248 lines
9.8 KiB
C++
248 lines
9.8 KiB
C++
/****************************************************************************
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*
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* Copyright (c) 2020-2023 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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#include "tim/vx/context.h"
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#include "tim/vx/graph.h"
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#include "tim/vx/ops/instancenormalization.h"
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#include "test_utils.h"
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#include "gtest/gtest.h"
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TEST(InstanceNorm, shape_2_2_2_2_float) {
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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 io_shape({2, 2, 2, 2}); //nchw
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tim::vx::ShapeType param_shape({2});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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io_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec param_spec(tim::vx::DataType::FLOAT32,
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param_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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io_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto gamma_tensor = graph->CreateTensor(param_spec);
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auto beta_tensor = graph->CreateTensor(param_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data = {
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0.0f, 0.0f, 0.0f, 0.0f, 1.0f, 2.0f, 2.0f, 4.0f, 1.0f, -1.0f, -1.0f, 1.0f, -1.0f, 2.0f, -2.0f, 4.0f
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};
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std::vector<float> gamma = {1.0f, 1.0f};
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std::vector<float> beta = {.0f, .0f};
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std::vector<float> golden = {
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0.0f, 0.0f, 0.0f, 0.0f, -1.1470304f, -0.22940612f, -0.22940612f, 1.6058424f, 0.99995005f,
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-0.99995005f, -0.99995005f, 0.99995005f, -0.7337929f, 0.52413774f, -1.1531031f, 1.3627582f
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};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
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EXPECT_TRUE(gamma_tensor->CopyDataToTensor(gamma.data(), gamma.size() * sizeof(float)));
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EXPECT_TRUE(beta_tensor->CopyDataToTensor(beta.data(), beta.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::InstanceNormalization>(1e-4f);
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(*op).BindInputs({input_tensor, beta_tensor, gamma_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(16);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
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}
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TEST(InstanceNorm, shape_3_6_1_float) {
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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 io_shape({3, 6, 1});
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tim::vx::ShapeType param_shape({6});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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io_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec param_spec(tim::vx::DataType::FLOAT32,
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param_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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io_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto gamma_tensor = graph->CreateTensor(param_spec);
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auto beta_tensor = graph->CreateTensor(param_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data = {
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-2, 0, 2,
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-3, 0, 3,
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-4, 0, 4,
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-5, 0, 5,
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-6, 0, 6,
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-7, 0, 7 };
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std::vector<float> gamma = {
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1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f
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};
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std::vector<float> beta = {
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.0f, .0f, .0f,
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.0f, .0f, .0f
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};
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std::vector<float> golden = {
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-1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f,
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};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
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EXPECT_TRUE(gamma_tensor->CopyDataToTensor(gamma.data(), gamma.size() * sizeof(float)));
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EXPECT_TRUE(beta_tensor->CopyDataToTensor(beta.data(), beta.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::InstanceNormalization>(2e-5f);
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(*op).BindInputs({input_tensor, beta_tensor, gamma_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(18);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
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}
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TEST(InstanceNorm, shape_3_3_6_1_float) {
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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 io_shape({2, 3, 6, 1});
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tim::vx::ShapeType param_shape({6});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
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io_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec param_spec(tim::vx::DataType::FLOAT32,
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param_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
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io_shape, tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto gamma_tensor = graph->CreateTensor(param_spec);
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auto beta_tensor = graph->CreateTensor(param_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data = {
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-2, 0, 2, -2, 0, 2,
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-3, 0, 3, -3, 0, 3,
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-4, 0, 4, -4, 0, 4,
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-5, 0, 5, -5, 0, 5,
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-6, 0, 6, -6, 0, 6,
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-7, 0, 7, -7, 0, 7,
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};
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std::vector<float> gamma = {
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1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f
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};
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std::vector<float> beta = {
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.0f, .0f, .0f,
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.0f, .0f, .0f
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};
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std::vector<float> golden = {
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-1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f,
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-1.22474f, 0, 1.22474f, -1.22474f, 0, 1.22474f,
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};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
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EXPECT_TRUE(gamma_tensor->CopyDataToTensor(gamma.data(), gamma.size() * sizeof(float)));
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EXPECT_TRUE(beta_tensor->CopyDataToTensor(beta.data(), beta.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::InstanceNormalization>(2e-5f);
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(*op).BindInputs({input_tensor, beta_tensor, gamma_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(36);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
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}
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#if 0
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// Fail case
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TEST(OP, instance_norm_shape_3_6_1_uint8) {
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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 io_shape({3, 6, 1});
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tim::vx::ShapeType param_shape({6});
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tim::vx::Quantization input_quant(tim::vx::QuantType::ASYMMETRIC, 1, 7);
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tim::vx::Quantization output_quant(tim::vx::QuantType::ASYMMETRIC, 1.22474f, 1);
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tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8,
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io_shape, tim::vx::TensorAttribute::INPUT, input_quant);
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tim::vx::TensorSpec param_spec(tim::vx::DataType::FLOAT32,
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param_shape, tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
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io_shape, tim::vx::TensorAttribute::OUTPUT, output_quant);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto gamma_tensor = graph->CreateTensor(param_spec);
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auto beta_tensor = graph->CreateTensor(param_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data = {
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5, 7, 9,
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4, 7, 10,
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3, 7, 11,
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2, 7, 12,
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1, 7, 13,
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0, 7, 14 };
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std::vector<float> gamma = {
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1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f
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};
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std::vector<float> beta = {
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.0f, .0f, .0f,
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.0f, .0f, .0f
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};
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std::vector<float> golden = {
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0, 1, 2,
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0, 1, 2,
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0, 1, 2,
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0, 1, 2,
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0, 1, 2,
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0, 1, 2,
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};
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EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
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EXPECT_TRUE(gamma_tensor->CopyDataToTensor(gamma.data(), gamma.size() * sizeof(float)));
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EXPECT_TRUE(beta_tensor->CopyDataToTensor(beta.data(), beta.size() * sizeof(float)));
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auto op = graph->CreateOperation<tim::vx::ops::InstanceNormalization>(2e-5f);
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(*op).BindInputs({input_tensor, beta_tensor, gamma_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(18);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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#endif
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