matmul issue while performing caliters_perm

[JKViswanadham14]

---

RuntimeError Traceback (most recent call last)
/tmp/ipykernel_3678/3876408280.py in
3 P1_gt_copy_inv = P1_gt_copy.clone()
4 P2_gt_copy_inv = P2_gt_copy.clone()
----> 5 s_perm_mat = caliters_perm(model.float(), P1_gt_copy.float(), P2_gt_copy.float(), torch.from_numpy(A1_gt), torch.from_numpy(A2_gt), n1_gt, n2_gt, estimate_iters)
6 """if cfg.EVAL.CYCLE:
7 s_perm_mat_inv = caliters_perm(model, P2_gt_copy_inv, P1_gt_copy_inv, A2_gt, A1_gt, n2_gt, n1_gt, estimate_iters)

/tmp/ipykernel_3678/871858585.py in caliters_perm(model, P1_gt_copy, P2_gt_copy, A1_gt, A2_gt, n1_gt, n2_gt, estimate_iters)
228 for estimate_iter in range(estimate_iters):
229 s_prem_i, Inlier_src_pre, Inlier_ref_pre = model(P1_gt_copy, P2_gt_copy,
--> 230 A1_gt, A2_gt, n1_gt, n2_gt)
231 if cfg.PGM.USEINLIERRATE:
232 s_prem_i = Inlier_src_pre * s_prem_i * Inlier_ref_pre.transpose(2, 1).contiguous()

/opt/conda/envs/fcgf/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),

/tmp/ipykernel_3678/871858585.py in forward(self, P_src, P_tgt, A_src, A_tgt, ns_src, ns_tgt)
101 emb_src, emb_tgt = gnn_layer([A_src1, emb_src], [A_tgt1, emb_tgt])
102 else:
--> 103 emb_src, emb_tgt = gnn_layer([A_src, emb_src], [A_tgt, emb_tgt])
104 affinity = getattr(self, 'affinity_{}'.format(i))
105 # emb_src_norm = torch.norm(emb_src, p=2, dim=2, keepdim=True).detach()

/opt/conda/envs/fcgf/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),

/home/ubuntu/PointCloudRegistration/AIModels/RGM/models/gconv.py in forward(self, g1, g2)
34
35 def forward(self, g1, g2):
---> 36 emb1 = self.gconv(*g1)
37 emb2 = self.gconv(*g2)
38 # embx are tensors of size (bs, N, num_features)

/opt/conda/envs/fcgf/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),

/home/ubuntu/PointCloudRegistration/AIModels/RGM/models/gconv.py in forward(self, A, x, norm)
19 A = F.normalize(A, p=1, dim=-2)
20 print(x.shape)
---> 21 ax = self.a_fc(x)
22 ux = self.u_fc(x)
23 x = torch.bmm(A, F.relu(ax)) + F.relu(ux) # has size (bs, N, num_outputs)

/opt/conda/envs/fcgf/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),

/opt/conda/envs/fcgf/lib/python3.7/site-packages/torch/nn/modules/linear.py in forward(self, input)
92
93 def forward(self, input: Tensor) -> Tensor:
---> 94 return F.linear(input, self.weight, self.bias)
95
96 def extra_repr(self) -> str:

/opt/conda/envs/fcgf/lib/python3.7/site-packages/torch/nn/functional.py in linear(input, weight, bias)
1751 if has_torch_function_variadic(input, weight):
1752 return handle_torch_function(linear, (input, weight), input, weight, bias=bias)
-> 1753 return torch._C._nn.linear(input, weight, bias)
1754
1755

RuntimeError: mat1 and mat2 shapes cannot be multiplied (76x1024 and 640x1024)

[Sabershou]

I have met the same problem, have you solved it now?

[JyothiKalyan]

not yet.

[Sabershou]

Change the FEATURE_NODE_CHANNEL and FEATURE_EDGE_CHANNEL may help, it should be caused by nn.linear(in_features, out_features), previous out_features need to be equal to the next in_features

[fukexue]

If you haven't modified the source code, you can see “self.a_fc” is (input channel =1024, output channel = 512) in line 21 of "gconv.py".