Model Training

utils/model_io.py

@@ -1,5 +1,6 @@
 from argparse import Namespace
 from collections import Mapping
+from copy import deepcopy
 from pathlib import Path
 
 import torch
@@ -8,12 +9,12 @@ from torch import nn
 
 
 # Hyperparamter Object
-class ModelParameters(Mapping, Namespace):
+class ModelParameters(Namespace, Mapping):
 
     @property
-    def module_paramters(self):
-        paramter_mapping = dict()
-        paramter_mapping.update(self.model_param.__dict__)
+    def module_kwargs(self):
+
+        paramter_mapping = deepcopy(self.__dict__)
 
         paramter_mapping.update(
             dict(
@@ -21,26 +22,37 @@ class ModelParameters(Mapping, Namespace):
             )
         )
 
-        del paramter_mapping['in_shape']
-
         return paramter_mapping
 
+    @property
+    def test_activation(self):
+        try:
+            return self._activations[self.model.activation]
+        except KeyError:
+            return nn.ReLU
+
     def __getitem__(self, k):
         # k: _KT -> _VT_co
         return self.__dict__[k]
 
     def __len__(self):
         # -> int
-        return len(self.__dict__.keys())
+        return len(self.__dict__)
 
     def __iter__(self):
         # -> Iterator[_T_co]
         return iter(list(self.__dict__.keys()))
 
     def __delitem__(self, key):
-        self.__dict__.__delitem__(key)
+        self.__delattr__(key)
         return True
 
+    def __getattribute__(self, name):
+        if name == 'activation':
+            return self._activations[self['activation']]
+        else:
+            return super(ModelParameters, self).__getattribute__(name)
+
     _activations = dict(
         leaky_relu=nn.LeakyReLU,
         relu=nn.ReLU,
@@ -48,22 +60,8 @@ class ModelParameters(Mapping, Namespace):
         tanh=nn.Tanh
     )
 
-    def __init__(self, model_param, train_param, data_param):
-        self.model_param = model_param
-        self.train_param = train_param
-        self.data_param = data_param
-        kwargs = vars(model_param)
-        kwargs.update(vars(train_param))
-        kwargs.update(vars(data_param))
-        super(ModelParameters, self).__init__(**kwargs)
-
-    def __getattribute__(self, item):
-        if item == 'activation':
-            try:
-                return self._activations[item]
-            except KeyError:
-                return nn.ReLU
-        return super(ModelParameters, self).__getattribute__(item)
+    def __init__(self, parameter_mapping):
+        super(ModelParameters, self).__init__(**parameter_mapping)
 
 
 class SavedLightningModels(object):