SoundLoader: A Function To Create Custom Dataloader in PyTorch for An Environmental Sound Classification

Import Classes from SoundCustomDataloader_Train.py and SoundCustomDataloader_Test.py to Get the Training and Test Dataset for Sound Classification. Forward this dataset as an argument to dataloader in PyTorch.
Sample Example Code:

1. import torch
   
2. from torch.utils.data import DataLoader
   
3. from torchvision.transforms import transforms
   
4. from SoundCustomDataloader_Train import Sound_Data_Train
   
5. from SoundCustomDataloader_Test import Sound_Data_Test
   
6. transform = transforms.Compose([transforms.Resize([224,224]),transforms.ToTensor(),transforms.Normalize(mean=[0.485], std=[0.229])])
   
7. batch_size=32
   
8. Training_Data=Sound_Data_Train(transform=transform)
   
9. Test_Data=Sound_Data_Test(transform=transform)
   
10. Train_loader=DataLoader(dataset=Train_Data,batch_size=batch_size,shuffle=True) # Pytorch dataloader for Training
    
11. Test_loader=DataLoader(dataset=Test_Data,batch_size=batch_size,shuffle=True) Pytorch dataloader for Test