RuntimeError: shape '[31, 147]' is invalid for input of size 651 in extracting embbeding from pdb files #59

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1. Oxer11 commented on Dec 17, 2023

   

   Oxer11

   on Dec 17, 2023

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   Hi, I'm wondering how you use the graph_construction_model. Usually, we use graph_contruction_model to define a graph with 7 edge types and perform relational message passing. The implementation of relational message passing is a little different from what you thought. You can find the code here. I guess that the error your reported is because you don't have the graph constucted correctly with 7 edge types.
2. steffanpaul commented on Dec 23, 2023

   

   steffanpaul

   on Dec 23, 2023

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   Hello! I am running into the same issue as above. I am using the graph_construction_model as used in the torchprotein tutorial. However, I run into the same import error as above. When I run the same code but using the downloaded EC proteins from the tutorial, I don't run into this issue. Is this to do with incorrect use of the data.Protein.from_pdb function?

   My code is as below (I'm reading in a pdb downloaded from rcsb).

   import os, sys
   pdb_dir = '/n/groups/marks/users/steffan/calibration_project/structures/pdb_repo'
   pdb_file = os.path.join(pdb_dir, os.listdir(pdb_dir)[10])
   pthdir = '/n/groups/marks/users/steffan/calibration_project/structure_kernels/gearnet/checkpoints'
   pthfile = os.path.join(pthdir, 'angle_gearnet_edge.pth')
   
   from torchdrug import data
   from torchdrug import layers
   from torchdrug.layers import geometry
   from torchdrug import models
   import torch
   
   # protein
   protein = data.Protein.from_pdb(pdb_file, atom_feature="position", bond_feature="length", residue_feature="symbol")
   _protein = data.Protein.pack([protein])
   graph_construction_model = layers.GraphConstruction(node_layers=[geometry.AlphaCarbonNode()], 
                                                       edge_layers=[geometry.SpatialEdge(radius=10.0, min_distance=5),
                                                                    geometry.KNNEdge(k=10, min_distance=5),
                                                                    geometry.SequentialEdge(max_distance=2)],
                                                      edge_feature="gearnet")
   protein_ = graph_construction_model(_protein)
   
   # model
   gearnet_edge = models.GearNet(input_dim=21, hidden_dims=[512, 512, 512, 512, 512, 512],
                                 num_relation=7, edge_input_dim=59, num_angle_bin=8,
                                 batch_norm=True, concat_hidden=True, short_cut=True, readout="sum")
   
   net = torch.load(pthfile, map_location=torch.device('cpu'))
   gearnet_edge.load_state_dict(net)
   
   #output
   gearnet_edge.eval()
   with torch.no_grad():
       output = gearnet_edge(protein_, protein_.node_feature.float(), all_loss=None, metric=None)
   

   And the error readout is

   ---------------------------------------------------------------------------
   RuntimeError                              Traceback (most recent call last)
   Cell In[23], line 34
        32 gearnet_edge.eval()
        33 with torch.no_grad():
   ---> 34     output = gearnet_edge(protein_, protein_.node_feature.float(), all_loss=None, metric=None)
   
   File /n/groups/marks/users/steffan/.conda/envs/seqmodels-calibration/lib/python3.9/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
      1496 # If we don't have any hooks, we want to skip the rest of the logic in
      1497 # this function, and just call forward.
      1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
      1499         or _global_backward_pre_hooks or _global_backward_hooks
      1500         or _global_forward_hooks or _global_forward_pre_hooks):
   -> 1501     return forward_call(*args, **kwargs)
      1502 # Do not call functions when jit is used
      1503 full_backward_hooks, non_full_backward_hooks = [], []
   
   File /n/groups/marks/users/steffan/.conda/envs/seqmodels-calibration/lib/python3.9/site-packages/torchdrug/models/gearnet.py:95, in GeometryAwareRelationalGraphNeuralNetwork.forward(self, graph, input, all_loss, metric)
        92     edge_input = line_graph.node_feature.float()
        94 for i in range(len(self.layers)):
   ---> 95     hidden = self.layers[i](graph, layer_input)
        96     if self.short_cut and hidden.shape == layer_input.shape:
        97         hidden = hidden + layer_input
   
