Merge pull request #83 from deel-ai/fix_pylint_warnings

Fix Pylint warnings

deel/lip/__init__.py

@@ -1,8 +1,32 @@
+# -*- coding: utf-8 -*-
 # Copyright IRT Antoine de Saint Exupéry et Université Paul Sabatier Toulouse III - All
 # rights reserved. DEEL is a research program operated by IVADO, IRT Saint Exupéry,
 # CRIAQ and ANITI - https://www.deel.ai/
-# =====================================================================================
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+DEEL-LIP
+--------
 
+DEEL-LIP provides a simple interface to build and train Lipschitz-constrained neural
+networks based on TensorFlow/Keras framework.
+"""
 from os import path
 
 with open(path.join(path.dirname(__file__), "VERSION")) as f:

deel/lip/callbacks.py

@@ -120,7 +120,7 @@ def _monitor(self, step):
                 ).numpy()
                 sig = sigmas[0]
             else:
-                RuntimeWarning(
+                raise RuntimeWarning(
                     f"[MonitorCallback] layer {layer_name} has no "
                     f"attribute {self.target}"
                 )
@@ -137,11 +137,13 @@ def _monitor(self, step):
                         sigmas,
                         step=step,
                         buckets=None,
-                        description="distribution of singular values for layer %s"
-                        % layer_name,
+                        description=(
+                            f"distribution of singular values for layer "
+                            f"{layer_name}"
+                        ),
                     )
             if not result:
-                RuntimeWarning(
+                raise RuntimeWarning(
                     "[MonitorCallback] unable to find filewriter, no logs were written,"
                 )
 

deel/lip/constraints.py

@@ -108,7 +108,7 @@ def __init__(
         super(SpectralConstraint, self).__init__()
 
     def __call__(self, w):
-        wbar, u, sigma = reshaped_kernel_orthogonalization(
+        wbar, _, _ = reshaped_kernel_orthogonalization(
             w,
             self.u,
             self.k_coef_lip,

deel/lip/initializers.py

@@ -2,6 +2,11 @@
 # rights reserved. DEEL is a research program operated by IVADO, IRT Saint Exupéry,
 # CRIAQ and ANITI - https://www.deel.ai/
 # =====================================================================================
+"""
+This module contains extra Keras initializers, e.g. SpectralInitializer for 1-Lipschitz
+matrix initialization.
+They can be used as kernel initializers in any Keras layer.
+"""
 from tensorflow.keras.initializers import Initializer
 from tensorflow.keras import initializers
 from .normalizers import (
@@ -44,7 +49,7 @@ def __init__(
 
     def __call__(self, shape, dtype=None, partition_info=None):
         w = self.base_initializer(shape=shape, dtype=dtype)
-        wbar, u, sigma = reshaped_kernel_orthogonalization(
+        wbar, _, _ = reshaped_kernel_orthogonalization(
             w,
             None,
             self.k_coef_lip,

deel/lip/layers/__init__.py

@@ -1,3 +1,30 @@
+# -*- coding: utf-8 -*-
+# Copyright IRT Antoine de Saint Exupéry et Université Paul Sabatier Toulouse III - All
+# rights reserved. DEEL is a research program operated by IVADO, IRT Saint Exupéry,
+# CRIAQ and ANITI - https://www.deel.ai/
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+"""
+The submodule `deel.lip.layers` contains all custom Keras layers to build
+Lipschitz-constrained neural networks. They all inherit from `keras.layers.Layer` from
+Keras API.
+"""
 from . import unconstrained
 from .activations import FullSort, GroupSort, GroupSort2, Householder, MaxMin, PReLUlip
 from .base_layer import Condensable, LipschitzLayer

deel/lip/layers/activations.py

@@ -18,14 +18,13 @@
 
 @register_keras_serializable("deel-lip", "MaxMin")
 class MaxMin(Layer, LipschitzLayer):
-    def __init__(self, data_format="channels_last", k_coef_lip=1.0, *args, **kwargs):
+    def __init__(self, data_format="channels_last", k_coef_lip=1.0, **kwargs):
         """
         MaxMin activation [Relu(x),reLU(-x)]
 
         Args:
             data_format (str): either channels_first or channels_last
             k_coef_lip (float): the lipschitz coefficient to be enforced
-            *args: params passed to Layers
             **kwargs: params passed to layers (named fashion)
 
         Input shape:
@@ -43,7 +42,7 @@ def __init__(self, data_format="channels_last", k_coef_lip=1.0, *args, **kwargs)
 
         """
         self.set_klip_factor(k_coef_lip)
-        super(MaxMin, self).__init__(*args, **kwargs)
+        super(MaxMin, self).__init__(**kwargs)
         if data_format == "channels_last":
             self.channel_axis = -1
         elif data_format == "channels_first":
@@ -59,7 +58,7 @@ def build(self, input_shape):
     def _compute_lip_coef(self, input_shape=None):
         return 1.0
 
-    def call(self, x, **kwargs):
+    def call(self, x):
         return (
             K.concatenate(
                 (K.relu(x, alpha=0), K.relu(-x, alpha=0)), axis=self.channel_axis
@@ -83,9 +82,7 @@ def compute_output_shape(self, input_shape):
 
 @register_keras_serializable("deel-lip", "GroupSort")
 class GroupSort(Layer, LipschitzLayer):
-    def __init__(
-        self, n=None, data_format="channels_last", k_coef_lip=1.0, *args, **kwargs
-    ):
+    def __init__(self, n=None, data_format="channels_last", k_coef_lip=1.0, **kwargs):
         """
         GroupSort activation
 
