The picture has to be pre-trained

[Aaron198513]

The picture has to be pre-trained, how do I change the parameters of the 2D convolution into the parameters of the 3D convolution

[Andy1621]

Please check

UniFormer/video_classification/slowfast/models/uniformer.py

Lines 387 to 421 in f92e423

	def inflate_weight(self, weight_2d, time_dim, center=False):
	if center:
	weight_3d = torch.zeros(*weight_2d.shape)
	weight_3d = weight_3d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
	middle_idx = time_dim // 2
	weight_3d[:, :, middle_idx, :, :] = weight_2d
	else:
	weight_3d = weight_2d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)
	weight_3d = weight_3d / time_dim
	return weight_3d
	def get_pretrained_model(self, cfg):
	if cfg.UNIFORMER.PRETRAIN_NAME:
	checkpoint = torch.load(model_path[cfg.UNIFORMER.PRETRAIN_NAME], map_location='cpu')
	if 'model' in checkpoint:
	checkpoint = checkpoint['model']
	elif 'model_state' in checkpoint:
	checkpoint = checkpoint['model_state']
	state_dict_3d = self.state_dict()
	for k in checkpoint.keys():
	if checkpoint[k].shape != state_dict_3d[k].shape:
	if len(state_dict_3d[k].shape) <= 2:
	logger.info(f'Ignore: {k}')
	continue
	logger.info(f'Inflate: {k}, {checkpoint[k].shape} => {state_dict_3d[k].shape}')
	time_dim = state_dict_3d[k].shape[2]
	checkpoint[k] = self.inflate_weight(checkpoint[k], time_dim)
	if self.num_classes != checkpoint['head.weight'].shape[0]:
	del checkpoint['head.weight']
	del checkpoint['head.bias']
	return checkpoint
	else:
	return None