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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 *ker, const float *expected_ofm, const Shape &ifm_shape,
const Shape ker_shape, const Shape ofm_shape, const uint32_t stride_v,
const uint32_t stride_h, const uint32_t pad_top = 0, const uint32_t pad_bottom = 0,
const uint32_t pad_left = 0, const uint32_t pad_right = 0)
{
auto g = loco::make_graph();
// Fill output data of FeatureEncode from ifm
auto ifm_enc = g->nodes()->create<loco::FeatureEncode>();
{
auto ifm_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();
ifm_enc_buf.at(ind) = ifm_overlay.at(ind);
}
auto enc_data = locomotiv::make_data(ifm_enc_buf);
locomotiv::annot_data(ifm_enc, std::move(enc_data));
locomotiv::annot_domain(ifm_enc, loco::Domain::Feature);
}
// Fill output data of DepthwiseFilterEncode from ker
auto ker_enc = g->nodes()->create<loco::DepthwiseFilterEncode>();
{
auto ker_enc_buf = make_buffer<float, LexicalLayout>(ker_shape);
auto ker_overlay = make_overlay<float, LexicalLayout>(ker_shape, const_cast<float *>(ker));
for (nncc::core::ADT::tensor::IndexEnumerator e{ker_shape}; e.valid(); e.advance())
{
const auto &ind = e.current();
ker_enc_buf.at(ind) = ker_overlay.at(ind);
}
auto enc_data = locomotiv::make_data(ker_enc_buf);
locomotiv::annot_data(ker_enc, std::move(enc_data));
locomotiv::annot_domain(ker_enc, loco::Domain::DepthwiseFilter);
}
// build DepthwiseConv2D
auto dw_conv2d = g->nodes()->create<loco::DepthwiseConv2D>();
dw_conv2d->ifm(ifm_enc);
dw_conv2d->ker(ker_enc);
dw_conv2d->stride()->vertical(stride_v);
dw_conv2d->stride()->horizontal(stride_h);
dw_conv2d->pad()->top(pad_top);
dw_conv2d->pad()->bottom(pad_bottom);
dw_conv2d->pad()->left(pad_left);
dw_conv2d->pad()->right(pad_right);
// run interpreter
locomotiv::NodeExecution::get().run(dw_conv2d);
// get result of calculation
auto dw_conv2d_result = locomotiv::annot_data(dw_conv2d);
// check the result
ASSERT_NE(dw_conv2d_result, nullptr);
ASSERT_TRUE(dw_conv2d_result->dtype() == loco::DataType::FLOAT32);
ASSERT_TRUE(*(dw_conv2d_result->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), dw_conv2d_result->as_f32_bufptr()->at(ind));
}
ASSERT_EQ(loco::Domain::Feature, locomotiv::annot_domain(dw_conv2d));
}
} // namespace
// clang-format off
/* ifm, ker and ofm are from the code below:
ifm = tf.random_normal([1, 5, 5, 2], stddev=1.1)
ker = tf.random_normal([4, 4, 2, 3], stddev=1.1)
out = tf.nn.depthwise_conv2d(ifm, ker, strides = [1, 1, 1, 1], padding= 'VALID')
with tf.Session() as sess:
print(sess.run(out))
*/
TEST(NodeExecution_DepthwiseConv2D, f32_random_valid)
{
using nncc::core::ADT::tensor::Shape;
const float ifm[] = {0.8122538, 1.209147, 0.6903842, -0.26646265, 1.516799, -1.8540707,
-0.74240327, 1.7811562, -0.03699546, -0.44468504, -1.4982721, -1.1858582,
-0.21140318, -0.974522, 1.0000849, -1.294535, -0.6108882, 0.25827602,
1.3631831, -0.5180266, 0.20870179, 0.18333802, -0.42263857, -1.6694735,
0.0415236, -0.3903758, 2.0933757, -0.29660916, 2.1218338, -1.1599928,
0.57163256, 0.48865932, -1.3622656, 0.35924262, 1.2951899, -0.1769997,
0.74513537, -0.31920406, -1.2902768, -0.7095059, 1.9157801, -0.41028237,
1.2502829, 0.3354887, 1.4199319, -0.20366786, -0.8828556, 0.5173567,
1.7708117, -0.30096334};
const float ker[] = {
-0.19805557, 0.58464956, -0.7804337, 0.06974592, 0.45790604, 0.24833807, 0.43393376,
0.2541043, -0.04406675, -0.32167575, 1.0546446, -1.4978354, 0.20829494, 1.1659569,
0.37908667, -0.94137955, 0.293349, -1.1023049, 0.76133233, 0.55595005, 1.4458209,
1.6128604, 1.5655615, -2.183877, -0.90535915, -0.49858555, 1.7168728, -1.1590382,
0.6706056, 1.2215618, -0.06603386, 0.16559464, 0.541991, -0.44488335, 0.766181,
1.0227629, -0.6352362, -1.670828, -0.63334507, 0.0313305, -0.6721083, 0.50112915,
-0.15218066, 0.67222077, -0.3613627, -0.08516614, -0.5024078, -0.9503976, -2.1892295,
1.8308185, -0.15187284, 1.5761136, 0.24869336, -1.7378871, -0.22518761, 1.0175673,
0.7084485, -0.74157554, -1.8185995, -1.3330095, -0.04427439, 1.0556892, -0.68243974,
0.32001218, 2.0901792, -1.1612813, 0.7294674, 0.05740008, -0.00832882, 1.0446658,
0.4477195, -0.09174404, -1.0176039, 1.5066665, -2.148343, 0.29421416, 0.93011874,
-0.15737922, -1.6444012, 0.25780794, -0.6545867, -0.3488956, 0.26167992, -0.154414,
0.2798124, -0.8590068, 2.0494444, 0.48268002, 0.81941164, -0.4848027, 0.76870304,
0.7102261, 0.45778143, 0.23214905, -0.17742023, -0.75016516};
const float ofm[] = {4.474646, 0.6792067, -1.9799856, 7.484751, 4.3087378, -1.905938,
1.4887369, 0.4361322, 0.79539883, -3.8583446, -4.502204, 4.356392,
-5.3030324, 3.493003, -4.349277, 2.3069482, -3.8881323, -0.73901534,
-0.6629516, 2.1247253, -4.9229584, 1.6716996, -3.0208125, 1.0597891};
run_test(ifm, ker, ofm,
Shape{1, 5, 5, 2}, Shape{4, 4, 2, 3}, Shape{1, 2, 2, 6}, // shapes of input, ker, output
1, 1 // stride
);
}
// TODO Add same padding test
// clang-format on
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