Enable yolov5 Example for TF 3x API (#1943)

Signed-off-by: zehao-intel <[email protected]>

Author: zehao-intel

examples/.config/model_params_tensorflow_3x.json

@@ -93,6 +93,13 @@
       "main_script": "main.py",
       "batch_size": 1000
     },
+    "yolo_v5": {
+      "model_src_dir": "object_detection/yolo_v5/quantization/ptq",
+      "dataset_location": "/tf_dataset2/datasets/coco_yolov5/coco",
+      "input_model": "/tf_dataset2/models/tensorflow/yolo_v5/yolov5s.pb",
+      "main_script": "main.py", 
+      "batch_size": 1
+    },     
     "faster_rcnn_resnet50": {
       "model_src_dir": "object_detection/faster_rcnn_resnet50/quantization/ptq",
       "dataset_location": "/tf_dataset/tensorflow/coco_val.record",

examples/3.x_api/tensorflow/object_detection/yolo_v5/quantization/ptq/README.md

@@ -0,0 +1,96 @@
+This document describes the step-by-step to reproduce Yolo-v5 tuning result with Neural Compressor. This example can run on Intel CPUs and GPUs.
+
+# Prerequisite
+
+
+## 1. Environment
+Recommend python 3.10 or higher version.
+
+### Install Intel® Neural Compressor
+```shell
+pip install neural-compressor
+```
+
+### Install Tensorflow
+```shell
+pip install tensorflow
+```
+> Note: Validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+### Installation Dependency packages
+```shell
+cd examples/3.x_api/tensorflow/object_detection/yolo_v5/quantization/ptq
+pip install -r requirements.txt
+```
+
+### Install Intel Extension for Tensorflow
+
+#### Quantizing the model on Intel GPU(Mandatory to install ITEX)
+Intel Extension for Tensorflow is mandatory to be installed for quantizing the model on Intel GPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[xpu]
+```
+For any more details, please follow the procedure in [install-gpu-drivers](https://github.com/intel/intel-extension-for-tensorflow/blob/main/docs/install/install_for_xpu.md#install-gpu-drivers)
+
+#### Quantizing the model on Intel CPU(Optional to install ITEX)
+Intel Extension for Tensorflow for Intel CPUs is experimental currently. It's not mandatory for quantizing the model on Intel CPUs.
+
+```shell
+pip install --upgrade intel-extension-for-tensorflow[cpu]
+```
+
+> **Note**: 
+> The version compatibility of stock Tensorflow and ITEX can be checked [here](https://github.com/intel/intel-extension-for-tensorflow#compatibility-table). Please make sure you have installed compatible Tensorflow and ITEX.
+
+## 2. Prepare model
+
+Users can choose to automatically or manually download the model.
+### Automatic download
+
+Run the `prepare_model.sh` script.
+```shell
+. prepare_model.sh
+```
+
+This script will load yolov5 model to `./yolov5/yolov5s.pb`.
+
+### Manual download
+
+To get a TensorFlow pretrained model, you need to export it from a PyTorch model. Clone the [Ultralytics yolov5 repository](https://github.com/ultralytics/yolov5.git).
+Generate the pretrained PyTorch model and then export to a Tensorflow supported format with the following commands:
+```shell
+python yolov5/models/tf.py --weights yolov5/yolov5s.pt
+python yolov5/export.py --weights yolov5/yolov5s.pt --include pb
+```
+
+The yolov5 model will be loaded to `./yolov5/yolov5s.pb`.
+
+## 3. Prepare Dataset
+
+Users can choose to automatically or manually download the dataset.
+### Automatic download
+
+Run the `prepare_dataset.sh` script.
+```shell
+. prepare_dataset.sh
+```
+The validation set of coco2017 will be downloaded into a `./coco` folder.
+
+# Run
+
+## 1. Quantization
+```python
+bash run_quant.sh --input_model=./yolov5/yolov5s.pb --output_model=yolov5s_int8.pb --dataset_location=/path/to/dataset
+```
+
+## 2. Benchmark
+```python
+# run performance benchmark
+bash run_benchmark.sh --input_model=yolov5s_int8.pb --dataset_location=/path/to/dataset --mode=performance
+
+# run accuracy benchmark
+bash run_benchmark.sh --input_model=yolov5s_int8.pb --dataset_location=/path/to/dataset --mode=accuracy
+```
+
+Finally, the program will generate the quantized Yolo-v5 model with relative 1% loss.