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| | | from functools import cmp_to_key |
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| | | |
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| | | # @torch.no_grad() |
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| | | # def average_nbest_models( |
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| | | # output_dir: Path, |
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| | | # best_model_criterion: Sequence[Sequence[str]], |
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| | | # nbest: Union[Collection[int], int], |
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| | | # suffix: Optional[str] = None, |
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| | | # oss_bucket=None, |
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| | | # pai_output_dir=None, |
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| | | # ) -> None: |
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| | | # """Generate averaged model from n-best models |
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| | | # |
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| | | # Args: |
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| | | # output_dir: The directory contains the model file for each epoch |
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| | | # reporter: Reporter instance |
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| | | # best_model_criterion: Give criterions to decide the best model. |
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| | | # e.g. [("valid", "loss", "min"), ("train", "acc", "max")] |
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| | | # nbest: Number of best model files to be averaged |
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| | | # suffix: A suffix added to the averaged model file name |
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| | | # """ |
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| | | # if isinstance(nbest, int): |
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| | | # nbests = [nbest] |
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| | | # else: |
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| | | # nbests = list(nbest) |
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| | | # if len(nbests) == 0: |
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| | | # warnings.warn("At least 1 nbest values are required") |
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| | | # nbests = [1] |
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| | | # if suffix is not None: |
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| | | # suffix = suffix + "." |
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| | | # else: |
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| | | # suffix = "" |
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| | | # |
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| | | # # 1. Get nbests: List[Tuple[str, str, List[Tuple[epoch, value]]]] |
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| | | # nbest_epochs = [ |
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| | | # (ph, k, reporter.sort_epochs_and_values(ph, k, m)[: max(nbests)]) |
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| | | # for ph, k, m in best_model_criterion |
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| | | # if reporter.has(ph, k) |
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| | | # ] |
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| | | # |
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| | | # _loaded = {} |
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| | | # for ph, cr, epoch_and_values in nbest_epochs: |
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| | | # _nbests = [i for i in nbests if i <= len(epoch_and_values)] |
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| | | # if len(_nbests) == 0: |
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| | | # _nbests = [1] |
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| | | # |
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| | | # for n in _nbests: |
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| | | # if n == 0: |
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| | | # continue |
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| | | # elif n == 1: |
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| | | # # The averaged model is same as the best model |
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| | | # e, _ = epoch_and_values[0] |
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| | | # op = output_dir / f"{e}epoch.pb" |
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| | | # sym_op = output_dir / f"{ph}.{cr}.ave_1best.{suffix}pb" |
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| | | # if sym_op.is_symlink() or sym_op.exists(): |
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| | | # sym_op.unlink() |
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| | | # sym_op.symlink_to(op.name) |
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| | | # else: |
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| | | # op = output_dir / f"{ph}.{cr}.ave_{n}best.{suffix}pb" |
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| | | # logging.info( |
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| | | # f"Averaging {n}best models: " f'criterion="{ph}.{cr}": {op}' |
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| | | # ) |
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| | | # |
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| | | # avg = None |
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| | | # # 2.a. Averaging model |
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| | | # for e, _ in epoch_and_values[:n]: |
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| | | # if e not in _loaded: |
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| | | # if oss_bucket is None: |
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| | | # _loaded[e] = torch.load( |
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| | | # output_dir / f"{e}epoch.pb", |
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| | | # map_location="cpu", |
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| | | # ) |
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| | | # else: |
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| | | # buffer = BytesIO( |
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| | | # oss_bucket.get_object(os.path.join(pai_output_dir, f"{e}epoch.pb")).read()) |
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| | | # _loaded[e] = torch.load(buffer) |
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| | | # states = _loaded[e] |
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| | | # |
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| | | # if avg is None: |
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| | | # avg = states |
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| | | # else: |
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| | | # # Accumulated |
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| | | # for k in avg: |
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| | | # avg[k] = avg[k] + states[k] |
