forked from mindspore-Ecosystem/mindspore
68 lines
2.3 KiB
Python
Executable File
68 lines
2.3 KiB
Python
Executable File
# 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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""" create train dataset. """
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from functools import partial
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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, config, repeat_num=1, batch_size=32):
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"""
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create a train dataset
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Args:
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dataset_path(string): the path of dataset.
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config(EasyDict)：the basic config for training
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repeat_num(int): the repeat times of dataset. Default: 1.
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batch_size(int): the batch size of dataset. Default: 32.
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Returns:
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dataset
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"""
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load_func = partial(ds.Cifar10Dataset, dataset_path)
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data_set = load_func(num_parallel_workers=8, shuffle=False)
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resize_height = config.image_height
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resize_width = config.image_width
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mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
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std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
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# define map operations
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resize_op = C.Resize((resize_height, resize_width))
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normalize_op = C.Normalize(mean=mean, std=std)
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changeswap_op = C.HWC2CHW()
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c_trans = [resize_op, normalize_op, changeswap_op]
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type_cast_op = C2.TypeCast(mstype.int32)
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data_set = data_set.map(operations=c_trans, input_columns="image",
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num_parallel_workers=8)
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data_set = data_set.map(operations=type_cast_op,
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input_columns="label", num_parallel_workers=8)
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# apply batch operations
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data_set = data_set.batch(batch_size, drop_remainder=True)
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# apply dataset repeat operation
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data_set = data_set.repeat(repeat_num)
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return data_set
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