Inception v3 pre-trained model

vision
1

I’m trying to train a pre-trained Inception v3 model for my task, which gives as input 178x178 images. It has 5 possible classes so I changed the fully-connected layer to have 5 output feature. My code is the following:


# Pre-trained models
model = models.inception_v3(pretrained=True)

### ResNet or Inception
classifier_input = model.fc.in_features
num_labels = 5

# Replace default classifier with new classifier
model.fc = nn.Linear(classifier_input, num_labels)
model.cuda()

However, I’m getting the following error: RuntimeError: Calculated padded input size per channel: (2 x 2). Kernel size: (5 x 5). Kernel size can’t be greater than actual input size.

I’m not sure what’s the problem as I adopted a similar strategy when training a pre-trained VGG and ResNet.

1 Like
2

Inception models expect an input of 299x299 spatial size, so your input might just bee too small for this architecture.

3

Changed the images size to 299x299 but now getting this error instead:

TypeError: max() received an invalid combination of arguments - got (InceptionOutputs, int), but expected one of:
 * (Tensor input)
 * (Tensor input, name dim, bool keepdim, tuple of Tensors out)
 * (Tensor input, Tensor other, Tensor out)
 * (Tensor input, int dim, bool keepdim, tuple of Tensors out)
4

The output of inception will now return an InceptionOutput, which will contain the .logits and .aux_logits, if specified.
If you don’t need the aus_logits, just use output.logits in your further processing.

1 Like
5

I tried doing this and have researched more but have not been successful.

My training code is the following:

def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0
    best_epoch = -1

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                if train_on_gpu:
                    inputs, labels = inputs.cuda(), labels.cuda()

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # statistics
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)
            if phase == 'train':
                scheduler.step()

            # deep copy the model
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = copy.deepcopy(model.state_dict())
                best_epoch = epoch

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:4f}'.format(best_acc))
    print('Best epoch: {:4f}'.format(best_epoch))

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model

And I specifically altered this line:

outputs = model(inputs)

to

outputs = model(inputs).logits

But still getting the same error…

7

Did you get to solve this problem?

8

This solution worked;

model = models.inception_v3(pretrained=True)
model.aux_logits = False

Found the solution here;