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)