clean and linter

Author: franckma31

deel/torchlip/modules/downsampling.py

@@ -24,11 +24,8 @@
 # rights reserved. DEEL is a research program operated by IVADO, IRT Saint Exupéry,
 # CRIAQ and ANITI - https://www.deel.ai/
 # =====================================================================================
-from typing import Tuple
-
 import torch
 
-from .. import functional as F
 from .module import LipschitzModule
 
 
@@ -43,15 +40,3 @@ def vanilla_export(self):
         else:
             return self
 
-
-# class InvertibleDownSampling(torch.nn.Module, LipschitzModule):
-#     def __init__(self, kernel_size: Tuple[int, int], k_coef_lip: float = 1.0):
-#         torch.nn.Module.__init__(self)
-#         LipschitzModule.__init__(self, k_coef_lip)
-#         self.kernel_size = kernel_size
-
-#     def forward(self, input: torch.Tensor) -> torch.Tensor:
-#         return F.invertible_downsample(input, self.kernel_size) * self._coefficient_lip
-
-#     def vanilla_export(self):
-#         return self

deel/torchlip/modules/residual.py

@@ -24,21 +24,20 @@
 # rights reserved. DEEL is a research program operated by IVADO, IRT Saint Exupéry,
 # CRIAQ and ANITI - https://www.deel.ai/
 # =====================================================================================
-from typing import Tuple
-from typing import Union
 
 import torch
 from torch import nn
 
 
-class LipResidual(nn.Module):    
+class LipResidual(nn.Module):
     """
     This class is a 1-Lipschitz residual connection
     With a learnable parameter alpha that give a tradeoff
     between the x and the layer y=l(x)
-    
+
     Args:
     """
+
     def __init__(self):
         super().__init__()
         self.alpha = nn.Parameter(torch.tensor(0.0), requires_grad=True)

deel/torchlip/modules/unconstrained.py

@@ -156,9 +156,10 @@ def __init__(
         self.old_padding_mode = padding_mode
         if padding_mode.lower() == "symmetric":
             # symmetric padding of one pixel can be replaced by replicate
-            if (isinstance(padding, int) and padding <= 1) or \
-                (isinstance(padding, tuple) and padding[0] <= 1 and padding[1] <= 1): 
-                    self.old_padding_mode = padding_mode = "replicate"
+            if (isinstance(padding, int) and padding <= 1) or (
+                isinstance(padding, tuple) and padding[0] <= 1 and padding[1] <= 1
+            ):
+                self.old_padding_mode = padding_mode = "replicate"
             else:
                 padding_mode = "zeros"
                 padding = "valid"

deel/torchlip/modules/upsampling.py

@@ -24,8 +24,6 @@
 # rights reserved. DEEL is a research program operated by IVADO, IRT Saint Exupéry,
 # CRIAQ and ANITI - https://www.deel.ai/
 # =====================================================================================
-from typing import Tuple
-from typing import Union
 
 import torch
 

tests/test_unconstrained_layers.py

@@ -46,26 +46,18 @@ def compare(x, x_ref, index_x=[], index_x_ref=[]):
         np.testing.assert_allclose(x_cropped, np.zeros(x_cropped.shape), 1e-2, 0)
     else:
         np.testing.assert_allclose(
-            x_cropped-
-            x_ref[
+            x_cropped
+            - x_ref[
                 :, :, index_x_ref[0] : index_x_ref[1], index_x_ref[3] : index_x_ref[4]
             ][:, :, :: index_x_ref[2], :: index_x_ref[5]],
             np.zeros(x_cropped.shape),
             1e-2,
             0,
         )
-        # np.testing.assert_allclose(
-        #     x_cropped,
-        #     x_ref[
-        #         :, :, index_x_ref[0] : index_x_ref[1], index_x_ref[3] : index_x_ref[4]
-        #     ][:, :, :: index_x_ref[2], :: index_x_ref[5]],
-        #     1e-2,
-        #     0,
-        # )
 
 
 @pytest.mark.parametrize(
-    "padding_tested", ["circular", "constant", "symmetric", "reflect","replicate"]
+    "padding_tested", ["circular", "constant", "symmetric", "reflect", "replicate"]
 )
 @pytest.mark.parametrize(
     "input_shape, batch_size, kernel_size, filters",
@@ -167,7 +159,8 @@ def test_padding(padding_tested, input_shape, batch_size, kernel_size, filters):
     reason="PadConv2d not available",
 )
 @pytest.mark.parametrize(
-    "padding_tested", ["circular", "constant", "symmetric", "reflect", "replicate", "same", "valid"]
+    "padding_tested",
+    ["circular", "constant", "symmetric", "reflect", "replicate", "same", "valid"],
 )
 @pytest.mark.parametrize(
     "input_shape, batch_size, kernel_size, filters",
@@ -240,7 +233,8 @@ def test_predict(padding_tested, input_shape, batch_size, kernel_size, filters):
     reason="PadConv2d not available",
 )
 @pytest.mark.parametrize(
-    "padding_tested", ["circular", "constant", "symmetric", "reflect", "replicate", "same", "valid"]
+    "padding_tested",
+    ["circular", "constant", "symmetric", "reflect", "replicate", "same", "valid"],
 )
 @pytest.mark.parametrize(
     "input_shape, batch_size, kernel_size, filters",

tests/test_updownsampling.py

@@ -33,7 +33,6 @@
 
 
 def check_downsample(x, y, kernel_size):
-    shape = uft.get_NCHW(x)
     index = 0
     for dx in range(kernel_size):
         for dy in range(kernel_size):

tests/utils_framework.py

@@ -127,6 +127,7 @@
     "CategoricalHingeLoss",
     "process_labels_for_multi_gpu",
     "SpectralConv1d",
+    "LipResidual",
 ]
 
 
@@ -625,7 +626,7 @@ def is_supported_padding(padding):
         "reflect",
         "circular",
         "symmetric",
-        'replicate'
+        "replicate",
     ]  # "constant",
 
 
@@ -635,7 +636,7 @@ def pad_input(x, padding, kernel_size):
         kernel_size = [kernel_size, kernel_size]
     if padding.lower() in ["same", "valid"]:
         return x
-    elif padding.lower() in ["constant", "reflect", "circular",'replicate']:
+    elif padding.lower() in ["constant", "reflect", "circular", "replicate"]:
         p_vert, p_hor = kernel_size[0] // 2, kernel_size[1] // 2
         pad_sizes = [
             p_hor,