Allow to non-trainable models with factory function

NOTE: it is particularly important that this command also sets the batch-normalization layers to non-trainable, which now seems to be the standard with Tensorflow 2 + Keras, but is not yet handled well by, e.g., the models from `segmentation_models`. Cf. `freeze_model` from `segmentation_models/models/_utils.py` and, e.g., https://keras.io/getting_started/faq/#whats-the-difference-between-the-training-argument-in-call-and-the-trainable-attribute and keras-team/keras#9965.

src/bfseg/cl_models/base_cl_model.py

@@ -52,8 +52,14 @@ def _build_model(self):
     assert (self.run.config['cl_params']['cl_framework']
             in ["ewc", "finetune"
                ]), "Currently, only EWC and fine-tuning are supported."
+    # NOTE: by default the model is created as trainable. CL frameworks that
+    # require a fixed, non-trainable network from which to distill the
+    # information (e.g., in distillation experiments) should create additional
+    # models by overloading this method and calling `super()._build_model()` in
+    # the overload.
     self.encoder, self.model = create_model(
         model_name=self.run.config['network_params']['architecture'],
+        trainable=True,
         **self.run.config['network_params']['model_params'])
     self.new_model = keras.Model(
         inputs=self.model.input,

src/bfseg/cl_models/distillation_model.py

@@ -43,7 +43,8 @@ def __init__(self, run, root_output_dir):
           "Distillation model requires the CL parameter `distillation_type` "
           "to be specified.")
 
-    super(DistillationModel, self).__init__(run=run, root_output_dir=root_output_dir)
+    super(DistillationModel, self).__init__(run=run,
+                                            root_output_dir=root_output_dir)
 
     self._started_training_new_task = False
 

src/bfseg/utils/models.py

@@ -9,6 +9,7 @@
 def create_model(model_name,
                  image_h,
                  image_w,
+                 trainable,
                  log_params_used=True,
                  **model_params):
   r"""Factory function that creates a model with the given parameters.
@@ -18,6 +19,7 @@ def create_model(model_name,
       "fast_scnn", "unet".
     image_h (int): Image height.
     image_w (int): Image width.
+    trainable (bool): Whether or not the model should be trainable.
     log_params_used (bool): If True, the complete list of parameters used to
       instantiate the model is printed.
     ---
@@ -91,4 +93,17 @@ def create_model(model_name,
 
   encoder, model = model_fn(**model_params)
 
-  return encoder, model
+  # Optionally set the model as non-trainable.
+  if (not trainable):
+    # NOTE: it is particularly important that this command also sets the
+    # batch-normalization layers to non-trainable, which now seems to be the
+    # standard with Tensorflow 2 + Keras, but is not yet handled well by, e.g.,
+    # the models from `segmentation_models`.
+    # Cf. `freeze_model` from `segmentation_models/models/_utils.py` and, e.g.,
+    # https://keras.io/getting_started/faq/#whats-the-difference-between-the-
+    # training-argument-in-call-and-the-trainable-attribute and
+    # https://github.com/keras-team/keras/pull/9965.
+    encoder.trainable = False
+    model.trainable = False
+
+  return encoder, model

src/nyu_pretraining.py

@@ -38,6 +38,7 @@ def pretrain_nyu(_run,
   _, model = create_model(model_name="fast_scnn",
                           image_h=image_h,
                           image_w=image_w,
+                          trainable=True,
                           num_downsampling_layers=2)
 
   model.compile(