   File /n/groups/marks/users/steffan/.conda/envs/seqmodels-calibration/lib/python3.9/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
      1496 # If we don't have any hooks, we want to skip the rest of the logic in
      1497 # this function, and just call forward.
      1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
      1499         or _global_backward_pre_hooks or _global_backward_hooks
      1500         or _global_forward_hooks or _global_forward_pre_hooks):
   -> 1501     return forward_call(*args, **kwargs)
      1502 # Do not call functions when jit is used
      1503 full_backward_hooks, non_full_backward_hooks = [], []
   
   File /n/groups/marks/users/steffan/.conda/envs/seqmodels-calibration/lib/python3.9/site-packages/torchdrug/layers/conv.py:91, in MessagePassingBase.forward(self, graph, input)
        89     update = checkpoint.checkpoint(self._message_and_aggregate, *graph.to_tensors(), input)
        90 else:
   ---> 91     update = self.message_and_aggregate(graph, input)
        92 output = self.combine(input, update)
        93 return output
   
   File /n/groups/marks/users/steffan/.conda/envs/seqmodels-calibration/lib/python3.9/site-packages/torchdrug/layers/conv.py:813, in GeometricRelationalGraphConv.message_and_aggregate(self, graph, input)
       809     edge_update = scatter_add(edge_input * edge_weight, node_out, dim=0,
       810                               dim_size=graph.num_node * graph.num_relation)
       811     update += edge_update
   --> 813 return update.view(graph.num_node, self.num_relation * self.input_dim)
   
   RuntimeError: shape '[276, 147]' is invalid for input of size 5796
   
3. Oxer11 commented on Dec 28, 2023

   

   Oxer11

   on Dec 28, 2023

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   Hi! I think the error is that you forget to set protein_.view = 'residue'. This is done by using the transforms.ProteinView in the tutorial. If you don't do this, the node_feature you feed into the model is atom_feature instead of residue_feature.
4. gtamer2 commented on Jan 22, 2024

   

   gtamer2

   on Jan 22, 2024

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   I am following the tutorial and having the equivalent issue on the update.view operation in the graph convolution layer.
   I have confirmed I am using residue_feature.

   The graph has attributes PackedProtein(batch_size=1, num_atoms=[350], num_bonds=[6816], num_residues=[350])

   Relevant dimensions from the convolution are:

   self.num_relation = 7 # from config
   self.input_dim =  21    # from config
   update.shape= torch.Size([2450, 67]) # Note that 2450 = 350 * 7
   graph.num_node= tensor(350, device='cuda:0')
   self.num_relation * self.input_dim = 147
   

   and we get

    return update.view(graph.num_node, self.num_relation * self.input_dim)
   RuntimeError: shape '[350, 147]' is invalid for input of size 164150
   

   Note that 350 * 147 gives a size of 51,450, and 51450 / 21 gives 2450, which matches the update dimension.

   So the mismatch is we expect input_dim = 21 (number of amino acids), but we have 67. I have copied the protein transforms and graph construction verbatum from tutorial:

       truncate_transform = transforms.TruncateProtein(max_length=350, random=False)
       protein_view_transform = transforms.ProteinView(view="residue")
       transform = transforms.Compose([truncate_transform, protein_view_transform])
       ....
       graphs = data.Protein.pack(proteins)
       graph_construction_model = layers.GraphConstruction(node_layers=[geometry.AlphaCarbonNode()], 
                                                       edge_layers=[geometry.SpatialEdge(radius=10.0, min_distance=5),
                                                                    geometry.KNNEdge(k=10, min_distance=5),
                                                                    geometry.SequentialEdge(max_distance=2)],
                                                       edge_feature="gearnet")
       graphs = graph_construction_model(graphs)
   

   What might be the problem here?
5. Oxer11 commented on Jan 22, 2024

   

   Oxer11

   on Jan 22, 2024

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   Hi, sorry for the confusion. Could you please check again whether you pass the residue_feature instead of atom_feature into the model? If this works correctly, I think the shape of input and update tensors should be [350, 21] and [2450, 21]. I can't figure out where the number 67 in [2450, 67] comes from. I guess that you probably need to useproteins = [transform(protein) for protein in proteins] before the data.Protein.pack operation? Or you need to ensure proteins are sampled from a customized data.ProteinDataset, which will feed the protein into transform when sampling it.
6. gtamer2 commented on Jan 22, 2024

   

   gtamer2

   on Jan 22, 2024

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   You are correct -- turns out in the process of fixing something else, I was passing in my transform object to my the self.load_pdbs fxn for my dataset which inherits from torch.data.ProteinDataset, but was not actually applying the transform in the __getitem__ method.

   I re-added that back in and got this working.

   Thank you!