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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, query_embed, y_ind):
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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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y_emb = query_embed[y_ind].permute(1,0,2)
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tgt = torch.zeros_like(y_emb)
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memory = self.encoder(src)
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hs = self.decoder(tgt, memory, query_pos=y_emb)
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return torch.cat([hs.transpose(1, 2)[-1], y_emb.permute(1,0,2)], -1)
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class TransformerEncoder(nn.Module):
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def __init__(self, encoder_layer, num_layers, norm=None):
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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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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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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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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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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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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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def _get_clones(module, N):
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return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
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def build_transformer(args):
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return Transformer(
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d_model=args.hidden_dim,
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dropout=args.dropout,
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nhead=args.nheads,
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dim_feedforward=args.dim_feedforward,
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num_encoder_layers=args.enc_layers,
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num_decoder_layers=args.dec_layers,
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normalize_before=args.pre_norm,
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return_intermediate_dec=True,
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)
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def _get_activation_fn(activation):
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"""Return an activation function given a string"""
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if activation == "relu":
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return F.relu
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if activation == "gelu":
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return F.gelu
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if activation == "glu":
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return F.glu
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raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
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