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import copy
import torch.nn as nn
from ...builder import TRANSFORMERS
from ..layers import BaseTransformerLayer, MultiheadAttention, FFN
@TRANSFORMERS.register_module()
class DETREncoder(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,
):
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._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", "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,
):
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)
return query
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