Open NicTanghe opened 2 months ago
ERROR: ViTMatte1.Inference1: Exception caught processing model: The following operation failed in the TorchScript interpreter. Traceback of TorchScript, serialized code (most recent call last): File .../torch.py, line 13, in forward image_and_trimap = {"image": image, "trimap": trimap} model = self.model _0 = ((model).forward(image_and_trimap, ))["phas"]
return torch.contiguous(_0) File .../vitmatte.py, line 21, in forward images, H, W, = _0 backbone = self.backbone features = (backbone).forward(images, ) ~~~~~~~~~~~~~~~~~ <--- HERE decoder = self.decoder outputs = (decoder).forward(features, images, ) File .../vit.py, line 21, in forward _0 = __torch__.modeling.backbone.utils.get_abs_pos patch_embed = self.patch_embed x0 = (patch_embed).forward(x, ) ~~~~~~~~~~~~~~~~~~~~ <--- HERE pos_embed = self.pos_embed pretrain_use_cls_token = self.pretrain_use_cls_token File .../utils.py, line 10, in forward x: Tensor) -> Tensor: proj = self.proj x0 = (proj).forward(x, ) ~~~~~~~~~~~~~ <--- HERE return torch.permute(x0, [0, 2, 3, 1]) def get_abs_pos(abs_pos: Tensor, File .../conv.py, line 23, in forward weight = self.weight bias = self.bias _0 = (self)._conv_forward(input, weight, bias, ) ~~~~~~~~~~~~~~~~~~~ <--- HERE return _0 def _conv_forward(self: __torch__.torch.nn.modules.conv.Conv2d, File .../conv.py, line 29, in _conv_forward weight: Tensor, bias: Optional[Tensor]) -> Tensor: _1 = torch.conv2d(input, weight, bias, [16, 16], [0, 0], [1, 1]) ~~~~~~~~~~~~ <--- HERE return _1 Traceback of TorchScript, original code (most recent call last): File "nuke_vitmatte.py", line 72, in forward } return self.model(image_and_trimap)["phas"].contiguous() ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE File .../vitmatte.py, line 42, in forward images, H, W = self.preprocess_inputs(batched_inputs)
I have the same issue on my nuke indie 15.1v1.
ERROR: ViTMatte1.Inference1: Exception caught processing model: The following operation failed in the TorchScript interpreter. Traceback of TorchScript, serialized code (most recent call last): File .../torch.py, line 13, in forward image_and_trimap = {"image": image, "trimap": trimap} model = self.model _0 = ((model).forward(image_and_trimap, ))["phas"]