Renamed model

DeepDeformationMapRegistration/networks.py

@@ -10,7 +10,7 @@ import voxelmorph as vxm
 from voxelmorph.tf.modelio import LoadableModel, store_config_args
 
 
-class VxmWeaklySupervised(LoadableModel):
+class WeaklySupervised(LoadableModel):
 
     @store_config_args
     def __init__(self, inshape, all_labels: [list, tuple], nb_unet_features=None, int_steps=5, bidir=False, **kwargs):
@@ -61,3 +61,4 @@ class VxmWeaklySupervised(LoadableModel):
         img_input = tf.keras.Input(shape=mov_img.shape[1:], name='input_img')
         pred_img = vxm.layers.SpatialTransformer(interp_method=interp_method)([img_input, warp_model.output])
         return tf.keras.Model(warp_model.inputs, pred_img).predict([mov_segm, fix_segm, mov_img])
+

TrainingScripts/Train_3d_weaklySupervised.py

@@ -16,7 +16,7 @@ from datetime import datetime
 import DeepDeformationMapRegistration.utils.constants as C
 from DeepDeformationMapRegistration.data_generator import DataGeneratorManager
 from DeepDeformationMapRegistration.utils.misc import try_mkdir
-from DeepDeformationMapRegistration.networks import VxmWeaklySupervised
+from DeepDeformationMapRegistration.networks import WeaklySupervised
 from DeepDeformationMapRegistration.losses import HausdorffDistance
 from DeepDeformationMapRegistration.layers import UncertaintyWeighting
 
@@ -44,7 +44,7 @@ in_shape = train_generator.get_input_shape()[1:-1]
 enc_features = [16, 32, 32, 32, 32, 32]# const.ENCODER_FILTERS
 dec_features = [32, 32, 32, 32, 32, 32, 32, 16, 16]# const.ENCODER_FILTERS[::-1]
 nb_features = [enc_features, dec_features]
-vxm_model = VxmWeaklySupervised(inshape=in_shape, all_labels=[1], nb_unet_features=nb_features, int_steps=5)
+vxm_model = WeaklySupervised(inshape=in_shape, all_labels=[1], nb_unet_features=nb_features, int_steps=5)
 
 # Losses and loss weights