Remove uncessary warnings

tltorch/factorized_layers/factorized_convolution.py

@@ -61,10 +61,10 @@ def _ensure_array(layers_shape, order, value, one_per_order=True):
         return value
 
     if isinstance(value, int):
-        array = np.ones(target_shape, dtype=np.int)*value
+        array = np.ones(target_shape, dtype=np.int32)*value
     else:
         assert len(value) == order
-        array = np.ones(target_shape, dtype=np.int)
+        array = np.ones(target_shape, dtype=np.int32)
         array[..., :] = value
     return array
 

tltorch/factorized_tensors/core.py

@@ -142,7 +142,8 @@ def __init_subclass__(cls, name, **kwargs):
             cls._factorizations[name.lower()] = cls
             cls._name = name
         else:
-            warnings.warn(f'Creating a subclass of FactorizedTensor {cls.__name__} with no name.')
+            if cls.__name__ != "TensorizedTensor": # Don't display warning when instantiating the TensorizedTensor class
+                warnings.warn(f'Creating a subclass of FactorizedTensor {cls.__name__} with no name.')
 
     def __new__(cls, *args, **kwargs):
         """Customize the creation of a factorized convolution
@@ -515,7 +516,7 @@ def to_matrix(self):
 
         If several matrices are parametrized, a batch of matrices is returned
         """ 
-        warnings.warn(f'{self} is being reconstructed into a matrix, consider operating on the decomposed form.')
+        # warnings.warn(f'{self} is being reconstructed into a matrix, consider operating on the decomposed form.')
 
         return self.to_tensor().reshape(self.shape)
 

tltorch/functional/factorized_linear.py

@@ -4,7 +4,7 @@
 
 # Author: Jean Kossaifi
 
-def linear_tucker(tensor, tucker_matrix, transpose=True):
+def linear_tucker(tensor, tucker_matrix, transpose=True, channels_first=True):
     if transpose:
         contraction_axis = 1
     else: