--- a
+++ b/test_seg_patch.py
@@ -0,0 +1,99 @@
+import string
+import sys
+import lasagne as nn
+import numpy as np
+import theano
+import buffering
+import pathfinder
+import utils
+from configuration import config, set_configuration
+from utils_plots import plot_slice_3d_3
+import utils_lung
+import logger
+
+theano.config.warn_float64 = 'raise'
+
+if len(sys.argv) < 2:
+    sys.exit("Usage: train.py <configuration_name>")
+
+config_name = sys.argv[1]
+set_configuration('configs_seg_patch', config_name)
+
+# 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-test.log' % expid)
+sys.stderr = sys.stdout
+
+# predictions path
+predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
+outputs_path = predictions_dir + '/' + expid
+utils.auto_make_dir(outputs_path)
+
+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'])
+
+valid_loss = config().build_objective(model, deterministic=True)
+
+x_shared = nn.utils.shared_empty(dim=len(model.l_in.shape))
+
+givens_valid = {}
+givens_valid[model.l_in.input_var] = x_shared
+
+# theano functions
+iter_get_predictions = theano.function([], nn.layers.get_output(model.l_out, deterministic=True), givens=givens_valid)
+valid_data_iterator = config().valid_data_iterator
+
+print
+print 'Data'
+print 'n validation: %d' % valid_data_iterator.nsamples
+
+valid_losses_dice = []
+tp = 0
+for n, (x_chunk, y_chunk, id_chunk) in enumerate(buffering.buffered_gen_threaded(valid_data_iterator.generate())):
+    # load chunk to GPU
+    x_shared.set_value(x_chunk)
+    predictions = iter_get_predictions()
+    targets = y_chunk
+    inputs = x_chunk
+
+    if predictions.shape != targets.shape:
+        pad_width = (np.asarray(targets.shape) - np.asarray(predictions.shape)) / 2
+        pad_width = [(p, p) for p in pad_width]
+        predictions = np.pad(predictions, pad_width=pad_width, mode='constant')
+
+    dice = utils_lung.dice_index(predictions, targets)
+    print n, id_chunk, dice
+    valid_losses_dice.append(dice)
+    if np.sum(predictions * targets) / np.sum(targets) > 0.1:
+        tp += 1
+    else:
+        print 'not detected!!!!'
+
+    for k in xrange(predictions.shape[0]):
+        plot_slice_3d_3(input=inputs[k, 0], mask=targets[k, 0], prediction=predictions[k, 0],
+                        axis=0, pid='-'.join([str(n), str(k), str(id_chunk[k])]),
+                        img_dir=outputs_path)
+
+print 'Dice index validation loss', np.mean(valid_losses_dice)
+print 'TP', tp