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Référence pour ultralytics/nn/modules/utils.py

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



ultralytics.nn.modules.utils._get_clones(module, n)

Crée une liste de modules clonés à partir du module donné.

Code source dans ultralytics/nn/modules/utils.py
def _get_clones(module, n):
    """Create a list of cloned modules from the given module."""
    return nn.ModuleList([copy.deepcopy(module) for _ in range(n)])



ultralytics.nn.modules.utils.bias_init_with_prob(prior_prob=0.01)

Initialise la valeur du biais conv/fc en fonction d'une valeur de probabilité donnée.

Code source dans ultralytics/nn/modules/utils.py
def bias_init_with_prob(prior_prob=0.01):
    """Initialize conv/fc bias value according to a given probability value."""
    return float(-np.log((1 - prior_prob) / prior_prob))  # return bias_init



ultralytics.nn.modules.utils.linear_init(module)

Initialise le site weights and biases d'un module linéaire.

Code source dans ultralytics/nn/modules/utils.py
def linear_init(module):
    """Initialize the weights and biases of a linear module."""
    bound = 1 / math.sqrt(module.weight.shape[0])
    uniform_(module.weight, -bound, bound)
    if hasattr(module, "bias") and module.bias is not None:
        uniform_(module.bias, -bound, bound)



ultralytics.nn.modules.utils.inverse_sigmoid(x, eps=1e-05)

Calcule la fonction sigmoĂŻde inverse pour une tensor.

Code source dans ultralytics/nn/modules/utils.py
def inverse_sigmoid(x, eps=1e-5):
    """Calculate the inverse sigmoid function for a tensor."""
    x = x.clamp(min=0, max=1)
    x1 = x.clamp(min=eps)
    x2 = (1 - x).clamp(min=eps)
    return torch.log(x1 / x2)



ultralytics.nn.modules.utils.multi_scale_deformable_attn_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights)

Attention déformable à plusieurs échelles.

https://github.com/IDEA-Research/detrex/blob/main/detrex/layers/multi_scale_deform_attn.py

Code source dans ultralytics/nn/modules/utils.py
def multi_scale_deformable_attn_pytorch(
    value: torch.Tensor,
    value_spatial_shapes: torch.Tensor,
    sampling_locations: torch.Tensor,
    attention_weights: torch.Tensor,
) -> torch.Tensor:
    """
    Multi-scale deformable attention.

    https://github.com/IDEA-Research/detrex/blob/main/detrex/layers/multi_scale_deform_attn.py
    """

    bs, _, num_heads, embed_dims = value.shape
    _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
    value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
    sampling_grids = 2 * sampling_locations - 1
    sampling_value_list = []
    for level, (H_, W_) in enumerate(value_spatial_shapes):
        # bs, H_*W_, num_heads, embed_dims ->
        # bs, H_*W_, num_heads*embed_dims ->
        # bs, num_heads*embed_dims, H_*W_ ->
        # bs*num_heads, embed_dims, H_, W_
        value_l_ = value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, H_, W_)
        # bs, num_queries, num_heads, num_points, 2 ->
        # bs, num_heads, num_queries, num_points, 2 ->
        # bs*num_heads, num_queries, num_points, 2
        sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1)
        # bs*num_heads, embed_dims, num_queries, num_points
        sampling_value_l_ = F.grid_sample(
            value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
        )
        sampling_value_list.append(sampling_value_l_)
    # (bs, num_queries, num_heads, num_levels, num_points) ->
    # (bs, num_heads, num_queries, num_levels, num_points) ->
    # (bs, num_heads, 1, num_queries, num_levels*num_points)
    attention_weights = attention_weights.transpose(1, 2).reshape(
        bs * num_heads, 1, num_queries, num_levels * num_points
    )
    output = (
        (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
        .sum(-1)
        .view(bs, num_heads * embed_dims, num_queries)
    )
    return output.transpose(1, 2).contiguous()





Créé le 2023-11-12, Mis à jour le 2024-01-14
Auteurs : glenn-jocher (4)