Codacy Bug Report fixes

prototorch/modules/models.py

@@ -3,14 +3,15 @@ import torch
 from prototorch.modules.prototypes import Prototypes1D
 from prototorch.functions.distances import tangent_distance, euclidean_distance_matrix
 from prototorch.functions.normalization import orthogonalization
-from prototorch.functions.helper import _check_shapes,_int_and_mixed_shape
+from prototorch.functions.helper import _check_shapes, _int_and_mixed_shape
+
 
 class GTLVQ(nn.Module):
     r""" Generalized Tangent Learning Vector Quantization
 
     Parameters
     ----------
-    num_classes: int 
+    num_classes: int
         Number of classes of the given classification problem.
 
     subspace_data: torch.tensor of shape (n_batch,feature_dim,feature_dim)
@@ -19,7 +20,11 @@ class GTLVQ(nn.Module):
     prototype_data: torch.tensor of shape (n_init_data,feature_dim) (optional)
         prototype data for initalization of the prototypes used in GTLVQ.
 
-    tangent_projection_type: string 
+    subspace_size: int (default=256,optional)
+        Subspace dimension of the Projectors. Currently only supported
+        with tagnent_projection_type=global.
+
+    tangent_projection_type: string
         Specifies the tangent projection type
         options:    local
                     local_proj
@@ -28,33 +33,33 @@ class GTLVQ(nn.Module):
         data. Only distances are available
         local_proj: computs tangent distances and returns the projected data
         for further use. Be careful: data is repeated by number of prototypes
-        global: Number of subspaces is set to one and every prototypes 
-        uses the same. 
-    
+        global: Number of subspaces is set to one and every prototypes
+        uses the same.
+
     prototypes_per_class: int (default=2,optional)
     Number of prototypes per class
 
     feature_dim: int (default=256)
-    Dimensionality of the feature space specified as integer. 
-    Prototype dimension. 
+    Dimensionality of the feature space specified as integer.
+    Prototype dimension.
 
     Notes
     -----
-    The GTLVQ [1] is a prototype-based classification learning model. The 
-    GTLVQ uses the Tangent-Distances for a local point approximation 
-    of an assumed data manifold via prototypial representations. 
+    The GTLVQ [1] is a prototype-based classification learning model. The
+    GTLVQ uses the Tangent-Distances for a local point approximation
+    of an assumed data manifold via prototypial representations.
 
     The GTLVQ requires subspace projectors for transforming the data
-    and prototypes into the affine subspace. Every prototype is 
-    equipped with a specific subpspace and represents a point 
+    and prototypes into the affine subspace. Every prototype is
+    equipped with a specific subpspace and represents a point
     approximation of the assumed manifold.
 
-    In practice prototypes and data are projected on this manifold 
+    In practice prototypes and data are projected on this manifold
     and pairwise euclidean distance computes.
 
     References
     ----------
-    .. [1] Saralajew, Sascha; Villmann, Thomas: Transfer learning 
+    .. [1] Saralajew, Sascha; Villmann, Thomas: Transfer learning
     in classification based on manifolc. models and its relation
     to tangent metric learning. In: 2017 International Joint
     Conference on Neural Networks (IJCNN).
@@ -82,13 +87,9 @@ class GTLVQ(nn.Module):
         self.tpt = tangent_projection_type
         with torch.no_grad():
             if self.tpt == 'local' or self.tpt == 'local_proj':
-                self.subspaces = torch.nn.Parameter(
-                    self.init_local_subspace(
-                        subspace_data).clone().detach().requires_grad_(True))
+                self.init_local_subspace(subspace_data)
             elif self.tpt == 'global':
-                self.subspaces = torch.nn.Parameter(
-                    self.init_gobal_subspace(
-                        subspace_data).clone().detach().requires_grad_(True))
+                self.init_gobal_subspace(subspace_data, subspace_size)
             else:
                 self.subspaces = None
 
@@ -125,13 +126,17 @@ class GTLVQ(nn.Module):
     def init_gobal_subspace(self, data, num_subspaces):
         _, _, v = torch.svd(data)
         subspace = (torch.eye(v.shape[0]) - (v @ v.T)).T
-        return subspace[:, :num_subspaces]
+        subspaces = subspace[:, :num_subspaces]
+        self.subspaces = torch.nn.Parameter(
+            subspaces).clone().detach().requires_grad_(True)
 
     def init_local_subspace(self, data):
         _, _, v = torch.svd(data)
         inital_projector = (torch.eye(v.shape[0]) - (v @ v.T)).T
-        return inital_projector.unsqueeze(0).repeat_interleave(
+        subspaces = inital_projector.unsqueeze(0).repeat_interleave(
             self.num_protos, 0)
+        self.subspaces = torch.nn.Parameter(
+            subspaces).clone().detach().requires_grad_(True)
 
     def global_tangent_distances(self, x):
         # Tangent Projection
@@ -154,13 +159,11 @@ class GTLVQ(nn.Module):
         # Origin Author:
 
         signal_shape, signal_int_shape = _int_and_mixed_shape(signals)
-        proto_shape, proto_int_shape = _int_and_mixed_shape(protos)
+        _, proto_int_shape = _int_and_mixed_shape(protos)
 
         # check if the shapes are correct
         _check_shapes(signal_int_shape, proto_int_shape)
 
-        atom_axes = list(range(3, len(signal_int_shape)))
-
         # Tangent Data Projections
         projected_protos = torch.bmm(protos.unsqueeze(1), subspaces).squeeze(1)
         data = signals.squeeze(2).permute([1, 0, 2])
@@ -170,7 +173,7 @@ class GTLVQ(nn.Module):
         projected_diff = torch.reshape(
             diff, (signal_shape[1], signal_shape[0], signal_shape[2]) +
             signal_shape[3:])
-        diss = torch.norm(projected_diff,2,dim=-1)
+        diss = torch.norm(projected_diff, 2, dim=-1)
         return diss.permute([1, 0, 2]).squeeze(-1), projected_data.squeeze(1)
 
     def get_parameters(self):