Refactor tutorial.md and add 3 example link (intel#806)

* refactor tutorial

docs/tutorial.md

@@ -1,155 +1,12 @@
 Tutorial
 ========
 
-This tutorial provides instructions (with examples) on how to integrate models with Intel® Neural Compressor. 
+This tutorial provides instructions (with examples), basic and end-to-end examples to help user to learn optimize models by Intel® Neural Compressor. 
 
-The following diagram shows steps for enabling model with Neural Compressor:
+## Instructions with Examples
 
-![Tutorial](imgs/tutorial.png "Tutorial") 
+[Usage Example](/docs/tutorial/usage_example.md)
 
-## Usage Examples
-
-To write launcher code, a user needs to prepare four components:
-
-*	`Dataloader/Dataset`
-*	`Model`
-*	`Postprocess`      <span style="color:red">*optional*</span>
-*	`Metric`
-
-Neural Compressor constructs the whole quantization/pruning process using these four components. 
-
-Neural Compressor has added built-in support for popular dataloaders/datasets and metrics to ease the preparation. Refer to [dataset](./dataset.md) and [metric](./metric.md) to learn how to use them in yaml. 
-
-Neural Compressor also supports registering custom datasets and custom metrics by code. 
-
-As for model, Neural Compressor abstract a common API, named [neural_compressor.experimental.common.Model](../neural_compressor/experimental/common/model.py), to cover the case in which model, weight, and other necessary info are separately stored. Refer to [model](./model.md) to learn how to use it.
-
-Postprocess is treated as a special transform by Neural Compressor which is only needed when a model output is mismatching with the expected input of Neural Compressor built-in metrics. If a user is using a custom metric, the postprocess is not needed as the custom metric implementation needed ensures it can handle the model output correctly. On the other hand, the postprocess logic becomes part of the custom metric implementation.
-
-The example below shows how to enable Neural Compressor on TensorFlow mobilenet_v1 with a built-in dataloader, dataset, and metric.
-
-```python
-# main.py
-from neural_compressor.experimental import Quantization, common
-quantizer = Quantization('./conf.yaml')
-quantizer.model = "./mobilenet_v1_1.0_224_frozen.pb"
-quantized_model = quantizer.fit()
-```
-Quantization also support Quantization_Conf class as it's argument:
-```python
-# main.py
-from lpot.experimental import Quantization, common
-from lpot.conf.config import Quantization_Conf
-conf = Quantization_Conf('./conf.yaml')
-quantizer = Quantization(conf)
-quantizer.model = "./mobilenet_v1_1.0_224_frozen.pb"
-quantized_model = quantizer.fit()
-```
-
-
-```yaml
-# conf.yaml
-model:
-  name: mobilenet_v1 
-  framework: tensorflow
-quantization:
-  calibration:
-    sampling_size: 20
-    dataloader:
-      batch_size: 1
-      dataset:
-        ImageRecord:
-          root: /path/to/imagenet/
-      transform:
-        ParseDecodeImagenet: {}
-        BilinearImagenet: 
-          height: 224
-          width: 224
-evaluation:
-  accuracy:
-    metric:
-      topk: 1
-    dataloader:
-      batch_size: 32 
-      dataset:
-        ImageRecord:
-          root: /path/to/imagenet/
-      transform:
-        ParseDecodeImagenet: {}
-        BilinearImagenet: 
-          height: 224
-          width: 224
-
-```
-
-In this example, we use an Neural Compressor built-in `ImageRecord` dataset and a `topk` metric.
-
-If the user wants to use a dataset or metric that is not supported by built-in, the user can register a custom one as demonstrated in the below helloworld example.
-
-```python
-# main.py
-from neural_compressor.experimental import Quantization, common
-
-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
-  def __getitem__(self, index):
-      return self.test_images[index], self.labels[index]
-  def __len__(self):
-      return len(self.test_images)
-
-# Define a customized Metric function 
-class MyMetric(object):
-  def __init__(self, *args):
-      self.pred_list = []
-      self.label_list = []
-      self.samples = 0
-  def update(self, predict, label):
-      self.pred_list.extend(np.argmax(predict, axis=1))
-      self.label_list.extend(label)
-      self.samples += len(label)
-  def reset(self):
-      self.pred_list = []
-      self.label_list = []
-      self.samples = 0
-  def result(self):
-      correct_num = np.sum(
-            np.array(self.pred_list) == np.array(self.label_list))
-      return correct_num / self.samples
-
-# Quantize with customized dataloader and metric
-quantizer = Quantization('./conf.yaml')
-dataset = Dataset()
-quantizer.metric = common.Metric(MyMetric)
-quantizer.calib_dataloader = common.DataLoader(dataset, batch_size=1)
-quantizer.eval_dataloader = common.DataLoader(dataset, batch_size=1)
-quantizer.model = '../models/simple_model'
-q_model = quantizer.fit()
-```
-> **Note** 
->
-> In the customized dataset, the `__getitem__()` interface must be implemented and return a single sample and label. In this example, it returns the (image, label) pair. The user can return (image, 0) for a label-free case.
-
-In the customized metric, the update() function records the predicted result of each mini-batch. The result() function is invoked by Neural Compressor at the end of the evaluation to return a scalar to reflect model accuracy. By default, this scalar is higher-is-better. If this scalar returned from the customized metric is a lower-is-better value, `tuning.accuracy_criterion.higher_is_better` in yaml should be set to `False`.
-
-```yaml
-# conf.yaml
-model:
-  name: hello_world
-  framework: tensorflow
-  inputs: input
-  outputs: output
-
-tuning:
-  accuracy_criterion:
-    relative: 0.01
-  exit_policy:
-    timeout: 100
-  random_seed: 100
-```
 
