Parallel execution of TensorFlow Layer 1 Python tests (#22173)
* Update TF Layer 1 tests * Enable parallel execution of tests * Fix typo * Fix typo * Enable parallel tensorflow tests for windows --------- Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>
This commit is contained in:
Rajat Krishna
GitHub
parent
45f62850f3
commit
1a8f72f342
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@ -249,7 +249,7 @@ jobs:
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run: |
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# requires 'unit_tests' from 'mo'
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export PYTHONPATH=${INSTALL_TEST_DIR}/mo
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python3 -m pytest ${LAYER_TESTS_INSTALL_DIR}/tensorflow_tests/ --use_new_frontend -m precommit_tf_fe --junitxml=${INSTALL_TEST_DIR}/TEST-tf_fe.xml
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python3 -m pytest ${LAYER_TESTS_INSTALL_DIR}/tensorflow_tests/ --use_new_frontend -m precommit_tf_fe -n logical --junitxml=${INSTALL_TEST_DIR}/TEST-tf_fe.xml
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env:
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TEST_DEVICE: CPU
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TEST_PRECISION: FP16
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@ -473,7 +473,7 @@ jobs:
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run: |
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:: requires 'unit_tests' from 'tools/mo'
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set PYTHONPATH=${{ env.INSTALL_TEST_DIR }}\mo;%PYTHONPATH%
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python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/tensorflow_tests/ --use_new_frontend -m precommit_tf_fe --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tf_fe.xml
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python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/tensorflow_tests/ --use_new_frontend -n logical -m precommit_tf_fe --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tf_fe.xml
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env:
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TEST_DEVICE: CPU
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TEST_PRECISION: FP16
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@ -13,6 +13,11 @@ from common.tf_layer_test_class import CommonTFLayerTest
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# Documentation: https://www.tensorflow.org/api_docs/python/tf/raw_ops/ArgMin
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# https://www.tensorflow.org/api_docs/python/tf/raw_ops/ArgMax
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OPS = {
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'tf.raw_ops.ArgMax': tf.raw_ops.ArgMax,
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'tf.raw_ops.ArgMin': tf.raw_ops.ArgMin
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}
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class TestArgMinMax(CommonTFLayerTest):
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def _prepare_input(self, inputs_info):
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assert 'input' in inputs_info
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@ -41,24 +46,24 @@ class TestArgMinMax(CommonTFLayerTest):
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return tf_net, ref_net
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test_data = [
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dict(input_shape=[20], dimension=0),
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dict(input_shape=[20, 30], dimension=1),
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dict(input_shape=[2, 30, 3, 4], dimension=2),
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[[20], 0],
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[[20, 30], 1],
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[[2, 30, 3, 4], 2],
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]
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.parametrize("input_shape, dimension", test_data)
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@pytest.mark.parametrize("input_type", [np.float32, np.int32])
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@pytest.mark.parametrize("output_type", [tf.int32, tf.int64])
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@pytest.mark.parametrize("op_type", [tf.raw_ops.ArgMax, tf.raw_ops.ArgMin])
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@pytest.mark.parametrize("op_type", ['tf.raw_ops.ArgMax', 'tf.raw_ops.ArgMin'])
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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@pytest.mark.xfail(condition=platform.system() == 'Linux' and platform.machine() in ['arm', 'armv7l',
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'aarch64',
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'arm64', 'ARM64'],
