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/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "NodeExecution.h"
#include "locomotiv/NodeData.h"
#include "NodeDataImpl.h"
#include "NodeDomain.h"
#include <nncc/core/ADT/tensor/Shape.h>
#include <nncc/core/ADT/tensor/Buffer.h>
#include <nncc/core/ADT/tensor/LexicalLayout.h>
#include <nncc/core/ADT/tensor/Index.h>
#include <nncc/core/ADT/tensor/IndexEnumerator.h>
#include <gtest/gtest.h>
using nncc::core::ADT::tensor::Shape;
using nncc::core::ADT::tensor::LexicalLayout;
using nncc::core::ADT::tensor::make_buffer;
using nncc::core::ADT::tensor::IndexEnumerator;
/*
test case generated from the following:
x = tf.constant([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
shape=[1, 3, 3, 2], dtype=tf.float32)
y = tf.constant([18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1],
shape=[1, 3, 3, 2], dtype=tf.float32)
out = tf.math.maximum(x, y)
with tf.Session() as sess:
print(sess.run(out))
*/
TEST(NodeExecution_EltwiseMax, f32)
{
float x_val[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18};
float y_val[] = {18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1};
float out_val[] = {18, 17, 16, 15, 14, 13, 12, 11, 10, 10, 11, 12, 13, 14, 15, 16, 17, 18};
// make EltwiseMax(Pull, Pull)
auto g = loco::make_graph();
Shape input_shape{1, 3, 3, 2}; // NHWC
auto inp_lhs = g->nodes()->create<loco::Pull>();
{
inp_lhs->dtype(loco::DataType::FLOAT32);
inp_lhs->shape({1, 3, 3, 2});
}
auto inp_rhs = g->nodes()->create<loco::Pull>();
{
inp_rhs->dtype(loco::DataType::FLOAT32);
inp_rhs->shape({1, 3, 3, 2});
}
auto eltwise_max = g->nodes()->create<loco::EltwiseMax>();
{
eltwise_max->lhs(inp_lhs);
eltwise_max->rhs(inp_rhs);
}
// Make and assign data to two pull nodes
auto inp_lhs_buf = make_buffer<float, LexicalLayout>(input_shape);
{
int n = 0;
for (IndexEnumerator e{inp_lhs_buf.shape()}; e.valid(); e.advance())
{
inp_lhs_buf.at(e.current()) = x_val[n++];
}
}
auto inp_rhs_buf = make_buffer<float, LexicalLayout>(input_shape);
{
int n = 0;
for (IndexEnumerator e{inp_rhs_buf.shape()}; e.valid(); e.advance())
{
inp_rhs_buf.at(e.current()) = y_val[n++];
}
}
auto inp_lhs_data = locomotiv::make_data(inp_lhs_buf);
locomotiv::annot_data(inp_lhs, std::move(inp_lhs_data));
locomotiv::annot_domain(inp_lhs, loco::Domain::Tensor);
auto inp_rhs_data = locomotiv::make_data(inp_rhs_buf);
locomotiv::annot_data(inp_rhs, std::move(inp_rhs_data));
locomotiv::annot_domain(inp_rhs, loco::Domain::Tensor);
// run the network
locomotiv::NodeExecution::get().run(eltwise_max);
// get result
auto eltwise_max_data = locomotiv::annot_data(eltwise_max);
// comparing the result
ASSERT_NE(eltwise_max_data, nullptr);
ASSERT_EQ(eltwise_max_data->dtype(), loco::DataType::FLOAT32);
ASSERT_EQ(*(eltwise_max_data->shape()), Shape({1, 3, 3, 2}));
uint32_t n = 0;
for (IndexEnumerator e{*(eltwise_max_data->shape())}; e.valid(); e.advance())
{
ASSERT_FLOAT_EQ(eltwise_max_data->as_f32_bufptr()->at(e.current()), out_val[n++]);
}
ASSERT_EQ(locomotiv::annot_domain(eltwise_max), loco::Domain::Tensor);
}
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