constants.py

import os

#height and width to which images are resized before feeding into networks
image_size = (224, 224) 

# Token to be used if object or attribute variable is unknown
unknown_token = 'UNK'

# Data paths
data_absolute_path = '/home/tanmay/Data/VisualGenome'
image_dir = os.path.join(data_absolute_path, 'cropped_regions')
object_labels_json = os.path.join(
    data_absolute_path,
    'restructured/object_labels.json')
attribute_labels_json = os.path.join(
    data_absolute_path,
    'restructured/attribute_labels.json')
regions_json = os.path.join(
    data_absolute_path,
    'restructured/region_with_labels.json')
mean_image_filename = os.path.join(
    data_absolute_path,
    'restructured/mean_image.jpg')
# Regions data partition
# First 70% meant to be used for training
# Next 10% is set aside for validation
# Last 20% is to be used for testing
num_total_regions = 1951768
num_train_regions = 1366238 # First 70%
num_val_regions = 195176 # Next 10%
num_test_regions = num_total_regions \
                   - num_train_regions \
                   - num_val_regions