373 lines
15 KiB
C++
373 lines
15 KiB
C++
/****************************************************************************
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*
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* Copyright (c) 2021 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#if VSI_FEAT_OP_MAXPOOLWITHARGMAX
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#include "tim/vx/context.h"
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#include "tim/vx/graph.h"
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#include "tim/vx/ops/maxpoolwithargmax2.h"
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#include "tim/vx/ops/scatternd.h"
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#include "tim/vx/ops/reshape.h"
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#include "gtest/gtest.h"
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TEST(MaxpoolWithArgmax2, without_overlay) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType in_shape({6, 4, 1, 1});
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tim::vx::ShapeType out_shape({2, 2, 1, 1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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in_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec_indices(tim::vx::DataType::INT32,
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out_shape, tim::vx::TensorAttribute::OUTPUT);
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tim::vx::TensorSpec output_spec_values(tim::vx::DataType::FLOAT32,
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out_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto output_tensor_indices = graph->CreateTensor(output_spec_indices);
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auto output_tensor_values = graph->CreateTensor(output_spec_values);
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std::vector<float> in_data = {
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7, 2, 5, 3, 10, 2,
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3, 8, 9, 3, 4, 2,
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1, 5, 7, 5, 6, 1,
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0, 6, 2, 7, 2, 8};
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std::vector<float> values_golden = {
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9, 10,
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7, 8 };
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std::vector<int32_t> indices_golden = {
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8, 4,
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14, 23 };
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
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std::array<uint32_t, 2> ksize = {3, 2};
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std::array<uint32_t, 2> stride = {3, 2};
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auto op = graph->CreateOperation<tim::vx::ops::MaxpoolWithArgmax2>(
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tim::vx::PadType::VALID, ksize, stride);
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(*op).BindInputs({input_tensor}).BindOutputs({output_tensor_values, output_tensor_indices});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> output_values(4);
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std::vector<int32_t> output_indices(4);
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EXPECT_TRUE(output_tensor_values->CopyDataFromTensor(output_values.data()));
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EXPECT_TRUE(output_tensor_indices->CopyDataFromTensor(output_indices.data()));
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EXPECT_EQ(values_golden, output_values);
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EXPECT_EQ(indices_golden, output_indices);
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}
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TEST(MaxpoolWithArgmax2, with_overlay) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType in_shape({5, 4, 1, 1});
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tim::vx::ShapeType out_shape({2, 2, 1, 1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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in_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec_indices(tim::vx::DataType::INT32,
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out_shape, tim::vx::TensorAttribute::OUTPUT);
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tim::vx::TensorSpec output_spec_values(tim::vx::DataType::FLOAT32,
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out_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto output_tensor_indices = graph->CreateTensor(output_spec_indices);
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auto output_tensor_values = graph->CreateTensor(output_spec_values);
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std::vector<float> in_data = {
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7, 2, 5, 3, 8,
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3, 8, 9, 3, 4,
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1, 5, 7, 5, 6,
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0, 6, 2, 10, 2};
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std::vector<float> values_golden = {
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9, 9,
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7, 10 };
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std::vector<int32_t> indices_golden = {
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7, 7,
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12, 18 };
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
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std::array<uint32_t, 2> ksize = {3, 2};
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std::array<uint32_t, 2> stride = {2, 2};
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auto op = graph->CreateOperation<tim::vx::ops::MaxpoolWithArgmax2>(
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tim::vx::PadType::VALID, ksize, stride);
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(*op).BindInputs({input_tensor}).BindOutputs({output_tensor_values, output_tensor_indices});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> output_values(4);
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std::vector<int32_t> output_indices(4);
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EXPECT_TRUE(output_tensor_values->CopyDataFromTensor(output_values.data()));
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EXPECT_TRUE(output_tensor_indices->CopyDataFromTensor(output_indices.data()));
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EXPECT_EQ(values_golden, output_values);
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EXPECT_EQ(indices_golden, output_indices);
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}
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TEST(MaxpoolGrad, without_overlay) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType in_shape({6, 4, 1, 1});
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tim::vx::ShapeType out_shape({2, 2, 1, 1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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in_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec_indices(tim::vx::DataType::INT32,
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out_shape, tim::vx::TensorAttribute::TRANSIENT);
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tim::vx::TensorSpec output_spec_values(tim::vx::DataType::FLOAT32,
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out_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto output_tensor_indices = graph->CreateTensor(output_spec_indices);
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auto output_tensor_values = graph->CreateTensor(output_spec_values);
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auto output_tensor = graph->CreateTensor(input_spec);
