# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import gc
import math
import os
import random
import time
from contextlib import contextmanager
from copy import deepcopy
from datetime import datetime
from pathlib import Path
from typing import Union
import numpy as np
import thop
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from ultralytics.utils import (
DEFAULT_CFG_DICT,
DEFAULT_CFG_KEYS,
LOGGER,
NUM_THREADS,
PYTHON_VERSION,
TORCHVISION_VERSION,
WINDOWS,
__version__,
colorstr,
)
from ultralytics.utils.checks import check_version
# Version checks (all default to version>=min_version)
TORCH_1_9 = check_version(torch.__version__, "1.9.0")
TORCH_1_13 = check_version(torch.__version__, "1.13.0")
TORCH_2_0 = check_version(torch.__version__, "2.0.0")
TORCH_2_4 = check_version(torch.__version__, "2.4.0")
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")
TORCHVISION_0_18 = check_version(TORCHVISION_VERSION, "0.18.0")
if WINDOWS and check_version(torch.__version__, "==2.4.0"): # reject version 2.4.0 on Windows
LOGGER.warning(
"WARNING ⚠️ Known issue with torch==2.4.0 on Windows with CPU, recommend upgrading to torch>=2.4.1 to resolve "
"https://github.com/ultralytics/ultralytics/issues/15049"
)
@contextmanager
def torch_distributed_zero_first(local_rank: int):
"""Ensures all processes in distributed training wait for the local master (rank 0) to complete a task first."""
initialized = dist.is_available() and dist.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=[local_rank])
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 autocast(enabled: bool, device: str = "cuda"):
"""
Get the appropriate autocast context manager based on PyTorch version and AMP setting.
This function returns a context manager for automatic mixed precision (AMP) training that is compatible with both
older and newer versions of PyTorch. It handles the differences in the autocast API between PyTorch versions.
Args:
enabled (bool): Whether to enable automatic mixed precision.
device (str, optional): The device to use for autocast. Defaults to 'cuda'.
Returns:
(torch.amp.autocast): The appropriate autocast context manager.
Note:
- For PyTorch versions 1.13 and newer, it uses `torch.amp.autocast`.
- For older versions, it uses `torch.cuda.autocast`.
Example:
```python
with autocast(amp=True):
# Your mixed precision operations here
pass
```
"""
if TORCH_1_13:
return torch.amp.autocast(device, enabled=enabled)
else:
return torch.cuda.amp.autocast(enabled)
def get_cpu_info():
"""Return a string with system CPU information, i.e. 'Apple M2'."""
from ultralytics.utils import PERSISTENT_CACHE # avoid circular import error
if "cpu_info" not in PERSISTENT_CACHE:
try:
import cpuinfo # pip install py-cpuinfo
k = "brand_raw", "hardware_raw", "arch_string_raw" # keys sorted by preference
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")
PERSISTENT_CACHE["cpu_info"] = string.replace("(R)", "").replace("CPU ", "").replace("@ ", "")
except Exception:
pass
return PERSISTENT_CACHE.get("cpu_info", "unknown")
def get_gpu_info(index):
"""Return a string with system GPU information, i.e. 'Tesla T4, 15102MiB'."""
properties = torch.cuda.get_device_properties(index)
return f"{properties.name}, {properties.total_memory / (1 << 20):.0f}MiB"
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) or str(device).startswith("tpu"):
return device
s = f"Ultralytics {__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"
mps = device in {"mps", "mps:0"} # Apple Metal Performance Shaders (MPS)
if cpu or mps:
os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # force torch.cuda.is_available() = False
elif device: # non-cpu device requested
if device == "cuda":
device = "0"
if "," in device:
device = ",".join([x for x in device.split(",") if x]) # remove sequential commas, i.e. "0,,1" -> "0,1"
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.split(","))):
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 torch.cuda.is_available(): # prefer GPU if available
devices = device.split(",") if device else "0" # i.e. "0,1" -> ["0", "1"]
n = len(devices) # device count
if n > 1: # multi-GPU
if batch < 1:
raise ValueError(
"AutoBatch with batch<1 not supported for Multi-GPU training, "
"please specify a valid batch size, i.e. batch=16."