 ## Helloworld Examples
 
@@ -158,3 +15,12 @@ tuning:
 3.  Enable benchmark for performance and accuracy measurement: see [tf_example5](/examples/helloworld/tf_example5) for more details.
 4.  TensorFlow slim model: see [tf_example3](/examples/helloworld/tf_example3) for more details.
 
+## Tutorial Examples
+
+These examples are **easy** to run without preparing dataset. The script will download public dataset automatically or create fake dataset.
+
+1. Compare existed FP32 & INT8 ResNet50 model directly: [Performance of FP32 Vs. INT8 ResNet50 Model](/docs/tutorial/perf_fp32_int8_tf).
+2. An End-To-End pipeline to build up a CNN model by Pytorch to recognize fashion image and speed up AI model by Intel® Neural Compressor: [Intel® Neural Compressor Sample for PyTorch*](/docs/tutorial/pytorch/alexnet_fashion_mnist)
+3. An End-To-End pipeline to build up a CNN model by Tensorflow to recognize handwriting number and speed up AI model by Intel® Neural Compressor: [Intel® Neural Compressor Sample for TensorFlow*](/docs/tutorial/tensorflow/alexnet_mnist)
+
+

docs/tutorial/pytorch/alexnet_mnist/README.md → docs/tutorial/pytorch/alexnet_fashion_mnist/README.md

@@ -15,7 +15,7 @@ Please refer to the official website for detailed info and news: [https://github
 
 ## Introduction
 
-This is a demo to show an End-To-End pipeline to build up a CNN model to recognize handwriting number and speed up AI model by Intel® Neural Compressor.
+This is a demo to show an End-To-End pipeline to build up a CNN model by Pytorch to recognize fashion image and speed up AI model by Intel® Neural Compressor.
 
 1. Train a CNN AlexNet model by PyTorch based on dataset Fashion-MNIST.
 

docs/tutorial/tensorflow/alexnet_mnist/README.md

@@ -15,7 +15,7 @@ Please refer to the official website for detailed info and news: [https://github
 
 ## Introduction
 
-This is a demo to show an End-To-End pipeline to build up a CNN model to recognize handwriting number and speed up AI model by Intel® Neural Compressor.
+This is a demo to show an End-To-End pipeline to build up a CNN model by Tensorflow to recognize handwriting number and speed up AI model by Intel® Neural Compressor.
 
