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100 lines
3.5 KiB
100 lines
3.5 KiB
3 years ago
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# Copyright (c) 2022 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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# 超分辨率数据集定义
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class SRdataset(object):
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def __init__(self,
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mode,
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gt_floder,
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lq_floder,
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transforms,
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scale,
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num_workers=4,
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batch_size=8):
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if mode == 'train':
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preprocess = []
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preprocess.append({
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'name': 'LoadImageFromFile',
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'key': 'lq'
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}) # 加载方式
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preprocess.append({'name': 'LoadImageFromFile', 'key': 'gt'})
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preprocess.append(transforms) # 变换方式
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self.dataset = {
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'name': 'SRDataset',
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'gt_folder': gt_floder,
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'lq_folder': lq_floder,
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'num_workers': num_workers,
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'batch_size': batch_size,
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'scale': scale,
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'preprocess': preprocess
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}
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if mode == "test":
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preprocess = []
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preprocess.append({'name': 'LoadImageFromFile', 'key': 'lq'})
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preprocess.append({'name': 'LoadImageFromFile', 'key': 'gt'})
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preprocess.append(transforms)
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self.dataset = {
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'name': 'SRDataset',
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'gt_folder': gt_floder,
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'lq_folder': lq_floder,
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'scale': scale,
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'preprocess': preprocess
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}
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def __call__(self):
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return self.dataset
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# 对定义的transforms处理方式组合，返回字典
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class ComposeTrans(object):
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def __init__(self, input_keys, output_keys, pipelines):
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if not isinstance(pipelines, list):
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raise TypeError(
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'Type of transforms is invalid. Must be List, but received is {}'
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.format(type(pipelines)))
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if len(pipelines) < 1:
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raise ValueError(
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'Length of transforms must not be less than 1, but received is {}'
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.format(len(pipelines)))
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self.transforms = pipelines
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self.output_length = len(output_keys) # 当output_keys的长度为3时，是DRN训练
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self.input_keys = input_keys
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self.output_keys = output_keys
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def __call__(self):
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pipeline = []
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for op in self.transforms:
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if op['name'] == 'SRPairedRandomCrop':
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op['keys'] = ['image'] * 2
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else:
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op['keys'] = ['image'] * self.output_length
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pipeline.append(op)
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if self.output_length == 2:
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transform_dict = {
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'name': 'Transforms',
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'input_keys': self.input_keys,
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'pipeline': pipeline
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}
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else:
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transform_dict = {
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'name': 'Transforms',
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'input_keys': self.input_keys,
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'output_keys': self.output_keys,
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'pipeline': pipeline
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}
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return transform_dict
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