Absolute pos embedding, learned.
| 116 | |
| 117 | |
| 118 | class PositionEmbeddingLearned(nn.Module): |
| 119 | """Absolute pos embedding, learned.""" |
| 120 | def __init__(self, num_pos_feats=256): |
| 121 | super().__init__() |
| 122 | self.row_embed = nn.Embedding(50, num_pos_feats) |
| 123 | self.col_embed = nn.Embedding(50, num_pos_feats) |
| 124 | self.reset_parameters() |
| 125 | |
| 126 | def reset_parameters(self): |
| 127 | nn.init.uniform_(self.row_embed.weight) |
| 128 | nn.init.uniform_(self.col_embed.weight) |
| 129 | |
| 130 | def forward(self, tensor_list: NestedTensor): |
| 131 | x = tensor_list.tensors |
| 132 | h, w = x.shape[-2:] |
| 133 | i = torch.arange(w, device=x.device) |
| 134 | j = torch.arange(h, device=x.device) |
| 135 | x_emb = self.col_embed(i) |
| 136 | y_emb = self.row_embed(j) |
| 137 | pos = torch.cat([ |
| 138 | x_emb.unsqueeze(0).repeat(h, 1, 1), |
| 139 | y_emb.unsqueeze(1).repeat(1, w, 1), |
| 140 | ], |
| 141 | dim=-1).permute(2, 0, 1).unsqueeze(0).repeat( |
| 142 | x.shape[0], 1, 1, 1) |
| 143 | return pos |
| 144 | |
| 145 | |
| 146 | def build_position_encoding(args): |
no outgoing calls
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