Resolving "Dimension out of range" error in PyTorch model forward pass

Question

I'm working on a PyTorch project where I've implemented a custom neural network using two pretrained models (ResNet-18 and ResNet-50) and a color information component. However, during the training process, I encountered an issue with dimensions in the forward pass, leading to a "Dimension out of range" error.

I have a custom CNN architecture (ClothesRecognizer) that combines features from the ResNet models and color information. The error occurs in the forward method during the interpolation of color_info.

# Define the CNN architecture
class ClothesRecognizer(nn.Module):
    def __init__(self, num_classes):
        super(ClothesRecognizer, self).__init__()
        # First pretrained model (ResNet-18)
        self.model1 = models.resnet18(pretrained=True)
        in_features = self.model1.fc.in_features
        self.model1.fc = nn.Identity()

        # Second pretrained model (ResNet-50)
        self.model2 = models.resnet50(pretrained=True)
        in_features2 = self.model2.fc.in_features
        self.model2.fc = nn.Identity()

        # Custom classifier that combines the features from both models
        self.classifier = nn.Sequential(
            nn.Linear(in_features + in_features2 + 3, 512),  # Add 3 for the color channels
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(512, num_classes)
        )

    def forward(self, x, color_info):
        features1 = self.model1(x)
        features2 = self.model2(x)

        # Resize color_info to match the spatial dimensions of features1 and features2
        color_info_resized = nn.functional.interpolate(color_info, size=(features1.size(2), features1.size(3)), mode='bilinear', align_corners=False)

        combined_features = torch.cat((features1, features2, color_info_resized), dim=1)  # Concatenate color_info
        x = self.classifier(combined_features)
        return x

The specific error message is:

IndexError: Dimension out of range (expected to be in range of [-2, 1], but got 2)

I'm seeking guidance on how to properly handle the dimensions in the forward pass to resolve this error. I've attempted adjustments, including modifying the get_color_info function and the forward method, but the issue persist

here's my get_color function:

    # Function to get color information
def get_color_info(batch):
    # Assuming batch is a tuple with images and labels
    images = batch[0]
    color_info_list = []

    for image in images:
        # Convert the tensor to a PIL Image
        image_pil = transforms.ToPILImage()(image)

        # Check if the image has three channels
        if image_pil.mode != 'RGB':
            # If not, duplicate the single channel to create an RGB image
            image_pil = image_pil.convert('RGB')

        # Split the channels
        r, g, b = image_pil.split()

        # Convert each channel to a tensor
        r = transforms.ToTensor()(r)
        g = transforms.ToTensor()(g)
        b = transforms.ToTensor()(b)

        # Stack the tensors to get the color information
        color_info = torch.stack([r, g, b])

        # Add a batch dimension to color_info
        color_info_list.append(color_info.unsqueeze(0))

    return torch.cat(color_info_list, dim=0).unsqueeze(0)

if there's anything unclear or missing please don't hesitate to ask or say,I appreciate any insights or suggestions to help resolve this dimension-related error. Thank you.