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,
decrypt=None
)
For more information, see mindspore.dataset.VOCDataset.
Differences
PyTorch: Pascal VOC Segmentation Dataset.
MindSpore: A source dataset for reading and parsing VOC dataset.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameter |
Parameter1 |
root |
dataset_dir |
- |
Parameter2 |
year |
- |
Not supported by MindSpore |
|
Parameter3 |
image_set |
usage |
- |
|
Parameter4 |
download |
- |
Not supported by MindSpore |
|
Parameter5 |
transform |
- |
Supported by |
|
Parameter6 |
target_transform |
- |
Supported by |
|
Parameter7 |
transforms |
- |
Supported by |
|
Parameter8 |
- |
task |
Set the task type of reading voc data |
|
Parameter9 |
- |
class_indexing |
A str-to-int mapping from label name to index |
|
Parameter10 |
- |
num_samples |
The number of images to be included in the dataset |
|
Parameter11 |
- |
num_parallel_workers |
Number of worker threads to read the data |
|
Parameter12 |
- |
shuffle |
Whether to perform shuffle on the dataset |
|
Parameter13 |
- |
decode |
Whether to decode the images after reading |
|
Parameter14 |
- |
sampler |
Object used to choose samples from the dataset |
|
Parameter15 |
- |
num_shards |
Number of shards that the dataset will be divided into |
|
Parameter16 |
- |
shard_id |
The shard ID within num_shards |
|
Parameter17 |
- |
cache |
Use tensor caching service to speed up dataset processing |
|
Parameter18 |
- |
extra_metadata |
Flag to add extra meta-data to row |
|
Parameter19 |
- |
decrypt |
Image decryption function |
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))