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
