def compute_centroids_estimate(ds, net_wo_centroids, net_centroids, scaler, n_iter=3):
pred = np.argmax(net_wo_centroids.predict(ds.inputs, 1000), axis=1)
r = RegionCentroids(134)
r.update_barycentres(ds.vx, pred)
p = PickCentroidDistances(134)
distances = p.pick(ds.vx, None, None, r)[0]
ds.inputs[:, -134:] = distances
scaler.scale(ds.inputs)
# New evaluations
for i in range(n_iter):
d = compute_dice(pred, np.argmax(ds.outputs, axis=1), 134)
print np.mean(d)
pred = np.argmax(net_centroids.predict(ds.inputs, 1000), axis=1)
r.update_barycentres(ds.vx, pred)
distances = p.pick(ds.vx, None, None, r)[0]
ds.inputs[:, -134:] = distances
scaler.scale(ds.inputs)
# Create the data generator
data_gen = DataGeneratorBrain()
data_gen.init_from(file_list, pick_vx, pick_patch, pick_tg)
# Evaluate the centroids
net_wo_centroids_path = "./experiments/report_3_patches_balanced_conv/"
net_wo_centroids = NetworkThreePatchesConv()
net_wo_centroids.init(29, 135)
net_wo_centroids.load_parameters(
open_h5file(net_wo_centroids_path + "net.net"))
ds_testing = DatasetBrainParcellation()
ds_testing.read(data_path + "train.h5")
pred_wo_centroids = np.argmax(net_wo_centroids.predict(
ds_testing.inputs, 1000),
axis=1)
region_centroids = RegionCentroids(134)
region_centroids.update_barycentres(ds_testing.vx, pred_wo_centroids)
# Generate and evaluate the dataset
start_time = time.clock()
dices = np.zeros((n_files, 134))
errors = np.zeros((n_files, ))
pred_functions = {}
for atlas_id in xrange(n_files):
print "Atlas: {}".format(atlas_id)
ls_vx = []
ls_pred = []
brain_batches = data_gen.generate_single_atlas(atlas_id, None,