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Increase-test-coverage in
backend_utils (#940)
* Increase-test-coverage in `backend_utils` * Increase-test-coverage in `backend_utils` * Increase-test-coverage in `backend_utils` * ncrease-test-coverage in `backend_utils` * ncrease-test-coverage in `backend_utils` * ncrease-test-coverage in `backend_utils` * Increase-test-coverage in `backend_utils`
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -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) |