|
- # Copyright 2020 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 dataset. """
-
- from functools import partial
- import mindspore.dataset as ds
- import mindspore.common.dtype as mstype
- import mindspore.dataset.vision.c_transforms as C
- import mindspore.dataset.transforms.c_transforms as C2
-
-
- def create_dataset(dataset_path, config, repeat_num=1, batch_size=32):
- """
- create a train dataset
-
- Args:
- dataset_path(string): the path of dataset.
- config(EasyDict)：the basic config for training
- repeat_num(int): the repeat times of dataset. Default: 1.
- batch_size(int): the batch size of dataset. Default: 32.
-
- Returns:
- dataset
- """
-
- load_func = partial(ds.Cifar10Dataset, dataset_path)
- cifar_ds = load_func(num_parallel_workers=8, shuffle=False)
-
- resize_height = config.image_height
- resize_width = config.image_width
- rescale = 1.0 / 255.0
- shift = 0.0
-
- # define map operations
- # interpolation default BILINEAR
- resize_op = C.Resize((resize_height, resize_width))
- rescale_op = C.Rescale(rescale, shift)
- normalize_op = C.Normalize(
- (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
- changeswap_op = C.HWC2CHW()
- type_cast_op = C2.TypeCast(mstype.int32)
-
- c_trans = [resize_op, rescale_op, normalize_op, changeswap_op]
-
- # apply map operations on images
- cifar_ds = cifar_ds.map(input_columns="label", operations=type_cast_op)
- cifar_ds = cifar_ds.map(input_columns="image", operations=c_trans)
-
- # apply batch operations
- cifar_ds = cifar_ds.batch(batch_size, drop_remainder=True)
-
- # apply dataset repeat operation
- cifar_ds = cifar_ds.repeat(repeat_num)
-
- return cifar_ds
|
