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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # 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.
- # ============================================================================
- """
- Data operations, will be used in train.py and eval.py
- """
- import mindspore.common.dtype as mstype
- import mindspore.dataset.engine as de
- import mindspore.dataset.transforms.c_transforms as C2
- import mindspore.dataset.vision.c_transforms as C
-
-
- def create_dataset(dataset_path, config, do_train, repeat_num=1):
- """
- create a train or eval dataset
-
- Args:
- dataset_path(string): the path of dataset.
- config(dict): config of dataset.
- do_train(bool): whether dataset is used for train or eval.
- repeat_num(int): the repeat times of dataset. Default: 1.
-
- Returns:
- dataset
- """
- rank = config.rank
- group_size = config.group_size
- if group_size == 1:
- ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=config.work_nums, shuffle=True)
- else:
- ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=config.work_nums, shuffle=True,
- num_shards=group_size, shard_id=rank)
- # define map operations
- if do_train:
- trans = [
- C.RandomCropDecodeResize(config.image_size),
- C.RandomHorizontalFlip(prob=0.5),
- C.RandomColorAdjust(brightness=0.4, saturation=0.5) # fast mode
- # C.RandomColorAdjust(brightness=0.4, contrast=0.5, saturation=0.5, hue=0.2)
- ]
- else:
- trans = [
- C.Decode(),
- C.Resize(int(config.image_size / 0.875)),
- C.CenterCrop(config.image_size)
- ]
- trans += [
- C.Rescale(1.0 / 255.0, 0.0),
- C.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
- C.HWC2CHW()
- ]
- type_cast_op = C2.TypeCast(mstype.int32)
- ds = ds.map(input_columns="image", operations=trans, num_parallel_workers=config.work_nums)
- ds = ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=config.work_nums)
- # apply batch operations
- ds = ds.batch(config.batch_size, drop_remainder=True)
- # apply dataset repeat operation
- ds = ds.repeat(repeat_num)
- return ds
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