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ultralytics 8.0.80 single-line docstring fixes (#2060)

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Glenn Jocher 2023-04-16 15:20:11 +02:00 • committed by GitHub
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48 changed files with 418 additions and 420 deletions
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45
ultralytics/yolo/utils/torch_utils.py
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@ -30,7 +30,7 @@ TORCH_2_X = check_version(torch.__version__, minimum='2.0')
@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
"""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])
@ -40,7 +40,8 @@ def torch_distributed_zero_first(local_rank: int):
def smart_inference_mode():
# Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator
"""Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator."""
def decorate(fn):
return (torch.inference_mode if TORCH_1_9 else torch.no_grad)()(fn)
@ -48,7 +49,7 @@ def smart_inference_mode():
def select_device(device='', batch=0, newline=False, verbose=True):
# device = None or 'cpu' or 0 or '0' or '0,1,2,3'
"""Selects PyTorch Device. Options are device = None or 'cpu' or 0 or '0' or '0,1,2,3'."""
s = f'Ultralytics YOLOv{__version__} 🚀 Python-{platform.python_version()} torch-{torch.__version__} '
device = str(device).lower()
for remove in 'cuda:', 'none', '(', ')', '[', ']', "'", ' ':
@ -84,7 +85,7 @@ def select_device(device='', batch=0, newline=False, verbose=True):
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 getattr(torch, 'has_mps', False) and torch.backends.mps.is_available() and TORCH_2_X:
# prefer MPS if available
# Prefer MPS if available
s += 'MPS\n'
arg = 'mps'
else: # revert to CPU
@ -97,14 +98,14 @@ def select_device(device='', batch=0, newline=False, verbose=True):
def time_sync():
# PyTorch-accurate time
"""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/
"""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,
@ -128,7 +129,7 @@ def fuse_conv_and_bn(conv, bn):
def fuse_deconv_and_bn(deconv, bn):
# Fuse ConvTranspose2d() and BatchNorm2d() layers
"""Fuse ConvTranspose2d() and BatchNorm2d() layers."""
fuseddconv = nn.ConvTranspose2d(deconv.in_channels,
deconv.out_channels,
kernel_size=deconv.kernel_size,
@ -139,7 +140,7 @@ def fuse_deconv_and_bn(deconv, bn):
groups=deconv.groups,
bias=True).requires_grad_(False).to(deconv.weight.device)
# prepare filters
# 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))
@ -153,7 +154,7 @@ def fuse_deconv_and_bn(deconv, bn):
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]
"""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)
@ -174,17 +175,17 @@ def model_info(model, detailed=False, verbose=True, imgsz=640):
def get_num_params(model):
# Return the total number of parameters in a YOLO 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 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 get_flops(model, imgsz=640):
# Return a YOLO model's FLOPs
"""Return a YOLO model's FLOPs."""
try:
model = de_parallel(model)
p = next(model.parameters())
@ -199,7 +200,7 @@ def get_flops(model, imgsz=640):
def initialize_weights(model):
# Initialize model weights to random values
"""Initialize model weights to random values."""
for m in model.modules():
t = type(m)
if t is nn.Conv2d:
@ -224,7 +225,7 @@ def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416)
def make_divisible(x, divisor):
# Returns nearest x divisible by divisor
"""Returns nearest x divisible by divisor."""
if isinstance(divisor, torch.Tensor):
divisor = int(divisor.max()) # to int
return math.ceil(x / divisor) * divisor
@ -240,7 +241,7 @@ def copy_attr(a, b, include=(), exclude=()):
def get_latest_opset():
# Return second-most (for maturity) recently supported ONNX opset by this version of torch
"""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
@ -250,22 +251,22 @@ def intersect_dicts(da, db, exclude=()):
def is_parallel(model):
# Returns True if model is of type DP or DDP
"""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
"""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):
# lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf
"""Returns a lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf."""
return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
def init_seeds(seed=0, deterministic=False):
# Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html
"""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)
@ -280,14 +281,14 @@ def init_seeds(seed=0, deterministic=False):
class ModelEMA:
""" Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
"""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
"""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)
@ -296,7 +297,7 @@ class ModelEMA:
self.enabled = True
def update(self, model):
# Update EMA parameters
"""Update EMA parameters."""
if self.enabled:
self.updates += 1
d = self.decay(self.updates)