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import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.optim import AdamW, lr_scheduler
from torch.utils.data import Dataset
from torchvision import models
from sklearn.metrics import f1_score, accuracy_score
from metrics.event_based_metrics import event_metrics
from .audio_preprocessing import *
class AudioDataset(Dataset):
def __init__(self, fbank_features, annotation):
self.fbank_features = fbank_features
self.annotation = annotation
def __len__(self):
return len(self.fbank_features)
def __getitem__(self, idx):
fbank_features = self.fbank_features[idx]
annotation = self.annotation[idx]
fbank_features_array = np.array(fbank_features)
fbank_features_tensor = torch.tensor(fbank_features_array, dtype=torch.float32)
annotation_tensor = torch.tensor(annotation, dtype=torch.float32)
return fbank_features_tensor, annotation_tensor
class AudioMobileNetV2(nn.Module):
def __init__(self):
super(AudioMobileNetV2, self).__init__()
self.mobilenetv2 = models.mobilenet_v2(pretrained=True)
self.mobilenetv2.features[0][0] = nn.Conv2d(1, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
self.mobilenetv2.classifier[1] = nn.Linear(self.mobilenetv2.last_channel, 1) # Binary classification
def forward(self, x):
batch_size, num_frame, feature_dim = x.size()
x = x.view(batch_size * num_frame, 1, 1, feature_dim)
x = self.mobilenetv2(x)
x = x.view(batch_size, num_frame, -1)
return x
class AudioBiLSTM(nn.Module):
def __init__(self, num_features):
super().__init__()
self.lstm1 = nn.LSTM(num_features, 128, bidirectional=True, batch_first=True)
self.lstm2 = nn.LSTM(256, 128, bidirectional=True, batch_first=True)
self.dense = nn.Linear(256, 1)
def forward(self, x):
out, _ = self.lstm1(x)
out, _ = self.lstm2(out)
out = self.dense(out)
return out
class AudioTransformer(nn.Module):
def __init__(self, input_dim=41, hidden_dim=128, num_heads=4, num_layers=2):
super().__init__()
self.input_projection = nn.Linear(input_dim, hidden_dim)
encoder_layers = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=num_heads)
self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers=num_layers)
self.fc = nn.Linear(hidden_dim, 1)
def forward(self, x):
batch_size, num_frame, feature_dim = x.size()
x = x.view(batch_size*num_frame, 1, feature_dim)
x = self.input_projection(x)
x = x.permute(1, 0, 2)
transformer_out = self.transformer_encoder(x)
out = transformer_out[0, :, :]
out = self.fc(out)
out = out.view(batch_size, num_frame, 1)
return out
def train(model, train_loader, device, num_epochs=10):
criterion = nn.BCEWithLogitsLoss()
optimizer = AdamW(model.parameters(), lr=0.001)
scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=0)
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
count = 0
for inputs, labels in train_loader:
inputs, labels = inputs.to(device), labels.to(device)
count += 1
optimizer.zero_grad()
outputs = model(inputs)
outputs = outputs.view(-1)
labels = labels.view(-1).float() # Convert labels to float for BCEWithLogitsLoss
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
scheduler.step()
print(f'Epoch {epoch+1}/{num_epochs}, Loss: {running_loss/len(train_loader)}, LR: {scheduler.get_last_lr()[0]}')
def eval(model, test_loader, device):
model.eval()
acc_list = []
framef_list = []
eventf_list = []
iou_list = []
with torch.no_grad():
for inputs, labels in test_loader:
# Move inputs and labels to GPU
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
outputs = outputs.view(-1)
labels = labels.view(-1).float() # Convert labels to float for BCEWithLogitsLoss
preds = torch.sigmoid(outputs) # Apply sigmoid to get probabilities
