def train():
train_images_path = '../dataset/train/images'
train_gt_path = '../dataset/train/gt'
test_images_path = '../dataset/test/images'
test_gt_path = '../dataset/test/gt'
valid_images_path = '../dataset/valid/images'
valid_gt_path = '../dataset/valid/gt'
model_save_path = './models'
trained_model_files = [
'./models/differential_train/deep_patch_classifier.pkl',
'./models/differential_train/shallow_9x9.pkl',
'./models/differential_train/shallow_7x7.pkl',
'./models/differential_train/shallow_5x5.pkl'
]
datasets = {
'train': image_data_set(train_images_path,
train_gt_path,
do_shuffle=True),
'test': image_data_set(test_images_path, test_gt_path),
'valid': image_data_set(valid_images_path, valid_gt_path)
}
networks = [
deep_patch_classifier(),
shallow_net_9x9(),
shallow_net_7x7(),
shallow_net_5x5()
]
load_nets(trained_model_files, networks)
train_funcs, test_funcs, run_funcs = create_network_functions(networks)
path = os.path.join(model_save_path, 'coupled_train')
if not os.path.exists(path):
os.makedirs(path)
os.makedirs(os.path.join(path, 'snapshots'))
train_coupled(networks, datasets, train_funcs, test_funcs, run_funcs, path)
print('\n-------\nDONE.')
import numpy as np import tensorflow as tf from data_reader import image_data_set train_images_path = '/home/enningxie/Documents/Codes/python/scnn/dataset/train_img' train_gt_path = '/home/enningxie/Documents/Codes/python/scnn/dataset/train_gt' train_data = image_data_set(train_images_path, train_gt_path, do_shuffle=True)
# # if os.path.isfile(os.path.join(images_path, f))]
# # for f in image_files:
# # print(os.path.join(gt_path, 'GT_' + os.path.splitext(f)[0] + '.npy'))
# print('end')
# test_images_path = './dataset/val_img'
# test_gt_path = './dataset/val_gt'
trained_model_files = [
'./models/coupled_train/deep_patch_classifier.pkl',
'./models/coupled_train/shallow_9x9.pkl',
'./models/coupled_train/shallow_7x7.pkl',
'./models/coupled_train/shallow_5x5.pkl'
]
datasets = {
'test': image_data_set(test_images_path, test_gt_path)
}
networks = [
deep_patch_classifier(),
shallow_net_9x9(),
shallow_net_7x7(),
shallow_net_5x5()
]
load_nets(trained_model_files, networks)
train_funcs, test_funcs, run_funcs = create_network_functions(networks)
print('TESTING SCNN...')
_, _, txt = test_scnn(test_funcs, datasets['test'])
print(txt)