Python utils.evaluate方法代码示例

# 需要导入模块: import utils [as 别名]
# 或者: from utils import evaluate [as 别名]
def load_apilog(self, log_fname, limit):
        with open(log_fname, 'rb') as f:
            data = f.read().split('\n')[:-1]
        if len(data) %2 !=0:
            data = data[:-1]
        idx = 0
        apilogs = []
        while idx < len(data) and idx < limit*2:
            if data[idx][:2] == 'IN':
                il = utils.evaluate(data[idx][2:])
            else:
                utils.error('load_apilog: parse IN error')

            if data[idx+1][:3] == 'OUT' :
                ol = utils.evaluate(data[idx+1][3:])
            else:
                utils.error('load_apilog: parse OUT error')
            apilog = log.ApiLog(self.apis[il[0]], il, ol)
            apilogs.append(apilog)
            idx+=2
        return apilogs 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import evaluate [as 别名]
def main(args):
  """Run testing."""
  test_data = utils.read_data(args, "test")
  print("total test samples:%s" % test_data.num_examples)

  if args.random_other:
    print("warning, testing mode with 'random_other' will result in "
          "different results every run...")

  model = models.get_model(args, gpuid=args.gpuid)
  tfconfig = tf.ConfigProto(allow_soft_placement=True)
  tfconfig.gpu_options.allow_growth = True
  tfconfig.gpu_options.visible_device_list = "%s" % (
      ",".join(["%s" % i for i in [args.gpuid]]))

  with tf.Session(config=tfconfig) as sess:
    utils.initialize(load=True, load_best=args.load_best,
                     args=args, sess=sess)

    # load the graph and variables
    tester = models.Tester(model, args, sess)

    perf = utils.evaluate(test_data, args, sess, tester)

  print("performance:")
  numbers = []
  for k in sorted(perf.keys()):
    print("%s, %s" % (k, perf[k]))
    numbers.append("%s" % perf[k])
  print(" ".join(sorted(perf.keys())))
  print(" ".join(numbers)) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import evaluate [as 别名]
def main():
    args = parse_args()
    C = importlib.import_module(args.config).TrainConfig
    print("MODEL ID: {}".format(C.model_id))

    summary_writer = SummaryWriter(C.log_dpath)

    train_iter, val_iter, test_iter, vocab = build_loaders(C)

    model = build_model(C, vocab)

    optimizer = torch.optim.Adam(model.parameters(), lr=C.lr, weight_decay=C.weight_decay, amsgrad=True)
    lr_scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=C.lr_decay_gamma,
                                     patience=C.lr_decay_patience, verbose=True)

    best_val_scores = { 'CIDEr': 0. }
    best_epoch = 0
    best_ckpt_fpath = None
    for e in range(1, C.epochs + 1):
        ckpt_fpath = C.ckpt_fpath_tpl.format(e)

        """ Train """
        print("\n")
        train_loss = train(e, model, optimizer, train_iter, vocab, C.decoder.rnn_teacher_forcing_ratio,
                           C.reg_lambda, C.recon_lambda, C.gradient_clip)
        log_train(C, summary_writer, e, train_loss, get_lr(optimizer))

        """ Validation """
        val_loss = test(model, val_iter, vocab, C.reg_lambda, C.recon_lambda)
        val_scores = evaluate(val_iter, model, model.vocab)
        log_val(C, summary_writer, e, val_loss, val_scores)

        if e >= C.save_from and e % C.save_every == 0:
            print("Saving checkpoint at epoch={} to {}".format(e, ckpt_fpath))
            save_checkpoint(e, model, ckpt_fpath, C)

        if e >= C.lr_decay_start_from:
            lr_scheduler.step(val_loss['total'])
        if e == 1 or val_scores['CIDEr'] > best_val_scores['CIDEr']:
            best_epoch = e
            best_val_scores = val_scores
            best_ckpt_fpath = ckpt_fpath

    """ Test with Best Model """
    print("\n\n\n[BEST]")
    best_model = load_checkpoint(model, best_ckpt_fpath)
    test_scores = evaluate(test_iter, best_model, best_model.vocab)
    log_test(C, summary_writer, best_epoch, test_scores)
    save_checkpoint(best_epoch, best_model, C.ckpt_fpath_tpl.format("best"), C)