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# -*- coding: UTF-8 -*-
'''=================================================
@Project -> File pram -> utils
@IDE PyCharm
@Author [email protected]
@Date 29/01/2024 14:31
=================================================='''
import torch
def normalize_size(x, size, scale=0.7):
size = size.reshape([1, 2])
norm_fac = size.max() + 0.5
return (x - size / 2) / (norm_fac * scale)
def collect_batch(batch):
out = {}
# if len(batch) == 0:
# return batch
# else:
for k in batch[0].keys():
tmp = []
for v in batch:
tmp.append(v[k])
if isinstance(batch[0][k], str) or isinstance(batch[0][k], list):
out[k] = tmp
else:
out[k] = torch.cat([torch.from_numpy(i)[None] for i in tmp], dim=0)
return out
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