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448 lines
19 KiB
448 lines
19 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license
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import math
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import os
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import platform
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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 thop
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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.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, __version__
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from ultralytics.yolo.utils.checks import check_version
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TORCHVISION_0_10 = check_version(torchvision.__version__, '0.10.0')
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TORCH_1_9 = check_version(torch.__version__, '1.9.0')
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TORCH_1_11 = check_version(torch.__version__, '1.11.0')
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TORCH_1_12 = check_version(torch.__version__, '1.12.0')
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TORCH_2_X = check_version(torch.__version__, minimum='2.0')
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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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return (torch.inference_mode if TORCH_1_9 else torch.no_grad)()(fn)
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return decorate
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def select_device(device='', batch=0, newline=False, verbose=True):
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"""Selects PyTorch Device. Options are device = None or 'cpu' or 0 or '0' or '0,1,2,3'."""
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s = f'Ultralytics YOLOv{__version__} 🚀 Python-{platform.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 == 'mps' # 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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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.replace(',', ''))):
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LOGGER.info(s)
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install = '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' if torch.cuda.device_count() == 0 else ''
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raise ValueError(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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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(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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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 getattr(torch, 'has_mps', False) and torch.backends.mps.is_available() and TORCH_2_X:
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# Prefer MPS if available
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s += 'MPS\n'
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arg = 'mps'
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else: # revert to CPU
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s += 'CPU\n'
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arg = 'cpu'
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if verbose and RANK == -1:
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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 = nn.Conv2d(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).requires_grad_(False).to(conv.weight.device)
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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.size(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 = nn.ConvTranspose2d(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).requires_grad_(False).to(deconv.weight.device)
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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.size(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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"""Model information. imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320]."""
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if not verbose:
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return
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n_p = get_num_params(model)
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n_g = get_num_gradients(model) # number gradients
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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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for i, (name, p) in enumerate(model.named_parameters()):
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name = name.replace('module_list.', '')
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LOGGER.info('%5g %40s %9s %12g %20s %10.3g %10.3g' %
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(i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
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flops = get_flops(model, imgsz)
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fused = ' (fused)' if model.is_fused() else ''
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fs = f', {flops:.1f} GFLOPs' if flops else ''
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m = Path(getattr(model, 'yaml_file', '') or model.yaml.get('yaml_file', '')).stem.replace('yolo', 'YOLO') or 'Model'
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LOGGER.info(f'{m} summary{fused}: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}')
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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 get_flops(model, imgsz=640):
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"""Return a YOLO model's FLOPs."""
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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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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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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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except Exception:
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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): # img(16,3,256,416)
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# Scales img(bs,3,y,x) by ratio constrained to gs-multiple
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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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# Copy attributes from 'b' to 'a', options to only include [...] and to exclude [...]
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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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# Dictionary intersection of matching keys and shapes, omitting '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."""
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return model.module if is_parallel(model) else model
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def one_cycle(y1=0.0, y2=1.0, steps=100):
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"""Returns a lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf."""
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return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
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def init_seeds(seed=0, deterministic=False):
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"""Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html."""
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe
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# torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287
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if deterministic and TORCH_1_12: # https://github.com/ultralytics/yolov5/pull/8213
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torch.use_deterministic_algorithms(True)
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torch.backends.cudnn.deterministic = True
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os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
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os.environ['PYTHONHASHSEED'] = str(seed)
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class ModelEMA:
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"""Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
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Keeps a moving average of everything in the model state_dict (parameters and buffers)
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For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
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To disable EMA set the `enabled` attribute to `False`.
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"""
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def __init__(self, model, decay=0.9999, tau=2000, updates=0):
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"""Create EMA."""
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self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA
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self.updates = updates # number of EMA updates
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self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs)
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for p in self.ema.parameters():
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p.requires_grad_(False)
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self.enabled = True
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def update(self, model):
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"""Update EMA parameters."""
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if self.enabled:
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self.updates += 1
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d = self.decay(self.updates)
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msd = de_parallel(model).state_dict() # model state_dict
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for k, v in self.ema.state_dict().items():
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if v.dtype.is_floating_point: # true for FP16 and FP32
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v *= d
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v += (1 - d) * msd[k].detach()
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# assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype}, model {msd[k].dtype}'
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def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
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# Update EMA attributes
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if self.enabled:
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copy_attr(self.ema, model, include, exclude)
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def strip_optimizer(f: Union[str, Path] = 'best.pt', s: str = '') -> None:
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"""
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Strip optimizer from 'f' to finalize training, optionally save as 's'.
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Args:
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f (str): file path to model to strip the optimizer from. Default is 'best.pt'.
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s (str): file path to save the model with stripped optimizer to. If not provided, 'f' will be overwritten.
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Returns:
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None
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Usage:
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from pathlib import Path
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from ultralytics.yolo.utils.torch_utils import strip_optimizer
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for f in Path('/Users/glennjocher/Downloads/weights').rglob('*.pt'):
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strip_optimizer(f)
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"""
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x = torch.load(f, map_location=torch.device('cpu'))
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args = {**DEFAULT_CFG_DICT, **x['train_args']} # combine model args with default args, preferring model args
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if x.get('ema'):
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x['model'] = x['ema'] # replace model with ema
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for k in 'optimizer', 'best_fitness', 'ema', 'updates': # keys
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x[k] = None
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x['epoch'] = -1
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x['model'].half() # to FP16
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for p in x['model'].parameters():
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p.requires_grad = False
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x['train_args'] = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS} # strip non-default keys
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# x['model'].args = x['train_args']
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torch.save(x, s or f)
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mb = os.path.getsize(s or f) / 1E6 # filesize
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LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")
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def profile(input, ops, n=10, device=None):
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"""
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YOLOv8 speed/memory/FLOPs profiler
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Usage:
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input = torch.randn(16, 3, 640, 640)
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m1 = lambda x: x * torch.sigmoid(x)
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m2 = nn.SiLU()
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profile(input, [m1, m2], n=100) # profile over 100 iterations
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"""
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results = []
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if not isinstance(device, torch.device):
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device = select_device(device)
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LOGGER.info(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
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f"{'input':>24s}{'output':>24s}")
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for x in input if isinstance(input, list) else [input]:
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x = x.to(device)
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x.requires_grad = True
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for m in ops if isinstance(ops, list) else [ops]:
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m = m.to(device) if hasattr(m, 'to') else m # device
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m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
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tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward
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try:
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flops = thop.profile(m, inputs=[x], verbose=False)[0] / 1E9 * 2 # GFLOPs
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except Exception:
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flops = 0
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try:
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for _ in range(n):
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t[0] = time_sync()
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y = m(x)
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t[1] = time_sync()
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try:
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_ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
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t[2] = time_sync()
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except Exception: # no backward method
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# print(e) # for debug
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t[2] = float('nan')
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tf += (t[1] - t[0]) * 1000 / n # ms per op forward
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tb += (t[2] - t[1]) * 1000 / n # ms per op backward
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mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0 # (GB)
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s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y)) # shapes
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p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters
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|
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}')
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|
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
|