--- a
+++ b/test_class_dsb.py
@@ -0,0 +1,230 @@
+import string
+import sys
+import lasagne as nn
+import numpy as np
+import theano
+import utils
+import logger
+import buffering
+from configuration import config, set_configuration
+import pathfinder
+import utils_lung
+import os
+import evaluate_submission
+
+theano.config.warn_float64 = 'raise'
+
+if len(sys.argv) < 2:
+    sys.exit("Usage: test_class_dsb.py <configuration_name> <valid|test>")
+
+config_name = sys.argv[1]
+set_configuration('configs_class_dsb', config_name)
+
+set = sys.argv[2] if len(sys.argv) == 3 else 'test'
+
+# metadata
+metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
+metadata_path = utils.find_model_metadata(metadata_dir, config_name)
+
+metadata = utils.load_pkl(metadata_path)
+expid = metadata['experiment_id']
+
+# logs
+logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
+sys.stdout = logger.Logger(logs_dir + '/%s-%s.log' % (expid, set))
+sys.stderr = sys.stdout
+
+# predictions path
+predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
+output_pkl_file = predictions_dir + '/%s-%s.pkl' % (expid, set)
+
+submissions_dir = utils.get_dir_path('submissions', pathfinder.METADATA_PATH)
+output_csv_file = submissions_dir + '/%s-%s.csv' % (expid, set)
+
+# if os.path.isfile(output_pkl_file):
+#     pid2prediction = utils.load_pkl(output_pkl_file)
+#     utils_lung.write_submission(pid2prediction, output_csv_file)
+#     print 'saved csv'
+#     print output_csv_file
+#     sys.exit(0)
+
+print 'Build model'
+model = config().build_model()
+all_layers = nn.layers.get_all_layers(model.l_out)
+all_params = nn.layers.get_all_params(model.l_out)
+num_params = nn.layers.count_params(model.l_out)
+print '  number of parameters: %d' % num_params
+print string.ljust('  layer output shapes:', 36),
+print string.ljust('#params:', 10),
+print 'output shape:'
+for layer in all_layers:
+    name = string.ljust(layer.__class__.__name__, 32)
+    num_param = sum([np.prod(p.get_value().shape) for p in layer.get_params()])
+    num_param = string.ljust(num_param.__str__(), 10)
+    print '    %s %s %s' % (name, num_param, layer.output_shape)
+
+nn.layers.set_all_param_values(model.l_out, metadata['param_values'])
+
+# theano functions
+iter_test = theano.function([model.l_in.input_var], nn.layers.get_output(model.l_out, deterministic=True))
+
+if set == 'test':
+    pid2label = utils_lung.read_test_labels(pathfinder.TEST_LABELS_PATH)
+    data_iterator = config().test_data_iterator
+
+    print
+    print 'Data'
+    print 'n test: %d' % data_iterator.nsamples
+
+    pid2prediction = {}
+    for i, (x_test, _, id_test) in enumerate(buffering.buffered_gen_threaded(
+            data_iterator.generate())):
+        predictions = iter_test(x_test)
+        pid = id_test[0]
+        print predictions
+        pid2prediction[pid] = predictions[1] if predictions.shape[-1] == 2 else predictions[0]
+        print i, pid, predictions#, pid2label[pid]
+
+    utils.save_pkl(pid2prediction, output_pkl_file)
+    print 'Saved validation predictions into pkl', os.path.basename(output_pkl_file)
+
+    test_loss = utils_lung.evaluate_log_loss(pid2prediction, pid2label)
+    print 'Test loss', test_loss
+
+    utils_lung.write_submission(pid2prediction, output_csv_file)
+    print 'Saved predictions into csv'
+    loss = evaluate_submission.leaderboard_performance(output_csv_file)
+    print loss
+
+elif set == 'valid':
+    data_iterator = config().valid_data_iterator
+
+    print
+    print 'Data'
+    print 'n valid: %d' % data_iterator.nsamples
+
+    pid2prediction, pid2label = {}, {}
+    for i, (x_test, y_test, id_test) in enumerate(buffering.buffered_gen_threaded(
+            data_iterator.generate())):
+        predictions = iter_test(x_test)
+        pid = id_test[0]
+        pid2prediction[pid] = predictions[0, 1] if predictions.shape[-1] == 2 else predictions[0]
+        pid2label[pid] = y_test[0]
+        print i, pid, predictions, pid2label[pid]
+
+    utils.save_pkl(pid2prediction, output_pkl_file)
