How to show the class distribution in Dataset object in Tensorflow - tensorflow

Question

I am working on a multi-class classification task using my own images.
filenames = [] # a list of filenames
labels = [] # a list of labels corresponding to the filenames
full_ds = tf.data.Dataset.from_tensor_slices((filenames, labels))
This full dataset will be shuffled and split into train, valid and test dataset
full_ds_size = len(filenames)
full_ds = full_ds.shuffle(buffer_size=full_ds_size*2, seed=128) # seed is used for reproducibility
train_ds_size = int(0.64 * full_ds_size)
valid_ds_size = int(0.16 * full_ds_size)
train_ds = full_ds.take(train_ds_size)
remaining = full_ds.skip(train_ds_size)
valid_ds = remaining.take(valid_ds_size)
test_ds = remaining.skip(valid_ds_size)
Now I am struggling to understand how each class is distributed in train_ds, valid_ds and test_ds. An ugly solution is to iterate all the element in the dataset and count the occurrence of each class. Is there any better way to solve it?
My ugly solution:
def get_class_distribution(dataset):
class_distribution = {}
for element in dataset.as_numpy_iterator():
label = element[1]
if label in class_distribution.keys():
class_distribution[label] += 1
else:
class_distribution[label] = 0
# sort dict by key
class_distribution = collections.OrderedDict(sorted(class_distribution.items()))
return class_distribution
train_ds_class_dist = get_class_distribution(train_ds)
valid_ds_class_dist = get_class_distribution(valid_ds)
test_ds_class_dist = get_class_distribution(test_ds)
print(train_ds_class_dist)
print(valid_ds_class_dist)
print(test_ds_class_dist)

Answer 1

The answer below assumes:
there are five classes.
labels are integers from 0 to 4.
It can be modified to suit your needs.
Define a counter function:
def count_class(counts, batch, num_classes=5):
labels = batch['label']
for i in range(num_classes):
cc = tf.cast(labels == i, tf.int32)
counts[i] += tf.reduce_sum(cc)
return counts
Use the reduce operation:
initial_state = dict((i, 0) for i in range(5))
counts = train_ds.reduce(initial_state=initial_state,
reduce_func=count_class)
print([(k, v.numpy()) for k, v in counts.items()])

Answer 2

A solution inspired by user650654 's answer, only using TensorFlow primitives (with tf.unique_with_counts instead of for loop):
In theory, this should have better performance and scale better to large datasets, batches or class count.
num_classes = 5
#tf.function
def count_class(counts, batch):
y, _, c = tf.unique_with_counts(batch[1])
return tf.tensor_scatter_nd_add(counts, tf.expand_dims(y, axis=1), c)
counts = train_ds.reduce(
initial_state=tf.zeros(num_classes, tf.int32),
reduce_func=count_class)
print(counts.numpy())
Similar and simpler version with numpy that actually had better performances for my simple use-case:
count = np.zeros(num_classes, dtype=np.int32)
for _, labels in train_ds:
y, _, c = tf.unique_with_counts(labels)
count[y.numpy()] += c.numpy()
print(count)