cleaning

Author: Franck Mamalet

deel/torchlip/modules/activation.py

@@ -254,7 +254,6 @@ def forward(self, z, axis=1):
         for _ in range(len(z.shape) - len(theta.shape)):
             theta = theta.unsqueeze(-1)
         x, y = z.split(z.shape[axis] // 2, axis)
-        print("aaaa", x.shape, y.shape, theta.shape)
         selector = (x * torch.sin(0.5 * theta)) - (y * torch.cos(0.5 * theta))
 
         a_2 = x * torch.cos(theta) + y * torch.sin(theta)

deel/torchlip/modules/conv.py

@@ -215,7 +215,7 @@ def __init__(
     ) -> None:
         if dilation != 1:
             raise ValueError("SpectralConvTranspose2d does not support dilation rate")
-        if not output_padding in [0, None]:
+        if output_padding not in [0, None]:
             raise ValueError("SpectralConvTranspose2d only supports output_padding=0")
         torch.nn.ConvTranspose2d.__init__(
             self,

tests/utils_framework.py

@@ -143,15 +143,16 @@ def __call__(self, **kwargs):
         return None
 
 
+TauCategoricalCrossentropyLoss = TauCrossEntropyLoss
+TauSparseCategoricalCrossentropyLoss = TauCrossEntropyLoss
+TauBinaryCrossentropyLoss = TauBCEWithLogitsLoss
+
 tInput = module_Unavailable_foo
 AutoWeightClipConstraint = module_Unavailable_class
 SpectralConstraint = module_Unavailable_class
 FrobeniusConstraint = module_Unavailable_class
 CondenseCallback = module_Unavailable_class
 MonitorCallback = module_Unavailable_class
-TauCategoricalCrossentropyLoss = TauCrossEntropyLoss
-TauSparseCategoricalCrossentropyLoss = TauCrossEntropyLoss
-TauBinaryCrossentropyLoss = TauBCEWithLogitsLoss
 CategoricalProvableRobustAccuracy = module_Unavailable_class
 BinaryProvableRobustAccuracy = module_Unavailable_class
 CategoricalProvableAvgRobustness = module_Unavailable_class
@@ -236,35 +237,31 @@ def get_instance_withcheck(
         get_instance_withreplacement, dict_keys_replace={"data_format": None}
     ),
     KRLoss: partial(
-        get_instance_withcheck,
+        get_instance_withreplacement,
         dict_keys_replace={"name": None},
-        list_keys_notimplemented=[],
     ),
-    HingeMarginLoss: partial(get_instance_withcheck, dict_keys_replace={"name": None}),
+    HingeMarginLoss: partial(
+        get_instance_withreplacement, dict_keys_replace={"name": None}
+    ),
     HKRLoss: partial(
-        get_instance_withcheck,
+        get_instance_withreplacement,
         dict_keys_replace={"name": None},
-        list_keys_notimplemented=[],
     ),
     HingeMulticlassLoss: partial(
-        get_instance_withcheck,
+        get_instance_withreplacement,
         dict_keys_replace={"name": None},
-        list_keys_notimplemented=[],
     ),
     HKRMulticlassLoss: partial(
-        get_instance_withcheck,
+        get_instance_withreplacement,
         dict_keys_replace={"name": None},
-        list_keys_notimplemented=[],
     ),
     KRMulticlassLoss: partial(
-        get_instance_withcheck,
+        get_instance_withreplacement,
         dict_keys_replace={"name": None},
-        list_keys_notimplemented=[],
     ),
     SoftHKRMulticlassLoss: partial(
-        get_instance_withcheck,
+        get_instance_withreplacement,
         dict_keys_replace={"name": None},
-        list_keys_notimplemented=[],
     ),
     tLinear: partial(
         get_instance_withcheck,