Small ops fixes for Torch unit tests

keras_core/activations/activations.py

@@ -416,6 +416,7 @@ def mish(x):
 
     - [Misra, 2019](https://arxiv.org/abs/1908.08681)
     """
+    x = backend.convert_to_tensor(x)
     return Mish.static_call(x)
 
 

keras_core/backend/torch/math.py

@@ -58,7 +58,7 @@ def logsumexp(x, axis=None, keepdims=False):
         max_x = torch.max(x)
         return torch.log(torch.sum(torch.exp(x - max_x))) + max_x
 
-    max_x = torch.max(x, dim=axis, keepdim=True).values
+    max_x = torch.amax(x, dim=axis, keepdim=True)
     result = (
         torch.log(torch.sum(torch.exp(x - max_x), dim=axis, keepdim=True))
         + max_x

keras_core/backend/torch/numpy.py

@@ -134,6 +134,8 @@ def append(
 
 def arange(start, stop=None, step=1, dtype=None):
     dtype = to_torch_dtype(dtype)
+    if stop is None:
+        return torch.arange(end=start, dtype=dtype)
     return torch.arange(start, stop, step=step, dtype=dtype)
 
 

keras_core/constraints/constraints_test.py

@@ -1,5 +1,6 @@
 import numpy as np
 
+from keras_core import backend
 from keras_core import constraints
 from keras_core import testing
 
@@ -35,12 +36,14 @@ def test_non_neg(self):
     def test_unit_norm(self):
         constraint_fn = constraints.UnitNorm()
         output = constraint_fn(get_example_array())
+        output = backend.convert_to_numpy(output)
         l2 = np.sqrt(np.sum(np.square(output), axis=0))
         self.assertAllClose(l2, 1.0)
 
     def test_min_max_norm(self):
         constraint_fn = constraints.MinMaxNorm(min_value=0.2, max_value=0.5)
         output = constraint_fn(get_example_array())
+        output = backend.convert_to_numpy(output)
         l2 = np.sqrt(np.sum(np.square(output), axis=0))
         self.assertFalse(l2[l2 < 0.2])
         self.assertFalse(l2[l2 > 0.5 + 1e-6])

keras_core/layers/preprocessing/normalization_test.py

@@ -68,6 +68,7 @@ def test_normalization_adapt(self, input_type):
         layer.adapt(data)
         self.assertTrue(layer.built)
         output = layer(x)
+        output = backend.convert_to_numpy(output)
         self.assertAllClose(np.var(output, axis=0), 1.0, atol=1e-5)
         self.assertAllClose(np.mean(output, axis=0), 0.0, atol=1e-5)
 
@@ -84,6 +85,7 @@ def test_normalization_adapt(self, input_type):
         layer.adapt(data)
         self.assertTrue(layer.built)
         output = layer(x)
+        output = backend.convert_to_numpy(output)
         self.assertAllClose(np.var(output, axis=(0, 3)), 1.0, atol=1e-5)
         self.assertAllClose(np.mean(output, axis=(0, 3)), 0.0, atol=1e-5)
 

keras_core/layers/preprocessing/random_brightness_test.py

@@ -1,6 +1,7 @@
 import numpy as np
 import tensorflow as tf
 
+from keras_core import backend
 from keras_core import layers
 from keras_core import testing
 
@@ -36,6 +37,7 @@ def test_output(self):
         inputs = np.random.randint(0, 255, size=(224, 224, 3))
         output = layer(inputs)
         diff = output - inputs
+        diff = backend.convert_to_numpy(diff)
         self.assertTrue(np.amin(diff) >= 0)
         self.assertTrue(np.mean(diff) > 0)
 
@@ -45,6 +47,7 @@ def test_output(self):
         inputs = np.random.randint(0, 255, size=(224, 224, 3))
         output = layer(inputs)
         diff = output - inputs
+        diff = backend.convert_to_numpy(diff)
         self.assertTrue(np.amax(diff) <= 0)
         self.assertTrue(np.mean(diff) < 0)
 

keras_core/operations/operation_test.py

@@ -9,7 +9,8 @@
 
 class OpWithMultipleInputs(operation.Operation):
     def call(self, x, y, z=None):
-        return x + 2 * y + 3 * z
+        return 3 * z + x + 2 * y
+        # Order of operations issue with torch backend
 
     def compute_output_spec(self, x, y, z=None):
         return keras_tensor.KerasTensor(x.shape, x.dtype)