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mmdet.models.utils.positional_encoding 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import math

import torch
import torch.nn as nn
from mmcv.cnn.bricks.transformer import POSITIONAL_ENCODING
from mmcv.runner import BaseModule


[文档]@POSITIONAL_ENCODING.register_module() class SinePositionalEncoding(BaseModule): """Position encoding with sine and cosine functions. See `End-to-End Object Detection with Transformers <https://arxiv.org/pdf/2005.12872>`_ for details. Args: num_feats (int): The feature dimension for each position along x-axis or y-axis. Note the final returned dimension for each position is 2 times of this value. temperature (int, optional): The temperature used for scaling the position embedding. Defaults to 10000. normalize (bool, optional): Whether to normalize the position embedding. Defaults to False. scale (float, optional): A scale factor that scales the position embedding. The scale will be used only when `normalize` is True. Defaults to 2*pi. eps (float, optional): A value added to the denominator for numerical stability. Defaults to 1e-6. offset (float): offset add to embed when do the normalization. Defaults to 0. init_cfg (dict or list[dict], optional): Initialization config dict. Default: None """ def __init__(self, num_feats, temperature=10000, normalize=False, scale=2 * math.pi, eps=1e-6, offset=0., init_cfg=None): super(SinePositionalEncoding, self).__init__(init_cfg) if normalize: assert isinstance(scale, (float, int)), 'when normalize is set,' \ 'scale should be provided and in float or int type, ' \ f'found {type(scale)}' self.num_feats = num_feats self.temperature = temperature self.normalize = normalize self.scale = scale self.eps = eps self.offset = offset
[文档] def forward(self, mask): """Forward function for `SinePositionalEncoding`. Args: mask (Tensor): ByteTensor mask. Non-zero values representing ignored positions, while zero values means valid positions for this image. Shape [bs, h, w]. Returns: pos (Tensor): Returned position embedding with shape [bs, num_feats*2, h, w]. """ # For convenience of exporting to ONNX, it's required to convert # `masks` from bool to int. mask = mask.to(torch.int) not_mask = 1 - mask # logical_not y_embed = not_mask.cumsum(1, dtype=torch.float32) x_embed = not_mask.cumsum(2, dtype=torch.float32) if self.normalize: y_embed = (y_embed + self.offset) / \ (y_embed[:, -1:, :] + self.eps) * self.scale x_embed = (x_embed + self.offset) / \ (x_embed[:, :, -1:] + self.eps) * self.scale dim_t = torch.arange( self.num_feats, dtype=torch.float32, device=mask.device) dim_t = self.temperature**(2 * (dim_t // 2) / self.num_feats) pos_x = x_embed[:, :, :, None] / dim_t pos_y = y_embed[:, :, :, None] / dim_t # use `view` instead of `flatten` for dynamically exporting to ONNX B, H, W = mask.size() pos_x = torch.stack( (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).view(B, H, W, -1) pos_y = torch.stack( (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).view(B, H, W, -1) pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) return pos
def __repr__(self): """str: a string that describes the module""" repr_str = self.__class__.__name__ repr_str += f'(num_feats={self.num_feats}, ' repr_str += f'temperature={self.temperature}, ' repr_str += f'normalize={self.normalize}, ' repr_str += f'scale={self.scale}, ' repr_str += f'eps={self.eps})' return repr_str
[文档]@POSITIONAL_ENCODING.register_module() class LearnedPositionalEncoding(BaseModule): """Position embedding with learnable embedding weights. Args: num_feats (int): The feature dimension for each position along x-axis or y-axis. The final returned dimension for each position is 2 times of this value. row_num_embed (int, optional): The dictionary size of row embeddings. Default 50. col_num_embed (int, optional): The dictionary size of col embeddings. Default 50. init_cfg (dict or list[dict], optional): Initialization config dict. """ def __init__(self, num_feats, row_num_embed=50, col_num_embed=50, init_cfg=dict(type='Uniform', layer='Embedding')): super(LearnedPositionalEncoding, self).__init__(init_cfg) self.row_embed = nn.Embedding(row_num_embed, num_feats) self.col_embed = nn.Embedding(col_num_embed, num_feats) self.num_feats = num_feats self.row_num_embed = row_num_embed self.col_num_embed = col_num_embed
[文档] def forward(self, mask): """Forward function for `LearnedPositionalEncoding`. Args: mask (Tensor): ByteTensor mask. Non-zero values representing ignored positions, while zero values means valid positions for this image. Shape [bs, h, w]. Returns: pos (Tensor): Returned position embedding with shape [bs, num_feats*2, h, w]. """ h, w = mask.shape[-2:] x = torch.arange(w, device=mask.device) y = torch.arange(h, device=mask.device) x_embed = self.col_embed(x) y_embed = self.row_embed(y) pos = torch.cat( (x_embed.unsqueeze(0).repeat(h, 1, 1), y_embed.unsqueeze(1).repeat( 1, w, 1)), dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(mask.shape[0], 1, 1, 1) return pos
def __repr__(self): """str: a string that describes the module""" repr_str = self.__class__.__name__ repr_str += f'(num_feats={self.num_feats}, ' repr_str += f'row_num_embed={self.row_num_embed}, ' repr_str += f'col_num_embed={self.col_num_embed})' return repr_str
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