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from typing import Optional
import torch
from torch import nn, Tensor
from torch.nn import functional as F
from timm.models.layers import trunc_normal_
from detectron2.layers import Conv2d
import fvcore.nn.weight_init as weight_init
from ..utils import MultiheadAttention
class SelfAttentionLayer(nn.Module):
def __init__(self, d_model, nhead, dropout=0.0,
activation="relu", normalize_before=False):
super().__init__()
self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout)
self.norm = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
self.activation = _get_activation_fn(activation)
self.normalize_before = normalize_before
self._reset_parameters()
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
return tensor if pos is None else tensor + pos
def forward_post(self, tgt,
tgt_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
q = k = self.with_pos_embed(tgt, query_pos)
tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask)[0]
tgt = tgt + self.dropout(tgt2)
tgt = self.norm(tgt)
return tgt
def forward_pre(self, tgt,
tgt_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
tgt2 = self.norm(tgt)
q = k = self.with_pos_embed(tgt2, query_pos)
tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask)[0]
tgt = tgt + self.dropout(tgt2)
return tgt
def forward(self, tgt,
tgt_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
if self.normalize_before:
return self.forward_pre(tgt, tgt_mask,
tgt_key_padding_mask, query_pos)
return self.forward_post(tgt, tgt_mask,
tgt_key_padding_mask, query_pos)
class CrossAttentionLayer(nn.Module):
def __init__(self, d_model, nhead, dropout=0.0,
activation="relu", normalize_before=False):
super().__init__()
self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.norm = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
self.activation = _get_activation_fn(activation)
self.normalize_before = normalize_before
self._reset_parameters()
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
return tensor if pos is None else tensor + pos
def forward_post(self, tgt, memory,
memory_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
tgt2, avg_attn = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),
key=self.with_pos_embed(memory, pos),
value=memory, attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask)
tgt = tgt + self.dropout(tgt2)
tgt = self.norm(tgt)
return tgt, avg_attn
def forward_pre(self, tgt, memory,
memory_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
tgt2 = self.norm(tgt)
tgt2, avg_attn = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
key=self.with_pos_embed(memory, pos),
value=memory, attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask)
tgt = tgt + self.dropout(tgt2)
return tgt, avg_attn
def forward(self, tgt, memory,
memory_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None,
pos: Optional[Tensor] = None,
query_pos: Optional[Tensor] = None):
if self.normalize_before:
return self.forward_pre(tgt, memory, memory_mask,
memory_key_padding_mask, pos, query_pos)
return self.forward_post(tgt, memory, memory_mask,
memory_key_padding_mask, pos, query_pos)
class FFNLayer(nn.Module):
def __init__(self, d_model, dim_feedforward=2048, dropout=0.0,
activation="relu", normalize_before=False):
super().__init__()
# 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.norm = nn.LayerNorm(d_model)
self.activation = _get_activation_fn(activation)
self.normalize_before = normalize_before
self._reset_parameters()
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def with_pos_embed(self, tensor, pos: Optional[Tensor]):
return tensor if pos is None else tensor + pos
def forward_post(self, tgt):
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout(tgt2)
tgt = self.norm(tgt)
return tgt
def forward_pre(self, tgt):
tgt2 = self.norm(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
tgt = tgt + self.dropout(tgt2)
return tgt
def forward(self, tgt):
if self.normalize_before:
return self.forward_pre(tgt)
return self.forward_post(tgt)
def _get_activation_fn(activation):
"""Return an activation function given a string"""
if activation == "relu":
return F.relu
if activation == "gelu":
return F.gelu
if activation == "glu":
return F.glu
raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
class MLP(nn.Module):
""" Very simple multi-layer perceptron (also called FFN)"""
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
super().__init__()
self.num_layers = num_layers
h = [hidden_dim] * (num_layers - 1)
self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
def forward(self, x):
for i, layer in enumerate(self.layers):
x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
return x
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