[Nano] Add a generalized how-to guide for accelerate PyTorch cv data process pipeline

[Oscilloscope98]

Description

Add a generalized how-to guide for accelerate PyTorch cv data process pipeline。

Related python scripts:

• https://github.com/intel-analytics/BigDL/blob/main/python/nano/tutorial/training/pytorch-lightning/lightning_cv_data_pipeline.py
• https://github.com/intel-analytics/BigDL/blob/main/python/nano/tutorial/training/pytorch/pytorch_cv_data_pipeline.py

1. Why the change?

The cv data process pipeline acceleration are exactly the same for PyTorch and PyTorch Lightning applications. There is no need to add separated how-to guides in PyTorch/PyTorch Lightning Training sections.

2. Summary of the change

• Add a Nano how-to guide section "Preprocessing"

• Add how to guide “How to accelerate a computer vision data processing pipeline” for PyTorch

• Restyled quote blocks for better note/warning/related reading box styles
  Before:
  
  

  After:
  
  

3. How to test?

• Document test: https://yuwentestdocs.readthedocs.io/en/nano-pytorch-cv-pipeline/doc/Nano/Howto/index.html
• Github Notebook preview: https://github.com/Oscilloscope98/BigDL/blob/nano-pytorch-cv-pipeline/python/nano/tutorial/notebook/preprocessing/pytorch/accelerate_pytorch_cv_data_pipeline.ipynb
• Notebook test locally (conda create an empty environment with python=3.7)

[rnwang04]

1. maybe we can split below code into two parts to make it more clear :

xxxx

# from torchvision import transforms
# from torchvision.datasets import OxfordIIITPet
from bigdl.nano.pytorch.vision import transforms
from bigdl.nano.pytorch.vision.datasets import OxfordIIITPet

# Data processing steps are the same as using torchvision
train_transform = transforms.Compose([transforms.Resize(256),
                                        transforms.RandomCrop(224),
                                        transforms.RandomHorizontalFlip(),
                                        transforms.ColorJitter(brightness=.5, hue=.3),
                                        transforms.ToTensor(),
                                        transforms.Normalize([0.485, 0.456, 0.406],
                                                            [0.229, 0.224, 0.225])])
val_transform = transforms.Compose([transforms.Resize(256),
                                    transforms.CenterCrop(224),
                                    transforms.ToTensor(),
                                    transforms.Normalize([0.485, 0.456, 0.406],
                                                            [0.229, 0.224, 0.225])])

train_dataset = OxfordIIITPet(root="/tmp/data", transform=train_transform, download=True)
val_dataset = OxfordIIITPet(root="/tmp/data", transform=val_transform)

xxx

# obtain training indices that will be used for validation
import torch

indices = torch.randperm(len(train_dataset))
val_size = len(train_dataset) // 4
train_dataset = torch.utils.data.Subset(train_dataset, indices[:-val_size])
val_dataset = torch.utils.data.Subset(val_dataset, indices[-val_size:])

# create dataloaders
from torch.utils.data.dataloader import DataLoader

train_dataloader = DataLoader(train_dataset, batch_size=32)
val_dataloader = DataLoader(val_dataset, batch_size=32)

2. what preprocess acceleration does bigdl.nano.pytorch.vision do? shall we explain this a bit?

[rnwang04]

LGTM