Andrey Kashchikhin
dfc91186a1
* extract cpu func tests into a reusable workflow
* add needs
* check paths
* correct paths
* rm usage of setupvars
* extract samples
* use yaml string
* split mac workflow
* check variables
* try github workspace
* check full dir
* set variables
* try with sub directory
* Revert "try with sub directory"
This reverts commit d891ee3f28.
* extract c++ tests from linux
* change
* use cxx workflow in mac workflows
* add noninteractive
* extract cpu func tests
* rm unused
* enable mac
* fix reusable workflow name
* correct path for sparse-checkout
* extract python UT
* remove unnecessary asterisk
* remove another unnecessary asterisk
* use reusable action for linux cc
* add check for setupvars existence
* check with manually installed opencl
* add components input to samples workflow; rm pr triggers for mac workflows
* add missing needs
* add missing option for deps install script
* use disables in test themselves instead of ifs in workflows
* use reusable workflow for cxx tests in linux arm
* use python reusable workflow in linux arm
* add missing endif
* use self-hosted for samples, add x86_64 constraint for jax
* check paths
* find gompby partial name
* skip failing tests on arm; correct gomp finding for ovc
* check tests
* add debian packages job; use job_ prefix for reusable workflows with jobs
* extract tf hub model tests
* extract tf model hub perf tests
* extract pytorch models tests
* do not use container on GHA runners
* extract onnx runtime
* add missing deps
* skip test for linux arm
* rm always()s
* fix quotes
* correct paths
* correct ifs, check dir for onnxruntime
* correct path for onnxruntime utils folder; install python3
* use self-hosted as input
* check for self-hosted runner via name, pass version
* skip cpu plugin unittest
* check cxx tests
* rm pr trigger
65 lines
2.7 KiB
Python
65 lines
2.7 KiB
Python
# Copyright (C) 2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import platform
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import numpy as np
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import pytest
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import tensorflow as tf
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from common.tf_layer_test_class import CommonTFLayerTest
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# Testing operation ArgMin, ArgMax (Initial Implementation)
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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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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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input_shape = inputs_info['input']
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inputs_data = {}
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rng = np.random.default_rng()
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inputs_data['input'] = rng.integers(-8, 8, input_shape).astype(self.input_type)
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return inputs_data
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def create_argmin_max_net(self, input_shape, dimension, input_type, output_type, op_type):
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self.input_type = input_type
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tf.compat.v1.reset_default_graph()
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# Create the graph and model
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with tf.compat.v1.Session() as sess:
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tf_input = tf.compat.v1.placeholder(input_type, input_shape, 'input')
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tf_dimension = tf.constant(dimension)
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op_type(input=tf_input, dimension=tf_dimension, output_type=output_type)
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tf.compat.v1.global_variables_initializer()
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tf_net = sess.graph_def
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ref_net = None
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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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]
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@pytest.mark.parametrize("params", 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.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, use_old_api):
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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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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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