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Add layout inference and unit test for BatchNorm (#243) 

Signed-off-by: Zongwu Yang <zongwu.yang@verisilicon.com>
This commit is contained in:
Zongwu.Yang 2021-12-22 09:47:57 +08:00 committed by GitHub
parent e42faad710
commit aed3a48248
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 263 additions and 3 deletions
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2
include/tim/vx/ops/batchnorm.h
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@ -42,7 +42,7 @@ namespace ops {
class BatchNorm : public Operation { class BatchNorm : public Operation {
public: public:
BatchNorm(Graph* graph, float eps); BatchNorm(Graph* graph, float eps, DataLayout input_layout = DataLayout::WHCN);
std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override; std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;

2
src/tim/transform/layout_inference.cc
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@ -57,6 +57,7 @@
#include "ops/logical_layout_inference.h" #include "ops/logical_layout_inference.h"
#include "ops/arg_layout_inference.h" #include "ops/arg_layout_inference.h"
#include "ops/deconv2d_layout_inference.h" #include "ops/deconv2d_layout_inference.h"
#include "ops/batchnorm_layout_inference.h"
#include "ops/default_layout_inference.h" #include "ops/default_layout_inference.h"
#include <algorithm> #include <algorithm>
@ -255,6 +256,7 @@ std::vector<std::shared_ptr<vx::Tensor>> HandleLayoutInfer(
REGIST_LAYOUT_INFERENCE(VSI_NN_OP_ARGMAX, Arg); REGIST_LAYOUT_INFERENCE(VSI_NN_OP_ARGMAX, Arg);
REGIST_LAYOUT_INFERENCE(VSI_NN_OP_ARGMIN, Arg); REGIST_LAYOUT_INFERENCE(VSI_NN_OP_ARGMIN, Arg);
REGIST_LAYOUT_INFERENCE(VSI_NN_OP_DECONVOLUTION, DeConv2d); REGIST_LAYOUT_INFERENCE(VSI_NN_OP_DECONVOLUTION, DeConv2d);
REGIST_LAYOUT_INFERENCE(VSI_NN_OP_BATCH_NORM, BatchNorm);
REGIST_LOGICAL_LAYOUT_INFERENCE(VSI_NN_OP_LOGICAL_OPS); REGIST_LOGICAL_LAYOUT_INFERENCE(VSI_NN_OP_LOGICAL_OPS);
REGIST_REDUCE_LAYOUT_INFERENCE(VSI_NN_OP_REDUCE); REGIST_REDUCE_LAYOUT_INFERENCE(VSI_NN_OP_REDUCE);
// use default layout inference // use default layout inference

90
src/tim/transform/ops/batchnorm_layout_inference.h Normal file
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@ -0,0 +1,90 @@
/****************************************************************************
*
* Copyright (c) 2020 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.
*
*****************************************************************************/
#ifndef TIM_LAYOUT_INFER_BATCHNORM_LAYOUT_INFERENCE_H_
#define TIM_LAYOUT_INFER_BATCHNORM_LAYOUT_INFERENCE_H_
#include "tim/vx/ops/batchnorm.h"
#include "ops/op_layout_inference.h"
#include "permute_vector.h"
#include "operation_private.h"
namespace tim {
namespace transform {
class BatchNormLayoutInfer : public OpLayoutInfer {
public:
BatchNormLayoutInfer(
const std::shared_ptr<vx::Operation> op,
std::shared_ptr<layout_inference_impl::LayoutInferContext>& context)
: OpLayoutInfer(op, context) {}
void OnInputs(
std::vector<std::shared_ptr<vx::Tensor>>& next_tensors) override {
vx::DataLayout layout = op_->impl()->layout_;
auto required_pv = MakeShared(4);
if (layout == vx::DataLayout::CWHN) {
required_pv = std::make_shared<PermuteVector<4>>(kCWHN2WHCN);
}
auto input_tensors = op_->impl()->InputsTensor();
assert(input_tensors.size() == 5);
for (uint32_t idx = 0; idx < input_tensors.size(); idx++) {
std::shared_ptr<vx::Tensor> perm_out;
std::shared_ptr<IPermuteVector> input_pv;
auto src_in = input_tensors[idx];
if (src_in->IsConstTensor()) {
perm_out = context_->infer_graph_->CreateTensor(src_in->GetSpec(), src_in->GetDataRef());
input_pv = MakeShared(src_in->GetShape().size());
} else {
perm_out = context_->GetMapedTensor(src_in);
input_pv = context_->GetPermuteVector(src_in);
context_->SetPermuteVector(src_in, input_pv);
if (idx == 0) {
auto final_pv = input_pv->Reverse()->Add(required_pv);
if (!final_pv->IsAligned()) {
perm_out = InsertPermute(perm_out, required_pv);
context_->SetPermuteVector(src_in, required_pv);
}
}
}
context_->UpdateTensorMap(src_in, perm_out);
}
auto batchnorm = op_->Clone(context_->infer_graph_);
auto out_tensor_infer = CreateOutputsTensor(required_pv);
(*batchnorm).BindInput(context_->GetMapedTensor(input_tensors[0]));
(*batchnorm).BindInput(context_->GetMapedTensor(input_tensors[1]));
(*batchnorm).BindInput(context_->GetMapedTensor(input_tensors[2]));
(*batchnorm).BindInput(context_->GetMapedTensor(input_tensors[3]));
(*batchnorm).BindInput(context_->GetMapedTensor(input_tensors[4]));
(*batchnorm).BindOutput(out_tensor_infer[0]);
context_->SetPermuteVector(op_->impl()->OutputsTensor()[0], required_pv);
// Add out tensor of src_graph into next_tensor
next_tensors.push_back(op_->impl()->OutputsTensor()[0]);
}
};
} // namespace transform
} // namespace tim
#endif

