from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import _init_paths

import os

import torch
import torch.utils.data
from opts import opts
from models.model import create_model, load_model, save_model
from models.data_parallel import DataParallel
from logger import Logger
from datasets.dataset_factory import get_dataset
from trains.train_factory import train_factory


def main(opt):
 torch.manual_seed(opt.seed)
 torch.backends.cudnn.benchmark = not opt.not_cuda_benchmark and not opt.test
 Dataset = get_dataset(opt.dataset, opt.task)
 opt = opts().update_dataset_info_and_set_heads(opt, Dataset)
 print(opt)

 logger = Logger(opt)

 os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str
 opt.device = torch.device('cuda' if opt.gpus[0] >= 0 else 'cpu')
 
 print('Creating model...')
 model = create_model(opt.arch, opt.heads, opt.head_conv)
 optimizer = torch.optim.Adam(model.parameters(), opt.lr)
 start_epoch = 0
 if opt.load_model != '':
 model, optimizer, start_epoch = load_model(
 model, opt.load_model, optimizer, opt.resume, opt.lr, opt.lr_step)

 Trainer = train_factory[opt.task]
 trainer = Trainer(opt, model, optimizer)
 trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)

 print('Setting up data...')
 val_loader = torch.utils.data.DataLoader(
 Dataset(opt, 'val'), 
 batch_size=1, 
 shuffle=False,
 num_workers=1,
 pin_memory=True
 )

 if opt.test:
 _, preds = trainer.val(0, val_loader)
 val_loader.dataset.run_eval(preds, opt.save_dir)
 return

 train_loader = torch.utils.data.DataLoader(
 Dataset(opt, 'train'), 
 batch_size=opt.batch_size, 
 shuffle=True,
 num_workers=opt.num_workers,
 pin_memory=True,
 drop_last=True
 )

 print('Starting training...')
 best = 1e10
 for epoch in range(start_epoch + 1, opt.num_epochs + 1):
 mark = epoch if opt.save_all else 'last'
 log_dict_train, _ = trainer.train(epoch, train_loader)
 logger.write('epoch: {} |'.format(epoch))
 for k, v in log_dict_train.items():
 logger.scalar_summary('train_{}'.format(k), v, epoch)
 logger.write('{} {:8f} | '.format(k, v))
 if opt.val_intervals > 0 and epoch % opt.val_intervals == 0:
 save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(mark)), 
 epoch, model, optimizer)
 with torch.no_grad():
 log_dict_val, preds = trainer.val(epoch, val_loader)
 for k, v in log_dict_val.items():
 logger.scalar_summary('val_{}'.format(k), v, epoch)
 logger.write('{} {:8f} | '.format(k, v))
 if log_dict_val[opt.metric] < best:
 best = log_dict_val[opt.metric]
 save_model(os.path.join(opt.save_dir, 'model_best.pth'), 
 epoch, model)
 else:
 save_model(os.path.join(opt.save_dir, 'model_last.pth'), 
 epoch, model, optimizer)
 logger.write('\n')
 if epoch in opt.lr_step:
 save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(epoch)), 
 epoch, model, optimizer)
 lr = opt.lr * (0.1 ** (opt.lr_step.index(epoch) + 1))
 print('Drop LR to', lr)
 for param_group in optimizer.param_groups:
 param_group['lr'] = lr
 logger.close()

if __name__ == '__main__':
 opt = opts().parse()
 main(opt)