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- # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
- """
- Auto-batch utils
- """
-
- from copy import deepcopy
-
- import numpy as np
- import torch
- from torch.cuda import amp
-
- from utils.general import colorstr
- from utils.torch_utils import profile
-
-
- def check_train_batch_size(model, imgsz=640):
- # Check YOLOv5 training batch size
- with amp.autocast():
- return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size
-
-
- def autobatch(model, imgsz=640, fraction=0.9, batch_size=16):
- # Automatically estimate best batch size to use `fraction` of available CUDA memory
- # Usage:
- # import torch
- # from utils.autobatch import autobatch
- # model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
- # print(autobatch(model))
-
- prefix = colorstr('autobatch: ')
- print(f'{prefix}Computing optimal batch size for --imgsz {imgsz}')
- device = next(model.parameters()).device # get model device
- if device.type == 'cpu':
- 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}{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:
- img = [torch.zeros(b, 3, imgsz, imgsz) for b in batch_sizes]
- y = profile(img, model, n=3, device=device)
- except Exception as e:
- print(f'{prefix}{e}')
-
- 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
- b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size)
- print(f'{prefix}Using colorstr(batch-size {b}) for {d} {t * fraction:.3g}G/{t:.3g}G ({fraction * 100:.0f}%)')
- return b
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