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import torch.nn as nn
import os
import sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from gatedResidualBlock import GatedAlteredResidualBlock, StackedGatedResidualBlock
from loadImageModelClassifier import loadModels
from metablock import MetaBlock
from metanet import MetaNet
from multimodalMDNet import MDNet
class MultimodalModel(nn.Module):
def __init__(
self,
num_classes,
num_heads,
device,
cnn_model_name,
text_model_name,
batch_size=32,
common_dim=512,
text_encoder_dim_output=512,
vocab_size=91,
unfreeze_weights="frozen_weights",
attention_mecanism="concatenation",
n=2
):
super().__init__()
self.device = device
self.common_dim = common_dim
self.num_heads = num_heads
self.attention_mecanism = attention_mecanism
self.n = n
self.vocab_size = vocab_size
self.num_classes = num_classes
self.cnn_model_name = cnn_model_name
self.text_model_name = text_model_name
self.unfreeze_weights = unfreeze_weights
# =====================================================
# Text Encoder
# =====================================================
self.text_encoder_dim_output = text_encoder_dim_output
# =====================================================
# Image Encoder
# =====================================================
self.image_encoder, self.cnn_dim_output = loadModels.loadModelImageEncoder(
cnn_model_name=self.cnn_model_name,
backbone_train_mode=self.unfreeze_weights
)
self.image_projector = nn.Linear(self.cnn_dim_output, self.common_dim)
if text_model_name == "one-hot-encoder":
self.text_fc = nn.Sequential(
nn.Linear(self.vocab_size, 256),
nn.ReLU(),
nn.Linear(256, 512),
nn.ReLU(),
nn.Linear(512, self.text_encoder_dim_output)
)
self.text_encoder = None
else:
self.text_encoder, self.text_encoder_dim_output, _ = loadModels.loadTextModelEncoder(
text_model_encoder=self.text_model_name,
train_mode=self.unfreeze_weights
)
self.text_fc = None
self.text_projector = nn.Linear(self.text_encoder_dim_output, self.common_dim)
# =====================================================
# Attention blocks
# =====================================================
self.image_self_attention = nn.MultiheadAttention(
embed_dim=self.common_dim,
num_heads=self.num_heads,
batch_first=False
)
self.text_self_attention = nn.MultiheadAttention(
embed_dim=self.common_dim,
num_heads=self.num_heads,
batch_first=False
)
self.image_cross_attention = nn.MultiheadAttention(
embed_dim=self.common_dim,
num_heads=self.num_heads,
batch_first=False
)
self.text_cross_attention = nn.MultiheadAttention(
embed_dim=self.common_dim,
num_heads=self.num_heads,
batch_first=False
)
# =====================================================
# Gating
# =====================================================
self.img_gate = nn.Linear(self.common_dim, self.common_dim)
self.txt_gate = nn.Linear(self.common_dim, self.common_dim)
# =====================================================
# MetaBlock (vetorial)
# =====================================================
# Bloco do Metablock, caso queira usar
self.meta_block = MetaBlock(
V_dim=self.common_dim if self.attention_mecanism in ["att-intramodal+residual+cross-attention-metadados+metablock"] else self.cnn_dim_output,
U_dim=self.common_dim if self.attention_mecanism in ["att-intramodal+residual+cross-attention-metadados+metablock", "metablock-se"] else self.text_encoder_dim_output
)
# Residual Blocks
# -------------------------
self.image_residual = GatedAlteredResidualBlock(dim=self.common_dim)
self.text_residual = GatedAlteredResidualBlock(dim=self.common_dim)
# =====================================================
# Fusion MLP
# =====================================================
self.fc_fusion = self.fc_mlp_module(
n=1 if attention_mecanism == "no-metadata" else self.n
)
# Usado no Metablock
self.fc_visual_only = nn.Linear(self.cnn_dim_output, self.num_classes)
