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""" |
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Various positional encodings for the transformer. |
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""" |
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import math |
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import torch |
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from torch import nn |
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class PositionEmbeddingSine(nn.Module): |
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""" |
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This is a more standard version of the position embedding, very similar to the one |
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used by the Attention is all you need paper, generalized to work on images. |
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""" |
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def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): |
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super().__init__() |
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self.num_pos_feats = num_pos_feats |
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self.temperature = temperature |
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self.normalize = normalize |
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if scale is not None and normalize is False: |
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raise ValueError("normalize should be True if scale is passed") |
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if scale is None: |
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scale = 2 * math.pi |
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self.scale = scale |
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def forward(self, token_tensors): |
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x = token_tensors |
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h, w = x.shape[-2:] |
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identity_map= torch.ones((h,w), device=x.device) |
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y_embed = identity_map.cumsum(0, dtype=torch.float32) |
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x_embed = identity_map.cumsum(1, dtype=torch.float32) |
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if self.normalize: |
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eps = 1e-6 |
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y_embed = y_embed / (y_embed[-1:, :] + eps) * self.scale |
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x_embed = x_embed / (x_embed[:, -1:] + eps) * self.scale |
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dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) |
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dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) |
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pos_x = x_embed[:, :, None] / dim_t |
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pos_y = y_embed[:, :, None] / dim_t |
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pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2) |
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pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2) |
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pos = torch.cat((pos_y, pos_x), dim=2).permute(2, 0, 1) |
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batch_pos = pos.unsqueeze(0).repeat(x.shape[0], 1, 1, 1) |
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return batch_pos |
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class PositionEmbeddingLearned(nn.Module): |
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""" |
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Absolute pos embedding, learned. |
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""" |
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def __init__(self, n_pos_x=16, n_pos_y=16, num_pos_feats=64): |
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super().__init__() |
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self.row_embed = nn.Embedding(n_pos_y, num_pos_feats) |
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self.col_embed = nn.Embedding(n_pos_x, num_pos_feats) |
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self.reset_parameters() |
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def reset_parameters(self): |
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nn.init.uniform_(self.row_embed.weight) |
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nn.init.uniform_(self.col_embed.weight) |
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def forward(self, token_tensors): |
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x = token_tensors |
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h, w = x.shape[-2:] |
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i = torch.arange(w, device=x.device) |
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j = torch.arange(h, device=x.device) |
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x_emb = self.col_embed(i) |
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y_emb = self.row_embed(j) |
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pos = torch.cat([ |
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x_emb.unsqueeze(0).repeat(h, 1, 1), |
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y_emb.unsqueeze(1).repeat(1, w, 1), |
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], dim=-1).permute(2, 0, 1) |
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batch_pos = pos.unsqueeze(0).repeat(x.shape[0], 1, 1, 1) |
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return batch_pos |
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def build_position_encoding(num_pos_feats=64, n_pos_x=16, n_pos_y=16, is_learned=False): |
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if is_learned: |
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position_embedding = PositionEmbeddingLearned(n_pos_x, n_pos_y, num_pos_feats) |
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else: |
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position_embedding = PositionEmbeddingSine(num_pos_feats, normalize=True) |
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return position_embedding |
