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Fix TFLite INT8 for OBB (#7989)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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AdamP 2024-02-06 13:59:32 +01:00 committed by GitHub
parent e62d9cfe07
commit 70a6ef9c7e
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1 changed files with 11 additions and 10 deletions
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21
ultralytics/nn/modules/head.py
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@ -59,16 +59,17 @@ class Detect(nn.Module):
cls = x_cat[:, self.reg_max * 4 :] cls = x_cat[:, self.reg_max * 4 :]
else: else:
box, cls = x_cat.split((self.reg_max * 4, self.nc), 1) box, cls = x_cat.split((self.reg_max * 4, self.nc), 1)
dbox = self.decode_bboxes(box)
if self.export and self.format in ("tflite", "edgetpu"): if self.export and self.format in ("tflite", "edgetpu"):
# Precompute normalization factor to increase numerical stability # Precompute normalization factor to increase numerical stability
# See https://github.com/ultralytics/ultralytics/issues/7371 # See https://github.com/ultralytics/ultralytics/issues/7371
img_h = shape[2] grid_h = shape[2]
img_w = shape[3] grid_w = shape[3]
img_size = torch.tensor([img_w, img_h, img_w, img_h], device=box.device).reshape(1, 4, 1) 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] * img_size) norm = self.strides / (self.stride[0] * grid_size)
dbox = dist2bbox(self.dfl(box) * norm, self.anchors.unsqueeze(0) * norm[:, :2], xywh=True, dim=1) dbox = self.decode_bboxes(self.dfl(box) * norm, self.anchors.unsqueeze(0) * norm[:, :2])
else:
dbox = self.decode_bboxes(self.dfl(box), self.anchors.unsqueeze(0)) * self.strides
y = torch.cat((dbox, cls.sigmoid()), 1) y = torch.cat((dbox, cls.sigmoid()), 1)
return y if self.export else (y, x) return y if self.export else (y, x)
@ -82,9 +83,9 @@ class Detect(nn.Module):
a[-1].bias.data[:] = 1.0 # box 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) 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): def decode_bboxes(self, bboxes, anchors):
"""Decode bounding boxes.""" """Decode bounding boxes."""
return dist2bbox(self.dfl(bboxes), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides return dist2bbox(bboxes, anchors, xywh=True, dim=1)
class Segment(Detect): class Segment(Detect):
@ -139,9 +140,9 @@ class OBB(Detect):
return x, angle return x, angle
return torch.cat([x, angle], 1) if self.export else (torch.cat([x[0], angle], 1), (x[1], angle)) return torch.cat([x, angle], 1) if self.export else (torch.cat([x[0], angle], 1), (x[1], angle))
def decode_bboxes(self, bboxes): def decode_bboxes(self, bboxes, anchors):
"""Decode rotated bounding boxes.""" """Decode rotated bounding boxes."""
return dist2rbox(self.dfl(bboxes), self.angle, self.anchors.unsqueeze(0), dim=1) * self.strides return dist2rbox(bboxes, self.angle, anchors, dim=1)
class Pose(Detect): class Pose(Detect):