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""" |
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Code modified from DETR tranformer: |
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https://github.com/facebookresearch/detr |
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Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved |
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""" |
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import copy |
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from typing import Optional |
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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 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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T,B,C = memory.shape |
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intermediate = [] |
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atten_layers = [] |
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for n,layer in enumerate(self.layers): |
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residual=True |
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output,ws = 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,residual=residual) |
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atten_layers.append(ws) |
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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,atten_layers |
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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.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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residual=True): |
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q = k = self.with_pos_embed(tgt, query_pos) |
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tgt2,ws = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask, |
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key_padding_mask=tgt_key_padding_mask) |
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tgt = self.norm1(tgt) |
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tgt2,ws = 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) |
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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,ws |
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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.norm2(tgt) |
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tgt2,attn_weights = 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) |
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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,attn_weights |
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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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residual=True): |
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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,residual) |
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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 _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}.") |
