mindspore/example/vgg16_cifar10/dataset.py

# 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.
# ============================================================================
"""
Data operations, will be used in train.py and eval.py
"""
import os
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.transforms.vision.c_transforms as vision
import mindspore.common.dtype as mstype
from config import cifar_cfg as cfg

def create_dataset(data_home, repeat_num=1, training=True):
    """Data operations."""
    ds.config.set_seed(1)
    data_dir = os.path.join(data_home, "cifar-10-batches-bin")
    if not training:
        data_dir = os.path.join(data_home, "cifar-10-verify-bin")
    data_set = ds.Cifar10Dataset(data_dir)
    resize_height = cfg.image_height
    resize_width = cfg.image_width
    rescale = 1.0 / 255.0
    shift = 0.0

    # define map operations
    random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4))  # padding_mode default CONSTANT
    random_horizontal_op = vision.RandomHorizontalFlip()
    resize_op = vision.Resize((resize_height, resize_width))  # interpolation default BILINEAR
    rescale_op = vision.Rescale(rescale, shift)
    normalize_op = vision.Normalize((0.4465, 0.4822, 0.4914), (0.2010, 0.1994, 0.2023))
    changeswap_op = vision.HWC2CHW()
    type_cast_op = C.TypeCast(mstype.int32)

    c_trans = []
    if training:
        c_trans = [random_crop_op, random_horizontal_op]
    c_trans += [resize_op, rescale_op, normalize_op,
                changeswap_op]

    # apply map operations on images
    data_set = data_set.map(input_columns="label", operations=type_cast_op)
    data_set = data_set.map(input_columns="image", operations=c_trans)

    # apply repeat operations
    data_set = data_set.repeat(repeat_num)

    # apply shuffle operations
    data_set = data_set.shuffle(buffer_size=10)

    # apply batch operations
    data_set = data_set.batch(batch_size=cfg.batch_size, drop_remainder=True)

    return data_set