Number of instances per class in pytorch dataset

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I'm trying to make a simple image classifier using PyTorch. This is how I load the data into a dataset and dataLoader:

batch_size = 64
validation_split = 0.2
data_dir = PROJECT_PATH+"/categorized_products"
transform = transforms.Compose([transforms.Grayscale(), CustomToTensor()])

dataset = ImageFolder(data_dir, transform=transform)

indices = list(range(len(dataset)))

train_indices = indices[:int(len(indices)*0.8)] 
test_indices = indices[int(len(indices)*0.8):]

train_sampler = SubsetRandomSampler(train_indices)
test_sampler = SubsetRandomSampler(test_indices)

train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, sampler=train_sampler, num_workers=16)
test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, sampler=test_sampler, num_workers=16)

I want to print out the number of images in each class in training and test data separately, something like this:

In train data:

  • shoes: 20
  • shirts: 14

In test data:

  • shoes: 4
  • shirts: 3

I tried this:

from collections import Counter
print(dict(Counter(sample_tup[1] for sample_tup in dataset.imgs)))

but I got this error:

AttributeError: 'MyDataset' object has no attribute 'img'
torch dataloader pytorch python
2 Answers

You need to use .targets to access the labels of data i.e.

print(dict(Counter(dataset.targets)))

It'll print something like this (e.g. in MNIST dataset):

{5: 5421, 0: 5923, 4: 5842, 1: 6742, 9: 5949, 2: 5958, 3: 6131, 6: 5918, 7: 6265, 8: 5851}

Also, you can use .classes or .class_to_idx to get mapping of label id to classes:

print(dataset.class_to_idx)
{'0 - zero': 0,
 '1 - one': 1,
 '2 - two': 2,
 '3 - three': 3,
 '4 - four': 4,
 '5 - five': 5,
 '6 - six': 6,
 '7 - seven': 7,
 '8 - eight': 8,
 '9 - nine': 9}

Edit: Method 1

From the comments, in order to get class distribution of training and testing set separately, you can simply iterate over subset as below:

train_size = int(0.8 * len(dataset))
test_size = len(dataset) - train_size
train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])

# labels in training set
train_classes = [label for _, label in train_dataset]
Counter(train_classes)
Counter({0: 4757,
         1: 5363,
         2: 4782,
         3: 4874,
         4: 4678,
         5: 4321,
         6: 4747,
         7: 5024,
         8: 4684,
         9: 4770})

Edit (2): Method 2

Since you've a large dataset, and as you said it takes considerable time to iterate over all training set, there is another way:

You can use .indices of subset, which referes to indices in the original dataset selected for subset.

i.e. 

train_classes = [dataset.targets[i] for i in train_dataset.indices]
Counter(train_classes) # if doesn' work: Counter(i.item() for i in train_classes)
user_62108070

Simple and easy
if you have dataset class which in your case in ImageFolder

dataset = MyDataset() # which in your case in ImageFolder
labels = torch.zeros(num_classes, dtype=torch.long)

for _, target in dataset:
    labels += target
user_133325820
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