add resnet50 inference UT

paddle/fluid/inference/tests/api/CMakeLists.txt

@@ -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

paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc

@@ -0,0 +1,92 @@
+/* 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