_weights) # load weights - super().__init__(args) - - def get_model(self): - """ - Returns the model instance. - """ - return self.model - - def get_representative_dataset(self, representative_dataset_folder, n_iter, batch_size): - """ - Create a representative dataset generator - Args: - representative_dataset_folder: Dataset location folder, in YOLO format (see ultralytics docs), for example: //coco/images/train2017 - n_iter: number batches to run in the generator - batch_size: number of images in each batch - - Returns: - A generator for the representative dataset, as the MCT expects - - """ - image_data_loader = FolderImageLoader(representative_dataset_folder, - preprocessing=[self.preprocess], - batch_size=batch_size) - def representative_data_gen() -> list: - for _ in range(n_iter): - yield [image_data_loader.sample()] - - return representative_data_gen - - def evaluate(self, model): - """ - Evaluates the model's performance. - - Args: - model: The model to evaluate. - - Returns: - acc: The accuracy of the model. - DatasetInfo: Information about the dataset used. - """ - # Use Cuda device if available - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - model.to(device) - - # Some attributes are required for the evaluation of the quantized model - self.ultralytics_model = prepare_model_for_ultralytics_val(self.ultralytics_model, model, quantized=isinstance(model, PytorchModel)) - - # Evaluation using ultralytics interface - if self.args[VALIDATION_DATASET_FOLDER] is not None: - logging.warning('The provided value for "validation_dataset_folder" is ignored. ' - 'Ultralytics utilizes the dataset path specified in the coco.yaml file. ' - 'By default, the dataset path is taken from "/home/user/.config/Ultralytics/settings.yaml", depends on your operating system.') - - results = self.ultralytics_model.val(batch=int(self.args[BATCH_SIZE])) # evaluate model performance on the validation set - map_res = results.mean_results()[-1] - dataset_info = DatasetInfo(self.dataset_name, 5000) - return map_res, dataset_info - - - diff --git a/tutorials/quick_start/pytorch_fw/ultralytics_lib/pose_replacers.py b/tutorials/quick_start/pytorch_fw/ultralytics_lib/pose_replacers.py deleted file mode 100644 index 158dcbc2f..000000000 --- a/tutorials/quick_start/pytorch_fw/ultralytics_lib/pose_replacers.py +++ /dev/null @@ -1,97 +0,0 @@ -# Copyright 2024 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== - -""" - Parts of this file were copied from https://github.com/ultralytics/ultralytics and modified for this project needs. - - The Licence of the ultralytics project is shown in: https://github.com/ultralytics/ultralytics/blob/main/LICENSE -""" -from typing import Tuple - -import torch -from torch import Tensor -from ultralytics.models.yolo.pose import PoseValidator -from ultralytics.nn.modules import Pose -from ultralytics.utils.tal import make_anchors, dist2bbox - -from tutorials.quick_start.common.model_lib import ModuleReplacer -from tutorials.quick_start.pytorch_fw.ultralytics_lib.detect_replacers import DetectReplacer, DetectionValidatorReplacer -from tutorials.quick_start.pytorch_fw.ultralytics_lib.replace_module import replace_2d_deg_module - - -class PoseReplacer(Pose): - """ - Replaces the Pose module to use the replaced Detect forward function. - To improve quantization (due to different data types), we removes the output concatenation. - This will be added back in post_process. - Also removes the key points decoding part, which will be added back in post_process. - """ - - def __init__(self, nc=80, kpt_shape=(17, 3), ch=()): - super().__init__(nc, kpt_shape, ch) - self.detect = DetectReplacer.forward - - def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor]: - """Perform forward pass through YOLO model and return predictions.""" - bs = x[0].shape[0] # batch size - kpt = torch.cat([self.cv4[i](x[i]).view(bs, self.nk, -1) for i in range(self.nl)], -1) # (bs, 17*3, h*w) - y_bb, y_cls = self.detect(self, x) - return y_bb, y_cls, kpt - - -class PoseModuleReplacer(ModuleReplacer): - """ - A module replacer for Segment modules. - """ - - def get_new_module(self, config: list) -> PoseReplacer: - return PoseReplacer(*config) - - def get_config(self, c: torch.nn.Module) -> list: - nc = c.nc - kpt_shape = c.kpt_shape - ch = [next(next(x.children()).children()).in_channels for x in c.cv2.children()] - return [nc, kpt_shape, ch] - - def replace(self, model: torch.nn.Module) -> torch.nn.Module: - return replace_2d_deg_module(model, Pose, self.get_new_module, self.get_config) - - -class PoseValidatorReplacer(PoseValidator, DetectionValidatorReplacer): - """ - Replaces the PoseValidator to include missing functionality from the Pose module. - Uses the post process function from the DetectionValidatorReplacer, and adds the keypoint post-processing. - """ - def postprocess(self, preds: Tuple[Tensor, Tensor, Tensor]) -> list[Tensor]: - kpt_shape = (17, 3) - a, s = self.create_anchors_strides() - - y_bb, y_cls, kpts = preds - dbox = dist2bbox(y_bb, a.unsqueeze(0), xywh=True, dim=1) * s - detect_out = torch.cat((dbox, y_cls), 1) - # additional part for pose estimation - ndim = kpt_shape[1] - pred_kpt = kpts.clone() - if ndim == 3: - pred_kpt[:, 2::3] = pred_kpt[:, 2::3].sigmoid() # sigmoid (WARNING: inplace .sigmoid_() Apple MPS bug) - pred_kpt[:, 0::ndim] = (pred_kpt[:, 0::ndim] * 2.0 + (a[0] - 0.5)) * s - pred_kpt[:, 1::ndim] = (pred_kpt[:, 1::ndim] * 2.0 + (a[1] - 0.5)) * s - preds = (torch.cat([detect_out, pred_kpt], 1), (y_cls, kpts)) - - # Original post-processing part - preds = super().postprocess(preds) - - return preds - \ No newline at end of file diff --git a/tutorials/quick_start/pytorch_fw/ultralytics_lib/replace_module.py b/tutorials/quick_start/pytorch_fw/ultralytics_lib/replace_module.py deleted file mode 100644 index dd0f4cf4b..000000000 --- a/tutorials/quick_start/pytorch_fw/ultralytics_lib/replace_module.py +++ /dev/null @@ -1,25 +0,0 @@ -import torch.nn - - -def replace_2d_deg_module(model: torch.nn.Module, old_module: type, new_module: callable, get_config: callable): - """ - Replaces a 2nd degree submodule in the given ultralytics model with a new module. - - Args: - model (torch.nn.Module): The model to modify. - old_module (type): The old module type to replace. - new_module (callable): A function or callable that creates the new module. - get_config (callable): A function or callable that retrieves the configuration for creating the new module. - - Returns: - torch.nn.Module: The modified model. - """ - for n, m in model.named_children(): - for name, c in m.named_children(): - if isinstance(c, old_module): - l = new_module(get_config(c)) - setattr(l, 'f', c.f) - setattr(l, 'i', c.i) - setattr(l, 'type', c.type) - setattr(m, name, l) - return model \ No newline at end of file diff --git a/tutorials/quick_start/pytorch_fw/ultralytics_lib/segment_replacers.py b/tutorials/quick_start/pytorch_fw/ultralytics_lib/segment_replacers.py deleted file mode 100644 index 0baebf778..000000000 --- a/tutorials/quick_start/pytorch_fw/ultralytics_lib/segment_replacers.py +++ /dev/null @@ -1,90 +0,0 @@ -# Copyright 2024 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== - -""" - Parts of this file were copied from https://github.com/ultralytics/ultralytics and modified for this project needs. - - The Licence of the ultralytics project is shown in: https://github.com/ultralytics/ultralytics/blob/main/LICENSE -""" -from typing import Tuple, Any - -import torch -from torch import Tensor -from ultralytics.models.yolo.segment import SegmentationValidator -from ultralytics.nn.modules import Segment -from ultralytics.utils.tal import make_anchors, dist2bbox - -from tutorials.quick_start.common.model_lib import ModuleReplacer -from tutorials.quick_start.pytorch_fw.ultralytics_lib.detect_replacers import DetectReplacer, DetectionValidatorReplacer -from tutorials.quick_start.pytorch_fw.ultralytics_lib.replace_module import replace_2d_deg_module - - -class SegmentReplacer(Segment): - """ - Replaces the Segment module to use the replaced Detect forward function. - To improve quantization (due to different data types), we removes the output concatenation. - This will be added back in post_process. - """ - - def __init__(self, nc=80, nm=32, npr=256, ch=()): - super().__init__(nc, nm, npr, ch) - self.detect = DetectReplacer.forward - - def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Any]: - p = self.proto(x[0]) # mask protos - bs = p.shape[0] # batch size - - mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2) # mask coefficients - y_bb, y_cls = self.detect(self, x) - - return y_bb, y_cls, mc, p - - -class SegmentModuleReplacer(ModuleReplacer): - """ - A module replacer for Segment modules. - """ - - def get_new_module(self, config: list) -> SegmentReplacer: - return SegmentReplacer(*config) - - def get_config(self, c: torch.nn.Module) -> list[int, int, int, tuple]: - nc = c.nc - nm = c.nm - npr = c.npr - ch = [next(next(x.children()).children()).in_channels for x in c.cv2.children()] - return [nc, nm, npr, ch] - - def replace(self, model: torch.nn.Module) -> torch.nn.Module: - return replace_2d_deg_module(model, Segment, self.get_new_module, self.get_config) - - -class