193 lines
6.2 KiB
Python
193 lines
6.2 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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"""
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Various positional encodings for the transformer.
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"""
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import math
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from typing import List, Optional
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import numpy as np
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import torch
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from torch import Tensor, nn
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# from util.misc import NestedTensor
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class NestedTensor(object):
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def __init__(self, tensors, mask: Optional[Tensor]):
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self.tensors = tensors
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self.mask = mask
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def to(self, device):
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# type: (Device) -> NestedTensor # noqa
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cast_tensor = self.tensors.to(device)
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mask = self.mask
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if mask is not None:
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assert mask is not None
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cast_mask = mask.to(device)
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else:
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cast_mask = None
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return NestedTensor(cast_tensor, cast_mask)
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def decompose(self):
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return self.tensors, self.mask
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def __repr__(self):
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return str(self.tensors)
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class PositionEmbeddingSine(nn.Module):
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"""
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This is a more standard version of the position embedding, very similar to the one
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used by the Attention is all you need paper, generalized to work on images.
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"""
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def __init__(self,
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num_pos_feats=64,
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temperature=10000,
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normalize=False,
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scale=None):
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super().__init__()
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self.num_pos_feats = num_pos_feats
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self.temperature = temperature
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self.normalize = normalize
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if scale is not None and normalize is False:
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raise ValueError("normalize should be True if scale is passed")
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if scale is None:
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scale = 2 * math.pi
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self.scale = scale
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def forward(self, tensor_list: NestedTensor):
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x = tensor_list.tensors
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mask = tensor_list.mask
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assert mask is not None
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not_mask = ~mask
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y_embed = not_mask.cumsum(1, dtype=torch.float32)
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x_embed = not_mask.cumsum(2, dtype=torch.float32)
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if self.normalize:
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eps = 1e-6
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y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
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x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
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dim_t = torch.arange(self.num_pos_feats,
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dtype=torch.float32,
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device=x.device)
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dim_t = self.temperature**(2 * (dim_t // 2) / self.num_pos_feats)
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pos_x = x_embed[:, :, :, None] / dim_t
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pos_y = y_embed[:, :, :, None] / dim_t
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pos_x = torch.stack(
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(pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
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dim=4).flatten(3)
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pos_y = torch.stack(
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(pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
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dim=4).flatten(3)
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pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
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return pos
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class PositionEmbeddingLearned(nn.Module):
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"""
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Absolute pos embedding, learned.
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"""
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def __init__(self, num_pos_feats=256):
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super().__init__()
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self.row_embed = nn.Embedding(50, num_pos_feats)
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self.col_embed = nn.Embedding(50, num_pos_feats)
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self.reset_parameters()
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def reset_parameters(self):
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nn.init.uniform_(self.row_embed.weight)
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nn.init.uniform_(self.col_embed.weight)
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def forward(self, tensor_list: NestedTensor):
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x = tensor_list.tensors
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h, w = x.shape[-2:]
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i = torch.arange(w, device=x.device)
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j = torch.arange(h, device=x.device)
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x_emb = self.col_embed(i)
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y_emb = self.row_embed(j)
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pos = torch.cat([
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x_emb.unsqueeze(0).repeat(h, 1, 1),
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y_emb.unsqueeze(1).repeat(1, w, 1),
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],
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dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(
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x.shape[0], 1, 1, 1)
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return pos
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class PositionEmbeddingSine1D(nn.Module):
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def __init__(self, d_model, max_len=500, batch_first=False):
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super().__init__()
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self.batch_first = batch_first
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pe = torch.zeros(max_len, d_model)
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position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
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div_term = torch.exp(
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torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model))
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pe[:, 0::2] = torch.sin(position * div_term)
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pe[:, 1::2] = torch.cos(position * div_term)
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pe = pe.unsqueeze(0).transpose(0, 1)
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self.register_buffer('pe', pe)
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def forward(self, x):
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# not used in the final model
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if self.batch_first:
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pos = self.pe.permute(1, 0, 2)[:, :x.shape[1], :]
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else:
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pos = self.pe[:x.shape[0], :]
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return pos
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class PositionEmbeddingLearned1D(nn.Module):
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def __init__(self, d_model, max_len=500, batch_first=False):
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super().__init__()
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self.batch_first = batch_first
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# self.dropout = nn.Dropout(p=dropout)
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self.pe = nn.Parameter(torch.zeros(max_len, 1, d_model))
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# self.pe = pe.unsqueeze(0).transpose(0, 1)
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self.reset_parameters()
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def reset_parameters(self):
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nn.init.uniform_(self.pe)
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def forward(self, x):
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# not used in the final model
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if self.batch_first:
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pos = self.pe.permute(1, 0, 2)[:, :x.shape[1], :]
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else:
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x = x + self.pe[:x.shape[0], :]
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return x
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# return self.dropout(x)
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def build_position_encoding(N_steps,
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position_embedding="sine",
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embedding_dim="1D"):
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# N_steps = hidden_dim // 2
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if embedding_dim == "1D":
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if position_embedding in ('v2', 'sine'):
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position_embedding = PositionEmbeddingSine1D(N_steps)
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elif position_embedding in ('v3', 'learned'):
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position_embedding = PositionEmbeddingLearned1D(N_steps)
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else:
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raise ValueError(f"not supported {position_embedding}")
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elif embedding_dim == "2D":
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if position_embedding in ('v2', 'sine'):
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# TODO find a better way of exposing other arguments
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position_embedding = PositionEmbeddingSine(N_steps, normalize=True)
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elif position_embedding in ('v3', 'learned'):
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position_embedding = PositionEmbeddingLearned(N_steps)
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else:
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raise ValueError(f"not supported {position_embedding}")
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else:
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raise ValueError(f"not supported {embedding_dim}")
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return position_embedding
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