train.py

import logging
import argparse
import collections
import torch
import os
import numpy as np
import data_loader.data_loaders as module_data
import model.loss as module_loss
import model.metric as module_metric
import model.model as module_arch
from parse_config import ConfigParser
from trainer import COWCTrainer
from trainer import COWCGANTrainer
from trainer import COWCFRCNNTrainer
from trainer import COWCGANFrcnnTrainer
from utils import setup_logger, dict2str
'''
python train.py -c config_GAN.json
'''

# fix random seeds for reproducibility
SEED = 123
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(SEED)

def main(config):
    #logger = config.get_logger('train')
    # config loggers. Before it, the log will not work
    setup_logger('base', config['path']['log'], 'train_' + config['name'], level=logging.INFO,
                      screen=True, tofile=True)
    setup_logger('val', config['path']['log'], 'val_' + config['name'], level=logging.INFO,
                      screen=True, tofile=True)
    logger = logging.getLogger('base')
    #logger.info(dict2str(config))


    # setup data_loader instances
    data_loader = config.init_obj('data_loader', module_data)
    #change later this valid_data_loader using init_obj
    valid_data_loader = module_data.COWCGANFrcnnDataLoader('/home/jakaria/Super_Resolution/Datasets/COWC/DetectionPatches_256x256/Potsdam_ISPRS/HR/x4/valid_img/',
    '/home/jakaria/Super_Resolution/Datasets/COWC/DetectionPatches_256x256/Potsdam_ISPRS/LR/x4/valid_img/', 1, training = False)

    # build model architecture, then print to console
    #model = config.init_obj('arch', module_arch)
    #logger.info(model)

    # get function handles of loss and metrics
    #criterion = getattr(module_loss, config['loss'])
    #metrics = [getattr(module_metric, met) for met in config['metrics']]

    # build optimizer, learning rate scheduler. delete every lines containing lr_scheduler for disabling scheduler
    #trainable_params = filter(lambda p: p.requires_grad, model.parameters())
    #optimizer = config.init_obj('optimizer', torch.optim, trainable_params)

    #lr_scheduler = config.init_obj('lr_scheduler', torch.optim.lr_scheduler, optimizer)
    '''
    trainer = COWCGANTrainer(model, criterion, metrics, optimizer,
                      config=config,
                      data_loader=data_loader,
                      valid_data_loader=valid_data_loader,
                      lr_scheduler=lr_scheduler)
    '''
    '''
    trainer = COWCGANTrainer(config=config,data_loader=data_loader,
                     valid_data_loader=valid_data_loader
                     )
    '''

    trainer = COWCGANFrcnnTrainer(config=config, data_loader=data_loader,
                     valid_data_loader=valid_data_loader)
    trainer.train()
    '''
    trainer = COWCFRCNNTrainer(config=config)
    trainer.train()
    '''
if __name__ == '__main__':
    args = argparse.ArgumentParser(description='PyTorch Template')
    args.add_argument('-c', '--config', default=None, type=str,
                      help='config file path (default: None)')
    args.add_argument('-r', '--resume', default=None, type=str,
                      help='path to latest checkpoint (default: None)')
    args.add_argument('-d', '--device', default=None, type=str,
                      help='indices of GPUs to enable (default: all)')

    # custom cli options to modify configuration from default values given in json file.
    CustomArgs = collections.namedtuple('CustomArgs', 'flags type target')
    options = [
        CustomArgs(['--lr', '--learning_rate'], type=float, target='optimizer;args;lr'),
        CustomArgs(['--bs', '--batch_size'], type=int, target='data_loader;args;batch_size')
    ]
    config = ConfigParser.from_args(args, options)
    main(config)