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117 lines
4.5 KiB
117 lines
4.5 KiB
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
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namespace paddle {
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namespace inference {
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namespace tensorrt {
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namespace plugin {
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inline void Seria(void*& buffer, // NOLINT
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const std::vector<nvinfer1::Dims>& input_dims,
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size_t max_batch_size, nvinfer1::DataType data_type,
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nvinfer1::PluginFormat data_format, bool with_fp16) {
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SerializeValue(&buffer, input_dims);
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SerializeValue(&buffer, max_batch_size);
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SerializeValue(&buffer, data_type);
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SerializeValue(&buffer, data_format);
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SerializeValue(&buffer, with_fp16);
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}
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inline void Deseria(void const*& serial_data, size_t& serial_length, // NOLINT
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std::vector<nvinfer1::Dims>* input_dims,
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size_t* max_batch_size, nvinfer1::DataType* data_type,
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nvinfer1::PluginFormat* data_format, bool* with_fp16) {
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DeserializeValue(&serial_data, &serial_length, input_dims);
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DeserializeValue(&serial_data, &serial_length, max_batch_size);
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DeserializeValue(&serial_data, &serial_length, data_type);
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DeserializeValue(&serial_data, &serial_length, data_format);
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DeserializeValue(&serial_data, &serial_length, with_fp16);
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}
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inline size_t SeriaSize(const std::vector<nvinfer1::Dims>& input_dims,
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size_t max_batch_size, nvinfer1::DataType data_type,
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nvinfer1::PluginFormat data_format, bool with_fp16) {
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return (SerializedSize(input_dims) + SerializedSize(max_batch_size) +
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SerializedSize(data_type) + SerializedSize(data_format) +
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SerializedSize(with_fp16));
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}
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void PluginTensorRT::serializeBase(void*& buffer) {
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Seria(buffer, input_dims_, max_batch_size_, data_type_, data_format_,
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with_fp16_);
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}
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void PluginTensorRT::deserializeBase(void const*& serial_data,
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size_t& serial_length) {
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Deseria(serial_data, serial_length, &input_dims_, &max_batch_size_,
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&data_type_, &data_format_, &with_fp16_);
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}
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size_t PluginTensorRT::getBaseSerializationSize() {
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return SeriaSize(input_dims_, max_batch_size_, data_type_, data_format_,
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with_fp16_);
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}
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bool PluginTensorRT::supportsFormat(nvinfer1::DataType type,
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nvinfer1::PluginFormat format) const {
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return ((type == nvinfer1::DataType::kFLOAT) &&
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(format == nvinfer1::PluginFormat::kNCHW));
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}
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void PluginTensorRT::configureWithFormat(
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const nvinfer1::Dims* input_dims, int num_inputs,
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const nvinfer1::Dims* output_dims, int num_outputs, nvinfer1::DataType type,
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nvinfer1::PluginFormat format, int max_batch_size) {
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data_type_ = type;
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data_format_ = format;
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input_dims_.assign(input_dims, input_dims + num_inputs);
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max_batch_size_ = max_batch_size;
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}
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void PluginTensorRTV2Ext::serializeBase(void*& buffer) const {
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Seria(buffer, input_dims_, max_batch_size_, data_type_, data_format_,
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with_fp16_);
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}
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void PluginTensorRTV2Ext::deserializeBase(void const*& serial_data,
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size_t& serial_length) {
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Deseria(serial_data, serial_length, &input_dims_, &max_batch_size_,
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&data_type_, &data_format_, &with_fp16_);
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}
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size_t PluginTensorRTV2Ext::getBaseSerializationSize() const {
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return SeriaSize(input_dims_, max_batch_size_, data_type_, data_format_,
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with_fp16_);
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}
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void PluginTensorRTV2Ext::configurePlugin(
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const nvinfer1::Dims* input_dims, int32_t nb_inputs,
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const nvinfer1::Dims* output_dims, int32_t nb_outputs,
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const nvinfer1::DataType* input_types,
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const nvinfer1::DataType* output_types, const bool* input_is_broadcast,
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const bool* output_is_broadcast, nvinfer1::PluginFormat float_format,
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int32_t max_batch_size) {
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input_dims_.assign(input_dims, input_dims + nb_inputs);
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max_batch_size_ = max_batch_size;
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data_format_ = float_format;
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data_type_ = input_types[0];
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}
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} // namespace plugin
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} // namespace tensorrt
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} // namespace inference
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} // namespace paddle
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