)
if 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):
s += f"{'' if i == 0 else space}CUDA:{d} ({get_gpu_info(i)})\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 arg in {"cpu", "mps"}:
torch.set_num_threads(NUM_THREADS) # reset OMP_NUM_THREADS for cpu training
if verbose:
LOGGER.info(s if newline else s.rstrip())
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.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.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):
"""Print and return detailed model information layer by layer."""
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':>10}{'parameters':>12}{'shape':>20}{'mu':>10}{'sigma':>10}")
for i, (name, p) in enumerate(model.named_parameters()):
name = name.replace("module_list.", "")
LOGGER.info(
f"{i:>5g}{name:>40s}{p.requires_grad!r:>10}{p.numel():>12g}{str(list(p.shape)):>20s}"
f"{p.mean():>10.3g}{p.std():>10.3g}{str(p.dtype):>15s}"
)
flops = get_flops(model, imgsz) # imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320]
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."""
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 package alternative, but 2-10x slower unfortunately)."""
if not TORCH_2_0: # torch profiler implemented in torch>=2.0
return 0.0
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) * 2 # max stride
im = torch.empty((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
flops = flops * imgsz[0] / stride * imgsz[1] / stride # 640x640 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
with torch.profiler.profile(with_flops=True) as prof:
model(im)
flops = sum(x.flops for x in prof.key_averages()) / 1e9
return flops
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, optionally maintaining aspect ratio and padding to gs multiple."""
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 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 the second-most recent ONNX opset version supported by this version of PyTorch, adjusted for maturity."""
if TORCH_1_13:
# If the PyTorch>=1.13, dynamically compute the latest opset minus one using 'symbolic_opset'
return max(int(k[14:]) for k in vars(torch.onnx) if "symbolic_opset" in k) - 1
# Otherwise for PyTorch<=1.12 return the corresponding predefined opset
version = torch.onnx.producer_version.rsplit(".", 1)[0] # i.e. '2.3'
return {"1.12": 15, "1.11": 14, "1.10": 13, "1.9": 12, "1.8": 12}.get(version, 12)
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):
"""Initialize EMA for 'model' with given arguments."""
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 = "", updates: dict = None) -> dict:
"""
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.
updates (dict): a dictionary of updates to overlay onto the checkpoint before saving.
Returns:
(dict): The combined checkpoint dictionary.
Example:
```python
from pathlib import Path
from ultralytics.utils.torch_utils import strip_optimizer
for f in Path("path/to/model/checkpoints").rglob("*.pt"):
strip_optimizer(f)
```
Note:
Use `ultralytics.nn.torch_safe_load` for missing modules with `x = torch_safe_load(f)[0]`
"""
try:
x = torch.load(f, map_location=torch.device("cpu"))
assert isinstance(x, dict), "checkpoint is not a Python dictionary"
assert "model" in x, "'model' missing from checkpoint"
except Exception as e:
LOGGER.warning(f"WARNING ⚠️ Skipping {f}, not a valid Ultralytics model: {e}")
return {}
metadata = {
"date": datetime.now().isoformat(),
"version": __version__,
"license": "AGPL-3.0 License (https://ultralytics.com/license)",
"docs": "https://docs.ultralytics.com",
}
# Update model
if x.get("ema"):
x["model"] = x["ema"] # replace model with EMA
if hasattr(x["model"], "args"):
x["model"].args = dict(x["model"].args) # convert from IterableSimpleNamespace to dict
if hasattr(x["model"], "criterion"):
x["model"].criterion = None # strip loss criterion
x["model"].half() # to FP16
for p in x["model"].parameters():
p.requires_grad = False
# Update other keys
args = {**DEFAULT_CFG_DICT, **x.get("train_args", {})} # combine args
for k in "optimizer", "best_fitness", "ema", "updates": # keys
x[k] = None
x["epoch"] = -1
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']
# Save
combined = {**metadata, **x, **(updates or {})}
torch.save(combined, s or f) # combine dicts (prefer to the right)
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")
return combined
def convert_optimizer_state_dict_to_fp16(state_dict):
"""
Converts the state_dict of a given optimizer to FP16, focusing on the 'state' key for tensor conversions.
