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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 # 128 | |
| self.temperatureH = temperatureH # 20 | |
| self.temperatureW = temperatureW | |
| self.normalize = normalize # true | |
| 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) | |
| # import pdb; pdb.set_trace() | |
| 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) | |
| # import pdb; pdb.set_trace() | |
| 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 # 256//2 | |
| if args.position_embedding in ('v2', 'sine'): # sine | |
| # TODO find a better way of exposing other arguments | |
| 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 | |