wehub-resource-sync
88 lines
3.0 KiB
C++
88 lines
3.0 KiB
C++
/* Copyright (c) 2022 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 <glog/logging.h>
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#include <gtest/gtest.h>
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#include <cmath>
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#include "paddle/common/flags.h"
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#include "test/cpp/inference/api/tester_helper.h"
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namespace paddle {
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namespace inference {
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// Compare results with 1 batch
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TEST(Analyzer_Resnet50_ipu, compare_results_1_batch) {
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std::string model_dir = FLAGS_infer_model + "/" + "model";
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AnalysisConfig config;
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// ipu_device_num, ipu_micro_batch_size, ipu_enable_pipelining
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config.EnableIpu(1, 1, false);
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// ipu_enable_fp16, ipu_replica_num, ipu_available_memory_proportion,
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// ipu_enable_half_partial
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config.SetIpuConfig(true, 1, 1.0, true);
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config.SetModel(model_dir + "/model", model_dir + "/params");
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std::vector<PaddleTensor> inputs;
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auto predictor = CreatePaddlePredictor(config);
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const int batch = 1;
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const int channel = 3;
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const int height = 318;
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const int width = 318;
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const int input_num = batch * channel * height * width;
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std::vector<float> input(input_num, 1);
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PaddleTensor in;
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in.shape = {batch, channel, height, width};
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in.data =
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PaddleBuf(static_cast<void*>(input.data()), input_num * sizeof(float));
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in.dtype = PaddleDType::FLOAT32;
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ConvertFP32toFP16(in);
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inputs.emplace_back(in);
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std::vector<PaddleTensor> outputs;
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ASSERT_TRUE(predictor->Run(inputs, &outputs));
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const std::vector<float> truth_values = {
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127.779f, 738.165f, 1013.22f, -438.17f, 366.401f, 927.659f,
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736.222f, -633.684f, -329.927f, -430.155f, -633.062f, -146.548f,
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-1324.28f, -1349.36f, -242.675f, 117.448f, -801.723f, -391.514f,
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-404.818f, 454.16f, 515.48f, -133.031f, 69.293f, 590.096f,
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-1434.69f, -1070.89f, 307.074f, 400.525f, -316.12f, -587.125f,
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-161.056f, 800.363f, -96.4708f, 748.706f, 868.174f, -447.938f,
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112.737f, 1127.2f, 47.4355f, 677.72f, 593.186f, -336.4f,
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551.362f, 397.823f, 78.3979f, -715.398f, 405.969f, 404.256f,
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246.019f, -8.42969f, 131.365f, -648.051f};
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const size_t expected_size = 1;
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EXPECT_EQ(outputs.size(), expected_size);
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auto output = outputs.front();
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ConvertFP16toFP32(output);
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auto outputs_size = 1;
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for (auto dim : output.shape) {
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outputs_size *= dim;
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}
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float* fp32_data = reinterpret_cast<float*>(output.data.data());
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for (size_t j = 0; j < outputs_size; j += 10) {
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EXPECT_NEAR(
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(fp32_data[j] - truth_values[j / 10]) / truth_values[j / 10], 0., 9e-2);
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}
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}
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} // namespace inference
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} // namespace paddle
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