RuntimeError: mat1 and mat2 shapes cannot be multiplied (123x18 and 19x56)
Closed this issue · 2 comments
When i follow the README file and run “high_throughput_virtual_screening_LRRK2_WDR.ipynb” and "prediction_example_using_PDB_6hd6.ipynb” file ，i got the erro info as follow:
RuntimeError Traceback (most recent call last)
File ~/anaconda3/envs/tankbind_py38/lib/python3.8/site-packages/torch/nn/modules/module.py:1110, in Module._call_impl(self, *input, **kwargs)
1106 # If we don't have any hooks, we want to skip the rest of the logic in
1107 # this function, and just call forward.
1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1109 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110 return forward_call(*input, **kwargs)
1111 # Do not call functions when jit is used
1112 full_backward_hooks, non_full_backward_hooks = [], []
File ~/TankBind-main-new/TankBind-main/examples/../tankbind/model.py:343, in IaBNet_with_affinity.forward(self, data)
341 edge_weight = data[("compound", "c2c", "compound")].edge_weight
342 compound_batch = data['compound'].batch
--> 343 compound_out = self.conv_compound(compound_edge_index,edge_weight,compound_edge_feature,compound_x.shape[0],compound_x)['node_feature']
345 # protein_batch version could further process b matrix. better than for loop.
346 # protein_out_batched of shape b, n, c
...
File ~/anaconda3/envs/tankbind_py38/lib/python3.8/site-packages/torch/nn/modules/linear.py:103, in Linear.forward(self, input)
102 def forward(self, input: Tensor) -> Tensor:
--> 103 return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (123x18 and 19x56)
it seems like the example data given not match GVP size, i'd appreciate it If you could help me fix it.
Please use torchdrug v0.1.2
There is a compatibility issue with the v0.1.3
thank you so much, this solution indeed fix it.