210 lines
7.6 KiB
C++
210 lines
7.6 KiB
C++
/****************************************************************************
|
|
*
|
|
* Copyright (c) 2021 Vivante Corporation
|
|
*
|
|
* Permission is hereby granted, free of charge, to any person obtaining a
|
|
* copy of this software and associated documentation files (the "Software"),
|
|
* to deal in the Software without restriction, including without limitation
|
|
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
|
|
* and/or sell copies of the Software, and to permit persons to whom the
|
|
* Software is furnished to do so, subject to the following conditions:
|
|
*
|
|
* The above copyright notice and this permission notice shall be included in
|
|
* all copies or substantial portions of the Software.
|
|
*
|
|
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
|
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
|
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
|
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
|
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
|
|
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
|
|
* DEALINGS IN THE SOFTWARE.
|
|
*
|
|
*****************************************************************************/
|
|
#include "tim/vx/context.h"
|
|
#include "tim/vx/graph.h"
|
|
#include "tim/vx/ops/maxpoolgrad.h"
|
|
#include "tim/vx/ops/scatternd.h"
|
|
#include "tim/vx/ops/reshape.h"
|
|
|
|
#include "gtest/gtest.h"
|
|
|
|
TEST(Fuse_MaxpoolGrad, without_overlay) {
|
|
auto ctx = tim::vx::Context::Create();
|
|
auto graph = ctx->CreateGraph();
|
|
|
|
tim::vx::ShapeType in_shape({6, 4, 1, 1});
|
|
tim::vx::ShapeType updates_shape({2, 2, 1, 1});
|
|
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
|
|
in_shape, tim::vx::TensorAttribute::INPUT);
|
|
tim::vx::TensorSpec updates_spec(tim::vx::DataType::FLOAT32,
|
|
updates_shape, tim::vx::TensorAttribute::INPUT);
|
|
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
|
|
in_shape, tim::vx::TensorAttribute::OUTPUT);
|
|
|
|
auto input_tensor = graph->CreateTensor(input_spec);
|
|
auto updates_tensor = graph->CreateTensor(updates_spec);
|
|
auto output_tensor = graph->CreateTensor(output_spec);
|
|
|
|
std::vector<float> in_data = {
|
|
7, 2, 5, 3, 10, 2,
|
|
3, 8, 9, 3, 4, 2,
|
|
1, 5, 7, 5, 6, 1,
|
|
0, 6, 2, 7, 2, 8};
|
|
std::vector<float> updates_data = {
|
|
2, 6,
|
|
3, 1
|
|
};
|
|
std::vector<float> golden = {
|
|
0, 0, 0, 0, 6, 0,
|
|
0, 0, 2, 0, 0, 0,
|
|
0, 0, 3, 0, 0, 0,
|
|
0, 0, 0, 0, 0, 1};
|
|
|
|
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
|
|
EXPECT_TRUE(updates_tensor->CopyDataToTensor(updates_data.data(), updates_data.size() * sizeof(float)));
|
|
|
|
std::array<uint32_t, 2> ksize = {3, 2};
|
|
std::array<uint32_t, 2> stride = {3, 2};
|
|
auto op = graph->CreateOperation<tim::vx::ops::MaxpoolGrad>(
|
|
tim::vx::PadType::VALID, ksize, stride);
|
|
(*op).BindInputs({input_tensor, updates_tensor}).BindOutputs({output_tensor});
|
|
|
|
EXPECT_TRUE(graph->Compile());
|
|
EXPECT_TRUE(graph->Run());
|
|
|
|
std::vector<float> output_values(golden.size());
|
|
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output_values.data()));
|
|
EXPECT_EQ(golden, output_values);
|
|
}
|
|
|
|
TEST(Fuse_MaxpoolGrad, with_overlay) {
|
|
auto ctx = tim::vx::Context::Create();
|
|
auto graph = ctx->CreateGraph();
|
|
|
|
tim::vx::ShapeType in_shape({5, 4, 1, 1});
|
|
tim::vx::ShapeType updates_shape({2, 2, 1, 1});
|
|
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
|
|
in_shape, tim::vx::TensorAttribute::INPUT);
|
|
tim::vx::TensorSpec updates_spec(tim::vx::DataType::FLOAT32,
|
|