This method aims to reduce storage size without altering 'param_groups' as they contain non-tensor data.
"""
for state in state_dict["state"].values():
for k, v in state.items():
if k != "step" and isinstance(v, torch.Tensor) and v.dtype is torch.float32:
state[k] = v.half()
return state_dict
@contextmanager
def cuda_memory_usage(device=None):
"""
Monitor and manage CUDA memory usage.
This function checks if CUDA is available and, if so, empties the CUDA cache to free up unused memory.
It then yields a dictionary containing memory usage information, which can be updated by the caller.
Finally, it updates the dictionary with the amount of memory reserved by CUDA on the specified device.
Args:
device (torch.device, optional): The CUDA device to query memory usage for. Defaults to None.
Yields:
(dict): A dictionary with a key 'memory' initialized to 0, which will be updated with the reserved memory.
"""
cuda_info = dict(memory=0)
if torch.cuda.is_available():
torch.cuda.empty_cache()
try:
yield cuda_info
finally:
cuda_info["memory"] = torch.cuda.memory_reserved(device)
else:
yield cuda_info
def profile(input, ops, n=10, device=None, max_num_obj=0):
"""
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}"
)
gc.collect() # attempt to free unused memory
torch.cuda.empty_cache()
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 # GFLOPs
except Exception:
flops = 0
try:
mem = 0
for _ in range(n):
with cuda_memory_usage(device) as cuda_info:
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")
mem += cuda_info["memory"] / 1e9 # (GB)
tf += (t[1] - t[0]) * 1000 / n # ms per op forward
tb += (t[2] - t[1]) * 1000 / n # ms per op backward
if max_num_obj: # simulate training with predictions per image grid (for AutoBatch)
with cuda_memory_usage(device) as cuda_info:
torch.randn(
x.shape[0],
max_num_obj,
int(sum((x.shape[-1] / s) * (x.shape[-2] / s) for s in m.stride.tolist())),
device=device,
dtype=torch.float32,
)
mem += cuda_info["memory"] / 1e9 # (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)
finally:
gc.collect() # attempt to free unused memory
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:
prefix = colorstr("EarlyStopping: ")
LOGGER.info(
f"{prefix}Training stopped 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
class FXModel(nn.Module):
"""
A custom model class for torch.fx compatibility.
This class extends `torch.nn.Module` and is designed to ensure compatibility with torch.fx for tracing and graph manipulation.
It copies attributes from an existing model and explicitly sets the model attribute to ensure proper copying.
Args:
model (torch.nn.Module): The original model to wrap for torch.fx compatibility.
"""
def __init__(self, model):
"""
Initialize the FXModel.
Args:
model (torch.nn.Module): The original model to wrap for torch.fx compatibility.
"""
super().__init__()
copy_attr(self, model)
# Explicitly set `model` since `copy_attr` somehow does not copy it.
self.model = model.model
def forward(self, x):
"""
Forward pass through the model.
This method performs the forward pass through the model, handling the dependencies between layers and saving intermediate outputs.
Args:
x (torch.Tensor): The input tensor to the model.
Returns:
(torch.Tensor): The output tensor from the model.
"""
y = [] # outputs
for m in self.model:
if m.f != -1: # if not from previous layer
# from earlier layers
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]
x = m(x) # run
y.append(x) # save output
return x