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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-AvgPool2D 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 TF AvgPool2D
auto avgpool2d = g->nodes()->create<loco::AvgPool2D>();
avgpool2d->ifm(enc);
avgpool2d->convention(loco::AvgPool2D::Convention::Valid);
avgpool2d->window()->vertical(window_v);
avgpool2d->window()->horizontal(window_h);
avgpool2d->stride()->vertical(stride_v);
avgpool2d->stride()->horizontal(stride_h);
avgpool2d->pad()->top(pad_top);
avgpool2d->pad()->bottom(pad_bottom);
avgpool2d->pad()->left(pad_left);
avgpool2d->pad()->right(pad_right);
// run interpreter
locomotiv::NodeExecution::get().run(avgpool2d);
// get result of calculation
auto avgpool2d_data = locomotiv::annot_data(avgpool2d);
// check the result
ASSERT_NE(avgpool2d_data, nullptr);
ASSERT_TRUE(avgpool2d_data->dtype() == loco::DataType::FLOAT32);
ASSERT_TRUE(*(avgpool2d_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), avgpool2d_data->as_f32_bufptr()->at(ind));
}
ASSERT_EQ(loco::Domain::Feature, locomotiv::annot_domain(avgpool2d));
}
} // namespace
// clang-format off
/* ifm and ofm are from the code below:
import tensorflow as tf
value = tf.constant([[[[-0.281157], [-1.0601869], [-0.622261], [-1.1777412]],
[[1.4411974], [0.01408334], [0.06958964], [-0.08663343]],
[[1.3424183], [-0.89015573], [0.2520576], [0.04843695]],
[[-1.6668711], [-0.02187406], [1.9362065], [1.3341236]]]])
avgpool = tf.nn.avg_pool(value, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding= 'VALID',
data_format="NHWC")
with tf.Session() as sess:
print(sess.run(avgpool))
*/
TEST(NodeExecution_AvgPool2D, f32_1x4x4x1_calculation)
{
using nncc::core::ADT::tensor::Shape;
const float ifm[] =
{
-0.281157, -1.0601869, -0.622261, -1.1777412,
1.4411974, 0.01408334, 0.06958964, -0.08663343,
1.3424183, -0.89015573, 0.2520576, 0.04843695,
-1.6668711, -0.02187406, 1.9362065, 1.3341236
};
const float ofm[] =
{
0.02848421, -0.45426148,
-0.30912063, 0.89270616
};
run_test(ifm, ofm,
Shape{1, 4, 4, 1}, Shape{1, 2, 2, 1}, // input shape , output shape
2, 2, // kernel
2, 2, // stride
0, 0, 0, 0 // padding
);
}
// clang-format on
// clang-format off
/* ifm and ofm are from the code below:
import tensorflow as tf
value = tf.constant([[[[-0.281157], [-1.0601869], [-0.622261]],
[[1.4411974], [0.01408334], [0.06958964]],
[[1.3424183], [-0.89015573], [0.2520576]]]])
avgpool = tf.nn.avg_pool(value, ksize = [1, 2, 2, 1], strides = [1, 1, 1, 1], padding= 'SAME',
data_format="NHWC")
with tf.Session() as sess:
print(sess.run(avgpool))
*/
TEST(NodeExecution_AvgPool2D, f32_1x3x3x1_calculation)
{
using nncc::core::ADT::tensor::Shape;
const float ifm[] =
{
-0.281157, -1.0601869, -0.622261,
1.4411974, 0.01408334, 0.06958964,
1.3424183, -0.89015573, 0.2520576
};
const float ofm[] =
{
0.02848421, -0.39969373, -0.2763357,
0.4768858, -0.13860628, 0.16082363,
0.22613129, -0.31904906, 0.2520576
};
run_test(ifm, ofm,
Shape{1, 3, 3, 1}, Shape{1, 3, 3, 1}, // input shape , output shape
2, 2, // kernel
1, 1, // stride
0, 1, 0, 1 // padding
);
}
// clang-format on
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