478 lines
19 KiB
Python
478 lines
19 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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"""
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DETR Transformer class.
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Copy-paste from torch.nn.Transformer with modifications:
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* positional encodings are passed in MHattention
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* extra LN at the end of encoder is removed
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* decoder returns a stack of activations from all decoding layers
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"""
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import copy
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from typing import Optional, List
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import torch
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import torch.nn.functional as F
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from torch import nn, Tensor
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class Transformer(nn.Module):
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def __init__(self, d_model=512, nhead=8, num_encoder_layers=6,
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num_decoder_layers=6, dim_feedforward=2048, dropout=0.1,
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activation="relu", normalize_before=False,
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return_intermediate_dec=False):
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super().__init__()
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encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward,
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dropout, activation, normalize_before)
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encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
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self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
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decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward,
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dropout, activation, normalize_before)
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decoder_norm = nn.LayerNorm(d_model)
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self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm,
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return_intermediate=return_intermediate_dec)
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self._reset_parameters()
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self.d_model = d_model
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self.nhead = nhead
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def _reset_parameters(self):
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for p in self.parameters():
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if p.dim() > 1:
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nn.init.xavier_uniform_(p)
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def forward(self, src, mask, query_embed, pos_embed):
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# flatten NxCxHxW to HWxNxC
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bs, c, h, w = src.shape
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src = src.flatten(2).permute(2, 0, 1)
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pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
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query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)
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mask = mask.flatten(1)
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tgt = torch.zeros_like(query_embed)
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memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)
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hs = self.decoder(tgt, memory, memory_key_padding_mask=mask,
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pos=pos_embed, query_pos=query_embed)
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return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w)
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class TransformerEncoder(nn.Module):
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def __init__(self, encoder_layer, num_layers, norm=None, batch_first=True):
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super().__init__()
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self.layers = _get_clones(encoder_layer, num_layers)
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self.num_layers = num_layers
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self.norm = norm
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self.batch_first = batch_first
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def forward(self, src,
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mask: Optional[Tensor] = None,
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src_key_padding_mask: Optional[Tensor] = None,
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pos: Optional[Tensor] = None):
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output = src
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if self.batch_first:
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output = output.transpose(0, 1)
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for layer in self.layers:
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output = layer(output, src_mask=mask,
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src_key_padding_mask=src_key_padding_mask, pos=pos)
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if self.norm is not None:
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output = self.norm(output)
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if self.batch_first:
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output = output.transpose(0, 1)
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return output
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class TransformerDecoder(nn.Module):
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def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):
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super().__init__()
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self.layers = _get_clones(decoder_layer, num_layers)
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self.num_layers = num_layers
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self.norm = norm
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self.return_intermediate = return_intermediate
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def forward(self, tgt, memory,
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tgt_mask: Optional[Tensor] = None,
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memory_mask: Optional[Tensor] = None,
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tgt_key_padding_mask: Optional[Tensor] = None,
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memory_key_padding_mask: Optional[Tensor] = None,
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pos: Optional[Tensor] = None,
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query_pos: Optional[Tensor] = None):
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output = tgt
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intermediate = []
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for layer in self.layers:
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output = layer(output, memory, tgt_mask=tgt_mask,
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memory_mask=memory_mask,
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tgt_key_padding_mask=tgt_key_padding_mask,
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memory_key_padding_mask=memory_key_padding_mask,
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pos=pos, query_pos=query_pos)
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if self.return_intermediate:
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intermediate.append(self.norm(output))
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if self.norm is not None:
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output = self.norm(output)
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if self.return_intermediate:
