| """ |
| Code modified from DETR tranformer: |
| https://github.com/facebookresearch/detr |
| Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved |
| """ |
|
|
| import copy |
| from typing import Optional, List |
| import pickle as cp |
|
|
| import torch |
| import torch.nn.functional as F |
| from torch import nn, Tensor |
|
|
|
|
| class TransformerDecoder(nn.Module): |
|
|
| def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): |
| super().__init__() |
| self.layers = _get_clones(decoder_layer, num_layers) |
| self.num_layers = num_layers |
| self.norm = norm |
| self.return_intermediate = return_intermediate |
|
|
| def forward(self, tgt, memory, |
| tgt_mask: Optional[Tensor] = None, |
| memory_mask: Optional[Tensor] = None, |
| tgt_key_padding_mask: Optional[Tensor] = None, |
| memory_key_padding_mask: Optional[Tensor] = None, |
| pos: Optional[Tensor] = None, |
| query_pos: Optional[Tensor] = None): |
| output = tgt |
| T,B,C = memory.shape |
| intermediate = [] |
| atten_layers = [] |
| for n,layer in enumerate(self.layers): |
| |
| residual=True |
| output,ws = layer(output, memory, tgt_mask=tgt_mask, |
| memory_mask=memory_mask, |
| tgt_key_padding_mask=tgt_key_padding_mask, |
| memory_key_padding_mask=memory_key_padding_mask, |
| pos=pos, query_pos=query_pos,residual=residual) |
| atten_layers.append(ws) |
| if self.return_intermediate: |
| intermediate.append(self.norm(output)) |
| if self.norm is not None: |
| output = self.norm(output) |
| if self.return_intermediate: |
| intermediate.pop() |
| intermediate.append(output) |
|
|
| if self.return_intermediate: |
| return torch.stack(intermediate) |
| return output,atten_layers |
|
|
|
|
| class TransformerDecoderLayer(nn.Module): |
|
|
| def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, |
| activation="relu", normalize_before=False): |
| super().__init__() |
| |
| self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) |
| |
| self.linear1 = nn.Linear(d_model, dim_feedforward) |
| self.dropout = nn.Dropout(dropout) |
| self.linear2 = nn.Linear(dim_feedforward, d_model) |
|
|
| self.norm1 = nn.LayerNorm(d_model) |
| self.norm2 = nn.LayerNorm(d_model) |
| self.norm3 = nn.LayerNorm(d_model) |
| self.dropout1 = nn.Dropout(dropout) |
| self.dropout2 = nn.Dropout(dropout) |
| self.dropout3 = nn.Dropout(dropout) |
|
|
| self.activation = _get_activation_fn(activation) |
| self.normalize_before = normalize_before |
| def with_pos_embed(self, tensor, pos: Optional[Tensor]): |
| return tensor if pos is None else tensor + pos |
|
|
| def forward_post(self, tgt, memory, |
| tgt_mask: Optional[Tensor] = None, |
| memory_mask: Optional[Tensor] = None, |
| tgt_key_padding_mask: Optional[Tensor] = None, |
| memory_key_padding_mask: Optional[Tensor] = None, |
| pos: Optional[Tensor] = None, |
| query_pos: Optional[Tensor] = None, |
| residual=True): |
| q = k = self.with_pos_embed(tgt, query_pos) |
| tgt2,ws = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, |
| key_padding_mask=tgt_key_padding_mask) |
| tgt = self.norm1(tgt) |
| tgt2,ws = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos), |
| key=self.with_pos_embed(memory, pos), |
| value=memory, attn_mask=memory_mask, |
| key_padding_mask=memory_key_padding_mask) |
|
|
| |
| tgt = tgt + self.dropout2(tgt2) |
| tgt = self.norm2(tgt) |
| tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt)))) |
| tgt = tgt + self.dropout3(tgt2) |
| tgt = self.norm3(tgt) |
| return tgt,ws |
|
|
| def forward_pre(self, tgt, memory, |
| tgt_mask: Optional[Tensor] = None, |
| memory_mask: Optional[Tensor] = None, |
| tgt_key_padding_mask: Optional[Tensor] = None, |
| memory_key_padding_mask: Optional[Tensor] = None, |
| pos: Optional[Tensor] = None, |
| query_pos: Optional[Tensor] = None): |
|
|
| tgt2 = self.norm2(tgt) |
| tgt2,attn_weights = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos), |
| key=self.with_pos_embed(memory, pos), |
| value=memory, attn_mask=memory_mask, |
| key_padding_mask=memory_key_padding_mask) |
| tgt = tgt + self.dropout2(tgt2) |
| tgt2 = self.norm3(tgt) |
| tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) |
| tgt = tgt + self.dropout3(tgt2) |
| return tgt,attn_weights |
|
|
| def forward(self, tgt, memory, |
| tgt_mask: Optional[Tensor] = None, |
| memory_mask: Optional[Tensor] = None, |
| tgt_key_padding_mask: Optional[Tensor] = None, |
| memory_key_padding_mask: Optional[Tensor] = None, |
| pos: Optional[Tensor] = None, |
| query_pos: Optional[Tensor] = None, |
| residual=True): |
| if self.normalize_before: |
| return self.forward_pre(tgt, memory, tgt_mask, memory_mask, |
| tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos) |
| return self.forward_post(tgt, memory, tgt_mask, memory_mask, |
| tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos,residual) |
|
|
|
|
| def _get_clones(module, N): |
| return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) |
|
|
|
|
|
|
| def _get_activation_fn(activation): |
| """Return an activation function given a string""" |
| if activation == "relu": |
| return F.relu |
| if activation == "gelu": |
| return F.gelu |
| if activation == "glu": |
| return F.glu |
| raise RuntimeError(F"activation should be relu/gelu, not {activation}.") |
