[FIXED] How can i solve this error with Pytorch summary?

Issue

I am trying to load a DNN pytorch model using:

from torch import nn
import torch.nn.functional as F

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

    # layers definition

    # first convolutional block
    self.conv1 = nn.Conv1d(in_channels=2, out_channels=8, kernel_size=7) 
    self.pool1 = nn.MaxPool1d(kernel_size = 2, stride=2) 

    # second convolutional block
    self.conv2 = nn.Conv1d(in_channels=8, out_channels=16, kernel_size=3) 
    self.pool2 = nn.MaxPool1d(kernel_size = 2, stride=2) 

    # third convolutional block
    self.conv3 = nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3) 
    self.pool3 = nn.MaxPool1d(kernel_size = 2, stride=2) 

    # fourth convolutional block
    self.conv4 = nn.Conv1d(in_channels=32, out_channels=64, kernel_size=3) 
    self.pool4 = nn.MaxPool1d(kernel_size = 2, stride=2) 

    # fifth convolutional block
    self.conv5 = nn.Conv1d(in_channels=64, out_channels=128, kernel_size=3) 
    self.pool5 = nn.MaxPool1d(kernel_size = 2, stride=2) 

    
    self.flatten = nn.Flatten() 
    self.drop1 = nn.Dropout(p=0.5) 
    self.fc1 = nn.Linear(in_features=3200, out_features=50) #3200 is a random number, 
    probably wrong
    self.drop2 = nn.Dropout(p=0.5) #dropout 
    self.fc2 = nn.Linear(in_features=50, out_features=25)
    self.fc3 = nn.Linear(in_features=25, out_features=2)


  def forward(self, x, y):  
    x = F.relu(self.conv1(x))  
    x = self.pool1(x)
    x = F.relu(self.conv2(x)) 
    x = self.pool2(x)
    x = F.relu(self.conv3(x))
    x = self.pool3(x)
    x = F.relu(self.conv4(x))
    x = self.pool3(x)
    x = F.relu(self.conv5(x))
    x = self.pool5(x)
    
    y = F.relu(self.conv1(y))  
    y = self.pool1(y)
    y = F.relu(self.conv2(y)) 
    y = self.pool2(y)
    y = F.relu(self.conv3(y))
    y = self.pool3(y)
    y = F.relu(self.conv4(y))
    y = self.pool3(y)
    y = F.relu(self.conv5(y))
    y = self.pool5(y)

    #flatten
    x = self.flatten(x)
    y = self.flatten(y)

    w = torch.cat(x,y,1) 
    
    w = self.drop1(w) #dropout layer
    w = F.relu(self.fc1(w)) #layer fully connected with re lu
    w = self.drop2(w)
    w = F.relu(self.fc2(w)) #layer fully connected with re lu 

    w = self.fc3(w) #layer fully connected
    out = F.log_softmax(w, dim=1) 

    return out

The DNN that i am trying to reproduce is the one mentioned in this paper:

https://iopscience.iop.org/article/10.1088/2399-6528/aa83fa/pdf

I am not sure that it's correct, anyway, when I load Pytorch's summary function:

model = myDNN()
print(model)

from torchsummary import summary
if torch.cuda.is_available():
  summary(model.cuda(), input_size = [(1,892),(1,492)])
else:
  summary(model, input_size = [(1,892),(1,492)])

but i get this error:

RuntimeError                              Traceback (most recent call last)
<ipython-input-123-6846b44c144c> in <module>()
      6 from torchsummary import summary
      7 if torch.cuda.is_available():
----> 8   summary(model.cuda(), input_size = [(1,892),(1,492)]) 
      9 else:
     10   summary(model, input_size = [(1,892),(1,492)])

5 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
    297                             _single(0), self.dilation, self.groups)
    298         return F.conv1d(input, weight, bias, self.stride,
--> 299                         self.padding, self.dilation, self.groups)
    300 
    301     def forward(self, input: Tensor) -> Tensor:

RuntimeError: Given groups=1, weight of size [8, 2, 7], expected input[2, 1, 892] to have 2 channels, but got 1 channels instead

Please any help would be grateful, thank you

Solution

The first layer of the model expects two channels rather than one. Simply pass the correct input shape to "summary" as follows:

summary(model, ((2, dim1),(2,dim2))

Edit: In the forward function I would do the concatenation as follows (if both model's inputs have the same shape):

w = torch.cat([x,y], dim=1)
w = self.flatten(w)

