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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """
- python dataset.py
- """
- import os
- import mindspore.common.dtype as mstype
- import mindspore.dataset.engine as de
- import mindspore.dataset.vision.c_transforms as C
- import mindspore.dataset.transforms.c_transforms as C2
- from mindspore.communication.management import init, get_rank, get_group_size
-
-
- def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
- """
- create a train dataset
-
- Args:
- dataset_path(string): the path of dataset.
- do_train(bool): whether dataset is used for train or eval.
- repeat_num(int): the repeat times of dataset. Default: 1
- batch_size(int): the batch size of dataset. Default: 32
- target(str): the device target. Default: Ascend
-
- Returns:
- dataset
- """
- if target == "Ascend":
- device_num, rank_id = _get_rank_info()
- else:
- init("nccl")
- rank_id = get_rank()
- device_num = get_group_size()
-
- if device_num == 1:
- ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
- else:
- ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
- num_shards=device_num, shard_id=rank_id)
-
- image_size = 112
- mean = [0.5 * 255, 0.5 * 255, 0.5 * 255]
- std = [0.5 * 255, 0.5 * 255, 0.5 * 255]
-
- # define map operations
- if do_train:
- trans = [
- # C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- C.Decode(),
- C.RandomHorizontalFlip(prob=0.5),
- C.Normalize(mean=mean, std=std),
- C.HWC2CHW()
- ]
- else:
- trans = [
- C.Decode(),
- C.Resize(256),
- C.CenterCrop(image_size),
- C.Normalize(mean=mean, std=std),
- C.HWC2CHW()
- ]
-
- type_cast_op = C2.TypeCast(mstype.int32)
-
- ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
- ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
-
- # apply batch operations
- ds = ds.batch(batch_size, drop_remainder=True)
-
- # apply dataset repeat operation
- ds = ds.repeat(repeat_num)
-
- return ds
-
-
- def _get_rank_info():
- """
- get rank size and rank id
- """
- rank_size = int(os.environ.get("RANK_SIZE", 1))
-
- if rank_size > 1:
- rank_size = int(os.environ.get("RANK_SIZE"))
- rank_id = int(os.environ.get("RANK_ID"))
- else:
- rank_size = 1
- rank_id = 0
-
- return rank_size, rank_id
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