from collections import defaultdict import utils import logger import theano.tensor as T import buffering from configuration import config, set_configuration import pathfinder theano.config.warn_float64 = 'raise' if len(sys.argv) < 2: sys.exit("Usage: train.py <configuration_name>") config_name = sys.argv[1] set_configuration('configs_luna_props_patch', config_name) expid = utils.generate_expid(config_name) print print "Experiment ID: %s" % expid print # metadata metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH) metadata_path = metadata_dir + '/%s.pkl' % expid # logs logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH) sys.stdout = logger.Logger(logs_dir + '/%s.log' % expid) sys.stderr = sys.stdout print 'Build model' model = config().build_model()
from datetime import datetime, timedelta import utils import logger import theano.tensor as T import buffering from configuration import config, set_configuration import pathfinder theano.config.warn_float64 = 'raise' if len(sys.argv) < 2: sys.exit("Usage: train.py <configuration_name>") config_name = sys.argv[1] set_configuration('configs_class_dsb', config_name) expid = utils.generate_expid(config_name) print() print("Experiment ID: %s" % expid) print() # metadata metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH) metadata_path = metadata_dir + '/%s.pkl' % expid # logs logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH) sys.stdout = logger.Logger(logs_dir + '/%s.log' % expid) sys.stderr = sys.stdout print('Build model') model = config().build_model()
""" Run with: python submit.py [-p mypredictions] [-c myconfigfile] """ import argparse from application.submission import generate_submission from utils.configuration import set_configuration import utils if __name__ == "__main__": NotImplementedError() parser = argparse.ArgumentParser(description=__doc__) required = parser.add_argument_group('required arguments') required.add_argument('-c', '--config', help='configuration to run', required=True) optional = parser.add_argument_group('optional arguments') optional.add_argument('-m', '--metadata', help='metadatafile to use', required=False) args = parser.parse_args() set_configuration(args.config) expid = utils.generate_expid(args.config) generate_submission(expid)
'predictions_per_slice': predictions, }, f, pickle.HIGHEST_PROTOCOL) print "prediction file dumped" return if __name__ == "__main__": parser = argparse.ArgumentParser(description=__doc__) required = parser.add_argument_group('required arguments') required.add_argument('-c', '--config', help='configuration to run', required=True) required.add_argument('-o', '--output', help='output file', required=True) optional = parser.add_argument_group('optional arguments') optional.add_argument('-m', '--metadata', help='metadatafile to use', required=False) args = parser.parse_args() set_configuration(args.config) expid = utils.generate_expid(args.config) mfile = args.metadata ofile = args.output predict_slice_model(expid, ofile, mfile)
preds = [] for x in range(patch_count[0]): preds_y = [] for y in range(patch_count[1]): ofs = y * patch_count[2] + x * patch_count[2] * patch_count[1] preds_z = np.concatenate(p[ofs:ofs + patch_count[2]], axis=2) preds_y.append(preds_z) preds_y = np.concatenate(preds_y, axis=1) preds.append(preds_y) preds = np.concatenate(preds, axis=0) preds = preds[:int(round(norm_shape[0])), :int(round(norm_shape[1])), :int(round(norm_shape[2]))] return preds if __name__ == "__main__": parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("config", help='configuration to run',) args = parser.parse_args() set_configuration(args.config) expid = utils.generate_expid(get_configuration_name()) log_file = LOGS_PATH + "%s-train.log" % expid with print_to_file(log_file): print "Running configuration:", config.__name__ print "Current git version:", utils.get_git_revision_hash() extract_rois(expid) print "log saved to '%s'" % log_file