4
src/tim/vx/ops/batchnorm.cc
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@ -30,8 +30,8 @@ namespace tim {
namespace vx { namespace vx {
namespace ops { namespace ops {
BatchNorm::BatchNorm(Graph* graph, float eps) BatchNorm::BatchNorm(Graph* graph, float eps, DataLayout input_layout)
: Operation(graph, VSI_NN_OP_BATCH_NORM), eps_(eps) { : Operation(graph, VSI_NN_OP_BATCH_NORM, 0, 0, input_layout), eps_(eps) {
this->impl()->node()->nn_param.batch_norm.eps = eps_; this->impl()->node()->nn_param.batch_norm.eps = eps_;
} }

168
src/tim/vx/ops/batchnorm_test.cc Normal file
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@ -0,0 +1,168 @@
/****************************************************************************
*
* 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/batchnorm.h"
#include "tim/transform/layout_inference.h"
#include "gtest/gtest.h"
#include "test_utils.h"
TEST(BatchNorm, shape_3_3_2_1_fp32_cwhn) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType in_shape({2, 3, 3, 1});
tim::vx::ShapeType out_shape({2, 3, 3, 1});
tim::vx::ShapeType mean_shape({2});
tim::vx::ShapeType var_shape({2});
tim::vx::ShapeType gamma_shape({2});
tim::vx::ShapeType beta_shape({2});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
in_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec mean_spec(tim::vx::DataType::FLOAT32, mean_shape, tim::vx::CONSTANT);
tim::vx::TensorSpec var_spec(tim::vx::DataType::FLOAT32, var_shape, tim::vx::CONSTANT);
tim::vx::TensorSpec gamma_spec(tim::vx::DataType::FLOAT32, gamma_shape, tim::vx::CONSTANT);
tim::vx::TensorSpec beta_spec(tim::vx::DataType::FLOAT32, beta_shape, tim::vx::CONSTANT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
out_shape, tim::vx::TensorAttribute::OUTPUT);
std::vector<float> in_data = {
0.59885779, 0.62662862, 0.63011179, 0.82569427, 0.64772359, 0.42895413,
0.30216458, 0.01351635, 0.32545444, 0.0360674, 0.33967769, 0.18092504,
0.09479915, 0.52258112, 0.46735646, 0.95689111, 0.51619059, 0.82685718};
std::vector<float> golden = {
0.92227477, 0.40612271, 1.09906762,
1.00176775, 1.19869136, -0.18535967,
-0.7560139, -1.42843423, -0.62427138,
-1.3609569, -0.54381545, -0.92751329,
-1.92900686, 0.09479138, 0.17841823,
1.39433545, 0.45465564, 1.0052474,
};
std::vector<float> mean_data = {
0.43581513, 0.49090168
};
std::vector<float> var_data = {
0.03025229, 0.11069085
};
std::vector<float> gamma_data = {
1,1
};
std::vector<float> beta_data = {
0, 0
};
auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
auto mean = graph->CreateTensor(mean_spec, mean_data.data());
auto var = graph->CreateTensor(var_spec, var_data.data());
auto gamma = graph->CreateTensor(gamma_spec, gamma_data.data());
auto beta = graph->CreateTensor(beta_spec, beta_data.data());
float epsilon = 0.001;
auto op = graph->CreateOperation<tim::vx::ops::BatchNorm>(epsilon, tim::vx::DataLayout::CWHN);
