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data_loader.py
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data_loader.py
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"""
@AmineHorseman
Sep, 1st, 2016
"""
from parameters import DATASET, NETWORK
import numpy as np
def load_data(validation=False, test=False):
data_dict = dict()
validation_dict = dict()
test_dict = dict()
if DATASET.name == "Fer2013":
# load train set
data_dict['X'] = np.load(DATASET.train_folder + '/images.npy')
data_dict['X'] = data_dict['X'].reshape([-1, NETWORK.input_size, NETWORK.input_size, 1])
if NETWORK.use_landmarks:
data_dict['X2'] = np.load(DATASET.train_folder + '/landmarks.npy')
if NETWORK.use_hog_and_landmarks:
data_dict['X2'] = np.load(DATASET.train_folder + '/landmarks.npy')
data_dict['X2'] = np.array([x.flatten() for x in data_dict['X2']])
data_dict['X2'] = np.concatenate((data_dict['X2'], np.load(DATASET.train_folder + '/hog_features.npy')), axis=1)
data_dict['Y'] = np.load(DATASET.train_folder + '/labels.npy')
if DATASET.trunc_trainset_to > 0:
data_dict['X'] = data_dict['X'][0:DATASET.trunc_trainset_to, :, :]
if NETWORK.use_landmarks and NETWORK.use_hog_and_landmarks:
data_dict['X2'] = data_dict['X2'][0:DATASET.trunc_trainset_to, :]
elif NETWORK.use_landmarks:
data_dict['X2'] = data_dict['X2'][0:DATASET.trunc_trainset_to, :, :]
data_dict['Y'] = data_dict['Y'][0:DATASET.trunc_trainset_to, :]
if validation:
# load validation set
validation_dict['X'] = np.load(DATASET.validation_folder + '/images.npy')
validation_dict['X'] = validation_dict['X'].reshape([-1, NETWORK.input_size, NETWORK.input_size, 1])
if NETWORK.use_landmarks:
validation_dict['X2'] = np.load(DATASET.validation_folder + '/landmarks.npy')
if NETWORK.use_hog_and_landmarks:
validation_dict['X2'] = np.load(DATASET.validation_folder + '/landmarks.npy')
validation_dict['X2'] = np.array([x.flatten() for x in validation_dict['X2']])
validation_dict['X2'] = np.concatenate((validation_dict['X2'], np.load(DATASET.validation_folder + '/hog_features.npy')), axis=1)
validation_dict['Y'] = np.load(DATASET.validation_folder + '/labels.npy')
if DATASET.trunc_validationset_to > 0:
validation_dict['X'] = validation_dict['X'][0:DATASET.trunc_validationset_to, :, :]
if NETWORK.use_landmarks and NETWORK.use_hog_and_landmarks:
validation_dict['X2'] = validation_dict['X2'][0:DATASET.trunc_validationset_to, :]
elif NETWORK.use_landmarks:
validation_dict['X2'] = validation_dict['X2'][0:DATASET.trunc_validationset_to, :, :]
validation_dict['Y'] = validation_dict['Y'][0:DATASET.trunc_validationset_to, :]
if test:
# load test set
test_dict['X'] = np.load(DATASET.test_folder + '/images.npy')
test_dict['X'] = test_dict['X'].reshape([-1, NETWORK.input_size, NETWORK.input_size, 1])
if NETWORK.use_landmarks:
test_dict['X2'] = np.load(DATASET.test_folder + '/landmarks.npy')
if NETWORK.use_hog_and_landmarks:
test_dict['X2'] = np.load(DATASET.test_folder + '/landmarks.npy')
test_dict['X2'] = np.array([x.flatten() for x in test_dict['X2']])
test_dict['X2'] = np.concatenate((test_dict['X2'], np.load(DATASET.test_folder + '/hog_features.npy')), axis=1)
test_dict['Y'] = np.load(DATASET.test_folder + '/labels.npy')
if DATASET.trunc_testset_to > 0:
test_dict['X'] = test_dict['X'][0:DATASET.trunc_testset_to, :, :]
if NETWORK.use_landmarks and NETWORK.use_hog_and_landmarks:
test_dict['X2'] = test_dict['X2'][0:DATASET.trunc_testset_to, :]
elif NETWORK.use_landmarks:
test_dict['X2'] = test_dict['X2'][0:DATASET.trunc_testset_to, :, :]
test_dict['Y'] = test_dict['Y'][0:DATASET.trunc_testset_to, :]
if not validation and not test:
return data_dict
elif not test:
return data_dict, validation_dict
else:
return data_dict, validation_dict, test_dict
else:
print( "Unknown dataset")
exit()