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add batch norm trt converter

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dataset_flowers_-_change_md5
nhzlx 7 years ago
parent 4069262f0e
commit 133ec69625
3 changed files with 202 additions and 1 deletions
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5
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
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@ -1,7 +1,7 @@
# Add TRT tests
nv_library(tensorrt_converter
SRCS mul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc elementwise_op.cc
activation_op.cc softmax_op.cc
batch_norm_op.cc activation_op.cc softmax_op.cc
DEPS tensorrt_engine operator scope framework_proto op_registry)
nv_test(test_op_converter SRCS test_op_converter.cc DEPS
@ -24,3 +24,6 @@ nv_test(test_trt_elementwise_op SRCS test_elementwise_op.cc elementwise_op.cc
nv_test(test_trt_softmax_op SRCS test_softmax_op.cc softmax_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine softmax_op SERIAL)
nv_test(test_trt_batch_norm_op SRCS test_batch_norm_op.cc batch_norm_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine batch_norm_op SERIAL)

131
paddle/fluid/inference/tensorrt/convert/batch_norm_op.cc
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/* Copyright (c) 2018 PaddlePaddle Authors. 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 "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include <math.h>
namespace paddle {
namespace inference {
namespace tensorrt {
class BatchNormOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
LOG(INFO)
<< "convert a fluid batch norm op to tensorrt batch_norm";
framework::OpDesc op_desc(op, nullptr);
PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
PADDLE_ENFORCE_EQ(op_desc.Input("Bias").size(), 1); // Bias is a weight
PADDLE_ENFORCE_EQ(op_desc.Input("Mean").size(), 1); // Mean is a weight
PADDLE_ENFORCE_EQ(op_desc.Input("Scale").size(), 1); // Scale is a weight
PADDLE_ENFORCE_EQ(op_desc.Input("Variance").size(), 1); // Variance is a weight
PADDLE_ENFORCE_EQ(op_desc.Output("Y").size(), 1);
auto* X = engine_->GetITensor(op_desc.Input("X").front());
// Declare weights
auto* Bias_v = scope.FindVar(op_desc.Input("Bias").front());
auto* Mean_v = scope.FindVar(op_desc.Input("Mean").front());
auto* Scale_v = scope.FindVar(op_desc.Input("Scale").front());
auto* Variance_v = scope.FindVar(op_desc.Input("Variance").front());
const float eps = boost::get<float>(op_desc.GetAttr("epsilon"));
PADDLE_ENFORCE_NOT_NULL(Bias_v);
PADDLE_ENFORCE_NOT_NULL(Mean_v);
PADDLE_ENFORCE_NOT_NULL(Scale_v);
PADDLE_ENFORCE_NOT_NULL(Variance_v);
// get tensor
auto* Bias_t = Bias_v->GetMutable<framework::LoDTensor>();
auto* Mean_t = Mean_v->GetMutable<framework::LoDTensor>();
auto* Scale_t = Scale_v->GetMutable<framework::LoDTensor>();
auto* Variance_t = Variance_v->GetMutable<framework::LoDTensor>();
// create temp tensor for weights
framework::LoDTensor bias_tensor;
framework::LoDTensor mean_tensor;
framework::LoDTensor scale_tensor;
framework::LoDTensor variance_tensor;
bias_tensor.Resize(Bias_t->dims());
mean_tensor.Resize(Mean_t->dims());
scale_tensor.Resize(Scale_t->dims());
variance_tensor.Resize(Variance_t->dims());
platform::CPUPlace cpu_place;
// copy data from gpu to cpu
TensorCopySync((*Bias_t), cpu_place, &bias_tensor);
TensorCopySync((*Mean_t), cpu_place, &mean_tensor);
TensorCopySync((*Scale_t), cpu_place, &scale_tensor);
TensorCopySync((*Variance_t), cpu_place, &variance_tensor);
auto* bias_data = bias_tensor.mutable_data<float>(platform::CPUPlace());
auto* mean_data = mean_tensor.mutable_data<float>(platform::CPUPlace());
auto* scale_data = scale_tensor.mutable_data<float>(platform::CPUPlace());
auto* variance_data = variance_tensor.mutable_data<float>(platform::CPUPlace());
framework::LoDTensor *combile_scale_tensor = new framework::LoDTensor();
framework::LoDTensor *combile_bias_tensor = new framework::LoDTensor();
combile_scale_tensor->Resize(scale_tensor.dims());
combile_bias_tensor->Resize(bias_tensor.dims());
auto* combile_scale_data = combile_scale_tensor->mutable_data<float>(platform::CPUPlace());
auto* combile_bias_data = combile_bias_tensor->mutable_data<float>(platform::CPUPlace());
engine_->weight_map_[op_desc.Input("Bias").front()] = std::move(std::unique_ptr<framework::Tensor>(combile_bias_tensor));
engine_->weight_map_[op_desc.Input("Scale").front()] = std::move(std::unique_ptr<framework::Tensor>(combile_scale_tensor));
size_t ele_num = combile_scale_tensor->memory_size()/sizeof(float);
for (size_t i = 0; i < ele_num; i++) {
float scale = scale_data[i];
float bias = bias_data[i];
float mean = mean_data[i];
float variance = variance_data[i];
combile_scale_data[i] = scale / sqrtf(variance + eps);
combile_bias_data[i] = bias - mean * combile_scale_data[i];
}
TensorRTEngine::Weight scale_weights{nvinfer1::DataType::kFLOAT,
static_cast<void*>(combile_scale_data),
combile_scale_tensor->memory_size() / sizeof(float)};
TensorRTEngine::Weight shift_weights{nvinfer1::DataType::kFLOAT,
static_cast<void *>(combile_bias_data),
combile_bias_tensor->memory_size()/ sizeof(float)};
TensorRTEngine::Weight power_weights{nvinfer1::DataType::kFLOAT, nullptr,
0};
nvinfer1::IScaleLayer* layer = TRT_ENGINE_ADD_LAYER(
engine_, Scale, *const_cast<nvinfer1::ITensor*>(X), nvinfer1::ScaleMode::kCHANNEL,
shift_weights.get(), scale_weights.get(), power_weights.get());
auto output_name = op_desc.Output("Y").front();
engine_->SetITensor(output_name, layer->getOutput(0));
if (test_mode) {
engine_->DeclareOutput(output_name);
}
}
};
} // namespace tensorrt
} // namespace inference
} // namespace paddle
REGISTER_TRT_OP_CONVERTER(batch_norm, BatchNormOpConverter);

