Update Examples for TF 3x API

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

examples/.config/model_params_tensorflow_3x.json

@@ -8,6 +8,20 @@
       "batch_size": 64,
       "fp32_model_url": "https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_7_0/fp32_bert_squad.pb"
     },
+    "distilbert_base": {
+      "model_src_dir": "nlp/distilbert_base/quantization/ptq",
+      "dataset_location": "/tf_dataset2/datasets/sst2_validation_dataset",
+      "input_model": "/tf_dataset2/models/tensorflow/distilbert_base/fp32/distilbert_base_fp32.pb",
+      "main_script": "run_inference.py",
+      "batch_size": 128
+    },
+    "distilbert_base_sq": {
+      "model_src_dir": "nlp/distilbert_base/quantization/ptq",
+      "dataset_location": "/tf_dataset2/datasets/sst2_validation_dataset",
+      "input_model": "/tf_dataset2/models/tensorflow/distilbert_base/fp32/distilbert_base_fp32.pb",
+      "main_script": "run_inference.py",
+      "batch_size": 128
+    },
     "opt_125m_sq": {
       "model_src_dir": "nlp/large_language_models/quantization/ptq/smoothquant",
       "dataset_location": "",
@@ -29,8 +43,44 @@
       "main_script": "main.py",
       "batch_size": 1
     },
+    "transformer_lt": {
+      "model_src_dir": "nlp/transformer_lt/quantization/ptq",
+      "dataset_location": "/tf_dataset/tensorflow/transformer-lt-official-fp32-inference/transformer_lt_official_fp32_pretrained_model/data",
+      "input_model": "/tf_dataset/tensorflow/transformer-lt-official-fp32-inference/transformer_lt_official_fp32_pretrained_model/graph/fp32_graphdef.pb",
+      "main_script": "main.py",
+      "batch_size": 64
+    },
+    "densenet121": {
+      "model_src_dir": "cv/densenet121/quantization/ptq",
+      "dataset_location": "/tf_dataset/dataset/imagenet",
+      "input_model": "/tf_dataset/tensorflow/densenet/densenet-121.pb",
+      "main_script": "main.py",
+      "batch_size": 32
+    },
+    "inception_v3": {
+      "model_src_dir": "cv/inception_v3/quantization/ptq",
+      "dataset_location": "/tf_dataset/dataset/imagenet",
+      "input_model": "/tf_dataset/pre-trained-models/inceptionv3/fp32/freezed_inceptionv3.pb",
+      "main_script": "main.py",
+      "batch_size": 32,
+      "fp32_model_url": "https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_8/inceptionv3_fp32_pretrained_model.pb"
+    },
+    "mobilenetv2": {
+      "model_src_dir": "cv/mobilenet_v2/quantization/ptq",
+      "dataset_location": "/tf_dataset/dataset/imagenet",
+      "input_model": "/tf_dataset/pre-train-model-slim/pbfile/frozen_pb/frozen_mobilenet_v2.pb",
+      "main_script": "main.py",
+      "batch_size": 32
+    },
+    "vgg16": {
+      "model_src_dir": "cv/vgg16/quantization/ptq",
+      "dataset_location": "/tf_dataset/dataset/imagenet",
+      "input_model": "/tf_dataset/pre-train-model-slim/pbfile/frozen_pb/frozen_vgg16.pb",
+      "main_script": "main.py",
+      "batch_size": 32
+    },
     "ViT": {
-      "model_src_dir": "image_recognition/vision_transformer/quantization/ptq",
+      "model_src_dir": "cv/vision_transformer/quantization/ptq",
       "dataset_location": "/tf_dataset/dataset/imagenet",
       "input_model": "/tf_dataset/tensorflow/vit/HF-ViT-Base16-Img224-frozen.pb",
       "main_script": "main.py",
@@ -42,6 +92,63 @@
       "input_model": "/tf_dataset/tensorflow/graphsage/graphsage_frozen_model.pb",
       "main_script": "main.py",
       "batch_size": 1000
+    },
+    "faster_rcnn_resnet50": {
+      "model_src_dir": "object_detection/faster_rcnn_resnet50/quantization/ptq",
+      "dataset_location": "/tf_dataset/tensorflow/coco_val.record",
+      "input_model": "/tf_dataset/pre-train-model-oob/object_detection/faster_rcnn_resnet50/frozen_inference_graph.pb",
+      "main_script": "main.py",
+      "batch_size": 10
+    },
+    "mask_rcnn_inception_v2": {
+      "model_src_dir": "object_detection/mask_rcnn_inception_v2/quantization/ptq",
+      "dataset_location": "/tf_dataset/tensorflow/coco_val.record",
+      "input_model": "/tf_dataset/pre-train-model-oob/object_detection/mask_rcnn_inception_v2/frozen_inference_graph.pb",
+      "main_script": "main.py",
+      "batch_size": 10
+    },
+    "mask_rcnn_inception_v2_ckpt": {
+      "model_src_dir": "object_detection/mask_rcnn_inception_v2/quantization/ptq",
+      "dataset_location": "/tf_dataset/tensorflow/coco_val.record",
+      "input_model": "/tf_dataset/pre-train-model-oob/object_detection/mask_rcnn_inception_v2",
+      "main_script": "main.py",
+      "batch_size": 10
+    },
+    "ssd_mobilenet_v1": {
+      "model_src_dir": "object_detection/ssd_mobilenet_v1/quantization/ptq",
+      "dataset_location": "/tf_dataset/tensorflow/coco_val.record",
+      "input_model": "/tf_dataset/pre-train-model-oob/object_detection/ssd_mobilenet_v1/frozen_inference_graph.pb",
+      "main_script": "main.py",       
+      "batch_size": 10
+    },
+    "ssd_mobilenet_v1_ckpt": {
+      "model_src_dir": "object_detection/ssd_mobilenet_v1/quantization/ptq",
+      "dataset_location": "/tf_dataset/tensorflow/coco_val.record",
+      "input_model": "/tf_dataset/pre-train-model-oob/object_detection/ssd_mobilenet_v1",
+      "main_script": "main.py",       
+      "batch_size": 10
+    },
+    "wide_deep_large_ds": {
+      "model_src_dir": "recommendation/wide_deep_large_ds/quantization/ptq",
+      "dataset_location": "/tf_dataset/tensorflow/wide_deep_large_ds/dataset",
+      "input_model": "/tf_dataset/tensorflow/wide_deep_large_ds/fp32_optimized_graph.pb",
+      "main_script": "inference.py",
+      "batch_size": 256,
+      "fp32_model_url": "https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_8/wide_deep_fp32_pretrained_model.pb"
+    },
+    "3dunet-mlperf": {
+      "model_src_dir": "semantic_image_segmentation/3dunet-mlperf/quantization/ptq",
+      "dataset_location": "/tf_dataset2/models/tensorflow/3dunet/build",
+      "input_model": "/tf_dataset2/models/tensorflow/3dunet/3dunet_dynamic_ndhwc.pb",
+      "main_script": "run_accuracy.py",
+      "batch_size": 100
+    },
+    "style_transfer": {
+      "model_src_dir": "style_transfer/arbitrary_style_transfer/quantization/ptq",
+      "dataset_location": "style_images,content_images",
+      "input_model": "/tf_dataset/tensorflow/style_transfer/arbitrary_style_transfer/model.ckpt",
+      "main_script": "style_tune.py",
+      "batch_size": 1
     }
   }
 }

