Merge branch 'keras-team:main' into main

keras_core/backend/common/backend_utils_test.py

@@ -0,0 +1,235 @@
+from keras_core.backend.common.backend_utils import (
+    _convert_conv_tranpose_padding_args_from_keras_to_jax,
+)
+from keras_core.backend.common.backend_utils import (
+    _convert_conv_tranpose_padding_args_from_keras_to_torch,
+)
+from keras_core.backend.common.backend_utils import (
+    _get_output_shape_given_tf_padding,
+)
+from keras_core.backend.common.backend_utils import (
+    compute_conv_transpose_padding_args_for_jax,
+)
+from keras_core.backend.common.backend_utils import (
+    compute_conv_transpose_padding_args_for_torch,
+)
+from keras_core.testing import test_case
+
+
+class ConvertConvTransposePaddingArgsJAXTest(test_case.TestCase):
+    def test_valid_padding_without_output_padding(self):
+        """Test conversion with 'valid' padding and no output padding"""
+        (
+            left_pad,
+            right_pad,
+        ) = _convert_conv_tranpose_padding_args_from_keras_to_jax(
+            kernel_size=3,
+            stride=2,
+            dilation_rate=1,
+            padding="valid",
+            output_padding=None,
+        )
+        self.assertEqual(left_pad, 2)
+        self.assertEqual(right_pad, 2)
+
+    def test_same_padding_without_output_padding(self):
+        """Test conversion with 'same' padding and no output padding."""
+        (
+            left_pad,
+            right_pad,
+        ) = _convert_conv_tranpose_padding_args_from_keras_to_jax(
+            kernel_size=3,
+            stride=2,
+            dilation_rate=1,
+            padding="same",
+            output_padding=None,
+        )
+        self.assertEqual(left_pad, 2)
+        self.assertEqual(right_pad, 1)
+
+
+class ConvertConvTransposePaddingArgsTorchTest(test_case.TestCase):
+    def test_valid_padding_without_output_padding(self):
+        """Test conversion with 'valid' padding and no output padding"""
+        (
+            torch_padding,
+            torch_output_padding,
+        ) = _convert_conv_tranpose_padding_args_from_keras_to_torch(
+            kernel_size=3,
+            stride=2,
+            dilation_rate=1,
+            padding="valid",
+            output_padding=None,
+        )
+        self.assertEqual(torch_padding, 0)
+        self.assertEqual(torch_output_padding, 0)
+
+    def test_same_padding_without_output_padding(self):
+        """Test conversion with 'same' padding and no output padding"""
+        (
+            torch_padding,
+            torch_output_padding,
+        ) = _convert_conv_tranpose_padding_args_from_keras_to_torch(
+            kernel_size=3,
+            stride=2,
+            dilation_rate=1,
+            padding="same",
+            output_padding=None,
+        )
+        self.assertEqual(torch_padding, 1)
+        self.assertEqual(torch_output_padding, 1)
+
+
+class ComputeConvTransposePaddingArgsForJAXTest(test_case.TestCase):
+    def test_valid_padding_without_output_padding(self):
+        """Test computation with 'valid' padding and no output padding"""
+        jax_padding = compute_conv_transpose_padding_args_for_jax(
+            input_shape=(1, 5, 5, 3),
+            kernel_shape=(3, 3, 3, 3),
+            strides=2,
+            padding="valid",
+            output_padding=None,
+            dilation_rate=1,
+        )
+        self.assertEqual(jax_padding, [(2, 2), (2, 2)])
+
+    def test_same_padding_without_output_padding(self):
+        """Test computation with 'same' padding and no output padding"""
+        jax_padding = compute_conv_transpose_padding_args_for_jax(
+            input_shape=(1, 5, 5, 3),
+            kernel_shape=(3, 3, 3, 3),
+            strides=2,
+            padding="same",
+            output_padding=None,
+            dilation_rate=1,
+        )
+
+        self.assertEqual(jax_padding, [(2, 1), (2, 1)])
+
+
+class ComputeConvTransposePaddingArgsForTorchTest(test_case.TestCase):
+    def test_valid_padding_without_output_padding(self):
+        """Test computation with 'valid' padding and no output padding"""
+        (
+            torch_paddings,
+            torch_output_paddings,
+        ) = compute_conv_transpose_padding_args_for_torch(
