def test_to_json(self): config = load_config(self.yaml_file) output = config.to_json('hola.json') output = config.to_yaml('copy.yml') # Test that we can read from the copy again config = load_config('copy.yml') self.assertEqual(config.data.loader.workers, 20) self.assertEqual(config.name, 'random-run')
def main(): args = parse_args() op = load_config(args.config) op.test = args.test print('Testing data....', op.test) # Set random seed for reproducibility manualSeed = op.seed print("Random Seed: ", manualSeed) random.seed(manualSeed) torch.manual_seed(manualSeed) # Load data/models on GPU? device = gpu_check(op) print('Creating data...') real_data_loader = create_train_data(op, device) if op.test: print('Done testing data!') return # Init wandb wandb.init(project='dfdf', config=op) print('==== Config ====', wandb.config) # Now we can enter the training loop! print('Creating models...') awesome = create_model(op, device) print('Starting training loop..') awesome.train(real_data_loader)
def test_can_read(self): config = load_config(self.yaml_file) self.assertEqual(config.data.loader.workers, 20) self.assertEqual(config.name, 'random-run')
def test_can_nested_write(self): config = load_config(self.yaml_file) config.data.name = 'wando' self.assertEqual(config.data.name, 'wando') self.assertEqual(config.data['clone_again'], False)
def test_can_write(self): config = load_config(self.yaml_file) config.foo = 2 self.assertEqual(config.foo, 2)
def test_not_exists(self): config = load_config(self.yaml_file) self.assertFalse('random_stuff' in config) with self.assertRaises(BoxKeyError): print(config.random_stuff)