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run_rat_spn_fashion_mnist.py
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import os
import filelock
import utils
import sys
import subprocess
import time
import json
print("")
print("Discriminative Training of RAT-SPNs on fashion-mnist")
print("")
with open('configurations.json') as f:
configs = json.loads(f.read())
start_time = time.time()
time_limit_seconds = configs['worker_time_limit']
dont_start_if_less_than_seconds = 600.0
base_result_path = "results/ratspn/fashion-mnist/"
structure_dict = {}
# depth 1
structure_dict[1] = [
{'num_recursive_splits': 9, 'num_input_distributions': 10, 'num_sums': 10},
{'num_recursive_splits': 14, 'num_input_distributions': 15, 'num_sums': 10},
{'num_recursive_splits': 19, 'num_input_distributions': 20, 'num_sums': 10},
{'num_recursive_splits': 29, 'num_input_distributions': 25, 'num_sums': 10},
{'num_recursive_splits': 40, 'num_input_distributions': 33, 'num_sums': 10}]
# depth 2
structure_dict[2] = [
{'num_recursive_splits': 8, 'num_input_distributions': 10, 'num_sums': 10},
{'num_recursive_splits': 12, 'num_input_distributions': 15, 'num_sums': 15},
{'num_recursive_splits': 19, 'num_input_distributions': 20, 'num_sums': 18},
{'num_recursive_splits': 30, 'num_input_distributions': 25, 'num_sums': 25},
{'num_recursive_splits': 40, 'num_input_distributions': 37, 'num_sums': 35}]
# depth 3
structure_dict[3] = [
{'num_recursive_splits': 10, 'num_input_distributions': 8, 'num_sums': 8},
{'num_recursive_splits': 12, 'num_input_distributions': 14, 'num_sums': 12},
{'num_recursive_splits': 15, 'num_input_distributions': 20, 'num_sums': 18},
{'num_recursive_splits': 30, 'num_input_distributions': 25, 'num_sums': 20},
{'num_recursive_splits': 40, 'num_input_distributions': 35, 'num_sums': 30}]
# depth 4
structure_dict[4] = [
{'num_recursive_splits': 5, 'num_input_distributions': 10, 'num_sums': 9},
{'num_recursive_splits': 10, 'num_input_distributions': 15, 'num_sums': 10},
{'num_recursive_splits': 14, 'num_input_distributions': 20, 'num_sums': 14},
{'num_recursive_splits': 28, 'num_input_distributions': 20, 'num_sums': 20},
{'num_recursive_splits': 40, 'num_input_distributions': 30, 'num_sums': 26}]
param_configs = [
{'dropout_rate_input': 1.0, 'dropout_rate_sums': 1.0},
{'dropout_rate_input': 1.0, 'dropout_rate_sums': 0.75},
{'dropout_rate_input': 1.0, 'dropout_rate_sums': 0.5},
{'dropout_rate_input': 1.0, 'dropout_rate_sums': 0.25},
{'dropout_rate_input': 0.75, 'dropout_rate_sums': 1.0},
{'dropout_rate_input': 0.75, 'dropout_rate_sums': 0.75},
{'dropout_rate_input': 0.75, 'dropout_rate_sums': 0.5},
{'dropout_rate_input': 0.75, 'dropout_rate_sums': 0.25},
{'dropout_rate_input': 0.5, 'dropout_rate_sums': 1.0},
{'dropout_rate_input': 0.5, 'dropout_rate_sums': 0.75},
{'dropout_rate_input': 0.5, 'dropout_rate_sums': 0.5},
{'dropout_rate_input': 0.5, 'dropout_rate_sums': 0.25},
{'dropout_rate_input': 0.25, 'dropout_rate_sums': 1.0},
{'dropout_rate_input': 0.25, 'dropout_rate_sums': 0.75},
{'dropout_rate_input': 0.25, 'dropout_rate_sums': 0.5},
{'dropout_rate_input': 0.25, 'dropout_rate_sums': 0.25}]
num_epochs = 200
def run():
for split_depth in structure_dict:
for structure_config in structure_dict[split_depth]:
for config_dict in param_configs:
remaining_time = time_limit_seconds - (time.time() - start_time)
if remaining_time < dont_start_if_less_than_seconds:
print("Only {} seconds remaining, stop worker".format(remaining_time))
sys.exit(0)
cmd = "python train_rat_spn.py --store_best_valid_loss --store_best_valid_acc --num_epochs {}".format(num_epochs)
cmd += " --timeout_seconds {}".format(remaining_time)
cmd += " --split_depth {}".format(split_depth)
cmd += " --data_path data/fashion-mnist/"
for key in sorted(structure_config.keys()):
cmd += " --{} {}".format(key, structure_config[key])
for key in sorted(config_dict.keys()):
cmd += " --{} {}".format(key, config_dict[key])
comb_string = ""
comb_string += "split_depth_{}".format(split_depth)
for key in sorted(structure_config.keys()):
comb_string += "__{}_{}".format(key, structure_config[key])
for key in sorted(config_dict.keys()):
comb_string += "__{}_{}".format(key, config_dict[key])
result_path = base_result_path + comb_string
cmd += " --result_path " + result_path
###
print(cmd)
utils.mkdir_p(result_path)
lock_file = result_path + "/file.lock"
done_file = result_path + "/file.done"
lock = filelock.FileLock(lock_file)
try:
lock.acquire(timeout=0.1)
if os.path.isfile(done_file):
print(" already done -> skip")
else:
sys.stdout.flush()
ret_val = subprocess.call(cmd, shell=True)
if ret_val == 7:
lock.release()
print("Task timed out, stop worker")
sys.exit(0)
os.system("touch {}".format(done_file))
lock.release()
except filelock.Timeout:
print(" locked -> skip")
if __name__ == '__main__':
run()