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# -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2020 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: [email protected]
from typing import List, Dict
from torch import Tensor
def collate_tensor_with_padding(batch: List[Tensor]) -> Tensor:
dims = batch[0].dim()
max_size = [max([b.size(i) for b in batch]) for i in range(dims)]
size = (len(batch),) + tuple(max_size)
canvas = batch[0].new_zeros(size=size)
for i, b in enumerate(batch):
sub_tensor = canvas[i]
for d in range(dims):
sub_tensor = sub_tensor.narrow(d, 0, b.size(d))
sub_tensor.add_(b)
return canvas
def collate_datastruct_and_text(lst_elements: List) -> Dict:
collate_datastruct = lst_elements[0]["datastruct"].transforms.collate
batch = {
# Collate with padding for the datastruct
"datastruct": collate_datastruct([x["datastruct"] for x in lst_elements]),
# Collate normally for the length
"length": [x["length"] for x in lst_elements],
# Collate the text
"text": [x["text"] for x in lst_elements]}
# add keyid for example
otherkeys = [x for x in lst_elements[0].keys() if x not in batch]
for key in otherkeys:
batch[key] = [x[key] for x in lst_elements]
return batch
def collate_length_and_text(lst_elements: List) -> Dict:
batch = {
"length_0": [x["length_0"] for x in lst_elements],
"length_1": [x["length_1"] for x in lst_elements],
"length_transition": [x["length_transition"] for x in lst_elements],
"length_1_with_transition": [x["length_1_with_transition"] for x in lst_elements],
"text_0": [x["text_0"] for x in lst_elements],
"text_1": [x["text_1"] for x in lst_elements]
}
return batch
def collate_pairs_and_text(lst_elements: List, ) -> Dict:
if 'features_0' not in lst_elements[0]: # test set
collate_datastruct = lst_elements[0]["datastruct"].transforms.collate
batch = {"datastruct": collate_datastruct([x["datastruct"] for x in lst_elements]),
"length_0": [x["length_0"] for x in lst_elements],
"length_1": [x["length_1"] for x in lst_elements],
"length_transition": [x["length_transition"] for x in lst_elements],
"length_1_with_transition": [x["length_1_with_transition"] for x in lst_elements],
"text_0": [x["text_0"] for x in lst_elements],
"text_1": [x["text_1"] for x in lst_elements]
}
else:
batch = {"motion_feats_0": collate_tensor_with_padding([el["features_0"] for el in lst_elements]),
"motion_feats_1": collate_tensor_with_padding([el["features_1"] for el in lst_elements]),
"motion_feats_1_with_transition": collate_tensor_with_padding([el["features_1_with_transition"] for el in lst_elements]),
"length_0": [x["length_0"] for x in lst_elements],
"length_1": [x["length_1"] for x in lst_elements],
"length_transition": [x["length_transition"] for x in lst_elements],
"length_1_with_transition": [x["length_1_with_transition"] for x in lst_elements],
"text_0": [x["text_0"] for x in lst_elements],
"text_1": [x["text_1"] for x in lst_elements]
}
return batch
def collate_text_and_length(lst_elements: Dict) -> Dict:
batch = {"length": [x["length"] for x in lst_elements],
"text": [x["text"] for x in lst_elements]}
# add keyid for example
otherkeys = [x for x in lst_elements[0].keys() if x not in batch and x != "datastruct"]
for key in otherkeys:
batch[key] = [x[key] for x in lst_elements]
return batch
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