add W8A8 support and example

Signed-off-by: Mengni Wang <[email protected]>

examples/image_recognition/resnet50/quantization/ptq_static/README.md

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+# Step-by-Step
+
+This example load an image classification model from [ONNX Model Zoo](https://github.com/onnx/models) and confirm its accuracy and speed based on [ILSVR2012 validation Imagenet dataset](http://www.image-net.org/challenges/LSVRC/2012/downloads). You need to download this dataset yourself.
+
+# Prerequisite
+
+## 1. Environment
+
+```shell
+pip install onnx-neural-compressor
+pip install -r requirements.txt
+```
+
+> Note: Validated ONNX Runtime [Version](/docs/source/installation_guide.md#validated-software-environment).
+
+## 2. Prepare Model
+
+```shell
+python prepare_model.py --output_model='resnet50-v1-12.onnx'
+```
+
+## 3. Prepare Dataset
+
+Download dataset [ILSVR2012 validation Imagenet dataset](http://www.image-net.org/challenges/LSVRC/2012/downloads).
+
+Download label:
+
+```shell
+wget http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz
+tar -xvzf caffe_ilsvrc12.tar.gz val.txt
+```
+
+# Run
+
+
+## 1. Quantization
+
+Quantize model with QLinearOps:
+
+```bash
+bash run_quant.sh --input_model=path/to/model \  # model path as *.onnx
+                   --dataset_location=/path/to/imagenet \
+                   --label_path=/path/to/val.txt \
+                   --output_model=path/to/save
+```
+
+Quantize model with QDQ mode:
+
+```bash
+bash run_quant.sh --input_model=path/to/model \  # model path as *.onnx
+                   --dataset_location=/path/to/imagenet \
+                   --label_path=/path/to/val.txt \
+                   --output_model=path/to/save \
+                   --quant_format=QDQ
+```
+
+## 2. Benchmark
+
+```bash
+bash run_benchmark.sh --input_model=path/to/model \  # model path as *.onnx
+                      --dataset_location=/path/to/imagenet \
+                      --label_path=/path/to/val.txt \
+                      --mode=performance # or accuracy
+```