回传数据解析，兼容v5和v10
This commit is contained in:
jiajie555
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ultralytics/utils/torch_utils.py
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610
ultralytics/utils/torch_utils.py
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import math
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import os
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import random
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import time
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from contextlib import contextmanager
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from copy import deepcopy
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from pathlib import Path
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from typing import Union
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import numpy as np
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision
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from ultralytics.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, __version__
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from ultralytics.utils.checks import PYTHON_VERSION, check_version
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try:
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import thop
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except ImportError:
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thop = None
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# Version checks (all default to version>=min_version)
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TORCH_1_9 = check_version(torch.__version__, "1.9.0")
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TORCH_1_13 = check_version(torch.__version__, "1.13.0")
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TORCH_2_0 = check_version(torch.__version__, "2.0.0")
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TORCHVISION_0_10 = check_version(torchvision.__version__, "0.10.0")
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TORCHVISION_0_11 = check_version(torchvision.__version__, "0.11.0")
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TORCHVISION_0_13 = check_version(torchvision.__version__, "0.13.0")
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@contextmanager
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def torch_distributed_zero_first(local_rank: int):
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"""Decorator to make all processes in distributed training wait for each local_master to do something."""
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initialized = torch.distributed.is_available() and torch.distributed.is_initialized()
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if initialized and local_rank not in (-1, 0):
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dist.barrier(device_ids=[local_rank])
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yield
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if initialized and local_rank == 0:
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dist.barrier(device_ids=[0])
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def smart_inference_mode():
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"""Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator."""
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def decorate(fn):
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"""Applies appropriate torch decorator for inference mode based on torch version."""
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if TORCH_1_9 and torch.is_inference_mode_enabled():
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return fn # already in inference_mode, act as a pass-through
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else:
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return (torch.inference_mode if TORCH_1_9 else torch.no_grad)()(fn)
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return decorate
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def get_cpu_info():
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"""Return a string with system CPU information, i.e. 'Apple M2'."""
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import cpuinfo # pip install py-cpuinfo
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k = "brand_raw", "hardware_raw", "arch_string_raw" # info keys sorted by preference (not all keys always available)
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info = cpuinfo.get_cpu_info() # info dict
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string = info.get(k[0] if k[0] in info else k[1] if k[1] in info else k[2], "unknown")
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return string.replace("(R)", "").replace("CPU ", "").replace("@ ", "")
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def select_device(device="", batch=0, newline=False, verbose=True):
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"""
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Selects the appropriate PyTorch device based on the provided arguments.
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The function takes a string specifying the device or a torch.device object and returns a torch.device object
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representing the selected device. The function also validates the number of available devices and raises an
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exception if the requested device(s) are not available.
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Args:
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device (str | torch.device, optional): Device string or torch.device object.
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Options are 'None', 'cpu', or 'cuda', or '0' or '0,1,2,3'. Defaults to an empty string, which auto-selects
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the first available GPU, or CPU if no GPU is available.
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batch (int, optional): Batch size being used in your model. Defaults to 0.
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newline (bool, optional): If True, adds a newline at the end of the log string. Defaults to False.
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verbose (bool, optional): If True, logs the device information. Defaults to True.
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Returns:
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(torch.device): Selected device.
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Raises:
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ValueError: If the specified device is not available or if the batch size is not a multiple of the number of
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devices when using multiple GPUs.
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Examples:
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>>> select_device('cuda:0')
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device(type='cuda', index=0)
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>>> select_device('cpu')
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device(type='cpu')
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Note:
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Sets the 'CUDA_VISIBLE_DEVICES' environment variable for specifying which GPUs to use.
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"""
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if isinstance(device, torch.device):
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return device
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s = f"Ultralytics YOLOv{__version__} 🚀 Python-{PYTHON_VERSION} torch-{torch.__version__} "
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device = str(device).lower()
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for remove in "cuda:", "none", "(", ")", "[", "]", "'", " ":
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device = device.replace(remove, "") # to string, 'cuda:0' -> '0' and '(0, 1)' -> '0,1'
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cpu = device == "cpu"
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mps = device in ("mps", "mps:0") # Apple Metal Performance Shaders (MPS)
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if cpu or mps:
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os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # force torch.cuda.is_available() = False
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elif device: # non-cpu device requested
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if device == "cuda":
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device = "0"
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visible = os.environ.get("CUDA_VISIBLE_DEVICES", None)
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os.environ["CUDA_VISIBLE_DEVICES"] = device # set environment variable - must be before assert is_available()
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if not (torch.cuda.is_available() and torch.cuda.device_count() >= len(device.split(","))):
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LOGGER.info(s)
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install = (
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"See https://pytorch.org/get-started/locally/ for up-to-date torch install instructions if no "
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"CUDA devices are seen by torch.\n"
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if torch.cuda.device_count() == 0
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else ""
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)
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raise ValueError(
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f"Invalid CUDA 'device={device}' requested."
