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Running
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A10G
| import math | |
| import torch | |
| import torch.nn as nn | |
| from mmcv.cnn.bricks.transformer import POSITIONAL_ENCODING | |
| from mmcv.runner import BaseModule | |
| 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 | |
| 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 | |
| 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 | |
| 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 |