| import copy |
| import torch |
| import torch.nn as nn |
| from ...builder import TRANSFORMERS |
| from ..layers import BaseTransformerLayer, MultiheadAttention, FFN |
|
|
|
|
| @TRANSFORMERS.register_module() |
| class DETRDecoder(nn.Module): |
| def __init__( |
| self, |
| embed_dim=256, |
| num_heads=8, |
| attn_dropout=0.1, |
| ffn_dim=2048, |
| ffn_dropout=0.1, |
| num_layers=6, |
| post_norm=True, |
| batch_first=True, |
| return_intermediate=True, |
| ): |
| super().__init__() |
|
|
| self.embed_dim = embed_dim |
| self.num_heads = num_heads |
| self.attn_dropout = attn_dropout |
| self.ffn_dim = ffn_dim |
| self.ffn_dropout = ffn_dropout |
| self.num_layers = num_layers |
| self.batch_first = batch_first |
| self.return_intermediate = return_intermediate |
|
|
| self._init_transformer_layers() |
|
|
| if post_norm: |
| self.post_norm_layer = nn.LayerNorm(self.embed_dim) |
| else: |
| self.post_norm_layer = None |
|
|
| def _init_transformer_layers(self): |
| transformer_layers = BaseTransformerLayer( |
| attn=MultiheadAttention( |
| embed_dim=self.embed_dim, |
| num_heads=self.num_heads, |
| attn_drop=self.attn_dropout, |
| batch_first=self.batch_first, |
| ), |
| ffn=FFN( |
| embed_dim=self.embed_dim, |
| ffn_dim=self.ffn_dim, |
| ffn_drop=self.ffn_dropout, |
| ), |
| norm=nn.LayerNorm( |
| normalized_shape=self.embed_dim, |
| ), |
| operation_order=("self_attn", "norm", "cross_attn", "norm", "ffn", "norm"), |
| ) |
| self.layers = nn.ModuleList([copy.deepcopy(transformer_layers) for _ in range(self.num_layers)]) |
|
|
| def forward( |
| self, |
| query, |
| key, |
| value, |
| query_pos=None, |
| key_pos=None, |
| attn_masks=None, |
| query_key_padding_mask=None, |
| key_padding_mask=None, |
| **kwargs, |
| ): |
| if not self.return_intermediate: |
| for layer in self.layers: |
| query = layer( |
| query, |
| key, |
| value, |
| query_pos=query_pos, |
| key_pos=key_pos, |
| attn_masks=attn_masks, |
| query_key_padding_mask=query_key_padding_mask, |
| key_padding_mask=key_padding_mask, |
| **kwargs, |
| ) |
|
|
| if self.post_norm_layer is not None: |
| query = self.post_norm_layer(query)[None] |
| return query |
|
|
| |
| intermediate = [] |
| for layer in self.layers: |
| query = layer( |
| query, |
| key, |
| value, |
| query_pos=query_pos, |
| key_pos=key_pos, |
| attn_masks=attn_masks, |
| query_key_padding_mask=query_key_padding_mask, |
| key_padding_mask=key_padding_mask, |
| **kwargs, |
| ) |
|
|
| if self.return_intermediate: |
| if self.post_norm_layer is not None: |
| intermediate.append(self.post_norm_layer(query)) |
| else: |
| intermediate.append(query) |
|
|
| return torch.stack(intermediate) |
|
|