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f" Use 'device=cpu' or pass valid CUDA device(s) if available,"
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f" i.e. 'device=0' or 'device=0,1,2,3' for Multi-GPU.\n"
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f"\ntorch.cuda.is_available(): {torch.cuda.is_available()}"
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f"\ntorch.cuda.device_count(): {torch.cuda.device_count()}"
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f"\nos.environ['CUDA_VISIBLE_DEVICES']: {visible}\n"
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f"{install}"
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)
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if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available
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devices = device.split(",") if device else "0" # range(torch.cuda.device_count()) # i.e. 0,1,6,7
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n = len(devices) # device count
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if n > 1 and batch > 0 and batch % n != 0: # check batch_size is divisible by device_count
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raise ValueError(
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f"'batch={batch}' must be a multiple of GPU count {n}. Try 'batch={batch // n * n}' or "
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f"'batch={batch // n * n + n}', the nearest batch sizes evenly divisible by {n}."
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)
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space = " " * (len(s) + 1)
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for i, d in enumerate(devices):
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p = torch.cuda.get_device_properties(i)
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s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB
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arg = "cuda:0"
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elif mps and TORCH_2_0 and torch.backends.mps.is_available():
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# Prefer MPS if available
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s += f"MPS ({get_cpu_info()})\n"
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arg = "mps"
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else: # revert to CPU
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s += f"CPU ({get_cpu_info()})\n"
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arg = "cpu"
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if verbose:
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LOGGER.info(s if newline else s.rstrip())
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return torch.device(arg)
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def time_sync():
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"""PyTorch-accurate time."""
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if torch.cuda.is_available():
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torch.cuda.synchronize()
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return time.time()
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def fuse_conv_and_bn(conv, bn):
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"""Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/."""
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fusedconv = (
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nn.Conv2d(
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conv.in_channels,
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conv.out_channels,
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kernel_size=conv.kernel_size,
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stride=conv.stride,
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padding=conv.padding,
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dilation=conv.dilation,
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groups=conv.groups,
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bias=True,
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)
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.requires_grad_(False)
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.to(conv.weight.device)
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)
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# Prepare filters
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w_conv = conv.weight.clone().view(conv.out_channels, -1)
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w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
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fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
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# Prepare spatial bias
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b_conv = torch.zeros(conv.weight.shape[0], device=conv.weight.device) if conv.bias is None else conv.bias
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b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
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fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
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return fusedconv
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def fuse_deconv_and_bn(deconv, bn):
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"""Fuse ConvTranspose2d() and BatchNorm2d() layers."""
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fuseddconv = (
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nn.ConvTranspose2d(
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deconv.in_channels,
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deconv.out_channels,
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kernel_size=deconv.kernel_size,
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stride=deconv.stride,
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padding=deconv.padding,
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output_padding=deconv.output_padding,
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dilation=deconv.dilation,
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groups=deconv.groups,
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bias=True,
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)
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.requires_grad_(False)
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.to(deconv.weight.device)
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)
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# Prepare filters
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w_deconv = deconv.weight.clone().view(deconv.out_channels, -1)
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w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
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fuseddconv.weight.copy_(torch.mm(w_bn, w_deconv).view(fuseddconv.weight.shape))
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# Prepare spatial bias
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b_conv = torch.zeros(deconv.weight.shape[1], device=deconv.weight.device) if deconv.bias is None else deconv.bias
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b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
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fuseddconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
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return fuseddconv
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def model_info(model, detailed=False, verbose=True, imgsz=640):
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"""
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Model information.
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imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320].
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"""
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if not verbose:
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return
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n_p = get_num_params(model) # number of parameters
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n_g = get_num_gradients(model) # number of gradients
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n_l = len(list(model.modules())) # number of layers
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if detailed:
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LOGGER.info(
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f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}"
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)
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for i, (name, p) in enumerate(model.named_parameters()):
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name = name.replace("module_list.", "")
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LOGGER.info(
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"%5g %40s %9s %12g %20s %10.3g %10.3g %10s"
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% (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std(), p.dtype)
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)
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flops = get_flops(model, imgsz)
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fused = " (fused)" if getattr(model, "is_fused", lambda: False)() else ""
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fs = f", {flops:.1f} GFLOPs" if flops else ""
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yaml_file = getattr(model, "yaml_file", "") or getattr(model, "yaml", {}).get("yaml_file", "")
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model_name = Path(yaml_file).stem.replace("yolo", "YOLO") or "Model"
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LOGGER.info(f"{model_name} summary{fused}: {n_l} layers, {n_p} parameters, {n_g} gradients{fs}")
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return n_l, n_p, n_g, flops
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def get_num_params(model):
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"""Return the total number of parameters in a YOLO model."""