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std::vector<float> in_data = {
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7, 2, 5, 3, 10, 2,
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3, 8, 9, 3, 4, 2,
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1, 5, 7, 5, 6, 1,
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0, 6, 2, 7, 2, 8};
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std::vector<float> updates_data = {
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2, 6,
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3, 1
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};
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std::vector<float> golden = {
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0, 0, 0, 0, 6, 0,
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0, 0, 2, 0, 0, 0,
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0, 0, 3, 0, 0, 0,
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0, 0, 0, 0, 0, 1};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
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std::array<uint32_t, 2> ksize = {3, 2};
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std::array<uint32_t, 2> stride = {3, 2};
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auto op = graph->CreateOperation<tim::vx::ops::MaxpoolWithArgmax2>(
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tim::vx::PadType::VALID, ksize, stride);
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(*op).BindInputs({input_tensor}).BindOutputs({output_tensor_values, output_tensor_indices});
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std::vector<uint32_t> shape = {4};
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tim::vx::TensorSpec input_spec_indices(tim::vx::DataType::INT32,
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shape, tim::vx::TensorAttribute::TRANSIENT);
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auto input_tensor_indices = graph->CreateTensor(input_spec_indices);
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auto op1 = graph->CreateOperation<tim::vx::ops::Reshape>(shape);
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(*op1).BindInputs({output_tensor_indices}).BindOutputs({input_tensor_indices});
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std::vector<uint32_t> out2_shape = {24};
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tim::vx::TensorSpec updates_spec(tim::vx::DataType::FLOAT32,
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shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output2_spec(tim::vx::DataType::FLOAT32,
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out2_shape, tim::vx::TensorAttribute::TRANSIENT);
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auto updates_tensor = graph->CreateTensor(updates_spec);
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auto output2_tensor = graph->CreateTensor(output2_spec);
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EXPECT_TRUE(updates_tensor->CopyDataToTensor(
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updates_data.data(), updates_data.size() * 4));
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auto op2 = graph->CreateOperation<tim::vx::ops::ScatterND>(out2_shape);
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(*op2).BindInputs({input_tensor_indices, updates_tensor}).BindOutputs({output2_tensor});
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auto op3 = graph->CreateOperation<tim::vx::ops::Reshape>(in_shape);
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(*op3).BindInputs({output2_tensor}).BindOutputs({output_tensor});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> output_values(24);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output_values.data()));
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EXPECT_EQ(golden, output_values);
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}
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TEST(MaxpoolGrad, with_overlay) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType in_shape({5, 4, 1, 1});
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tim::vx::ShapeType out_shape({2, 2, 1, 1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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in_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec_indices(tim::vx::DataType::INT32,
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out_shape, tim::vx::TensorAttribute::TRANSIENT);
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tim::vx::TensorSpec output_spec_values(tim::vx::DataType::FLOAT32,
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out_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto output_tensor_indices = graph->CreateTensor(output_spec_indices);
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auto output_tensor_values = graph->CreateTensor(output_spec_values);
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auto output_tensor = graph->CreateTensor(input_spec);
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std::vector<float> in_data = {
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7, 2, 5, 3, 8,
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3, 8, 9, 3, 4,
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1, 5, 7, 5, 6,
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0, 6, 2, 10, 2};
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std::vector<float> updates_data = {
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2, 6,
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3, 1
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};
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std::vector<float> golden = {
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0, 0, 0, 0, 0,
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0, 0, 8, 0, 0,
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0, 0, 3, 0, 0,
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0, 0, 0, 1, 0};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
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std::array<uint32_t, 2> ksize = {3, 2};
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std::array<uint32_t, 2> stride = {2, 2};
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auto op = graph->CreateOperation<tim::vx::ops::MaxpoolWithArgmax2>(
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tim::vx::PadType::VALID, ksize, stride);
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(*op).BindInputs({input_tensor}).BindOutputs({output_tensor_values, output_tensor_indices});
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std::vector<uint32_t> shape = {4};
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tim::vx::TensorSpec input_spec_indices(tim::vx::DataType::INT32,
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shape, tim::vx::TensorAttribute::TRANSIENT);
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auto input_tensor_indices = graph->CreateTensor(input_spec_indices);
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auto op1 = graph->CreateOperation<tim::vx::ops::Reshape>(shape);
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(*op1).BindInputs({output_tensor_indices}).BindOutputs({input_tensor_indices});
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std::vector<uint32_t> out2_shape = {20};
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tim::vx::TensorSpec updates_spec(tim::vx::DataType::FLOAT32,
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shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output2_spec(tim::vx::DataType::FLOAT32,
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out2_shape, tim::vx::TensorAttribute::TRANSIENT);
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auto updates_tensor = graph->CreateTensor(updates_spec);
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auto output2_tensor = graph->CreateTensor(output2_spec);
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EXPECT_TRUE(updates_tensor->CopyDataToTensor(
