You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
71 lines
3.0 KiB
Python
71 lines
3.0 KiB
Python
3 weeks ago
|
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
|
||
|
|
"""Auto-batch utils."""
|
||
|
|
|
||
|
|
from copy import deepcopy
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import torch
|
||
|
|
|
||
|
|
from utils.general import LOGGER, colorstr
|
||
|
|
from utils.torch_utils import profile
|
||
|
|
|
||
|
|
|
||
|
|
def check_train_batch_size(model, imgsz=640, amp=True):
|
||
|
|
"""Checks and computes optimal training batch size for YOLOv5 model, given image size and AMP setting."""
|
||
|
|
with torch.cuda.amp.autocast(amp):
|
||
|
|
return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size
|
||
|
|
|
||
|
|
|
||
|
|
def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
|
||
|
|
"""Estimates optimal YOLOv5 batch size using `fraction` of CUDA memory."""
|
||
|
|
# Usage:
|
||
|
|
# import torch
|
||
|
|
# from utils.autobatch import autobatch
|
||
|
|
# model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
|
||
|
|
# print(autobatch(model))
|
||
|
|
|
||
|
|
# 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)
|
||
|
|
except Exception as e:
|
||
|
|
LOGGER.warning(f"{prefix}{e}")
|
||
|
|
|
||
|
|
# 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.warning(f"{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.")
|
||
|
|
|
||
|
|
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
|