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reason='Ticket - 126314')
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def test_argmin_max_net(self, params, input_type, output_type, op_type, ie_device, precision, ir_version, temp_dir,
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use_new_frontend):
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def test_argmin_max_net(self, input_shape, dimension, input_type, output_type, op_type, ie_device, precision, ir_version, temp_dir, use_new_frontend):
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params = dict(input_shape=input_shape, dimension=dimension)
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self._test(*self.create_argmin_max_net(**params, input_type=input_type,
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output_type=output_type, op_type=op_type),
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output_type=output_type, op_type=OPS[op_type]),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend)
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@ -7,6 +7,11 @@ import tensorflow as tf
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from common.tf_layer_test_class import CommonTFLayerTest
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OPS = {
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"tf.raw_ops.CheckNumerics": tf.raw_ops.CheckNumerics,
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"tf.raw_ops.CheckNumericsV2": tf.raw_ops.CheckNumericsV2
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}
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class TestCheckNumerics(CommonTFLayerTest):
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def _prepare_input(self, inputs_info):
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assert 'x' in inputs_info
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@ -33,15 +38,16 @@ class TestCheckNumerics(CommonTFLayerTest):
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return tf_net, None
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test_data_basic = [
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dict(input_shape=[2, 6], input_type=np.float32, op=tf.raw_ops.CheckNumerics),
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dict(input_shape=[3, 4, 5], input_type=np.float32, op=tf.raw_ops.CheckNumericsV2),
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[[2, 6], np.float32, 'tf.raw_ops.CheckNumerics'],
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[[3, 4, 5], np.float32, 'tf.raw_ops.CheckNumericsV2'],
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]
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@pytest.mark.parametrize("params", test_data_basic)
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@pytest.mark.parametrize("input_shape, input_type, op", test_data_basic)
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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def test_check_numerics_basic(self, params, ie_device, precision, ir_version, temp_dir,
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def test_check_numerics_basic(self, input_shape, input_type, op, ie_device, precision, ir_version, temp_dir,
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use_new_frontend):
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params = dict(input_shape=input_shape, input_type=input_type, op=OPS[op])
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self._test(*self.create_check_numerics_net(**params),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend)
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@ -9,6 +9,21 @@ import tensorflow as tf
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from common.tf_layer_test_class import CommonTFLayerTest
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OPS = {
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'tf.raw_ops.IRFFT': tf.raw_ops.IRFFT,
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'tf.raw_ops.IRFFT2D': tf.raw_ops.IRFFT2D,
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'tf.raw_ops.IRFFT3D': tf.raw_ops.IRFFT3D,
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'tf.raw_ops.FFT': tf.raw_ops.FFT,
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'tf.raw_ops.FFT2D': tf.raw_ops.FFT2D,
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'tf.raw_ops.FFT3D': tf.raw_ops.FFT3D,
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'tf.raw_ops.IFFT': tf.raw_ops.IFFT,
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'tf.raw_ops.IFFT2D': tf.raw_ops.IFFT2D,
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'tf.raw_ops.IFFT3D': tf.raw_ops.IFFT3D,
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'tf.raw_ops.RFFT': tf.raw_ops.RFFT,
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'tf.raw_ops.RFFT2D': tf.raw_ops.RFFT2D,
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'tf.raw_ops.RFFT3D': tf.raw_ops.RFFT3D