updates_shape, tim::vx::TensorAttribute::INPUT);
|
|
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
|
|
in_shape, tim::vx::TensorAttribute::OUTPUT);
|
|
|
|
auto input_tensor = graph->CreateTensor(input_spec);
|
|
auto updates_tensor = graph->CreateTensor(updates_spec);
|
|
auto output_tensor = graph->CreateTensor(input_spec);
|
|
|
|
std::vector<float> in_data = {
|
|
7, 2, 5, 3, 8,
|
|
3, 8, 9, 3, 4,
|
|
1, 5, 7, 5, 6,
|
|
0, 6, 2, 10, 2};
|
|
std::vector<float> updates_data = {
|
|
2, 6,
|
|
3, 1
|
|
};
|
|
std::vector<float> golden = {
|
|
0, 0, 0, 0, 0,
|
|
0, 0, 8, 0, 0,
|
|
0, 0, 3, 0, 0,
|
|
0, 0, 0, 1, 0};
|
|
|
|
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
|
|
EXPECT_TRUE(updates_tensor->CopyDataToTensor(updates_data.data(), updates_data.size() * sizeof(float)));
|
|
|
|
|
|
std::array<uint32_t, 2> ksize = {3, 2};
|
|
std::array<uint32_t, 2> stride = {2, 2};
|
|
auto op = graph->CreateOperation<tim::vx::ops::MaxpoolGrad>(
|
|
tim::vx::PadType::VALID, ksize, stride);
|
|
(*op).BindInputs({input_tensor, updates_tensor}).BindOutputs({output_tensor});
|
|
|
|
EXPECT_TRUE(graph->Compile());
|
|
EXPECT_TRUE(graph->Run());
|
|
|
|
std::vector<float> output_values(golden.size());
|
|
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output_values.data()));
|
|
EXPECT_EQ(golden, output_values);
|
|
}
|
|
|
|
TEST(Fuse_MaxpoolGrad, with_overlay_multi_channel_multi_batch) {
|
|
auto ctx = tim::vx::Context::Create();
|
|
auto graph = ctx->CreateGraph();
|
|
|
|
tim::vx::ShapeType in_shape({5, 4, 2, 2});
|
|
tim::vx::ShapeType updates_shape({2, 2, 2, 2});
|
|
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
|
|
in_shape, tim::vx::TensorAttribute::INPUT);
|
|
tim::vx::TensorSpec updates_spec(tim::vx::DataType::FLOAT32,
|
|
updates_shape, tim::vx::TensorAttribute::INPUT);
|
|
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
|
|
in_shape, tim::vx::TensorAttribute::OUTPUT);
|
|
|
|
auto input_tensor = graph->CreateTensor(input_spec);
|
|
auto updates_tensor = graph->CreateTensor(updates_spec);
|
|
auto output_tensor = graph->CreateTensor(input_spec);
|
|
|
|
std::vector<float> in_data = {
|
|
7, 2, 5, 3, 8,
|
|
3, 8, 9, 3, 4,
|
|
1, 5, 7, 5, 6,
|
|
0, 6, 2, 10, 2,
|
|
7, 2, 5, 3, 8,
|
|
3, 8, 9, 3, 4,
|
|
1, 5, 7, 5, 6,
|
|
0, 6, 2, 10, 2,
|
|
7, 2, 5, 3, 8,
|
|
3, 8, 9, 3, 4,
|
|
1, 5, 7, 5, 6,
|
|
0, 6, 2, 10, 2,
|
|
7, 2, 5, 3, 8,
|
|
3, 8, 9, 3, 4,
|
|
1, 5, 7, 5, 6,
|
|
0, 6, 2, 10, 2};
|
|
std::vector<float> updates_data = {
|
|
2, 6,
|
|
3, 1,
|
|
2, 6,
|
|
3, 1,
|
|
2, 6,
|
|
3, 1,
|
|
2, 6,
|
|
3, 1,
|
|
};
|
|
std::vector<float> golden = {
|
|
0, 0, 0, 0, 0,
|
|
0, 0, 8, 0, 0,
|
|
0, 0, 3, 0, 0,
|
|
0, 0, 0, 1, 0,
|
|
0, 0, 0, 0, 0,
|
|
0, 0, 8, 0, 0,
|
|
0, 0, 3, 0, 0,
|
|
0, 0, 0, 1, 0,
|
|
0, 0, 0, 0, 0,
|
|
0, 0, 8, 0, 0,
|
|
0, 0, 3, 0, 0,
|
|
0, 0, 0, 1, 0,
|
|
0, 0, 0, 0, 0,
|
|
0, 0, 8, 0, 0,
|
|
0, 0, 3, 0, 0,
|
|
0, 0, 0, 1, 0};
|
|
|
|
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
|
|
EXPECT_TRUE(updates_tensor->CopyDataToTensor(updates_data.data(), updates_data.size() * sizeof(float)));
|
|
|
|
|
|
std::array<uint32_t, 2> ksize = {3, 2};
|
|
std::array<uint32_t, 2> stride = {2, 2};
|
|
auto op = graph->CreateOperation<tim::vx::ops::MaxpoolGrad>(
|
|
tim::vx::PadType::VALID, ksize, stride);
|
|
(*op).BindInputs({input_tensor, updates_tensor}).BindOutputs({output_tensor});
|
|
|
|
EXPECT_TRUE(graph->Compile());
|
|
EXPECT_TRUE(graph->Run());
|
|
|
|
std::vector<float> output_values(golden.size());
|
|
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output_values.data()));
|
|
EXPECT_EQ(golden, output_values);
|
|
}
|