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ultralytics 8.0.80 single-line docstring fixes (#2060)

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Glenn Jocher 2023-04-16 15:20:11 +02:00 • committed by GitHub
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48 changed files with 418 additions and 420 deletions
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6
ultralytics/yolo/engine/trainer.py
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@ -163,7 +163,7 @@ class BaseTrainer:
callback(self)
def train(self):
# Allow device='', device=None on Multi-GPU systems to default to device=0
"""Allow device='', device=None on Multi-GPU systems to default to device=0."""
if isinstance(self.args.device, int) or self.args.device: # i.e. device=0 or device=[0,1,2,3]
world_size = torch.cuda.device_count()
elif torch.cuda.is_available(): # i.e. device=None or device=''
@ -306,7 +306,7 @@ class BaseTrainer:
xi = [0, nw] # x interp
self.accumulate = max(1, np.interp(ni, xi, [1, self.args.nbs / self.batch_size]).round())
for j, x in enumerate(self.optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
# Bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(
ni, xi, [self.args.warmup_bias_lr if j == 0 else 0.0, x['initial_lr'] * self.lf(epoch)])
if 'momentum' in x:
@ -631,7 +631,7 @@ def check_amp(model):
return False # AMP only used on CUDA devices
def amp_allclose(m, im):
# All close FP32 vs AMP results
"""All close FP32 vs AMP results."""
a = m(im, device=device, verbose=False)[0].boxes.data # FP32 inference
with torch.cuda.amp.autocast(True):
b = m(im, device=device, verbose=False)[0].boxes.data # AMP inference