def main():
config = Config()
create_dirs([config.summary_dir, config.checkpoint_dir])
sess = tf.Session()
train_data = Dataset(config.root,
config.train_image_file,
config.type,
transform=Augmentaton(size=config.resize,
mean=config.means[config.type],
std=config.stds[config.type]),
max_samples=None)
valid_data = Dataset(config.root,
config.valid_image_file,
config.type,
transform=Augmentaton(size=config.resize,
mean=config.means[config.type],
std=config.stds[config.type]),
max_samples=None)
train_data_loader = DataLoader(train_data)
valid_data_loader = DataLoader(valid_data)
model = DenseNet(config)
logger = Logger(sess, config)
trainer = DenseNetTrainer(sess, model, train_data_loader,
valid_data_loader, config, logger)
model.load(sess)
if config.phase == "train":
trainer.train()
elif config.phase == "test":
trainer.test("prediction.csv")
def evaluate():
config = Config()
valid_data = Dataset(config.root,
valid_image_paths,
config.type,
transform=Augmentaton(size=config.resize,
mean=config.means[config.type],
std=config.stds[config.type]),
max_samples=10)
valid_data_loader = DataLoader(valid_data)
sess = tf.Session()
model = DenseNet(config)
logger = Logger(sess, config)
trainer = DenseNetTrainer(sess, model, valid_data_loader,
valid_data_loader, config, logger)
model.load(sess)
if config.phase == "train":
trainer.train()
elif config.phase == "test":
trainer.test(output_prediction_path)