@@ -94,7 +91,6 @@ def __init__(
                 size (fullSort behavior)
             data_format (str): either channels_first or channels_last
             k_coef_lip (float): the lipschitz coefficient to be enforced
-            *args: params passed to Layers
             **kwargs: params passed to layers (named fashion)
 
         Input shape:
@@ -107,14 +103,13 @@ def __init__(
 
         """
         self.set_klip_factor(k_coef_lip)
-        super(GroupSort, self).__init__(*args, **kwargs)
+        super(GroupSort, self).__init__(**kwargs)
         if data_format == "channels_last":
             self.channel_axis = -1
         elif data_format == "channels_first":
             raise RuntimeError(
                 "channels_first not implemented for GroupSort activation"
             )
-            self.channel_axis = 1
         else:
             raise RuntimeError("data format not understood")
         self.n = n
@@ -138,7 +133,7 @@ def _compute_lip_coef(self, input_shape=None):
         return 1.0
 
     @tf.function
-    def call(self, x, **kwargs):
+    def call(self, x):
         fv = tf.reshape(x, self.flat_shape)
         if self.n == 2:
             b, c = tf.split(fv, 2, -1)

deel/lip/layers/convolutional.py

@@ -63,7 +63,7 @@ def _compute_conv_lip_factor(kernel_size, strides, input_shape, data_format):
     elif data_format == "channels_first":
         h, w = input_shape[-2], input_shape[-1]
     else:
-        raise RuntimeError("data_format not understood: " % data_format)
+        raise RuntimeError(f"data_format not understood: {data_format}")
 
     if stride == 1:
         return np.sqrt(
@@ -99,7 +99,7 @@ def __init__(
         beta_bjorck=DEFAULT_BETA_BJORCK,
         maxiter_spectral=DEFAULT_MAXITER_SPECTRAL,
         maxiter_bjorck=DEFAULT_MAXITER_BJORCK,
-        **kwargs
+        **kwargs,
     ):
         """
         This class is a Conv2D Layer constrained such that all singular of it's kernel
@@ -164,11 +164,7 @@ def __init__(
 
         This documentation reuse the body of the original keras.layers.Conv2D doc.
         """
-        if not (
-            (dilation_rate == (1, 1))
-            or (dilation_rate == [1, 1])
-            or (dilation_rate == 1)
-        ):
+        if dilation_rate not in ((1, 1), [1, 1], 1):
             raise RuntimeError("SpectralConv2D does not support dilation rate")
         if padding != "same":
             raise RuntimeError("SpectralConv2D only supports padding='same'")
@@ -188,7 +184,7 @@ def __init__(
             activity_regularizer=activity_regularizer,
             kernel_constraint=kernel_constraint,
             bias_constraint=bias_constraint,
-            **kwargs
+            **kwargs,
         )
         self._kwargs = kwargs
         self.set_klip_factor(k_coef_lip)
@@ -299,7 +295,7 @@ def vanilla_export(self):
             use_bias=self.use_bias,
             kernel_initializer="glorot_uniform",
             bias_initializer="zeros",
-            **self._kwargs
+            **self._kwargs,
         )
         layer.build(self.input_shape)
         layer.kernel.assign(self.wbar)
@@ -334,7 +330,7 @@ def __init__(
         beta_bjorck=DEFAULT_BETA_BJORCK,
         maxiter_spectral=DEFAULT_MAXITER_SPECTRAL,
         maxiter_bjorck=DEFAULT_MAXITER_BJORCK,
-        **kwargs
+        **kwargs,
     ):
         """
         This class is a Conv2DTranspose layer constrained such that all singular values
@@ -419,7 +415,7 @@ def __init__(
             activity_regularizer,
             kernel_constraint,
             bias_constraint,
-            **kwargs
+            **kwargs,
         )
 
         if self.dilation_rate != (1, 1):
@@ -598,7 +594,7 @@ def vanilla_export(self):
             data_format=self.data_format,
             activation=self.activation,
             use_bias=self.use_bias,
-            **self._kwargs
+            **self._kwargs,
         )
         layer.build(self.input_shape)
         layer.kernel.assign(self.wbar)
@@ -631,15 +627,11 @@ def __init__(
         kernel_constraint=None,
         bias_constraint=None,
         k_coef_lip=1.0,
-        **kwargs
+        **kwargs,
     ):
-        if not ((strides == (1, 1)) or (strides == [1, 1]) or (strides == 1)):
+        if strides not in ((1, 1), [1, 1], 1):
             raise RuntimeError("FrobeniusConv2D does not support strides")
-        if not (
-            (dilation_rate == (1, 1))
-            or (dilation_rate == [1, 1])
-            or (dilation_rate == 1)
-        ):
+        if dilation_rate not in ((1, 1), [1, 1], 1):
             raise RuntimeError("FrobeniusConv2D does not support dilation rate")
         if padding != "same":
             raise RuntimeError("FrobeniusConv2D only supports padding='same'")
@@ -667,7 +659,7 @@ def __init__(
             activity_regularizer=activity_regularizer,
             kernel_constraint=kernel_constraint,
             bias_constraint=bias_constraint,
-            **kwargs
+            **kwargs,
         )
         self.set_klip_factor(k_coef_lip)
         self.wbar = None