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from timm import create_model
import torch
import torch.nn as nn
from transformers import RobertaModel
EMBEDDING_DIM = 512
class ImageEncoder(nn.Module):
def __init__(self):
super(ImageEncoder, self).__init__()
# Load the Swin Transformer with features_only=True
self.swin = create_model("swin_base_patch4_window7_224.ms_in22k", pretrained=True, features_only=True)
for param in self.swin.parameters():
param.requires_grad = True
# Get the feature size of the final stage
self.swin_output_dim = self.swin.feature_info.channels()[-1] # Last stage: 1024 channels
# Define FC layer
self.fc1 = nn.Linear(self.swin_output_dim * 7 * 7, EMBEDDING_DIM) # Flattened input size
nn.init.xavier_uniform_(self.fc1.weight)
nn.init.zeros_(self.fc1.bias)
for param in self.fc1.parameters():
param.requires_grad = True
def forward(self, x):
# Extract features from Swin
swin_features = self.swin(x)[-1] # Use the last stage feature map (e.g., [B, 1024, 7, 7])
# Flatten feature map
swin_features = swin_features.view(swin_features.size(0), -1) # Shape: (B, 1024*7*7)
# Pass through FC layer
output = self.fc1(swin_features) # Shape: (B, embedding_dim)
return output
class RobertaEncoder(nn.Module):
def __init__(self, roberta_model_path="roberta-base"):
super(RobertaEncoder, self).__init__()
# Load pre-trained RoBERTa model
self.roberta = RobertaModel.from_pretrained(roberta_model_path)
# Add a linear projection layer to reduce dimensionality
self.projection = nn.Linear(self.roberta.config.hidden_size, EMBEDDING_DIM)
# Initialize the projection layer weights
nn.init.xavier_uniform_(self.projection.weight)
nn.init.zeros_(self.projection.bias)
# Allow fine-tuning of the RoBERTa model
for param in self.roberta.parameters():
param.requires_grad = True
def forward(self, input_ids, attention_mask):
"""
Forward pass through RoBERTa.
Args:
input_ids: Tensor of shape (batch_size, seq_length)
attention_mask: Tensor of shape (batch_size, seq_length)
Returns:
Embedding: Tensor of shape (batch_size, EMBEDDING_DIM)
"""
roberta_output = self.roberta(input_ids=input_ids, attention_mask=attention_mask)
cls_token = roberta_output.last_hidden_state[:, 0, :] # Use CLS token
pooled_output = torch.mean(roberta_output.last_hidden_state, dim=1) # Mean pooling
return self.projection(cls_token+pooled_output)
class LVL(nn.Module):
def __init__(self):
super(LVL, self).__init__()
self.image_encoder = ImageEncoder()
self.text_encoder = RobertaEncoder()
self.t_prime = nn.Parameter(torch.ones([]) * np.log(0.07))
self.b = nn.Parameter(torch.ones([]) * 0)
def get_images_features(self,images):
image_embeddings = self.image_encoder(images) # (batch_size, EMBEDDING_DIM)
image_embeddings = nn.functional.normalize(image_embeddings, p=2, dim=-1)
return image_embeddings
def get_texts_feature(self,input_ids,attention_mask):
text_embeddings = self.text_encoder(input_ids, attention_mask) # (batch_size, EMBEDDING_DIM)
text_embeddings = nn.functional.normalize(text_embeddings, p=2, dim=-1)
return text_embeddings
def forward(self, images, input_ids, attention_mask):
"""
Args:
images: Tensor of shape (batch_size, 3, 224, 224)
input_ids: Tensor of shape (batch_size, seq_length)
attention_mask: Tensor of shape (batch_size, seq_length)
Returns:
Image and text embeddings normalized for similarity calculation
"""
image_embeddings = self.get_images_features(images)
text_embeddings = self.get_texts_feature(input_ids, attention_mask)
return image_embeddings, text_embeddings
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