Spaces:
Runtime error
Runtime error
| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved | |
| """ | |
| Various positional encodings for the transformer. | |
| """ | |
| import math | |
| import torch | |
| from torch import nn | |
| import numpy as np | |
| def PositionalEncoding(n_position, d_hid): | |
| def get_position_angle_vec(position, d_hid): | |
| return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] | |
| sinusoid_table = np.array([get_position_angle_vec(pos_i, d_hid) for pos_i in range(n_position)]) | |
| sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i | |
| sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 | |
| return torch.FloatTensor(sinusoid_table) # shape:(1, maxLen(n_position), d_hid) | |
| class TrainablePositionalEncoding(nn.Module): | |
| """Construct the embeddings from word, position and token_type embeddings. | |
| """ | |
| def __init__(self, max_position_embeddings, hidden_size, dropout=0.1): | |
| super(TrainablePositionalEncoding, self).__init__() | |
| self.position_embeddings = nn.Embedding(max_position_embeddings, hidden_size) | |
| self.LayerNorm = nn.LayerNorm(hidden_size) | |
| self.dropout = nn.Dropout(dropout) | |
| def forward(self, input_feat): | |
| """ | |
| Args: | |
| input_feat: (N, L, D) | |
| """ | |
| bsz, seq_length = input_feat.shape[:2] | |
| position_ids = torch.arange(seq_length, dtype=torch.long, device=input_feat.device) | |
| position_ids = position_ids.unsqueeze(0).repeat(bsz, 1) # (N, L) | |
| position_embeddings = self.position_embeddings(position_ids) | |
| embeddings = self.LayerNorm(input_feat + position_embeddings) | |
| embeddings = self.dropout(embeddings) | |
| return embeddings | |
| 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. (To 1D sequences) | |
| """ | |
| 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, x, mask): | |
| """ | |
| Args: | |
| x: torch.tensor, (batch_size, L, d) | |
| mask: torch.tensor, (batch_size, L), with 1 as valid | |
| Returns: | |
| """ | |
| assert mask is not None | |
| x_embed = mask.cumsum(1, dtype=torch.float32) # (bsz, L) | |
| if self.normalize: | |
| eps = 1e-6 | |
| x_embed = x_embed / (x_embed[:, -1:] + eps) * self.scale | |
| dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) | |
| # import pdb; pdb.set_trace() | |
| # dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) | |
| dim_t = self.temperature ** (2 * torch.div(dim_t, 2).int() / self.num_pos_feats) | |
| pos_x = x_embed[:, :, None] / dim_t # (bsz, L, num_pos_feats) | |
| pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2) # (bsz, L, num_pos_feats*2) | |
| # import ipdb; ipdb.set_trace() | |
| return pos_x # .permute(0, 2, 1) # (bsz, num_pos_feats*2, L) | |
| 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, x, mask): | |
| 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 | |
| if args.position_embedding in ('v2', 'sine'): | |
| # TODO find a better way of exposing other arguments | |
| position_embedding = PositionEmbeddingSine(N_steps, normalize=True) | |
| # elif args.position_embedding in ('v3', 'learned'): | |
| # position_embedding = PositionEmbeddingLearned(N_steps) | |
| else: | |
| raise ValueError(f"not supported {args.position_embedding}") | |
| txt_pos_embed = TrainablePositionalEncoding( | |
| max_position_embeddings=args.max_q_l, | |
| hidden_size=args.hidden_dim, dropout=args.input_dropout) | |
| return position_embedding, txt_pos_embed | |