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# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
DETR Transformer class.
Copy-paste from torch.nn.Transformer with modifications:
* positional encodings are passed in MHattention
* extra LN at the end of encoder is removed
* decoder returns a stack of activations from all decoding layers
"""
import torch
from torch import Tensor, nn
from typing import List, Optional
from .utils import _get_activation_fn, _get_clones
class TextTransformer(nn.Module):
def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1):
super().__init__()
self.num_layers = num_layers
self.d_model = d_model
self.nheads = nheads
self.dim_feedforward = dim_feedforward
self.norm = None
single_encoder_layer = TransformerEncoderLayer(d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout)
self.layers = _get_clones(single_encoder_layer, num_layers)
def forward(self, memory_text:torch.Tensor, text_attention_mask:torch.Tensor):
"""
Args:
text_attention_mask: bs, num_token
memory_text: bs, num_token, d_model
Raises:
RuntimeError: _description_
Returns:
output: bs, num_token, d_model
"""
output = memory_text.transpose(0, 1)
for layer in self.layers:
output = layer(output, src_key_padding_mask=text_attention_mask)
if self.norm is not None:
output = self.norm(output)
return output.transpose(0, 1)
class TransformerEncoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", normalize_before=False):
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
# Implementation of Feedforward model
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.activation = _get_activation_fn(activation)
self.normalize_before = normalize_before
self.nhead = nhead
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
return tensor if pos is None else tensor + pos
def forward(
self,
src,
src_mask: Optional[Tensor] = None,
src_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None,
):
# repeat attn mask
if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]:
# bs, num_q, num_k
src_mask = src_mask.repeat(self.nhead, 1, 1)
q = k = self.with_pos_embed(src, pos)
src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0]
# src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
src = src + self.dropout1(src2)
src = self.norm1(src)
src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
src = src + self.dropout2(src2)
src = self.norm2(src)
return src
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