55 lines
2.3 KiB
Python
55 lines
2.3 KiB
Python
# 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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"""
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Produce the dataset
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"""
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from config import alexnet_cfg as cfg
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.c_transforms as C
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import mindspore.dataset.transforms.vision.c_transforms as CV
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from mindspore.common import dtype as mstype
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def create_dataset(data_path, batch_size=32, repeat_size=1, status="train"):
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"""
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create dataset for train or test
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"""
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cifar_ds = ds.Cifar10Dataset(data_path)
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rescale = 1.0 / 255.0
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shift = 0.0
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resize_op = CV.Resize((cfg.image_height, cfg.image_width))
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rescale_op = CV.Rescale(rescale, shift)
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normalize_op = CV.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
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if status == "train":
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random_crop_op = CV.RandomCrop([32, 32], [4, 4, 4, 4])
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random_horizontal_op = CV.RandomHorizontalFlip()
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channel_swap_op = CV.HWC2CHW()
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typecast_op = C.TypeCast(mstype.int32)
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cifar_ds = cifar_ds.map(input_columns="label", operations=typecast_op)
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if status == "train":
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cifar_ds = cifar_ds.map(input_columns="image", operations=random_crop_op)
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cifar_ds = cifar_ds.map(input_columns="image", operations=random_horizontal_op)
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cifar_ds = cifar_ds.map(input_columns="image", operations=resize_op)
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cifar_ds = cifar_ds.map(input_columns="image", operations=rescale_op)
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cifar_ds = cifar_ds.map(input_columns="image", operations=normalize_op)
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cifar_ds = cifar_ds.map(input_columns="image", operations=channel_swap_op)
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cifar_ds = cifar_ds.shuffle(buffer_size=cfg.buffer_size)
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cifar_ds = cifar_ds.batch(batch_size, drop_remainder=True)
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cifar_ds = cifar_ds.repeat(repeat_size)
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return cifar_ds
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