def test_update_args(args, classifier_modified): """ Args: args(@pytest.fixture): Namespace classifier_modified(@pytest.fixture): ClassifierModified Asserts: True if exp.ARGS is updated adequately. """ expected_classifier_args_before_update = {'dropout': 0.2, 'dim_h': 100} expected_classifier_args_after_update = {'dropout': 0.1, 'dim_h': 100} args_for_update = { 'data': { 'batch_size': 128 }, 'model': { 'classifier_args': { 'dropout': 0.1 } } } config.set_config() classifier_modified = setup_experiment(args, model=classifier_modified, testmode=True) assert exp.ARGS['model'][ 'classifier_args'] == expected_classifier_args_before_update update_args(args_for_update, exp.ARGS) assert exp.ARGS['model'][ 'classifier_args'] == expected_classifier_args_after_update assert exp.ARGS['data']['batch_size'] == 128
def run(model=None): '''Main function. ''' # Parse the command-line arguments try: args = setup_cortex(model=model) if args.command == 'setup': # Performs setup only. config.setup() exit(0) else: config.set_config() print_section('EXPERIMENT') model, reload_nets = setup_experiment(args, model=model) print_section('DATA') data.setup(**exp.ARGS['data']) print_section('MODEL') model.reload_nets(reload_nets) model.build() print_section('OPTIMIZER') optimizer.setup(model, **exp.ARGS['optimizer']) except KeyboardInterrupt: print('Cancelled') exit(0) print_section('RUNNING') train.main_loop(model, **exp.ARGS['train'])
def test_update_nested_dicts(args, classifier_modified): """ Args: args(@pytest.fixture): Namespace classifier_modified(@pytest.fixture): ClassifierModified Asserts: True if a dict. arg. is being updated to a nested dict. (not overridden). """ expected_classifier_args_before_update = {'dropout': 0.2} expected_classifier_args_after_update = {'dropout': 0.2, 'dim_h': 100} args_for_update = { 'data': { 'batch_size': 122 }, 'model': { 'classifier_args': { 'dropout': 0.2, 'dim_h': 100 } } } config.set_config() classifier_modified = setup_experiment(args, model=classifier_modified, testmode=True) assert exp.ARGS['model'][ 'classifier_args'] == expected_classifier_args_before_update update_nested_dicts(args_for_update, exp.ARGS) assert exp.ARGS['model'][ 'classifier_args'] == expected_classifier_args_after_update assert exp.ARGS['data']['batch_size'] == 122
def test_update_args(args, classifier_modified): """ Args: args(@pytest.fixture): Namespace classifier_modified(@pytest.fixture): ClassifierModified Asserts: True if exp.ARGS is updated adequately. """ expected_classifier_args_before_update = {'dropout': 0.2} expected_classifier_args_after_update = {'dropout': 0.1} args_for_update = { 'data': { 'batch_size': 128 }, 'model': { 'classifier_args': { 'dropout': 0.1 } } } config.set_config() classifier_modified = setup_experiment( args, model=classifier_modified, testmode=True) assert exp.ARGS['model'][ 'classifier_args'] == expected_classifier_args_before_update update_args(args_for_update, exp.ARGS) assert exp.ARGS['model'][ 'classifier_args'] == expected_classifier_args_after_update assert exp.ARGS['data']['batch_size'] == 128
def test_update_nested_dicts(args, classifier_modified): """ Args: args(@pytest.fixture): Namespace classifier_modified(@pytest.fixture): ClassifierModified Asserts: True if a dict. arg. is being updated to a nested dict. (not overridden). """ expected_classifier_args_before_update = {'dropout': 0.2} expected_classifier_args_after_update = {'dropout': 0.2, 'dim_h': 100} args_for_update = { 'data': { 'batch_size': 122 }, 'model': { 'classifier_args': { 'dropout': 0.2, 'dim_h': 100 } } } config.set_config() classifier_modified = setup_experiment( args, model=classifier_modified, testmode=True) assert exp.ARGS['model'][ 'classifier_args'] == expected_classifier_args_before_update update_nested_dicts(args_for_update, exp.ARGS) assert exp.ARGS['model'][ 'classifier_args'] == expected_classifier_args_after_update assert exp.ARGS['data']['batch_size'] == 122
def run(model=None): '''Main function. ''' # Parse the command-line arguments try: args = setup_cortex(model=model) if args.command == 'setup': # Performs setup only. config.setup() exit(0) else: config.set_config() print_section('EXPERIMENT') model, reload_nets = setup_experiment(args, model=model) print_section('DATA') data.setup(**exp.ARGS['data']) print_section('MODEL') model.reload_nets(reload_nets) model.build() print_section('OPTIMIZER') optimizer.setup(model, **exp.ARGS['optimizer']) except KeyboardInterrupt: print('Cancelled') exit(0) print_section('RUNNING') train.main_loop(model, **exp.ARGS['train'])
def test_static_override_parameters(args, classifier_modified): """ Args: args(@pytest.fixture): Namespace classifier_modified(@pytest.fixture): ClassifierModified Asserts: True if default attribute is overriding parameters values. """ expected_type = 'convnet' config.set_config() classifier_modified = setup_experiment( args, model=classifier_modified, testmode=True) assert exp.ARGS['model']['classifier_type'] == expected_type
def test_static_override_parameters(args, classifier_modified): """ Args: args(@pytest.fixture): Namespace classifier_modified(@pytest.fixture): ClassifierModified Asserts: True if default attribute is overriding parameters values. """ expected_type = 'convnet' config.set_config() classifier_modified = setup_experiment(args, model=classifier_modified, testmode=True) assert exp.ARGS['model']['classifier_type'] == expected_type
def test_command_override_static(args): """ Args: args(@pytest.fixture): Namespace Asserts: True if passing a command line arg, the exp.ARGS is changing the value from default for the command line one. """ expected_type = 'resnet' args.__dict__['classifier_type'] = expected_type classifier_defaults = ImageClassification() config.set_config() # NOTE: exp.ARGS is being populated inside setup_experiment() call classifier_defaults = setup_experiment( args, model=classifier_defaults, testmode=True) assert exp.ARGS['model']['classifier_type'] == expected_type
def test_command_override_static(args): """ Args: args(@pytest.fixture): Namespace Asserts: True if passing a command line arg, the exp.ARGS is changing the value from default for the command line one. """ expected_type = 'resnet' args.__dict__['classifier_type'] = expected_type classifier_defaults = ImageClassification() config.set_config() # NOTE: exp.ARGS is being populated inside setup_experiment() call classifier_defaults = setup_experiment(args, model=classifier_defaults, testmode=True) assert exp.ARGS['model']['classifier_type'] == expected_type