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import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from tqdm import tqdm
from torch.utils.data import DataLoader
from sklearn.metrics import f1_score, precision_score, recall_score, balanced_accuracy_score
import wandb
import argparse
from transformers import AutoModel, AutoConfig, Wav2Vec2FeatureExtractor
from ICASSP_2026.MERT.datalib import FakeMusicCapsDataset, train_files, train_labels, val_files, val_labels
from ICASSP_2026.MERT.networks import MERTFeatureExtractor
# Set device
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Seed for reproducibility
torch.manual_seed(42)
random.seed(42)
np.random.seed(42)
# Initialize wandb
wandb.init(project="mert", name=f"hpfilter_pretrain_{args.pretrain_epochs}_finetune_{args.finetune_epochs}", config=args)
# Load datasets
print("🔍 Preparing datasets...")
train_dataset = FakeMusicCapsDataset(train_files, train_labels)
val_dataset = FakeMusicCapsDataset(val_files, val_labels)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=4, collate_fn=FakeMusicCapsDataset.collate_fn)
val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=4, collate_fn=FakeMusicCapsDataset.collate_fn)
# Model Checkpoint Paths
pretrain_ckpt = os.path.join(args.checkpoint_dir, f"mert_pretrain_{args.pretrain_epochs}.pth")
finetune_ckpt = os.path.join(args.checkpoint_dir, f"mert_finetune_{args.finetune_epochs}.pth")
# Load Music2Vec Model for Pretraining
print("🔍 Initializing MERT model for Pretraining...")
config = AutoConfig.from_pretrained("m-a-p/MERT-v1-95M", trust_remote_code=True)
if not hasattr(config, "conv_pos_batch_norm"):
setattr(config, "conv_pos_batch_norm", False)
mert_model = AutoModel.from_pretrained("m-a-p/MERT-v1-95M", config=config, trust_remote_code=True).to(device)
mert_model = MERTFeatureExtractor().to(device)
# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(mert_model.parameters(), lr=args.learning_rate, weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)
# Training function
def train(model, dataloader, optimizer, criterion, device, epoch, phase="Pretrain"):
model.train()
total_loss, total_correct, total_samples = 0, 0, 0
all_preds, all_labels = [], []
for inputs, labels in tqdm(dataloader, desc=f"{phase} Training Epoch {epoch+1}"):
labels = labels.to(device)
inputs = inputs.to(device)
# inputs = inputs.float()
# output = model(inputs)
output = model(inputs)
# Check if the output is a tensor or an object with logits
if isinstance(output, torch.Tensor):
logits = output
elif hasattr(output, "logits"):
logits = output.logits
elif isinstance(output, (tuple, list)):
logits = output[0]
else:
raise ValueError("Unexpected model output type")
loss = criterion(logits, labels)
# loss = criterion(output, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
preds = output.argmax(dim=1)
total_correct += (preds == labels).sum().item()
total_samples += labels.size(0)
all_preds.extend(preds.cpu().numpy())
all_labels.extend(labels.cpu().numpy())
scheduler.step()
accuracy = total_correct / total_samples
f1 = f1_score(all_labels, all_preds, average="binary")
precision = precision_score(all_labels, all_preds, average="binary")
recall = recall_score(all_labels, all_preds, average="binary", pos_label=1)
balanced_acc = balanced_accuracy_score(all_labels, all_preds)
wandb.log({
f"{phase} Train Loss": total_loss / len(dataloader),
f"{phase} Train Accuracy": accuracy,
f"{phase} Train F1 Score": f1,
f"{phase} Train Precision": precision,
f"{phase} Train Recall": recall,
f"{phase} Train Balanced Accuracy": balanced_acc,
})
print(f"{phase} Train Epoch {epoch+1}: Train Loss: {total_loss / len(dataloader):.4f}, "
f"Train Acc: {accuracy:.4f}, Train F1: {f1:.4f}, Train Prec: {precision:.4f}, Train Rec: {recall:.4f}, B_ACC: {balanced_acc:.4f}")
def validate(model, dataloader, optimizer, criterion, device, epoch, phase="Validation"):
model.eval()
total_loss, total_correct, total_samples = 0, 0, 0
all_preds, all_labels = [], []
for inputs, labels in tqdm(dataloader, desc=f"{phase} Validation Epoch {epoch+1}"):
labels = labels.to(device)
inputs = inputs.to(device)
output = model(inputs)
# Check if the output is a tensor or an object with logits
if isinstance(output, torch.Tensor):
logits = output
elif hasattr(output, "logits"):
logits = output.logits
elif isinstance(output, (tuple, list)):
logits = output[0]
else:
raise ValueError("Unexpected model output type")
loss = criterion(logits, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
preds = outputs.argmax(dim=1)
total_correct += (preds == labels).sum().item()
total_samples += labels.size(0)
all_preds.extend(preds.cpu().numpy())
all_labels.extend(labels.cpu().numpy())
scheduler.step()
accuracy = total_correct / total_samples
val_f1 = f1_score(all_labels, all_preds, average="weighted")
val_precision = precision_score(all_labels, all_preds, average="binary")
val_recall = recall_score(all_labels, all_preds, average="binary")
val_bal_acc = balanced_accuracy_score(all_labels, all_preds)
wandb.log({
f"{phase} Val Loss": total_loss / len(dataloader),
f"{phase} Val Accuracy": accuracy,
f"{phase} Val F1 Score": val_f1,
f"{phase} Val Precision": val_precision,
f"{phase} Val Recall": val_recall,
f"{phase} Val Balanced Accuracy": val_bal_acc,
})
print(f"{phase} Val Loss: {total_loss / len(dataloader):.4f}, "
f"Val Acc: {accuracy:.4f}, Val F1: {val_f1:.4f}, Val Prec: {val_precision:.4f}, Val Rec: {val_recall:.4f}, Val B_ACC: {val_bal_acc:.4f}")
return total_loss / len(dataloader), accuracy, val_f1
print("\n🔍 Step 1: Self-Supervised Pretraining on REAL Data")
# for epoch in range(args.pretrain_epochs):
# train(mert_model, train_loader, optimizer, criterion, device, epoch, phase="Pretrain")
# torch.save(mert_model.state_dict(), pretrain_ckpt)
# print(f"\nPretraining completed! Model saved at: {pretrain_ckpt}")
# print("\n🔍 Initializing CCV Model for Fine-Tuning...")
# mert_model = AutoModel.from_pretrained("m-a-p/MERT-v1-95M", config=config, trust_remote_code=True).to(device)
# mert_model.feature_extractor.load_state_dict(torch.load(pretrain_ckpt), strict=False)
# optimizer = optim.Adam(mert_model.parameters(), lr=args.finetune_lr, weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)
print("\n🔍 Step 2: Fine-Tuning CCV Model")
for epoch in range(args.finetune_epochs):
train(mert_model, train_loader, optimizer, criterion, device, epoch, phase="Fine-Tune")
torch.save(mert_model.state_dict(), finetune_ckpt)
print(f"\nFine-Tuning completed! Model saved at: {finetune_ckpt}")
print("\n🔍 Step 2: Fine-Tuning MERT Model")
mert_model.load_state_dict(torch.load(pretrain_ckpt), strict=False)
optimizer = optim.Adam(mert_model.parameters(), lr=args.finetune_lr, weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)
for epoch in range(args.finetune_epochs):
train(mert_model, train_loader, optimizer, criterion, device, epoch, phase="Fine-Tune")
torch.save(mert_model.state_dict(), finetune_ckpt)
print(f"\nFine-Tuning completed! Model saved at: {finetune_ckpt}") |