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}
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class TestComplexFFT(CommonTFLayerTest):
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def _prepare_input(self, inputs_info):
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rng = np.random.default_rng()
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@ -41,30 +56,31 @@ class TestComplexFFT(CommonTFLayerTest):
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return tf_net, None
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test_data_basic = [
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dict(input_shape=[1, 50, 2], shift_roll=[10, 1], axis_roll=[-2, -1]),
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dict(input_shape=[4, 20, 3], shift_roll=[2, 10], axis_roll=[0, 1]),
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dict(input_shape=[1, 50, 50, 2], shift_roll=[10, 20], axis_roll=[-2, -1]),
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dict(input_shape=[4, 20, 30, 3], shift_roll=[2, 10], axis_roll=[0, 1]),
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dict(input_shape=[1, 50, 50, 30, 2], shift_roll=[10, 20, 4], axis_roll=[-3, -2, -1]),
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dict(input_shape=[4, 20, 30, 10, 3], shift_roll=[2, 10], axis_roll=[1, 2]),
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[[1, 50, 2], [10, 1], [-2, -1]],
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[[4, 20, 3], [2, 10], [0, 1]],
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[[1, 50, 50, 2], [10, 20], [-2, -1]],
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[[4, 20, 30, 3], [2, 10], [0, 1]],
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[[1, 50, 50, 30, 2], [10, 20, 4], [-3, -2, -1]],
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[[4, 20, 30, 10, 3], [2, 10], [1, 2]],
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]
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@pytest.mark.parametrize("fft_op", [
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tf.raw_ops.FFT, tf.raw_ops.FFT2D, tf.raw_ops.FFT3D,
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tf.raw_ops.IFFT, tf.raw_ops.IFFT2D, tf.raw_ops.IFFT3D
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"tf.raw_ops.FFT", "tf.raw_ops.FFT2D", "tf.raw_ops.FFT3D",
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"tf.raw_ops.IFFT", "tf.raw_ops.IFFT2D", "tf.raw_ops.IFFT3D"
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])
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@pytest.mark.parametrize("params", test_data_basic)
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@pytest.mark.parametrize("input_shape, shift_roll, axis_roll", test_data_basic)
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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@pytest.mark.xfail(condition=platform.system() == 'Linux' and platform.machine() in ['arm', 'armv7l',
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'aarch64',
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'arm64', 'ARM64'],
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reason='Ticket - 126314')
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def test_complex_fft_basic(self, params, fft_op,
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def test_complex_fft_basic(self, input_shape, shift_roll, axis_roll, fft_op,
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ie_device, precision, ir_version, temp_dir,
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use_new_frontend):
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params = dict(input_shape=input_shape, shift_roll=shift_roll, axis_roll=axis_roll)
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self._test(
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*self.create_complex_fft_net(**params, fft_op=fft_op),
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*self.create_complex_fft_net(**params, fft_op=OPS[fft_op]),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, custom_eps=1e-2)
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@ -95,20 +111,19 @@ class TestComplexAbs(CommonTFLayerTest):
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return tf_net, None
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test_data_basic = [
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dict(input_shape=[]),
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dict(input_shape=[2]),
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dict(input_shape=[1, 3]),
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dict(input_shape=[2, 3, 4]),
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dict(input_shape=[3, 4, 5, 6]),
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[],
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[2],
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[1, 3],