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updates_data.data(), updates_data.size() * 4));
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auto op2 = graph->CreateOperation<tim::vx::ops::ScatterND>(out2_shape);
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(*op2).BindInputs({input_tensor_indices, updates_tensor}).BindOutputs({output2_tensor});
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auto op3 = graph->CreateOperation<tim::vx::ops::Reshape>(in_shape);
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(*op3).BindInputs({output2_tensor}).BindOutputs({output_tensor});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> output_values(20);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output_values.data()));
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EXPECT_EQ(golden, output_values);
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}
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TEST(MaxpoolGrad, with_overlay_multi_channel_multi_batch) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType in_shape({5, 4, 2, 2});
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tim::vx::ShapeType out_shape({2, 2, 2, 2});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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in_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec_indices(tim::vx::DataType::INT32,
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out_shape, tim::vx::TensorAttribute::TRANSIENT);
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tim::vx::TensorSpec output_spec_values(tim::vx::DataType::FLOAT32,
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out_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto output_tensor_indices = graph->CreateTensor(output_spec_indices);
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auto output_tensor_values = graph->CreateTensor(output_spec_values);
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auto output_tensor = graph->CreateTensor(input_spec);
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std::vector<float> in_data = {
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7, 2, 5, 3, 8,
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3, 8, 9, 3, 4,
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1, 5, 7, 5, 6,
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0, 6, 2, 10, 2,
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7, 2, 5, 3, 8,
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3, 8, 9, 3, 4,
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1, 5, 7, 5, 6,
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0, 6, 2, 10, 2,
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7, 2, 5, 3, 8,
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3, 8, 9, 3, 4,
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1, 5, 7, 5, 6,
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0, 6, 2, 10, 2,
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7, 2, 5, 3, 8,
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3, 8, 9, 3, 4,
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1, 5, 7, 5, 6,
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0, 6, 2, 10, 2};
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std::vector<float> updates_data = {
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2, 6,
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3, 1,
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2, 6,
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3, 1,
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2, 6,
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3, 1,
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2, 6,
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3, 1,
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};
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std::vector<float> golden = {
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0, 0, 0, 0, 0,
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0, 0, 8, 0, 0,
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0, 0, 3, 0, 0,
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0, 0, 0, 1, 0,
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0, 0, 0, 0, 0,
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0, 0, 8, 0, 0,
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0, 0, 3, 0, 0,
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0, 0, 0, 1, 0,
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0, 0, 0, 0, 0,
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0, 0, 8, 0, 0,
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0, 0, 3, 0, 0,
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0, 0, 0, 1, 0,
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0, 0, 0, 0, 0,
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0, 0, 8, 0, 0,
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0, 0, 3, 0, 0,
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0, 0, 0, 1, 0};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
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std::array<uint32_t, 2> ksize = {3, 2};
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std::array<uint32_t, 2> stride = {2, 2};
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auto op = graph->CreateOperation<tim::vx::ops::MaxpoolWithArgmax2>(
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tim::vx::PadType::VALID, ksize, stride);
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(*op).BindInputs({input_tensor}).BindOutputs({output_tensor_values, output_tensor_indices});
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std::vector<uint32_t> shape = {16};
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tim::vx::TensorSpec input_spec_indices(tim::vx::DataType::INT32,
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shape, tim::vx::TensorAttribute::TRANSIENT);
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auto input_tensor_indices = graph->CreateTensor(input_spec_indices);
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auto op1 = graph->CreateOperation<tim::vx::ops::Reshape>(shape);
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(*op1).BindInputs({output_tensor_indices}).BindOutputs({input_tensor_indices});
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std::vector<uint32_t> out2_shape = {80};
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tim::vx::TensorSpec updates_spec(tim::vx::DataType::FLOAT32,
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shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output2_spec(tim::vx::DataType::FLOAT32,
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out2_shape, tim::vx::TensorAttribute::TRANSIENT);
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auto updates_tensor = graph->CreateTensor(updates_spec);
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auto output2_tensor = graph->CreateTensor(output2_spec);
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EXPECT_TRUE(updates_tensor->CopyDataToTensor(
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updates_data.data(), updates_data.size() * 4));
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auto op2 = graph->CreateOperation<tim::vx::ops::ScatterND>(out2_shape);
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(*op2).BindInputs({input_tensor_indices, updates_tensor}).BindOutputs({output2_tensor});
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auto op3 = graph->CreateOperation<tim::vx::ops::Reshape>(in_shape);
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(*op3).BindInputs({output2_tensor}).BindOutputs({output_tensor});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> output_values(80);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output_values.data()));
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EXPECT_EQ(golden, output_values);
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}
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#endif //(VSI_FEAT_OP_MAXPOOLWITHARGMAX)
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