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Referência para ultralytics/utils/torch_utils.py

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Este ficheiro está disponível em https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/utils/ torch_utils .py. Se detectares um problema, por favor ajuda a corrigi-lo contribuindo com um Pull Request 🛠️. Obrigado 🙏!



ultralytics.utils.torch_utils.ModelEMA

Média Móvel Exponencial (EMA) actualizada de https://github.com/rwightman/pytorch-image-models Mantém uma média móvel de tudo no modelo state_dict (parâmetros e buffers) Para obter detalhes sobre a EMA, consulte https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage Para desativar a EMA, define o parâmetro enabled atributo para False.

Código fonte em ultralytics/utils/torch_utils.py
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)

__init__(model, decay=0.9999, tau=2000, updates=0)

Cria a EMA.

Código fonte em ultralytics/utils/torch_utils.py
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

update(model)

Actualiza os parâmetros da EMA.

Código fonte em ultralytics/utils/torch_utils.py
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()

update_attr(model, include=(), exclude=('process_group', 'reducer'))

Atualiza os atributos e salva o modelo despojado com o otimizador removido.

Código fonte em ultralytics/utils/torch_utils.py
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)



ultralytics.utils.torch_utils.EarlyStopping

Classe de paragem antecipada que pára o treino quando um número especificado de épocas tiver passado sem melhorias.

Código fonte em ultralytics/utils/torch_utils.py
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

__call__(epoch, fitness)

Verifica se o treino deve ser interrompido.

Parâmetros:

Nome Tipo Descrição Predefinição
epoch int

Época atual de formação

necessário
fitness float

Valor de aptidão da época atual

necessário

Devolve:

Tipo Descrição
bool

Verdadeiro se a formação deve parar, Falso caso contrário

Código fonte em ultralytics/utils/torch_utils.py
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

__init__(patience=50)

Inicializa o objeto de parada antecipada.

Parâmetros:

Nome Tipo Descrição Predefinição
patience int

Número de épocas a esperar depois de a aptidão deixar de melhorar antes de parar.

50
Código fonte em ultralytics/utils/torch_utils.py
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



ultralytics.utils.torch_utils.torch_distributed_zero_first(local_rank)

Decorador para fazer com que todos os processos em treinamento distribuído esperem que cada local_master faça alguma coisa.

Código fonte em ultralytics/utils/torch_utils.py
@contextmanager
def torch_distributed_zero_first(local_rank: int):
    """Decorator to make all processes in distributed training wait for each local_master to do something."""
    initialized = torch.distributed.is_available() and torch.distributed.is_initialized()
    if initialized and local_rank not in (-1, 0):
        dist.barrier(device_ids=[local_rank])
    yield
    if initialized and local_rank == 0:
        dist.barrier(device_ids=[0])



ultralytics.utils.torch_utils.smart_inference_mode()

Aplica o decorador torch.inference_mode() se torch>=1.9.0 senão o decorador torch.no_grad().

Código fonte em ultralytics/utils/torch_utils.py
def smart_inference_mode():
    """Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator."""

    def decorate(fn):
        """Applies appropriate torch decorator for inference mode based on torch version."""
        if TORCH_1_9 and torch.is_inference_mode_enabled():
            return fn  # already in inference_mode, act as a pass-through
        else:
            return (torch.inference_mode if TORCH_1_9 else torch.no_grad)()(fn)

    return decorate



ultralytics.utils.torch_utils.get_cpu_info()

Retorna uma string com informações da CPU do sistema, ou seja, 'Apple M2'.

Código fonte em ultralytics/utils/torch_utils.py
def get_cpu_info():
    """Return a string with system CPU information, i.e. 'Apple M2'."""
    import cpuinfo  # pip install py-cpuinfo

    k = "brand_raw", "hardware_raw", "arch_string_raw"  # info keys sorted by preference (not all keys always available)
    info = cpuinfo.get_cpu_info()  # info dict
    string = info.get(k[0] if k[0] in info else k[1] if k[1] in info else k[2], "unknown")
    return string.replace("(R)", "").replace("CPU ", "").replace("@ ", "")



ultralytics.utils.torch_utils.select_device(device='', batch=0, newline=False, verbose=True)

Selecciona o dispositivo PyTorch adequado com base nos argumentos fornecidos.

