[Paddle-TRT] Better Paddle-TensorRT support for PaddleSlim quant models (#25097)
* Paddle-TensorRT support slim QAT. test=develop * add comments. test=develop * use RenameInput instead of ResetInputs. test=developfix_copy_if_different
Pei Yang
5 years ago
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/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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 <gflags/gflags.h>
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#include <glog/logging.h>
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#include <gtest/gtest.h>
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#include <numeric>
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#include "paddle/fluid/inference/tests/api/trt_test_helper.h"
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namespace paddle {
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namespace inference {
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TEST(quant_int8, yolov3_resnet50) {
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AnalysisConfig config;
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config.EnableUseGpu(100, 0);
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config.SetModel(FLAGS_infer_model + "/model", FLAGS_infer_model + "/params");
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config.SwitchUseFeedFetchOps(false);
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config.EnableTensorRtEngine(1 << 30, 1, 3, AnalysisConfig::Precision::kInt8,
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false, false);
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auto predictor = CreatePaddlePredictor(config);
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auto input_names = predictor->GetInputNames();
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int channels = 3;
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int height = 608;
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int width = 608;
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int input_num = channels * height * width * 1;
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float *input = new float[input_num];
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int32_t *im_shape = new int32_t[2];
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im_shape[0] = 608;
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im_shape[1] = 608;
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memset(input, 1.0, input_num * sizeof(float));
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auto input_t = predictor->GetInputTensor(input_names[0]);
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input_t->Reshape({1, channels, height, width});
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input_t->copy_from_cpu(input);
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auto input_t1 = predictor->GetInputTensor(input_names[1]);
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input_t1->Reshape({1, 2});
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input_t1->copy_from_cpu(im_shape);
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ASSERT_TRUE(predictor->ZeroCopyRun());
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std::vector<float> out_data;
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auto output_names = predictor->GetOutputNames();
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auto output_t = predictor->GetOutputTensor(output_names[0]);
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std::vector<int> output_shape = output_t->shape();
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int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
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std::multiplies<int>());
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out_data.resize(out_num);
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output_t->copy_to_cpu(out_data.data());
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}
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} // namespace inference
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} // namespace paddle
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