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