"Usage: predict.py <config_name> <n_tta_iterations> <average: arithmetic|geometric>"
)
config_name = sys.argv[1]
n_tta_iterations = int(sys.argv[2]) if len(sys.argv) >= 3 else 100
mean = sys.argv[3] if len(sys.argv) >= 4 else 'geometric'
print 'Make %s tta predictions for %s set using %s mean' % (
n_tta_iterations, "valid and test", mean)
metadata_dir = utils.get_dir_path('train', METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, config_name)
metadata = utils.load_pkl(metadata_path)
assert config_name == metadata['configuration']
if 'subconfiguration' in metadata:
set_subconfiguration(metadata['subconfiguration'])
set_configuration(config_name)
# predictions paths
jonas_prediction_path = PREDICTIONS_PATH + '/ira_%s.pkl' % config().__name__
prediction_dir = utils.get_dir_path('predictions', METADATA_PATH)
valid_prediction_path = prediction_dir + "/%s-%s-%s-%s.pkl" % (
metadata['experiment_id'], 'valid', n_tta_iterations, mean)
test_prediction_path = prediction_dir + "/%s-%s-%s-%s.pkl" % (
metadata['experiment_id'], 'test', n_tta_iterations, mean)
# submissions paths
submission_dir = utils.get_dir_path('submissions', METADATA_PATH)
submission_path = submission_dir + "/%s-%s-%s-%s.csv" % (
metadata['experiment_id'], 'test', n_tta_iterations, mean)
import lasagne as nn
import utils
import buffering
import utils_heart
from configuration import config, set_configuration, set_subconfiguration
from pathfinder import METADATA_PATH
if not (len(sys.argv) < 3):
sys.exit("Usage: predict.py <metadata_path>")
metadata_path = sys.argv[1]
metadata_dir = utils.get_dir_path('train', METADATA_PATH)
metadata = utils.load_pkl(metadata_dir + '/%s' % metadata_path)
config_name = metadata['configuration']
if 'subconfiguration' in metadata:
set_subconfiguration(metadata['subconfiguration'])
set_configuration(config_name)
# predictions paths
prediction_dir = utils.get_dir_path('predictions', METADATA_PATH)
prediction_path = prediction_dir + "/%s.pkl" % metadata['experiment_id']
prediction_mu_std_path = prediction_dir + "/%s_mu_sigma.pkl" % metadata['experiment_id']
print "Build model"
model = config().build_model()
all_layers = nn.layers.get_all_layers(model.l_top)
all_params = nn.layers.get_all_params(model.l_top)
num_params = nn.layers.count_params(model.l_top)
print ' number of parameters: %d' % num_params
nn.layers.set_all_param_values(model.l_top, metadata['param_values'])
if not (3 <= len(sys.argv) <= 5):
sys.exit("Usage: predict.py <config_name> <n_tta_iterations> <average: arithmetic|geometric>")
config_name = sys.argv[1]
n_tta_iterations = int(sys.argv[2]) if len(sys.argv) >= 3 else 100
mean = sys.argv[3] if len(sys.argv) >= 4 else "geometric"
print "Make %s tta predictions for %s set using %s mean" % (n_tta_iterations, "valid and test", mean)
metadata_dir = utils.get_dir_path("train", METADATA_PATH)
metadata_path = utils.find_model_metadata(metadata_dir, config_name)
metadata = utils.load_pkl(metadata_path)
assert config_name == metadata["configuration"]
if "subconfiguration" in metadata:
set_subconfiguration(metadata["subconfiguration"])
set_configuration(config_name)
# predictions paths
jonas_prediction_path = PREDICTIONS_PATH + "/ira_%s.pkl" % config().__name__
prediction_dir = utils.get_dir_path("predictions", METADATA_PATH)
valid_prediction_path = prediction_dir + "/%s-%s-%s-%s.pkl" % (
metadata["experiment_id"],
"valid",
n_tta_iterations,
mean,
)
test_prediction_path = prediction_dir + "/%s-%s-%s-%s.pkl" % (metadata["experiment_id"], "test", n_tta_iterations, mean)
# submissions paths
submission_dir = utils.get_dir_path("submissions", METADATA_PATH)
from configuration import config, set_configuration, set_subconfiguration
import pathfinder
if len(sys.argv) < 2:
sys.exit("Usage: train.py <meta_configuration_name>")
config_name = sys.argv[1]
subconfig_name = config_name.replace('meta_', '')
metadata_dir = utils.get_dir_path('train', pathfinder.METADATA_PATH)
submodel_metadata_path = utils.find_model_metadata(metadata_dir,
subconfig_name)
submodel_metadata = utils.load_pkl(submodel_metadata_path)
assert subconfig_name == submodel_metadata['configuration']
set_subconfiguration(subconfig_name)
set_configuration(config_name)
expid = utils.generate_expid(config_name)
print()
print("Experiment ID: %s" % expid)
print()
# meta metadata and logs paths
metadata_path = metadata_dir + '/%s.pkl' % expid
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % expid)
print('Build model')
model = config().build_model()
all_layers = nn.layers.get_all_layers(model.l_top)
import buffering
from configuration import config, set_configuration, set_subconfiguration
import pathfinder
if len(sys.argv) < 2:
sys.exit("Usage: train.py <meta_configuration_name>")
config_name = sys.argv[1]
subconfig_name = config_name.replace('meta_', '')
metadata_dir = utils.get_dir_path('train', pathfinder.METADATA_PATH)
submodel_metadata_path = utils.find_model_metadata(metadata_dir, subconfig_name)
submodel_metadata = utils.load_pkl(submodel_metadata_path)
assert subconfig_name == submodel_metadata['configuration']
set_subconfiguration(subconfig_name)
set_configuration(config_name)
expid = utils.generate_expid(config_name)
print
print "Experiment ID: %s" % expid
print
# meta metadata and logs paths
metadata_path = metadata_dir + '/%s.pkl' % expid
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % expid)
print 'Build model'
model = config().build_model()
all_layers = nn.layers.get_all_layers(model.l_top)