RuntimeError: mat1 and mat2 shapes cannot be multiplied (128x512 and 2048x4096)

[CasellaJr]

I am trying to run my notebook (that works fine on google colab or other similar platforms) on Intel OpenFL.
I am using MNIST with this net:

class Net(nn.Module):
     def __init__(self):
         super(Net, self).__init__()

        # calculate same padding:
        # (w - k + 2*p)/s + 1 = o
        # => p = (s(o-1) - w + k)/2

         self.block_1 = nn.Sequential(
            nn.Conv2d(in_channels=1,
                      out_channels=64,
                      kernel_size=(3, 3),
                      stride=(1, 1),
                      # (1(32-1)- 32 + 3)/2 = 1
                      padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(in_channels=64,
                      out_channels=64,
                      kernel_size=(3, 3),
                      stride=(1, 1),
                      padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=(2, 2),
                         stride=(2, 2))
         )

         self.block_2 = nn.Sequential(
            nn.Conv2d(in_channels=64,
                      out_channels=128,
                      kernel_size=(3, 3),
                      stride=(1, 1),
                      padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Conv2d(in_channels=128,
                      out_channels=128,
                      kernel_size=(3, 3),
                      stride=(1, 1),
                      padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=(2, 2),
                         stride=(2, 2))
         )   

         self.block_3 = nn.Sequential(
            nn.Conv2d(in_channels=128,
                      out_channels=256,
                      kernel_size=(3, 3),
                      stride=(1, 1),
                      padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Conv2d(in_channels=256,
                      out_channels=256,
                      kernel_size=(3, 3),
                      stride=(1, 1),
                      padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Conv2d(in_channels=256,
                      out_channels=256,
                      kernel_size=(3, 3),
                      stride=(1, 1),
                      padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=(2, 2),
                         stride=(2, 2))
         )

         self.block_4 = nn.Sequential(
            nn.Conv2d(in_channels=256,
                      out_channels=512,
                      kernel_size=(3, 3),
                      stride=(1, 1),
                      padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.Conv2d(in_channels=512,
                      out_channels=512,
                      kernel_size=(3, 3),
                      stride=(1, 1),
                      padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.Conv2d(in_channels=512,
                      out_channels=512,
                      kernel_size=(3, 3),
                      stride=(1, 1),
                      padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=(2, 2),
                         stride=(2, 2))
         )   
          
         self.classifier = nn.Sequential(
            nn.Linear(2048, 4096),
            nn.ReLU(True),
            nn.Dropout(p=0.65),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(p=0.65),
            nn.Linear(4096, classes) 
        )

         for m in self.modules():
            if isinstance(m, torch.nn.Conv2d) or isinstance(m, torch.nn.Linear):
                nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='leaky_relu')
#                 nn.init.xavier_normal_(m.weight)
                if m.bias is not None:
                    m.bias.detach().zero_()

        # self.avgpool = nn.AdaptiveAvgPool2d((7, 7))

     def forward(self, x):

        x = self.block_1(x)
        x = self.block_2(x)
        x = self.block_3(x)
        x = self.block_4(x)
        # x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

But I have this error:

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
/var/folders/pm/nj7yy3px76n6b62knyrn8r_40000gn/T/ipykernel_10337/666012611.py in <module>
----> 1 final_model = fx.run_experiment(collaborators,{'aggregator.settings.rounds_to_train':5})

/opt/anaconda3/envs/my_env/lib/python3.7/site-packages/openfl/native/native.py in run_experiment(collaborator_dict, override_config)
    282         for col in plan.authorized_cols:
    283             collaborator = collaborators[col]
--> 284             collaborator.run_simulation()
    285 
    286     # Set the weights for the final model

/opt/anaconda3/envs/my_env/lib/python3.7/site-packages/openfl/component/collaborator/collaborator.py in run_simulation(self)
    170                 self.logger.info(f'Received the following tasks: {tasks}')
    171                 for task in tasks:
--> 172                     self.do_task(task, round_number)
    173                 self.logger.info(f'All tasks completed on {self.collaborator_name} '
    174                                  f'for round {round_number}...')

/opt/anaconda3/envs/my_env/lib/python3.7/site-packages/openfl/component/collaborator/collaborator.py in do_task(self, task, round_number)
    245             round_num=round_number,
    246             input_tensor_dict=input_tensor_dict,
--> 247             **kwargs)
    248 
    249         # Save global and local output_tensor_dicts to TensorDB

/opt/anaconda3/envs/my_env/lib/python3.7/site-packages/openfl/federated/task/runner_pt.py in validate(self, col_name, round_num, input_tensor_dict, use_tqdm, **kwargs)
    106                 data, target = pt.tensor(data).to(self.device), pt.tensor(
    107                     target).to(self.device, dtype=pt.int64)
--> 108                 output = self(data)
    109                 # get the index of the max log-probability
    110                 pred = output.argmax(dim=1, keepdim=True)

/opt/anaconda3/envs/my_env/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(),

/var/folders/pm/nj7yy3px76n6b62knyrn8r_40000gn/T/ipykernel_10337/3611293808.py in forward(self, x)
    125         # x = self.avgpool(x)
    126         x = x.view(x.size(0), -1)
--> 127         x = self.classifier(x)
    128         return x

/opt/anaconda3/envs/my_env/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/anaconda3/envs/my_env/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input)
    117     def forward(self, input):
    118         for module in self:
--> 119             input = module(input)
    120         return input
    121 

/opt/anaconda3/envs/my_env/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/anaconda3/envs/my_env/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/anaconda3/envs/my_env/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 (128x512 and 2048x4096)

I probably do not understand how works the plan and how to initialize it correctly. I think it is like that the training procedure is taken from the "torch_cnn_mnist" example and applied on my custom network. But being the networks different, it fails.

[daviddelriod]

Did you finally manage to solve the problem?

[CasellaJr]

It was my problem: I was using 28x28 images, while this network works with 32x32 images

[daviddelriod]

And once you realized about that, did the net work or did you have to change the federation plan (settings) in order to make it work?

[CasellaJr]

No need to change plan. Just resize images to 32x32 during preprocessing.