提交 1dcd6ee5 编写于 作者: T Tao Luo

add resnet50 inference UT

上级 643b6faa
......@@ -70,6 +70,14 @@ if (NOT EXISTS ${OCR_INSTALL_DIR})
endif()
inference_analysis_api_test(test_analyzer_ocr ${OCR_INSTALL_DIR} analyzer_vis_tester.cc)
# resnet50
set(RESNET50_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/resnet50")
if (NOT EXISTS ${RESNET50_INSTALL_DIR})
inference_download_and_uncompress(${RESNET50_INSTALL_DIR} ${INFERENCE_URL} "resnet50_model.tar.gz")
endif()
inference_analysis_test(test_analyzer_resnet50 SRCS analyzer_resnet50_tester.cc
EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} ARGS --infer_model=${RESNET50_INSTALL_DIR}/model)
# anakin
if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
# anakin rnn1
......
/* 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 <fstream>
#include <iostream>
#include "paddle/fluid/inference/tests/api/tester_helper.h"
namespace paddle {
namespace inference {
namespace analysis {
void SetConfig(AnalysisConfig *cfg) {
cfg->param_file = FLAGS_infer_model + "/params";
cfg->prog_file = FLAGS_infer_model + "/model";
cfg->use_gpu = false;
cfg->device = 0;
cfg->enable_ir_optim = true;
cfg->specify_input_name = true;
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
PADDLE_ENFORCE_EQ(FLAGS_test_all_data, 0, "Only have single batch of data.");
PaddleTensor input;
// channel=3, height/width=318
std::vector<int> shape({FLAGS_batch_size, 3, 318, 318});
input.shape = shape;
input.dtype = PaddleDType::FLOAT32;
// fill input data, for profile easily, do not use random data here.
size_t size = FLAGS_batch_size * 3 * 318 * 318;
input.data.Resize(size * sizeof(float));
float *input_data = static_cast<float *>(input.data.data());
for (size_t i = 0; i < size; i++) {
*(input_data + i) = static_cast<float>(i) / size;
}
std::vector<PaddleTensor> input_slots;
input_slots.assign({input});
(*inputs).emplace_back(input_slots);
}
// Easy for profiling independently.
TEST(Analyzer_resnet50, profile) {
AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<PaddleTensor> outputs;
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
TestPrediction(cfg, input_slots_all, &outputs, FLAGS_num_threads);
if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
PADDLE_ENFORCE_EQ(outputs.size(), 1UL);
size_t size = GetSize(outputs[0]);
// output is a 512-dimension feature
EXPECT_EQ(size, 512 * FLAGS_batch_size);
}
}
// Check the fuse status
TEST(Analyzer_resnet50, fuse_statis) {
AnalysisConfig cfg;
SetConfig(&cfg);
int num_ops;
GetFuseStatis(cfg, &num_ops);
}
// Compare result of NativeConfig and AnalysisConfig
TEST(Analyzer_resnet50, compare) {
AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
CompareNativeAndAnalysis(cfg, input_slots_all);
}
} // namespace analysis
} // namespace inference
} // namespace paddle
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