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""" |
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PyTorch utils |
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""" |
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import math |
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import os |
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import platform |
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import subprocess |
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import time |
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import warnings |
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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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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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from torch.nn.parallel import DistributedDataParallel as DDP |
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from utils.general import LOGGER, check_version, colorstr, file_date, git_describe |
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LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) |
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RANK = int(os.getenv('RANK', -1)) |
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WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) |
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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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warnings.filterwarnings('ignore', message='User provided device_type of \'cuda\', but CUDA is not available. Disabling') |
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warnings.filterwarnings('ignore', category=UserWarning) |
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def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')): |
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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 smartCrossEntropyLoss(label_smoothing=0.0): |
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if check_version(torch.__version__, '1.10.0'): |
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return nn.CrossEntropyLoss(label_smoothing=label_smoothing) |
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if label_smoothing > 0: |
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LOGGER.warning(f'WARNING β οΈ label smoothing {label_smoothing} requires torch>=1.10.0') |
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return nn.CrossEntropyLoss() |
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def smart_DDP(model): |
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assert not check_version(torch.__version__, '1.12.0', pinned=True), \ |
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'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \ |
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'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395' |
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if check_version(torch.__version__, '1.11.0'): |
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return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True) |
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else: |
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return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK) |
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def reshape_classifier_output(model, n=1000): |
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from models.common import Classify |
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name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1] |
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if isinstance(m, Classify): |
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if m.linear.out_features != n: |
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m.linear = nn.Linear(m.linear.in_features, n) |
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elif isinstance(m, nn.Linear): |
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if m.out_features != n: |
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setattr(model, name, nn.Linear(m.in_features, n)) |
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elif isinstance(m, nn.Sequential): |
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types = [type(x) for x in m] |
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if nn.Linear in types: |
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i = types.index(nn.Linear) |
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if m[i].out_features != n: |
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m[i] = nn.Linear(m[i].in_features, n) |
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elif nn.Conv2d in types: |
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i = types.index(nn.Conv2d) |
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if m[i].out_channels != n: |
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m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None) |
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@contextmanager |
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def torch_distributed_zero_first(local_rank: int): |
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if 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 local_rank == 0: |
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dist.barrier(device_ids=[0]) |
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def device_count(): |
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assert platform.system() in ('Linux', 'Windows'), 'device_count() only supported on Linux or Windows' |
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try: |
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cmd = 'nvidia-smi -L | wc -l' if platform.system() == 'Linux' else 'nvidia-smi -L | find /c /v ""' |
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return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]) |
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except Exception: |
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return 0 |
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def select_device(device='', batch_size=0, newline=True): |
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s = f'YOLOv5 π {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} ' |
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device = str(device).strip().lower().replace('cuda:', '').replace('none', '') |
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cpu = device == 'cpu' |
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mps = device == 'mps' |
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if cpu or mps: |
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os.environ['CUDA_VISIBLE_DEVICES'] = '-1' |
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elif device: |
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os.environ['CUDA_VISIBLE_DEVICES'] = device |
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assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \ |
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f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)" |
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if not cpu and not mps and torch.cuda.is_available(): |
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devices = device.split(',') if device else '0' |
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n = len(devices) |
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if n > 1 and batch_size > 0: |
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assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {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" |
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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(): |
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s += 'MPS\n' |
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arg = 'mps' |
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else: |
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s += 'CPU\n' |
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arg = 'cpu' |
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if not newline: |
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s = s.rstrip() |
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LOGGER.info(s) |
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return torch.device(arg) |
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def time_sync(): |
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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 profile(input, ops, n=10, device=None): |
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""" YOLOv5 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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print(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 |
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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] |
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try: |
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flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 |
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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: |
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t[2] = float('nan') |
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tf += (t[1] - t[0]) * 1000 / n |
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tb += (t[2] - t[1]) * 1000 / n |
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mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0 |
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s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y)) |
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p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 |
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print(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]) |
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except Exception as e: |
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print(e) |
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results.append(None) |
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torch.cuda.empty_cache() |
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return results |
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def is_parallel(model): |
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return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) |
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def de_parallel(model): |
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return model.module if is_parallel(model) else model |
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def initialize_weights(model): |
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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 |
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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 find_modules(model, mclass=nn.Conv2d): |
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return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] |
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def sparsity(model): |
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a, b = 0, 0 |
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for p in model.parameters(): |
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a += p.numel() |
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b += (p == 0).sum() |