SegmentationValidatorReplacer(SegmentationValidator, DetectionValidatorReplacer): - """ - Replaces the SegmentationValidator to include missing functionality from the Segment module. - Uses the post process function from the DetectionValidatorReplacer, and adds the segmentation post-processing. - """ - def postprocess(self, preds: Tuple[Tensor, Tensor, Tensor, Any]) -> Tuple[list[Tensor], Tensor]: - a, s = self.create_anchors_strides() - y_bb, y_cls, masks_coeffs, proto = preds - dbox = dist2bbox(y_bb, a.unsqueeze(0), xywh=True, dim=1) * s - y = torch.cat((dbox, y_cls), 1) - # additional part for segmentation - preds = (torch.cat([y, masks_coeffs], 1), (y_cls, masks_coeffs, proto)) - - # Original post-processing part - preds = super().postprocess(preds) - - return preds \ No newline at end of file diff --git a/tutorials/quick_start/pytorch_fw/utils.py b/tutorials/quick_start/pytorch_fw/utils.py deleted file mode 100644 index 923c8d7d8..000000000 --- a/tutorials/quick_start/pytorch_fw/utils.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright 2023 Sony Semiconductor Israel, Inc. 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. -# 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. -# ============================================================================== -import torch -import logging -from tqdm import tqdm - - -def classification_eval(model, data_loader, limit=None, num_images_to_display=1000): - logging.info(f'Start classification evaluation') - correct = 0 - total = 0 - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - model.to(device) - model.eval() - # since we're not training, we don't need to calculate the gradients for our outputs - with torch.no_grad(): - for data in tqdm(data_loader, desc="Classification evaluation"): - images, labels = data - # calculate outputs by running images through the network - outputs = model(images.to(device)) - # the class with the highest energy is what we choose as prediction - _, predicted = torch.max(outputs.data, 1) - total += labels.size(0) - correct += (predicted == labels.to(device)).sum().item() - if total % num_images_to_display == 0: - print(f'Num of images: {total}, Accuracy: {round(100 * correct / total, 2)} %') - if limit and total >= int(limit): - break - - logging.info(f'Num of images: {total}, Accuracy: {round(100 * correct / total, 2)} %') - - return correct / total, total - - -def get_representative_dataset(data_loader, n_iters, data_loader_key=0, transforms=None): - class RepresentativeDataset(object): - def __init__(self, in_data_loader): - self.dl = in_data_loader - self.iter = iter(self.dl) - - def __call__(self): - for _ in range(n_iters): - try: - x = next(self.iter)[data_loader_key] - except StopIteration: - self.iter = iter(self.dl) - x = next(self.iter)[data_loader_key] - if transforms is not None: - x = transforms(x.float()) - yield [x.cpu().numpy()] - - return RepresentativeDataset(data_loader) diff --git a/tutorials/quick_start/results/model_quantization_results.csv b/tutorials/quick_start/results/model_quantization_results.csv deleted file mode 100644 index 60d3060db..000000000 --- a/tutorials/quick_start/results/model_quantization_results.csv +++ /dev/null @@ -1,15 +0,0 @@ -ModelName,ModelLibrary,DatasetName,TotalImages,FloatAcc,QuantAcc,Size[MB],BitsConfig,QuantTechnique,TPC -mobilenet_v2,torchvision,ImageNet,50000,0.7215,0.7188,3.47,W8A8,PTQ,default_pytorch_tpc-v4 -regnet_y_400mf,torchvision,ImageNet,50000,0.7578,0.7542,4.31,W8A8,PTQ,default_pytorch_tpc-v4 -shufflenet_v2_x0_5,torchvision,ImageNet,50000,0.6055,0.597,1.36,W8A8,PTQ,default_pytorch_tpc-v4 -squeezenet1_0,torchvision,ImageNet,50000,0.581,0.5767,1.24,W8A8,PTQ,default_pytorch_tpc-v4 -regnetx_002,timm,ImageNet,50000,0.6876,0.6827,2.67,W8A8,PTQ,default_pytorch_tpc-v4 -regnety_008,timm,ImageNet,50000,0.7632,0.7598,6.23,W8A8,PTQ,default_pytorch_tpc-v4 -resnet10t,timm,ImageNet,50000,0.6656,0.6643,5.43,W8A8,PTQ,default_pytorch_tpc-v4 -resnet18,timm,ImageNet,50000,0.6976,0.6961,11.68,W8A8,PTQ,default_pytorch_tpc-v4 -efficientnet_es,timm,ImageNet,50000,0.7808,0.7774,5.39,W8A8,PTQ,default_pytorch_tpc-v4 -yolov8n,ultralytics,COCO,5000,0.3704,0.3617,3.15,W8A8,PTQ,default_pytorch_tpc-v4 -yolov8m,ultralytics,COCO,5000,0.4999,0.494,25.9,W8A8,PTQ,default_pytorch_tpc-v4 -MobileNetV2,keras_applications,ImageNet,50000,0.7185,0.7157,3.47,W8A8,PTQ,default_keras_tpc-v4 -EfficientNetV2B0,keras_applications,ImageNet,50000,0.7841,0.7744,7.06,W8A8,PTQ,default_keras_tpc-v4 -ResNet50,keras_applications,ImageNet,50000,0.7422,0.7408,25.5,W8A8,PTQ,default_keras_tpc-v4