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# model.py
import torch
import torch.nn as nn
import torch.nn.functional as F
import warnings
from transformers import (
HubertModel,
AutoProcessor,
AutoTokenizer,
AutoModel
)
warnings.filterwarnings("ignore")
import torchvision.transforms as transforms
from PIL import Image
from peft import (
LoraConfig,
get_peft_model,
TaskType,
)
#################################################################
# Audio Embedder
#################################################################
class AudioEmbedder(nn.Module):
"""
Uses a pre-trained HuBERT (or similar) to extract audio features from raw audio (16kHz).
Projects them down to a desired embedding dimension.
"""
def __init__(self, embedding_dim=512, hubert_name="facebook/hubert-base-ls960"):
super().__init__()
self.processor = AutoProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
self.hubert = HubertModel.from_pretrained(hubert_name)
self.projection = nn.Linear(self.hubert.config.hidden_size, embedding_dim)
for param in self.hubert.parameters():
param.requires_grad = True
for param in self.projection.parameters():
param.requires_grad = True
def forward(self, audio_input: torch.Tensor) -> torch.Tensor:
"""
Args:
audio_input: (B, T) raw audio waveform at 16kHz
Returns:
audio_feats: (B, Na, D)
B = batch size
Na = number of audio tokens (T/320 for Hubert)
D = embedding_dim
"""
if len(audio_input.shape) == 3: # shape: [B, 1, T]
audio_input = audio_input.squeeze(0) # squeeze first dim to get [B, T]
inputs = self.processor(
audio_input,
return_tensors="pt",
sampling_rate=16000,
padding=True,
return_attention_mask=True
).input_values.squeeze(0)
device = next(self.parameters()).device
inputs = inputs.to(device)
hubert_output = self.hubert(inputs).last_hidden_state # (B, T', hidden_size)
audio_feats = self.projection(hubert_output) # (B, T', D)
return audio_feats
#################################################################
# Text Embedder
#################################################################
class TextEmbedder(nn.Module):
"""
Uses a pre-trained BERT-like model (ModernBERT or similar) to extract text features.
Projects them down to a desired embedding dimension.
"""
def __init__(self, embedding_dim=512, model_name="answerdotai/ModernBERT-base"):
super().__init__()
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.encoder = AutoModel.from_pretrained(model_name)
self.projection = nn.Linear(self.encoder.config.hidden_size, embedding_dim)
print("Using text model: ", model_name)
for param in self.encoder.parameters():
param.requires_grad = True
for param in self.projection.parameters():
param.requires_grad = True
def forward(self, text_list):
"""
Args:
text_list: List[str], batch of text inputs
Returns:
text_feats: (B, Nt, D)
attention_mask: (B, Nt)
"""
inputs = self.tokenizer(
text_list,
padding=True,
truncation=True,
add_special_tokens=False,
max_length=128,
return_tensors="pt"
)
device = next(self.parameters()).device
for k in inputs:
inputs[k] = inputs[k].to(device)
outputs = self.encoder(**inputs) # (B, Nt, hidden_size)
hidden_states = outputs.last_hidden_state
text_feats = self.projection(hidden_states) # (B, Nt, D)
return text_feats, inputs["attention_mask"]
#################################################################
# Visual Embedder
#################################################################
class ViTLoRAEmbedder(nn.Module):
def __init__(self, model_name='facebookresearch/dinov2', arch='dinov2_vitb14',
embedding_dim=512, dropout_prob=0.1, lora_rank=16, lora_alpha=32):
super().__init__()
self.model = torch.hub.load(model_name, arch)
#print(f"Using DINOv2 model with LoRA adapters: {arch}")
for param in self.model.parameters():
param.requires_grad = False
target_modules = ["attn.qkv", "attn.proj"]
lora_config = LoraConfig(
task_type=TaskType.FEATURE_EXTRACTION,
inference_mode=True,
r=lora_rank,
lora_alpha=lora_alpha,
target_modules=target_modules,
lora_dropout=dropout_prob,
)
self.model = get_peft_model(self.model, lora_config)
#trainable_params = sum(p.numel() for p in self.model.parameters() if p.requires_grad)
#total_params = sum(p.numel() for p in self.model.parameters())
