Python utils.evaluate() Examples

The following are 3 code examples of utils.evaluate(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module utils , or try the search function .
Example #1
Source File: apifuzz.py    From IMF with MIT License 6 votes vote down vote up 
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
Example #2
Source File: test.py    From next-prediction with Apache License 2.0 5 votes vote down vote up 
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))
Example #3
Source File: train.py    From RecNet with MIT License 4 votes vote down vote up 
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)