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ultralytics 8.0.197 save P, R, F1 curves to metrics (#5354)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: erminkev1 <83356055+erminkev1@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Andy <39454881+yermandy@users.noreply.github.com>
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Glenn Jocher 2023-10-13 02:49:31 +02:00 • committed by GitHub
parent 7fd5dcbd86
commit 12e3eef844
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33 changed files with 337 additions and 195 deletions
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ultralytics/nn/modules/transformer.py
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@ -81,7 +81,7 @@ class AIFI(TransformerEncoderLayer):
"""Forward pass for the AIFI transformer layer."""
c, h, w = x.shape[1:]
pos_embed = self.build_2d_sincos_position_embedding(w, h, c)
# flatten [B, C, H, W] to [B, HxW, C]
# Flatten [B, C, H, W] to [B, HxW, C]
x = super().forward(x.flatten(2).permute(0, 2, 1), pos=pos_embed.to(device=x.device, dtype=x.dtype))
return x.permute(0, 2, 1).view([-1, c, h, w]).contiguous()
@ -213,7 +213,7 @@ class MSDeformAttn(nn.Module):
if d_model % n_heads != 0:
raise ValueError(f'd_model must be divisible by n_heads, but got {d_model} and {n_heads}')
_d_per_head = d_model // n_heads
# you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation
# Better to set _d_per_head to a power of 2 which is more efficient in a CUDA implementation
assert _d_per_head * n_heads == d_model, '`d_model` must be divisible by `n_heads`'
self.im2col_step = 64