(*op).BindInputs({input_tensor, mean, var,gamma, beta}).BindOutputs({output_tensor});
auto final_graph = tim::transform::LayoutInference(graph, ctx);
EXPECT_TRUE(final_graph.first->Compile());
final_graph.second[input_tensor]->CopyDataToTensor(
in_data.data(), in_data.size() * sizeof(float));
EXPECT_TRUE(final_graph.first->Run());
std::vector<float> output(golden.size());
EXPECT_TRUE(final_graph.second[output_tensor]->CopyDataFromTensor(output.data()));
for (uint32_t idx = 0; idx < golden.size(); idx++) {
EXPECT_TRUE(std::abs(golden[idx] - output[idx]) < 0.01);
}
}
TEST(BatchNorm, shape_3_3_2_1_fp32_whcn) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType in_shape({3, 3, 2, 1});
tim::vx::ShapeType out_shape({3, 3, 2, 1});
tim::vx::ShapeType mean_shape({2});
tim::vx::ShapeType var_shape({2});
tim::vx::ShapeType gamma_shape({2});
tim::vx::ShapeType beta_shape({2});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
in_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec mean_spec(tim::vx::DataType::FLOAT32, mean_shape, tim::vx::CONSTANT);
tim::vx::TensorSpec var_spec(tim::vx::DataType::FLOAT32, var_shape, tim::vx::CONSTANT);
tim::vx::TensorSpec gamma_spec(tim::vx::DataType::FLOAT32, gamma_shape, tim::vx::CONSTANT);
tim::vx::TensorSpec beta_spec(tim::vx::DataType::FLOAT32, beta_shape, tim::vx::CONSTANT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
out_shape, tim::vx::TensorAttribute::OUTPUT);
std::vector<float> in_data = {
0.598858, 0.630112, 0.647724, 0.302165, 0.325454, 0.339678,
0.094799, 0.467356, 0.516191, 0.626629, 0.825694, 0.428954,
0.013516, 0.036067, 0.180925, 0.522581, 0.956891, 0.826857};
std::vector<float> golden = {
0.922275, 1.099068, 1.198692, -0.756014, -0.624271, -0.543815,
-1.929007, 0.178418, 0.454656, 0.406123, 1.001768, -0.185360,
-1.428434, -1.360957, -0.927513, 0.094791, 1.394335, 1.005247};
std::vector<float> mean_data = {
0.43581513, 0.49090168
};
std::vector<float> var_data = {
0.03025229, 0.11069085
};
std::vector<float> gamma_data = {
1,1
};
std::vector<float> beta_data = {
0, 0
};
auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
auto mean = graph->CreateTensor(mean_spec, mean_data.data());
auto var = graph->CreateTensor(var_spec, var_data.data());
auto gamma = graph->CreateTensor(gamma_spec, gamma_data.data());
auto beta = graph->CreateTensor(beta_spec, beta_data.data());
float epsilon = 0.001;
auto op = graph->CreateOperation<tim::vx::ops::BatchNorm>(epsilon);
(*op).BindInputs({input_tensor, mean, var,gamma, beta}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
input_tensor->CopyDataToTensor(in_data.data(),
in_data.size() * sizeof(float));
EXPECT_TRUE(graph->Run());
std::vector<float> output(golden.size());
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
for (uint32_t idx = 0; idx < golden.size(); idx++) {
EXPECT_TRUE(std::abs(golden[idx] - output[idx]) < 0.01);
}
}