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Running
on
Zero
File size: 3,276 Bytes
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import argparse
import importlib
import os
import random
from datetime import date
from shutil import copyfile
import cv2 as cv
import numpy as np
import torch
import torch.backends.cudnn
import admin.settings as ws_settings
def run_sampling(train_module, train_name, seed, name, cudnn_benchmark=True, corruption=False):
"""Run a sampling scripts in train_settings.
args:
train_module: Name of module in the "train_settings/" folder.
train_name: Name of the train settings file.
cudnn_benchmark: Use cudnn benchmark or not (default is True).
"""
# This is needed to avoid strange crashes related to opencv
cv.setNumThreads(0)
torch.backends.cudnn.benchmark = cudnn_benchmark
# dd/mm/YY
today = date.today()
d1 = today.strftime("%d/%m/%Y")
print('Sampling: {} {}\nDate: {}'.format(train_module, train_name, d1))
settings = ws_settings.Settings()
settings.module_name = train_module
settings.script_name = train_name
settings.project_path = 'train_settings/{}/{}'.format(train_module, train_name)
settings.seed = seed
settings.name = name
# will save the checkpoints there
save_dir = os.path.join(settings.env.workspace_dir, settings.project_path)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
copyfile(settings.project_path + '.py', os.path.join(save_dir, settings.script_name + '.py'))
expr_module = importlib.import_module('train_settings.{}.{}'.format(train_module.replace('/', '.'),
train_name.replace('/', '.')))
expr_func = getattr(expr_module, 'run')
if corruption:
for severity in [5]:
settings.severity = severity
for corruption_number in range(0, 15):
# [0, 18]; useful for easy looping; 15, 16, 17, 18 are validation corruption numbers
settings.corruption_number = corruption_number
expr_func(settings)
else:
settings.severity = 0
settings.corruption_number = 0
expr_func(settings)
def main():
parser = argparse.ArgumentParser(description='Run a sampling scripts in train_settings.')
parser.add_argument('--train_module', type=str, help='Name of module in the "train_settings/" folder.')
parser.add_argument('--train_name', type=str, help='Name of the train settings file.')
parser.add_argument('--cudnn_benchmark', type=bool, default=True,
help='Set cudnn benchmark on (1) or off (0) (default is on).')
parser.add_argument('--seed', type=int, default=1992, help='Pseudo-RNG seed')
parser.add_argument('--name', type=str, default="Default", help='Name of the experiment')
parser.add_argument('--corruption', action='store_true')
args = parser.parse_args()
args.seed = random.randint(0, 3000000)
args.seed = torch.initial_seed() & (2 ** 32 - 1)
print('Seed is {}'.format(args.seed))
random.seed(int(args.seed))
np.random.seed(args.seed)
run_sampling(
args.train_module, args.train_name, cudnn_benchmark=args.cudnn_benchmark, seed=args.seed,
name=args.name, corruption=args.corruption)
if __name__ == '__main__':
main() |