56f2bc2f12
* Add support api 2.0 * Upd imports * Update onnx_tests * Upd tenserflow_tests * Fix test_split_concat.py * Fixed comparing
146 lines
6.7 KiB
Python
146 lines
6.7 KiB
Python
# Copyright (C) 2018-2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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from distutils.version import LooseVersion
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import numpy as np
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import pytest
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from common.layer_test_class import check_ir_version
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from common.tf_layer_test_class import CommonTFLayerTest
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from common.utils.tf_utils import permute_nchw_to_nhwc
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from openvino.tools.mo.front.common.partial_infer.utils import int64_array
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from unit_tests.utils.graph import build_graph
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class TestLogSoftmax(CommonTFLayerTest):
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def create_log_softmax_net(self, shape, reduction_axis, ir_version, use_new_frontend):
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"""
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Tensorflow net IR net
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Input->LogSoftmax => Input->Softmax->Log
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"""
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#
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# Create Tensorflow model
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#
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import tensorflow as tf
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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_x_shape = shape.copy()
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tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
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input = tf.compat.v1.placeholder(tf.float32, tf_x_shape, 'Input')
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if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
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tf.nn.log_softmax(input, name='Operation', axis=reduction_axis)
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else:
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tf.nn.log_softmax(input, axis=reduction_axis, name='Operation')
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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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reduce_sum_shape = np.copy(shape)
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rank = len(shape)
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if rank in {4, 5}:
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reduction_axis = reduction_axis if reduction_axis >= 0 else rank + reduction_axis
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if rank == 4:
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reduction_axis = {0: 0, 1: 2, 2: 3, 3: 1}[reduction_axis]
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else:
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reduction_axis = {0: 0, 1: 2, 2: 3, 3: 4, 4: 1}[reduction_axis]
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reduce_sum_shape[reduction_axis] = 1
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converted_shape = shape if rank != 1 else shape[0]
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if check_ir_version(10, None, ir_version) and not use_new_frontend:
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ref_nodes_attributes = {
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'input': {'kind': 'op', 'type': 'Parameter', 'shape': converted_shape},
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'input_data': {'shape': shape, 'kind': 'data', 'value': None},
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'reduce_max_axis_val': {'shape': int64_array([reduction_axis]).shape,
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'kind': 'data',
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'value': int64_array([reduction_axis])},
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'reduce_max_axis': {'type': 'Const', 'kind': 'op', 'shape': 1},
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'reduce_max_axis_data': {'shape': int64_array([1]), 'kind': 'data', 'value': None},
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'reduce_max': {'type': 'ReduceMax', 'kind': 'op', 'keep_dims': True},
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'reduce_max_data': {'shape': reduce_sum_shape, 'kind': 'data', 'value': None},
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'sub_first': {'type': 'Subtract', 'kind': 'op'},
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'sub_first_data': {'shape': shape, 'kind': 'data', 'value': None},
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'reduce_sum_axis_val': {'shape': int64_array([reduction_axis]).shape,
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'kind': 'data',
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'value': int64_array([reduction_axis])},
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'reduce_sum_axis': {'type': 'Const', 'kind': 'op', 'shape': 1},
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'reduce_sum_axis_data': {'shape': int64_array([1]), 'kind': 'data', 'value': None},
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'reduce_sum': {'type': 'ReduceSum', 'kind': 'op', 'keep_dims': True},
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'reduce_sum_data': {'shape': reduce_sum_shape, 'kind': 'data', 'value': None},
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'exp': {'type': 'Exp', 'kind': 'op'},
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'exp_data': {'shape': shape, 'kind': 'data', 'value': None},
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'log': {'type': 'Log', 'kind': 'op'},
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'log_data': {'shape': reduce_sum_shape, 'kind': 'data', 'value': None},
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'sub_second': {'type': 'Subtract', 'kind': 'op'},
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'sub_second_data': {'shape': shape, 'kind': 'data', 'value': None},
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'result': {'kind': 'op', 'type': 'Result'},
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}
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ref_edges = [
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('input', 'input_data'),
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('reduce_max_axis_val', 'reduce_max_axis'),
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('reduce_max_axis', 'reduce_max_axis_data'),
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('reduce_max_axis_data', 'reduce_max', {'in': 1}),
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('reduce_max', 'reduce_max_data'),
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('input_data', 'reduce_max', {'out': 0, 'in': 0}),
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('input_data', 'sub_first', {'out': 0, 'in': 0}),
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('reduce_max_data', 'sub_first', {'in': 1}),
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('sub_first', 'sub_first_data'),
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('reduce_sum_axis_val', 'reduce_sum_axis'),
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('reduce_sum_axis', 'reduce_sum_axis_data'),
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('reduce_sum_axis_data', 'reduce_sum', {'in': 1}),
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('reduce_sum', 'reduce_sum_data'),
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('sub_first_data', 'exp'),
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('exp', 'exp_data'),
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('exp_data', 'reduce_sum', {'in': 0}),
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('reduce_sum_data', 'log'),
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('log', 'log_data'),
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('log_data', 'sub_second', {'in': 1}),
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('sub_second', 'sub_second_data'),
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('sub_first_data', 'sub_second', {'out': 0, 'in': 0}),
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('sub_second_data', 'result'),
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]
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ref_net = build_graph(ref_nodes_attributes, ref_edges)
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return tf_net, ref_net
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test_data_precommit = [
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pytest.param(dict(shape=[3, 2, 3, 7, 6], reduction_axis=-1),
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marks=pytest.mark.skip(reason="Skipped until fixed"))
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]
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@pytest.mark.parametrize("params", test_data_precommit)
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@pytest.mark.precommit
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def test_log_softmax_precommit(self, params, ie_device, precision, ir_version, temp_dir,
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use_new_frontend, api_2):
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self._test(*self.create_log_softmax_net(**params, ir_version=ir_version,
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use_new_frontend=use_new_frontend),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, api_2=api_2)
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test_data = [dict(shape=[1], reduction_axis=-1),
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dict(shape=[2, 5], reduction_axis=-1),
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dict(shape=[5, 3, 7, 4], reduction_axis=-1),
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dict(shape=[3, 2, 3, 7, 6], reduction_axis=-1)]
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_log_softmax(self, params, ie_device, precision, ir_version, temp_dir, use_new_frontend,
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api_2):
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self._test(*self.create_log_softmax_net(**params, ir_version=ir_version,
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use_new_frontend=use_new_frontend),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, api_2=api_2)
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