binarization as a separate task of segmentation

train.py

@@ -96,7 +96,7 @@ def run(_config, n_classes, n_epochs, input_height,
         transformer_num_patches_xy, backbone_type, flip_index, dir_eval, dir_output,
         pretraining, learning_rate, task, f1_threshold_classification, classification_classes_name):
     
-    if task == "segmentation" or task == "enhancement":
+    if task == "segmentation" or task == "enhancement" or task == "binarization":
         if data_is_provided:
             dir_train_flowing = os.path.join(dir_output, 'train')
             dir_eval_flowing = os.path.join(dir_output, 'eval')
@@ -194,16 +194,16 @@ def run(_config, n_classes, n_epochs, input_height,
 
         if continue_training:
             if backbone_type=='nontransformer':
-                if is_loss_soft_dice and task == "segmentation":
+                if is_loss_soft_dice and (task == "segmentation" or task == "binarization"):
                     model = load_model(dir_of_start_model, compile=True, custom_objects={'soft_dice_loss': soft_dice_loss})
-                if weighted_loss and task == "segmentation":
+                if weighted_loss and (task == "segmentation" or task == "binarization"):
                     model = load_model(dir_of_start_model, compile=True, custom_objects={'loss': weighted_categorical_crossentropy(weights)})
                 if not is_loss_soft_dice and not weighted_loss:
                     model = load_model(dir_of_start_model , compile=True)
             elif backbone_type=='transformer':
-                if is_loss_soft_dice and task == "segmentation":
+                if is_loss_soft_dice and (task == "segmentation" or task == "binarization"):
                     model = load_model(dir_of_start_model, compile=True, custom_objects={"PatchEncoder": PatchEncoder, "Patches": Patches,'soft_dice_loss': soft_dice_loss})
-                if weighted_loss and task == "segmentation":
+                if weighted_loss and (task == "segmentation" or task == "binarization"):
                     model = load_model(dir_of_start_model, compile=True, custom_objects={'loss': weighted_categorical_crossentropy(weights)})
                 if not is_loss_soft_dice and not weighted_loss:
                     model = load_model(dir_of_start_model , compile=True,custom_objects = {"PatchEncoder": PatchEncoder, "Patches": Patches})
@@ -224,8 +224,9 @@ def run(_config, n_classes, n_epochs, input_height,
         
         #if you want to see the model structure just uncomment model summary.
         #model.summary()
+
         
-        if task == "segmentation":
+        if (task == "segmentation" or task == "binarization"):
             if not is_loss_soft_dice and not weighted_loss:
                 model.compile(loss='categorical_crossentropy',
                               optimizer=Adam(lr=learning_rate), metrics=['accuracy'])

utils.py

@@ -309,7 +309,7 @@ def data_gen(img_folder, mask_folder, batch_size, input_height, input_width, n_c
                                        interpolation=cv2.INTER_NEAREST)  # Read an image from folder and resize
 
                 img[i - c] = train_img  # add to array - img[0], img[1], and so on.
-                if task == "segmentation":
+                if task == "segmentation" or task=="binarization":
                     train_mask = cv2.imread(mask_folder + '/' + filename + '.png')
                     train_mask = get_one_hot(resize_image(train_mask, input_height, input_width), input_height, input_width,
                                             n_classes)
@@ -569,7 +569,7 @@ def provide_patches(imgs_list_train, segs_list_train, dir_img, dir_seg, dir_flow
     indexer = 0
     for im, seg_i in tqdm(zip(imgs_list_train, segs_list_train)):
         img_name = im.split('.')[0]
-        if task == "segmentation":
+        if task == "segmentation" or task == "binarization":
             dir_of_label_file = os.path.join(dir_seg, img_name + '.png')
         elif task=="enhancement":
             dir_of_label_file = os.path.join(dir_seg, im)