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# Ultralytics YOLO 🚀, AGPL-3.0 license |
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""" |
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Functions for estimating the best YOLO batch size to use a fraction of the available CUDA memory in PyTorch. |
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""" |
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from copy import deepcopy |
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import numpy as np |
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import torch |
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from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, colorstr |
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from ultralytics.yolo.utils.torch_utils import profile |
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def check_train_batch_size(model, imgsz=640, amp=True): |
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""" |
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Check YOLO training batch size using the autobatch() function. |
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Args: |
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model (torch.nn.Module): YOLO model to check batch size for. |
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imgsz (int): Image size used for training. |
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amp (bool): If True, use automatic mixed precision (AMP) for training. |
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Returns: |
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(int): Optimal batch size computed using the autobatch() function. |
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""" |
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with torch.cuda.amp.autocast(amp): |
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return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size |
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def autobatch(model, imgsz=640, fraction=0.67, batch_size=DEFAULT_CFG.batch): |
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""" |
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Automatically estimate the best YOLO batch size to use a fraction of the available CUDA memory. |
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Args: |
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model (torch.nn.module): YOLO model to compute batch size for. |
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imgsz (int, optional): The image size used as input for the YOLO model. Defaults to 640. |
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fraction (float, optional): The fraction of available CUDA memory to use. Defaults to 0.67. |
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batch_size (int, optional): The default batch size to use if an error is detected. Defaults to 16. |
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Returns: |
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(int): The optimal batch size. |
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""" |
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# Check device |
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prefix = colorstr('AutoBatch: ') |
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LOGGER.info(f'{prefix}Computing optimal batch size for imgsz={imgsz}') |
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device = next(model.parameters()).device # get model device |
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if device.type == 'cpu': |
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LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}') |
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return batch_size |
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if torch.backends.cudnn.benchmark: |
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LOGGER.info(f'{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}') |
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return batch_size |
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# Inspect CUDA memory |
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gb = 1 << 30 # bytes to GiB (1024 ** 3) |
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d = str(device).upper() # 'CUDA:0' |
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properties = torch.cuda.get_device_properties(device) # device properties |
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t = properties.total_memory / gb # GiB total |
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r = torch.cuda.memory_reserved(device) / gb # GiB reserved |
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a = torch.cuda.memory_allocated(device) / gb # GiB allocated |
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f = t - (r + a) # GiB free |
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LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free') |
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# Profile batch sizes |
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batch_sizes = [1, 2, 4, 8, 16] |
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try: |
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img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes] |
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results = profile(img, model, n=3, device=device) |
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# Fit a solution |
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y = [x[2] for x in results if x] # memory [2] |
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p = np.polyfit(batch_sizes[:len(y)], y, deg=1) # first degree polynomial fit |
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b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size) |
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if None in results: # some sizes failed |
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i = results.index(None) # first fail index |
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if b >= batch_sizes[i]: # y intercept above failure point |
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b = batch_sizes[max(i - 1, 0)] # select prior safe point |
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if b < 1 or b > 1024: # b outside of safe range |
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b = batch_size |
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LOGGER.info(f'{prefix}WARNING ⚠️ CUDA anomaly detected, using default batch-size {batch_size}.') |
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fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted |
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LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅') |
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return b |
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except Exception as e: |
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LOGGER.warning(f'{prefix}WARNING ⚠️ error detected: {e}, using default batch-size {batch_size}.') |
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return batch_size
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