On/Off filter can be turned on/off

Dataset.py

@@ -135,10 +135,13 @@ class DatasetMNIST(DatasetMaster):
         pattern = scripted_transforms(pattern)
 
         # => On/Off
+        if cfg.augmentation.use_on_off_filter is True:
             my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
             gray: torch.Tensor = my_on_off_filter(
                 pattern[:, 0:1, :, :],
             )
+        else:
+            gray = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
 
         return gray
 
@@ -166,10 +169,13 @@ class DatasetMNIST(DatasetMaster):
         pattern = scripted_transforms(pattern)
 
         # => On/Off
+        if cfg.augmentation.use_on_off_filter is True:
             my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
             gray: torch.Tensor = my_on_off_filter(
                 pattern[:, 0:1, :, :],
             )
+        else:
+            gray = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
 
         return gray
 
@@ -225,10 +231,13 @@ class DatasetFashionMNIST(DatasetMaster):
         pattern = scripted_transforms(pattern)
 
         # => On/Off
+        if cfg.augmentation.use_on_off_filter is True:
             my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
             gray: torch.Tensor = my_on_off_filter(
                 pattern[:, 0:1, :, :],
             )
+        else:
+            gray = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
 
         return gray
 
@@ -263,10 +272,13 @@ class DatasetFashionMNIST(DatasetMaster):
         pattern = scripted_transforms(pattern)
 
         # => On/Off
+        if cfg.augmentation.use_on_off_filter is True:
             my_on_off_filter: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
             gray: torch.Tensor = my_on_off_filter(
                 pattern[:, 0:1, :, :],
             )
+        else:
+            gray = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
 
         return gray
 
@@ -321,10 +333,16 @@ class DatasetCIFAR(DatasetMaster):
         pattern = scripted_transforms(pattern)
 
         # => On/Off
-
+        if cfg.augmentation.use_on_off_filter is True:
-        my_on_off_filter_r: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
+            my_on_off_filter_r: OnOffFilter = OnOffFilter(
-        my_on_off_filter_g: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[1])
+                p=cfg.image_statistics.mean[0]
-        my_on_off_filter_b: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[2])
+            )
+            my_on_off_filter_g: OnOffFilter = OnOffFilter(
+                p=cfg.image_statistics.mean[1]
+            )
+            my_on_off_filter_b: OnOffFilter = OnOffFilter(
+                p=cfg.image_statistics.mean[2]
+            )
             r: torch.Tensor = my_on_off_filter_r(
                 pattern[:, 0:1, :, :],
             )
@@ -334,6 +352,10 @@ class DatasetCIFAR(DatasetMaster):
             b: torch.Tensor = my_on_off_filter_b(
                 pattern[:, 2:3, :, :],
             )
+        else:
+            r = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
+            g = pattern[:, 1:2, :, :] + torch.finfo(torch.float32).eps
+            b = pattern[:, 2:3, :, :] + torch.finfo(torch.float32).eps
 
         new_tensor: torch.Tensor = torch.cat((r, g, b), dim=1)
         return new_tensor
@@ -370,9 +392,16 @@ class DatasetCIFAR(DatasetMaster):
         pattern = scripted_transforms(pattern)
 
         # => On/Off
-        my_on_off_filter_r: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[0])
+        if cfg.augmentation.use_on_off_filter is True:
-        my_on_off_filter_g: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[1])
+            my_on_off_filter_r: OnOffFilter = OnOffFilter(
-        my_on_off_filter_b: OnOffFilter = OnOffFilter(p=cfg.image_statistics.mean[2])
+                p=cfg.image_statistics.mean[0]
+            )
+            my_on_off_filter_g: OnOffFilter = OnOffFilter(
+                p=cfg.image_statistics.mean[1]
+            )
+            my_on_off_filter_b: OnOffFilter = OnOffFilter(
+                p=cfg.image_statistics.mean[2]
+            )
             r: torch.Tensor = my_on_off_filter_r(
                 pattern[:, 0:1, :, :],
             )
@@ -382,6 +411,10 @@ class DatasetCIFAR(DatasetMaster):
             b: torch.Tensor = my_on_off_filter_b(
                 pattern[:, 2:3, :, :],
             )
+        else:
+            r = pattern[:, 0:1, :, :] + torch.finfo(torch.float32).eps
+            g = pattern[:, 1:2, :, :] + torch.finfo(torch.float32).eps
+            b = pattern[:, 2:3, :, :] + torch.finfo(torch.float32).eps
 
