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import torch
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import torch.nn as nn
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from mmcv.cnn.bricks.transformer import POSITIONAL_ENCODING
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from mmcv.runner import BaseModule
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@POSITIONAL_ENCODING.register_module()
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class CustomLearnedPositionalEncoding(BaseModule):
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"""Position embedding with learnable embedding weights.
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Args:
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num_feats (int): The feature dimension for each position
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along x-axis or y-axis. The final returned dimension for
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each position is 2 times of this value.
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row_num_embed (int, optional): The dictionary size of row embeddings.
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Default 50.
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col_num_embed (int, optional): The dictionary size of col embeddings.
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Default 50.
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init_cfg (dict or list[dict], optional): Initialization config dict.
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"""
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def __init__(self,
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num_feats,
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row_num_embed=50,
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col_num_embed=50,
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init_cfg=dict(type='Uniform', layer='Embedding')):
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super(CustomLearnedPositionalEncoding, self).__init__(init_cfg)
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self.row_embed = nn.Embedding(row_num_embed, num_feats)
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self.col_embed = nn.Embedding(col_num_embed, num_feats)
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self.num_feats = num_feats
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self.row_num_embed = row_num_embed
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self.col_num_embed = col_num_embed
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def forward(self, bs, h, w, device):
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"""Forward function for `LearnedPositionalEncoding`.
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Args:
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mask (Tensor): ByteTensor mask. Non-zero values representing
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ignored positions, while zero values means valid positions
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for this image. Shape [bs, h, w].
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Returns:
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pos (Tensor): Returned position embedding with shape
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[bs, num_feats*2, h, w].
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"""
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x = torch.arange(w, device=device)
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y = torch.arange(h, device=device)
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x_embed = self.col_embed(x)
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y_embed = self.row_embed(y)
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pos = torch.cat(
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(x_embed.unsqueeze(0).repeat(h, 1, 1), y_embed.unsqueeze(1).repeat(
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1, w, 1)),
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dim=-1).permute(2, 0,
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1).unsqueeze(0).repeat(bs, 1, 1, 1)
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return pos
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