import torch
from torchdistill.datasets.collator import register_collate_func
[docs]def cat_list(images, fill_value=0):
"""
Concatenates a list of images with the max size for each of heights and widths and
fills empty spaces with a specified value.
:param images: batch tensor
:type images: torch.Tensor
:param fill_value: value to be filled
:type fill_value: int
:return: backbone model
:rtype: torch.Tensor
"""
if len(images) == 1 and not isinstance(images[0], torch.Tensor):
return images
max_size = tuple(max(s) for s in zip(*[img.shape for img in images]))
batch_shape = (len(images),) + max_size
batched_imgs = images[0].new(*batch_shape).fill_(fill_value)
for img, pad_img in zip(images, batched_imgs):
pad_img[..., :img.shape[-2], :img.shape[-1]].copy_(img)
return batched_imgs
[docs]@register_collate_func
def pascal_seg_collate_fn(batch):
"""
Collates input data for PASCAL VOC 2012 segmentation.
:param batch: list/tuple of triplets (image, target, supp_dict), where supp_dict can be an empty dict
:type batch: list or tuple
:return: collated images, targets, and supplementary dicts
:rtype: (torch.Tensor, tensor.Tensor, list[dict])
"""
images, targets, supp_dicts = list(zip(*batch))
batched_imgs = cat_list(images, fill_value=0)
batched_targets = cat_list(targets, fill_value=255)
return batched_imgs, batched_targets, supp_dicts
[docs]@register_collate_func
def pascal_seg_eval_collate_fn(batch):
"""
Collates input data for PASCAL VOC 2012 segmentation in evaluation
:param batch: list/tuple of tuples (image, target)
:type batch: list or tuple
:return: collated images and targets
:rtype: (torch.Tensor, tensor.Tensor)
"""
images, targets = list(zip(*batch))
batched_imgs = cat_list(images, fill_value=0)
batched_targets = cat_list(targets, fill_value=255)
return batched_imgs, batched_targets