# 参考资料 `ultralytics/nn/modules/utils.py`

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

 ```16 17 18``` ``````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)`

 ```21 22 23``` ``````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)`

 ```26 27 28 29 30 31``` ``````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)`

 ```34 35 36 37 38 39``` ``````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)`

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

 ```42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85``` ``````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() ``````