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ultralytics 8.0.21 Windows, segments, YAML fixes (#655)

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Glenn Jocher 2023-01-26 23:07:27 +01:00 • committed by GitHub
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commit 6c44ce21d9
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16 changed files with 147 additions and 146 deletions
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39
ultralytics/yolo/utils/torch_utils.py
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@ -17,11 +17,8 @@ import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parallel import DistributedDataParallel as DDP
import ultralytics
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER
from ultralytics.yolo.utils.checks import git_describe
from .checks import check_version
from ultralytics.yolo.utils.checks import check_version
LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
RANK = int(os.getenv('RANK', -1))
@ -60,8 +57,8 @@ def DDP_model(model):
def select_device(device='', batch=0, newline=False):
# device = None or 'cpu' or 0 or '0' or '0,1,2,3'
ver = git_describe() or ultralytics.__version__ # git commit or pip package version
s = f'Ultralytics YOLOv{ver} 🚀 Python-{platform.python_version()} torch-{torch.__version__} '
from ultralytics import __version__
s = f'Ultralytics YOLOv{__version__} 🚀 Python-{platform.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'
@ -247,6 +244,7 @@ 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):
@ -256,22 +254,25 @@ class ModelEMA:
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
self.updates += 1
d = self.decay(self.updates)
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} and model {msd[k].dtype} must be FP32'
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')):
# Update EMA attributes
copy_attr(self.ema, model, include, exclude)
if self.enabled:
copy_attr(self.ema, model, include, exclude)
def strip_optimizer(f='best.pt', s=''):
@ -285,8 +286,8 @@ def strip_optimizer(f='best.pt', s=''):
strip_optimizer(f)
Args:
f (str): file path to model state to strip the optimizer from. Default is 'best.pt'.
s (str): file path to save the model with stripped optimizer to. Default is ''. If not provided, the original file will be overwritten.
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
@ -364,12 +365,12 @@ class EarlyStopping:
Early stopping class that stops training when a specified number of epochs have passed without improvement.
"""
def __init__(self, patience=30):
def __init__(self, patience=50):
"""
Initialize early stopping object
Args:
patience (int, optional): Number of epochs to wait after fitness stops improving before stopping. Default is 30.
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