机器视觉-尝试使用directml运行yolov8
DirectML 是什么
DirectML 是微软提供的类似于CUDA的基础计算框架, 不同于CUDA必须使用N卡GPU, DirectML要求显卡支持DirectX 12即可, 所以AMD和intel的核显也能支持.
Pytorch 要使用 DirectML 作为计算的backend, 需要安装python库 torch-directml, 经测试torch-directml和DirectML 并不能支持所有的算子, 所以 暂时不能支持yolov8 .
准备Python测试环境
## 安装 python=3.9 虚拟环境
conda.exe create --name yolo_directml python=3.9
conda.exe activate yolo_directml
安装 torch-directml
该包更新的不是很频繁, 更新历史 https://pypi.org/project/torch-directml/#history
最后一个版本是 0.2.0.dev230426, 对应的pytorch 为 2.0; 次新版本是 0.1.13.1.dev230413, 对应的pytorch 为 1.13.1.
这两个版本在后面yolov8 使用中经常会使Windows蓝屏.
先不要安装 pytorch, 直接安装安装 torch-directml将会自动安装依赖 pytorch 版本.
pip install torch-directml==0.1.13.1.dev230413
检查环境
检查安装的 python 包:
pip list
检查torch和torch_directml的device输出:
import torch
torch.cuda.is_available()
import torch_directml
torch_directml.is_available()
torch_directml.device()
# 正常输出为: device(type='privateuseone', index=0)
安装 yolov8
pip install ultralytics
检查yolov8安装情况:
import ultralytics
ultralytics.checks()
改造 ultralytics\utils\torch_utils.py 文件
yolov8 仅支持device为cpu或0, 即CPU或cuda, 所以需增加一个device=dml的选项, 以启用DirectML 计算后台, 需要修改 torch_utils.py 文件:
# Ultralytics YOLO 🚀, AGPL-3.0 license
import math
import os
import random
import time
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
from typing import Union
import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from ultralytics.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, __version__
from ultralytics.utils.checks import PYTHON_VERSION, check_version
try:
import thop
except ImportError:
thop = None
TORCH_1_9 = check_version(torch.__version__, "1.9.0")
TORCH_2_0 = check_version(torch.__version__, "2.0.0")
TORCHVISION_0_10 = check_version(torchvision.__version__, "0.10.0")
TORCHVISION_0_11 = check_version(torchvision.__version__, "0.11.0")
TORCHVISION_0_13 = check_version(torchvision.__version__, "0.13.0")
@contextmanager
def torch_distributed_zero_first(local_rank: int):
"""Decorator to make all processes in distributed training wait for each local_master to do something."""
initialized = torch.distributed.is_available() and torch.distributed.is_initialized()
if initialized and local_rank not in (-1, 0):
dist.barrier(device_ids=[local_rank])
yield
if initialized and local_rank == 0:
dist.barrier(device_ids=[0])
def smart_inference_mode():
"""Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator."""
def decorate(fn):
"""Applies appropriate torch decorator for inference mode based on torch version."""
if TORCH_1_9 and torch.is_inference_mode_enabled():
return fn # already in inference_mode, act as a pass-through
else:
return (torch.inference_mode if TORCH_1_9 else torch.no_grad)()(fn)
return decorate
def get_cpu_info():
"""Return a string with system CPU information, i.e. 'Apple M2'."""
import cpuinfo # pip install py-cpuinfo
k = "brand_raw", "hardware_raw", "arch_string_raw" # info keys sorted by preference (not all keys always available)
info = cpuinfo.get_cpu_info() # info dict
string = info.get(k[0] if k[0] in info else k[1] if k[1] in info else k[2], "unknown")
return string.replace("(R)", "").replace("CPU ", "").replace("@ ", "")
def select_device(device="", batch=0, newline=False, verbose=True):
"""
Selects the appropriate PyTorch device based on the provided arguments.
The function takes a string specifying the device or a torch.device object and returns a torch.device object
representing the selected device. The function also validates the number of available devices and raises an
exception if the requested device(s) are not available.
Args:
device (str | torch.device, optional): Device string or torch.device object.
Options are 'None', 'cpu', or 'cuda', or '0' or '0,1,2,3'. Defaults to an empty string, which auto-selects
the first available GPU, or CPU if no GPU is available.
batch (int, optional): Batch size being used in your model. Defaults to 0.
newline (bool, optional): If True, adds a newline at the end of the log string. Defaults to False.
verbose (bool, optional): If True, logs the device information. Defaults to True.
