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import torch
import torch.nn as nn
import copy
from functools import partial
from .dasheng import LayerScale, Attention, Mlp
class Decoder_Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
drop=0.,
attn_drop=0.,
init_values=None,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
attention_type='Attention',
fusion='adaln',
):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(dim,
num_heads=num_heads,
qkv_bias=qkv_bias,
attn_drop=attn_drop,
proj_drop=drop)
self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = Mlp(in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=drop)
self.ls2 = LayerScale(
dim, init_values=init_values) if init_values else nn.Identity()
self.fusion = fusion
if fusion == 'adaln':
self.adaln = nn.Linear(dim, 6 * dim, bias=True)
def forward(self, x, c=None):
B, T, C = x.shape
if self.fusion == 'adaln':
ada = self.adaln(c)
(scale_msa, gate_msa, shift_msa,
scale_mlp, gate_mlp, shift_mlp) = ada.reshape(B, 6, -1).chunk(6, dim=1)
# self attention
x_norm = self.norm1(x) * (1 + scale_msa) + shift_msa
tanh_gate_msa = torch.tanh(1 - gate_msa)
x = x + tanh_gate_msa * self.ls1(self.attn(x_norm))
# mlp
x_norm = self.norm2(x) * (1 + scale_mlp) + shift_mlp
tanh_gate_mlp = torch.tanh(1 - gate_mlp)
x = x + tanh_gate_mlp * self.ls2(self.mlp(x_norm))
else:
x = x + self.ls1(self.attn(self.norm1(x)))
x = x + self.ls2(self.mlp(self.norm2(x)))
return x
class Decoder(nn.Module):
def __init__(
self,
embed_dim: int = 768,
depth: int = 2,
num_heads=8,
mlp_ratio=4.,
qkv_bias=True,
drop_rate=0.,
attn_drop_rate=0.,
cls_dim: int = 512,
fusion: str = 'adaln',
**kwargs
):
super().__init__()
norm_layer = partial(nn.LayerNorm, eps=1e-6)
act_layer = nn.GELU
init_values = None
block_function = Decoder_Block
self.blocks = nn.ModuleList([
block_function(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
init_values=init_values,
drop=drop_rate,
attn_drop=attn_drop_rate,
norm_layer=norm_layer,
act_layer=act_layer,
attention_type="Attention",
fusion=fusion,
) for _ in range(depth)
])
self.fusion = fusion
cls_out = embed_dim
self.cls_embed = nn.Sequential(
nn.Linear(cls_dim, embed_dim, bias=True),
nn.SiLU(),
nn.Linear(embed_dim, cls_out, bias=True),)
self.sed_head = nn.Linear(embed_dim, 1, bias=True)
self.norm = norm_layer(embed_dim)
self.apply(self.init_weights)
# self.energy_head = nn.Linear(embed_dim, 1, bias=True)
def init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.LayerNorm):
nn.init.constant_(module.bias, 0)
nn.init.constant_(module.weight, 1.0)
if self.fusion == 'adaln':
for block in self.blocks:
nn.init.constant_(block.adaln.weight, 0)
nn.init.constant_(block.adaln.bias, 0)
def forward(self, x, cls):
B, L, C = x.shape
_, N, D = cls.shape
# Expand x to shape (B, N, L, C)
x = x.unsqueeze(1).expand(-1, N, -1, -1)
# Reshape both tensors to (B*N, L, C) for processing
x = x.reshape(B * N, L, C)
cls = cls.reshape(B * N, D)
cls = self.cls_embed(cls)
shift = 0
if self.fusion == 'adaln':
pass
elif self.fusion == 'token':
cls = cls.unsqueeze(1)
x = torch.cat([cls, x], dim=1)
shift = 1
else:
raise NotImplementedError("unknown fusion")
for block in self.blocks:
x = block(x, cls)
x = x[:, shift:]
x = self.norm(x)
strong = self.sed_head(x)
return strong.transpose(1, 2)
class TSED_Wrapper(nn.Module):
def __init__(
self,
encoder,
decoder,
ft_blocks=[11, 12],
frozen_encoder=True
):
super().__init__()
self.encoder = encoder
self.decoder = decoder
print("Loading Dasheng weights for decoders...")
for i, blk_idx in enumerate(ft_blocks):
decoder_block = self.decoder.blocks[i]
encoder_block = self.encoder.blocks[blk_idx]
state_dict = copy.deepcopy(encoder_block.state_dict())
missing, unexpected = decoder_block.load_state_dict(state_dict, strict=False)
if missing or unexpected:
print(f"Block {blk_idx}:")
if missing:
print(f"✅ Expected missing keys: {missing}")
if unexpected:
print(f" Unexpected keys: {unexpected}")
# Copy norm_layer weights
self.decoder.norm.load_state_dict(copy.deepcopy(self.encoder.norm.state_dict()))
# Remove the injected blocks and norm_layer from the encoder
for blk_idx in sorted(ft_blocks, reverse=True):
# Reverse to avoid index shift issues
del self.encoder.blocks[blk_idx]
# Remove encoder norm layer
del self.encoder.norm
self.frozen_encoder = frozen_encoder
if frozen_encoder:
for param in self.encoder.parameters():
param.requires_grad = False
def forward_to_spec(self, x):
return self.encoder.forward_to_spec(x)
def forward_encoder(self, x):
if self.frozen_encoder:
with torch.no_grad():
x = self.encoder(x)
else:
x = self.encoder(x)
return x
def forward(self, x, cls):
x = self.forward_encoder(x)
pred = self.decoder(x, cls)
return pred
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