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- 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
- import mindspore.dataset.vision.py_transforms as py_vision
- from mindspore.communication.management import init, get_rank, get_group_size
- from mindspore.dataset.vision import Inter
- from src.network.transform import RandAugment
-
-
- def create_dataset_Cifar10(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
- """
- 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
-
- 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.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True)
- else:
- ds = de.Cifar10Dataset(dataset_path, num_parallel_workers=8, shuffle=True,
- num_shards=device_num, shard_id=rank_id)
-
- # define map operations
- trans = []
- if do_train:
- trans += [
- C.RandomCrop((32, 32), (4, 4, 4, 4)),
- C.RandomHorizontalFlip(prob=0.5)
- ]
-
- trans += [
- C.Resize((224, 224)),
- C.Rescale(1.0 / 255.0, 0.0),
- C.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
- C.HWC2CHW()
- ]
-
- type_cast_op = C2.TypeCast(mstype.int32)
-
- ds = ds.map(input_columns="label", num_parallel_workers=8, operations=type_cast_op)
- ds = ds.map(input_columns="image", num_parallel_workers=8, operations=trans)
-
- # apply batch operations
- ds = ds.batch(batch_size, drop_remainder=True)
- # apply dataset repeat operation
- ds = ds.repeat(repeat_num)
-
- return ds
-
-
- def create_dataset_ImageNet(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend"):
- """
- create a train or eval imagenet2012 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
-
- 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 = 224
- mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
- std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
-
- # define map operations
- efficient_rand_augment = RandAugment()
-
- if do_train:
- trans = [
- C.Decode(),
- C.RandomResizedCrop(size=(image_size, image_size),
- scale=(0.08, 1.0),
- ratio=(3. / 4., 4. / 3.),
- interpolation=Inter.BICUBIC),
- C.RandomHorizontalFlip(prob=0.5),
- ]
- 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,
- per_batch_map=efficient_rand_augment,
- input_columns=['image', 'label'],
- num_parallel_workers=2,
- 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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