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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."""
import os
from copy import deepcopy
import numpy as np
import torch
from ultralytics.utils import DEFAULT_CFG, LOGGER, colorstr
from ultralytics.utils.torch_utils import autocast, profile
def check_train_batch_size(model, imgsz=640, amp=True, batch=-1):
"""
Compute optimal YOLO training batch size using the autobatch() function.
Args:
model (torch.nn.Module): YOLO model to check batch size for.
imgsz (int, optional): Image size used for training.
amp (bool, optional): Use automatic mixed precision if True.
batch (float, optional): Fraction of GPU memory to use. If -1, use default.
Returns:
(int): Optimal batch size computed using the autobatch() function.
Note:
If 0.0 < batch < 1.0, it's used as the fraction of GPU memory to use.
Otherwise, a default fraction of 0.6 is used.
"""
with autocast(enabled=amp):
return autobatch(deepcopy(model).train(), imgsz, fraction=batch if 0.0 < batch < 1.0 else 0.6)
def autobatch(model, imgsz=640, fraction=0.60, 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 0.60.
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} at {fraction * 100}% CUDA memory utilization.")
device = next(model.parameters()).device # get model device
if device.type in {"cpu", "mps"}:
LOGGER.info(f"{prefix} ⚠️ intended for CUDA devices, using default 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 = f"CUDA:{os.getenv('CUDA_VISIBLE_DEVICES', '0').strip()[0]}" # '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] if t < 16 else [1, 2, 4, 8, 16, 32, 64]
try:
img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes]
results = profile(img, model, n=1, 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
finally:
torch.cuda.empty_cache()
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