chore: Move mixins into seperate file

prototorch/models/abstract.py

@@ -168,32 +168,3 @@ class SupervisedPrototypeModel(PrototypeModel):
         accuracy = torchmetrics.functional.accuracy(preds.int(), targets.int())
 
         self.log("test_acc", accuracy)
-
-
-class ProtoTorchMixin(object):
-    """All mixins are ProtoTorchMixins."""
-    pass
-
-
-class NonGradientMixin(ProtoTorchMixin):
-    """Mixin for custom non-gradient optimization."""
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.automatic_optimization = False
-
-    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
-        raise NotImplementedError
-
-
-class ImagePrototypesMixin(ProtoTorchMixin):
-    """Mixin for models with image prototypes."""
-    def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
-        """Constrain the components to the range [0, 1] by clamping after updates."""
-        self.proto_layer.components.data.clamp_(0.0, 1.0)
-
-    def get_prototype_grid(self, num_columns=2, return_channels_last=True):
-        from torchvision.utils import make_grid
-        grid = make_grid(self.components, nrow=num_columns)
-        if return_channels_last:
-            grid = grid.permute((1, 2, 0))
-        return grid.cpu()

prototorch/models/cbc.py

@@ -7,8 +7,8 @@ from prototorch.core.losses import MarginLoss
 from prototorch.core.similarities import euclidean_similarity
 from prototorch.nn.wrappers import LambdaLayer
 
-from .abstract import ImagePrototypesMixin
 from .glvq import SiameseGLVQ
+from .mixin import ImagePrototypesMixin
 
 
 class CBC(SiameseGLVQ):

prototorch/models/glvq.py

@@ -9,7 +9,8 @@ from prototorch.core.transforms import LinearTransform
 from prototorch.nn.wrappers import LambdaLayer, LossLayer
 from torch.nn.parameter import Parameter
 
-from .abstract import ImagePrototypesMixin, SupervisedPrototypeModel
+from .abstract import SupervisedPrototypeModel
+from .mixin import ImagePrototypesMixin
 
 
 class GLVQ(SupervisedPrototypeModel):

prototorch/models/lvq.py

@@ -4,8 +4,8 @@ from prototorch.core.losses import _get_dp_dm
 from prototorch.nn.activations import get_activation
 from prototorch.nn.wrappers import LambdaLayer
 
-from .abstract import NonGradientMixin
 from .glvq import GLVQ
+from .mixin import NonGradientMixin
 
 
 class LVQ1(NonGradientMixin, GLVQ):

prototorch/models/mixin.py

@@ -0,0 +1,27 @@
+class ProtoTorchMixin(object):
+    """All mixins are ProtoTorchMixins."""
+    pass
+
+
+class NonGradientMixin(ProtoTorchMixin):
+    """Mixin for custom non-gradient optimization."""
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.automatic_optimization = False
+
+    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
+        raise NotImplementedError
+
+
+class ImagePrototypesMixin(ProtoTorchMixin):
+    """Mixin for models with image prototypes."""
+    def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
+        """Constrain the components to the range [0, 1] by clamping after updates."""
+        self.proto_layer.components.data.clamp_(0.0, 1.0)
+
+    def get_prototype_grid(self, num_columns=2, return_channels_last=True):
+        from torchvision.utils import make_grid
+        grid = make_grid(self.components, nrow=num_columns)
+        if return_channels_last:
+            grid = grid.permute((1, 2, 0))
+        return grid.cpu()

prototorch/models/unsupervised.py

@@ -7,9 +7,10 @@ from prototorch.core.distances import squared_euclidean_distance
 from prototorch.core.losses import NeuralGasEnergy
 from prototorch.nn.wrappers import LambdaLayer
 
-from .abstract import NonGradientMixin, UnsupervisedPrototypeModel
+from .abstract import UnsupervisedPrototypeModel
 from .callbacks import GNGCallback
 from .extras import ConnectionTopology
+from .mixin import NonGradientMixin
 
 
 class KohonenSOM(NonGradientMixin, UnsupervisedPrototypeModel):