Returns:
(torch.device): Selected device.
Raises:
ValueError: If the specified device is not available or if the batch size is not a multiple of the number of
devices when using multiple GPUs.
Examples:
>>> select_device('cuda:0')
device(type='cuda', index=0)
>>> select_device('cpu')
device(type='cpu')
Note:
Sets the 'CUDA_VISIBLE_DEVICES' environment variable for specifying which GPUs to use.
"""
if isinstance(device, torch.device):
return device
s = f"Ultralytics YOLOv{__version__} 🚀 Python-{PYTHON_VERSION} torch-{torch.__version__} "
device = str(device).lower()
for remove in "cuda:", "none", "(", ")", "[", "]", "'", " ":
device = device.replace(remove, "") # to string, 'cuda:0' -> '0' and '(0, 1)' -> '0,1'
cpu = device == "cpu"
dml = device == "dml"
mps = device in ("mps", "mps:0") # Apple Metal Performance Shaders (MPS)
if cpu or mps or dml:
os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # force torch.cuda.is_available() = False
elif device: # non-cpu device requested
if device == "cuda":
device = "0"
visible = os.environ.get("CUDA_VISIBLE_DEVICES", None)
os.environ["CUDA_VISIBLE_DEVICES"] = device # set environment variable - must be before assert is_available()
if not (torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(",", ""))):
LOGGER.info(s)
install = (
"See https://pytorch.org/get-started/locally/ for up-to-date torch install instructions if no "
"CUDA devices are seen by torch.\n"
if torch.cuda.device_count() == 0
else ""
)
raise ValueError(
f"Invalid CUDA 'device={device}' requested."
f" Use 'device=cpu' or pass valid CUDA device(s) if available,"
f" i.e. 'device=0' or 'device=0,1,2,3' for Multi-GPU.\n"
f"\ntorch.cuda.is_available(): {torch.cuda.is_available()}"
f"\ntorch.cuda.device_count(): {torch.cuda.device_count()}"
f"\nos.environ['CUDA_VISIBLE_DEVICES']: {visible}\n"
f"{install}"
)
if not cpu and not mps and not dml and torch.cuda.is_available(): # prefer GPU if available
devices = device.split(",") if device else "0" # range(torch.cuda.device_count()) # i.e. 0,1,6,7
n = len(devices) # device count
if n > 1 and batch > 0 and batch % n != 0: # check batch_size is divisible by device_count
raise ValueError(
f"'batch={batch}' must be a multiple of GPU count {n}. Try 'batch={batch // n * n}' or "
f"'batch={batch // n * n + n}', the nearest batch sizes evenly divisible by {n}."
)
space = " " * (len(s) + 1)
for i, d in enumerate(devices):
p = torch.cuda.get_device_properties(i)
s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB
arg = "cuda:0"
elif mps and TORCH_2_0 and torch.backends.mps.is_available():
# Prefer MPS if available
s += f"MPS ({get_cpu_info()})\n"
arg = "mps"
else: # revert to CPU
s += f"CPU ({get_cpu_info()})\n"
arg = "cpu"
if verbose:
LOGGER.info(s if newline else s.rstrip())
if dml:
import torch_directml
return torch_directml.device()
else:
return torch.device(arg)
def time_sync():
"""PyTorch-accurate time."""
if torch.cuda.is_available():
torch.cuda.synchronize()
return time.time()
def fuse_conv_and_bn(conv, bn):
"""Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/."""
fusedconv = (
nn.Conv2d(
conv.in_channels,
conv.out_channels,
kernel_size=conv.kernel_size,
stride=conv.stride,
padding=conv.padding,
dilation=conv.dilation,
groups=conv.groups,
bias=True,
)
.requires_grad_(False)
.to(conv.weight.device)
)
# Prepare filters
w_conv = conv.weight.clone().view(conv.out_channels, -1)
w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
# Prepare spatial bias
b_conv = torch.zeros(conv.weight.shape[0], device=conv.weight.device) if conv.bias is None else conv.bias
b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
return fusedconv
def fuse_deconv_and_bn(deconv, bn):
"""Fuse ConvTranspose2d() and BatchNorm2d() layers."""