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| | | # for k in avg: |
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| | | # if str(avg[k].dtype).startswith("torch.int"): |
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| | | # # For int type, not averaged, but only accumulated. |
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| | | # # e.g. BatchNorm.num_batches_tracked |
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| | | # # (If there are any cases that requires averaging |
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| | | # # or the other reducing method, e.g. max/min, for integer type, |
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| | | # # please report.) |
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| | | # pass |
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| | | # else: |
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| | | # avg[k] = avg[k] / n |
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| | | # |
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| | | # # 2.b. Save the ave model and create a symlink |
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| | | # if oss_bucket is None: |
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| | | # torch.save(avg, op) |
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| | | # else: |
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| | | # buffer = BytesIO() |
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| | | # torch.save(avg, buffer) |
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| | | # oss_bucket.put_object(os.path.join(pai_output_dir, f"{ph}.{cr}.ave_{n}best.{suffix}pb"), |
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| | | # buffer.getvalue()) |
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| | | # |
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| | | # # 3. *.*.ave.pb is a symlink to the max ave model |
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| | | # if oss_bucket is None: |
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| | | # op = output_dir / f"{ph}.{cr}.ave_{max(_nbests)}best.{suffix}pb" |
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| | | # sym_op = output_dir / f"{ph}.{cr}.ave.{suffix}pb" |
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| | | # if sym_op.is_symlink() or sym_op.exists(): |
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| | | # sym_op.unlink() |
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| | | # sym_op.symlink_to(op.name) |
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| | | |
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| | | |
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| | | def _get_checkpoint_paths(output_dir: str, last_n: int=5): |
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| | | """ |
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| | | Get the paths of the last 'last_n' checkpoints by parsing filenames |
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| | | in the output directory. |
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| | | """ |
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| | | # List all files in the output directory |
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| | | files = os.listdir(output_dir) |
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| | | # Filter out checkpoint files and extract epoch numbers |
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| | | checkpoint_files = [f for f in files if f.startswith("model.pt.e")] |
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| | | # Sort files by epoch number in descending order |
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| | | checkpoint_files.sort(key=lambda x: int(re.search(r'(\d+)', x).group()), reverse=True) |
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| | | # Get the last 'last_n' checkpoint paths |
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| | | checkpoint_paths = [os.path.join(output_dir, f) for f in checkpoint_files[:last_n]] |
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| | | try: |
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| | | checkpoint = torch.load(os.path.exists(os.path.join(output_dir, "model.pt")), map_location="cpu") |
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| | | avg_keep_nbest_models_type = checkpoint["avg_keep_nbest_models_type"] |
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| | | val_step_or_eoch = checkpoint[f"val_{avg_keep_nbest_models_type}_step_or_eoch"] |
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| | | sorted_items = sorted(saved_ckpts.items(), key=lambda x: x[1], reverse=True) |
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| | | sorted_items = sorted_items[:last_n] if avg_keep_nbest_models_type == "acc" else sorted_items[-last_n:] |
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| | | checkpoint_paths = [os.path.join(output_dir, key) for key, value in sorted_items[:last_n]] |
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| | | except: |
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| | | # List all files in the output directory |
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| | | files = os.listdir(output_dir) |
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| | | # Filter out checkpoint files and extract epoch numbers |
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| | | checkpoint_files = [f for f in files if f.startswith("model.pt.e")] |
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| | | # Sort files by epoch number in descending order |
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| | | checkpoint_files.sort(key=lambda x: int(re.search(r'(\d+)', x).group()), reverse=True) |
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| | | # Get the last 'last_n' checkpoint paths |
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| | | checkpoint_paths = [os.path.join(output_dir, f) for f in checkpoint_files[:last_n]] |
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| | | return checkpoint_paths |
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| | | |
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| | | @torch.no_grad() |
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| | | def average_checkpoints(output_dir: str, last_n: int=5, val_acc_list=[]): |
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| | | def average_checkpoints(output_dir: str, last_n: int=5, **kwargs): |
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| | | """ |
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| | | Average the last 'last_n' checkpoints' model state_dicts. |
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| | | If a tensor is of type torch.int, perform sum instead of average. |
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| | | """ |
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| | | checkpoint_paths = _get_checkpoint_paths(output_dir, last_n) |
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| | | print(f"average_checkpoints: {checkpoint_paths}") |
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| | | state_dicts = [] |
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| | | |
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| | | # Load state_dicts from checkpoints |
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