Pytorch's model can't feed forward a DataLoader dataset, NotImplementedError
so I am experimenting with PyTorch library to train a CNN. There is nothing wrong with the model (I can feed forward a data w/ no error) and I prepare a custom dataset with DataLoader function.

This is my code for data prep (I've omitted some irrelevant variable declaration, etc.):

# Initiliaze model  
class neural_net_model(nn.Module):
      # omitted 
      ...

# Prep the dataset
train_data = torchvision.datasets.ImageFolder(root = TRAIN_DATA_PATH, transform = TRANSFORM_IMG)
train_data_loader = data_utils.DataLoader(train_data, batch_size = BATCH_SIZE, shuffle = True)

test_data = torchvision.datasets.ImageFolder(root = TEST_DATA_PATH, transform = TRANSFORM_IMG)
test_data_loader = data_utils.DataLoader(test_data, batch_size = BATCH_SIZE, shuffle = True)


But, in the training code (which I follow based on various online references), there is an error when I feed forward the model with this instruction:

...

for step, (data, label) in enumerate(train_data_loader):
    outputs = neural_net_model(data)
    ...


Which raise an error:

NotImplementedError                       Traceback (most recent call last)
&lt;ipython-input-12-690cfa6916ec&gt; in &lt;module&gt;
      6 
      7         # Forward pass
----&gt; 8         outputs = neural_net_model(images)
      9         loss = criterion(outputs, labels)
     10 

~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    487             result = self._slow_forward(*input, **kwargs)
    488         else:
--&gt; 489             result = self.forward(*input, **kwargs)
    490         for hook in self._forward_hooks.values():
    491             hook_result = hook(self, input, result)

~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in forward(self, *input)
     83             registered hooks while the latter silently ignores them.
     84         """
---&gt; 85         raise NotImplementedError
     86 
     87     def register_buffer(self, name, tensor):

NotImplementedError: 


I can't find a similar problems on the internet and it seems strange because I've followed the code much exactly as the references and the error is not really well defined in the docs (NotImplementedError:)

Do you guys know the cause and solution to this problem?


This is the code for the network


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

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

        # Layer 1
        self.convolution1 = nn.Conv2d(in_channels = 1, out_channels = 64, kernel_size = 7, stride = 2, padding = 3)
        self.pooling1 = nn.MaxPool2d(kernel_size = 3, stride = 2, padding = 0)

        # Layer FeatEx1
        self.convolution2a = nn.Conv2d(in_channels = 64, out_channels = 96, kernel_size = 1, stride = 1, padding = 0)
        self.convolution2b = nn.Conv2d(in_channels = 96, out_channels = 208, kernel_size = 3, stride = 1, padding = 1)

        self.pooling2a = nn.MaxPool2d(kernel_size = 3, stride = 1, padding = 1)
        self.convolution2c = nn.Conv2d(in_channels = 64, out_channels = 64, kernel_size = 1, stride = 1, padding = 0)

        self.pooling2b = nn.MaxPool2d(kernel_size = 3, stride = 2, padding = 0)

        # Layer FeatEx2
        self.convolution3a = nn.Conv2d(in_channels = 272, out_channels = 96, kernel_size = 1, stride = 1, padding = 0)
        self.convolution3b = nn.Conv2d(in_channels = 96, out_channels = 208, kernel_size = 3, stride = 1, padding = 1)

        self.pooling3a = nn.MaxPool2d(kernel_size = 3, stride = 1, padding = 1)
        self.convolution3c = nn.Conv2d(in_channels = 272, out_channels = 64, kernel_size = 1, stride = 1, padding = 0)

        self.pooling3b = nn.MaxPool2d(kernel_size = 3, stride = 2, padding = 0)

        # Fully-connected Layer
        self.fc1 = nn.Linear(45968, 1024)
        self.fc2 = nn.Linear(1024, 64)
        self.fc3 = nn.Linear(64, 8)

    def net_forward(self, x):
        # Layer 1
        x = F.relu(self.convolution1(x))
        x = F.local_response_norm(self.pooling1(x), size = 2)
        y1 = x
        y2 = x
        # Layer FeatEx1
        y1 = F.relu(self.convolution2a(y1))
        y1 = F.relu(self.convolution2b(y1))

        y2 = self.pooling2a(y2)
        y2 = F.relu(self.convolution2c(y2))

        x = torch.zeros([y1.shape[0], y1.shape[1] + y2.shape[1], y1.shape[2], y1.shape[3]])
        x[:, 0:y1.shape[1], :, :] = y1
        x[:,  y1.shape[1]:, :, :] = y2

        x = self.pooling2b(x)
        y1 = x
        y2 = x
        # Layer FeatEx2
        y1 = F.relu(self.convolution3a(y1))
        y1 = F.relu(self.convolution3b(y1))

        y2 = self.pooling3a(y2)
        y2 = F.relu(self.convolution3c(y2))

        x = torch.zeros([y1.shape[0], y1.shape[1] + y2.shape[1], y1.shape[2], y1.shape[3]])
        x[:, 0:y1.shape[1], :, :] = y1
        x[:,  y1.shape[1]:, :, :] = y2

        x = self.pooling3b(x)
        # Fully-connected layer
        x = x.view(-1, x.shape[0] * x.shape[1] * x.shape[2] * x.shape[3])
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.log_softmax(self.fc3(x), dim = None)

        return x 


Your network class implemented a net_forward method. However, nn.Module expects its derived classes to implement forward method (without net_ prefix).
Simply rename net_forward to just forward and your code should be okay.

You can learn more about inheritance and overloaded methods here.



Old Answer:

The code you are running, and the code you post are not the same.
You posted a code:


for step, (data, label) in enumerate(train_data_loader):
    neural_net_model(data)



While the code you run (as it appears in the error message posted) is:


# Forward pass
outputs = model(images)



The error you get indicates that model to which you feed images is of class nn.Module and not an actual implementation derived from nn.Module. Therefore, the actual model you are trying to use has no explicit implementation of forward method. Make sure you are using the actual model you implemented.

