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- # 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.
- # ============================================================================
- """
- dataset processing.
- """
- import os
- from mindspore.common import dtype as mstype
- import mindspore.dataset as de
- import mindspore.dataset.transforms.c_transforms as C
- import mindspore.dataset.transforms.vision.c_transforms as V_C
- from PIL import Image, ImageFile
- from src.utils.sampler import DistributedSampler
-
- ImageFile.LOAD_TRUNCATED_IMAGES = True
-
- class TxtDataset():
- """
- create txt dataset.
-
- Args:
- Returns:
- de_dataset.
- """
- def __init__(self, root, txt_name):
- super(TxtDataset, self).__init__()
- self.imgs = []
- self.labels = []
- fin = open(txt_name, "r")
- for line in fin:
- img_name, label = line.strip().split(' ')
- self.imgs.append(os.path.join(root, img_name))
- self.labels.append(int(label))
- fin.close()
-
- def __getitem__(self, index):
- img = Image.open(self.imgs[index]).convert('RGB')
- return img, self.labels[index]
-
- def __len__(self):
- return len(self.imgs)
-
-
- def classification_dataset(data_dir, image_size, per_batch_size, max_epoch, rank, group_size,
- mode='train',
- input_mode='folder',
- root='',
- num_parallel_workers=None,
- shuffle=None,
- sampler=None,
- class_indexing=None,
- drop_remainder=True,
- transform=None,
- target_transform=None):
- """
- A function that returns a dataset for classification. The mode of input dataset could be "folder" or "txt".
- If it is "folder", all images within one folder have the same label. If it is "txt", all paths of images
- are written into a textfile.
-
- Args:
- data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"".
- Or path of the textfile that contains every image's path of the dataset.
- image_size (str): Size of the input images.
- per_batch_size (int): the batch size of evey step during training.
- max_epoch (int): the number of epochs.
- rank (int): The shard ID within num_shards (default=None).
- group_size (int): Number of shards that the dataset should be divided
- into (default=None).
- mode (str): "train" or others. Default: " train".
- input_mode (str): The form of the input dataset. "folder" or "txt". Default: "folder".
- root (str): the images path for "input_mode="txt"". Default: " ".
- num_parallel_workers (int): Number of workers to read the data. Default: None.
- shuffle (bool): Whether or not to perform shuffle on the dataset
- (default=None, performs shuffle).
- sampler (Sampler): Object used to choose samples from the dataset. Default: None.
- class_indexing (dict): A str-to-int mapping from folder name to index
- (default=None, the folder names will be sorted
- alphabetically and each class will be given a
- unique index starting from 0).
-
- Examples:
- >>> from mindvision.common.datasets.classification import classification_dataset
- >>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images
- >>> dataset_dir = "/path/to/imagefolder_directory"
- >>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244],
- >>> per_batch_size=64, max_epoch=100,
- >>> rank=0, group_size=4)
- >>> # Path of the textfile that contains every image's path of the dataset.
- >>> dataset_dir = "/path/to/dataset/images/train.txt"
- >>> images_dir = "/path/to/dataset/images"
- >>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244],
- >>> per_batch_size=64, max_epoch=100,
- >>> rank=0, group_size=4,
- >>> input_mode="txt", root=images_dir)
- """
-
- mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
- std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
-
- if transform is None:
- if mode == 'train':
- transform_img = [
- V_C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
- V_C.RandomHorizontalFlip(prob=0.5),
- V_C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4),
- V_C.Normalize(mean=mean, std=std),
- V_C.HWC2CHW()
- ]
- else:
- transform_img = [
- V_C.Decode(),
- V_C.Resize((256, 256)),
- V_C.CenterCrop(image_size),
- V_C.Normalize(mean=mean, std=std),
- V_C.HWC2CHW()
- ]
- else:
- transform_img = transform
-
- if target_transform is None:
- transform_label = [C.TypeCast(mstype.int32)]
- else:
- transform_label = target_transform
-
- if input_mode == 'folder':
- de_dataset = de.ImageFolderDatasetV2(data_dir, num_parallel_workers=num_parallel_workers,
- shuffle=shuffle, sampler=sampler, class_indexing=class_indexing,
- num_shards=group_size, shard_id=rank)
- else:
- dataset = TxtDataset(root, data_dir)
- sampler = DistributedSampler(dataset, rank, group_size, shuffle=shuffle)
- de_dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=sampler)
- de_dataset.set_dataset_size(len(sampler))
-
- de_dataset = de_dataset.map(input_columns="image", num_parallel_workers=8, operations=transform_img)
- de_dataset = de_dataset.map(input_columns="label", num_parallel_workers=8, operations=transform_label)
-
- columns_to_project = ["image", "label"]
- de_dataset = de_dataset.project(columns=columns_to_project)
-
- de_dataset = de_dataset.batch(per_batch_size, drop_remainder=drop_remainder)
- de_dataset = de_dataset.repeat(max_epoch)
-
- return de_dataset
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