def main():
# capture the config path from the run arguments
# then process the json configration file
# try:
data_loader = DataLoader(data_dir, config)
data_loader.load_directory('.tif')
data_loader.create_np_arrays()
data_loader.create_data_label_pairs()
preptt = PrepTrainTest(config, data_loader)
for data_label_pair in data_loader.data_label_pairs:
x_data = data_label_pair[0][data_loader]
y_true = data_label_pair[1][data_loader.data_label_pairs[i][1][:, :, 0]]
preptt.add_data(x_data, y_true)
# Create the experiments dirs
create_dirs([config.summary_dir, config.checkpoint_dir, config.input_dir])
# Create tensorflow session
sess = tf.Session()
# Create instance of the model you want
model = PopModel(config)
# Load model if exist
model.load(sess)
# Create Tensorboard logger
logger = Logger(sess, config)
logger.log_config()
# Create your data generator
data = DataGenerator(config, preptraintest = preptt)
data.create_traintest_data()
# Create trainer and path all previous components to it
trainer = PopTrainer(sess, model, data, config, logger)
# Train model
trainer.train()
def main():
# capture the config path from the run arguments
# then process the json configration file
# try:
args = get_args()
if args.config != 'None':
config = process_config(args.config)
else:
config = process_config(os.path.join(config_dir, 'example.json'))
data_loader = DataLoader(data_dir)
data_loader.load_directory('.tif')
data_loader.create_np_arrays()
preptt = PrepTrainTest(data_loader.arrays[0], data_loader.arrays[1], config.batch_size, config.chunk_height, config.chunk_width)
prepd = PrepData(data_loader.arrays[0], data_loader.arrays[1], config.batch_size, config.chunk_height, config.chunk_width)
# create the experiments dirs
create_dirs([config.summary_dir, config.checkpoint_dir])
# create tensorflow session
sess = tf.Session()
# create instance of the model you want
model = PopModel(config)
#load model if exist
model.load(sess)
# create your data generator
data = DataGenerator(config, preptt, prepd)
data.create_traintest_data()
data.create_data()
# Create Tensorboard logger
logger = Logger(sess, config)
# Create trainer and path all previous components to it
tester = PopTrainer(sess, model, data, config, logger)
# Test model
tester.test()