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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 <loco/IR/PermutingCodec.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/IndexEnumerator.h>
#include <gtest/gtest.h>
using nncc::core::ADT::tensor::Index;
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(NodeExecution_DepthwiseFilterEncode, f32)
{
const uint32_t H = 2;
const uint32_t W = 3;
const uint32_t C = 4;
const uint32_t M = 5;
auto g = loco::make_graph();
// Pull
auto pull = g->nodes()->create<loco::Pull>();
pull->dtype(loco::DataType::FLOAT32);
// Make and assign "MHWC" data to pull node
auto pull_buf = make_buffer<float, LexicalLayout>(Shape{M, H, W, C});
float f = 1;
for (IndexEnumerator e{pull_buf.shape()}; e.valid(); e.advance())
{
pull_buf.at(e.current()) = f;
f += 0.1f; // Doesn't matter what it is
}
auto pull_data = locomotiv::make_data(pull_buf);
locomotiv::annot_data(pull, std::move(pull_data));
locomotiv::annot_domain(pull, loco::Domain::Tensor);
// Encoder to correctly read input tensor as MHWC
auto encoder = std::unique_ptr<loco::PermutingEncoder<loco::Domain::DepthwiseFilter>>(
new loco::PermutingEncoder<loco::Domain::DepthwiseFilter>);
encoder->perm()->axis(loco::DepthwiseFilterAxis::Multiplier) = 0;
encoder->perm()->axis(loco::DepthwiseFilterAxis::Height) = 1;
encoder->perm()->axis(loco::DepthwiseFilterAxis::Width) = 2;
encoder->perm()->axis(loco::DepthwiseFilterAxis::Depth) = 3;
// DepthwiseFilterEncode
auto enc = g->nodes()->create<loco::DepthwiseFilterEncode>();
enc->input(pull);
enc->encoder(std::move(encoder));
locomotiv::NodeExecution::get().run(enc);
auto enc_data = locomotiv::annot_data(enc);
ASSERT_NE(enc_data, nullptr);
ASSERT_EQ(enc_data->dtype(), loco::DataType::FLOAT32);
ASSERT_EQ(*(enc_data->shape()), (Shape{H, W, C, M})); // locomotiv depthwise filter is HWCM
auto enc_buf = enc_data->as_f32_bufptr();
for (uint32_t h = 0; h < H; ++h)
for (uint32_t w = 0; w < W; ++w)
for (uint32_t c = 0; c < C; ++c)
for (uint32_t m = 0; m < M; ++m)
ASSERT_FLOAT_EQ(pull_buf.at(Index{m, h, w, c}), enc_buf->at(Index{h, w, c, m}));
ASSERT_EQ(locomotiv::annot_domain(enc), loco::Domain::DepthwiseFilter);
}
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