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intermediate.pop()
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intermediate.append(output)
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if self.return_intermediate:
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return torch.stack(intermediate)
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return output.unsqueeze(0)
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class TransformerEncoderLayer(nn.Module):
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def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
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activation="relu", normalize_before=False):
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super().__init__()
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self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
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# Implementation of Feedforward model
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self.linear1 = nn.Linear(d_model, dim_feedforward)
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self.dropout = nn.Dropout(dropout)
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self.linear2 = nn.Linear(dim_feedforward, d_model)
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self.norm1 = nn.LayerNorm(d_model)
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self.norm2 = nn.LayerNorm(d_model)
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self.dropout1 = nn.Dropout(dropout)
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self.dropout2 = nn.Dropout(dropout)
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self.activation = _get_activation_fn(activation)
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self.normalize_before = normalize_before
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def with_pos_embed(self, tensor, pos: Optional[Tensor]):
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return tensor if pos is None else tensor + pos
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def forward_post(self,
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src,
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src_mask: Optional[Tensor] = None,
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src_key_padding_mask: Optional[Tensor] = None,
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pos: Optional[Tensor] = None):
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q = k = self.with_pos_embed(src, pos)
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src2 = self.self_attn(q, k, value=src, attn_mask=src_mask,
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key_padding_mask=src_key_padding_mask)[0]
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src = src + self.dropout1(src2)
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src = self.norm1(src)
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src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
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src = src + self.dropout2(src2)
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src = self.norm2(src)
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return src
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def forward_pre(self, src,
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src_mask: Optional[Tensor] = None,
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src_key_padding_mask: Optional[Tensor] = None,
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pos: Optional[Tensor] = None):
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src2 = self.norm1(src)
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q = k = self.with_pos_embed(src2, pos)
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src2 = self.self_attn(q, k, value=src2, attn_mask=src_mask,
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key_padding_mask=src_key_padding_mask)[0]
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src = src + self.dropout1(src2)
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src2 = self.norm2(src)
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src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
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src = src + self.dropout2(src2)
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return src
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def forward(self, src,
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src_mask: Optional[Tensor] = None,
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src_key_padding_mask: Optional[Tensor] = None,
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pos: Optional[Tensor] = None):
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if self.normalize_before:
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return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
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return self.forward_post(src, src_mask, src_key_padding_mask, pos)
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class TransformerDecoderLayer(nn.Module):
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def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
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activation="relu", normalize_before=False):
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super().__init__()
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self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
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self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
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# Implementation of Feedforward model
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self.linear1 = nn.Linear(d_model, dim_feedforward)
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self.dropout = nn.Dropout(dropout)
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self.linear2 = nn.Linear(dim_feedforward, d_model)
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self.norm1 = nn.LayerNorm(d_model)
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self.norm2 = nn.LayerNorm(d_model)
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self.norm3 = nn.LayerNorm(d_model)
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self.dropout1 = nn.Dropout(dropout)
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self.dropout2 = nn.Dropout(dropout)
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self.dropout3 = nn.Dropout(dropout)
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self.activation = _get_activation_fn(activation)
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self.normalize_before = normalize_before
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def with_pos_embed(self, tensor, pos: Optional[Tensor]):
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return tensor if pos is None else tensor + pos
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def forward_post(self, tgt, memory,
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tgt_mask: Optional[Tensor] = None,
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memory_mask: Optional[Tensor] = None,
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tgt_key_padding_mask: Optional[Tensor] = None,
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memory_key_padding_mask: Optional[Tensor] = None,
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pos: Optional[Tensor] = None,
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query_pos: Optional[Tensor] = None):
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q = k = self.with_pos_embed(tgt, query_pos)
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tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,
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key_padding_mask=tgt_key_padding_mask)[0]
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tgt = tgt + self.dropout1(tgt2)
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tgt = self.norm1(tgt)
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tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),
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key=self.with_pos_embed(memory, pos),
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value=memory, attn_mask=memory_mask,
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key_padding_mask=memory_key_padding_mask)[0]
