forked from mindspore-Ecosystem/mindspore
86 lines
2.9 KiB
Python
Executable File
86 lines
2.9 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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"""
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create train or eval dataset.
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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.engine as de
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import mindspore.dataset.transforms.vision.c_transforms as C
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import mindspore.dataset.transforms.c_transforms as C2
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from mindspore.communication.management import init, get_rank, get_group_size
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def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
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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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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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target(str): the device target. Default: Ascend
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Returns:
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dataset
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"""
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if target == "Ascend":
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device_num = int(os.getenv("DEVICE_NUM"))
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rank_id = int(os.getenv("RANK_ID"))
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else:
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init("nccl")
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rank_id = get_rank()
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device_num = get_group_size()
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if device_num == 1:
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ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True)
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else:
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ds = de.ImageFolderDatasetV2(dataset_path, num_parallel_workers=8, shuffle=True,
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num_shards=device_num, shard_id=rank_id)
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image_size = 224
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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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if do_train:
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trans = [
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C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
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C.RandomHorizontalFlip(prob=0.5),
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C.Normalize(mean=mean, std=std),
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C.HWC2CHW()
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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((256, 256)),
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C.CenterCrop(image_size),
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C.Normalize(mean=mean, std=std),
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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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ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
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ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
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# apply batch operations
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ds = ds.batch(batch_size, drop_remainder=True)
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# apply dataset repeat operation
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ds = ds.repeat(repeat_num)
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return ds
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