print('PID: {}'.format(pid)) f = open('pid_' + str(train_split), 'wb') f.write(str(pid) + '\n') f.close() # Load model model = SupervisedModel('experiment', './', learning_rate=1e-2) monitor = util.Monitor(model, checkpoint_directory='checkpoints_' + str(train_split), save_steps=1000) # Loading CK+ dataset print('Loading Data') supervised_data_loader = SupervisedDataLoaderCrossVal( data_paths.ck_plus_data_path) train_data_container = supervised_data_loader.load('train', train_split) test_data_container = supervised_data_loader.load('test', train_split) X_train = train_data_container.X X_train = numpy.float32(X_train) X_train /= 255.0 X_train *= 2.0 y_train = train_data_container.y X_test = test_data_container.X X_test = numpy.float32(X_test) X_test /= 255.0 X_test *= 2.0 y_test = test_data_container.y train_dataset = supervised_dataset.SupervisedDataset(X_train, y_train)
parser.add_argument("checkpoint_file", help="Path to a single model checkpoint (.pkl file).") args = parser.parse_args() checkpoint_file = args.checkpoint_file fold = int(args.split) dataset_path = data_paths.ck_plus_data_path print "Checkpoint: %s" % checkpoint_file print "Testing on split %d\n" % fold # Load model model = SupervisedModel("evaluation", "./") # Load dataset supervised_data_loader = SupervisedDataLoaderCrossVal(dataset_path) test_data_container = supervised_data_loader.load(mode="test", fold=fold) test_data_container.X = numpy.float32(test_data_container.X) test_data_container.X /= 255.0 test_data_container.X *= 2.0 # Remove samples with neutral and contempt labels mask = numpy.logical_and(test_data_container.y != 0, test_data_container.y != 2) test_data_container.X = test_data_container.X[mask, :, :, :] test_data_container.y = test_data_container.y[mask] test_data_container.y = reindex_labels(test_data_container.y) num_test_samples = len(test_data_container.y) if fold == 9: test_data_container.X, test_data_container.y = add_padding(test_data_container.X, test_data_container.y) # Construct evaluator
print('PID: {}'.format(pid)) f = open('pid_'+str(train_split), 'wb') f.write(str(pid)+'\n') f.close() # Load model model = SupervisedModel('experiment', './', learning_rate=1e-2) monitor = util.Monitor(model, checkpoint_directory='checkpoints_'+str(train_split), save_steps=1000) # Loading CK+ dataset print('Loading Data') supervised_data_loader = SupervisedDataLoaderCrossVal( data_paths.ck_plus_data_path) train_data_container = supervised_data_loader.load('train', train_split) test_data_container = supervised_data_loader.load('test', train_split) X_train = train_data_container.X X_train = numpy.float32(X_train) X_train /= 255.0 X_train *= 2.0 y_train = train_data_container.y X_test = test_data_container.X X_test = numpy.float32(X_test) X_test /= 255.0 X_test *= 2.0 y_test = test_data_container.y train_dataset = supervised_dataset.SupervisedDataset(X_train, y_train)
help='Path to a single model checkpoint (.pkl file).') args = parser.parse_args() checkpoint_file = args.checkpoint_file fold = int(args.split) dataset_path = data_paths.ck_plus_data_path print 'Checkpoint: %s' % checkpoint_file print 'Testing on split %d\n' % fold # Load model model = SupervisedModel('evaluation', './') # Load dataset supervised_data_loader = SupervisedDataLoaderCrossVal(dataset_path) test_data_container = supervised_data_loader.load(mode='test', fold=fold) test_data_container.X = numpy.float32(test_data_container.X) test_data_container.X /= 255.0 test_data_container.X *= 2.0 # Construct evaluator preprocessor = [util.Normer3(filter_size=5, num_channels=1)] evaluator = util.Evaluator(model, test_data_container, checkpoint_file, preprocessor) # For the inputted checkpoint, compute the overall test accuracy accuracies = [] print 'Checkpoint: %s' % os.path.split(checkpoint_file)[1] evaluator.set_checkpoint(checkpoint_file) accuracy = evaluator.run()