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main.py
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main.py
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import os
import cv2
import random
import numpy as np
import torch
import argparse
from shutil import copyfile
from src.config import Config
from src.D4 import D4
def main(mode=None):
r"""starts the model
Args:
mode (int): 1: train, 2: test, 3: eval, reads from config file if not specified
"""
config = load_config(mode)
# cuda visble devices
os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(str(e) for e in config.GPU)
# init device
if torch.cuda.is_available():
config.DEVICE = torch.device("cuda")
torch.backends.cudnn.benchmark = True # cudnn auto-tuner
else:
config.DEVICE = torch.device("cpu")
# set cv2 running threads to 1 (prevents deadlocks with pytorch dataloader)
cv2.setNumThreads(0)
# initialize random seed
torch.manual_seed(config.SEED)
torch.cuda.manual_seed_all(config.SEED)
np.random.seed(config.SEED)
random.seed(config.SEED)
# build the model and initialize
model = D4(config)
model.load()
# model training
if config.MODE == 1:
config.print()
print('\nstart training...\n')
model.train()
# model test
elif config.MODE == 2:
print('\nstart testing...\n')
model.test()
# eval mode
else:
print('\nstart eval...\n')
model.eval()
def load_config(mode=None):
r"""loads model config
Args:
mode (int): 1: train, 2: test, 3: eval, reads from config file if not specified
"""
parser = argparse.ArgumentParser()
parser.add_argument('--path', '--checkpoints', type=str, default='./checkpoints', help='model checkpoints path (default: ./checkpoints)')
parser.add_argument('--model', type=int, choices=[1, 2, 3, 4, 5, 6, 7], help='1: edge model, 2: inpaint model, 3: edge-inpaint model, 4: joint model')
# test mode
if mode == 2:
parser.add_argument('--input', type=str, help='path to the input images directory or an input image')
parser.add_argument('--output', type=str, help='path to the output directory')
parser.add_argument('--crop', type=bool)
parser.add_argument('--crop_size', type=int, nargs=2)
args = parser.parse_args()
config_path = os.path.join(args.path, 'config.yml')
# create checkpoints path if does't exist
if not os.path.exists(args.path):
os.makedirs(args.path)
# copy config template if does't exist
if not os.path.exists(config_path):
copyfile('./config.yml.example', config_path)
# load config file
config = Config(config_path)
# train mode
if mode == 1:
config.MODE = 1
if args.model:
config.MODEL = args.model
# test mode
elif mode == 2:
config.MODE = 2
config.MODEL = args.model if args.model is not None else 3
config.INPUT_SIZE = 0
if args.input is not None:
config.TEST_FLIST = args.input
if args.output is not None:
config.RESULTS = args.output
if args.crop is not None and args.crop_size is not None:
config.CROP = args.crop
config.CROP_SIZE = args.crop_size
# eval mode
elif mode == 3:
config.MODE = 3
config.MODEL = args.model if args.model is not None else 3
return config
if __name__ == "__main__":
main()