| 259 | } |
| 260 | |
| 261 | static bool SetRandomInputs(const vector<vector<int64_t>>& input_shapes, Runtime* runtime, vector<string>* input_data) { |
| 262 | for (uint32_t c = 0; c < runtime->GetInputCount(); ++c) { |
| 263 | auto t = runtime->GetInputTensor(c); |
| 264 | auto shape = t->GetShape(); |
| 265 | |
| 266 | if (input_shapes.empty()) { |
| 267 | auto dim_count = shape->GetRealDimCount(); |
| 268 | auto dims = GenerateRandomDims(dim_count); |
| 269 | for (uint32_t j = 2; j < dim_count; ++j) { |
| 270 | if (shape->GetDim(j) == 1) { |
| 271 | shape->SetDim(j, dims[j]); |
| 272 | } |
| 273 | } |
| 274 | } else { |
| 275 | shape->Reshape(input_shapes[c]); |
| 276 | } |
| 277 | |
| 278 | auto nr_element = shape->CalcBytesIncludingPadding() / sizeof(float); |
| 279 | vector<float> buffer(nr_element); |
| 280 | |
| 281 | std::default_random_engine eng; |
| 282 | std::uniform_real_distribution<float> dis(-1.0f, 1.0f); |
| 283 | for (uint32_t i = 0; i < nr_element; ++i) { |
| 284 | buffer[i] = dis(eng); |
| 285 | } |
| 286 | |
| 287 | auto status = t->ReallocBuffer(); |
| 288 | if (status != RC_SUCCESS) { |
| 289 | LOG(ERROR) << "ReallocBuffer for tensor[" << t->GetName() << "] failed: " << GetRetCodeStr(status); |
| 290 | return false; |
| 291 | } |
| 292 | |
| 293 | TensorShape src_desc = *t->GetShape(); |
| 294 | src_desc.SetDataFormat(DATAFORMAT_NDARRAY); |
| 295 | status = t->ConvertFromHost(buffer.data(), src_desc); |
| 296 | if (status != RC_SUCCESS) { |
| 297 | LOG(ERROR) << "set tensor[" << t->GetName() << "] content failed: " << GetRetCodeStr(status); |
| 298 | return false; |
| 299 | } |
| 300 | |
| 301 | input_data->emplace_back(string((const char*)buffer.data(), buffer.size() * sizeof(float))); |
| 302 | } |
| 303 | |
| 304 | return true; |
| 305 | } |
| 306 | |
| 307 | static bool SetInputsAllInOne(const string& input_file, const vector<vector<int64_t>>& input_shapes, Runtime* runtime, |
| 308 | vector<string>* input_data) { |
no test coverage detected