fuseddconv = (
nn.ConvTranspose2d(
deconv.in_channels,
deconv.out_channels,
kernel_size=deconv.kernel_size,
stride=deconv.stride,
padding=deconv.padding,
output_padding=deconv.output_padding,
dilation=deconv.dilation,
groups=deconv.groups,
bias=True,
)
.requires_grad_(False)
.to(deconv.weight.device)
)
# Prepare filters
w_deconv = deconv.weight.clone().view(deconv.out_channels, -1)
w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
fuseddconv.weight.copy_(torch.mm(w_bn, w_deconv).view(fuseddconv.weight.shape))
# Prepare spatial bias
b_conv = torch.zeros(deconv.weight.shape[1], device=deconv.weight.device) if deconv.bias is None else deconv.bias
b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
fuseddconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
return fuseddconv
def model_info(model, detailed=False, verbose=True, imgsz=640):
"""
Model information.
imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320].
"""
if not verbose:
return
n_p = get_num_params(model) # number of parameters
n_g = get_num_gradients(model) # number of gradients
n_l = len(list(model.modules())) # number of layers
if detailed:
LOGGER.info(
f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}"
)
for i, (name, p) in enumerate(model.named_parameters()):
name = name.replace("module_list.", "")
LOGGER.info(
"%5g %40s %9s %12g %20s %10.3g %10.3g %10s"
% (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std(), p.dtype)
)
flops = get_flops(model, imgsz)
fused = " (fused)" if getattr(model, "is_fused", lambda: False)() else ""
fs = f", {flops:.1f} GFLOPs" if flops else ""
yaml_file = getattr(model, "yaml_file", "") or getattr(model, "yaml", {}).get("yaml_file", "")
model_name = Path(yaml_file).stem.replace("yolo", "YOLO") or "Model"
LOGGER.info(f"{model_name} summary{fused}: {n_l} layers, {n_p} parameters, {n_g} gradients{fs}")
return n_l, n_p, n_g, flops
def get_num_params(model):
"""Return the total number of parameters in a YOLO model."""
return sum(x.numel() for x in model.parameters())
def get_num_gradients(model):
"""Return the total number of parameters with gradients in a YOLO model."""
return sum(x.numel() for x in model.parameters() if x.requires_grad)
def model_info_for_loggers(trainer):
"""
Return model info dict with useful model information.
Example:
YOLOv8n info for loggers
```python
results = {'model/parameters': 3151904,
'model/GFLOPs': 8.746,
'model/speed_ONNX(ms)': 41.244,
'model/speed_TensorRT(ms)': 3.211,
'model/speed_PyTorch(ms)': 18.755}
```
"""
if trainer.args.profile: # profile ONNX and TensorRT times
from ultralytics.utils.benchmarks import ProfileModels
results = ProfileModels([trainer.last], device=trainer.device).profile()[0]
results.pop("model/name")
else: # only return PyTorch times from most recent validation
results = {
"model/parameters": get_num_params(trainer.model),
"model/GFLOPs": round(get_flops(trainer.model), 3),
}
results["model/speed_PyTorch(ms)"] = round(trainer.validator.speed["inference"], 3)
return results
def get_flops(model, imgsz=640):
"""Return a YOLO model's FLOPs."""
if not thop:
return 0.0 # if not installed return 0.0 GFLOPs
try:
model = de_parallel(model)
p = next(model.parameters())
if not isinstance(imgsz, list):
imgsz = [imgsz, imgsz] # expand if int/float
try:
# Use stride size for input tensor
stride = max(int(model.stride.max()), 32) if hasattr(model, "stride") else 32 # max stride
im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
flops = thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1e9 * 2 # stride GFLOPs
return flops * imgsz[0] / stride * imgsz[1] / stride # imgsz GFLOPs
except Exception:
# Use actual image size for input tensor (i.e. required for RTDETR models)
im = torch.empty((1, p.shape[1], *imgsz), device=p.device) # input image in BCHW format
return thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1e9 * 2 # imgsz GFLOPs
except Exception:
return 0.0
def get_flops_with_torch_profiler(model, imgsz=640):
"""Compute model FLOPs (thop alternative)."""
if TORCH_2_0:
model = de_parallel(model)
p = next(model.parameters())
stride = (max(int(model.stride.max()), 32) if hasattr(model, "stride") else 32) * 2 # max stride
im = torch.zeros((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
with torch.profiler.profile(with_flops=True) as prof:
model(im)
flops = sum(x.flops for x in prof.key_averages()) / 1e9
imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float
flops = flops * imgsz[0] / stride * imgsz[1] / stride # 640x640 GFLOPs
return flops
return 0
def initialize_weights(model):
"""Initialize model weights to random values."""
