Debugging Runtime Error Given groups=1, weight of size [64, 368, 1, 1, 1], expected input[2, 64, 8, 90, 60] to have 368 channels, but got 64 channels instead

Common runtime error encountered in deep neural network architectures. The error message “RuntimeError: Given groups=1, weight of size [64, 368, 1, 1, 1] , expected input[2, 64, 8, 90, 60] to have 368 channels, but got 64 channels instead” often perplexes developers and can be challenging to resolve. We will explore the possible causes of this error and provide strategies to debug and fix it effectively.

Table of Contents:

1. Understanding the Error Message
2. Examining the Code
3. Identifying the Source of the Error
4. Analyzing the Input and Output Channels
5. Troubleshooting Strategies 5.1 Checking Channel Mismatch 5.2 Reviewing the Architecture 5.3 Verifying Input Size and Batch Dimension 5.4 Handling ChannelAttention and SpatialAttention
6. Implementing the Fixes
7. Testing the Updated Network
8. Conclusion
9. Understanding the Error Message The error message indicates that there is a mismatch between the expected number of channels and the actual number of channels in the input tensor. It specifies the sizes of the weight and input tensors and highlights the channel discrepancy.
10. Examining the Code We will start by examining the code provided in the question. It includes a network architecture implemented in PyTorch, which consists of various modules such as CBAM (Convolutional Block Attention Module), ChannelAttention, SpatialAttention, Conv3d_BN, InceptionModule_with_CBAM, and I3D_with_CBAM.
11. Identifying the Source of the Error To identify the source of the error, we need to analyze the code and understand the expected channel configurations in different parts of the network.
12. Analyzing the Input and Output Channels By inspecting the code and print statements, we can determine the expected input and output channel sizes at various stages of the network. We observe that the ChannelAttention class expects a specific number of input channels, which is determined by the out_channels values in the InceptionModule_with_CBAM class.
13. Troubleshooting Strategies We will explore several strategies to resolve the channel mismatch error: 5.1 Checking Channel Mismatch: Compare the expected and actual channel sizes at different stages of the network to identify any discrepancies. 5.2 Reviewing the Architecture: Verify the architecture of the network, ensuring that the number of input and output channels are correctly defined in each module. 5.3 Verifying Input Size and Batch Dimension: Ensure that the input tensor has the correct size and the batch dimension matches the expected value. 5.4 Handling ChannelAttention and SpatialAttention: Review the implementations of ChannelAttention and SpatialAttention modules to ensure they handle the input channels correctly.
14. Implementing the Fixes Based on the analysis and troubleshooting strategies, we will implement the necessary fixes in the code to address the channel mismatch error.
15. Testing the Updated Network After applying the fixes, we will test the updated network with sample inputs to ensure that the error no longer occurs.