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[2, 3, 4],
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[3, 4, 5, 6],
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]
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@pytest.mark.parametrize("params", test_data_basic)
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@pytest.mark.parametrize("input_shape", test_data_basic)
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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def test_complex_abs_basic(self, params, ie_device, precision, ir_version, temp_dir,
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def test_complex_abs_basic(self, input_shape, ie_device, precision, ir_version, temp_dir,
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use_new_frontend):
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self._test(
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*self.create_complex_abs_net(**params),
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*self.create_complex_abs_net(input_shape),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend)
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@ -138,18 +153,19 @@ class TestComplexRFFT(CommonTFLayerTest):
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return tf_net, None
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test_data_basic = [
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dict(input_shape=[1, 3, 20], fft_length=[10], rfft_op=tf.raw_ops.RFFT),
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dict(input_shape=[1, 3, 20], fft_length=[20], rfft_op=tf.raw_ops.RFFT),
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dict(input_shape=[1, 3, 20, 40], fft_length=[20, 10], rfft_op=tf.raw_ops.RFFT2D),
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dict(input_shape=[1, 3, 20, 40], fft_length=[10, 40], rfft_op=tf.raw_ops.RFFT2D),
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dict(input_shape=[1, 2, 10, 20, 5], fft_length=[2, 5, 3], rfft_op=tf.raw_ops.RFFT3D),
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[[1, 3, 20], [10], 'tf.raw_ops.RFFT'],
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[[1, 3, 20], [20], 'tf.raw_ops.RFFT'],
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[[1, 3, 20, 40], [20, 10], 'tf.raw_ops.RFFT2D'],
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[[1, 3, 20, 40], [10, 40], 'tf.raw_ops.RFFT2D'],
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[[1, 2, 10, 20, 5], [2, 5, 3], 'tf.raw_ops.RFFT3D']
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]
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@pytest.mark.parametrize("params", test_data_basic)
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@pytest.mark.parametrize("input_shape, fft_length, rfft_op", test_data_basic)
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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def test_complex_rfft_basic(self, params, ie_device, precision, ir_version, temp_dir,
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def test_complex_rfft_basic(self, input_shape, fft_length, rfft_op, ie_device, precision, ir_version, temp_dir,
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use_new_frontend):
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params = dict(input_shape=input_shape, fft_length=fft_length, rfft_op=OPS[rfft_op])
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self._test(
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*self.create_complex_rfft_net(**params),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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@ -183,19 +199,19 @@ class TestComplexIRFFT(CommonTFLayerTest):
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return tf_net, None
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test_data_basic = [
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dict(input_shape=[1, 3, 20], fft_length=[10], irfft_op=tf.raw_ops.IRFFT),
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dict(input_shape=[1, 3, 20], fft_length=[20], irfft_op=tf.raw_ops.IRFFT),
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dict(input_shape=[1, 3, 20, 40], fft_length=[20, 10], irfft_op=tf.raw_ops.IRFFT2D),
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dict(input_shape=[1, 3, 20, 40], fft_length=[10, 40], irfft_op=tf.raw_ops.IRFFT2D),
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pytest.param(dict(input_shape=[1, 10, 20, 30, 5], fft_length=[2, 3, 4], irfft_op=tf.raw_ops.IRFFT3D),
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marks=pytest.mark.xfail(reason="accuracy-issue-124452"))
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[[1, 3, 20], [10], 'tf.raw_ops.IRFFT'],
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[[1, 3, 20], [20], 'tf.raw_ops.IRFFT'],
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[[1, 3, 20, 40], [20, 10], 'tf.raw_ops.IRFFT2D'],
