# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import tarfile import numpy as np import six from PIL import Image from six.moves import cPickle as pickle import paddle from paddle.io import Dataset from paddle.dataset.common import _check_exists_and_download __all__ = ['Cifar10', 'Cifar100'] URL_PREFIX = 'https://dataset.bj.bcebos.com/cifar/' CIFAR10_URL = URL_PREFIX + 'cifar-10-python.tar.gz' CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a' CIFAR100_URL = URL_PREFIX + 'cifar-100-python.tar.gz' CIFAR100_MD5 = 'eb9058c3a382ffc7106e4002c42a8d85' MODE_FLAG_MAP = { 'train10': 'data_batch', 'test10': 'test_batch', 'train100': 'train', 'test100': 'test' } class Cifar10(Dataset): """ Implementation of `Cifar-10 `_ dataset, which has 10 categories. Args: data_file(str): path to data file, can be set None if :attr:`download` is True. Default None mode(str): 'train', 'test' mode. Default 'train'. transform(callable): transform to perform on image, None for on transform. download(bool): whether to download dataset automatically if :attr:`data_file` is not set. Default True backend(str, optional): Specifies which type of image to be returned: PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}. If this option is not set, will get backend from ``paddle.vsion.get_image_backend`` , default backend is 'pil'. Default: None. Returns: Dataset: instance of cifar-10 dataset Examples: .. code-block:: python import paddle import paddle.nn as nn from paddle.vision.datasets import Cifar10 from paddle.vision.transforms import Normalize class SimpleNet(paddle.nn.Layer): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Sequential( nn.Linear(3072, 10), nn.Softmax()) def forward(self, image, label): image = paddle.reshape(image, (1, -1)) return self.fc(image), label paddle.disable_static() normalize = Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], data_format='HWC') cifar10 = Cifar10(mode='train', transform=normalize) for i in range(10): image, label = cifar10[i] image = paddle.to_tensor(image) label = paddle.to_tensor(label) model = SimpleNet() image, label = model(image, label) print(image.numpy().shape, label.numpy().shape) """ def __init__(self, data_file=None, mode='train', transform=None, download=True, backend=None): assert mode.lower() in ['train', 'test', 'train', 'test'], \ "mode should be 'train10', 'test10', 'train100' or 'test100', but got {}".format(mode) self.mode = mode.lower() if backend is None: backend = paddle.vision.get_image_backend() if backend not in ['pil', 'cv2']: raise ValueError( "Expected backend are one of ['pil', 'cv2'], but got {}" .format(backend)) self.backend = backend self._init_url_md5_flag() self.data_file = data_file if self.data_file is None: assert download, "data_file is not set and downloading automatically is disabled" self.data_file = _check_exists_and_download( data_file, self.data_url, self.data_md5, 'cifar', download) self.transform = transform # read dataset into memory self._load_data() self.dtype = paddle.get_default_dtype() def _init_url_md5_flag(self): self.data_url = CIFAR10_URL self.data_md5 = CIFAR10_MD5 self.flag = MODE_FLAG_MAP[self.mode + '10'] def _load_data(self): self.data = [] with tarfile.open(self.data_file, mode='r') as f: names = (each_item.name for each_item in f if self.flag in each_item.name) for name in names: if six.PY2: batch = pickle.load(f.extractfile(name)) else: batch = pickle.load(f.extractfile(name), encoding='bytes') data = batch[six.b('data')] labels = batch.get( six.b('labels'), batch.get(six.b('fine_labels'), None)) assert labels is not None for sample, label in six.moves.zip(data, labels): self.data.append((sample, label)) def __getitem__(self, idx): image, label = self.data[idx] image = np.reshape(image, [3, 32, 32]) image = image.transpose([1, 2, 0]) if self.backend == 'pil': image = Image.fromarray(image) if self.transform is not None: image = self.transform(image) if self.backend == 'pil': return image, np.array(label).astype('int64') return image.astype(self.dtype), np.array(label).astype('int64') def __len__(self): return len(self.data) class Cifar100(Cifar10): """ Implementation of `Cifar-100 `_ dataset, which has 100 categories. Args: data_file(str): path to data file, can be set None if :attr:`download` is True. Default None mode(str): 'train', 'test' mode. Default 'train'. transform(callable): transform to perform on image, None for on transform. download(bool): whether to download dataset automatically if :attr:`data_file` is not set. Default True backend(str, optional): Specifies which type of image to be returned: PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}. If this option is not set, will get backend from ``paddle.vsion.get_image_backend`` , default backend is 'pil'. Default: None. Returns: Dataset: instance of cifar-100 dataset Examples: .. code-block:: python import paddle import paddle.nn as nn from paddle.vision.datasets import Cifar100 from paddle.vision.transforms import Normalize class SimpleNet(paddle.nn.Layer): def __init__(self): super(SimpleNet, self).__init__() self.fc = nn.Sequential( nn.Linear(3072, 10), nn.Softmax()) def forward(self, image, label): image = paddle.reshape(image, (1, -1)) return self.fc(image), label paddle.disable_static() normalize = Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], data_format='HWC') cifar100 = Cifar100(mode='train', transform=normalize) for i in range(10): image, label = cifar100[i] image = paddle.to_tensor(image) label = paddle.to_tensor(label) model = SimpleNet() image, label = model(image, label) print(image.numpy().shape, label.numpy().shape) """ def __init__(self, data_file=None, mode='train', transform=None, download=True, backend=None): super(Cifar100, self).__init__(data_file, mode, transform, download, backend) def _init_url_md5_flag(self): self.data_url = CIFAR100_URL self.data_md5 = CIFAR100_MD5 self.flag = MODE_FLAG_MAP[self.mode + '100']