A função recebe uma cadeia de caracteres que especifica o dispositivo ou um objeto torch.device e devolve um objeto torch.device que representa o dispositivo selecionado. A função também valida o número de dispositivos disponíveis e levanta uma exceção se o(s) dispositivo(s) solicitado(s) não estiver(em) disponível(eis).

Parâmetros:

Nome Tipo Descrição Predefinição
device str | device

Cadeia de caracteres do dispositivo ou torch.device object. As opções são 'None', 'cpu', ou 'cuda', ou '0' ou '0,1,2,3'. Usa como padrão uma string vazia, que seleciona automaticamente seleciona automaticamente a primeira GPU disponível, ou CPU se nenhuma GPU estiver disponível.

''
batch int

Tamanho do lote que está a ser utilizado no teu modelo. O valor predefinido é 0.

0
newline bool

Se for Verdadeiro, adiciona uma nova linha no final da cadeia de registo. A predefinição é False.

False
verbose bool

Se for Verdadeiro, regista as informações do dispositivo. A predefinição é Verdadeiro.

True

Devolve:

Tipo Descrição
device

Dispositivo selecionado.

Aumenta:

Tipo Descrição
ValueError

Se o dispositivo especificado não estiver disponível ou se o tamanho do lote não for um múltiplo do número de dispositivos dispositivos ao usar várias GPUs.

Exemplos:

>>> select_device('cuda:0')
device(type='cuda', index=0)
>>> select_device('cpu')
device(type='cpu')
Nota

Define a variável de ambiente 'CUDA_VISIBLE_DEVICES' para especificar quais GPUs usar.

Código fonte em ultralytics/utils/torch_utils.py
def select_device(device="", batch=0, newline=False, verbose=True):
    """
    Selects the appropriate PyTorch device based on the provided arguments.

    The function takes a string specifying the device or a torch.device object and returns a torch.device object
    representing the selected device. The function also validates the number of available devices and raises an
    exception if the requested device(s) are not available.

    Args:
        device (str | torch.device, optional): Device string or torch.device object.
            Options are 'None', 'cpu', or 'cuda', or '0' or '0,1,2,3'. Defaults to an empty string, which auto-selects
            the first available GPU, or CPU if no GPU is available.
        batch (int, optional): Batch size being used in your model. Defaults to 0.
        newline (bool, optional): If True, adds a newline at the end of the log string. Defaults to False.
        verbose (bool, optional): If True, logs the device information. Defaults to True.

    Returns:
        (torch.device): Selected device.

    Raises:
        ValueError: If the specified device is not available or if the batch size is not a multiple of the number of
            devices when using multiple GPUs.

    Examples:
        >>> select_device('cuda:0')
        device(type='cuda', index=0)

        >>> select_device('cpu')
        device(type='cpu')

    Note:
        Sets the 'CUDA_VISIBLE_DEVICES' environment variable for specifying which GPUs to use.
    """

