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# Multi-HMR
# Copyright (c) 2024-present NAVER Corp.
# CC BY-NC-SA 4.0 license
import torch
def rebatch(idx_0, idx_det):
# Rebuild the batch dimension : (N, ...) is turned into (batch_dim, nb_max, ...)
# with zero padding for batch elements with fewer people.
values, counts = torch.unique(idx_0, sorted=True, return_counts=True)
#print(idx_0)
if not len(values) == values.max() + 1:
# Abnormal jumps in the idx_0: some images in the batch did not produce any inputs.
jumps = (values - torch.concat([torch.Tensor([-1]).to(values.device), values])[:-1]) - 1
offsets = torch.cumsum(jumps.int(), dim=0)
# Correcting idx_0 to account for missing batch elements
# This is actually wrong: in the case where we have 2 consecutive images without ppl, this will fail.
# But two consecutive jumps has proba so close to 0 that I consider it 'impossible'.
offsets = [c * [o] for o, c in [(offsets[i], counts[i]) for i in range(offsets.shape[0])]]
offsets = torch.Tensor([e for o in offsets for e in o]).to(jumps.device).int()
idx_0 = idx_0 - offsets
idx_det_0 = idx_det[0] - offsets
else:
idx_det_0 = idx_det[0]
return counts, idx_det_0
def pad(x, padlen, dim):
assert x.shape[dim] <= padlen, "Incoherent dimensions"
if not dim == 1:
raise NotImplementedError("Not implemented for this dim.")
padded = torch.concat([x, x.new_zeros((x.shape[0], padlen - x.shape[dim],) + x.shape[2:])], dim=dim)
mask = torch.concat([x.new_ones((x.shape[0], x.shape[dim])), x.new_zeros((x.shape[0], padlen - x.shape[dim]))], dim=dim)
return padded, mask
def pad_to_max(x_central, counts):
"""Pad so that each batch images has the same number of x_central queries.
Mask is used in attention to remove the fact queries. """
max_count = counts.max()
xlist = torch.split(x_central, tuple(counts), dim=0)
xlist2 = [x.unsqueeze(0) for x in xlist]
xlist3 = [pad(x, max_count, dim=1) for x in xlist2]
xlist4, mask = [x[0] for x in xlist3], [x[1] for x in xlist3]
x_central, mask = torch.concat(xlist4, dim=0), torch.concat(mask, dim=0)
return x_central, mask
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