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tgt = tgt + self.dropout2(tgt2)
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tgt = self.norm2(tgt)
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tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
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tgt = tgt + self.dropout3(tgt2)
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tgt = self.norm3(tgt)
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return tgt
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def forward_pre(self, tgt, memory,
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tgt_mask: Optional[Tensor] = None,
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memory_mask: Optional[Tensor] = None,
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tgt_key_padding_mask: Optional[Tensor] = None,
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memory_key_padding_mask: Optional[Tensor] = None,
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pos: Optional[Tensor] = None,
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query_pos: Optional[Tensor] = None):
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tgt2 = self.norm1(tgt)
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q = k = self.with_pos_embed(tgt2, query_pos)
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tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
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key_padding_mask=tgt_key_padding_mask)[0]
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tgt = tgt + self.dropout1(tgt2)
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tgt2 = self.norm2(tgt)
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tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
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key=self.with_pos_embed(memory, pos),
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value=memory, attn_mask=memory_mask,
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key_padding_mask=memory_key_padding_mask)[0]
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tgt = tgt + self.dropout2(tgt2)
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tgt2 = self.norm3(tgt)
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tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
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tgt = tgt + self.dropout3(tgt2)
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return tgt
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def forward(self, tgt, memory,
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tgt_mask: Optional[Tensor] = None,
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memory_mask: Optional[Tensor] = None,
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tgt_key_padding_mask: Optional[Tensor] = None,
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memory_key_padding_mask: Optional[Tensor] = None,
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pos: Optional[Tensor] = None,
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query_pos: Optional[Tensor] = None):
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if self.normalize_before:
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return self.forward_pre(tgt, memory, tgt_mask, memory_mask,
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tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)
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return self.forward_post(tgt, memory, tgt_mask, memory_mask,
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tgt_key_padding_mask, memory_key_padding_mask, pos, query_pos)
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class TransformerDecoder3(nn.Module):
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def __init__(self, mm_fusion, decoder_layer, num_layers, norm=None, return_intermediate=False, batch_first=True):
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super().__init__()
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self.mm_fusion = mm_fusion # stack, cat
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assert self.mm_fusion in ['stack', 'cat']
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self.layers = _get_clones(decoder_layer, num_layers)
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self.num_layers = num_layers
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self.norm = norm
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self.return_intermediate = return_intermediate
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self.batch_first = batch_first
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def forward(
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self, tgt, text_memory, hist_memory,
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tgt_key_padding_mask: Optional[Tensor] = None,
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text_memory_key_padding_mask: Optional[Tensor] = None,
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hist_memory_key_padding_mask: Optional[Tensor] = None,
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):
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output = tgt
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if self.batch_first:
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output = output.transpose(0, 1)
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text_memory = text_memory.transpose(0, 1)
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hist_memory = hist_memory.transpose(0, 1)
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intermediate = []
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if self.mm_fusion == 'cat':
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memory_key_padding_mask = torch.cat([text_memory_key_padding_mask, hist_memory_key_padding_mask], dim=1)
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memory = torch.cat([text_memory, hist_memory], dim=0)
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for layer in self.layers:
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output = layer(output, memory, tgt_key_padding_mask=tgt_key_padding_mask, memory_key_padding_mask=memory_key_padding_mask)
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if self.return_intermediate:
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intermediate.append(self.norm(output))
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elif self.mm_fusion == 'stack':
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for layer in self.layers:
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output = layer(
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output, text_memory, hist_memory,
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tgt_key_padding_mask=tgt_key_padding_mask,
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text_memory_key_padding_mask=text_memory_key_padding_mask,
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hist_memory_key_padding_mask=hist_memory_key_padding_mask,
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)
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if self.return_intermediate:
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intermediate.append(self.norm(output))
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if self.norm is not None:
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output = self.norm(output)
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if self.return_intermediate:
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intermediate.pop()
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intermediate.append(output)
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if self.return_intermediate:
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return torch.stack(intermediate)
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if self.batch_first:
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output = output.transpose(0, 1)
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return output
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class TransformerDecoderLayer3(nn.Module):
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def __init__(
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self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
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activation="relu", normalize_before=False
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):
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super().__init__()