    if isinstance(device, torch.device):
        return device

    s = f"Ultralytics YOLOv{__version__} 🚀 Python-{PYTHON_VERSION} torch-{torch.__version__} "
    device = str(device).lower()
    for remove in "cuda:", "none", "(", ")", "[", "]", "'", " ":
        device = device.replace(remove, "")  # to string, 'cuda:0' -> '0' and '(0, 1)' -> '0,1'
    cpu = device == "cpu"
    mps = device in ("mps", "mps:0")  # Apple Metal Performance Shaders (MPS)
    if cpu or mps:
        os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # force torch.cuda.is_available() = False
    elif device:  # non-cpu device requested
        if device == "cuda":
            device = "0"
        visible = os.environ.get("CUDA_VISIBLE_DEVICES", None)
        os.environ["CUDA_VISIBLE_DEVICES"] = device  # set environment variable - must be before assert is_available()
        if not (torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(",", ""))):
            LOGGER.info(s)
            install = (
                "See https://pytorch.org/get-started/locally/ for up-to-date torch install instructions if no "
                "CUDA devices are seen by torch.\n"
                if torch.cuda.device_count() == 0
                else ""
            )
            raise ValueError(
                f"Invalid CUDA 'device={device}' requested."
                f" Use 'device=cpu' or pass valid CUDA device(s) if available,"
                f" i.e. 'device=0' or 'device=0,1,2,3' for Multi-GPU.\n"
                f"\ntorch.cuda.is_available(): {torch.cuda.is_available()}"
                f"\ntorch.cuda.device_count(): {torch.cuda.device_count()}"
                f"\nos.environ['CUDA_VISIBLE_DEVICES']: {visible}\n"
                f"{install}"
            )

    if not cpu and not mps and torch.cuda.is_available():  # prefer GPU if available
        devices = device.split(",") if device else "0"  # range(torch.cuda.device_count())  # i.e. 0,1,6,7
        n = len(devices)  # device count
        if n > 1 and batch > 0 and batch % n != 0:  # check batch_size is divisible by device_count
            raise ValueError(
                f"'batch={batch}' must be a multiple of GPU count {n}. Try 'batch={batch // n * n}' or "
                f"'batch={batch // n * n + n}', the nearest batch sizes evenly divisible by {n}."
            )
        space = " " * (len(s) + 1)
        for i, d in enumerate(devices):
            p = torch.cuda.get_device_properties(i)
            s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n"  # bytes to MB
        arg = "cuda:0"
    elif mps and TORCH_2_0 and torch.backends.mps.is_available():
        # Prefer MPS if available
        s += f"MPS ({get_cpu_info()})\n"
        arg = "mps"
    else:  # revert to CPU
        s += f"CPU ({get_cpu_info()})\n"
        arg = "cpu"

    if verbose:
        LOGGER.info(s if newline else s.rstrip())
    return torch.device(arg)



ultralytics.utils.torch_utils.time_sync()

PyTorch-tempo exato.

Código fonte em ultralytics/utils/torch_utils.py
def time_sync():
    """PyTorch-accurate time."""
    if torch.cuda.is_available():
        torch.cuda.synchronize()
    return time.time()



ultralytics.utils.torch_utils.fuse_conv_and_bn(conv, bn)

Funde as camadas Conv2d() e BatchNorm2d() https://tehnokv.com/posts/fusing-batchnorm-and-conv/.

Código fonte em ultralytics/utils/torch_utils.py
def fuse_conv_and_bn(conv, bn):
    """Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/."""
    fusedconv = (
        nn.Conv2d(
            conv.in_channels,
            conv.out_channels,
            kernel_size=conv.kernel_size,
            stride=conv.stride,
            padding=conv.padding,
            dilation=conv.dilation,
            groups=conv.groups,
            bias=True,
        )
        .requires_grad_(False)
        .to(conv.weight.device)
    )

    # Prepare filters
    w_conv = conv.weight.clone().view(conv.out_channels, -1)
    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))

    # Prepare spatial bias
    b_conv = torch.zeros(conv.weight.shape[0], device=conv.weight.device) if conv.bias is None else conv.bias
    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)

    return fusedconv



ultralytics.utils.torch_utils.fuse_deconv_and_bn(deconv, bn)

Funde as camadas ConvTranspose2d() e BatchNorm2d().