examples/3.x_api/tensorflow/cv/densenet121/quantization/ptq/README.md

@@ -0,0 +1,78 @@
+Step-by-Step
+============
+
+This document list steps of reproducing densenet121 model tuning and benchmark results via Neural Compressor.
+This example can run on Intel CPUs and GPUs.
+
+> **Note**: 
+> The models is supported in Validated TensorFlow [Version](/docs/source/installation_guide.md#validated-software-environment).
+# Prerequisite
+
+## 1. Environment
+
+### Installation
+Recommend python 3.9 or higher version.
+```shell
+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 pre-trained model
+The densenet-series comes from [tensorflow-densenet](https://github.com/pudae/tensorflow-densenet), please also follow the step [Prepare pre-trained model](#3-prepare-pre-trained-model) to get the pb files or use openvino download tools.
+   ```shell
+   git clone https://github.com/openvinotoolkit/open_model_zoo.git
+   cd open_model_zoo/tools/downloader
+   git checkout tags/2021.2
+   pip install -r requirements.in
+   python downloader.py --name densenet-{121|161|169}-tf -o /PATH/TO/MODEL
+   ```
+
+## 3. Prepare Dataset
+
+  TensorFlow [models](https://github.com/tensorflow/models) repo provides [scripts and instructions](https://github.com/tensorflow/models/tree/master/research/slim#an-automated-script-for-processing-imagenet-data) to download, process and convert the ImageNet dataset to the TF records format.
+  We also prepared related scripts in ` examples/3.x_api/tensorflow/cv` directory. To download the raw images, the user must create an account with image-net.org. If you have downloaded the raw data and preprocessed the validation data by moving the images into the appropriate sub-directory based on the label (synset) of the image. we can use below command ro convert it to tf records format.
+
+  ```shell
+  cd examples/3.x_api/tensorflow/cv
+  # convert validation subset
+  bash prepare_dataset.sh --output_dir=./densenet121/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
+  # convert train subset
+  bash prepare_dataset.sh --output_dir=./densenet121/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
+  ```
+> **Note**: 
+> The raw ImageNet dataset resides in JPEG files should be in the following directory structure. Taking validation set as an example:<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000293.JPEG<br>
+> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;/PATH/TO/img_raw/val/n01440764/ILSVRC2012_val_00000543.JPEG<br>
+> where 'n01440764' is the unique synset label associated with these images.
+
+# Run
+
+## 1 Quantization
+
+  ```shell
+  bash run_quant.sh --input_model=/PATH/TO/densenet-121.pb \
+      --output_model=./nc_densenet121.pb --dataset_location=/path/to/ImageNet/
+  ```
+
+## 2. Benchmark
+  ```shell
+  bash run_benchmark.sh --input_model=./nc_densenet121.pb --mode=accuracy --dataset_location=/path/to/ImageNet/ --batch_size=32
+  bash run_benchmark.sh --input_model=./nc_densenet121.pb --mode=performance --dataset_location=/path/to/ImageNet/ --batch_size=1
+  ```