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- # Copyright 2022 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.
- # ============================================================================
- """
- create train or eval dataset.
- """
- import os
- import mindspore
- import mindspore.common.dtype as mstype
- import mindspore.dataset as ds
- import mindspore.dataset.transforms.c_transforms as C2
- import mindspore.dataset.vision.c_transforms as C
- import mindspore.dataset.vision.py_transforms as py_transforms
- import mindspore.dataset.transforms.py_transforms as py_transforms2
- from mindspore.dataset.vision import Inter
- from mindspore.communication.management import init, get_rank, get_group_size
- from src.model_utils.config import config
- from src.model_utils.device_adapter import get_device_num, get_rank_id
-
- def create_dataset1(dataset_path, do_train, repeat_num=1, batch_size=32,
- target="Ascend", distribute=False):
- """
- create a train or evaluate cifar10 dataset for resnet50
- 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
- distribute(bool): data for distribute or not. Default: False
- enable_cache(bool): whether tensor caching service is used for eval. Default: False
- cache_session_id(int): If enable_cache, cache session_id need to be provided. Default: None
-
- Returns:
- dataset
- """
- if target == "Ascend":
- device_num, rank_id = _get_rank_info()
- else:
- if distribute:
- init()
- rank_id = get_rank()
- device_num = get_group_size()
- else:
- device_num = 1
- ds.config.set_prefetch_size(64)
- if do_train:
- usage = "train"
- transform = py_transforms2.Compose([py_transforms.ToPIL(),
- py_transforms.RandomHorizontalFlip(),
- py_transforms.RandomCrop((32, 32), (4, 4, 4, 4)),
- py_transforms.ToTensor()])
- else:
- usage = "test"
- transform = py_transforms2.Compose([py_transforms.ToPIL(),
- py_transforms.ToTensor()])
- if device_num == 1:
- dataset = ds.Cifar10Dataset(dataset_path, usage=usage, num_parallel_workers=8, shuffle=True)
- else:
- dataset = ds.Cifar10Dataset(dataset_path, usage=usage, num_parallel_workers=8,
- shuffle=True, num_shards=device_num, shard_id=rank_id)
- type_cast_op = C2.TypeCast(mindspore.int32)
- dataset = dataset.map(operations=transform, input_columns="image")
- dataset = dataset.map(operations=type_cast_op, input_columns="label")
- # apply batch operations
- dataset = dataset.batch(batch_size, drop_remainder=True)
- # apply dataset repeat operation
- dataset = dataset.repeat(repeat_num)
- return dataset
-
- def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, train_image_size=224, eval_image_size=224,
- target="Ascend", distribute=False, enable_cache=False, cache_session_id=None):
- if target == "Ascend":
- device_num, rank_id = _get_rank_info()
- else:
- if distribute:
- init()
- rank_id = get_rank()
- device_num = get_group_size()
- else:
- device_num = 1
- ds.config.set_prefetch_size(64)
- if device_num == 1:
- data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=12, shuffle=True)
- else:
- data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=12, shuffle=True,
- num_shards=device_num, shard_id=rank_id)
- mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
- std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
- # define map operations
- if do_train:
- trans = [
- C.RandomCropDecodeResize(train_image_size, scale=(0.08, 1.0), interpolation=Inter.BICUBIC),
- C.RandomHorizontalFlip(prob=0.5),
- C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
- C.Normalize(mean=mean, std=std),
- C.HWC2CHW()
- ]
- else:
- trans = [
- C.Decode(),
- C.Resize(256),
- C.CenterCrop(eval_image_size),
- C.Normalize(mean=mean, std=std),
- C.HWC2CHW()
- ]
-
- type_cast_op = C2.TypeCast(mstype.int32)
- data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=12)
- data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=12)
- # apply batch operations
- data_set = data_set.batch(batch_size, drop_remainder=True)
- # apply dataset repeat operation
- data_set = data_set.repeat(repeat_num)
- return data_set
-
- def _get_rank_info():
- """
- get rank size and rank id
- """
- rank_size = int(os.environ.get("RANK_SIZE", 1))
-
- if config.device_target == "Ascend":
- if rank_size > 1:
- rank_size = get_device_num()
- rank_id = get_rank_id()
- else:
- rank_size = 1
- rank_id = 0
- else:
- if rank_size > 1:
- rank_size = get_group_size()
- rank_id = get_rank()
- else:
- rank_size = 1
- rank_id = 0
-
- return rank_size, rank_id
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