for m in model.modules():
t = type(m)
if t is nn.Conv2d:
pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif t is nn.BatchNorm2d:
m.eps = 1e-3
m.momentum = 0.03
elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
m.inplace = True
def scale_img(img, ratio=1.0, same_shape=False, gs=32):
"""Scales and pads an image tensor of shape img(bs,3,y,x) based on given ratio and grid size gs, optionally
retaining the original shape.
"""
if ratio == 1.0:
return img
h, w = img.shape[2:]
s = (int(h * ratio), int(w * ratio)) # new size
img = F.interpolate(img, size=s, mode="bilinear", align_corners=False) # resize
if not same_shape: # pad/crop img
h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))
return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean
def make_divisible(x, divisor):
"""Returns nearest x divisible by divisor."""
if isinstance(divisor, torch.Tensor):
divisor = int(divisor.max()) # to int
return math.ceil(x / divisor) * divisor
def copy_attr(a, b, include=(), exclude=()):
"""Copies attributes from object 'b' to object 'a', with options to include/exclude certain attributes."""
for k, v in b.__dict__.items():
if (len(include) and k not in include) or k.startswith("_") or k in exclude:
continue
else:
setattr(a, k, v)
def get_latest_opset():
"""Return second-most (for maturity) recently supported ONNX opset by this version of torch."""
return max(int(k[14:]) for k in vars(torch.onnx) if "symbolic_opset" in k) - 1 # opset
def intersect_dicts(da, db, exclude=()):
"""Returns a dictionary of intersecting keys with matching shapes, excluding 'exclude' keys, using da values."""
return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape}
def is_parallel(model):
"""Returns True if model is of type DP or DDP."""
return isinstance(model, (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel))
def de_parallel(model):
"""De-parallelize a model: returns single-GPU model if model is of type DP or DDP."""
return model.module if is_parallel(model) else model
def one_cycle(y1=0.0, y2=1.0, steps=100):
"""Returns a lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf."""
return lambda x: max((1 - math.cos(x * math.pi / steps)) / 2, 0) * (y2 - y1) + y1
def init_seeds(seed=0, deterministic=False):
"""Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html."""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe
# torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287
if deterministic:
if TORCH_2_0:
torch.use_deterministic_algorithms(True, warn_only=True) # warn if deterministic is not possible
torch.backends.cudnn.deterministic = True
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
os.environ["PYTHONHASHSEED"] = str(seed)
else:
LOGGER.warning("WARNING ⚠️ Upgrade to torch>=2.0.0 for deterministic training.")
else:
torch.use_deterministic_algorithms(False)
torch.backends.cudnn.deterministic = False
class ModelEMA:
"""Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
Keeps a moving average of everything in the model state_dict (parameters and buffers)
For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
To disable EMA set the `enabled` attribute to `False`.
"""
def __init__(self, model, decay=0.9999, tau=2000, updates=0):
"""Create EMA."""
self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA
self.updates = updates # number of EMA updates
self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs)
for p in self.ema.parameters():
p.requires_grad_(False)
self.enabled = True
def update(self, model):
"""Update EMA parameters."""
if self.enabled:
self.updates += 1
d = self.decay(self.updates)
msd = de_parallel(model).state_dict() # model state_dict
for k, v in self.ema.state_dict().items():
if v.dtype.is_floating_point: # true for FP16 and FP32
v *= d
v += (1 - d) * msd[k].detach()
# assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype}, model {msd[k].dtype}'
def update_attr(self, model, include=(), exclude=("process_group", "reducer")):
"""Updates attributes and saves stripped model with optimizer removed."""
if self.enabled:
copy_attr(self.ema, model, include, exclude)
def strip_optimizer(f: Union[str, Path] = "best.pt", s: str = "") -> None:
"""
Strip optimizer from 'f' to finalize training, optionally save as 's'.
Args:
f (str): file path to model to strip the optimizer from. Default is 'best.pt'.
s (str): file path to save the model with stripped optimizer to. If not provided, 'f' will be overwritten.
