fix(): revert kfold implementation and fix CQT sampling rate

main.py

@@ -176,6 +176,7 @@ def train(parser, device):
 
 
     train_set = dataset_loader.ASVDataset(is_train=True, transform=transforms)
+    # dev_set = dataset_loader.ASVDataset(is_train=False, transform=transforms)
 
     # number_of_epochs = int(args.num_epochs / args.num_folds)
     # checkpoint_epoch = args.epoch % number_of_epochs
@@ -195,6 +196,17 @@ def train(parser, device):
     # weighted_sampler = torch.utils.data.WeightedRandomSampler(weights=weights, num_samples=len(train_ids),
     #                                                           replacement=True)
     train_loader, validation_loader = split_dataset_to_train_and_val(k_fold, train_set, batch_size=args.batch_size)
+
+    # train_loader_part = torch.utils.data.DataLoader(
+    #     train_set,
+    #     batch_size=args.batch_size,
+    #     shuffle=True
+    # )
+    # validation_loader_part = torch.utils.data.DataLoader(
+    #     train_set,
+    #     batch_size=args.batch_size, shuffle=True
+    # )
+
     model.train()
 
     # train_sub_sampler = torch.utils.data.Subset(train_set, train_ids)

model.py

@@ -28,14 +28,14 @@ def forward(self, x, labels, is_train=True):
 
         x = self.amp_to_db(x)
 
-        x = self.resnet(x.unsqueeze(1).float().to(self.device))
-
-        x = F.relu(self.mlp_layer1(x))
-        self.drop_out(x)
-        x = F.relu(self.mlp_layer2(x))
-        self.drop_out(x)
-        x = F.relu(self.mlp_layer3(x))
-        feat = x
+        feat, mu = self.resnet(x.unsqueeze(1).float().to(self.device))
+
+        # x = F.relu(self.mlp_layer1(x))
+        # self.drop_out(x)
+        # x = F.relu(self.mlp_layer2(x))
+        # self.drop_out(x)
+        # x = F.relu(self.mlp_layer3(x))
+        # feat = x
 
 
         return self.oc_softmax(feat, labels, is_train)

resnet.py

@@ -191,4 +191,4 @@ def forward(self, x):
 
         mu = self.fc_mu(feat)
 
-        return mu
+        return feat, mu

tools/dataset_loader.py

@@ -31,7 +31,7 @@ def __init__(self,
 
         self.data_root = data_root
 
-        self.dset_name = 'eval2021' if is_eval2021 else 'eval' if is_eval else 'train' if is_train else 'train'
+        self.dset_name = 'eval2021' if is_eval2021 else 'eval' if is_eval else 'train' if is_train else 'dev'
 
         self.protocols_fname = os.path.join(self.data_root, self.dset_name + '.protocol.txt')