Source code for espnet2.main_funcs.collect_stats

import logging
from collections import defaultdict
from pathlib import Path
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np
import torch
from torch.nn.parallel import data_parallel
from import DataLoader
from typeguard import check_argument_types

from espnet2.fileio.datadir_writer import DatadirWriter
from espnet2.fileio.npy_scp import NpyScpWriter
from espnet2.torch_utils.device_funcs import to_device
from espnet2.torch_utils.forward_adaptor import ForwardAdaptor
from espnet2.train.abs_espnet_model import AbsESPnetModel

[docs]@torch.no_grad() def collect_stats( model: Union[AbsESPnetModel, None], train_iter: DataLoader and Iterable[Tuple[List[str], Dict[str, torch.Tensor]]], valid_iter: DataLoader and Iterable[Tuple[List[str], Dict[str, torch.Tensor]]], output_dir: Path, ngpu: Optional[int], log_interval: Optional[int], write_collected_feats: bool, ) -> None: """Perform on collect_stats mode. Running for deriving the shape information from data and gathering statistics. This method is used before executing train(). """ assert check_argument_types() npy_scp_writers = {} for itr, mode in zip([train_iter, valid_iter], ["train", "valid"]): if log_interval is None: try: log_interval = max(len(itr) // 20, 10) except TypeError: log_interval = 100 sum_dict = defaultdict(lambda: 0) sq_dict = defaultdict(lambda: 0) count_dict = defaultdict(lambda: 0) with DatadirWriter(output_dir / mode) as datadir_writer: for iiter, (keys, batch) in enumerate(itr, 1): batch = to_device(batch, "cuda" if ngpu > 0 else "cpu") # 1. Write shape file for name in batch: if name.endswith("_lengths"): continue for i, (key, data) in enumerate(zip(keys, batch[name])): if f"{name}_lengths" in batch: lg = int(batch[f"{name}_lengths"][i]) data = data[:lg] datadir_writer[f"{name}_shape"][key] = ",".join( map(str, data.shape) ) if model is not None: # 2. Extract feats if ngpu <= 1: data = model.collect_feats(**batch) else: # Note that data_parallel can parallelize only "forward()" data = data_parallel( ForwardAdaptor(model, "collect_feats"), (), range(ngpu), module_kwargs=batch, ) # 3. Calculate sum and square sum for key, v in data.items(): for i, (uttid, seq) in enumerate(zip(keys, v.cpu().numpy())): # Truncate zero-padding region if f"{key}_lengths" in data: length = data[f"{key}_lengths"][i] # seq: (Length, Dim, ...) seq = seq[:length] else: # seq: (Dim, ...) -> (1, Dim, ...) seq = seq[None] # Accumulate value, its square, and count sum_dict[key] += seq.sum(0) sq_dict[key] += (seq**2).sum(0) count_dict[key] += len(seq) # 4. [Option] Write derived features as npy format file. if write_collected_feats: # Instantiate NpyScpWriter for the first iteration if (key, mode) not in npy_scp_writers: p = output_dir / mode / "collect_feats" npy_scp_writers[(key, mode)] = NpyScpWriter( p / f"data_{key}", p / f"{key}.scp" ) # Save array as npy file npy_scp_writers[(key, mode)][uttid] = seq if iiter % log_interval == 0:"Niter: {iiter}") for key in sum_dict: np.savez( output_dir / mode / f"{key}_stats.npz", count=count_dict[key], sum=sum_dict[key], sum_square=sq_dict[key], ) # batch_keys and stats_keys are used by with (output_dir / mode / "batch_keys").open("w", encoding="utf-8") as f: f.write( "\n".join(filter(lambda x: not x.endswith("_lengths"), batch)) + "\n" ) with (output_dir / mode / "stats_keys").open("w", encoding="utf-8") as f: f.write("\n".join(sum_dict) + "\n")