worker_module.py

import torch
import torch.nn as nn
import torch.optim as optim
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader, Dataset, DistributedSampler
import os
import matplotlib.pyplot as plt
from Deep_ANC_model_trim import CRN
import logging
from Pre_processing import Preprocessing
import random
from torch.optim.lr_scheduler import CosineAnnealingLR
from ranger import Ranger
from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts
from torch.optim.lr_scheduler import OneCycleLR
# from deap import base, creator, tools, algorithms  # For GA
# import pickle
# import json
# import optuna 
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# class NoisySpeechDataset(Dataset):
#     def __init__(self, noisy_dir, clean_dir):
#         self.noisy_files = sorted([os.path.join(noisy_dir, f) for f in os.listdir(noisy_dir) if f.endswith('.pt')])
#         self.clean_files = sorted([os.path.join(clean_dir, f) for f in os.listdir(clean_dir) if f.endswith('.pt')])

#     def __len__(self):
#         return len(self.noisy_files)

#     def __getitem__(self, idx):
#         noisy_spectrogram = torch.load(self.noisy_files[idx], weights_only=True)
#         clean_spectrogram = torch.load(self.clean_files[idx], weights_only=True)
#         assert noisy_spectrogram.shape == clean_spectrogram.shape, "Mismatched tensor shapes"
#         return noisy_spectrogram, clean_spectrogram
# class NoisySpeechDataset(Dataset):
#     def __init__(self, noisy_dir, clean_dir, subset_size=50000):
#         self.noisy_files = sorted([os.path.join(noisy_dir, f) for f in os.listdir(noisy_dir) if f.endswith('.pt')])
#         self.clean_files = sorted([os.path.join(clean_dir, f) for f in os.listdir(clean_dir) if f.endswith('.pt')])
#         assert len(self.noisy_files) == len(self.clean_files), "Mismatched noisy and clean datasets"
#         subset_size = min(subset_size, len(self.noisy_files))
#         self.noisy_files = self.noisy_files[:subset_size]
#         self.clean_files = self.clean_files[:subset_size]

#     def __len__(self):
#         return len(self.noisy_files)

#     def __getitem__(self, idx):
#         noisy_spectrogram = torch.load(self.noisy_files[idx], weights_only=True)
#         clean_spectrogram = torch.load(self.clean_files[idx], weights_only=True)
#         return noisy_spectrogram, clean_spectrogram
def custom_loss_function(output, target):
    if output.size() != target.size():
        min_size = min(output.size(2), target.size(2))
        output = output[:, :, :min_size, :]
        target = target[:, :, :min_size, :]
    return torch.mean((output - target) ** 2)
class NoisySpeechDataset(Dataset):
    def __init__(self, noisy_dir, clean_dir, subset_size=50000, shuffle=True):
        self.noisy_files = sorted([os.path.join(noisy_dir, f) for f in os.listdir(noisy_dir) if f.endswith('.pt')])
        self.clean_files = sorted([os.path.join(clean_dir, f) for f in os.listdir(clean_dir) if f.endswith('.pt')])
        assert len(self.noisy_files) == len(self.clean_files), "Mismatched noisy and clean datasets"

        # If shuffle is True, shuffle the dataset
        if shuffle:
            combined = list(zip(self.noisy_files, self.clean_files))
            random.shuffle(combined)
            self.noisy_files, self.clean_files = zip(*combined)

        # Limit the subset size if provided
        subset_size = min(subset_size, len(self.noisy_files))
        self.noisy_files = self.noisy_files[:subset_size]
        self.clean_files = self.clean_files[:subset_size]

    def __len__(self):
        return len(self.noisy_files)

    def __getitem__(self, idx):
        noisy_spectrogram = torch.load(self.noisy_files[idx], weights_only=True)
        clean_spectrogram = torch.load(self.clean_files[idx], weights_only=True)
        return noisy_spectrogram, clean_spectrogram
# def snr_improvement(noisy, clean, enhanced):
#     min_size = min(noisy.size(2), clean.size(2), enhanced.size(2))
#     noisy = noisy[:, :, :min_size, :]
#     clean = clean[:, :, :min_size, :]
#     enhanced = enhanced[:, :, :min_size, :]
#     noise = noisy - clean
#     noise_est = enhanced - clean
#     snr_before = torch.mean(clean ** 2) / torch.mean(noise ** 2)
#     snr_after = torch.mean(clean ** 2) / torch.mean(noise_est ** 2)
#     return 10 * torch.log10(snr_after / snr_before)
def snr_improvement(noisy, clean, enhanced):
    min_size = min(noisy.size(2), clean.size(2), enhanced.size(2))
    noisy = noisy[:, :, :min_size, :]
    clean = clean[:, :, :min_size, :]
    enhanced = enhanced[:, :, :min_size, :]
    
    noise = noisy - clean
    noise_est = enhanced - clean

    # Ensure the denominator isn't zero to avoid NaN values
    noise_power = torch.mean(noise ** 2)
    noise_est_power = torch.mean(noise_est ** 2)

