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- #! /usr/bin/python
- # -*- coding: utf-8 -*-
-
- import os
- # os.environ['TL_BACKEND'] = 'tensorflow'
- os.environ['TL_BACKEND'] = 'mindspore'
-
- import tensorlayerx as tlx
- from tensorlayerx.dataflow import Dataset, DataLoader
- from tensorlayerx.vision.transforms import (
- Compose, Resize, RandomFlipHorizontal, RandomContrast, RandomBrightness, StandardizePerImage, RandomCrop
- )
-
- X_train, y_train, X_test, y_test = tlx.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)
-
-
- train_transforms = Compose(
- [
- RandomCrop(size=[24, 24]),
- RandomFlipHorizontal(),
- RandomBrightness(brightness_factor=(0.5, 1.5)),
- RandomContrast(contrast_factor=(0.5, 1.5)),
- StandardizePerImage()
- ])
-
-
- class make_dataset(Dataset):
- def __init__(self, data, label, transforms):
- self.data = data
- self.label = label
- self.transforms = transforms
-
- def __getitem__(self, idx):
- x = self.data[idx].astype('uint8')
- y = self.label[idx].astype('int64')
- x = self.transforms(x)
-
- return x, y
-
- def __len__(self):
-
- return len(self.label)
-
-
- train_dataset = make_dataset(data=X_train, label=y_train, transforms=train_transforms)
- train_dataset = DataLoader(train_dataset, batch_size=128, shuffle=True)
-
- for X_batch, y_batch in train_dataset:
- print(X_batch.shape, type(X_batch), y_batch)
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