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return sum(x.numel() for x in model.parameters())
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def get_num_gradients(model):
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"""Return the total number of parameters with gradients in a YOLO model."""
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return sum(x.numel() for x in model.parameters() if x.requires_grad)
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def model_info_for_loggers(trainer):
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"""
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Return model info dict with useful model information.
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Example:
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YOLOv8n info for loggers
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```python
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results = {'model/parameters': 3151904,
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'model/GFLOPs': 8.746,
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'model/speed_ONNX(ms)': 41.244,
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'model/speed_TensorRT(ms)': 3.211,
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'model/speed_PyTorch(ms)': 18.755}
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```
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"""
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if trainer.args.profile: # profile ONNX and TensorRT times
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from ultralytics.utils.benchmarks import ProfileModels
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results = ProfileModels([trainer.last], device=trainer.device).profile()[0]
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results.pop("model/name")
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else: # only return PyTorch times from most recent validation
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results = {
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"model/parameters": get_num_params(trainer.model),
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"model/GFLOPs": round(get_flops(trainer.model), 3),
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}
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results["model/speed_PyTorch(ms)"] = round(trainer.validator.speed["inference"], 3)
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return results
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def get_flops(model, imgsz=640):
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"""Return a YOLO model's FLOPs."""
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if not thop:
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return 0.0 # if not installed return 0.0 GFLOPs
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try:
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model = de_parallel(model)
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p = next(model.parameters())
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if not isinstance(imgsz, list):
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imgsz = [imgsz, imgsz] # expand if int/float
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try:
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# Use stride size for input tensor
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# stride = max(int(model.stride.max()), 32) if hasattr(model, "stride") else 32 # max stride
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# im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
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# flops = thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1e9 * 2 # stride GFLOPs
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# return flops * imgsz[0] / stride * imgsz[1] / stride # imgsz GFLOPs
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raise Exception
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except Exception:
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# Use actual image size for input tensor (i.e. required for RTDETR models)
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im = torch.empty((1, p.shape[1], *imgsz), device=p.device) # input image in BCHW format
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return thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1e9 * 2 # imgsz GFLOPs
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except Exception:
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return 0.0
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def get_flops_with_torch_profiler(model, imgsz=640):
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"""Compute model FLOPs (thop alternative)."""
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if TORCH_2_0:
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model = de_parallel(model)
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p = next(model.parameters())
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stride = (max(int(model.stride.max()), 32) if hasattr(model, "stride") else 32) * 2 # max stride
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im = torch.zeros((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
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with torch.profiler.profile(with_flops=True) as prof:
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model(im)
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flops = sum(x.flops for x in prof.key_averages()) / 1e9
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imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float
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flops = flops * imgsz[0] / stride * imgsz[1] / stride # 640x640 GFLOPs
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return flops
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return 0
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def initialize_weights(model):
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"""Initialize model weights to random values."""
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for m in model.modules():
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t = type(m)
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if t is nn.Conv2d:
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pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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elif t is nn.BatchNorm2d:
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m.eps = 1e-3
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m.momentum = 0.03
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elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
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m.inplace = True
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def scale_img(img, ratio=1.0, same_shape=False, gs=32):
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"""Scales and pads an image tensor of shape img(bs,3,y,x) based on given ratio and grid size gs, optionally
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retaining the original shape.
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"""
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if ratio == 1.0:
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return img
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h, w = img.shape[2:]
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s = (int(h * ratio), int(w * ratio)) # new size
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img = F.interpolate(img, size=s, mode="bilinear", align_corners=False) # resize
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if not same_shape: # pad/crop img
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h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))
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return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean
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def make_divisible(x, divisor):
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"""Returns nearest x divisible by divisor."""
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if isinstance(divisor, torch.Tensor):
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divisor = int(divisor.max()) # to int
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return math.ceil(x / divisor) * divisor
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def copy_attr(a, b, include=(), exclude=()):
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"""Copies attributes from object 'b' to object 'a', with options to include/exclude certain attributes."""
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for k, v in b.__dict__.items():
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if (len(include) and k not in include) or k.startswith("_") or k in exclude:
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continue
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else:
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setattr(a, k, v)
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def get_latest_opset():
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"""Return second-most (for maturity) recently supported ONNX opset by this version of torch."""
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return max(int(k[14:]) for k in vars(torch.onnx) if "symbolic_opset" in k) - 1 # opset
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def intersect_dicts(da, db, exclude=()):
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"""Returns a dictionary of intersecting keys with matching shapes, excluding 'exclude' keys, using da values."""
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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}
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def is_parallel(model):
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"""Returns True if model is of type DP or DDP."""
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return isinstance(model, (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel))
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def de_parallel(model):
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"""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
|
||||
Reference in New Issue
Block a user