ResNet: A Deep Residual Network for Image Classification

The error message "Calculated padded input size per channel: (1 x 1). Kernel size: (3 x 3). Kernel size can't be greater than actual input size" indicates that the input to a convolutional layer is too small for the kernel size used. This typically happens when the input image is progressively downsampled by convolutional layers with stride > 1, eventually leading to an input feature map that is smaller than the kernel size. To address this, there are a few solutions:

• Increase the input size: If possible, use larger input images to the network. This can be done by either resizing the original images or adding more layers before the convolutional layer that encounters the error, preserving a larger input feature map.
• Decrease the kernel size: You can reduce the kernel size of the convolutional layers in the later stages of the network, ensuring the kernel is smaller than the input feature map. You can adjust the kernel sizes based on the expected output size and the downsampling factors used in previous layers.
• Adjust the downsampling factors: Carefully examine the strides used in the convolutional layers before the layer causing the error. You might need to adjust the strides to ensure the input feature map size doesn't shrink too rapidly, preventing the issue.
• Use padding: Adding padding around the input features can effectively increase the input size, allowing for larger kernel sizes. The padding argument in nn.Conv2d can be used to control the amount of padding.

Additionally, carefully consider the following:

• Network design: Review the overall network architecture, ensuring that the downsampling process is gradual and appropriate for the input image size and the intended output size.
• Data augmentation: Data augmentation techniques like random cropping or padding can help introduce variations in the input image size, reducing the likelihood of this error.

By addressing these potential issues, you can resolve the error "Calculated padded input size per channel: (1 x 1). Kernel size: (3 x 3). Kernel size can't be greater than actual input size" and effectively train your ResNet model.