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ultralytics 8.1.4 RTDETR TensorBoard graph visualization fix (#7725)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-01-21 22:28:24 +01:00 • committed by GitHub
parent 6535bcde2b
commit 7a0c27c7d7
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8 changed files with 65 additions and 26 deletions
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2
ultralytics/nn/modules/head.py
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@ -376,7 +376,7 @@ class RTDETRDecoder(nn.Module):
def _get_decoder_input(self, feats, shapes, dn_embed=None, dn_bbox=None):
"""Generates and prepares the input required for the decoder from the provided features and shapes."""
bs = len(feats)
bs = feats.shape[0]
# Prepare input for decoder
anchors, valid_mask = self._generate_anchors(shapes, dtype=feats.dtype, device=feats.device)
features = self.enc_output(valid_mask * feats) # bs, h*w, 256

6
ultralytics/nn/modules/transformer.py
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@ -101,10 +101,10 @@ class AIFI(TransformerEncoderLayer):
@staticmethod
def build_2d_sincos_position_embedding(w, h, embed_dim=256, temperature=10000.0):
"""Builds 2D sine-cosine position embedding."""
grid_w = torch.arange(int(w), dtype=torch.float32)
grid_h = torch.arange(int(h), dtype=torch.float32)
grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="ij")
assert embed_dim % 4 == 0, "Embed dimension must be divisible by 4 for 2D sin-cos position embedding"
grid_w = torch.arange(w, dtype=torch.float32)
grid_h = torch.arange(h, dtype=torch.float32)
grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="ij")
pos_dim = embed_dim // 4
omega = torch.arange(pos_dim, dtype=torch.float32) / pos_dim
omega = 1.0 / (temperature**omega)