Returns:
None
Example:
```python
from pathlib import Path
from ultralytics.utils.torch_utils import strip_optimizer
for f in Path('path/to/weights').rglob('*.pt'):
strip_optimizer(f)
```
"""
x = torch.load(f, map_location=torch.device("cpu"))
if "model" not in x:
LOGGER.info(f"Skipping {f}, not a valid Ultralytics model.")
return
if hasattr(x["model"], "args"):
x["model"].args = dict(x["model"].args) # convert from IterableSimpleNamespace to dict
args = {**DEFAULT_CFG_DICT, **x["train_args"]} if "train_args" in x else None # combine args
if x.get("ema"):
x["model"] = x["ema"] # replace model with ema
for k in "optimizer", "best_fitness", "ema", "updates": # keys
x[k] = None
x["epoch"] = -1
x["model"].half() # to FP16
for p in x["model"].parameters():
p.requires_grad = False
x["train_args"] = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS} # strip non-default keys
# x['model'].args = x['train_args']
torch.save(x, s or f)
mb = os.path.getsize(s or f) / 1e6 # file size
LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")
def profile(input, ops, n=10, device=None):
"""
Ultralytics speed, memory and FLOPs profiler.
Example:
```python
from ultralytics.utils.torch_utils import profile
input = torch.randn(16, 3, 640, 640)
m1 = lambda x: x * torch.sigmoid(x)
m2 = nn.SiLU()
profile(input, [m1, m2], n=100) # profile over 100 iterations
```
"""
results = []
if not isinstance(device, torch.device):
device = select_device(device)
LOGGER.info(
f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
f"{'input':>24s}{'output':>24s}"
)
for x in input if isinstance(input, list) else [input]:
x = x.to(device)
x.requires_grad = True
for m in ops if isinstance(ops, list) else [ops]:
m = m.to(device) if hasattr(m, "to") else m # device
m = m.half() if hasattr(m, "half") and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward
try:
flops = thop.profile(m, inputs=[x], verbose=False)[0] / 1e9 * 2 if thop else 0 # GFLOPs
except Exception:
flops = 0
try:
for _ in range(n):
t[0] = time_sync()
y = m(x)
t[1] = time_sync()
try:
(sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
t[2] = time_sync()
except Exception: # no backward method
# print(e) # for debug
t[2] = float("nan")
tf += (t[1] - t[0]) * 1000 / n # ms per op forward
tb += (t[2] - t[1]) * 1000 / n # ms per op backward
mem = torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0 # (GB)
s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else "list" for x in (x, y)) # shapes
p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters
LOGGER.info(f"{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}")
results.append([p, flops, mem, tf, tb, s_in, s_out])
except Exception as e:
LOGGER.info(e)
results.append(None)
torch.cuda.empty_cache()
return results
class EarlyStopping:
"""Early stopping class that stops training when a specified number of epochs have passed without improvement."""
def __init__(self, patience=50):
"""
Initialize early stopping object.
Args:
patience (int, optional): Number of epochs to wait after fitness stops improving before stopping.
"""
self.best_fitness = 0.0 # i.e. mAP
self.best_epoch = 0
self.patience = patience or float("inf") # epochs to wait after fitness stops improving to stop
self.possible_stop = False # possible stop may occur next epoch
def __call__(self, epoch, fitness):
"""
Check whether to stop training.
Args:
epoch (int): Current epoch of training
fitness (float): Fitness value of current epoch
Returns:
(bool): True if training should stop, False otherwise
"""
if fitness is None: # check if fitness=None (happens when val=False)
return False
if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training
self.best_epoch = epoch
self.best_fitness = fitness
delta = epoch - self.best_epoch # epochs without improvement
self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch
stop = delta >= self.patience # stop training if patience exceeded
if stop:
LOGGER.info(
f"Stopping training early as no improvement observed in last {self.patience} epochs. "
f"Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n"
f"To update EarlyStopping(patience={self.patience}) pass a new patience value, "
f"i.e. `patience=300` or use `patience=0` to disable EarlyStopping."
)
return stop
进行 yolov8 训练
使用命令行进行训练, directML不能支持混合精度 AMP, 所以关闭AMP特性:
yolo.exe task=detect mode=train val=True data=d:\data.yaml model=D:\yolov8n.pt epochs=1 workers=0 imgsz=640 seed=1 device=dml amp=False verbose=True
但在训练过程中仍然报错: RuntimeError: Return counts not implemented for unique operator for DirectML.