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[[1, 3, 20, 40], [10, 40], 'tf.raw_ops.IRFFT2D'],
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pytest.param([1, 10, 20, 30, 5], [2, 3, 4], 'tf.raw_ops.IRFFT3D',
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marks=pytest.mark.xfail(reason="accuracy-issue-124452"))
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]
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@pytest.mark.parametrize("params", test_data_basic)
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@pytest.mark.parametrize("input_shape, fft_length, irfft_op", test_data_basic)
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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def test_complex_irfft_basic(self, params, ie_device, precision, ir_version, temp_dir,
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def test_complex_irfft_basic(self, input_shape, fft_length, irfft_op, ie_device, precision, ir_version, temp_dir,
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use_new_frontend):
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params = dict(input_shape=input_shape, fft_length=fft_length, irfft_op=OPS[irfft_op])
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self._test(
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*self.create_complex_irfft_net(**params),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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@ -9,6 +9,12 @@ import tensorflow as tf
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from common.tf_layer_test_class import CommonTFLayerTest
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OPS = {
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'tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel': tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel,
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'tf.raw_ops.FakeQuantWithMinMaxVars': tf.raw_ops.FakeQuantWithMinMaxVars,
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'tf.raw_ops.FakeQuantWithMinMaxArgs': tf.raw_ops.FakeQuantWithMinMaxArgs
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}
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class TestFakeQuantWithMinMaxVars(CommonTFLayerTest):
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def _prepare_input(self, inputs_info):
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# generate elements so that the input tensor may contain repeating elements
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@ -32,38 +38,36 @@ class TestFakeQuantWithMinMaxVars(CommonTFLayerTest):
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test_basic = [
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# test FakeQuantWithMinMaxVars
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dict(inputs_shape=[2, 6, 4], min_value=-3, max_value=4, num_bits=None, narrow_range=None),
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dict(inputs_shape=[3, 2, 1, 5], min_value=-4, max_value=5, num_bits=14, narrow_range=True),
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dict(inputs_shape=[3, 2, 4], min_value=2, max_value=4, num_bits=10, narrow_range=False),
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dict(inputs_shape=[1, 2, 3], min_value=-6, max_value=-3, num_bits=8, narrow_range=True),
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[[2, 6, 4], -3, 4, None, None],
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[[3, 2, 1, 5], -4, 5, 14, True],
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[[3, 2, 4], 2, 4, 10, False],
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[[1, 2, 3], -6, -3, 8, True],
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]
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@pytest.mark.parametrize("params", test_basic)
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@pytest.mark.parametrize("inputs_shape, min_value, max_value, num_bits, narrow_range", test_basic)
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@pytest.mark.parametrize("fake_quant_op", [
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tf.raw_ops.FakeQuantWithMinMaxVars, tf.raw_ops.FakeQuantWithMinMaxArgs
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'tf.raw_ops.FakeQuantWithMinMaxVars', 'tf.raw_ops.FakeQuantWithMinMaxArgs'
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])
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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@pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
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reason='Ticket - 122716')
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def test_fake_quant_with_min_max_vars_basic(self, params, fake_quant_op, ie_device, precision, ir_version, temp_dir,
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use_new_frontend):
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self._test(*self.create_fake_quant_with_min_max_vars_net(**params, fake_quant_op=fake_quant_op),