    if noise_power == 0 or noise_est_power == 0:
        return torch.tensor(0.0)  # Avoid division by zero and return 0 SNR improvement

    snr_before = torch.mean(clean ** 2) / noise_power
    snr_after = torch.mean(clean ** 2) / noise_est_power
    
    return 10 * torch.log10(snr_after / snr_before)

def plot_metrics(train_metrics, val_metrics, metric_name):
    epochs = range(1, len(train_metrics) + 1)
    plt.plot(epochs, train_metrics, 'bo', label=f'Training {metric_name}')
    plt.plot(epochs, val_metrics, 'b', label=f'Validation {metric_name}')
    plt.title(f'Training and Validation {metric_name}')
    plt.xlabel('Epochs')
    plt.ylabel(metric_name)
    plt.legend()
    plt.show()


def train_model(rank, world_size, model, train_loader, val_loader, num_epochs, learning_rate, save_path, best_save_path, checkpoint_path=None):
    try:
        # Enable anomaly detection
        torch.autograd.set_detect_anomaly(True)

        # Set the device for the current process
        torch.cuda.set_device(rank)
        model = model.to(rank)
        model = DDP(model, device_ids=[rank])

        #  # Apply weight initialization
        # def init_weights(m):
        #     if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
        #         nn.init.kaiming_uniform_(m.weight, nonlinearity='relu')  # He initialization
        #         if m.bias is not None:
        #             nn.init.constant_(m.bias, 0)
        #     elif isinstance(m, nn.LSTM):
        #         for name, param in m.named_parameters():
        #             if 'weight' in name:
        #                 nn.init.xavier_uniform_(param)  # Xavier initialization
        #             elif 'bias' in name:
        #                 nn.init.constant_(param, 0)

        # # Initialize the weights of the model
        # model.apply(init_weights)
        # Initialize the Adam optimizer
        optimizer = optim.Adam(model.parameters(), lr=0.001, amsgrad=True)
        # Set up the learning rate scheduler
        # scheduler = OneCycleLR(optimizer, max_lr=0.01, steps_per_epoch=1250, epochs=1500)
        # Define the Cosine Annealing Warm Restarts scheduler
        scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', 
                                                 factor=0.1, patience=20, 
                                                 verbose=True)
        
        # Initialize variables for tracking progress
        start_epoch = 0
        best_val_loss = float('inf')
        best_val_snr_improvement = float('-inf')

        # Load checkpoint if provided and if it exists
        if checkpoint_path and os.path.exists(checkpoint_path):
            try:
                checkpoint = torch.load(checkpoint_path, map_location=torch.device(f'cuda:{rank}'))
                print(f"Checkpoint keys: {checkpoint.keys()}")  # Inspect the checkpoint keys

                # Directly load the state dictionary into the model
                model.load_state_dict(checkpoint)
                logger.info(f"Model state loaded directly from checkpoint.")

                # If available, load optimizer and scheduler states
                if 'optimizer_state_dict' in checkpoint:
                    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])

                if 'scheduler_state_dict' in checkpoint:
                    scheduler.load_state_dict(checkpoint['scheduler_state_dict'])

                # Load additional tracking variables if available
                start_epoch = checkpoint.get('epoch', 0) + 1
                best_val_loss = checkpoint.get('best_val_loss', float('inf'))
                best_val_snr_improvement = checkpoint.get('best_val_snr_improvement', float('-inf'))
                logger.info(f"Resuming training from epoch {start_epoch}")
            
            except Exception as e:
                logger.error(f"Error loading checkpoint: {e}")
                raise e  # Re-raise the exception to prevent further issues

        # Training loop
        model.train()
        training_snr_improvements = []
        validation_snr_improvements = []

        for epoch in range(start_epoch, start_epoch + num_epochs):
            running_loss = 0.0
            train_snr_improvement = 0.0
            total_samples = 0
            batch_snr_improvements = []

            for i, (noisy_spectrogram, clean_spectrogram) in enumerate(train_loader):
                noisy_spectrogram = noisy_spectrogram.cuda(rank, non_blocking=True)
                clean_spectrogram = clean_spectrogram.cuda(rank, non_blocking=True)

                optimizer.zero_grad()

                # Perform forward and backward pass with anomaly detection
                with torch.amp.autocast(device_type='cuda'):
                    output = model(noisy_spectrogram)
                    loss = custom_loss_function(output, clean_spectrogram)
                
                # Check for NaNs or Infs in loss
                if torch.isnan(loss).any() or torch.isinf(loss).any():
                    print(f"NaN or Inf detected in loss at iteration {i}, epoch {epoch}")
                    continue

                loss.backward()

                # Apply gradient clipping
                torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)

                optimizer.step()

                running_loss += loss.item()

