f32530f0d7
Remove useless and confusing comments Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com> Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
134 lines
5.0 KiB
Python
134 lines
5.0 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.tf_layer_test_class import CommonTFLayerTest
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from common.utils.tf_utils import permute_nchw_to_nhwc
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def generate_input(op_type, size):
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narrow_borders = ["Pow"]
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logical_type = ['LogicalAnd', 'LogicalOr', 'LogicalXor']
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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 op_type in narrow_borders:
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lower = 0
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upper = 16
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if op_type in logical_type:
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return np.random.randint(0, 1, size).astype(np.bool)
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elif op_type in narrow_borders:
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return np.random.uniform(lower, upper, size).astype(np.float32)
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else:
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return np.random.uniform(lower, upper, size).astype(np.float32)
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class TestBinaryOps(CommonTFLayerTest):
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def _prepare_input(self, inputs_dict):
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for input in inputs_dict.keys():
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inputs_dict[input] = generate_input(self.current_op_type, inputs_dict[input])
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return inputs_dict
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def create_add_placeholder_const_net(self, x_shape, y_shape, ir_version, op_type,
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use_new_frontend):
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"""
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Tensorflow net IR net
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Placeholder->BinaryOp => Placeholder->BinaryOp
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/ /
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Const-------/ Const-------/
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"""
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self.current_op_type = op_type
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import tensorflow as tf
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op_type_to_tf = {
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'Add': tf.math.add,
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'Sub': tf.math.subtract,
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'Mul': tf.math.multiply,
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'Div': tf.math.divide,
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'RealDiv': tf.realdiv,
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'SquaredDifference': tf.math.squared_difference,
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'Pow': tf.math.pow,
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'Maximum': tf.math.maximum,
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'Minimum': tf.math.minimum,
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'Equal': tf.math.equal,
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'NotEqual': tf.math.not_equal,
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'Mod': tf.math.mod,
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'Greater': tf.math.greater,
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'GreaterEqual': tf.math.greater_equal,
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'Less': tf.math.less,
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'LessEqual': tf.math.less_equal,
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'LogicalAnd': tf.math.logical_and,
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'LogicalOr': tf.math.logical_or,
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'LogicalXor': tf.math.logical_xor,
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'FloorMod': tf.math.floormod,
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}
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type = np.float32
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if op_type in ["LogicalAnd", "LogicalOr", "LogicalXor"]:
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type = np.bool
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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 = x_shape.copy()
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tf_y_shape = y_shape.copy()
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tf_x_shape = permute_nchw_to_nhwc(tf_x_shape, use_new_frontend)
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tf_y_shape = permute_nchw_to_nhwc(tf_y_shape, use_new_frontend)
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x = tf.compat.v1.placeholder(type, tf_x_shape, 'Input')
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constant_value = generate_input(op_type, tf_y_shape)
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if (constant_value == 0).all():
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# Avoid elimination of the layer from IR
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constant_value = constant_value + 1
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y = tf.constant(constant_value, dtype=type)
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op = op_type_to_tf[op_type](x, y, 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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return tf_net, ref_net
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test_data_precommits = [dict(x_shape=[2, 3, 4], y_shape=[2, 3, 4]),
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pytest.param(dict(x_shape=[2, 3, 4, 5], y_shape=[2, 3, 4, 5]),
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marks=pytest.mark.precommit_tf_fe)]
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@pytest.mark.parametrize("params", test_data_precommits)
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@pytest.mark.parametrize("op_type",
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['Add', 'Sub', 'Mul', 'Div', 'RealDiv', 'SquaredDifference', 'Pow',
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'Maximum', 'Minimum',
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'Equal', 'NotEqual', 'Mod', 'Greater', 'GreaterEqual', 'Less',
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'LessEqual',
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'LogicalAnd', 'LogicalOr', 'LogicalXor', 'FloorMod'])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_binary_op(self, params, ie_device, precision, ir_version, temp_dir, op_type,
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use_new_frontend, use_old_api):
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if ie_device == 'GPU' and precision == "FP16":
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pytest.skip("BinaryOps tests temporary skipped on GPU with FP16 precision."
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"Several tests don't pass accuracy checks.")
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self._test(
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*self.create_add_placeholder_const_net(**params, ir_version=ir_version, op_type=op_type,
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use_new_frontend=use_new_frontend), ie_device,
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precision,
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ir_version, temp_dir=temp_dir, use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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