70 lines
2.7 KiB
Python
70 lines
2.7 KiB
Python
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Auto-batch utils
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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 app.yolov5.utils.general import LOGGER, colorstr
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from app.yolov5.utils.torch_utils import profile
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def check_train_batch_size(model, imgsz=640, amp=True):
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# Check YOLOv5 training batch size
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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=0.8, batch_size=16):
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# Automatically estimate best batch size to use `fraction` of available CUDA memory
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# Usage:
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# import torch
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# from utils.autobatch import autobatch
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# model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
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# print(autobatch(model))
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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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# 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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except Exception as e:
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LOGGER.warning(f'{prefix}{e}')
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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.warning(f'{prefix}WARNING: ⚠️ CUDA anomaly detected, recommend restart environment and retry command.')
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fraction = np.polyval(p, b) / 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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