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244 lines
8.5 KiB
244 lines
8.5 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import tarfile
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import numpy as np
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import six
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from PIL import Image
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from six.moves import cPickle as pickle
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import paddle
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from paddle.io import Dataset
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from paddle.dataset.common import _check_exists_and_download
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__all__ = ['Cifar10', 'Cifar100']
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URL_PREFIX = 'https://dataset.bj.bcebos.com/cifar/'
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CIFAR10_URL = URL_PREFIX + 'cifar-10-python.tar.gz'
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CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a'
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CIFAR100_URL = URL_PREFIX + 'cifar-100-python.tar.gz'
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CIFAR100_MD5 = 'eb9058c3a382ffc7106e4002c42a8d85'
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MODE_FLAG_MAP = {
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'train10': 'data_batch',
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'test10': 'test_batch',
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'train100': 'train',
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'test100': 'test'
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}
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class Cifar10(Dataset):
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"""
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Implementation of `Cifar-10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_
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dataset, which has 10 categories.
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Args:
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data_file(str): path to data file, can be set None if
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:attr:`download` is True. Default None, default data path: ~/.cache/paddle/dataset/cifar
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mode(str): 'train', 'test' mode. Default 'train'.
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transform(callable): transform to perform on image, None for no transform.
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download(bool): download dataset automatically if :attr:`data_file` is None. Default True
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backend(str, optional): Specifies which type of image to be returned:
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PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
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If this option is not set, will get backend from ``paddle.vsion.get_image_backend`` ,
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default backend is 'pil'. Default: None.
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Returns:
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Dataset: instance of cifar-10 dataset
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Examples:
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.. code-block:: python
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import paddle
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import paddle.nn as nn
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from paddle.vision.datasets import Cifar10
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from paddle.vision.transforms import Normalize
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class SimpleNet(paddle.nn.Layer):
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def __init__(self):
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super(SimpleNet, self).__init__()
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self.fc = nn.Sequential(
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nn.Linear(3072, 10),
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nn.Softmax())
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def forward(self, image, label):
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image = paddle.reshape(image, (1, -1))
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return self.fc(image), label
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normalize = Normalize(mean=[0.5, 0.5, 0.5],
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std=[0.5, 0.5, 0.5],
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data_format='HWC')
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cifar10 = Cifar10(mode='train', transform=normalize)
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for i in range(10):
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image, label = cifar10[i]
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image = paddle.to_tensor(image)
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label = paddle.to_tensor(label)
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model = SimpleNet()
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image, label = model(image, label)
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print(image.numpy().shape, label.numpy().shape)
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"""
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def __init__(self,
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data_file=None,
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mode='train',
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transform=None,
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download=True,
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backend=None):
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assert mode.lower() in ['train', 'test', 'train', 'test'], \
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"mode should be 'train10', 'test10', 'train100' or 'test100', but got {}".format(mode)
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self.mode = mode.lower()
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if backend is None:
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backend = paddle.vision.get_image_backend()
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if backend not in ['pil', 'cv2']:
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raise ValueError(
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"Expected backend are one of ['pil', 'cv2'], but got {}"
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.format(backend))
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self.backend = backend
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self._init_url_md5_flag()
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self.data_file = data_file
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if self.data_file is None:
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assert download, "data_file is not set and downloading automatically is disabled"
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self.data_file = _check_exists_and_download(
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data_file, self.data_url, self.data_md5, 'cifar', download)
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self.transform = transform
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# read dataset into memory
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self._load_data()
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self.dtype = paddle.get_default_dtype()
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def _init_url_md5_flag(self):
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self.data_url = CIFAR10_URL
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self.data_md5 = CIFAR10_MD5
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self.flag = MODE_FLAG_MAP[self.mode + '10']
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def _load_data(self):
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self.data = []
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with tarfile.open(self.data_file, mode='r') as f:
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names = (each_item.name for each_item in f
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if self.flag in each_item.name)
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for name in names:
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if six.PY2:
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batch = pickle.load(f.extractfile(name))
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else:
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batch = pickle.load(f.extractfile(name), encoding='bytes')
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data = batch[six.b('data')]
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labels = batch.get(
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six.b('labels'), batch.get(six.b('fine_labels'), None))
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assert labels is not None
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for sample, label in six.moves.zip(data, labels):
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self.data.append((sample, label))
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def __getitem__(self, idx):
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image, label = self.data[idx]
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image = np.reshape(image, [3, 32, 32])
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image = image.transpose([1, 2, 0])
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if self.backend == 'pil':
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image = Image.fromarray(image.astype('uint8'))
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if self.transform is not None:
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image = self.transform(image)
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if self.backend == 'pil':
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return image, np.array(label).astype('int64')
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return image.astype(self.dtype), np.array(label).astype('int64')
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def __len__(self):
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return len(self.data)
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class Cifar100(Cifar10):
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"""
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Implementation of `Cifar-100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_
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dataset, which has 100 categories.
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Args:
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data_file(str): path to data file, can be set None if
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:attr:`download` is True. Default None, default data path: ~/.cache/paddle/dataset/cifar
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mode(str): 'train', 'test' mode. Default 'train'.
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transform(callable): transform to perform on image, None for no transform.
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download(bool): download dataset automatically if :attr:`data_file` is None. Default True
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backend(str, optional): Specifies which type of image to be returned:
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PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
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If this option is not set, will get backend from ``paddle.vsion.get_image_backend`` ,
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default backend is 'pil'. Default: None.
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Returns:
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Dataset: instance of cifar-100 dataset
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Examples:
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.. code-block:: python
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import paddle
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import paddle.nn as nn
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from paddle.vision.datasets import Cifar100
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from paddle.vision.transforms import Normalize
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class SimpleNet(paddle.nn.Layer):
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def __init__(self):
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super(SimpleNet, self).__init__()
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self.fc = nn.Sequential(
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nn.Linear(3072, 10),
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nn.Softmax())
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def forward(self, image, label):
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image = paddle.reshape(image, (1, -1))
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return self.fc(image), label
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normalize = Normalize(mean=[0.5, 0.5, 0.5],
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std=[0.5, 0.5, 0.5],
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data_format='HWC')
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cifar100 = Cifar100(mode='train', transform=normalize)
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for i in range(10):
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image, label = cifar100[i]
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image = paddle.to_tensor(image)
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label = paddle.to_tensor(label)
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model = SimpleNet()
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image, label = model(image, label)
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print(image.numpy().shape, label.numpy().shape)
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"""
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def __init__(self,
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data_file=None,
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mode='train',
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transform=None,
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download=True,
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backend=None):
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super(Cifar100, self).__init__(data_file, mode, transform, download,
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backend)
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def _init_url_md5_flag(self):
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self.data_url = CIFAR100_URL
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self.data_md5 = CIFAR100_MD5
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self.flag = MODE_FLAG_MAP[self.mode + '100']
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