---

Edit: Here is a working code using the correct implementation

from torch import nn
import torch.nn.functional as F
import torch

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

    # layers definition

    # first convolutional block
    self.path1_conv1 = nn.Conv1d(in_channels=2, out_channels=8, kernel_size=7)
    self.path1_pool1 = nn.MaxPool1d(kernel_size = 2, stride=2)

    # second convolutional block
    self.path1_conv2 = nn.Conv1d(in_channels=8, out_channels=16, kernel_size=3)
    self.path1_pool2 = nn.MaxPool1d(kernel_size = 2, stride=2)

    # third convolutional block
    self.path1_conv3 = nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3)
    self.path1_pool3 = nn.MaxPool1d(kernel_size = 2, stride=2)

    # fourth convolutional block
    self.path1_conv4 = nn.Conv1d(in_channels=32, out_channels=64, kernel_size=3)
    self.path1_pool4 = nn.MaxPool1d(kernel_size = 2, stride=2)

    # fifth convolutional block
    self.path1_conv5 = nn.Conv1d(in_channels=64, out_channels=128, kernel_size=3)
    self.path1_pool5 = nn.MaxPool1d(kernel_size = 2, stride=2)

    self.path2_conv1 = nn.Conv1d(in_channels=2, out_channels=8, kernel_size=7)
    self.path2_pool1 = nn.MaxPool1d(kernel_size = 2, stride=2)

    # second convolutional block
    self.path2_conv2 = nn.Conv1d(in_channels=8, out_channels=16, kernel_size=3)
    self.path2_pool2 = nn.MaxPool1d(kernel_size = 2, stride=2)

    # third convolutional block
    self.path2_conv3 = nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3)
    self.path2_pool3 = nn.MaxPool1d(kernel_size = 2, stride=2)

    # fourth convolutional block
    self.path2_conv4 = nn.Conv1d(in_channels=32, out_channels=64, kernel_size=3)
    self.path2_pool4 = nn.MaxPool1d(kernel_size = 2, stride=2)

    # fifth convolutional block
    self.path2_conv5 = nn.Conv1d(in_channels=64, out_channels=128, kernel_size=3)
    self.path2_pool5 = nn.MaxPool1d(kernel_size = 2, stride=2)


    self.flatten = nn.Flatten()
    self.drop1 = nn.Dropout(p=0.5)
    self.fc1 = nn.Linear(in_features=2048, out_features=50) #3200 is a random number,probably wrong
    self.drop2 = nn.Dropout(p=0.5) #dropout
    self.fc2 = nn.Linear(in_features=50, out_features=25)
    self.fc3 = nn.Linear(in_features=25, out_features=2)


  def forward(self, x, y):
    x = F.relu(self.path1_conv1(x))
    x = self.path1_pool1(x)
    x = F.relu(self.path1_conv2(x))
    x = self.path1_pool2(x)
    x = F.relu(self.path1_conv3(x))
    x = self.path1_pool3(x)
    x = F.relu(self.path1_conv4(x))
    x = self.path1_pool3(x)
    x = F.relu(self.path1_conv5(x))
    x = self.path1_pool5(x)

    y = F.relu(self.path2_conv1(y))
    y = self.path2_pool1(y)
    y = F.relu(self.path2_conv2(y))
    y = self.path2_pool2(y)
    y = F.relu(self.path2_conv3(y))
    y = self.path2_pool3(y)
    y = F.relu(self.path2_conv4(y))
    y = self.path2_pool3(y)
    y = F.relu(self.path2_conv5(y))
    y = self.path2_pool5(y)

    #flatten
    x = self.flatten(x)
    y = self.flatten(y)

    w = torch.cat([x,y],dim=1)
    print(w.shape)
    w = self.drop1(w) #dropout layer
    w = F.relu(self.fc1(w)) #layer fully connected with re lu
    w = self.drop2(w)
    w = F.relu(self.fc2(w)) #layer fully connected with re lu

    w = self.fc3(w) #layer fully connected
    out = F.log_softmax(w, dim=1)

    return out

def main():
    model = myDNN()
    print(model)
    from torchsummary import summary
    if torch.cuda.is_available():
        summary(model.cuda(), input_size = [(2,246),(2,447)])
    else:
        summary(model, input_size = [(2,246),(2,447)])
if __name__ == '__main__':
    main()

Answered By - Hatem