preds = (preds > 0.5).float() # Convert probabilities to binary predictions
labels = labels.cpu().numpy()
preds = preds.cpu().numpy()
# Frame-based accuracy
accuracy = accuracy_score(labels, preds)
acc_list.append(accuracy)
# Frame-based F1 score
framef = f1_score(labels, preds)
framef_list.append(framef)
# Event-based metrics
eventf, iou, counted_events, fake_events, undetected_events = event_metrics(labels, preds, tolerance=9, overlap_threshold=0.75)
eventf_list.append(eventf)
iou_list.append(iou)
return acc_list, framef_list, eventf_list, iou_list
def save_model(model, path):
torch.save(model.state_dict(), path)
print(f"Model saved to {path}")
class AsymmetricalFocalLoss(nn.Module):
def __init__(self, gamma=0, zeta=0):
super(AsymmetricalFocalLoss, self).__init__()
self.gamma = gamma # balancing between classes
self.zeta = zeta # balancing between active/inactive frames
def forward(self, pred, target):
losses = - (((1 - pred) ** self.gamma) * target * torch.clamp_min(torch.log(pred), -100) +
(pred ** self.zeta) * (1 - target) * torch.clamp_min(torch.log(1 - pred), -100))
return torch.mean(losses)
def train_FDYSED(model, train_loader, device, num_epochs=10):
criterion = AsymmetricalFocalLoss(gamma=2, zeta=0.5)
optimizer = AdamW(model.parameters(), lr=0.001)
scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=0)
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
count = 0
for inputs, labels in train_loader:
inputs, labels = inputs.to(device), labels.to(device)
count += 1
optimizer.zero_grad()
outputs = model(inputs)
outputs = outputs.view(-1)
labels = labels.view(-1).float() # Convert labels to float for BCEWithLogitsLoss
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
scheduler.step()
print(f'Epoch {epoch+1}/{num_epochs}, Loss: {running_loss/len(train_loader)}, LR: {scheduler.get_last_lr()[0]}')
# MDFDSED
def obtain_loss(train_cfg, model_outs, labels, weak_labels, mask_strong, mask_weak):
strong_pred_stud, strong_pred_tch, weak_pred_stud, weak_pred_tch = model_outs
loss_total = 0
# loss_class_weak = train_cfg["criterion_class"](weak_pred_stud[mask_weak], weak_labels)
# loss_cons_weak = train_cfg["criterion_cons"](weak_pred_stud, weak_pred_tch.detach())
w_cons = train_cfg["w_cons_max"] * train_cfg["scheduler"]._get_scaling_factor()
loss_class_strong = train_cfg["criterion_class"](strong_pred_stud[:], labels[:]) #strong masked label size = [bs_strong, n_class, frames]
loss_cons_strong = train_cfg["criterion_cons"](strong_pred_stud, strong_pred_tch.detach())
loss_total += loss_class_strong + w_cons * (loss_cons_strong) # train_cfg["w_weak"] * loss_class_weak + \ + train_cfg["w_weak_cons"] * loss_cons_weak
return loss_total #, loss_class_strong, loss_class_weak, loss_cons_strong, loss_cons_weak
def train_MDFDSED(model, train_loader, device, num_epochs=10):
train_cfg = yaml.load(open("./config_MDFDbest.yaml", "r"), Loader=yaml.Loader)
criterion = obtain_loss
optimizer = AdamW(model.parameters(), lr=0.001)
scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=0)
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
count = 0
for inputs, labels in train_loader:
inputs, labels = inputs.to(device), labels.to(device)
count += 1
optimizer.zero_grad()
outputs = model(inputs)
outputs = outputs.view(-1)
labels = labels.view(-1).float() # Convert labels to float for BCEWithLogitsLoss
loss = criterion(train_cfg, outputs, labels, None, None, None)
loss.backward()
optimizer.step()
running_loss += loss.item()
scheduler.step()
print(f'Epoch {epoch+1}/{num_epochs}, Loss: {running_loss/len(train_loader)}, LR: {scheduler.get_last_lr()[0]}')
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