Codacy Bug Report fixes

examples/gtlvq_mnist.py

@@ -7,10 +7,7 @@ Siamnese fashion
 For more info about GTLVQ see:
 DOI:10.1109/IJCNN.2016.7727534
 """
-import sys
 
-from torch.nn import parameter
-from matplotlib.pyplot import fill
 import numpy as np
 import torch
 import torch.nn as nn

prototorch/functions/normalization.py

@@ -24,7 +24,7 @@ def orthogonalization(tensors):
     return out
 
 
-def trace_normalization(tensors, epsilon=[1e-10]):
+def trace_normalization(tensors):
     r""" Trace normalization
     """
     epsilon = torch.tensor([1e-10], dtype=torch.float64)

prototorch/modules/models.py

@@ -5,6 +5,7 @@ from prototorch.functions.distances import tangent_distance, euclidean_distance_
 from prototorch.functions.normalization import orthogonalization
 from prototorch.functions.helper import _check_shapes, _int_and_mixed_shape
 
+
 class GTLVQ(nn.Module):
     r""" Generalized Tangent Learning Vector Quantization
 
@@ -19,6 +20,10 @@ 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.
 
+    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
@@ -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])