Illustrates a Siamese network structure with a contrastive or triplet loss to learn aligned embeddings.
import torch
import torch.nn as nn
import torch.nn.functional as F
# Assume pre-defined ImageEncoder and TextEncoder similar to those in VLM or AVL examples
class ImageEncoder(nn.Module): # Simplified placeholder
def __init__(self, embedding_dim):
super().__init__()
self.fc = nn.Linear(2048, embedding_dim) # Assuming input from a ResNet-like backbone
def forward(self, x): return self.fc(x)
class TextEncoder(nn.Module): # Simplified placeholder
def __init__(self, embedding_dim):
super().__init__()
self.fc = nn.Linear(768, embedding_dim) # Assuming input from a BERT-like backbone
def forward(self, x): return self.fc(x)
class CrossModalRetrievalNet(nn.Module):
def __init__(self, image_feature_dim, text_feature_dim, shared_embedding_dim):
super().__init__()
self.image_encoder = ImageEncoder(image_feature_dim) # This should be a proper vision model
self.text_encoder = TextEncoder(text_feature_dim) # This should be a proper language model
# Projection heads to map to the shared embedding space
self.image_projection = nn.Linear(image_feature_dim, shared_embedding_dim)
self.text_projection = nn.Linear(text_feature_dim, shared_embedding_dim)
def encode_image(self, image_features_raw):
# In a real scenario, image_features_raw would be processed by a full vision backbone
projected = self.image_projection(self.image_encoder(image_features_raw))
return F.normalize(projected, p=2, dim=-1)
def encode_text(self, text_features_raw):
# In a real scenario, text_features_raw would be processed by a full text model
projected = self.text_projection(self.text_encoder(text_features_raw))
return F.normalize(projected, p=2, dim=-1)
def forward(self, image_features_raw, text_features_raw):
image_embeddings = self.encode_image(image_features_raw)
text_embeddings = self.encode_text(text_features_raw)
return image_embeddings, text_embeddings
# Conceptual Triplet Loss Training Snippet
# model = CrossModalRetrievalNet(image_feature_dim=2048, text_feature_dim=768, shared_embedding_dim=256)
# triplet_loss = nn.TripletMarginLoss(margin=0.2, p=2)
# optimizer = torch.optim.Adam(model.parameters())
# # Assume:
# # anchor_images_features: Features of anchor images
# # positive_texts_features: Features of texts corresponding to anchor images
# # negative_texts_features: Features of texts not corresponding to anchor images
# anchor_img_embed = model.encode_image(anchor_images_features)
# positive_text_embed = model.encode_text(positive_texts_features)
# negative_text_embed = model.encode_text(negative_texts_features)
# loss = triplet_loss(anchor_img_embed, positive_text_embed, negative_text_embed)
# optimizer.zero_grad()
# loss.backward()
# optimizer.step()
# # Symmetrically, you could have text anchors and image positives/negatives.
# # For contrastive loss (like CLIP):
# # image_embeddings, text_embeddings = model(image_batch_features, text_batch_features)
# # logits = image_embeddings @ text_embeddings.t() * logit_scale.exp()
# # labels = torch.arange(image_embeddings.size(0))
# # loss = (F.cross_entropy(logits, labels) + F.cross_entropy(logits.t(), labels)) / 2