         new_tensor: torch.Tensor = torch.cat((r, g, b), dim=1)
         return new_tensor

Parameter.py

@@ -42,12 +42,14 @@ class Network:
     its layers and the number of output neurons."""
 
     number_of_output_neurons: int = field(default=0)
-    forward_kernel_size: list[list[int]] = field(default_factory=list)
     forward_neuron_numbers: list[list[int]] = field(default_factory=list)
+    is_pooling_layer: list[bool] = field(default_factory=list)
+
+    forward_kernel_size: list[list[int]] = field(default_factory=list)
     strides: list[list[int]] = field(default_factory=list)
     dilation: list[list[int]] = field(default_factory=list)
     padding: list[list[int]] = field(default_factory=list)
-    is_pooling_layer: list[bool] = field(default_factory=list)
+
     w_trainable: list[bool] = field(default_factory=list)
     eps_xy_trainable: list[bool] = field(default_factory=list)
     eps_xy_mean: list[bool] = field(default_factory=list)
@@ -57,24 +59,34 @@ class Network:
 class LearningParameters:
     """Parameter required for training"""
 
+    learning_active: bool = field(default=True)
+
     loss_coeffs_mse: float = field(default=0.5)
     loss_coeffs_kldiv: float = field(default=1.0)
+
+    optimizer_name: str = field(default="Adam")
     learning_rate_gamma_w: float = field(default=-1.0)
     learning_rate_gamma_eps_xy: float = field(default=-1.0)
     learning_rate_threshold_w: float = field(default=0.00001)
     learning_rate_threshold_eps_xy: float = field(default=0.00001)
-    learning_active: bool = field(default=True)
+
+    lr_schedule_name: str = field(default="ReduceLROnPlateau")
+    lr_scheduler_factor_w: float = field(default=0.75)
+    lr_scheduler_patience_w: int = field(default=-1)
+
+    lr_scheduler_factor_eps_xy: float = field(default=0.75)
+    lr_scheduler_patience_eps_xy: int = field(default=-1)
+
+    number_of_batches_for_one_update: int = field(default=1)
+    overload_path: str = field(default="./Previous")
+
     weight_noise_amplitude: float = field(default=0.01)
     eps_xy_intitial: float = field(default=0.1)
+
     test_every_x_learning_steps: int = field(default=50)
     test_during_learning: bool = field(default=True)
-    lr_scheduler_factor: float = field(default=0.75)
+
-    lr_scheduler_patience: int = field(default=10)
-    optimizer_name: str = field(default="Adam")
-    lr_schedule_name: str = field(default="ReduceLROnPlateau")
-    number_of_batches_for_one_update: int = field(default=1)
     alpha_number_of_iterations: int = field(default=0)
-    overload_path: str = field(default="./Previous")
 
 
 @dataclass
@@ -82,12 +94,16 @@ class Augmentation:
     """Parameters used for data augmentation."""
 
     crop_width_in_pixel: int = field(default=2)
+
     flip_p: float = field(default=0.5)
+
     jitter_brightness: float = field(default=0.5)
     jitter_contrast: float = field(default=0.1)
     jitter_saturation: float = field(default=0.1)
     jitter_hue: float = field(default=0.15)
 
+    use_on_off_filter: bool = field(default=True)
+
 
 @dataclass
 class ImageStatistics: