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/*
* Copyright (c) 2019 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/Overlay.h>
#include <nncc/core/ADT/tensor/LexicalLayout.h>
#include "nncc/core/ADT/tensor/IndexEnumerator.h"
#include <gtest/gtest.h>
namespace
{
using nncc::core::ADT::tensor::Shape;
using nncc::core::ADT::tensor::LexicalLayout;
using nncc::core::ADT::tensor::make_buffer;
using nncc::core::ADT::tensor::make_overlay;
void run_test(const float *ifm, const float *expected_ofm, const Shape &ifm_shape,
const Shape &ofm_shape, const uint32_t window_v, const uint32_t window_h,
const uint32_t stride_v, const uint32_t stride_h, const uint32_t pad_top,
const uint32_t pad_bottom, const uint32_t pad_left, const uint32_t pad_right)
{
// Let's make FeatureEncode-MaxPool2D graph
auto g = loco::make_graph();
auto enc = g->nodes()->create<loco::FeatureEncode>();
// Fill output data of FeatureEncode from ifm
auto enc_buf = make_buffer<float, LexicalLayout>(ifm_shape);
auto ifm_overlay = make_overlay<float, LexicalLayout>(ifm_shape, const_cast<float *>(ifm));
for (nncc::core::ADT::tensor::IndexEnumerator e{ifm_shape}; e.valid(); e.advance())
{
const auto &ind = e.current();
enc_buf.at(ind) = ifm_overlay.at(ind);
}
auto enc_data = locomotiv::make_data(enc_buf);
locomotiv::annot_data(enc, std::move(enc_data));
locomotiv::annot_domain(enc, loco::Domain::Feature);
// build MaxPool2D
auto maxpool2d = g->nodes()->create<loco::MaxPool2D>();
maxpool2d->ifm(enc);
maxpool2d->window()->vertical(window_v);
maxpool2d->window()->horizontal(window_h);
maxpool2d->stride()->vertical(stride_v);
maxpool2d->stride()->horizontal(stride_h);
maxpool2d->pad()->top(pad_top);
maxpool2d->pad()->bottom(pad_bottom);
maxpool2d->pad()->left(pad_left);
maxpool2d->pad()->right(pad_right);
// run interpreter
locomotiv::NodeExecution::get().run(maxpool2d);
// get result of calculation
auto maxpool2d_data = locomotiv::annot_data(maxpool2d);
// check the result
ASSERT_NE(maxpool2d_data, nullptr);
ASSERT_TRUE(maxpool2d_data->dtype() == loco::DataType::FLOAT32);
ASSERT_TRUE(*(maxpool2d_data->shape()) == ofm_shape);
auto ofm_overlay =
make_overlay<float, LexicalLayout>(ofm_shape, const_cast<float *>(expected_ofm));
for (nncc::core::ADT::tensor::IndexEnumerator e{ofm_shape}; e.valid(); e.advance())
{
const auto &ind = e.current();
ASSERT_FLOAT_EQ(ofm_overlay.at(ind), maxpool2d_data->as_f32_bufptr()->at(ind));
}
ASSERT_EQ(loco::Domain::Feature, locomotiv::annot_domain(maxpool2d));
}
} // namespace
// clang-format off
/* ifm and ofm are from the code below:
value = tf.random_normal([1, 3, 3, 1], stddev=1)
maxpool = tf.nn.max_pool(value, ksize = [1, 2, 2, 1], strides = [1, 1, 1, 1], padding= 'VALID',
data_format="NHWC")
with tf.Session() as sess:
print(sess.run(maxpool))
*/
TEST(NodeExecution_MaxPool2D, f32_1x3x3x1_calculation)
{
using nncc::core::ADT::tensor::Shape;
const float ifm[] =
{
-1.5510627, 0.3653609, 1.9002001,
-0.15861237, -0.32944828, 1.2053918,
0.50054574, -0.8533826, 0.131492,
};
const float ofm[] =
{
0.3653609, 1.9002001,
0.50054574, 1.2053918
};
run_test(ifm, ofm,
Shape{1, 3, 3, 1}, Shape{1, 2, 2, 1}, // input shape , output shape
2, 2, // kernel
1, 1, // stride
0, 0, 0, 0 // padding
);
}
TEST(NodeExecution_MaxPool2D, with_padding)
{
using nncc::core::ADT::tensor::Shape;
const float ifm[] =
{
1, 2, 3, 4, 5,
6, 7, 8, 9, 10,
11, 12, 13, 14, 15,
16, 17, 18, 19, 20,
21, 22, 23, 24, 25
};
const float ofm[] =
{
7, 9, 10,
17, 19, 20,
22, 24, 25
};
run_test(ifm, ofm,
Shape{1, 5, 5, 1}, Shape{1, 3, 3, 1}, // input shape , output shape
3, 3, // kernel
2, 2, // stride
1, 1, 1, 1 // padding - this mimics SAME padding
);
}
// clang-format on
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