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def test_fake_quant_with_min_max_vars_basic(self, inputs_shape, min_value, max_value, num_bits, narrow_range, fake_quant_op, ie_device, precision, ir_version, temp_dir, use_new_frontend):
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params = dict(inputs_shape=inputs_shape, min_value=min_value, max_value=max_value, num_bits=num_bits, narrow_range=narrow_range)
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self._test(*self.create_fake_quant_with_min_max_vars_net(**params, fake_quant_op=OPS[fake_quant_op]),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend)
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test_per_channel_basic = [
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dict(inputs_shape=[2, 6, 4], min_value=[-4, -3, -5, -8], max_value=[4, 7, 9, 5], num_bits=None,
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narrow_range=None,
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fake_quant_op=tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel),
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[[2, 6, 4], [-4, -3, -5, -8], [4, 7, 9, 5], None, None, 'tf.raw_ops.FakeQuantWithMinMaxVarsPerChannel'],
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]
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@pytest.mark.parametrize("params", test_per_channel_basic)
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@pytest.mark.parametrize("inputs_shape, min_value, max_value, num_bits, narrow_range, fake_quant_op", test_per_channel_basic)
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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@pytest.mark.xfail("104822")
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def test_fake_quant_with_min_max_vars_per_channel_basic(self, params, ie_device, precision, ir_version, temp_dir,
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use_new_frontend):
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def test_fake_quant_with_min_max_vars_per_channel_basic(self, inputs_shape, min_value, max_value, num_bits, narrow_range, fake_quant_op, ie_device, precision, ir_version, temp_dir, use_new_frontend):
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params=dict(inputs_shape=inputs_shape, min_value=min_value, max_value=max_value, num_bits=num_bits, narrow_range=narrow_range, fake_quant_op=OPS[fake_quant_op])
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self._test(*self.create_fake_quant_with_min_max_vars_net(**params),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend)
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|
|
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@ -6,6 +6,13 @@ import tensorflow as tf
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from common.tf_layer_test_class import CommonTFLayerTest
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OPS = {
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'tf.raw_ops.Identity': tf.raw_ops.Identity,
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'tf.raw_ops.PreventGradient': tf.raw_ops.PreventGradient,
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'tf.raw_ops.Snapshot': tf.raw_ops.Snapshot,
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'tf.raw_ops.StopGradient': tf.raw_ops.StopGradient,
|
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}
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class TestIdentity(CommonTFLayerTest):
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def create_identity_net(self, input_shape, identity_op):
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tf.compat.v1.reset_default_graph()
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|
|
@ -22,17 +29,18 @@ class TestIdentity(CommonTFLayerTest):
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return tf_net, None
|
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test_data_basic = [
|
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dict(input_shape=[2], identity_op=tf.raw_ops.Identity),
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||||
dict(input_shape=[2, 3], identity_op=tf.raw_ops.PreventGradient),
|
||||
dict(input_shape=[], identity_op=tf.raw_ops.Snapshot),
|
||||
dict(input_shape=[1, 2, 3], identity_op=tf.raw_ops.StopGradient)
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[[2], 'tf.raw_ops.Identity'],
|
||||
[[2, 3], 'tf.raw_ops.PreventGradient'],
|
||||
[[], 'tf.raw_ops.Snapshot'],
|
||||
[[1, 2, 3], 'tf.raw_ops.StopGradient']
|
||||
]
|
||||
|
||||
@pytest.mark.parametrize("params", test_data_basic)
|
||||
@pytest.mark.parametrize("input_shape, identity_op", test_data_basic)
|
||||
@pytest.mark.precommit_tf_fe
|
||||
@pytest.mark.nightly
|
||||
def test_identity_basic(self, params, ie_device, precision, ir_version, temp_dir,
|
||||
def test_identity_basic(self, input_shape, identity_op, ie_device, precision, ir_version, temp_dir,
|
||||
