chore: Allow no-self-use for some class members

Classes are used as common interface and connection to pytorch.

prototorch/core/competitions.py

@@ -48,7 +48,7 @@ class WTAC(torch.nn.Module):
     Thin wrapper over the `wtac` function.
 
     """
-    def forward(self, distances, labels):
+    def forward(self, distances, labels):  # pylint: disable=no-self-use
         return wtac(distances, labels)
 
 
@@ -58,7 +58,7 @@ class LTAC(torch.nn.Module):
     Thin wrapper over the `wtac` function.
 
     """
-    def forward(self, probs, labels):
+    def forward(self, probs, labels):  # pylint: disable=no-self-use
         return wtac(-1.0 * probs, labels)
 
 
@@ -85,5 +85,5 @@ class CBCC(torch.nn.Module):
     Thin wrapper over the `cbcc` function.
 
     """
-    def forward(self, detections, reasonings):
+    def forward(self, detections, reasonings):  # pylint: disable=no-self-use
         return cbcc(detections, reasonings)

prototorch/core/initializers.py

@@ -303,17 +303,18 @@ class OneHotLabelsInitializer(LabelsInitializer):
 
 
 # Reasonings
-class AbstractReasoningsInitializer(ABC):
-    """Abstract class for all reasonings initializers."""
-    def __init__(self, components_first: bool = True):
-        self.components_first = components_first
-
-    def compute_shape(self, distribution):
+def compute_distribution_shape(distribution):
     distribution = parse_distribution(distribution)
     num_components = sum(distribution.values())
     num_classes = len(distribution.keys())
     return (num_components, num_classes, 2)
 
+
+class AbstractReasoningsInitializer(ABC):
+    """Abstract class for all reasonings initializers."""
+    def __init__(self, components_first: bool = True):
+        self.components_first = components_first
+
     def generate_end_hook(self, reasonings):
         if not self.components_first:
             reasonings = reasonings.permute(2, 1, 0)
@@ -349,7 +350,7 @@ class LiteralReasoningsInitializer(AbstractReasoningsInitializer):
 class ZerosReasoningsInitializer(AbstractReasoningsInitializer):
     """Reasonings are all initialized with zeros."""
     def generate(self, distribution: Union[dict, list, tuple]):
-        shape = self.compute_shape(distribution)
+        shape = compute_distribution_shape(distribution)
         reasonings = torch.zeros(*shape)
         reasonings = self.generate_end_hook(reasonings)
         return reasonings
@@ -358,7 +359,7 @@ class ZerosReasoningsInitializer(AbstractReasoningsInitializer):
 class OnesReasoningsInitializer(AbstractReasoningsInitializer):
     """Reasonings are all initialized with ones."""
     def generate(self, distribution: Union[dict, list, tuple]):
-        shape = self.compute_shape(distribution)
+        shape = compute_distribution_shape(distribution)
         reasonings = torch.ones(*shape)
         reasonings = self.generate_end_hook(reasonings)
         return reasonings
@@ -372,7 +373,7 @@ class RandomReasoningsInitializer(AbstractReasoningsInitializer):
         self.maximum = maximum
 
     def generate(self, distribution: Union[dict, list, tuple]):
-        shape = self.compute_shape(distribution)
+        shape = compute_distribution_shape(distribution)
         reasonings = torch.ones(*shape).uniform_(self.minimum, self.maximum)
         reasonings = self.generate_end_hook(reasonings)
         return reasonings
@@ -381,7 +382,8 @@ class RandomReasoningsInitializer(AbstractReasoningsInitializer):
 class PurePositiveReasoningsInitializer(AbstractReasoningsInitializer):
     """Each component reasons positively for exactly one class."""
     def generate(self, distribution: Union[dict, list, tuple]):
-        num_components, num_classes, _ = self.compute_shape(distribution)
+        num_components, num_classes, _ = compute_distribution_shape(
+            distribution)
         A = OneHotLabelsInitializer().generate(distribution)
         B = torch.zeros(num_components, num_classes)
         reasonings = torch.stack([A, B], dim=-1)

prototorch/core/pooling.py

@@ -82,23 +82,23 @@ def stratified_prod_pooling(values: torch.Tensor,
 
 class StratifiedSumPooling(torch.nn.Module):
     """Thin wrapper over the `stratified_sum_pooling` function."""
-    def forward(self, values, labels):
+    def forward(self, values, labels):  # pylint: disable=no-self-use
         return stratified_sum_pooling(values, labels)
 
 
 class StratifiedProdPooling(torch.nn.Module):
     """Thin wrapper over the `stratified_prod_pooling` function."""
-    def forward(self, values, labels):
+    def forward(self, values, labels):  # pylint: disable=no-self-use
         return stratified_prod_pooling(values, labels)
 
 
 class StratifiedMinPooling(torch.nn.Module):
     """Thin wrapper over the `stratified_min_pooling` function."""
-    def forward(self, values, labels):
+    def forward(self, values, labels):  # pylint: disable=no-self-use
         return stratified_min_pooling(values, labels)
 
 
 class StratifiedMaxPooling(torch.nn.Module):
     """Thin wrapper over the `stratified_max_pooling` function."""
-    def forward(self, values, labels):
+    def forward(self, values, labels):  # pylint: disable=no-self-use
         return stratified_max_pooling(values, labels)