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| import torch | |
| def lengths_to_mask(lengths): | |
| max_len = max(lengths) | |
| mask = torch.arange(max_len, device=lengths.device).expand( | |
| len(lengths), max_len) < lengths.unsqueeze(1) | |
| return mask | |
| def collate_tensors(batch): | |
| 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(batch): | |
| databatch = [b[0] for b in batch] | |
| labelbatch = [b[1] for b in batch] | |
| lenbatch = [len(b[0][0][0]) for b in batch] | |
| databatchTensor = collate_tensors(databatch) | |
| labelbatchTensor = torch.as_tensor(labelbatch) | |
| lenbatchTensor = torch.as_tensor(lenbatch) | |
| maskbatchTensor = lengths_to_mask(lenbatchTensor) | |
| # x - [bs, njoints, nfeats, lengths] | |
| # - nfeats, the representation of a joint | |
| # y - [bs] | |
| # mask - [bs, lengths] | |
| # lengths - [bs] | |
| batch = {"x": databatchTensor, "y": labelbatchTensor, | |
| "mask": maskbatchTensor, 'lengths': lenbatchTensor} | |
| return batch | |
| # slow version with padding | |
| def collate_data3d_slow(batch): | |
| batchTensor = {} | |
| for key in batch[0].keys(): | |
| databatch = [b[key] for b in batch] | |
| batchTensor[key] = collate_tensors(databatch) | |
| batch = batchTensor | |
| # theta - [bs, lengths, 85], theta shape (85,) | |
| # - (np.array([1., 0., 0.]), pose(72), shape(10)), axis=0) | |
| # kp_2d - [bs, lengths, njoints, nfeats], nfeats (x,y,weight) | |
| # kp_3d - [bs, lengths, njoints, nfeats], nfeats (x,y,z) | |
| # w_smpl - [bs, lengths] zeros | |
| # w_3d - [bs, lengths] zeros | |
| return batch | |
| def collate_data3d(batch): | |
| batchTensor = {} | |
| for key in batch[0].keys(): | |
| databatch = [b[key] for b in batch] | |
| if key == "paths": | |
| batchTensor[key] = databatch | |
| else: | |
| batchTensor[key] = torch.stack(databatch,axis=0) | |
| batch = batchTensor | |
| # theta - [bs, lengths, 85], theta shape (85,) | |
| # - (np.array([1., 0., 0.]), pose(72), shape(10)), axis=0) | |
| # kp_2d - [bs, lengths, njoints, nfeats], nfeats (x,y,weight) | |
| # kp_3d - [bs, lengths, njoints, nfeats], nfeats (x,y,z) | |
| # w_smpl - [bs, lengths] zeros | |
| # w_3d - [bs, lengths] zeros | |
| return batch | |