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67 lines
2.6 KiB
67 lines
2.6 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""Data operations, will be used in train.py and eval.py"""
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from src.config import config
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import mindspore.common.dtype as mstype
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.c_transforms as C2
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import mindspore.dataset.vision.c_transforms as C
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def create_dataset(dataset_path, do_train, device_num=1, rank=0):
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"""
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create a train or eval dataset
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Args:
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dataset_path(string): the path of dataset.
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do_train(bool): whether dataset is used for train or eval.
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rank (int): The shard ID within num_shards (default=None).
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group_size (int): Number of shards that the dataset should be divided into (default=None).
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repeat_num(int): the repeat times of dataset. Default: 1.
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Returns:
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dataset
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"""
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if device_num == 1:
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data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
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else:
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data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
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num_shards=device_num, shard_id=rank)
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# define map operations
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if do_train:
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trans = [
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C.RandomCropDecodeResize(224),
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C.RandomHorizontalFlip(prob=0.5),
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C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
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]
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else:
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trans = [
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C.Decode(),
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C.Resize(239),
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C.CenterCrop(224)
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]
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trans += [
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C.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255], std=[0.229 * 255, 0.224 * 255, 0.225 * 255]),
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C.HWC2CHW(),
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]
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type_cast_op = C2.TypeCast(mstype.int32)
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data_set = data_set.map(input_columns="image", operations=trans, num_parallel_workers=8)
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data_set = data_set.map(input_columns="label", operations=type_cast_op, num_parallel_workers=8)
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# apply batch operations
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data_set = data_set.batch(config.batch_size, drop_remainder=True)
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return data_set
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