composer.datasets.ade20k#
ADE20K Semantic segmentation and scene parsing dataset.
Please refer to the ADE20K dataset for more details about this dataset.
Classes
PyTorch Dataset for ADE20k. |
|
Implementation of the ADE20k dataset using StreamingDataset. |
- class composer.datasets.ade20k.ADE20k(datadir, split='train', both_transforms=None, image_transforms=None, target_transforms=None)[source]#
Bases:
torch.utils.data.dataset.Dataset
PyTorch Dataset for ADE20k.
- Parameters
datadir (str) โ the path to the ADE20k folder.
split (str) โ the dataset split to use, either โtrainโ, โvalโ, or โtestโ. Default:
'train'
.both_transforms (Module) โ transformations to apply to the image and target simultaneously. Default:
None
.image_transforms (Module) โ transformations to apply to the image only. Default:
None
.target_transforms (Module) โ transformations to apply to the target only. Default
None
.
- class composer.datasets.ade20k.PadToSize(size, fill=0)[source]#
Bases:
torch.nn.modules.module.Module
Pad an image to a specified size.
- class composer.datasets.ade20k.PhotometricDistoration(brightness, contrast, saturation, hue)[source]#
Bases:
torch.nn.modules.module.Module
Applies a combination of brightness, contrast, saturation, and hue jitters with random intensity.
This is a less severe form of PyTorchโs ColorJitter used by the mmsegmentation library here: https://github.com/open-mmlab/mmsegmentation/blob/aa50358c71fe9c4cccdd2abe42433bdf702e757b/mmseg/datasets/pipelines/transforms.py#L861
- class composer.datasets.ade20k.RandomCropPair(crop_size, class_max_percent=1.0, num_retry=1)[source]#
Bases:
torch.nn.modules.module.Module
Crop the image and target at a randomly sampled position.
- Parameters
crop_size (Tuple[int, int]) โ the size (height x width) of the crop.
class_max_percent (float) โ the maximum percent of the image area a single class should occupy. Default is 1.0.
num_retry (int) โ the number of times to resample the crop if
class_max_percent
threshold is not reached. Default is 1.
- class composer.datasets.ade20k.RandomHFlipPair(probability=0.5)[source]#
Bases:
torch.nn.modules.module.Module
Flip the image and target horizontally with a specified probability.
- Parameters
probability (float) โ the probability of flipping the image and target. Default:
0.5
.
- class composer.datasets.ade20k.RandomResizePair(min_scale, max_scale, base_size=None)[source]#
Bases:
torch.nn.modules.module.Module
Resize the image and target to
base_size
scaled by a randomly sampled value.- Parameters
min_scale (float) โ the minimum value the samples can be rescaled.
max_scale (float) โ the maximum value the samples can be rescaled.
base_size (Tuple[int, int]) โ a specified base size (height x width) to scale to get the resized dimensions. When this is None, use the input image size. Default:
None
.
- class composer.datasets.ade20k.StreamingADE20k(remote, local, split, shuffle, base_size=512, min_resize_scale=0.5, max_resize_scale=2.0, final_size=512, batch_size=None)[source]#
Bases:
composer.datasets.streaming.dataset.StreamingDataset
Implementation of the ADE20k dataset using StreamingDataset.
- Parameters
remote (str) โ Remote directory (S3 or local filesystem) where dataset is stored.
local (str) โ Local filesystem directory where dataset is cached during operation.
split (str) โ The dataset split to use, either โtrainโ or โvalโ.
shuffle (bool) โ Whether to shuffle the samples in this dataset.
base_size (int) โ initial size of the image and target before other augmentations. Default:
512
.min_resize_scale (float) โ the minimum value the samples can be rescaled. Default:
0.5
.max_resize_scale (float) โ the maximum value the samples can be rescaled. Default:
2.0
.final_size (int) โ the final size of the image and target. Default:
512
.batch_size (Optional[int]) โ Hint the batch_size that will be used on each deviceโs DataLoader. Default:
None
.