 1. Train a CNN AlexNet model by Keras and Intel Optimization for Tensorflow based on dataset MNIST.
 

docs/tutorial/usage_example.md

@@ -0,0 +1,157 @@
+# Usage Example
+
+## Steps
+The following diagram shows steps for enabling model with Neural Compressor:
+
+<img src="/docs/imgs/tutorial.png" alt="Tutorial" width="50%"/>
+
+## Example
+
+To write launcher code, a user needs to prepare four components:
+
+*	`Dataloader/Dataset`
+*	`Model`
+*	`Postprocess`      <span style="color:red">*optional*</span>
+*	`Metric`
+
+Neural Compressor constructs the whole quantization/pruning process using these four components. 
+
+Neural Compressor has added built-in support for popular dataloaders/datasets and metrics to ease the preparation. Refer to [dataset](./dataset.md) and [metric](./metric.md) to learn how to use them in yaml. 
+
+Neural Compressor also supports registering custom datasets and custom metrics by code. 
+
+As for model, Neural Compressor abstract a common API, named [neural_compressor.experimental.common.Model](../neural_compressor/experimental/common/model.py), to cover the case in which model, weight, and other necessary info are separately stored. Refer to [model](./model.md) to learn how to use it.
+
+Postprocess is treated as a special transform by Neural Compressor which is only needed when a model output is mismatching with the expected input of Neural Compressor built-in metrics. If a user is using a custom metric, the postprocess is not needed as the custom metric implementation needed ensures it can handle the model output correctly. On the other hand, the postprocess logic becomes part of the custom metric implementation.
+
+1. Basic + built-in dataloader, dataset and metric
+
+The example below shows how to enable Neural Compressor on TensorFlow mobilenet_v1 with a built-in dataloader, dataset, and metric.
+
+```python
+# main.py
+from neural_compressor.experimental import Quantization, common
+quantizer = Quantization('./conf.yaml')
+quantizer.model = "./mobilenet_v1_1.0_224_frozen.pb"
+quantized_model = quantizer.fit()
+```
+
+2. Basic + Quantization_Conf + built-in dataloader, dataset and metric 
+
+Quantization also support Quantization_Conf class as it's argument:
+```python
+# main.py
+from lpot.experimental import Quantization, common
+from lpot.conf.config import Quantization_Conf
+conf = Quantization_Conf('./conf.yaml')
+quantizer = Quantization(conf)
+quantizer.model = "./mobilenet_v1_1.0_224_frozen.pb"
+quantized_model = quantizer.fit()
+```
+
+
+```yaml
+# conf.yaml
+model:
+  name: mobilenet_v1 
+  framework: tensorflow
+quantization:
+  calibration:
+    sampling_size: 20
+    dataloader:
+      batch_size: 1
+      dataset:
+        ImageRecord:
+          root: /path/to/imagenet/
+      transform:
+        ParseDecodeImagenet: {}
+        BilinearImagenet: 
+          height: 224
+          width: 224
+evaluation:
+  accuracy:
+    metric:
+      topk: 1
+    dataloader:
+      batch_size: 32 
+      dataset:
+        ImageRecord:
+          root: /path/to/imagenet/
+      transform:
+        ParseDecodeImagenet: {}
+        BilinearImagenet: 
+          height: 224
+          width: 224
+
+```
+
+In this example, we use an Neural Compressor built-in `ImageRecord` dataset and a `topk` metric.
+
+3. Basic + customed dataloader and metric
+
+If the user wants to use a dataset or metric that is not supported by built-in, the user can register a custom one as demonstrated in the below helloworld example.
+
+```python
+# main.py
+from neural_compressor.experimental import Quantization, common
+
+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
+  def __getitem__(self, index):
+      return self.test_images[index], self.labels[index]
+  def __len__(self):
+      return len(self.test_images)
+
+# Define a customized Metric function 
+class MyMetric(object):
+  def __init__(self, *args):
+      self.pred_list = []
+      self.label_list = []
+      self.samples = 0
+  def update(self, predict, label):
+      self.pred_list.extend(np.argmax(predict, axis=1))
+      self.label_list.extend(label)
+      self.samples += len(label)
+  def reset(self):
+      self.pred_list = []
+      self.label_list = []
+      self.samples = 0
+  def result(self):
+      correct_num = np.sum(
+            np.array(self.pred_list) == np.array(self.label_list))
+      return correct_num / self.samples
+
+# Quantize with customized dataloader and metric
+quantizer = Quantization('./conf.yaml')
+dataset = Dataset()
+quantizer.metric = common.Metric(MyMetric)
+quantizer.calib_dataloader = common.DataLoader(dataset, batch_size=1)
+quantizer.eval_dataloader = common.DataLoader(dataset, batch_size=1)
+quantizer.model = '../models/simple_model'
+q_model = quantizer.fit()
+```
+> **Note** 
+>
+> In the customized dataset, the `__getitem__()` interface must be implemented and return a single sample and label. In this example, it returns the (image, label) pair. The user can return (image, 0) for a label-free case.
+
+In the customized metric, the update() function records the predicted result of each mini-batch. The result() function is invoked by Neural Compressor at the end of the evaluation to return a scalar to reflect model accuracy. By default, this scalar is higher-is-better. If this scalar returned from the customized metric is a lower-is-better value, `tuning.accuracy_criterion.higher_is_better` in yaml should be set to `False`.
+
+```yaml
+# conf.yaml
+model:
+  name: hello_world
+  framework: tensorflow
+  inputs: input
+  outputs: output
+
+tuning:
+  accuracy_criterion:
+    relative: 0.01
+  exit_policy:
+    timeout: 100
+  random_seed: 100
+```