[FIXED] Using WeightedRandomSampler in PyTorch

Issue

I need to implement a multi-label image classification model in PyTorch. However my data is not balanced, so I used the WeightedRandomSampler in PyTorch to create a custom dataloader. But when I iterate through the custom dataloader, I get the error : IndexError: list index out of range

Implemented the following code using this link :https://discuss.pytorch.org/t/balanced-sampling-between-classes-with-torchvision-dataloader/2703/3?u=surajsubramanian

def make_weights_for_balanced_classes(images, nclasses):                        
    count = [0] * nclasses                                                      
    for item in images:                                                         
        count[item[1]] += 1                                                     
    weight_per_class = [0.] * nclasses                                      
    N = float(sum(count))                                                   
    for i in range(nclasses):                                                   
        weight_per_class[i] = N/float(count[i])                                 
    weight = [0] * len(images)                                              
    for idx, val in enumerate(images):                                          
        weight[idx] = weight_per_class[val[1]]                                  
    return weight 

weights = make_weights_for_balanced_classes(train_dataset.imgs, len(full_dataset.classes))
weights = torch.DoubleTensor(weights)
sampler = WeightedRandomSampler(weights, len(weights))

train_loader = DataLoader(train_dataset, batch_size=4,sampler = sampler, pin_memory=True)   

Based on the answer in https://stackoverflow.com/a/60813495/10077354, the following is my updated code. But then too when I create a dataloader :loader = DataLoader(full_dataset, batch_size=4, sampler=sampler), len(loader) returns 1.

class_counts = [1691, 743, 2278, 1271]
num_samples = np.sum(class_counts)
labels = [tag for _,tag in full_dataset.imgs] 

class_weights = [num_samples/class_counts[i] for i in range(len(class_counts)]
weights = [class_weights[labels[i]] for i in range(num_samples)]
sampler = WeightedRandomSampler(torch.DoubleTensor(weights), num_samples)

Thanks a lot in advance !

I included an utility function based on the accepted answer below :

def sampler_(dataset):
    dataset_counts = imageCount(dataset)
    num_samples = sum(dataset_counts)
    labels = [tag for _,tag in dataset]

    class_weights = [num_samples/dataset_counts[i] for i in range(n_classes)]
    weights = [class_weights[labels[i]] for i in range(num_samples)]
    sampler = WeightedRandomSampler(torch.DoubleTensor(weights), int(num_samples))
    return sampler

The imageCount function finds number of images of each class in the dataset. Each row in the dataset contains the image and the class, so we take the second element in the tuple into consideration.

def imageCount(dataset):
    image_count = [0]*(n_classes)
    for img in dataset:
        image_count[img[1]] += 1
    return image_count

Solution

That code looks a bit complex... You can try the following:

#Let there be 9 samples and 1 sample in class 0 and 1 respectively
class_counts = [9.0, 1.0]
num_samples = sum(class_counts)
labels = [0, 0,..., 0, 1] #corresponding labels of samples

class_weights = [num_samples/class_counts[i] for i in range(len(class_counts))]
weights = [class_weights[labels[i]] for i in range(int(num_samples))]
sampler = WeightedRandomSampler(torch.DoubleTensor(weights), int(num_samples))

Answered By - ccl