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Ce fichier est disponible à l'adresse https://github.com/ultralytics/ ultralytics/blob/main/ ultralytics/utils/ torch_utils .py. Si tu repères un problème, aide à le corriger en contribuant à une Pull Request 🛠️. Merci 🙏 !



ultralytics.utils.torch_utils.ModelEMA

Moyenne mobile exponentielle (EMA) mise à jour à partir de https://github.com/rwightman/pytorch-image-models Conserve une moyenne mobile de tout ce qui se trouve dans le modèle state_dict (paramètres et tampons). Pour plus de détails sur l'EMA, voir https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage Pour désactiver l'EMA, définis le paramètre enabled à l'attribut False.

Code source dans 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)

Créer un EMA.

Code source dans 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)

Mets à jour les paramètres de l'EMA.

Code source dans 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'))

Met à jour les attributs et enregistre le modèle dépouillé dont l'optimiseur a été supprimé.

Code source dans 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 d'arrêt précoce qui arrête la formation lorsqu'un nombre spécifié d'époques s'est écoulé sans amélioration.

Code source dans 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:
            prefix = colorstr("EarlyStopping: ")
            LOGGER.info(
                f"{prefix}Training stopped early as no improvement observed in last {self.patience} epochs. "
                f"Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n"
                f"To update EarlyStopping(patience={self.patience}) pass a new patience value, "
                f"i.e. `patience=300` or use `patience=0` to disable EarlyStopping."
            )
        return stop

__call__(epoch, fitness)

Vérifie s'il faut arrêter la formation.

Paramètres :

Nom Type Description Défaut
epoch int

Époque actuelle de la formation

requis
fitness float

Valeur d'aptitude de l'époque actuelle

requis

Retourne :

Type Description
bool

Vrai si la formation doit s'arrêter, Faux sinon

Code source dans 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:
        prefix = colorstr("EarlyStopping: ")
        LOGGER.info(
            f"{prefix}Training stopped early as no improvement observed in last {self.patience} epochs. "
            f"Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n"
            f"To update EarlyStopping(patience={self.patience}) pass a new patience value, "
            f"i.e. `patience=300` or use `patience=0` to disable EarlyStopping."
        )
    return stop

__init__(patience=50)

Initialise l'objet d'arrêt précoce.

Paramètres :

Nom Type Description Défaut
patience int

Nombre d'époques à attendre après l'arrêt de l'amélioration de la condition physique avant d'arrêter.

50
Code source dans 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)

Décorateur permettant à tous les processus de la formation distribuée d'attendre que chaque local_master fasse quelque chose.

Code source dans 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()

Applique le décorateur torch.inference_mode() si torch>=1.9.0 sinon torch.no_grad().

Code source dans 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()

Renvoie une chaîne contenant des informations sur l'unité centrale du système, par exemple 'Apple M2'.

Code source dans 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)

Sélectionne le dispositif PyTorch approprié en fonction des arguments fournis.

La fonction prend une chaîne de caractères spécifiant l'appareil ou un objet torch.device et renvoie un objet torch.device représentant l'appareil sélectionné. La fonction valide également le nombre d'appareils disponibles et lève une exception si l'appareil demandé n'est pas disponible. une exception si le(s) appareil(s) demandé(s) n'est (ne sont) pas disponible(s).

Paramètres :

Nom Type Description Défaut
device str | device

Chaîne de périphérique ou objet torch.device. Les options sont 'None', 'cpu', ou 'cuda', ou '0' ou '0,1,2,3'. La valeur par défaut est une chaîne vide, qui sélectionne automatiquement le premier GPU ou CPU disponible. le premier GPU disponible, ou le CPU si aucun GPU n'est disponible.

''
batch int

Taille du lot utilisé dans ton modèle. La valeur par défaut est 0.

0
newline bool

Si True, ajoute une nouvelle ligne à la fin de la chaîne du journal. La valeur par défaut est False.

False
verbose bool

Si True, enregistre les informations relatives à l'appareil. La valeur par défaut est True.

True

Retourne :

Type Description
device

Appareil sélectionné.

Augmente :

Type Description
ValueError

Si le périphérique spécifié n'est pas disponible ou si la taille du lot n'est pas un multiple du nombre de périphériques. périphériques lors de l'utilisation de plusieurs GPU.

Exemples :

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

Définit la variable d'environnement 'CUDA_VISIBLE_DEVICES' pour spécifier les GPU à utiliser.

