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ultralytics 8.3.29 Sony IMX500 export (#14878)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
Co-authored-by: Francesco Mattioli <Francesco.mttl@gmail.com>
Co-authored-by: Lakshantha Dissanayake <lakshantha@ultralytics.com>
Co-authored-by: Lakshantha Dissanayake <lakshanthad@yahoo.com>
Co-authored-by: Chizkiyahu Raful <37312901+Chizkiyahu@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
Co-authored-by: Mohammed Yasin <32206511+Y-T-G@users.noreply.github.com>
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Laughing 2024-11-11 21:20:21 +08:00 • committed by GitHub
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commit 0fa1d7d5a6
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16 changed files with 281 additions and 17 deletions
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12
ultralytics/nn/modules/head.py
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@ -23,6 +23,7 @@ class Detect(nn.Module):
dynamic = False # force grid reconstruction
export = False # export mode
format = None # export format
end2end = False # end2end
max_det = 300 # max_det
shape = None
@ -101,7 +102,7 @@ class Detect(nn.Module):
# Inference path
shape = x[0].shape # BCHW
x_cat = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2)
if self.dynamic or self.shape != shape:
if self.format != "imx" and (self.dynamic or self.shape != shape):
self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5))
self.shape = shape
@ -119,6 +120,11 @@ class Detect(nn.Module):
grid_size = torch.tensor([grid_w, grid_h, grid_w, grid_h], device=box.device).reshape(1, 4, 1)
norm = self.strides / (self.stride[0] * grid_size)
dbox = self.decode_bboxes(self.dfl(box) * norm, self.anchors.unsqueeze(0) * norm[:, :2])
elif self.export and self.format == "imx":
dbox = self.decode_bboxes(
self.dfl(box) * self.strides, self.anchors.unsqueeze(0) * self.strides, xywh=False
)
return dbox.transpose(1, 2), cls.sigmoid().permute(0, 2, 1)
else:
dbox = self.decode_bboxes(self.dfl(box), self.anchors.unsqueeze(0)) * self.strides
@ -137,9 +143,9 @@ class Detect(nn.Module):
a[-1].bias.data[:] = 1.0 # box
b[-1].bias.data[: m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (.01 objects, 80 classes, 640 img)
def decode_bboxes(self, bboxes, anchors):
def decode_bboxes(self, bboxes, anchors, xywh=True):
"""Decode bounding boxes."""
return dist2bbox(bboxes, anchors, xywh=not self.end2end, dim=1)
return dist2bbox(bboxes, anchors, xywh=xywh and (not self.end2end), dim=1)
@staticmethod
def postprocess(preds: torch.Tensor, max_det: int, nc: int = 80):