PyTorch NotImplementedError in forward

Solution for PyTorch NotImplementedError in forward
is Given Below:

import torch
import torch.nn as nn

device = torch.device('cuda' if torch.cuda.is_available() else 
'cpu')

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.layer = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2), # 16x16x650
            nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1), # 32x16x650
            nn.ReLU(),
            nn.Dropout2d(0.5),
            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1), # 64x16x650
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2), # 64x8x325
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.ReLU()) # 64x8x325

        self.fc = nn.Sequential(
            nn.Linear(64*8*325, 128),
            nn.ReLU(),
            nn.Linear(128, 256),
            nn.ReLU(),
            nn.Linear(256, 1),
        )

        def forward(self, x):
            out = self.layer1(x)
            out = self.layer2(out)
            out = out.reshape(out.size(0), -1)
            out = self.fc(out)
            return out

# HYPERPARAMETER
learning_rate = 0.0001 
num_epochs = 15

import data

def main():
    model = Model().to(device)

    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), 
lr=learning_rate)

    total_step = len(data.train_loader)
    for epoch in range(num_epochs):
        for i, (images, labels) in enumerate(data.train_loader):
            images = images.to(device)
            labels = labels.to(device)

            outputs = model(images)
            loss = criterion(outputs, labels)

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

        if (i + 1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))

    model.eval()
    with torch.no_grad():
        correct = 0
        total = 0
        for images, labels in data.test_loader:
            images = images.to(device)
            labels = labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

        print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))

if __name__ == '__main__':
    main()

Error:

File "/home/rladhkstn8/Desktop/SWID/tmp/pycharm_project_853/model.py", line 82, in <module>
    main()
  File "/home/rladhkstn8/Desktop/SWID/tmp/pycharm_project_853/model.py", line 56, in main
    outputs = model(images)
  File "/home/rladhkstn8/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 477, in __call__
    result = self.forward(*input, **kwargs)
  File "/home/rladhkstn8/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 83, in forward
    raise NotImplementedError
NotImplementedError

I do not know where the problem is. I know that NotImplementedError should be implemented, but it happens when there is unimplemented code.

please look carefully at the indentation of your __init__ function: your forward is part of __init__ not part of your module.

This error happens when you don’t implement the required method from super class, in my case, i had typo on the function name forward. I recommend you check your code indentation.

Just unindent your forward method in Model class.

like this:

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.layer = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2), # 16x16x650
            nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1), # 32x16x650
            nn.ReLU(),
            nn.Dropout2d(0.5),
            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1), # 64x16x650
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2), # 64x8x325
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.ReLU()) # 64x8x325

        self.fc = nn.Sequential(
            nn.Linear(64*8*325, 128),
            nn.ReLU(),
            nn.Linear(128, 256),
            nn.ReLU(),
            nn.Linear(256, 1),
        )

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out