67
paddle/fluid/inference/tensorrt/convert/test_batch_norm_op.cc
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/* Copyright (c) 2018 PaddlePaddle Authors. 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 <gtest/gtest.h>
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
namespace paddle {
namespace inference {
namespace tensorrt {
TEST(batch_norm_op, test) {
std::unordered_set<std::string> parameters({"batch_norm_scale",
"batch_norm_bias", "batch_norm_mean", "batch_norm_variance" });
framework::Scope scope;
TRTConvertValidation validator(5, parameters, scope, 1 << 15);
std::vector<int> param_shape{2};
validator.DeclInputVar("batch_norm_X", nvinfer1::DimsCHW(2, 5, 5));
validator.DeclParamVar("batch_norm_scale", param_shape);
validator.DeclParamVar("batch_norm_bias", param_shape);
validator.DeclParamVar("batch_norm_mean", param_shape);
validator.DeclParamVar("batch_norm_variance", param_shape);
validator.DeclOutputVar("batch_norm_Y", nvinfer1::DimsCHW(2, 5, 5));
validator.DeclOutputVar("batch_norm_save_mean", param_shape);
validator.DeclOutputVar("batch_norm_save_variance", param_shape);
// Prepare Op description
framework::OpDesc desc;
desc.SetType("batch_norm");
desc.SetInput("X", {"batch_norm_X"});
desc.SetInput("Scale", {"batch_norm_scale"});
desc.SetInput("Bias", {"batch_norm_bias"});
desc.SetInput("Mean", {"batch_norm_mean"});
desc.SetInput("Variance", {"batch_norm_variance"});
desc.SetOutput("Y", {"batch_norm_Y"});
desc.SetOutput("MeanOut", {"batch_norm_mean"});
desc.SetOutput("VarianceOut", {"batch_norm_variance"});
desc.SetOutput("SavedMean", {"batch_norm_save_mean"});
desc.SetOutput("SavedVariance", {"batch_norm_save_variance"});
float eps = 1e-5f;
bool is_test = true;
desc.SetAttr("epsilon", eps);
desc.SetAttr("is_test", is_test);
validator.SetOp(*desc.Proto());
std::unordered_set<std::string> neglected_output = {"batch_norm_save_mean", "batch_norm_save_variance", "batch_norm_mean", "batch_norm_variance"};
validator.Execute(3, neglected_output);
}
} // namespace tensorrt
} // namespace inference
} // namespace paddle
USE_OP(batch_norm);
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