New TensorFlow TFCrossConv() module

[glenn-jocher]

🛠️ PR Summary

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🌟 Summary

Introducing CrossConv: a new cross-convolutional layer and updating autopad functionality.

📊 Key Changes

• Modified the autopad function to create lists instead of generators.
• Added a new CrossConv class to common.py, representing a cross-convolutional downsampling layer.
• Introduced C3x class to common.py, which is an extended version of the C3 class with cross-convolutions.
• Moved CrossConv from experimental.py to common.py as it seems to be a more stable feature now.
• Updated the TensorFlow model definitions in tf.py to include the new C3x and CrossConv classes, along with adjustments for padding.
• Refactored yolo.py to recognize C3x as one of the core components of the model.

🎯 Purpose & Impact

• 🛠️ Enhances model flexibility with the addition of cross-convolutional layers which can help in capturing better features from input data.
• 🏎️ May improve the YOLOv5 model's accuracy and efficiency by enabling more complex feature mapping.
• ✅ Keeps TensorFlow and PyTorch implementations consistent with the addition of C3x and CrossConv in both model definitions.
• 📐 Adjustments in padding logic ensure that feature maps are properly aligned and there are no discrepancies between different layers' outputs.
• 🔄 Moving CrossConv to common.py suggests it has matured from an experimental feature to a more widely applicable component, potentially affecting a range of current and future models.