| import math |
| import torch |
| from torch import nn |
|
|
| from tools.utils 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 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(config): |
| N_steps = config.hidden_dim // 2 |
| if config.position_embedding in ('v2', 'sine'): |
| |
| position_embedding = PositionEmbeddingSine(N_steps, normalize=True) |
| elif config.position_embedding in ('v3', 'learned'): |
| position_embedding = PositionEmbeddingLearned(N_steps) |
| else: |
| raise ValueError(f"not supported {config.position_embedding}") |
|
|
| return position_embedding |
