Debugging und hparam als Mapping

utils/config.py

@@ -7,7 +7,6 @@ from configparser import ConfigParser
 from pathlib import Path
 
 from ml_lib.utils.model_io import ModelParameters
-from ml_lib.utils.transforms import AsArray
 
 
 def is_jsonable(x):
@@ -116,7 +115,7 @@ class Config(ConfigParser, ABC):
     def build_model(self):
         return self.model_class(self.model_paramters)
 
-    def build_and_init_model(self, in_shape, weight_init_function):
+    def build_and_init_model(self, weight_init_function):
         model = self.build_model()
         model.init_weights(weight_init_function)
         return model

utils/model_io.py

@@ -1,4 +1,5 @@
 from argparse import Namespace
+from collections import Mapping
 from pathlib import Path
 
 import torch
@@ -7,7 +8,19 @@ from torch import nn
 
 
 # Hyperparamter Object
-class ModelParameters(Namespace):
+class ModelParameters(Mapping, Namespace):
+
+    def __getitem__(self, k):
+        # k: _KT -> _VT_co
+        return self.__dict__[k]
+
+    def __len__(self):
+        # -> int
+        return len(self.__dict__.keys())
+
+    def __iter__(self):
+        # -> Iterator[_T_co]
+        return iter(list(self.__dict__.keys()))
 
     _activations = dict(
         leaky_relu=nn.LeakyReLU,