self.fc_mlp_module_after_metablock_fusion_module = self.fc_mlp_module_after_metablock()
def fc_mlp_module(self, n=1):
fc_fusion = nn.Sequential(
nn.Linear(self.common_dim * n, self.common_dim),
nn.LayerNorm(self.common_dim),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(self.common_dim, self.common_dim // 2),
nn.LayerNorm(self.common_dim // 2),
nn.ReLU(),
nn.Dropout(0.5),
nn.Linear(self.common_dim // 2, self.num_classes)
)
return fc_fusion
def fc_mlp_module_after_metablock(self):
fc_fusion = nn.Sequential(
nn.Linear(self.cnn_dim_output, self.common_dim),
nn.LayerNorm(self.common_dim),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(self.common_dim, self.common_dim // 2),
nn.LayerNorm(self.common_dim // 2),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(self.common_dim // 2, self.num_classes)
)
return fc_fusion
def forward(self, image, text_metadata):
# =====================================================
# Image encoding → (B, D)
# =====================================================
image = image.to(self.device)
img_feat = self.image_encoder(image)
if img_feat.dim() == 4:
img_feat = img_feat.mean(dim=(-2, -1))
proj_img_feat = self.image_projector(img_feat)
# =====================================================
# Text encoding → (B, D)
# =====================================================
if self.text_model_name == "one-hot-encoder":
txt_feat = self.text_fc(text_metadata.to(self.device))
else:
input_ids = text_metadata["input_ids"].squeeze(1).to(self.device)
attention_mask = text_metadata["attention_mask"].squeeze(1).to(self.device)
outputs = self.text_encoder(input_ids=input_ids, attention_mask=attention_mask)
txt_feat = outputs.last_hidden_state[:, 0, :]
proj_txt_feat = self.text_projector(txt_feat)
# =====================================================
# Attention (seq_len = 1)
# =====================================================
img_seq = proj_img_feat.unsqueeze(0)
txt_seq = proj_txt_feat.unsqueeze(0)
img_att, _ = self.image_self_attention(img_seq, img_seq, img_seq)
txt_att, _ = self.text_self_attention(txt_seq, txt_seq, txt_seq)
img_cross, _ = self.image_cross_attention(img_att, txt_att, txt_att)
txt_cross, _ = self.text_cross_attention(txt_att, img_att, img_att)
img_pooled = img_cross.squeeze(0)
txt_pooled = txt_cross.squeeze(0)
# =====================================================
# Fusion strategies
# =====================================================
if self.attention_mecanism == "no-metadata":
return self.fc_fusion(proj_img_feat)
elif self.attention_mecanism == "no-metadata-without-mlp":
return self.fc_visual_only(img_seq)
elif self.attention_mecanism == "concatenation":
fused = torch.cat([proj_img_feat, proj_txt_feat], dim=1)
return self.fc_fusion(fused)
elif self.attention_mecanism == "crossattention":
fused = torch.cat([img_pooled, txt_pooled], dim=1)
return self.fc_fusion(fused)
elif self.attention_mecanism == "weighted":
alpha_img = torch.sigmoid(self.img_gate(proj_img_feat))
alpha_txt = torch.sigmoid(self.txt_gate(proj_txt_feat))
fused = torch.cat([alpha_img * proj_img_feat, alpha_txt * proj_txt_feat], dim=1)
return self.fc_fusion(fused)
elif self.attention_mecanism == "gfcam":
alpha_img = torch.sigmoid(self.img_gate(img_pooled))
alpha_txt = torch.sigmoid(self.txt_gate(txt_pooled))
fused = torch.cat([alpha_img * img_pooled, alpha_txt * txt_pooled], dim=1)
return self.fc_fusion(fused)
elif self.attention_mecanism == "cross-weights-after-crossattention":
alpha_img = torch.sigmoid(self.img_gate(img_pooled))
alpha_txt = torch.sigmoid(self.txt_gate(txt_pooled))
fused = torch.cat([alpha_txt * img_pooled, alpha_img * txt_pooled], dim=1)
return self.fc_fusion(fused)
elif self.attention_mecanism == "metablock":
meta_features = self.meta_block(
img_feat,
txt_feat
)
return self.fc_mlp_module_after_metablock_fusion_module(meta_features)
