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[TF FE] Fix LRN operation and test it (#15145)

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It allows to fix inference results mismatch for some models

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
This commit is contained in:
Roman Kazantsev 2023-01-16 21:31:24 +04:00 committed by GitHub
parent 74f4a5e47d
commit 04c9c28869
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2 changed files with 56 additions and 15 deletions
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38
src/frontends/tensorflow/src/op/lrn.cpp
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@ -4,6 +4,7 @@
#include "op_table.hpp"
#include "openvino/opsets/opset8.hpp"
#include "utils.hpp"
using namespace std;
using namespace ov::opset8;
@ -14,23 +15,30 @@ namespace tensorflow {
namespace op {
OutputVector translate_lrn_op(const NodeContext& node) {
// The normalization is performed along the last dimension
default_op_checks(node, 1, {"LRN"});
auto input = node.get_input(0);
auto alpha = node.get_attribute<float>("alpha");
auto beta = node.get_attribute<float>("beta");
auto bias = node.get_attribute<float>("bias");
auto depth_radius = node.get_attribute<int64_t>("depth_radius");
// OV: Each input value is divided by (bias+(alpha/size)*sum(xi^2 for every xi
// in the local region))^beta
// TF: sqr_sum[a, b, c, d] = sum(input[a, b, c, d - depth_radius : d +
// depth_radius + 1] ** 2)
// output = input / (bias + alpha * sqr_sum) ** beta
int64_t size = depth_radius * 2 + 1;
alpha = alpha * size;
// todo: input is in NHWC, need to apply NHWC to NCHW?
auto res = make_shared<LRN>(input, alpha, beta, bias, static_cast<size_t>(size));
set_node_name(node.get_name(), res);
return res->outputs();
// retrieve attributes
auto depth_radius = node.get_attribute<int64_t>("depth_radius", 5);
auto bias = static_cast<double>(node.get_attribute<float>("bias", 1));
auto alpha = static_cast<double>(node.get_attribute<float>("alpha", 1));
auto beta = static_cast<double>(node.get_attribute<float>("beta", 0.5));
// adjust attribute values for opset LRN operation
size_t attr_size = static_cast<size_t>(depth_radius) * 2 + 1;
alpha *= static_cast<double>(attr_size);
// currently, plugins fallback to the template to execute this operation
// the current implementation supports only the normalization across channel (axes={1})
// and spatial dimensions (axes={2,3})
// so we have to transpose inputs and outputs due to this limitation
auto axis = make_shared<Constant>(element::i32, Shape{1}, 1);
ov::frontend::tensorflow::convert_nhwc_to_nchw(true, input);
auto lrn = make_shared<LRN>(input, axis, alpha, beta, bias, attr_size)->output(0);
ov::frontend::tensorflow::convert_nchw_to_nhwc(true, lrn);
set_node_name(node.get_name(), lrn.get_node_shared_ptr());
return {lrn};
}
} // namespace op

33
tests/layer_tests/tensorflow_tests/test_tf_LRN.py Normal file
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@ -0,0 +1,33 @@
# Copyright (C) 2018-2023 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import pytest
import tensorflow as tf
from common.tf_layer_test_class import CommonTFLayerTest
class TestLRN(CommonTFLayerTest):
def create_lrn_net(self, input_shape, depth_radius, bias, alpha, beta):
tf.compat.v1.reset_default_graph()
# Create the graph and model
with tf.compat.v1.Session() as sess:
input = tf.compat.v1.placeholder(tf.float32, input_shape, 'input')
tf.raw_ops.LRN(input=input, depth_radius=depth_radius, bias=bias, alpha=alpha, beta=beta)
tf.compat.v1.global_variables_initializer()
tf_net = sess.graph_def
return tf_net, None
test_data_basic = [
dict(input_shape=[2, 20, 30, 3], depth_radius=3, bias=0.2, alpha=4, beta=0.75),
dict(input_shape=[1, 10, 30, 2], depth_radius=4, bias=1, alpha=3, beta=0.15),
]
@pytest.mark.parametrize("params", test_data_basic)
@pytest.mark.precommit_tf_fe
def test_lrn_basic(self, params, ie_device, precision, ir_version, temp_dir,
use_new_frontend, use_old_api):
self._test(*self.create_lrn_net(**params),
ie_device, precision, ir_version, temp_dir=temp_dir,
use_new_frontend=use_new_frontend, use_old_api=use_old_api)