Code source dans 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.split(","))):
            LOGGER.info(s)
            install = (
                "See https://pytorch.org/get-started/locally/ for up-to-date torch install instructions if no "
                "CUDA devices are seen by torch.\n"
                if torch.cuda.device_count() == 0
                else ""
            )
            raise ValueError(
                f"Invalid CUDA 'device={device}' requested."
                f" Use 'device=cpu' or pass valid CUDA device(s) if available,"
                f" i.e. 'device=0' or 'device=0,1,2,3' for Multi-GPU.\n"
                f"\ntorch.cuda.is_available(): {torch.cuda.is_available()}"
                f"\ntorch.cuda.device_count(): {torch.cuda.device_count()}"
                f"\nos.environ['CUDA_VISIBLE_DEVICES']: {visible}\n"
                f"{install}"
            )

    if not cpu and not mps and torch.cuda.is_available():  # prefer GPU if available
        devices = device.split(",") if device else "0"  # 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-Heure précise.

Code source dans 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)

Fusionne les couches Conv2d() et BatchNorm2d() https://tehnokv.com/posts/fusing-batchnorm-and-conv/.

Code source dans 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)

Fusionne les couches ConvTranspose2d() et BatchNorm2d().

Code source dans 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)

Informations sur le modèle.

imgsz peut être un int ou une liste, c'est-à-dire imgsz=640 ou imgsz=[640, 320].

Code source dans 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)

Renvoie le nombre total de paramètres dans un modèle YOLO .

Code source dans 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)

Renvoie le nombre total de paramètres avec des gradients dans un modèle YOLO .

Code source dans 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)

Retourne le dict info modèle avec des informations utiles sur le modèle.

Exemple

YOLOv8n info pour les bûcherons

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}

Code source dans 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)

Renvoie les FLOPs d'un modèle YOLO .

Code source dans 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)

Calcule les FLOPs du modèle (alternative thop).

Code source dans 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)

Initialise les poids du modèle à des valeurs aléatoires.

Code source dans 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)

Met à l'échelle et tamponne une image tensor de forme img(bs,3,y,x) en fonction d'un rapport donné et de la taille de la grille gs, en conservant éventuellement la forme d'origine. en conservant la forme originale.

Code source dans 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)

Renvoie le x le plus proche divisible par le diviseur.

Code source dans 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=())

Copie les attributs de l'objet "b" dans l'objet "a", avec des options permettant d'inclure/exclure certains attributs.

Code source dans 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()

Retourne l'opset ONNX le plus récemment pris en charge (pour la maturité) par cette version de torch.

Code source dans 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=())

Renvoie un dictionnaire de clés intersectées avec des formes correspondantes, à l'exclusion des clés "exclure", en utilisant les valeurs da.

Code source dans 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)

Renvoie True si le modèle est de type DP ou DDP.

Code source dans 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)

Déparalléliser un modèle : renvoie un modèle à un seul GPU si le modèle est de type DP ou DDP.

Code source dans 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)

Renvoie une fonction lambda pour une rampe sinusoïdale de y1 à y2 https://arxiv.org/pdf/1812.01187.pdf.

Code source dans 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)

Initialise les graines du générateur de nombres aléatoires (RNG) https://pytorch.org/docs/stable/notes/randomness.html.

Code source dans 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='')

Supprime l'optimiseur de 'f' pour finaliser l'entraînement, enregistre éventuellement sous 's'.

Paramètres :

Nom Type Description Défaut
f str

chemin d'accès au fichier du modèle à partir duquel l'optimiseur doit être supprimé. La valeur par défaut est 'best.pt'.

'best.pt'
s str

chemin du fichier dans lequel enregistrer le modèle avec l'optimiseur dépouillé. S'il n'est pas fourni, 'f' sera remplacé.

''

Retourne :

Type Description
None

Aucun

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

for f in Path('path/to/weights').rglob('*.pt'):
    strip_optimizer(f)
Code source dans 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.convert_optimizer_state_dict_to_fp16(state_dict)

Convertit le state_dict d'un optimiseur donné en FP16, en se concentrant sur la clé 'state' pour les conversions tensor .

Cette méthode vise à réduire la taille du stockage sans modifier les "groupes de paramètres", car ils contiennent des données nontensor .

Code source dans ultralytics/utils/torch_utils.py
def convert_optimizer_state_dict_to_fp16(state_dict):
    """
    Converts the state_dict of a given optimizer to FP16, focusing on the 'state' key for tensor conversions.

    This method aims to reduce storage size without altering 'param_groups' as they contain non-tensor data.
    """
    for state in state_dict["state"].values():
        for k, v in state.items():
            if k != "step" and isinstance(v, torch.Tensor) and v.dtype is torch.float32:
                state[k] = v.half()

    return state_dict



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

Ultralytics profileur de vitesse, de mémoire et de FLOPs.

Exemple
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
Code source dans 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





Créé le 2023-11-12, Mis à jour le 2024-03-31
Auteurs : glenn-jocher (4), Laughing-q (1)