Function differences with torchvision.datasets.VOCSegmentation
torchvision.datasets.VOCSegmentation
class torchvision.datasets.VOCSegmentation(
root: str,
year: str='2012',
image_set: str='train',
download: bool=False,
transform: Optional[Callable]=None,
target_transform: Optional[Callable]=None,
transforms: Optional[Callable]=None
)
For more information, see torchvision.datasets.VOCSegmentation.
mindspore.dataset.VOCDataset
class mindspore.dataset.VOCDataset(
dataset_dir,
task="Segmentation",
usage="train",
class_indexing=None,
num_samples=None,
num_parallel_workers=None,
shuffle=None,
decode=False,
sampler=None,
num_shards=None,
shard_id=None,
cache=None,
extra_metadata=False
)
For more information, see mindspore.dataset.VOCDataset.
Differences
PyTorch: Pascal VOC Segmentation Dataset.
MindSpore: A source dataset for reading and parsing VOC dataset. The generated dataset with different task settings has different output columns.
Code Example
import mindspore.dataset as ds
import torchvision.transforms as T
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
# In MindSpore, the generated dataset with different task setting has different output columns.
voc_dataset_dir = "/path/to/voc_dataset_directory/"
# task = Segmentation, output columns: [image, dtype=uint8], [target,dtype=uint8].
dataset = ds.VOCDataset(dataset_dir=voc_dataset_dir, task="Segmentation", usage="train")
for item in dataset:
print("item:", item[0])
print(len(item[0]))
break
# Out:
# item: [255 216 255 ... 73 255 217]
# 52544
# In torch, the output will be result of transform, eg. RandomCrop
root = "/path/to/voc_dataset_directory2/"
dataset = datasets.VOCSegmentation(root, image_set='train', year='2012', transform=T.RandomCrop(300))
print(dataset)
print(type(dataset))