PaddleClas/ppcls/data/dataloader/customized_cifar10.py

66 lines
2.3 KiB
Python

# Copyright (c) 2021 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.
import paddle
from paddle.vision.datasets import Cifar10
from paddle.vision import transforms
from paddle.dataset.common import _check_exists_and_download
import numpy as np
import os
from PIL import Image
class CustomizedCifar10(Cifar10):
def __init__(self,
data_file=None,
mode='train',
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 = transforms.Compose([
transforms.Resize(224), transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
self._load_data()
self.dtype = paddle.get_default_dtype()
def __getitem__(self, index):
img, target = self.data[index]
img = np.reshape(img, [3, 32, 32])
img = img.transpose([1, 2, 0]).astype("uint8")
img = Image.fromarray(img)
img = self.transform(img)
return (img, target)