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self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
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self.text_cross_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
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self.hist_cross_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
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# Implementation of Feedforward model
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self.linear1 = nn.Linear(d_model, dim_feedforward)
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self.dropout = nn.Dropout(dropout)
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self.linear2 = nn.Linear(dim_feedforward, d_model)
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self.norm1 = nn.LayerNorm(d_model)
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self.norm2 = nn.LayerNorm(d_model)
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self.norm3 = nn.LayerNorm(d_model)
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self.norm4 = nn.LayerNorm(d_model)
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self.dropout1 = nn.Dropout(dropout)
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self.dropout2 = nn.Dropout(dropout)
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self.dropout3 = nn.Dropout(dropout)
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self.dropout4 = nn.Dropout(dropout)
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self.activation = _get_activation_fn(activation)
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self.normalize_before = normalize_before
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def forward(
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self, tgt, text_memory, hist_memory,
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tgt_key_padding_mask: Optional[Tensor] = None,
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text_memory_key_padding_mask: Optional[Tensor] = None,
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hist_memory_key_padding_mask: Optional[Tensor] = None,
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):
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# pre normalization
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tgt2 = self.norm1(tgt)
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tgt2 = self.self_attn(
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tgt2, tgt2, value=tgt2,
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key_padding_mask=tgt_key_padding_mask
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)[0]
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tgt = tgt + self.dropout1(tgt2)
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tgt2 = self.norm2(tgt)
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tgt2 = self.hist_cross_attn(
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query=tgt2, key=hist_memory, value=hist_memory,
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key_padding_mask=hist_memory_key_padding_mask
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)[0]
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tgt = tgt + self.dropout2(tgt2)
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tgt2 = self.norm3(tgt)
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tgt2 = self.text_cross_attn(
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query=tgt2, key=text_memory, value=text_memory,
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key_padding_mask=text_memory_key_padding_mask
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)[0]
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tgt = tgt + self.dropout3(tgt2)
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tgt2 = self.norm4(tgt)
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tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
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tgt = tgt + self.dropout4(tgt2)
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return tgt
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class TransformerDecoderLayer3Add(nn.Module):
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def __init__(
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self, d_model, nhead, dim_feedforward=2048, dropout=0.1,
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activation="relu", normalize_before=False
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):
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super().__init__()
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self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
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self.text_cross_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
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self.hist_cross_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
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# Implementation of Feedforward model
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self.linear1 = nn.Linear(d_model, dim_feedforward)
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self.dropout = nn.Dropout(dropout)
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self.linear2 = nn.Linear(dim_feedforward, d_model)
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self.norm1 = nn.LayerNorm(d_model)
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self.norm2 = nn.LayerNorm(d_model)
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self.norm3 = nn.LayerNorm(d_model)
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self.dropout1 = nn.Dropout(dropout)
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self.dropout2 = nn.Dropout(dropout)
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self.dropout3 = nn.Dropout(dropout)
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self.activation = _get_activation_fn(activation)
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self.normalize_before = normalize_before
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def forward(
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self, tgt, text_memory, hist_memory,
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tgt_key_padding_mask: Optional[Tensor] = None,
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text_memory_key_padding_mask: Optional[Tensor] = None,
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hist_memory_key_padding_mask: Optional[Tensor] = None,
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):
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# pre normalization
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tgt2 = self.norm1(tgt)
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tgt2 = self.self_attn(
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tgt2, tgt2, value=tgt2,
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key_padding_mask=tgt_key_padding_mask
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)[0]
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tgt = tgt + self.dropout1(tgt2)
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tgt2 = self.norm2(tgt)
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hist_tgt = self.hist_cross_attn(
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query=tgt2, key=hist_memory, value=hist_memory,
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key_padding_mask=hist_memory_key_padding_mask
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)[0]
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txt_tgt = self.text_cross_attn(
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query=tgt2, key=text_memory, value=text_memory,
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key_padding_mask=text_memory_key_padding_mask
|
|
)[0]
|
|
tgt = tgt + self.dropout2(hist_tgt) + self.dropout2(txt_tgt)
|
|
|
|
tgt2 = self.norm3(tgt)
|
|
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
|
|
tgt = tgt + self.dropout3(tgt2)
|
|
return tgt
|
|
|
|
|
|
def _get_clones(module, N):
|
|
return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
|
|
|
|
|
|
def build_transformer(args):
|
|
return Transformer(
|
|
d_model=args.hidden_dim,
|
|
dropout=args.dropout,
|
|
nhead=args.nheads,
|
|
dim_feedforward=args.dim_feedforward,
|
|
num_encoder_layers=args.enc_layers,
|
|
num_decoder_layers=args.dec_layers,
|
|
normalize_before=args.pre_norm,
|
|
return_intermediate_dec=True,
|
|
)
|
|
|
|
|
|
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}.")
|