56f2bc2f12
* Add support api 2.0 * Upd imports * Update onnx_tests * Upd tenserflow_tests * Fix test_split_concat.py * Fixed comparing
208 lines
8.1 KiB
Python
208 lines
8.1 KiB
Python
# Copyright (C) 2018-2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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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 unit_tests.utils.graph import build_graph
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class TestUnaryOps(CommonTFLayerTest):
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current_op_type = None
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def _prepare_input(self, inputs_dict):
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non_negative = ['Sqrt', 'Log']
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narrow_borders = ["Sinh", "Cosh", "Tanh", "Exp"]
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within_one = ['Asin', 'Acos', 'Atanh']
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from_one = ['Acosh']
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logical_type = ['LogicalNot']
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# usual function domain
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lower = -256
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upper = 256
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# specific domains
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if self.current_op_type in non_negative:
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lower = 0
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elif self.current_op_type in narrow_borders:
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lower = -16
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upper = 16
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elif self.current_op_type in from_one:
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lower = 1
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elif self.current_op_type in within_one:
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lower = -1
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upper = 1
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for input in inputs_dict.keys():
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if self.current_op_type in logical_type:
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inputs_dict[input] = np.random.randint(0, 1, inputs_dict[input]).astype(np.bool)
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else:
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inputs_dict[input] = np.random.uniform(lower, upper, inputs_dict[input]).astype(
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np.float32)
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return inputs_dict
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def create_net_with_unary_op(self, shape, ir_version, op_type, use_new_frontend):
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"""
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Tensorflow net IR net
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Input->UnaryOp => Input->UnaryOp
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"""
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import tensorflow as tf
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self.current_op_type = op_type
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op_type_to_tf = {
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'Abs': tf.math.abs,
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'Acos': tf.math.acos,
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'Acosh': tf.math.acosh,
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'Asin': tf.math.asin,
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'Asinh': tf.math.asinh,
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'Atan': tf.math.atan,
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'Atanh': tf.math.atanh,
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'Ceiling': tf.math.ceil,
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'Cos': tf.math.cos,
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'Cosh': tf.math.cosh,
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'Elu': tf.nn.elu,
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'Exp': tf.math.exp,
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'Floor': tf.math.floor,
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'Log': tf.math.log,
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'LogicalNot': tf.math.logical_not,
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'Negative': tf.math.negative,
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'Sigmoid': tf.nn.sigmoid,
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'Sign': tf.math.sign,
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'Sin': tf.math.sin,
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'Sinh': tf.math.sinh,
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'SoftPlus': tf.nn.softplus,
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'Tan': tf.math.tan,
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'Tanh': tf.math.tanh,
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'ReLU': tf.nn.relu,
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}
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#
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# Create Tensorflow model
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#
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tf.compat.v1.reset_default_graph()
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type = tf.float32
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if op_type == "LogicalNot":
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type = tf.bool
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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(type, tf_x_shape, 'Input')
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op_type_to_tf[self.current_op_type](input, 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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#
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# Create reference IR net
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# Please, specify 'type': 'Input' for input node
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# Moreover, do not forget to validate ALL layer attributes!!!
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#
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ref_net = None
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if check_ir_version(10, None, ir_version) and not use_new_frontend:
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nodes_attributes = {
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'input': {'kind': 'op', 'type': 'Parameter'},
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'input_data': {'shape': shape, 'kind': 'data'},
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'testing_op': {'kind': 'op', 'type': self.current_op_type},
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'testing_data': {'shape': shape, 'kind': 'data'},
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'result': {'kind': 'op', 'type': 'Result'}
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}
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ref_net = build_graph(nodes_attributes,
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[('input', 'input_data'),
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('input_data', 'testing_op'),
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('testing_op', 'testing_data'),
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('testing_data', 'result')
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])
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return tf_net, ref_net
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test_data_precommit = [dict(shape=[4, 6, 8, 10, 12])]
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@pytest.mark.parametrize("params", test_data_precommit)
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@pytest.mark.parametrize("op_type", ['Elu',
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'Sigmoid',
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'Sin',
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'Sinh',
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'Cos',
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'Cosh',
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'Abs',
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'Negative',
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'Exp',
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'Tan',
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'Tanh',
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'Floor',
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'ReLU',
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'Ceiling',
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'Asin',
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'Acos',
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'Atan',
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'Log',
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'Sign',
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'SoftPlus',
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'Atanh',
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'Acosh',
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'Asinh',
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'LogicalNot',
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])
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@pytest.mark.precommit
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def test_unary_op_precommit(self, params, ie_device, precision, ir_version, temp_dir, op_type,
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use_new_frontend, api_2):
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if ie_device == 'GPU':
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pytest.skip("5D tensors is not supported on GPU")
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self._test(*self.create_net_with_unary_op(**params, ir_version=ir_version, op_type=op_type,
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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=[10, 12]),
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dict(shape=[8, 10, 12]),
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dict(shape=[6, 8, 10, 12]),
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dict(shape=[4, 6, 8, 10, 12])]
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.parametrize("op_type", ['Elu',
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'Sigmoid',
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'Sin',
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'Sinh',
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'Cos',
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'Cosh',
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'Abs',
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'Negative',
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'Exp',
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'Tan',
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'Tanh',
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'Floor',
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'ReLU',
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'Ceiling',
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'Asin',
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'Acos',
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'Atan',
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'Log',
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'LogicalNot',
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'Sign',
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'SoftPlus',
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'Atanh',
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'Acosh',
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'Asinh'])
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@pytest.mark.nightly
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def test_unary_op(self, params, ie_device, precision, ir_version, temp_dir, op_type,
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use_new_frontend, api_2):
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if ie_device == 'GPU':
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pytest.skip("5D tensors is not supported on GPU")
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self._test(*self.create_net_with_unary_op(**params, ir_version=ir_version, op_type=op_type,
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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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