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Fix per-layer FLOPs profiling (#4048)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2023-07-30 22:24:28 +02:00 committed by GitHub
parent e6d18cc944
commit 379f31904a
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1 changed files with 7 additions and 8 deletions
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15
ultralytics/nn/tasks.py
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@ -87,9 +87,8 @@ class BaseModel(nn.Module):
def _predict_augment(self, x):
"""Perform augmentations on input image x and return augmented inference."""
LOGGER.warning(
f'WARNING ⚠️ {self.__class__.__name__} has not supported augment inference yet! Now using single-scale inference instead.'
)
LOGGER.warning(f'WARNING ⚠️ {self.__class__.__name__} does not support augmented inference yet. '
f'Reverting to single-scale inference instead.')
return self._predict_once(x)
def _profile_one_layer(self, m, x, dt):
@ -105,15 +104,15 @@ class BaseModel(nn.Module):
Returns:
None
"""
c = m == self.model[-1] # is final layer, copy input as inplace fix
o = thop.profile(m, inputs=[x.clone() if c else x], verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
c = m == self.model[-1] and isinstance(x, list) # is final layer list, copy input as inplace fix
flops = thop.profile(m, inputs=[x.copy() if c else x], verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
t = time_sync()
for _ in range(10):
m(x.clone() if c else x)
m(x.copy() if c else x)
dt.append((time_sync() - t) * 100)
if m == self.model[0]:
LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module")
LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}')
LOGGER.info(f'{dt[-1]:10.2f} {flops:10.2f} {m.np:10.0f} {m.type}')
if c:
LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
@ -338,7 +337,7 @@ class ClassificationModel(BaseModel):
"""YOLOv8 classification model."""
def __init__(self,
cfg=None,
cfg='yolov8n-cls.yaml',
model=None,
ch=3,
nc=None,