import os, sys, time, torch, random, argparse
from PIL     import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from copy    import deepcopy
from pathlib import Path

lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from config_utils import load_config, dict2config
from procedures   import get_procedures, get_optim_scheduler
from datasets     import get_datasets
from models       import obtain_model
from utils        import get_model_infos
from log_utils    import PrintLogger, time_string


assert torch.cuda.is_available(), 'torch.cuda is not available'


def main(args):

  assert os.path.isdir ( args.data_path ) , 'invalid data-path : {:}'.format(args.data_path)
  assert os.path.isfile( args.checkpoint ), 'invalid checkpoint : {:}'.format(args.checkpoint)

  checkpoint = torch.load( args.checkpoint )
  xargs      = checkpoint['args']
  train_data, valid_data, xshape, class_num = get_datasets(xargs.dataset, args.data_path, xargs.cutout_length)
  valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=xargs.batch_size, shuffle=False, num_workers=xargs.workers, pin_memory=True)

  logger       = PrintLogger()
  model_config = dict2config(checkpoint['model-config'], logger)
  base_model   = obtain_model(model_config)
  flop, param  = get_model_infos(base_model, xshape)
  logger.log('model ====>>>>:\n{:}'.format(base_model))
  logger.log('model information : {:}'.format(base_model.get_message()))
  logger.log('-'*50)
  logger.log('Params={:.2f} MB, FLOPs={:.2f} M ... = {:.2f} G'.format(param, flop, flop/1e3))
  logger.log('-'*50)
  logger.log('valid_data : {:}'.format(valid_data))
  optim_config = dict2config(checkpoint['optim-config'], logger)
  _, _, criterion = get_optim_scheduler(base_model.parameters(), optim_config)
  logger.log('criterion  : {:}'.format(criterion))
  base_model.load_state_dict( checkpoint['base-model'] )
  _, valid_func = get_procedures(xargs.procedure)
  logger.log('initialize the CNN done, evaluate it using {:}'.format(valid_func))
  network = torch.nn.DataParallel(base_model).cuda()
  
  try:
    valid_loss, valid_acc1, valid_acc5 = valid_func(valid_loader, network, criterion, optim_config, 'pure-evaluation', xargs.print_freq_eval, logger)
  except:
    _, valid_func = get_procedures('basic')
    valid_loss, valid_acc1, valid_acc5 = valid_func(valid_loader, network, criterion, optim_config, 'pure-evaluation', xargs.print_freq_eval, logger)
  
  num_bytes = torch.cuda.max_memory_cached( next(network.parameters()).device ) * 1.0
  logger.log('***{:s}*** EVALUATION loss = {:.6f}, accuracy@1 = {:.2f}, accuracy@5 = {:.2f}, error@1 = {:.2f}, error@5 = {:.2f}'.format(time_string(), valid_loss, valid_acc1, valid_acc5, 100-valid_acc1, 100-valid_acc5))
  logger.log('[GPU-Memory-Usage on {:} is {:} bytes, {:.2f} KB, {:.2f} MB, {:.2f} GB.]'.format(next(network.parameters()).device, int(num_bytes), num_bytes / 1e3, num_bytes / 1e6, num_bytes / 1e9))
  logger.close()


if __name__ == '__main__':
  parser = argparse.ArgumentParser("Evaluate-CNN")
  parser.add_argument('--data_path',         type=str,   help='Path to dataset.')
  parser.add_argument('--checkpoint',        type=str,   help='Choose between Cifar10/100 and ImageNet.')
  args = parser.parse_args()
  main(args)
