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experiment.py
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experiment.py
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"""
Entry point for running an SDNet experiment.
"""
import argparse
import importlib
import json
import logging
import os
import matplotlib
matplotlib.use('Agg') # environment for non-interactive environments
from easydict import EasyDict
from numpy.random import seed
from tensorflow import set_random_seed
seed(1)
set_random_seed(1)
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.Session(config=config)
KTF.set_session(sess)
class Experiment(object):
"""
Experiment class reads the configuration parameters (stored under configuration folder) and execute the experiment.
Required command line arguments are:
--config the configuration file name.
--split split number for cross validation, e.g. 0, 1, ...
Optional command line arguments are:
--test only test a model defined by the configuration file
--l_mix float [0, 1]. Sets the amount of labelled data.
--augment Use data augmentation
--modality Set the modality to load. Used in multimodal datasets.
"""
def __init__(self):
self.log = None
def init_logging(self, config):
if not os.path.exists(config.folder):
os.makedirs(config.folder)
logging.basicConfig(filename=config.folder + '/logfile.log', level=logging.DEBUG, format='%(asctime)s %(message)s')
logging.getLogger().addHandler(logging.StreamHandler())
self.log = logging.getLogger()
self.log.debug(config.items())
self.log.info('---- Setting up experiment at ' + config.folder + '----')
def get_config(self, split, args):
"""
Read a config file and convert it into an object for easy processing.
:param split: the cross-validation split id
:param args: the command arguments
:return: config object(namespace)
"""
data_type = args.config.split('-')[0].split('_')[-1]
config_script = args.config
l_mix = None if not args.l_mix else args.l_mix
# ul_mix = None if not args.ul_mix else args.ul_mix
# modality = None if not args.modality else args.modality
config_dict = importlib.import_module('configuration.'+ data_type+ '.' + config_script).get()
config = EasyDict(config_dict)
config.split = int(split)
data_type = args.config.split('-')[0].split('_')[-1]
config.folder = os.path.join('exp_data', data_type + '/' + config.folder)
if l_mix is not None:
config.l_mix = l_mix
pat_sup_pctg = int(float(l_mix.split('-')[0])*100)
per_volume_pctg = int(float(l_mix.split('-')[1])*100)
config.folder += '-%dSupPat-Partial%dPerVol' % (pat_sup_pctg, per_volume_pctg)
# if ul_mix is not None:
# config.ul_mix = float(ul_mix)
# config.folder += '_ul%s' % ul_mix
# if modality is not None:
# config.modality = modality
# config.folder += '_' + modality
config.folder += '_split%s' % split
config.folder = config.folder.replace('.', '')
if args.augment:
config.augment = args.augment
self.save_config(config)
return config
def save_config(self, config):
if not os.path.exists(config.folder):
os.makedirs(config.folder)
with open(config.folder + '/experiment_configuration.json', 'w') as outfile:
json.dump(dict(config.items()), outfile)
def run(self):
args = Experiment.read_console_parameters()
configuration = self.get_config(args.split, args)
self.init_logging(configuration)
self.run_experiment(configuration, args.test)
def run_experiment(self, configuration, test):
executor = self.get_executor(configuration)
if test:
executor.test()
else:
executor.train()
with open(configuration.folder + '/experiment_configuration.json', 'w') as outfile:
json.dump(vars(configuration), outfile)
executor.test()
@staticmethod
def read_console_parameters():
parser = argparse.ArgumentParser(description='')
parser.add_argument('--config', default='', help='The experiment configuration file', required=True)
parser.add_argument('--test', help='Evaluate the model on test data', type=bool)
parser.add_argument('--split', help='Data split to run.', required=True)
parser.add_argument('--l_mix', help='Percentage of labelled data')
# parser.add_argument('--ul_mix', help='Percentage of unlabelled data')
parser.add_argument('--augment', help='Augment training data', type=bool)
# parser.add_argument('--modality', help='Modality to load', choices=['MR', 'CT', 'all', 'cine', 'BOLD'])
return parser.parse_args()
def get_executor(self, config):
# Initialise model
module_name = config.model.split('.')[0]
model_name = config.model.split('.')[1]
model = getattr(importlib.import_module('models.' + module_name), model_name)(config)
mark = model.build()
if not mark:
return
if config.l_mix == 0.015 and config.split == 1:
config.seed = 10
# Initialise executor
module_name = config.executor.split('.')[0]
model_name = config.executor.split('.')[1]
executor = getattr(importlib.import_module('model_executors.' + module_name), model_name)(config, model)
return executor
if __name__ == '__main__':
exp = Experiment()
exp.run()