def test_add_to_parser():
parser = HyperOptArgumentParser(strategy='grid_search')
utils.add_to_parser(parser, 'test0', '4')
utils.add_to_parser(parser, 'test1', '5')
utils.add_to_parser(parser, 'test2', [1, 2, 3])
namespace, _ = parser.parse_known_args(['--test0', '3'])
# single argument
assert namespace.test0 == '3' # user defined arg
assert namespace.test1 == '5' # default arg
# list argument
assert namespace.test2 is None
assert parser.opt_args['--test2'].opt_values == [1, 2, 3]
assert parser.opt_args['--test2'].tunable
# optimize across all gpus
print('submitting jobs...')
cluster.optimize_parallel_cluster_gpu(main,
nb_trials=hyperparams.nb_hopt_trials,
job_name=job_display_name)
if __name__ == '__main__':
root_dir = os.path.split(os.path.dirname(
sys.modules['__main__'].__file__))[0]
parser = HyperOptArgumentParser(add_help=False, strategy='grid_search')
parser = Trainer.add_argparse_args(parser)
parser.add_argument('--model', type=str, default='galaxypred_model')
(temp_args, arr) = parser.parse_known_args()
model_name = temp_args.model
MODEL_CLASS = MODEL_NAMES[model_name]
# give the module a chance to add own params
# good practice to define LightningModule specific params in the module
parser = MODEL_CLASS.add_model_specific_args(parser)
parser.add_argument('--nodes', type=int, default=1)
parser.add_argument('--conda_env', type=str, default='dm2gal')
parser.add_argument('--on_cluster', default=True, action='store_true')
parser.add_argument('-n', '--tt_name', default='speckled')
parser.add_argument('-d',
'--tt_description',
default='predict a central mass')
parser.add_argument('--logs_save_path',
default='/projects/QUIJOTE/Noah/logs')
def test_add_dependent_params(tmpdir):
# -----------------
# ae
# -----------------
# arch params correctly added to parser
parser = HyperOptArgumentParser(strategy='grid_search')
utils.add_to_parser(parser, 'model_class', 'ae')
utils.add_to_parser(parser, 'model_type', 'conv')
utils.add_to_parser(parser, 'n_ae_latents', 32)
utils.add_to_parser(parser, 'n_input_channels', 2)
utils.add_to_parser(parser, 'y_pixels', 32)
utils.add_to_parser(parser, 'x_pixels', 32)
utils.add_to_parser(parser, 'ae_arch_json', None)
utils.add_to_parser(parser, 'approx_batch_size', 200)
utils.add_to_parser(parser, 'mem_limit_gb', 10)
namespace, _ = parser.parse_known_args([])
utils.add_dependent_params(parser, namespace)
assert '--architecture_params' in parser.opt_args
# linear autoencoder
parser = HyperOptArgumentParser(strategy='grid_search')
utils.add_to_parser(parser, 'model_class', 'ae')
utils.add_to_parser(parser, 'model_type', 'linear')
utils.add_to_parser(parser, 'n_ae_latents', 32)
utils.add_to_parser(parser, 'n_input_channels', 2)
utils.add_to_parser(parser, 'y_pixels', 32)
utils.add_to_parser(parser, 'x_pixels', 32)
utils.add_to_parser(parser, 'ae_arch_json', None)
utils.add_to_parser(parser, 'approx_batch_size', 200)
utils.add_to_parser(parser, 'mem_limit_gb', 10)
namespace, _ = parser.parse_known_args([])
utils.add_dependent_params(parser, namespace)
print(parser)
assert namespace.model_type == 'linear'
assert namespace.n_latents == '32'
# raise exception when max latents exceeded
# parser = HyperOptArgumentParser(strategy='grid_search')
# utils.add_to_parser(parser, 'model_class', 'ae')
# utils.add_to_parser(parser, 'n_ae_latents', 100000)
# utils.add_to_parser(parser, 'n_input_channels', 2)
# utils.add_to_parser(parser, 'y_pixels', 32)
# utils.add_to_parser(parser, 'x_pixels', 32)
# utils.add_to_parser(parser, 'ae_arch_json', None)
# utils.add_to_parser(parser, 'approx_batch_size', 200)
# utils.add_to_parser(parser, 'mem_limit_gb', 10)
# namespace, _ = parser.parse_known_args([])
# with pytest.raises(ValueError):
# utils.add_dependent_params(parser, namespace)
# -----------------
# vae
# -----------------
# arch params correctly added to parser
parser = HyperOptArgumentParser(strategy='grid_search')