                # Calculate SNR Improvement
                batch_snr_improvement = 0.0
                for j in range(noisy_spectrogram.size(0)):
                    single_snr_improvement = snr_improvement(
                        noisy_spectrogram[j:j+1], clean_spectrogram[j:j+1], output[j:j+1]
                    ).item()
                    batch_snr_improvement += single_snr_improvement

                batch_snr_improvement /= noisy_spectrogram.size(0)
                batch_snr_improvements.append(batch_snr_improvement)
                total_samples += noisy_spectrogram.size(0)

            # Aggregate batch SNR improvements
            training_snr_improvement_avg = sum(batch_snr_improvements) / len(batch_snr_improvements)
            training_snr_improvements.append(training_snr_improvement_avg)

            print(f"Epoch {epoch+1}, Training SNR Improvement: {training_snr_improvement_avg}")
            print(f"Epoch {epoch+1}, Total Samples Processed: {total_samples}")

            # Validation phase
            model.eval()
            val_loss = 0.0
            val_snr_improvement = 0.0
            with torch.no_grad():
                for noisy_spectrogram, clean_spectrogram in val_loader:
                    noisy_spectrogram = noisy_spectrogram.cuda(rank, non_blocking=True)
                    clean_spectrogram = clean_spectrogram.cuda(rank, non_blocking=True)
                    with torch.amp.autocast(device_type='cuda'):
                        output = model(noisy_spectrogram)
                        loss = custom_loss_function(output, clean_spectrogram)

                    val_loss += loss.item()
                    val_snr_improvement += snr_improvement(noisy_spectrogram, clean_spectrogram, output).item()

            val_loss /= len(val_loader)
            val_snr_improvement /= len(val_loader)
            validation_snr_improvements.append(val_snr_improvement)

            print(f"Epoch {epoch+1}, Validation Loss: {val_loss}, Validation SNR Improvement: {val_snr_improvement}")
            model.train()

            # Save the model every 50 epochs
            if rank == 0:
                if (epoch + 1) % 50 == 0:
                    torch.save(model.state_dict(), save_path)
                    print(f"Model saved at epoch {epoch+1}")

                if val_loss < best_val_loss:
                    best_val_loss = val_loss
                    torch.save(model.state_dict(), best_save_path)
                    print(f"Best model saved at epoch {epoch+1} with validation loss {best_val_loss}")

                if val_snr_improvement > best_val_snr_improvement:
                    best_val_snr_improvement = val_snr_improvement

            # Step the learning rate scheduler
            scheduler.step(val_loss)

        if rank == 0:
            print(f"Training complete for batch size {train_loader.batch_size}, learning rate {learning_rate}, epochs {num_epochs}")
            print(f"Best Validation Loss: {best_val_loss}, Best Validation SNR Improvement: {best_val_snr_improvement}")
            plot_metrics(training_snr_improvements, validation_snr_improvements, 'SNR Improvement')

    except Exception as e:
        print(f"Rank {rank} encountered an error: {e}")
    finally:
        torch.cuda.synchronize()  # Ensure all operations are complete before cleanup
        cleanup()

def setup(rank, world_size):
    logger.info(f"Setting up distributed training on rank {rank}")
    dist.init_process_group("nccl", rank=rank, world_size=world_size)
    torch.cuda.set_device(rank)
    # torch.distributed.barrier()
def cleanup():
    try:
        dist.destroy_process_group()
    except Exception as e:
        print(f"Error during cleanup: {e}")
        
def main_worker(rank, world_size, noisy_dir, clean_dir, save_dir, num_epochs, learning_rate, batch_size, checkpoint_path):
    try:
        setup(rank, world_size)

        # Preprocessing the data
        # preprocessor = Preprocessing(sample_rate=16000, n_fft=1024, hop_length=512, win_length=1024)
        # preprocessor.create_dataset(noisy_dir, clean_dir, save_dir)

        dataset = NoisySpeechDataset(os.path.join(save_dir, 'noisy'), os.path.join(save_dir, 'clean'), subset_size=50000)

        train_size = int(0.8 * len(dataset))
        val_size = len(dataset) - train_size
        train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])

        train_sampler = DistributedSampler(train_dataset, num_replicas=world_size, rank=rank)
        val_sampler = DistributedSampler(val_dataset, num_replicas=world_size, rank=rank, shuffle=False)

        train_loader = DataLoader(train_dataset, batch_size=batch_size, sampler=train_sampler, num_workers=2)
        val_loader = DataLoader(val_dataset, batch_size=batch_size, sampler=val_sampler)

        model = CRN()

        save_path = f"/home/siddharth/Sid/ASR/ANC/DEEP_ANC_MODEL_trim_bs{batch_size}_lr{learning_rate}_ep{num_epochs}_og_trial.pth"
        best_save_path = f"/home/siddharth/Sid/ASR/ANC/DEEP_ANC_MODEL_best_bs{batch_size}_lr{learning_rate}_ep{num_epochs}_og_trial.pth"

        train_model(rank, world_size, model, train_loader, val_loader, num_epochs, learning_rate, save_path, best_save_path, checkpoint_path)
        
    except Exception as e:
        logger.error(f"An error occurred on rank {rank}: {e}")
    finally:
        cleanup()