Andrey KashchikhinandIlya Lavrenov
1454e77bbf
* start transferring * start with samples * start with initial two stages * change name * skip pytorch tests; rm unused comments * rm setupvars sourcing; make test steps similar to those in linux pipeline * add missing options and setupvars sourcing * add skips for mac * install wheels directly * add deployment target * add skips for pytorch layer tests; experiment with samples * do not exclude files for archives; set rpath * apply comments; rm unnecessary stages * Update mac.yml fixed MO Python API tests * Update .github/workflows/mac.yml * Update openvino.cmake add LC_RPATH to libopenvino.dylib * Update src/cmake/openvino.cmake * Update CMakeLists.txt reverted changes in samples build * Update openvino.cmake removed rpath changes * add setupvars * disable pr trigger --------- Co-authored-by: Ilya Lavrenov <ilya.lavrenov@intel.com>
125 lines
5.4 KiB
Python
125 lines
5.4 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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from sys 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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class TestWhile(CommonTFLayerTest):
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def _prepare_input(self, inputs_info):
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assert 'x' in inputs_info, "Test error: inputs_info must contain `x`"
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assert 'y' in inputs_info, "Test error: inputs_info must contain `y`"
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x_shape = inputs_info['x']
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y_shape = inputs_info['y']
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inputs_data = {}
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inputs_data['x'] = np.random.randint(1, 10, x_shape).astype(np.int32)
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inputs_data['y'] = np.random.randint(-50, 50, y_shape).astype(np.int32)
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return inputs_data
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def create_while_net(self, y_shape, data_type, lower_control_flow):
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from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
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def while_function(x, y):
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@tf.function
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def cond(x, y):
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return tf.less(x, 10)
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@tf.function
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def body(x, y):
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y_new = tf.add(y, tf.constant(2, dtype=data_type))
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x_new = tf.add(x, 1)
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return x_new, y_new
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return tf.while_loop(cond, body, [x, y])
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tf_while_graph = tf.function(while_function)
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x = np.random.randint(1, 10, []).astype(data_type)
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y = np.random.randint(-50, 50, y_shape).astype(data_type)
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concrete_func = tf_while_graph.get_concrete_function(x, y)
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# lower_control_flow defines representation of While operation
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# in case of lower_control_flow=True it is decomposed into LoopCond, NextIteration and TensorArray operations
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frozen_func = convert_variables_to_constants_v2(concrete_func,
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lower_control_flow=lower_control_flow)
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graph_def = frozen_func.graph.as_graph_def(add_shapes=True)
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return graph_def, None
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test_data_basic = [
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dict(y_shape=[2, 3], data_type=np.int32, lower_control_flow=False),
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dict(y_shape=[2, 1, 4], data_type=np.int32, lower_control_flow=False),
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dict(y_shape=[2, 1, 4], data_type=np.int32, lower_control_flow=True)
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]
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@pytest.mark.parametrize("params", 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.skipif(platform == 'darwin', reason="Ticket - 122182")
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def test_while_basic(self, params, 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_while_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, use_old_api=use_old_api)
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class TestWhileShapeVariant(CommonTFLayerTest):
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def _prepare_input(self, inputs_info):
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assert 'x' in inputs_info, "Test error: inputs_info must contain `x`"
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assert 'y' in inputs_info, "Test error: inputs_info must contain `y`"
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x_shape = inputs_info['x']
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y_shape = inputs_info['y']
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inputs_data = {}
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inputs_data['x'] = np.random.randint(1, 10, x_shape).astype(np.int32)
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inputs_data['y'] = np.random.randint(-50, 50, y_shape).astype(np.float32)
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return inputs_data
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def create_while_net(self, y_shape, lower_control_flow):
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from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
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def while_function(x, y):
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@tf.function
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def cond(x, y):
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return tf.less(x, 10)
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@tf.function
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def body(x, y):
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add_2 = tf.add(y, tf.constant(2, dtype=tf.float32))
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y_new = tf.concat(values=[y, add_2], axis=0)
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x_new = tf.add(x, tf.constant(1, tf.int32))
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return x_new, y_new
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return tf.while_loop(cond, body, [x, y],
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shape_invariants=[tf.TensorShape([]),
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tf.TensorShape([None] + y_shape[1:])])
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tf_while_graph = tf.function(while_function)
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x = np.random.randint(1, 10, []).astype(np.int32)
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y = np.random.randint(-50, 50, y_shape).astype(np.float32)
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concrete_func = tf_while_graph.get_concrete_function(x, y)
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# lower_control_flow defines representation of While operation
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# in case of lower_control_flow=True it is decomposed into LoopCond, NextIteration and TensorArray operations
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frozen_func = convert_variables_to_constants_v2(concrete_func,
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lower_control_flow=lower_control_flow)
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graph_def = frozen_func.graph.as_graph_def(add_shapes=True)
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return graph_def, None
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test_data_basic = [
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dict(y_shape=[2, 3], lower_control_flow=False),
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dict(y_shape=[2, 1, 4], lower_control_flow=False),
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dict(y_shape=[2, 1, 4], lower_control_flow=True)
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]
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@pytest.mark.parametrize("params", 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.skipif(platform == 'darwin', reason="Ticket - 122182")
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def test_while_basic(self, params, 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_while_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, use_old_api=use_old_api)
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