+            input_shape=(1, 5, 5, 3),
+            kernel_shape=(3, 3, 3, 3),
+            strides=2,
+            padding="valid",
+            output_padding=None,
+            dilation_rate=1,
+        )
+        self.assertEqual(torch_paddings, [0, 0])
+        self.assertEqual(torch_output_paddings, [0, 0])
+
+    def test_same_padding_without_output_padding(self):
+        """Test computation with 'same' padding and no output padding"""
+        (
+            torch_paddings,
+            torch_output_paddings,
+        ) = compute_conv_transpose_padding_args_for_torch(
+            input_shape=(1, 5, 5, 3),
+            kernel_shape=(3, 3, 3, 3),
+            strides=2,
+            padding="same",
+            output_padding=None,
+            dilation_rate=1,
+        )
+        self.assertEqual(torch_paddings, [1, 1])
+        self.assertEqual(torch_output_paddings, [1, 1])
+
+    def test_valid_padding_with_none_output_padding(self):
+        """Test conversion with 'valid' padding and no output padding"""
+        (
+            torch_padding,
+            torch_output_padding,
+        ) = _convert_conv_tranpose_padding_args_from_keras_to_torch(
+            kernel_size=3,
+            stride=2,
+            dilation_rate=1,
+            padding="valid",
+            output_padding=None,
+        )
+        self.assertEqual(torch_padding, 0)
+        self.assertEqual(torch_output_padding, 0)
+
+    def test_valid_padding_with_output_padding(self):
+        """Test conversion with 'valid' padding and output padding for Torch."""
+        (
+            torch_padding,
+            torch_output_padding,
+        ) = _convert_conv_tranpose_padding_args_from_keras_to_torch(
+            kernel_size=3,
+            stride=2,
+            dilation_rate=1,
+            padding="valid",
+            output_padding=1,
+        )
+        self.assertEqual(torch_padding, 0)
+        self.assertEqual(torch_output_padding, 1)
+
+
+class GetOutputShapeGivenTFPaddingTest(test_case.TestCase):
+    def test_valid_padding_without_output_padding(self):
+        """Test computation with 'valid' padding and no output padding."""
+        output_shape = _get_output_shape_given_tf_padding(
+            input_size=5,
+            kernel_size=3,
+            strides=2,
+            padding="valid",
+            output_padding=None,
+            dilation_rate=1,
+        )
+        self.assertEqual(output_shape, 11)
+
+    def test_same_padding_without_output_padding(self):
+        """Test computation with 'same' padding and no output padding."""
+        output_shape = _get_output_shape_given_tf_padding(
+            input_size=5,
+            kernel_size=3,
+            strides=2,
+            padding="same",
+            output_padding=None,
+            dilation_rate=1,
+        )
+        self.assertEqual(output_shape, 10)
+
+    def test_valid_padding_with_output_padding(self):
+        """Test computation with 'valid' padding and output padding."""
+        output_shape = _get_output_shape_given_tf_padding(
+            input_size=5,
+            kernel_size=3,
+            strides=2,
+            padding="valid",
+            output_padding=1,
+            dilation_rate=1,
+        )
+        self.assertEqual(output_shape, 12)
+
+    def test_warning_for_inconsistencies(self):
+        """Test that a warning is raised for potential inconsistencies"""
+        with self.assertWarns(Warning):
+            _convert_conv_tranpose_padding_args_from_keras_to_torch(
+                kernel_size=3,
+                stride=2,
+                dilation_rate=1,
+                padding="same",
+                output_padding=1,
+            )
+
+    def test_same_padding_without_output_padding_for_torch_(self):
+        """Test conversion with 'same' padding and no output padding."""
+        (
+            torch_padding,
+            torch_output_padding,
+        ) = _convert_conv_tranpose_padding_args_from_keras_to_torch(
+            kernel_size=3,
+            stride=2,
+            dilation_rate=1,
+            padding="same",
+            output_padding=None,
+        )
+        self.assertEqual(torch_padding, max(-((3 % 2 - 3) // 2), 0))
+        self.assertEqual(torch_output_padding, 1)