support more input fake data

paddle/fluid/inference/tests/api/tester_helper.h

@@ -132,7 +132,8 @@ std::unordered_map<std::string, int> GetFuseStatis(PaddlePredictor *predictor,
 void SetFakeImageInput(std::vector<std::vector<PaddleTensor>> *inputs,
                        const std::string &dirname, bool is_combined = true,
                        std::string model_filename = "model",
-                       std::string params_filename = "params") {
+                       std::string params_filename = "params",
+                       const std::vector<std::string> *feed_names = nullptr) {
   // Set fake_image_data
   PADDLE_ENFORCE_EQ(FLAGS_test_all_data, 0, "Only have single batch of data.");
   std::vector<std::vector<int64_t>> feed_target_shapes = GetFeedTargetShapes(
@@ -146,26 +147,32 @@ void SetFakeImageInput(std::vector<std::vector<PaddleTensor>> *inputs,
     os << "}\n";
   }
   LOG(INFO) << os.str();
-
+  if (feed_names) {
-  int dim1 = feed_target_shapes[0][1];
+    PADDLE_ENFORCE_EQ(feed_names->size(), feed_target_shapes.size());
-  int dim2 = feed_target_shapes[0][2];
+  }
-  int dim3 = feed_target_shapes[0][3];
+  std::vector<PaddleTensor> input_slots(feed_target_shapes.size());
-
+  for (size_t i = 0; i < feed_target_shapes.size(); ++i) {
-  PaddleTensor input;
+    const auto &feed_shape = feed_target_shapes[i];
-  std::vector<int> shape({FLAGS_batch_size, dim1, dim2, dim3});
+    auto &input = input_slots[i];
-  input.shape = shape;
+    std::vector<int> shape({FLAGS_batch_size});
-  input.dtype = PaddleDType::FLOAT32;
+    for (size_t s = 1; s < feed_shape.size(); ++s) {
-
+      shape.push_back(static_cast<int>(feed_shape[s]));
-  // fill input data, for profile easily, do not use random data here.
+    }
-  size_t size = FLAGS_batch_size * dim1 * dim2 * dim3;
+    if (feed_names) {
-  input.data.Resize(size * sizeof(float));
+      input.name = (*feed_names)[i];
-  float *input_data = static_cast<float *>(input.data.data());
+    }
-  for (size_t i = 0; i < size; i++) {
+    input.shape = shape;
-    *(input_data + i) = static_cast<float>(i) / size;
+    input.dtype = PaddleDType::FLOAT32;
+    size_t len = std::accumulate(shape.begin(), shape.end(), 1,
+                                 [](int a, int b) { return a * b; });
+    input.data.Resize(len * sizeof(float));
+    input.lod.assign({{0, static_cast<size_t>(FLAGS_batch_size)}});
+    float *input_data = static_cast<float *>(input.data.data());
+    // fill input data, for profile easily, do not use random data here.
+    for (size_t j = 0; j < len; ++j) {
+      *(input_data + j) = static_cast<float>(j) / len;
+    }
   }
-
-  std::vector<PaddleTensor> input_slots;
-  input_slots.assign({input});
   (*inputs).emplace_back(input_slots);
 }