Fix tensor shape mismatch in AAttn when dim is not divisible by num_heads

[ZoomZoneZero]

Description

I noticed that the AAttn class attempts to handle cases where dim is not divisible by num_heads by defining the all_head_dim variable. However, the current implementation doesn't use it consistently, which leads to crashes in both initialization and forward passes.

Specifically, when dim % num_heads != 0, the original code encounters 3 critical issues:

1. Initialization Crash (ValueError): In self.pe, the module is initialized with in_channels=all_head_dim but groups=dim. This triggers ValueError: in_channels must be divisible by groups.

2. Reshape Crash (RuntimeError): In the forward pass, x cannot be properly reshaped because it forces the use of the original C (dim) instead of the internally processed channel size.

3. Projection Dimension Mismatch: The addition x + pp results in a tensor with dim channels, but the projection layer self.proj is initialized to accept an input of all_head_dim channels. Passing it to self.proj(x) inevitably triggers a dimension mismatch error during the forward pass.

How this PR fixes it

This PR corrects the internal channel logic to consistently use the calculated all_head_dim (which resolves to head_dim * num_heads) for intermediate operations.

• No structural changes: It does not change the final input/output shapes.
• Logic preserved: All underlying operations (group convolutions, dimensional transformations) are kept exactly as intended.
• Robustness: The code now successfully handles ANY dim input, fulfilling the original intent of defining all_head_dim and significantly improving the module's robustness for custom/lightweight model designs.

Note on Reliability

This exact logic fix has been thoroughly tested and already merged into the official Ultralytics repository (PR #24114). I am submitting this PR to help keep the original research codebase robust and synchronized with industry standards.

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Minimum Reproducible Example (Before this PR)

import torch

# This will crash immediately before this PR
model = AAttn(dim=18, num_heads=4, area=2)
input_tensor = torch.randn(1, 18, 8, 8)
output = model(input_tensor)