run.py

import argparse
import json

import torch
from derender3d import setup_runtime, Trainer
from derender3d.model import Derender3D

import os
os.system("nvidia-smi")

## runtime arguments
parser = argparse.ArgumentParser(description='Training configurations.')
parser.add_argument('--config', default=None, type=str, help='Specify a config file path')
parser.add_argument('--gpu', default=None, type=int, help='Specify a GPU device')
parser.add_argument('--gpu_any', action='store_true')
parser.add_argument('--num_workers', default=4, type=int, help='Specify the number of worker threads for data loaders')
parser.add_argument('--seed', default=0, type=int, help='Specify a random seed')
parser.add_argument('--cfg_params', default='{}', type=str, help='Manually add entries to the config dict')
args = parser.parse_args()

## set up
cfgs = setup_runtime(args)
print(args.cfg_params)
cfg_params = json.loads(args.cfg_params)
cfgs.update(cfg_params)

if 'model' in cfgs:
    model = globals().get(cfgs.get('model'))
else:
    model = Derender3D
trainer = Trainer(cfgs, model)
run_train = cfgs.get('run_train', False)
run_test = cfgs.get('run_test', False)

## run
if run_train:
    trainer.train()
if run_test:
    trainer.test()