def stat_of_all_models(self, img_true, n_out):
'''
return two lists of dice coefficient and error of all models in the ensemble
'''
dices = []
errs = []
count = 1
for pred_raw in self.pred_raws:
# Compute img_pred
pred_all = np.argmax(pred_raw, axis=1)
img_pred = create_img_from_pred(self.vx_all, pred_all,
img_true.shape)
# Compute the dice coefficient and the error
non_zo = img_pred.nonzero() or img_true.nonzero()
pred = img_pred[non_zo]
true = img_true[non_zo]
dice_regions = compute_dice(pred, true, n_out)
dices.append(dice_regions.mean())
err_global = error_rate(pred, true)
errs.append(err_global)
count = count + 1
return dices, errs
brain_batches = data_gen.generate_single_atlas(atlas_id, None,
region_centroids,
batch_size, True)
vx_all, pred_all = net.predict_from_generator(brain_batches, scaler,
pred_functions)
# Construct the predicted image
img_true = data_gen.atlases[atlas_id][1]
img_pred = create_img_from_pred(vx_all, pred_all, img_true.shape)
# Compute the dice coefficient and the error
non_zo = img_pred.nonzero() or img_true.nonzero()
pred = img_pred[non_zo]
true = img_true[non_zo]
dice_regions = compute_dice(pred, true, n_out)
err_global = error_rate(pred, true)
end_time = time.clock()
print "It took {} seconds to evaluate the whole brain.".format(
end_time - start_time)
print "The mean dice is {}".format(dice_regions.mean())
print "The error rateis {}".format(err_global)
# Save the results
errors[atlas_id] = err_global
dices[atlas_id, :] = dice_regions
# Diff Image
img_diff = (img_pred == img_true).astype(np.uint8)
img_diff += 1
from spynet.utils.utilities import open_h5file
if __name__ == '__main__':
"""
Evaluate a trained network (without approximating the centroids)
"""
experiment_path = "./experiments/paper_ultimate_conv/"
data_path = "./datasets/paper_ultimate_conv/"
# Load the network
net = NetworkUltimateConv()
net.init(33, 29, 5, 134, 135)
net.load_parameters(open_h5file(experiment_path + "net.net"))
n_out = net.n_out
# Load the scaler
scaler = pickle.load(open(experiment_path + "s.scaler", "rb"))
testing_data_path = data_path + "test.h5"
ds_testing = DatasetBrainParcellation()
ds_testing.read(testing_data_path)
scaler.scale(ds_testing.inputs)
out_pred = net.predict(ds_testing.inputs, 1000)
errors = np.argmax(out_pred, axis=1) != np.argmax(ds_testing.outputs,
axis=1)
dice = compute_dice(np.argmax(out_pred, axis=1),
np.argmax(ds_testing.outputs, axis=1), 134)
print np.mean(dice)
print np.mean(errors)
ls_vx = []
ls_pred = []
brain_batches = data_gen.generate_single_atlas(atlas_id, None, region_centroids, batch_size, True)
vx_all, pred_all = net.predict_from_generator(brain_batches, scaler, pred_functions)
# Construct the predicted image
img_true = data_gen.atlases[atlas_id][1]
img_pred = create_img_from_pred(vx_all, pred_all, img_true.shape)
# Compute the dice coefficient and the error
non_zo = img_pred.nonzero() or img_true.nonzero()
pred = img_pred[non_zo]
true = img_true[non_zo]
dice_regions = compute_dice(pred, true, n_out)
err_global = error_rate(pred, true)
end_time = time.clock()
print "It took {} seconds to evaluate the whole brain.".format(end_time - start_time)
print "The mean dice is {}".format(dice_regions.mean())
print "The error rateis {}".format(err_global)
# Save the results
errors[atlas_id] = err_global
dices[atlas_id, :] = dice_regions
# Diff Image
img_diff = (img_pred == img_true).astype(np.uint8)
img_diff += 1
img_diff[img_pred == 0] = 0
from data_brain_parcellation import DatasetBrainParcellation, DataGeneratorBrain, list_miccai_files, RegionCentroids
from spynet.utils.utilities import open_h5file
if __name__ == '__main__':
"""
Evaluate a trained network (without approximating the centroids)
"""
experiment_path = "./experiments/paper_ultimate_conv/"
data_path = "./datasets/paper_ultimate_conv/"
# Load the network
net = NetworkUltimateConv()
net.init(33, 29, 5, 134, 135)
net.load_parameters(open_h5file(experiment_path + "net.net"))
n_out = net.n_out
# Load the scaler
scaler = pickle.load(open(experiment_path + "s.scaler", "rb"))
testing_data_path = data_path + "test.h5"
ds_testing = DatasetBrainParcellation()
ds_testing.read(testing_data_path)
scaler.scale(ds_testing.inputs)
out_pred = net.predict(ds_testing.inputs, 1000)
errors = np.argmax(out_pred, axis=1) != np.argmax(ds_testing.outputs, axis=1)
dice = compute_dice(np.argmax(out_pred, axis=1), np.argmax(ds_testing.outputs, axis=1), 134)
print np.mean(dice)
print np.mean(errors)