# =====================================================
# Residual-based multimodal fusion
# =====================================================
# =====================================================
# Residual-based multimodal fusion (corrigidos)
# =====================================================
elif self.attention_mecanism == "only-with-att-intramodal+residual":
# Residual direto (sem self-att explícito)
# No nosso forward atual, a forma padrão é (seq_len=1, B, D) => img_seq/txt_seq
img_res = self.image_residual(img_seq, txt_seq, txt_seq) # (1, B, D)
txt_res = self.text_residual(txt_seq, img_seq, img_seq) # (1, B, D)
img_res = img_res.squeeze(0) # (B, D)
txt_res = txt_res.squeeze(0) # (B, D)
fused = torch.cat([img_res, txt_res], dim=1) # (B, 2D)
return self.fc_fusion(fused)
elif self.attention_mecanism == "att-intramodal+residual":
# Self-att já calculado acima: img_att, txt_att
# Residual usa (base, att, att)
img_res = self.image_residual(img_seq, img_att, img_att) # (1, B, D)
txt_res = self.text_residual(txt_seq, txt_att, txt_att) # (1, B, D)
img_res = img_res.squeeze(0) # (B, D)
txt_res = txt_res.squeeze(0) # (B, D)
fused = torch.cat([img_res, txt_res], dim=1) # (B, 2D)
return self.fc_fusion(fused)
elif self.attention_mecanism == "att-intramodal+residual+cross-attention-metadados":
# Self-att já calculado: img_att, txt_att
# Residual antes do cross
img_res = self.image_residual(img_seq, img_att, img_att) # (1, B, D)
txt_res = self.text_residual(txt_seq, txt_att, txt_att) # (1, B, D)
# Cross-attention entre os residuais
img_cross2, _ = self.image_cross_attention(
query=img_res, key=txt_res, value=txt_res
) # (1, B, D)
txt_cross2, _ = self.text_cross_attention(
query=txt_res, key=img_res, value=img_res
) # (1, B, D)
img_pooled2 = img_cross2.squeeze(0) # (B, D)
txt_pooled2 = txt_cross2.squeeze(0) # (B, D)
fused = torch.cat([img_pooled2, txt_pooled2], dim=1) # (B, 2D)
return self.fc_fusion(fused)
elif self.attention_mecanism == "att-intramodal+residual+cross-attention-metadados+metablock":
# Self-att já calculado: img_att, txt_att
# Residual antes do cross
img_res = self.image_residual(img_seq, img_att, img_att) # (1, B, D)
txt_res = self.text_residual(txt_seq, txt_att, txt_att) # (1, B, D)
# Cross-attention
img_cross2, _ = self.image_cross_attention(
query=img_res, key=txt_res, value=txt_res
) # (1, B, D)
txt_cross2, _ = self.text_cross_attention(
query=txt_res, key=img_res, value=img_res
) # (1, B, D)
img_pooled2 = img_cross2.squeeze(0) # (B, D)
txt_pooled2 = txt_cross2.squeeze(0) # (B, D)
# MetaBlock vetorial (B, D) + (B, D) -> (B, D)
fused_meta = self.meta_block(img_pooled2, txt_pooled2) # (B, D)
# Classificador no espaço comum
return self.fc_visual_only(fused_meta)
elif self.attention_mecanism == "att-intramodal+residual+cross-attention-metadados+att-intramodal+residual":
# Self-att inicial: img_att, txt_att
# Residual antes do cross
img_res1 = self.image_residual(img_seq, img_att, img_att) # (1, B, D)
txt_res1 = self.text_residual(txt_seq, txt_att, txt_att) # (1, B, D)
# Cross-attention
img_cross2, _ = self.image_cross_attention(
query=img_res1, key=txt_res1, value=txt_res1
) # (1, B, D)
txt_cross2, _ = self.text_cross_attention(
query=txt_res1, key=img_res1, value=img_res1
) # (1, B, D)
# Self-att depois do cross
img_att2, _ = self.image_self_attention(img_cross2, img_cross2, img_cross2)
txt_att2, _ = self.text_self_attention(txt_cross2, txt_cross2, txt_cross2)
# Residual final (base = cross2)
img_res2 = self.image_residual(img_cross2, img_att2, img_att2).squeeze(0) # (B, D)
txt_res2 = self.text_residual(txt_cross2, txt_att2, txt_att2).squeeze(0) # (B, D)
fused = torch.cat([img_res2, txt_res2], dim=1) # (B, 2D)
return self.fc_fusion(fused)
else:
raise ValueError(
f"Attention mechanism '{self.attention_mecanism}' not implemented."
)
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