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|
| | """ |
| | Various positional encodings for the transformer. |
| | """ |
| | import math |
| | import torch |
| | from torch import nn |
| |
|
| | from util.misc import NestedTensor |
| |
|
| |
|
| | class PositionEmbeddingSine(nn.Module): |
| | """ |
| | This is a more standard version of the position embedding, very similar to the one |
| | used by the Attention is all you need paper, generalized to work on images. |
| | """ |
| | def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None): |
| | super().__init__() |
| | self.num_pos_feats = num_pos_feats |
| | self.temperature = temperature |
| | self.normalize = normalize |
| | if scale is not None and normalize is False: |
| | raise ValueError("normalize should be True if scale is passed") |
| | if scale is None: |
| | scale = 2 * math.pi |
| | self.scale = scale |
| |
|
| | def forward(self, tensor_list: NestedTensor): |
| | x = tensor_list.tensors |
| | mask = tensor_list.mask |
| | assert mask is not None |
| | not_mask = ~mask |
| | y_embed = not_mask.cumsum(1, dtype=torch.float32) |
| | x_embed = not_mask.cumsum(2, dtype=torch.float32) |
| | if self.normalize: |
| | eps = 1e-6 |
| | |
| | |
| | |
| | |
| | y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale |
| | x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale |
| |
|
| | dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) |
| | dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) |
| |
|
| | pos_x = x_embed[:, :, :, None] / dim_t |
| | pos_y = y_embed[:, :, :, None] / dim_t |
| | pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) |
| | pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) |
| | pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) |
| | return pos |
| |
|
| | class PositionEmbeddingSineHW(nn.Module): |
| | """ |
| | This is a more standard version of the position embedding, very similar to the one |
| | used by the Attention is all you need paper, generalized to work on images. |
| | """ |
| | def __init__(self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None): |
| | super().__init__() |
| | self.num_pos_feats = num_pos_feats |
| | self.temperatureH = temperatureH |
| | self.temperatureW = temperatureW |
| | self.normalize = normalize |
| | if scale is not None and normalize is False: |
| | raise ValueError("normalize should be True if scale is passed") |
| | if scale is None: |
| | scale = 2 * math.pi |
| | self.scale = scale |
| |
|
| | def forward(self, tensor_list: NestedTensor): |
| | x = tensor_list.tensors |
| | mask = tensor_list.mask |
| | assert mask is not None |
| | not_mask = ~mask |
| | y_embed = not_mask.cumsum(1, dtype=torch.float32) |
| | x_embed = not_mask.cumsum(2, dtype=torch.float32) |
| |
|
| | |
| |
|
| | if self.normalize: |
| | eps = 1e-6 |
| | y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale |
| | x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale |
| |
|
| | dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) |
| | dim_tx = self.temperatureW ** (2 * (dim_tx // 2) / self.num_pos_feats) |
| | pos_x = x_embed[:, :, :, None] / dim_tx |
| |
|
| | dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) |
| | dim_ty = self.temperatureH ** (2 * (dim_ty // 2) / self.num_pos_feats) |
| | pos_y = y_embed[:, :, :, None] / dim_ty |
| |
|
| | pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) |
| | pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) |
| | pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) |
| |
|
| | |
| |
|
| | return pos |
| |
|
| | class PositionEmbeddingLearned(nn.Module): |
| | """ |
| | Absolute pos embedding, learned. |
| | """ |
| | def __init__(self, num_pos_feats=256): |
| | super().__init__() |
| | self.row_embed = nn.Embedding(50, num_pos_feats) |
| | self.col_embed = nn.Embedding(50, num_pos_feats) |
| | self.reset_parameters() |
| |
|
| | def reset_parameters(self): |
| | nn.init.uniform_(self.row_embed.weight) |
| | nn.init.uniform_(self.col_embed.weight) |
| |
|
| | def forward(self, tensor_list: NestedTensor): |
| | x = tensor_list.tensors |
| | h, w = x.shape[-2:] |
| | i = torch.arange(w, device=x.device) |
| | j = torch.arange(h, device=x.device) |
| | x_emb = self.col_embed(i) |
| | y_emb = self.row_embed(j) |
| | pos = torch.cat([ |
| | x_emb.unsqueeze(0).repeat(h, 1, 1), |
| | y_emb.unsqueeze(1).repeat(1, w, 1), |
| | ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1) |
| | return pos |
| |
|
| |
|
| | def build_position_encoding(args): |
| | N_steps = args.hidden_dim // 2 |
| | if args.position_embedding in ('v2', 'sine'): |
| | |
| | position_embedding = PositionEmbeddingSineHW( |
| | N_steps, |
| | temperatureH=args.pe_temperatureH, |
| | temperatureW=args.pe_temperatureW, |
| | normalize=True |
| | ) |
| | elif args.position_embedding in ('v3', 'learned'): |
| | position_embedding = PositionEmbeddingLearned(N_steps) |
| | else: |
| | raise ValueError(f"not supported {args.position_embedding}") |
| |
|
| | return position_embedding |
| |
|