#print(f"ViTLoRAEmbedder - Trainable parameters: {trainable_params:,} ({100 * trainable_params / total_params:.2f}% of total)")
self.projection = nn.Linear(self.model.embed_dim, embedding_dim)
self.dropout = nn.Dropout(p=dropout_prob)
def forward(self, x):
"""
Args:
x: (B, 3, H, W), e.g. (B,3,224,224) image batch
Returns:
visual_feats: (B, Nv, D)
Nv = number of visual tokens
D = embedding_dim
"""
if len(x.shape) == 5: # shape: [1, 1, 3, 224, 224]
x = x.squeeze(0) # get [1, 3, 224, 224]
if len(x.shape) == 3:
x = x.unsqueeze(0)
# Use intermediate layers for feature extraction - same as original
patches = self.model.get_intermediate_layers(x, n=1)[0]
feats = self.projection(patches)
feats = self.dropout(feats)
return feats
class Triad(nn.Module):
def __init__(
self,
audio_model_name="facebook/hubert-base-ls960",
text_model_name="distilbert/distilbert-base-uncased",
temperature=2.0,
patch_sparsity_threshold=0.3,
patch_sparsity_weight=0.1,
visual_dropout_prob=0.1,
lora_rank=8,
lora_alpha=16
):
super().__init__()
self.audio_embedder = AudioEmbedder(embedding_dim=512, hubert_name=audio_model_name)
self.text_embedder = TextEmbedder(embedding_dim=512, model_name=text_model_name)
self.visual_embedder = ViTLoRAEmbedder(
arch='dinov2_vitb14',
embedding_dim=512,
dropout_prob=visual_dropout_prob,
lora_rank=lora_rank,
lora_alpha=lora_alpha
)
self.temperature = nn.Parameter(torch.tensor(temperature))
self.patch_sparsity_threshold = patch_sparsity_threshold
self.patch_sparsity_weight = patch_sparsity_weight
def compute_similarity_matrix(self, feats1, feats2):
"""
Generic token-level dot-product similarity between feats1 and feats2.
feats1: (B, N1, D)
feats2: (B, N2, D)
Returns sim: (B, N1, N2)
"""
sim = torch.bmm(feats1, feats2.transpose(1, 2))
return sim / self.temperature
def forward(self, image=None, audio=None, text_list=None):
assert image is not None or audio is not None or text_list is not None, "At least one modality must be provided"
if image is not None: assert image is not str, "Frames should be a path to an image"
if audio is not None:
assert isinstance(audio, torch.Tensor) and len(audio.shape) == 2, "Audio must be a PyTorch tensor of shape (B, T)"
if text_list is not None:
assert isinstance(text_list, list) and len(text_list) == 1, "Text list must be a list of strings of length 1"
if image is not None:
device = next(self.parameters()).device
# Handle batch of file paths
if isinstance(image, list):
# Process a list of image paths
processed_images = []
for img_path in image:
img = Image.open(img_path).convert('RGB')
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
processed_img = transform(img).to(device)
processed_images.append(processed_img)
image = torch.stack(processed_images, dim=0) # [B, 3, 224, 224]
# Handle single file path
elif isinstance(image, str):
img = Image.open(image).convert('RGB')
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
image = transform(img).to(device).unsqueeze(0) # Add batch dimension [1, 3, 224, 224]
# Handle tensor input (assume it's already processed but may need device transfer)
elif isinstance(image, torch.Tensor):
# If single image without batch dimension
if image.dim() == 3:
image = image.unsqueeze(0) # Add batch dimension
image = image.to(device)
embeddings = {}
if image is not None:
embeddings['visual_feats'] = self.visual_embedder(image)
if audio is not None:
embeddings['audio_feats'] = self.audio_embedder(audio)
if text_list is not None:
embeddings['text_feats'], _ = self.text_embedder(text_list)
# if two or more modalities are present, we compute the similarity matrix
if image is not None and text_list is not None:
embeddings['vis_text_sim_matrix'] = self.compute_similarity_matrix(embeddings['text_feats'], embeddings['visual_feats'])
if audio is not None and image is not None:
embeddings['vis_audio_sim_matrix'] = self.compute_similarity_matrix(embeddings['audio_feats'], embeddings['visual_feats'])
if text_list is not None and audio is not None:
embeddings['text_audio_sim_matrix'] = self.compute_similarity_matrix(embeddings['text_feats'], embeddings['audio_feats'])
return embeddings
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