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return b / a |
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def prune(model, amount=0.3): |
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import torch.nn.utils.prune as prune |
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for name, m in model.named_modules(): |
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if isinstance(m, nn.Conv2d): |
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prune.l1_unstructured(m, name='weight', amount=amount) |
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prune.remove(m, 'weight') |
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LOGGER.info(f'Model pruned to {sparsity(model):.3g} global sparsity') |
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def fuse_conv_and_bn(conv, bn): |
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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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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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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 model_info(model, verbose=False, imgsz=640): |
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n_p = sum(x.numel() for x in model.parameters()) |
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n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) |
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if verbose: |
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print(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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print('%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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try: |
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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 |
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im = torch.empty((1, p.shape[1], stride, stride), device=p.device) |
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flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2 |
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imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] |
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fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs' |
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except Exception: |
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fs = '' |
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|
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name = Path(model.yaml_file).stem.replace('yolov5', 'YOLOv5') if hasattr(model, 'yaml_file') else 'Model' |
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LOGGER.info(f'{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}') |
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def scale_img(img, ratio=1.0, same_shape=False, gs=32): |
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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)) |
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img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) |
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if not same_shape: |
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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) |
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def copy_attr(a, b, include=(), 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 smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5): |
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g = [], [], [] |
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bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) |
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for v in model.modules(): |
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for p_name, p in v.named_parameters(recurse=0): |
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if p_name == 'bias': |
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g[2].append(p) |
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elif p_name == 'weight' and isinstance(v, bn): |
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g[1].append(p) |
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else: |
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g[0].append(p) |
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if name == 'Adam': |
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optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) |
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elif name == 'AdamW': |
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optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0) |
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elif name == 'RMSProp': |
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optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum) |
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elif name == 'SGD': |
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optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True) |
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else: |
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raise NotImplementedError(f'Optimizer {name} not implemented.') |
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|
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optimizer.add_param_group({'params': g[0], 'weight_decay': decay}) |
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optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0}) |
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LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups " |
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f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias') |
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return optimizer |
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def smart_hub_load(repo='ultralytics/yolov5', model='yolov5s', **kwargs): |
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|
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if check_version(torch.__version__, '1.9.1'): |
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kwargs['skip_validation'] = True |
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if check_version(torch.__version__, '1.12.0'): |
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kwargs['trust_repo'] = True |
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try: |
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return torch.hub.load(repo, model, **kwargs) |
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except Exception: |
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return torch.hub.load(repo, model, force_reload=True, **kwargs) |
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def smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True): |
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|
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best_fitness = 0.0 |
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start_epoch = ckpt['epoch'] + 1 |
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if ckpt['optimizer'] is not None: |
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optimizer.load_state_dict(ckpt['optimizer']) |
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best_fitness = ckpt['best_fitness'] |
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if ema and ckpt.get('ema'): |
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ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) |
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ema.updates = ckpt['updates'] |
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if resume: |
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assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.\n' \ |
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f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'" |
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LOGGER.info(f'Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs') |
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if epochs < start_epoch: |
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LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.") |
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epochs += ckpt['epoch'] |
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return best_fitness, start_epoch, epochs |
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|
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class EarlyStopping: |
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|
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def __init__(self, patience=30): |
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self.best_fitness = 0.0 |
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self.best_epoch = 0 |
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self.patience = patience or float('inf') |
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self.possible_stop = False |
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|
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def __call__(self, epoch, fitness): |
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if fitness >= self.best_fitness: |
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self.best_epoch = epoch |
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self.best_fitness = fitness |
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delta = epoch - self.best_epoch |
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self.possible_stop = delta >= (self.patience - 1) |
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stop = delta >= self.patience |
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if stop: |
|
LOGGER.info(f'Stopping training early as no improvement observed in last {self.patience} epochs. ' |
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f'Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n' |
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f'To update EarlyStopping(patience={self.patience}) pass a new patience value, ' |
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f'i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping.') |
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return stop |
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|
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|
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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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""" |
|
|
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def __init__(self, model, decay=0.9999, tau=2000, updates=0): |
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|
|
self.ema = deepcopy(de_parallel(model)).eval() |
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self.updates = updates |
|
self.decay = lambda x: decay * (1 - math.exp(-x / tau)) |
|
for p in self.ema.parameters(): |
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p.requires_grad_(False) |
|
|
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def update(self, model): |
|
|
|
self.updates += 1 |
|
d = self.decay(self.updates) |
|
|
|
msd = de_parallel(model).state_dict() |
|
for k, v in self.ema.state_dict().items(): |
|
if v.dtype.is_floating_point: |
|
v *= d |
|
v += (1 - d) * msd[k].detach() |
|
|
|
|
|
def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): |
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|
|
copy_attr(self.ema, model, include, exclude) |
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|