tf_example1

tf_example1 example

This example is used to demonstrate how to utilize Neural Compressor builtin dataloader and metric to enabling quantization without coding effort.

1. Installation

pip install -r requirements.txt

2. Prepare Dataset

TensorFlow models repo provides scripts and instructions to download, process and convert the ImageNet dataset to the TF records format. We also prepared related scripts in TF image_recognition example.

3. Download the FP32 model

wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/mobilenet_v1_1.0_224_frozen.pb

4. Update the root of dataset in conf.yaml

The configuration will create a dataloader of Imagenet and it will do Bilinear resampling to resize the image to 224x224. And it will create a TopK metric function for evaluation.

quantization:                                        # optional. tuning constraints on model-wise for advance user to reduce tuning space.
  calibration:
    sampling_size: 20                                # optional. default value is 100. used to set how many samples should be used in calibration.
    dataloader:
      dataset:
        ImageRecord:
          root: <DATASET>/TF_imagenet/val/           # NOTE: modify to calibration dataset location if needed
      transform:
        BilinearImagenet: 
          height: 224
          width: 224
  model_wise:                                        # optional. tuning constraints on model-wise for advance user to reduce tuning space.
    activation:
      algorithm: minmax
    weight:
      granularity: per_channel

evaluation:                                          # optional. required if user doesn't provide eval_func in neural_compressor.Quantization.
  accuracy:                                          # optional. required if user doesn't provide eval_func in neural_compressor.Quantization.
    metric:
      topk: 1                                        # built-in metrics are topk, map, f1, allow user to register new metric.
    dataloader:
      batch_size: 32
      dataset:
        ImageRecord:
          root: <DATASET>/TF_imagenet/val/           # NOTE: modify to evaluation dataset location if needed
      transform:
        BilinearImagenet: 
          height: 224
          width: 224

5. Run Command

python test.py

6. Introduction

We only need to add the following lines for quantization to create an int8 model.

    from neural_compressor.experimental import Quantization, common
    quantizer = Quantization('./conf.yaml')
    quantizer.model = common.Model("./mobilenet_v1_1.0_224_frozen.pb")
    quantized_model = quantizer.fit()