tf_example2

tf_example2 example

Step-by-Step

This is Hello World to demonstrate how to quickly start with Intel® Neural Compressor. It is a Keras model on mnist dataset defined by helloworld/train.py, we will implement a customized metric and a customized dataloader for quantization and evaluation.

Prerequisite

1. Installation

pip install -r requirements.txt

Note: Validated TensorFlow Version.

2. Prepare FP32 model

cd <WORK_DIR>/examples/helloworld
python train.py

Run Command

The cmd of quantization and predict with the quantized model

python test.py 

Introduction

This example can demonstrate the steps to do quantization on Keras generated saved model with customized dataloader and metric.

1. Define a customer dataloader for mnist

    class Dataset(object):
      def __init__(self):
          (train_images, train_labels), (test_images,
                     test_labels) = keras.datasets.fashion_mnist.load_data()
          self.test_images = test_images.astype(np.float32) / 255.0
          self.labels = test_labels
          pass
    
      def __getitem__(self, index):
          return self.test_images[index], self.labels[index]
    
      def __len__(self):
          return len(self.test_images)

2. Define a customized metric

This customized metric will calculate accuracy.

    class MyMetric(object):
      def __init__(self, *args):
          self.pred_list = []
          self.label_list = []
          self.samples = 0
          pass
    
      def update(self, predict, label):
          self.pred_list.extend(np.argmax(predict, axis=1))
          self.label_list.extend(label)
          self.samples += len(label) 
          pass
    
      def reset(self):
          self.pred_list = []
          self.label_list = []
          self.samples = 0
          pass
    
      def result(self):
          correct_num = np.sum(
                np.array(self.pred_list) == np.array(self.label_list))
          return correct_num / self.samples

3. Use the customized data loader and metric for quantization

    dataset = Dataset()
    dataloader = DataLoader(framework='tensorflow', dataset=dataset)
    config = PostTrainingQuantConfig(backend='itex')
    q_model = fit(
        model='../models/saved_model',
        conf=config,
        calib_dataloader=dataloader,
        eval_dataloader=dataloader,
        eval_metric=MyMetric())

4. Run quantized model

Please get the input and output op name from nc_workspace/tensorflow/hello_world/deploy.yaml

Run inference on the quantized model

    keras_model = q_model.model
    predictions = keras_model.predict_on_batch(dataset.test_images)
    print("Inference is done.")