I was able to use this weighted random data sampling using @ptrblck 's code.
here is a snippet:
   print('Loading data...')
   train_dataset = datasets.ImageFolder(root=traindir,
                                    transform=transform_train)
   test_dataset = datasets.ImageFolder(root=valdir,
                                   transform=transform_test)
   print('Loading is Done!')

   num_classes = len(train_dataset.classes)

   #TODO: read class counts from the file
   class_sample_counts = [10647, 5659, 31445, 40283,  800,  407, 1111, 22396,  610, 1288, 5708, 1538, 1848, 26015, 17639, 3859,  473, 2509,  579, 2636,  822, 1616, 1226,  949, 1725, 1306, 1758, 1704, 10637, 1091, 1036, 1292,  474,  569, 1682,  553,  506, 7571, 3598, 2280, 24291, 5725, 1319,  824, 5456, 1781, 4074, 2538, 5032,  503, 1623, 7251,  599, 9037, 12221, 2128, 2290,  459, 1549, 1739, 2297,  838,  469,  674, 1030,  994,  704,  672, 1690, 2442,  766,  578, 2032,  534,  552, 13934, 1138, 1372]

   # compute weight for all the samples in the dataset
   # samples_weights contain the probability for each example in dataset to be sampled  
   class_weights = 1./torch.Tensor(class_sample_counts)
   train_targets = [sample[1] for sample in train_dataset.imgs]
   train_samples_weight = [class_weights[class_id] for class_id in train_targets]
   test_targets = [sample[1] for sample in test_dataset.imgs]
   test_samples_weight = [class_weights[class_id] for class_id in test_targets]

   # now lets initialize samplers 
   train_sampler = torch.utils.data.sampler.WeightedRandomSampler(train_samples_weight, len(train_dataset))
   test_sampler = torch.utils.data.sampler.WeightedRandomSampler(test_samples_weight, len(test_dataset))

   train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size,sampler=train_sampler, **kwargs)
   val_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size, sampler=test_sampler, **kwargs)