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131 lines
4.6 KiB
131 lines
4.6 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 os
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import io
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import tarfile
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import numpy as np
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import scipy.io as scio
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from PIL import Image
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from paddle.io import Dataset
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from .utils import _check_exists_and_download
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__all__ = ["Flowers"]
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DATA_URL = 'http://paddlemodels.bj.bcebos.com/flowers/102flowers.tgz'
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LABEL_URL = 'http://paddlemodels.bj.bcebos.com/flowers/imagelabels.mat'
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SETID_URL = 'http://paddlemodels.bj.bcebos.com/flowers/setid.mat'
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DATA_MD5 = '52808999861908f626f3c1f4e79d11fa'
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LABEL_MD5 = 'e0620be6f572b9609742df49c70aed4d'
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SETID_MD5 = 'a5357ecc9cb78c4bef273ce3793fc85c'
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# In official 'readme', tstid is the flag of test data
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# and trnid is the flag of train data. But test data is more than train data.
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# So we exchange the train data and test data.
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MODE_FLAG_MAP = {'train': 'tstid', 'test': 'trnid', 'valid': 'valid'}
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class Flowers(Dataset):
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"""
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Implementation of `Flowers <https://www.robots.ox.ac.uk/~vgg/data/flowers/>`_
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dataset
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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
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label_file(str): path to label file, can be set None if
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:attr:`download` is True. Default None
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setid_file(str): path to subset index file, can be set
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None if :attr:`download` is True. Default None
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mode(str): 'train', 'valid' or 'test' mode. Default 'train'.
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transform(callable): transform to perform on image, None for on transform.
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download(bool): whether to download dataset automatically if
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:attr:`data_file` is not set. Default True
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Examples:
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.. code-block:: python
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from paddle.incubate.hapi.datasets import Flowers
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flowers = Flowers(mode='test')
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for i in range(len(flowers)):
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sample = flowers[i]
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print(sample[0].shape, sample[1])
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"""
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def __init__(self,
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data_file=None,
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label_file=None,
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setid_file=None,
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mode='train',
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transform=None,
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download=True):
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assert mode.lower() in ['train', 'valid', 'test'], \
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"mode should be 'train', 'valid' or 'test', but got {}".format(mode)
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self.flag = MODE_FLAG_MAP[mode.lower()]
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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, DATA_URL, DATA_MD5, 'flowers', download)
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self.label_file = label_file
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if self.label_file is None:
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assert download, "label_file is not set and downloading automatically is disabled"
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self.label_file = _check_exists_and_download(
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label_file, LABEL_URL, LABEL_MD5, 'flowers', download)
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self.setid_file = setid_file
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if self.setid_file is None:
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assert download, "setid_file is not set and downloading automatically is disabled"
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self.setid_file = _check_exists_and_download(
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setid_file, SETID_URL, SETID_MD5, 'flowers', download)
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self.transform = transform
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# read dataset into memory
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self._load_anno()
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def _load_anno(self):
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self.name2mem = {}
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self.data_tar = tarfile.open(self.data_file)
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for ele in self.data_tar.getmembers():
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self.name2mem[ele.name] = ele
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self.labels = scio.loadmat(self.label_file)['labels'][0]
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self.indexes = scio.loadmat(self.setid_file)[self.flag][0]
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def __getitem__(self, idx):
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index = self.indexes[idx]
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label = np.array([self.labels[index - 1]])
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img_name = "jpg/image_%05d.jpg" % index
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img_ele = self.name2mem[img_name]
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image = self.data_tar.extractfile(img_ele).read()
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image = np.array(Image.open(io.BytesIO(image)))
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if self.transform is not None:
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image = self.transform(image)
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return image, label.astype('int64')
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def __len__(self):
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return len(self.indexes)
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