use_new_frontend):
|
||||
params = dict(input_shape=input_shape, identity_op=OPS[identity_op])
|
||||
self._test(*self.create_identity_net(**params),
|
||||
ie_device, precision, ir_version, temp_dir=temp_dir,
|
||||
use_new_frontend=use_new_frontend)
|
||||
|
|
|
|||
|
|
@ -9,6 +9,11 @@ import tensorflow as tf
|
|||
from common.tf_layer_test_class import CommonTFLayerTest
|
||||
|
||||
|
||||
OPS = {
|
||||
'tf.raw_ops.ResizeBilinear': tf.raw_ops.ResizeBilinear,
|
||||
'tf.raw_ops.ResizeNearestNeighbor': tf.raw_ops.ResizeNearestNeighbor,
|
||||
}
|
||||
|
||||
class TestResize(CommonTFLayerTest):
|
||||
def _prepare_input(self, inputs_info):
|
||||
assert 'images' in inputs_info, "Test error: inputs_info must contain `x`"
|
||||
|
|
@ -34,38 +39,27 @@ class TestResize(CommonTFLayerTest):
|
|||
|
||||
test_data_basic = [
|
||||
# ResizeBilinear testing
|
||||
dict(images_shape=[1, 30, 30, 3], images_type=tf.float32, size_value=[40, 40], align_corners=False,
|
||||
half_pixel_centers=False, resize_op=tf.raw_ops.ResizeBilinear),
|
||||
dict(images_shape=[1, 30, 30, 3], images_type=tf.float64, size_value=[40, 40], align_corners=False,
|
||||
half_pixel_centers=False, resize_op=tf.raw_ops.ResizeBilinear),
|
||||
dict(images_shape=[2, 100, 100, 3], images_type=tf.float32, size_value=[40, 40], align_corners=True,
|
||||
half_pixel_centers=False, resize_op=tf.raw_ops.ResizeBilinear),
|
||||
dict(images_shape=[2, 10, 10, 3], images_type=tf.float32, size_value=[40, 40], align_corners=False,
|
||||
half_pixel_centers=True, resize_op=tf.raw_ops.ResizeBilinear),
|
||||
dict(images_shape=[2, 40, 40, 3], images_type=tf.uint8, size_value=[10, 10], align_corners=False,
|
||||
half_pixel_centers=False, resize_op=tf.raw_ops.ResizeBilinear),
|
||||
dict(images_shape=[1, 40, 40, 3], images_type=tf.int32, size_value=[10, 10], align_corners=False,
|
||||
half_pixel_centers=True, resize_op=tf.raw_ops.ResizeBilinear),
|
||||
[[1, 30, 30, 3], tf.float32, [40, 40], False, False, 'tf.raw_ops.ResizeBilinear'],
|
||||
[[1, 30, 30, 3], tf.float64, [40, 40], False, False, 'tf.raw_ops.ResizeBilinear'],
|
||||
[[2, 100, 100, 3], tf.float32, [40, 40], True, False, 'tf.raw_ops.ResizeBilinear'],
|
||||
[[2, 10, 10, 3], tf.float32, [40, 40], False, True, 'tf.raw_ops.ResizeBilinear'],
|
||||
[[2, 40, 40, 3], tf.uint8, [10, 10], False, False, 'tf.raw_ops.ResizeBilinear'],
|
||||
[[1, 40, 40, 3], tf.int32, [10, 10], False, True, 'tf.raw_ops.ResizeBilinear'],
|
||||
# ResizeNearestNeighbor testing
|
||||
dict(images_shape=[1, 30, 30, 3], images_type=tf.float32, size_value=[40, 40], align_corners=False,
|
||||
half_pixel_centers=False, resize_op=tf.raw_ops.ResizeNearestNeighbor),
|
||||
dict(images_shape=[2, 100, 100, 3], images_type=tf.float32, size_value=[40, 40], align_corners=True,
|
||||
half_pixel_centers=False, resize_op=tf.raw_ops.ResizeNearestNeighbor),
|
||||
dict(images_shape=[2, 10, 10, 3], images_type=tf.float32, size_value=[40, 40], align_corners=False,
|
||||
half_pixel_centers=True, resize_op=tf.raw_ops.ResizeNearestNeighbor),
|
||||
dict(images_shape=[2, 40, 40, 3], images_type=tf.uint8, size_value=[10, 10], align_corners=False,
|
||||
half_pixel_centers=False, resize_op=tf.raw_ops.ResizeNearestNeighbor),
|
||||
dict(images_shape=[1, 40, 40, 3], images_type=tf.int32, size_value=[10, 10], align_corners=False,
|
||||
half_pixel_centers=True, resize_op=tf.raw_ops.ResizeNearestNeighbor),
|
||||
[[1, 30, 30, 3], tf.float32, [40, 40], False, False, 'tf.raw_ops.ResizeNearestNeighbor'],
|
||||
[[2, 100, 100, 3], tf.float32, [40, 40], True, False, 'tf.raw_ops.ResizeNearestNeighbor'],
|
||||
[[2, 10, 10, 3], tf.float32, [40, 40], False, True, 'tf.raw_ops.ResizeNearestNeighbor'],
|
||||
[[2, 40, 40, 3], tf.uint8, [10, 10], False, False, 'tf.raw_ops.ResizeNearestNeighbor'],
|
||||
[[1, 40, 40, 3], tf.int32, [10, 10], False,True, 'tf.raw_ops.ResizeNearestNeighbor'],
|
||||
]
|
||||
|
||||
@pytest.mark.parametrize("params", test_data_basic)
|
||||
@pytest.mark.parametrize("images_shape, images_type, size_value, align_corners, half_pixel_centers, resize_op", test_data_basic)
|
||||
@pytest.mark.precommit_tf_fe
|
||||
@pytest.mark.nightly
|
||||
@pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
|
||||
reason='Ticket - 122716')
|
||||
def test_resize_basic(self, params, ie_device, precision, ir_version, temp_dir,
|
||||
use_new_frontend):
|
||||
def test_resize_basic(self, images_shape, images_type, size_value, align_corners, half_pixel_centers, resize_op, ie_device, precision, ir_version, temp_dir, use_new_frontend):
|
||||
params = dict(images_shape=images_shape, images_type=images_type, size_value=size_value, align_corners=align_corners, half_pixel_centers=half_pixel_centers, resize_op=OPS[resize_op])
|
||||
self._test(*self.create_resize_net(**params),
|
||||
ie_device, precision, ir_version, temp_dir=temp_dir,
|
||||
use_new_frontend=use_new_frontend)
|
||||
|
|
|
|||
Loading…
Reference in New Issue