Código fonte em ultralytics/utils/torch_utils.py
def fuse_deconv_and_bn(deconv, bn):
    """Fuse ConvTranspose2d() and BatchNorm2d() layers."""
    fuseddconv = (
        nn.ConvTranspose2d(
            deconv.in_channels,
            deconv.out_channels,
            kernel_size=deconv.kernel_size,
            stride=deconv.stride,
            padding=deconv.padding,
            output_padding=deconv.output_padding,
            dilation=deconv.dilation,
            groups=deconv.groups,
            bias=True,
        )
        .requires_grad_(False)
        .to(deconv.weight.device)
    )

    # Prepare filters
    w_deconv = deconv.weight.clone().view(deconv.out_channels, -1)
    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
    fuseddconv.weight.copy_(torch.mm(w_bn, w_deconv).view(fuseddconv.weight.shape))

    # Prepare spatial bias
    b_conv = torch.zeros(deconv.weight.shape[1], device=deconv.weight.device) if deconv.bias is None else deconv.bias
    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
    fuseddconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)

    return fuseddconv



ultralytics.utils.torch_utils.model_info(model, detailed=False, verbose=True, imgsz=640)

Informações sobre o modelo.

imgsz pode ser um int ou uma lista, ou seja, imgsz=640 ou imgsz=[640, 320].

Código fonte em ultralytics/utils/torch_utils.py
def model_info(model, detailed=False, verbose=True, imgsz=640):
    """
    Model information.

    imgsz may be int or list, i.e. imgsz=640 or imgsz=[640, 320].
    """
    if not verbose:
        return
    n_p = get_num_params(model)  # number of parameters
    n_g = get_num_gradients(model)  # number of gradients
    n_l = len(list(model.modules()))  # number of layers
    if detailed:
        LOGGER.info(
            f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}"
        )
        for i, (name, p) in enumerate(model.named_parameters()):
            name = name.replace("module_list.", "")
            LOGGER.info(
                "%5g %40s %9s %12g %20s %10.3g %10.3g %10s"
                % (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std(), p.dtype)
            )

    flops = get_flops(model, imgsz)
    fused = " (fused)" if getattr(model, "is_fused", lambda: False)() else ""
    fs = f", {flops:.1f} GFLOPs" if flops else ""
    yaml_file = getattr(model, "yaml_file", "") or getattr(model, "yaml", {}).get("yaml_file", "")
    model_name = Path(yaml_file).stem.replace("yolo", "YOLO") or "Model"
    LOGGER.info(f"{model_name} summary{fused}: {n_l} layers, {n_p} parameters, {n_g} gradients{fs}")
    return n_l, n_p, n_g, flops



ultralytics.utils.torch_utils.get_num_params(model)

Devolve o número total de parâmetros de um modelo YOLO .

Código fonte em ultralytics/utils/torch_utils.py
def get_num_params(model):
    """Return the total number of parameters in a YOLO model."""
    return sum(x.numel() for x in model.parameters())



ultralytics.utils.torch_utils.get_num_gradients(model)

Devolve o número total de parâmetros com gradientes num modelo YOLO .

Código fonte em ultralytics/utils/torch_utils.py
def get_num_gradients(model):
    """Return the total number of parameters with gradients in a YOLO model."""
    return sum(x.numel() for x in model.parameters() if x.requires_grad)



ultralytics.utils.torch_utils.model_info_for_loggers(trainer)

Devolve o dict de informações do modelo com informações úteis sobre o modelo.

Exemplo

YOLOv8n informação para madeireiros

results = {'model/parameters': 3151904,
           'model/GFLOPs': 8.746,
           'model/speed_ONNX(ms)': 41.244,
           'model/speed_TensorRT(ms)': 3.211,
           'model/speed_PyTorch(ms)': 18.755}

Código fonte em ultralytics/utils/torch_utils.py
def model_info_for_loggers(trainer):
    """
    Return model info dict with useful model information.

    Example:
        YOLOv8n info for loggers
        ```python
        results = {'model/parameters': 3151904,
                   'model/GFLOPs': 8.746,
                   'model/speed_ONNX(ms)': 41.244,
                   'model/speed_TensorRT(ms)': 3.211,
                   'model/speed_PyTorch(ms)': 18.755}
        ```
    """
    if trainer.args.profile:  # profile ONNX and TensorRT times
        from ultralytics.utils.benchmarks import ProfileModels

        results = ProfileModels([trainer.last], device=trainer.device).profile()[0]
        results.pop("model/name")
    else:  # only return PyTorch times from most recent validation
        results = {
            "model/parameters": get_num_params(trainer.model),
            "model/GFLOPs": round(get_flops(trainer.model), 3),
        }
    results["model/speed_PyTorch(ms)"] = round(trainer.validator.speed["inference"], 3)
    return results



ultralytics.utils.torch_utils.get_flops(model, imgsz=640)

Devolve os FLOPs de um modelo YOLO .