utils.add_to_parser(parser, 'model_class', 'vae')
utils.add_to_parser(parser, 'model_type', 'conv')
utils.add_to_parser(parser, 'n_ae_latents', 32)
utils.add_to_parser(parser, 'n_input_channels', 2)
utils.add_to_parser(parser, 'y_pixels', 32)
utils.add_to_parser(parser, 'x_pixels', 32)
utils.add_to_parser(parser, 'ae_arch_json', None)
utils.add_to_parser(parser, 'approx_batch_size', 200)
utils.add_to_parser(parser, 'mem_limit_gb', 10)
namespace, _ = parser.parse_known_args([])
utils.add_dependent_params(parser, namespace)
assert '--architecture_params' in parser.opt_args
# -----------------
# neural
# -----------------
# make tmp hdf5 file
path = tmpdir.join('data.hdf5')
idx_data = {
'i0': np.array([0, 1, 2]),
'i1': np.array([3, 4, 5]),
'i2': np.array([6, 7, 8])
}
with h5py.File(path, 'w') as f:
group0 = f.create_group('regions')
# groupa = f.create_group('neural')
group1 = group0.create_group('indxs')
group1.create_dataset('i0', data=idx_data['i0'])
group1.create_dataset('i1', data=idx_data['i1'])
group1.create_dataset('i2', data=idx_data['i2'])
# subsample idxs not added to parser when not requested
parser = HyperOptArgumentParser(strategy='grid_search')
utils.add_to_parser(parser, 'model_class', 'neural-ae')
utils.add_to_parser(parser, 'subsample_method', 'none')
namespace, _ = parser.parse_known_args([])
utils.add_dependent_params(parser, namespace)
assert '--subsample_idxs_name' not in parser.opt_args
# subsample idxs added to parser when requested (all datasets)
parser = HyperOptArgumentParser(strategy='grid_search')
utils.add_to_parser(parser, 'data_dir', tmpdir)
utils.add_to_parser(parser, 'lab', '')
utils.add_to_parser(parser, 'expt', '')
utils.add_to_parser(parser, 'animal', '')
utils.add_to_parser(parser, 'session', '')
utils.add_to_parser(parser, 'model_class', 'neural-ae')
utils.add_to_parser(parser, 'subsample_method', 'single')
utils.add_to_parser(parser, 'subsample_idxs_dataset', 'all')
namespace, _ = parser.parse_known_args([])
utils.add_dependent_params(parser, namespace)
assert '--subsample_idxs_name' in parser.opt_args
parser_vals = parser.opt_args['--subsample_idxs_name'].opt_values.keys()
assert sorted(['i0', 'i1', 'i2']) == sorted(parser_vals)
# subsample idxs added to parser when requested (single dataset)
parser = HyperOptArgumentParser(strategy='grid_search')
utils.add_to_parser(parser, 'data_dir', tmpdir)
utils.add_to_parser(parser, 'lab', '')
utils.add_to_parser(parser, 'expt', '')
utils.add_to_parser(parser, 'animal', '')
utils.add_to_parser(parser, 'session', '')
utils.add_to_parser(parser, 'model_class', 'neural-ae')
utils.add_to_parser(parser, 'subsample_method', 'single')
utils.add_to_parser(parser, 'subsample_idxs_dataset', 'i0')
namespace, _ = parser.parse_known_args([])
utils.add_dependent_params(parser, namespace)
parser.parse_args([])
assert parser.parsed_args['subsample_idxs_name'] == 'i0'
# raise exception when dataset is not a string
parser = HyperOptArgumentParser(strategy='grid_search')
utils.add_to_parser(parser, 'model_class', 'neural-ae')
utils.add_to_parser(parser, 'subsample_method', 'single')
utils.add_to_parser(parser, 'subsample_idxs_dataset', ['i0', 'i1'])
namespace, _ = parser.parse_known_args([])
with pytest.raises(ValueError):
utils.add_dependent_params(parser, namespace)
