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68 lines
2.6 KiB
68 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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"""
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Data operations, will be used in train.py and eval.py
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"""
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import os
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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 C
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import mindspore.dataset.transforms.vision.c_transforms as vision
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from config import cifar_cfg as cfg
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def create_dataset(data_home, repeat_num=1, training=True):
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"""Data operations."""
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ds.config.set_seed(1)
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data_dir = os.path.join(data_home, "cifar-10-batches-bin")
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if not training:
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data_dir = os.path.join(data_home, "cifar-10-verify-bin")
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rank_size = int(os.environ.get("RANK_SIZE")) if os.environ.get("RANK_SIZE") else None
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rank_id = int(os.environ.get("RANK_ID")) if os.environ.get("RANK_ID") else None
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data_set = ds.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id)
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resize_height = cfg.image_height
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resize_width = cfg.image_width
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# define map operations
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random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT
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random_horizontal_op = vision.RandomHorizontalFlip()
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resize_op = vision.Resize((resize_height, resize_width)) # interpolation default BILINEAR
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normalize_op = vision.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
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changeswap_op = vision.HWC2CHW()
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type_cast_op = C.TypeCast(mstype.int32)
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c_trans = []
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if training:
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c_trans = [random_crop_op, random_horizontal_op]
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c_trans += [resize_op, normalize_op, changeswap_op]
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# apply map operations on images
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data_set = data_set.map(input_columns="label", operations=type_cast_op)
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data_set = data_set.map(input_columns="image", operations=c_trans)
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# apply repeat operations
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data_set = data_set.repeat(repeat_num)
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# apply shuffle operations
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data_set = data_set.shuffle(buffer_size=10)
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# apply batch operations
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data_set = data_set.batch(batch_size=cfg.batch_size, drop_remainder=True)
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return data_set
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