Código fonte em ultralytics/utils/torch_utils.py
def get_flops(model, imgsz=640):
    """Return a YOLO model's FLOPs."""
    if not thop:
        return 0.0  # if not installed return 0.0 GFLOPs

    try:
        model = de_parallel(model)
        p = next(model.parameters())
        if not isinstance(imgsz, list):
            imgsz = [imgsz, imgsz]  # expand if int/float
        try:
            # Use stride size for input tensor
            stride = max(int(model.stride.max()), 32) if hasattr(model, "stride") else 32  # max stride
            im = torch.empty((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format
            flops = thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1e9 * 2  # stride GFLOPs
            return flops * imgsz[0] / stride * imgsz[1] / stride  # imgsz GFLOPs
        except Exception:
            # Use actual image size for input tensor (i.e. required for RTDETR models)
            im = torch.empty((1, p.shape[1], *imgsz), device=p.device)  # input image in BCHW format
            return thop.profile(deepcopy(model), inputs=[im], verbose=False)[0] / 1e9 * 2  # imgsz GFLOPs
    except Exception:
        return 0.0



ultralytics.utils.torch_utils.get_flops_with_torch_profiler(model, imgsz=640)

Calcula os FLOPs do modelo (alternativa thop).

Código fonte em ultralytics/utils/torch_utils.py
def get_flops_with_torch_profiler(model, imgsz=640):
    """Compute model FLOPs (thop alternative)."""
    if TORCH_2_0:
        model = de_parallel(model)
        p = next(model.parameters())
        stride = (max(int(model.stride.max()), 32) if hasattr(model, "stride") else 32) * 2  # max stride
        im = torch.zeros((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format
        with torch.profiler.profile(with_flops=True) as prof:
            model(im)
        flops = sum(x.flops for x in prof.key_averages()) / 1e9
        imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz]  # expand if int/float
        flops = flops * imgsz[0] / stride * imgsz[1] / stride  # 640x640 GFLOPs
        return flops
    return 0



ultralytics.utils.torch_utils.initialize_weights(model)

Inicializa os pesos do modelo com valores aleatórios.

Código fonte em ultralytics/utils/torch_utils.py
def initialize_weights(model):
    """Initialize model weights to random values."""
    for m in model.modules():
        t = type(m)
        if t is nn.Conv2d:
            pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        elif t is nn.BatchNorm2d:
            m.eps = 1e-3
            m.momentum = 0.03
        elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
            m.inplace = True



ultralytics.utils.torch_utils.scale_img(img, ratio=1.0, same_shape=False, gs=32)

Dimensiona e preenche uma imagem tensor de forma img(bs,3,y,x) com base no rácio dado e no tamanho da grelha gs, opcionalmente mantém a forma original.

Código fonte em ultralytics/utils/torch_utils.py
def scale_img(img, ratio=1.0, same_shape=False, gs=32):
    """Scales and pads an image tensor of shape img(bs,3,y,x) based on given ratio and grid size gs, optionally
    retaining the original shape.
    """
    if ratio == 1.0:
        return img
    h, w = img.shape[2:]
    s = (int(h * ratio), int(w * ratio))  # new size
    img = F.interpolate(img, size=s, mode="bilinear", align_corners=False)  # resize
    if not same_shape:  # pad/crop img
        h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))
    return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean



ultralytics.utils.torch_utils.make_divisible(x, divisor)

Devolve o x mais próximo divisível pelo divisor.

