| 277 | |
| 278 | |
| 279 | def main(): |
| 280 | parser = argparse.ArgumentParser( |
| 281 | description="load a megengine dumped model and export log file for tensorboard visualization.", |
| 282 | formatter_class=argparse.ArgumentDefaultsHelpFormatter, |
| 283 | ) |
| 284 | parser.add_argument("model_path", help="dumped model path.") |
| 285 | parser.add_argument("--log_path", help="tensorboard log path.") |
| 286 | parser.add_argument( |
| 287 | "--load_input_data", |
| 288 | help="load input data from pickle file; it should be a numpy array or a dict of numpy array", |
| 289 | ) |
| 290 | parser.add_argument( |
| 291 | "--bar_length_max", |
| 292 | type=int, |
| 293 | default=20, |
| 294 | help="size of bar indicating max flops or parameter size in net stats.", |
| 295 | ) |
| 296 | parser.add_argument( |
| 297 | "--cal_params", |
| 298 | action="store_true", |
| 299 | help="whether calculate and record params size.", |
| 300 | ) |
| 301 | parser.add_argument( |
| 302 | "--cal_flops", |
| 303 | action="store_true", |
| 304 | help="whether calculate and record op flops.", |
| 305 | ) |
| 306 | parser.add_argument( |
| 307 | "--cal_activations", |
| 308 | action="store_true", |
| 309 | help="whether calculate and record op activations.", |
| 310 | ) |
| 311 | parser.add_argument( |
| 312 | "--logging_to_stdout", |
| 313 | action="store_true", |
| 314 | help="whether print all calculated statistic details.", |
| 315 | ) |
| 316 | parser.add_argument( |
| 317 | "--all", |
| 318 | action="store_true", |
| 319 | help="whether print all stats. Tensorboard logs will be placed in './log' if not specified.", |
| 320 | ) |
| 321 | args = parser.parse_args() |
| 322 | if args.load_input_data: |
| 323 | logger.info("load data from {}".format(args.load_input_data)) |
| 324 | data = mge.load(args.load_input_data) |
| 325 | if isinstance(data, dict): |
| 326 | for v in data.values(): |
| 327 | assert isinstance( |
| 328 | v, np.ndarray |
| 329 | ), "data should provide ndarray; got {} instead".format(v) |
| 330 | args.inp_dict = data |
| 331 | elif isinstance(data, np.ndarray): |
| 332 | args.input = data |
| 333 | else: |
| 334 | logger.error("input data should be a numpy array or a dict of numpy array") |
| 335 | if args.all: |
| 336 | args.cal_params = True |