Function differences with torchvision.datasets.VOCDetection
torchvision.datasets.VOCDetection
class torchvision.datasets.VOCDetection(
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.VOCDetection.
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 Detection 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 = Detection, output columns: [image, dtype=uint8], [bbox, dtype=float32], [label, dtype=uint32], [difficult, dtype=uint32], [truncate, dtype=uint32].
dataset = ds.VOCDataset(dataset_dir=voc_dataset_dir, task="Detection", usage="train")
for item in dataset:
print("item:", item[0])
print(len(item[0]))
break
# Out:
# item: [255 216 255 ... 3 255 217]
# 147025
# In torch, the output will be result of transform, eg. RandomCrop
root = "/path/to/voc_dataset_directory2/"
dataset = datasets.VOCDetection(root, image_set='train', year='2012', transform=T.ToTensor())
dataloader = DataLoader(dataset=dataset, num_workers=8, batch_size=1, shuffle=True)
for epoch in range(1):
for i, (data, label) in enumerate(dataloader):
print((data, label)[0])
# Out:
# tensor([[[[0.7176, 0.7176, 0.7216, ..., 0.7843, 0.7843, 0.7843],
# [0.7216, 0.7216, 0.7216, ..., 0.7882, 0.7882, 0.7882],
# [0.7216, 0.7255, 0.7255, ..., 0.7882, 0.7882, 0.7882],
# ...,
# ...
# ...,
# [0.6667, 0.6667, 0.6667, ..., 0.8118, 0.8118, 0.8078],
# [0.6627, 0.6627, 0.6588, ..., 0.8078, 0.8039, 0.8000],
# [0.6627, 0.6627, 0.6588, ..., 0.8078, 0.8039, 0.8000]]]])
# {'annotation': {'folder': ['VOC2012'], 'filename': ['61.jpg'], 'source': {'database': ['simulate VOC2007 Database'],
# 'annotation': ['simulate VOC2007'], 'image': ['flickr']}, 'size': {'width': ['500'], 'height': ['333'], 'depth': ['3']}, 'segmented': ['1'],
# 'object': [{'name': ['train'], 'pose': ['Unspecified'], 'truncated': ['0'], 'difficult': ['0'], 'bndbox': {'xmin': ['252'], 'ymin': ['42'],
# 'xmax': ['445'], 'ymax': ['282']}}, {'name': ['person'], 'pose': ['Frontal'], 'truncated': ['0'], 'difficult': ['0'], 'bndbox': {'xmin': ['204'],
# 'ymin': ['198'], 'xmax': ['271'], 'ymax': ['293']}}]}}