Fixes and improvements:

- Fixed LSA/FT (crashed under certain conditions) - Fixed model_executer creation for TensorFlow models on ImageNet - Removed unsused code and improved naming of variables
fraunhoferhhi · Nov 21, 2022 · da45cfc · da45cfc
1 parent 9c44bed
commit da45cfc
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Showing 5 changed files with 15 additions and 31 deletions.
diff --git a/README.md b/README.md
@@ -57,7 +57,7 @@ source env/bin/activate
 
 **Note**: For further information on how to set up a virtual python environment (also on **Windows**) refer to https://docs.python.org/3/library/venv.html .
 
-When successfully installed, the software outputs the line : "Successfully installed NNC-0.1.0"
+When successfully installed, the software outputs the line : "Successfully installed NNC-0.1.1"
 
 ### Importing the main module
 

diff --git a/framework/pytorch_model/__init__.py b/framework/pytorch_model/__init__.py
@@ -573,6 +573,9 @@ def tune_model(
                    model_dict[module_name] = model_dict[module_name].reshape(base_model_arch[module_name].shape)
         self.model.load_state_dict(model_dict)
 
+        for param in parameters:
+            parameters[param] = copy.deepcopy(self.model.state_dict()[param])
+
         tuning_params = []
         for name, param in self.model.named_parameters():
             if lsa_flag and ft_flag and param_types[name] in nnc_core.nnr_model.O_TYPES:

diff --git a/framework/tensorflow_model/__init__.py b/framework/tensorflow_model/__init__.py
@@ -157,8 +157,6 @@ def create_NNC_model_instance_from_object(
                  model_struct=None,
                  model_name=None
                 ):
-
-    model_name = model_object.name
 
     TEFModel = TensorFlowModel()
     model_parameters, loaded_model_struct = TEFModel.init_model_from_model_object(model_object)
@@ -258,23 +256,6 @@ def load_model( self,
                 model_parameter_dict = {}
                 for name in layer_names:
                     model_parameter_dict[name] = model_file[name]
-
-                else:
-                    if 'layer_names' in model_file.attrs:
-                        module_names = [n.decode('utf8') for n in model_file.attrs['layer_names']]
-
-                    layer_names = []
-                    for mod_name in module_names:
-                        layer = model_file[mod_name]
-                        if 'weight_names' in layer.attrs:
-                            weight_names = [mod_name+'/'+n.decode('utf8') for n in layer.attrs['weight_names']]
-                            if weight_names:
-                                layer_names += weight_names
-
-                    model_parameter_dict = {}
-                    for name in layer_names:
-                        model_parameter_dict[name] = model_file[name]
-
         except:
             raise SystemExit("Can't read model: {}".format(model_path))
 

diff --git a/framework/use_case_init/__init__.py b/framework/use_case_init/__init__.py
@@ -109,36 +109,36 @@ def init_validation(self, dataset_path, batch_size, num_workers):
         return val_set, val_loader
 
     def init_test_tef(self, dataset_path, batch_size, num_workers, model_name):
-        val_set = self.dataset(
+        test_set = self.dataset(
             root=dataset_path,
             split='test'
         )
         self.__model_name = model_name
 
-        val_images, val_labels = zip(*val_set.imgs)
-        val_loader = tf.data.Dataset.from_tensor_slices((list(val_images), list(val_labels)))
-        val_loader = val_loader.map(lambda image, label: tf.py_function(self.preprocess,
+        test_images, test_labels = zip(*test_set.imgs)
+        test_loader = tf.data.Dataset.from_tensor_slices((list(test_images), list(test_labels)))
+        test_loader = test_loader.map(lambda image, label: tf.py_function(self.preprocess,
                                                                               inp=[image, label],
                                                                               Tout=[tf.float32, tf.int32]), num_parallel_calls=num_workers).batch(
                                                                                 batch_size)
 
-        return val_set, val_loader
+        return test_set, test_loader
 
     def init_validation_tef(self, dataset_path, batch_size, num_workers, model_name):
-        test_set = self.dataset(
+        val_set = self.dataset(
             root=dataset_path,
             split='val'
         )
         self.__model_name = model_name
 
-        val_images, val_labels = zip(*test_set.imgs)
-        test_loader = tf.data.Dataset.from_tensor_slices((list(val_images), list(val_labels)))
-        test_loader = test_loader.map(lambda image, label: tf.py_function(self.preprocess,
+        val_images, val_labels = zip(*val_set.imgs)
+        val_loader = tf.data.Dataset.from_tensor_slices((list(val_images), list(val_labels)))
+        val_loader = val_loader.map(lambda image, label: tf.py_function(self.preprocess,
                                                                               inp=[image, label],
                                                                               Tout=[tf.float32, tf.int32]), num_parallel_calls=num_workers).batch(
                                                                                 batch_size)
 
-        return test_set, test_loader
+        return val_set, val_loader
 
 
     def preprocess(

diff --git a/setup.py b/setup.py
@@ -49,7 +49,7 @@
 from setuptools.command.build_ext import build_ext
 import setuptools
 
-__version__ = '0.1.0'
+__version__ = '0.1.1'
 
 
 class get_pybind_include(object):