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| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| """ | |
| Various positional encodings for the transformer. | |
| """ | |
| import math | |
| from typing import List, Optional | |
| import numpy as np | |
| import torch | |
| from torch import Tensor, nn | |
| # from util.misc import NestedTensor | |
| class NestedTensor(object): | |
| def __init__(self, tensors, mask: Optional[Tensor]): | |
| self.tensors = tensors | |
| self.mask = mask | |
| def to(self, device): | |
| # type: (Device) -> NestedTensor # noqa | |
| cast_tensor = self.tensors.to(device) | |
| mask = self.mask | |
| if mask is not None: | |
| assert mask is not None | |
| cast_mask = mask.to(device) | |
| else: | |
| cast_mask = None | |
| return NestedTensor(cast_tensor, cast_mask) | |
| def decompose(self): | |
| return self.tensors, self.mask | |
| def __repr__(self): | |
| return str(self.tensors) | |
| 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 | |
| class PositionEmbeddingSine1D(nn.Module): | |
| def __init__(self, d_model, max_len=500, batch_first=False): | |
| super().__init__() | |
| self.batch_first = batch_first | |
| pe = torch.zeros(max_len, d_model) | |
| position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) | |
| div_term = torch.exp( | |
| torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model)) | |
| pe[:, 0::2] = torch.sin(position * div_term) | |
| pe[:, 1::2] = torch.cos(position * div_term) | |
| pe = pe.unsqueeze(0).transpose(0, 1) | |
| self.register_buffer('pe', pe) | |
| def forward(self, x): | |
| # not used in the final model | |
| if self.batch_first: | |
| pos = self.pe.permute(1, 0, 2)[:, :x.shape[1], :] | |
| else: | |
| pos = self.pe[:x.shape[0], :] | |
| return pos | |
| class PositionEmbeddingLearned1D(nn.Module): | |
| def __init__(self, d_model, max_len=500, batch_first=False): | |
| super().__init__() | |
| self.batch_first = batch_first | |
| # self.dropout = nn.Dropout(p=dropout) | |
| self.pe = nn.Parameter(torch.zeros(max_len, 1, d_model)) | |
| # self.pe = pe.unsqueeze(0).transpose(0, 1) | |
| self.reset_parameters() | |
| def reset_parameters(self): | |
| nn.init.uniform_(self.pe) | |
| def forward(self, x): | |
| # not used in the final model | |
| if self.batch_first: | |
| pos = self.pe.permute(1, 0, 2)[:, :x.shape[1], :] | |
| else: | |
| x = x + self.pe[:x.shape[0], :] | |
| return x | |
| # return self.dropout(x) | |
| def build_position_encoding(N_steps, | |
| position_embedding="sine", | |
| embedding_dim="1D"): | |
| # N_steps = hidden_dim // 2 | |
| if embedding_dim == "1D": | |
| if position_embedding in ('v2', 'sine'): | |
| position_embedding = PositionEmbeddingSine1D(N_steps) | |
| elif position_embedding in ('v3', 'learned'): | |
| position_embedding = PositionEmbeddingLearned1D(N_steps) | |
| else: | |
| raise ValueError(f"not supported {position_embedding}") | |
| elif embedding_dim == "2D": | |
| if position_embedding in ('v2', 'sine'): | |
| # TODO find a better way of exposing other arguments | |
| position_embedding = PositionEmbeddingSine(N_steps, normalize=True) | |
| elif position_embedding in ('v3', 'learned'): | |
| position_embedding = PositionEmbeddingLearned(N_steps) | |
| else: | |
| raise ValueError(f"not supported {position_embedding}") | |
| else: | |
| raise ValueError(f"not supported {embedding_dim}") | |
| return position_embedding | |