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Fine-tuning Model for GraPHFormer
Supports three modes: image_only, tree_only, multimodal
Loads pretrained weights from CLIP-style training checkpoints
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .fusion import (
CrossAttentionFusion, BiDirectionalCrossAttention,
GatedFusion, CMF, MultiHeadCrossModalAttention
)
class ArcMarginProduct(nn.Module):
"""ArcFace: Additive Angular Margin Loss"""
def __init__(self, in_features, out_features, s=30.0, m=0.50, easy_margin=False):
super(ArcMarginProduct, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.s = s
self.m = m
self.easy_margin = easy_margin
self.weight = nn.Parameter(torch.FloatTensor(out_features, in_features))
nn.init.xavier_uniform_(self.weight)
self.cos_m = np.cos(m)
self.sin_m = np.sin(m)
self.th = np.cos(np.pi - m)
self.mm = np.sin(np.pi - m) * m
def forward(self, input, label):
cosine = F.linear(F.normalize(input), F.normalize(self.weight))
sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
phi = cosine * self.cos_m - sine * self.sin_m
if self.easy_margin:
phi = torch.where(cosine > 0, phi, cosine)
else:
phi = torch.where(cosine > self.th, phi, cosine - self.mm)
one_hot = torch.zeros(cosine.size(), device=input.device)
one_hot.scatter_(1, label.view(-1, 1).long(), 1)
output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
output *= self.s
return output
class FineTuneModel(nn.Module):
"""Fine-tuning model with classification head"""
def __init__(
self,
pretrained_model,
num_classes,
mode='multimodal',
freeze_encoders=False,
fusion_mode='concat',
dropout=0.5,
label_smoothing=0.0,
use_projection=False,
use_arcface=False,
arcface_s=30.0,
arcface_m=0.50,
freeze_image_only=False
):
"""
Args:
pretrained_model: Pretrained CLIPModel
num_classes: Number of classes for classification
mode: 'image_only', 'tree_only', or 'multimodal'
freeze_encoders: If True, freeze encoder weights
fusion_mode: For multimodal - 'concat', 'add', 'cross_attention', 'bi_attention', 'gated', 'cmf', 'mhcma'
dropout: Dropout rate for first layer
label_smoothing: Label smoothing factor
use_projection: If True, use projection heads from pretrained model
use_arcface: If True, use ArcFace loss instead of CrossEntropy
arcface_s: ArcFace scale parameter
arcface_m: ArcFace margin parameter
freeze_image_only: If True, freeze only image encoder
"""
super(FineTuneModel, self).__init__()
self.mode = mode
self.fusion_mode = fusion_mode
self.use_projection = use_projection
self.use_arcface = use_arcface
self.tree_encoder_type = pretrained_model.tree_encoder_type
# Copy encoders from pretrained model
if mode in ['tree_only', 'multimodal']:
self.tree_encoder = pretrained_model.tree_encoder
if use_projection:
self.tree_projection = pretrained_model.tree_projection
if mode in ['image_only', 'multimodal']:
self.image_encoder = pretrained_model.image_encoder
if use_projection:
self.image_projection = pretrained_model.image_projection
# Freeze encoders if requested
if freeze_encoders:
if mode in ['tree_only', 'multimodal']:
for param in self.tree_encoder.parameters():
param.requires_grad = False
if use_projection:
for param in self.tree_projection.parameters():
param.requires_grad = False
if mode in ['image_only', 'multimodal']:
for param in self.image_encoder.parameters():
param.requires_grad = False
if use_projection:
for param in self.image_projection.parameters():
param.requires_grad = False
# Freeze only image encoder
if freeze_image_only and mode == 'multimodal':
for param in self.image_encoder.parameters():
param.requires_grad = False
if use_projection:
for param in self.image_projection.parameters():
param.requires_grad = False
# Get embedding dimensions
if use_projection:
tree_embed_dim = pretrained_model.tree_projection[-1].out_features
image_embed_dim = pretrained_model.image_projection[-1].out_features
else:
if mode in ['tree_only', 'multimodal']:
tree_embed_dim = pretrained_model.tree_encoder.h_size
else:
tree_embed_dim = 0
if mode in ['image_only', 'multimodal']:
if hasattr(pretrained_model.image_encoder, 'encoder'):
if hasattr(pretrained_model.image_encoder.encoder, 'feat_dim'):
image_embed_dim = pretrained_model.image_encoder.encoder.feat_dim
else:
image_embed_dim = pretrained_model.image_encoder.encoder[-1].in_features
elif hasattr(pretrained_model.image_encoder, 'feat_dim'):
image_embed_dim = pretrained_model.image_encoder.feat_dim
else:
image_embed_dim = pretrained_model.tree_encoder.h_size
else:
image_embed_dim = 0
# Setup fusion for multimodal
if mode == 'multimodal':
if fusion_mode == 'concat':
fusion_dim = tree_embed_dim + image_embed_dim
elif fusion_mode == 'add':
fusion_dim = min(tree_embed_dim, image_embed_dim)
if tree_embed_dim != image_embed_dim:
self.tree_dim_match = nn.Linear(tree_embed_dim, fusion_dim) if tree_embed_dim != fusion_dim else nn.Identity()
self.image_dim_match = nn.Linear(image_embed_dim, fusion_dim) if image_embed_dim != fusion_dim else nn.Identity()
elif fusion_mode == 'cross_attention':
fusion_dim = min(tree_embed_dim, image_embed_dim)
if tree_embed_dim != image_embed_dim:
self.tree_dim_match = nn.Linear(tree_embed_dim, fusion_dim) if tree_embed_dim != fusion_dim else nn.Identity()
self.image_dim_match = nn.Linear(image_embed_dim, fusion_dim) if image_embed_dim != fusion_dim else nn.Identity()
self.fusion_layer = CrossAttentionFusion(fusion_dim, num_heads=4)
elif fusion_mode == 'bi_attention':
fusion_dim = min(tree_embed_dim, image_embed_dim)
if tree_embed_dim != image_embed_dim:
self.tree_dim_match = nn.Linear(tree_embed_dim, fusion_dim) if tree_embed_dim != fusion_dim else nn.Identity()
self.image_dim_match = nn.Linear(image_embed_dim, fusion_dim) if image_embed_dim != fusion_dim else nn.Identity()
self.fusion_layer = BiDirectionalCrossAttention(fusion_dim, num_heads=4)
elif fusion_mode == 'gated':
fusion_dim = min(tree_embed_dim, image_embed_dim)
if tree_embed_dim != image_embed_dim:
self.tree_dim_match = nn.Linear(tree_embed_dim, fusion_dim) if tree_embed_dim != fusion_dim else nn.Identity()
self.image_dim_match = nn.Linear(image_embed_dim, fusion_dim) if image_embed_dim != fusion_dim else nn.Identity()
self.fusion_layer = GatedFusion(fusion_dim)
elif fusion_mode == 'cmf':
fusion_dim = min(tree_embed_dim, image_embed_dim)
if tree_embed_dim != image_embed_dim:
self.tree_dim_match = nn.Linear(tree_embed_dim, fusion_dim) if tree_embed_dim != fusion_dim else nn.Identity()
self.image_dim_match = nn.Linear(image_embed_dim, fusion_dim) if image_embed_dim != fusion_dim else nn.Identity()
self.fusion_layer = CMF(fusion_dim)
elif fusion_mode == 'mhcma':
fusion_dim = min(tree_embed_dim, image_embed_dim)
if tree_embed_dim != image_embed_dim:
self.tree_dim_match = nn.Linear(tree_embed_dim, fusion_dim) if tree_embed_dim != fusion_dim else nn.Identity()
self.image_dim_match = nn.Linear(image_embed_dim, fusion_dim) if image_embed_dim != fusion_dim else nn.Identity()
self.fusion_layer = MultiHeadCrossModalAttention(fusion_dim, num_heads=8)
else:
raise ValueError(f"Unknown fusion_mode: {fusion_mode}")
input_dim = fusion_dim
else:
if mode == 'tree_only':
input_dim = tree_embed_dim
else:
input_dim = image_embed_dim
# Classification head
if use_arcface:
self.feature_extractor = nn.Sequential(
nn.Dropout(dropout),
nn.Linear(input_dim, input_dim // 2),
nn.BatchNorm1d(input_dim // 2),
nn.ReLU(),
nn.Dropout(dropout * 0.7),
)
self.arcface = ArcMarginProduct(input_dim // 2, num_classes, s=arcface_s, m=arcface_m)
self.classifier = None
else:
self.classifier = nn.Sequential(
nn.Dropout(dropout),
nn.Linear(input_dim, input_dim // 2),
nn.BatchNorm1d(input_dim // 2),
nn.ReLU(),
nn.Dropout(dropout * 0.7),
nn.Linear(input_dim // 2, num_classes)
)
self.feature_extractor = None
self.arcface = None
self.criterion = nn.CrossEntropyLoss(label_smoothing=label_smoothing)
def encode_tree(self, batch):
"""Encode tree data"""
tree_feats = self.tree_encoder(batch)
if self.use_projection:
tree_embed = self.tree_projection(tree_feats)
return tree_embed
else:
return tree_feats
def encode_image(self, images):
"""Encode image data"""
image_feats = self.image_encoder(images)
if self.use_projection:
image_embed = self.image_projection(image_feats)
return image_embed
else:
return image_feats
def forward(self, batch, return_features=False):
"""
Forward pass
Args:
batch: contains batch.graph, batch.feats, batch.images, batch.label
return_features: if True, return embeddings along with logits
Returns:
loss: classification loss
logits: (B, num_classes)
features: (optional) embeddings
"""
images = batch.images.cuda() if not batch.images.is_cuda else batch.images
labels = batch.label.cuda() if not batch.label.is_cuda else batch.label
if self.mode == 'tree_only':
tree_embed = self.encode_tree(batch)
tree_embed = F.normalize(tree_embed, dim=-1)
features = tree_embed
elif self.mode == 'image_only':
image_embed = self.encode_image(images)
image_embed = F.normalize(image_embed, dim=-1)
features = image_embed
else: # multimodal
tree_embed = self.encode_tree(batch)
image_embed = self.encode_image(images)
tree_embed = F.normalize(tree_embed, dim=-1)
image_embed = F.normalize(image_embed, dim=-1)
if self.fusion_mode == 'concat':
features = torch.cat([tree_embed, image_embed], dim=1)
elif self.fusion_mode == 'add':
if hasattr(self, 'tree_dim_match'):
tree_embed = self.tree_dim_match(tree_embed)
image_embed = self.image_dim_match(image_embed)
features = tree_embed + image_embed
elif self.fusion_mode in ['gated', 'cmf', 'cross_attention', 'bi_attention', 'mhcma']:
if hasattr(self, 'tree_dim_match'):
tree_embed = self.tree_dim_match(tree_embed)
image_embed = self.image_dim_match(image_embed)
features = self.fusion_layer(tree_embed, image_embed)
else:
raise ValueError(f"Unknown fusion_mode: {self.fusion_mode}")
# Classification
if self.use_arcface:
extracted_features = self.feature_extractor(features)
logits = self.arcface(extracted_features, labels)
loss = self.criterion(logits, labels)
else:
logits = self.classifier(features)
loss = self.criterion(logits, labels)
if return_features:
return loss, logits, features
return loss, logits
def unfreeze_encoders(self):
"""Unfreeze encoder weights for full fine-tuning"""
if self.mode in ['tree_only', 'multimodal'] and hasattr(self, 'tree_encoder'):
for param in self.tree_encoder.parameters():
param.requires_grad = True
if self.use_projection and hasattr(self, 'tree_projection'):
for param in self.tree_projection.parameters():
param.requires_grad = True
if self.mode in ['image_only', 'multimodal'] and hasattr(self, 'image_encoder'):
for param in self.image_encoder.parameters():
param.requires_grad = True
if self.use_projection and hasattr(self, 'image_projection'):
for param in self.image_projection.parameters():
param.requires_grad = True
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