Código fonte em ultralytics/utils/torch_utils.py
def make_divisible(x, divisor):
    """Returns nearest x divisible by divisor."""
    if isinstance(divisor, torch.Tensor):
        divisor = int(divisor.max())  # to int
    return math.ceil(x / divisor) * divisor



ultralytics.utils.torch_utils.copy_attr(a, b, include=(), exclude=())

Copia atributos do objeto 'b' para o objeto 'a', com opções para incluir/excluir certos atributos.

Código fonte em ultralytics/utils/torch_utils.py
def copy_attr(a, b, include=(), exclude=()):
    """Copies attributes from object 'b' to object 'a', with options to include/exclude certain attributes."""
    for k, v in b.__dict__.items():
        if (len(include) and k not in include) or k.startswith("_") or k in exclude:
            continue
        else:
            setattr(a, k, v)



ultralytics.utils.torch_utils.get_latest_opset()

Devolve o segundo conjunto de opções mais recente (por maturidade) suportado ONNX por esta versão de torch.

Código fonte em ultralytics/utils/torch_utils.py
def get_latest_opset():
    """Return second-most (for maturity) recently supported ONNX opset by this version of torch."""
    return max(int(k[14:]) for k in vars(torch.onnx) if "symbolic_opset" in k) - 1  # opset



ultralytics.utils.torch_utils.intersect_dicts(da, db, exclude=())

Devolve um dicionário de chaves de intersecção com formas correspondentes, excluindo chaves de "exclusão", utilizando valores da.

Código fonte em ultralytics/utils/torch_utils.py
def intersect_dicts(da, db, exclude=()):
    """Returns a dictionary of intersecting keys with matching shapes, excluding 'exclude' keys, using da values."""
    return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape}



ultralytics.utils.torch_utils.is_parallel(model)

Devolve True se o modelo for do tipo DP ou DDP.

Código fonte em ultralytics/utils/torch_utils.py
def is_parallel(model):
    """Returns True if model is of type DP or DDP."""
    return isinstance(model, (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel))



ultralytics.utils.torch_utils.de_parallel(model)

Desparaleliza um modelo: retorna um modelo de GPU única se o modelo for do tipo DP ou DDP.

Código fonte em ultralytics/utils/torch_utils.py
def de_parallel(model):
    """De-parallelize a model: returns single-GPU model if model is of type DP or DDP."""
    return model.module if is_parallel(model) else model



ultralytics.utils.torch_utils.one_cycle(y1=0.0, y2=1.0, steps=100)

Devolve uma função lambda para uma rampa sinusoidal de y1 a y2 https://arxiv.org/pdf/1812.01187.pdf.

Código fonte em ultralytics/utils/torch_utils.py
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



ultralytics.utils.torch_utils.init_seeds(seed=0, deterministic=False)

Inicializa as sementes do gerador de números aleatórios (RNG) https://pytorch.org/docs/stable/notes/randomness.html.

Código fonte em ultralytics/utils/torch_utils.py
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



ultralytics.utils.torch_utils.strip_optimizer(f='best.pt', s='')

Retira o optimizador de 'f' para finalizar o treino e, opcionalmente, guarda como 's'.

Parâmetros:

Nome Tipo Descrição Predefinição
f str

caminho do ficheiro para o modelo do qual o optimizador deve ser retirado. A predefinição é 'best.pt'.

'best.pt'
s str

caminho do ficheiro para guardar o modelo com o optimizador despojado. Se não for fornecido, 'f' será substituído.

''

Devolve:

Tipo Descrição
None

Não tens

Exemplo
from pathlib import Path
from ultralytics.utils.torch_utils import strip_optimizer

for f in Path('path/to/weights').rglob('*.pt'):
    strip_optimizer(f)
Código fonte em ultralytics/utils/torch_utils.py
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")



ultralytics.utils.torch_utils.profile(input, ops, n=10, device=None)

Ultralytics perfil de velocidade, memória e FLOPs.

Exemplo
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
Código fonte em ultralytics/utils/torch_utils.py
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





Criado em 2023-11-12, Atualizado em 2023-11-25
Autores: glenn-jocher (3), Laughing-q (1)