比较与torchvision.datasets.Cityscapes的差异

torchvision.datasets.Cityscapes

class torchvision.datasets.Cityscapes(
    root: str,
    split: str,
    mode: str,
    target_type: str or list,
    transform: Optional[Callable] = None,
    target_transform: Optional[Callable] = None,
    transforms: Optional[Callable] = None
    )

更多内容详见torchvision.datasets.Cityscapes。

mindspore.dataset.CityscapesDataset

class mindspore.dataset.CityscapesDataset(
    dataset_dir,
    usage='train',
    quality_mode='fine',
    task='instance',
    num_samples=None,
    num_parallel_workers=None,
    shuffle=None,
    decode=None,
    sampler=None,
    num_shards=None,
    shard_id=None,
    cache=None
    )

更多内容详见mindspore.dataset.CityscapesDataset。

差异对比

PyTorch：读取Cityscapes数据集。

MindSpore：读取Cityscapes数据集，不支持下载。

分类	子类	PyTorch	MindSpore	差异
参数	参数1	root	dataset_dir	-
	参数2	split	usage	-
	参数3	mode	quality_mode	-
	参数4	target_type	task	-
	参数5	transform	-	MindSpore通过 mindspore.dataset.map 操作支持
	参数6	target_transform	-	MindSpore通过 mindspore.dataset.map 操作支持
	参数7	transforms	-	MindSpore通过 mindspore.dataset.map 操作支持
	参数8	-	num_samples	指定从数据集中读取的样本数
	参数9	-	num_parallel_workers	指定读取数据的工作线程数
	参数10	-	shuffle	指定是否混洗数据集
	参数11	-	decode	解码读取的图片
	参数12	-	sampler	指定从数据集中选取样本的采样器
	参数13	-	num_shards	指定分布式训练时将数据集进行划分的分片数
	参数14	-	shard_id	指定分布式训练时使用的分片ID号
	参数15	-	cache	指定单节点数据缓存服务

代码示例

# PyTorch
import torchvision.transforms as T
import torchvision.datasets as datasets
from torch.utils.data import DataLoader

root = "/path/to/dataset_directory/"
dataset = datasets.Cityscapes(root, split='train', mode='fine', target_type='semantic')
dataloader = DataLoader(dataset)

# MindSpore
import mindspore.dataset as ds
import mindspore.dataset.vision as vision

# Download the dataset files, unzip into the following structure
# .
# └── "/path/to/dataset_directory"
#      ├── leftImg8bit
#      |    ├── train
#      |    |    ├── aachen
#      |    |    |    ├── aachen_000000_000019_leftImg8bit.png
#      |    |    |    ├── aachen_000001_000019_leftImg8bit.png
#      |    |    |    ├── ...
#      |    |    ├── bochum
#      |    |    |    ├── ...
#      |    |    ├── ...
#      |    ├── test
#      |    |    ├── ...
#      |    ├── val
#      |    |    ├── ...
#      └── gtFine
#           ├── train
#           |    ├── aachen
#           |    |    ├── aachen_000000_000019_gtFine_color.png
#           |    |    ├── aachen_000000_000019_gtFine_instanceIds.png
#           |    |    ├── aachen_000000_000019_gtFine_labelIds.png
#           |    |    ├── aachen_000000_000019_gtFine_polygons.json
#           |    |    ├── aachen_000001_000019_gtFine_color.png
#           |    |    ├── aachen_000001_000019_gtFine_instanceIds.png
#           |    |    ├── aachen_000001_000019_gtFine_labelIds.png
#           |    |    ├── aachen_000001_000019_gtFine_polygons.json
#           |    |    ├── ...
#           |    ├── bochum
#           |    |    ├── ...
#           |    ├── ...
#           ├── test
#           |    ├── ...
#           └── val
#                ├── ...

root = "/path/to/dataset_directory/"
ms_dataloader = ds.CityscapesDataset(root, usage='train')
ms_dataloader = ms_dataloader.map(vision.RandomCrop((28, 28)), ["image"])