Cleanup printing

utils/autobatch.py

@@ -34,11 +34,12 @@ def autobatch(model, imgsz=640, fraction=0.9, batch_size=16):
  print(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')
  return batch_size
 
+ d = str(device).upper() # 'CUDA:0'
  t = torch.cuda.get_device_properties(device).total_memory / 1024 ** 3 # (GB)
  r = torch.cuda.memory_reserved(device) / 1024 ** 3 # (GB)
  a = torch.cuda.memory_allocated(device) / 1024 ** 3 # (GB)
  f = t - (r + a) # free inside reserved
- print(f'{prefix}{t:.3g}G total, {r:.3g}G reserved, {a:.3g}G allocated, {f:.3g}G free')
+ print(f'{prefix}{d} {t:.3g}G total, {r:.3g}G reserved, {a:.3g}G allocated, {f:.3g}G free')
 
  batch_sizes = [1, 2, 4, 8, 16]
  try:
@@ -50,9 +51,8 @@ def autobatch(model, imgsz=640, fraction=0.9, batch_size=16):
  y = [x[2] for x in y if x] # memory [2]
  batch_sizes = batch_sizes[:len(y)]
  p = np.polyfit(batch_sizes, y, deg=1) # first degree polynomial fit
- f_intercept = int((f * fraction - p[1]) / p[0]) # optimal batch size
- print(f'{prefix}batch-size {f_intercept} estimated to utilize '
- f'{str(device).upper()} {t * fraction:.3g}G/{t:.3g}G ({fraction * 100:.0f}%)')
- return f_intercept
+ b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size)
+ print(f'{prefix}batch-size {b} estimated to utilize {d} {t * fraction:.3g}G/{t:.3g}G ({fraction * 100:.0f}%)')
+ return b
 
 # autobatch(torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False))