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198 lines
8.1 KiB
198 lines
8.1 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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dataset processing.
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"""
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import os
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from mindspore.common import dtype as mstype
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import mindspore.dataset as de
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import mindspore.dataset.transforms.c_transforms as C
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import mindspore.dataset.transforms.vision.c_transforms as vision
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from PIL import Image, ImageFile
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from src.utils.sampler import DistributedSampler
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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def vgg_create_dataset(data_home, image_size, batch_size, rank_id=0, rank_size=1, repeat_num=1, training=True):
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"""Data operations."""
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de.config.set_seed(1)
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data_dir = os.path.join(data_home, "cifar-10-batches-bin")
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if not training:
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data_dir = os.path.join(data_home, "cifar-10-verify-bin")
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data_set = de.Cifar10Dataset(data_dir, num_shards=rank_size, shard_id=rank_id)
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rescale = 1.0 / 255.0
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shift = 0.0
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# define map operations
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random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT
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random_horizontal_op = vision.RandomHorizontalFlip()
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resize_op = vision.Resize(image_size) # interpolation default BILINEAR
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rescale_op = vision.Rescale(rescale, shift)
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normalize_op = vision.Normalize((0.4465, 0.4822, 0.4914), (0.2010, 0.1994, 0.2023))
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changeswap_op = vision.HWC2CHW()
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type_cast_op = C.TypeCast(mstype.int32)
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c_trans = []
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if training:
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c_trans = [random_crop_op, random_horizontal_op]
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c_trans += [resize_op, rescale_op, normalize_op,
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changeswap_op]
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# apply map operations on images
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data_set = data_set.map(input_columns="label", operations=type_cast_op)
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data_set = data_set.map(input_columns="image", operations=c_trans)
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# apply repeat operations
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data_set = data_set.repeat(repeat_num)
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# apply shuffle operations
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data_set = data_set.shuffle(buffer_size=10)
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# apply batch operations
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data_set = data_set.batch(batch_size=batch_size, drop_remainder=True)
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return data_set
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def classification_dataset(data_dir, image_size, per_batch_size, rank=0, group_size=1,
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mode='train',
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input_mode='folder',
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root='',
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num_parallel_workers=None,
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shuffle=None,
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sampler=None,
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repeat_num=1,
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class_indexing=None,
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drop_remainder=True,
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transform=None,
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target_transform=None):
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"""
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A function that returns a dataset for classification. The mode of input dataset could be "folder" or "txt".
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If it is "folder", all images within one folder have the same label. If it is "txt", all paths of images
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are written into a textfile.
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Args:
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data_dir (str): Path to the root directory that contains the dataset for "input_mode="folder"".
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Or path of the textfile that contains every image's path of the dataset.
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image_size (str): Size of the input images.
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per_batch_size (int): the batch size of evey step during training.
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rank (int): The shard ID within num_shards (default=None).
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group_size (int): Number of shards that the dataset should be divided
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into (default=None).
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mode (str): "train" or others. Default: " train".
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input_mode (str): The form of the input dataset. "folder" or "txt". Default: "folder".
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root (str): the images path for "input_mode="txt"". Default: " ".
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num_parallel_workers (int): Number of workers to read the data. Default: None.
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shuffle (bool): Whether or not to perform shuffle on the dataset
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(default=None, performs shuffle).
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sampler (Sampler): Object used to choose samples from the dataset. Default: None.
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repeat_num (int): the num of repeat dataset.
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class_indexing (dict): A str-to-int mapping from folder name to index
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(default=None, the folder names will be sorted
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alphabetically and each class will be given a
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unique index starting from 0).
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Examples:
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>>> from mindvision.common.datasets.classification import classification_dataset
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>>> # path to imagefolder directory. This directory needs to contain sub-directories which contain the images
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>>> dataset_dir = "/path/to/imagefolder_directory"
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>>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244],
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>>> per_batch_size=64, rank=0, group_size=4)
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>>> # Path of the textfile that contains every image's path of the dataset.
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>>> dataset_dir = "/path/to/dataset/images/train.txt"
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>>> images_dir = "/path/to/dataset/images"
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>>> de_dataset = classification_dataset(train_data_dir, image_size=[224, 244],
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>>> per_batch_size=64, rank=0, group_size=4,
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>>> input_mode="txt", root=images_dir)
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"""
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mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
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std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
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if transform is None:
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if mode == 'train':
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transform_img = [
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vision.RandomCropDecodeResize(image_size, scale=(0.08, 1.0)),
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vision.RandomHorizontalFlip(prob=0.5),
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vision.Normalize(mean=mean, std=std),
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vision.HWC2CHW()
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]
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else:
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transform_img = [
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vision.Decode(),
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vision.Resize((256, 256)),
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vision.CenterCrop(image_size),
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vision.Normalize(mean=mean, std=std),
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vision.HWC2CHW()
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]
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else:
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transform_img = transform
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if target_transform is None:
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transform_label = [C.TypeCast(mstype.int32)]
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else:
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transform_label = target_transform
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if input_mode == 'folder':
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de_dataset = de.ImageFolderDatasetV2(data_dir, num_parallel_workers=num_parallel_workers,
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shuffle=shuffle, sampler=sampler, class_indexing=class_indexing,
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num_shards=group_size, shard_id=rank)
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else:
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dataset = TxtDataset(root, data_dir)
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sampler = DistributedSampler(dataset, rank, group_size, shuffle=shuffle)
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de_dataset = de.GeneratorDataset(dataset, ["image", "label"], sampler=sampler)
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de_dataset.set_dataset_size(len(sampler))
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de_dataset = de_dataset.map(input_columns="image", num_parallel_workers=8, operations=transform_img)
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de_dataset = de_dataset.map(input_columns="label", num_parallel_workers=8, operations=transform_label)
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columns_to_project = ["image", "label"]
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de_dataset = de_dataset.project(columns=columns_to_project)
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de_dataset = de_dataset.batch(per_batch_size, drop_remainder=drop_remainder)
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de_dataset = de_dataset.repeat(repeat_num)
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return de_dataset
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class TxtDataset:
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"""
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create txt dataset.
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Args:
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Returns:
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de_dataset.
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"""
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def __init__(self, root, txt_name):
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super(TxtDataset, self).__init__()
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self.imgs = []
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self.labels = []
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fin = open(txt_name, "r")
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for line in fin:
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img_name, label = line.strip().split(' ')
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self.imgs.append(os.path.join(root, img_name))
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self.labels.append(int(label))
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fin.close()
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def __getitem__(self, index):
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img = Image.open(self.imgs[index]).convert('RGB')
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return img, self.labels[index]
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def __len__(self):
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return len(self.imgs)
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