+    print 'Saved validation predictions into pkl', os.path.basename(output_pkl_file)
+    valid_loss = utils_lung.evaluate_log_loss(pid2prediction, pid2label)
+    print 'Validation loss', valid_loss
+
+elif set == 'stage2':
+    data_iterator = config().test_data_iterator
+
+    print
+    print 'Data'
+    print 'n test: %d' % data_iterator.nsamples
+
+    pid2prediction = {}
+    for i, (x_test, _, id_test) in enumerate(buffering.buffered_gen_threaded(
+            data_iterator.generate())):
+        predictions = iter_test(x_test)
+        pid = id_test[0]
+        print predictions
+        pid2prediction[pid] = predictions[1] if predictions.shape[-1] == 2 else predictions[0]
+        print i, pid, predictions
+
+    utils.save_pkl(pid2prediction, output_pkl_file)
+    print 'Saved validation predictions into pkl', os.path.basename(output_pkl_file)
+
+    utils_lung.write_submission(pid2prediction, output_csv_file)
+    print 'Saved predictions into csv'
+
+elif set == 'tta_test':
+    pid2label = utils_lung.read_test_labels(pathfinder.TEST_LABELS_PATH)
+    data_iterator = config().tta_test_data_iterator
+    tta_bs = config().tta_batch_size
+
+    print
+    print 'Data'
+    print 'n valid: %d' % data_iterator.nsamples
+
+
+    pid2prediction = {}
+    for i, (x_test, _, id_test) in enumerate(buffering.buffered_gen_threaded(
+            data_iterator.generate())):
+        preds = []
+        for bidx, pos in enumerate(range(0,x_test.shape[0],tta_bs)):
+            predictions = iter_test(x_test[pos:pos+tta_bs])
+            predictions = predictions[:, 1] if predictions.shape[-1] == 2 else predictions
+            preds.append(predictions)
+        
+        preds = np.concatenate(preds)
+        pred = np.average(preds)
+        pid = id_test
+
+        pid2prediction[pid] = pred
+        print i, pid, pred, pid2label[pid]
+
+
+
+    output_pkl_file = predictions_dir + '/%s-%s-%s.pkl' % (expid, set, str(data_iterator.tta))
+    output_csv_file = submissions_dir + '/%s-%s-%s.csv' % (expid, set, str(data_iterator.tta))
+
+    utils.save_pkl(pid2prediction, output_pkl_file)
+    print 'Saved predictions into pkl', os.path.basename(output_pkl_file)
+
+    test_loss = utils_lung.evaluate_log_loss(pid2prediction, pid2label)
+    print 'Test loss', test_loss
+
+    utils_lung.write_submission(pid2prediction, output_csv_file)
+    print 'Saved predictions into csv'
+    loss = evaluate_submission.leaderboard_performance(output_csv_file)
+    print loss
+
+
+
+elif set == 'tta_valid':
+    pid2label = utils_lung.read_labels(pathfinder.LABELS_PATH)
+    data_iterator = config().tta_valid_data_iterator
+    tta_bs = config().tta_batch_size
+
+    print
+    print 'Data'
+    print 'n valid: %d' % data_iterator.nsamples
+
+
+    pid2prediction = {}
+    for i, (x_valid, _, pid) in enumerate(buffering.buffered_gen_threaded(
+            data_iterator.generate())):
+        preds = []
+        print x_valid.shape[0]
+        for bidx, pos in enumerate(range(0,x_valid.shape[0],tta_bs)):
+            predictions = iter_test(x_valid[pos:pos+tta_bs])
+            predictions = predictions[:, 1] if predictions.shape[-1] == 2 else predictions
+            preds.append(predictions)
+        
+        preds = np.concatenate(preds)
+        pred = np.average(preds)
+
+        pid2prediction[pid] = pred
+        print i, pid, pred, pid2label[pid]
+
+
+
+    output_pkl_file = predictions_dir + '/%s-%s-%s.pkl' % (expid, set, str(data_iterator.tta))
+    output_csv_file = submissions_dir + '/%s-%s-%s.csv' % (expid, set, str(data_iterator.tta))
+
+    utils.save_pkl(pid2prediction, output_pkl_file)
+    print 'Saved predictions into pkl', os.path.basename(output_pkl_file)
+
+    test_loss = utils_lung.evaluate_log_loss(pid2prediction, pid2label)
+    print 'Test loss', test_loss
+
+    utils_lung.write_submission(pid2prediction, output_csv_file)
+    print 'Saved predictions into csv'
+    loss = evaluate_submission.leaderboard_performance(output_csv_file)
+    print loss
+
+
+else:
+    raise ValueError('wrong set argument')