submodule/sapiens/engine/mmengine/hooks/logger_hook.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import logging
import os
import os.path as osp
from collections import OrderedDict
from pathlib import Path
from typing import Dict, Optional, Sequence, Union

import numpy as np
import torch

from mmengine.fileio import FileClient, dump
from mmengine.fileio.io import get_file_backend
from mmengine.hooks import Hook
from mmengine.logging import print_log
from mmengine.registry import HOOKS
from mmengine.utils import is_seq_of, scandir

DATA_BATCH = Optional[Union[dict, tuple, list]]
SUFFIX_TYPE = Union[Sequence[str], str]


@HOOKS.register_module()
class LoggerHook(Hook):
    """Collect logs from different components of ``Runner`` and write them to
    terminal, JSON file, tensorboard and wandb .etc.

    ``LoggerHook`` is used to record logs formatted by ``LogProcessor`` during
    training/validation/testing phase. It is used to control following
    behaviors:

    - The frequency of logs update in terminal, local, tensorboad wandb.etc.
    - The frequency of show experiment information in terminal.
    - The work directory to save logs.

    Args:
        interval (int): Logging interval (every k iterations).
            Defaults to 10.
        ignore_last (bool): Ignore the log of last iterations in each epoch if
            the number of remaining iterations is less than :attr:`interval`.
            Defaults to True.
        interval_exp_name (int): Logging interval for experiment name. This
            feature is to help users conveniently get the experiment
            information from screen or log file. Defaults to 1000.
        out_dir (str or Path, optional): The root directory to save
            checkpoints. If not specified, ``runner.work_dir`` will be used
            by default. If specified, the ``out_dir`` will be the concatenation
            of ``out_dir`` and the last level directory of ``runner.work_dir``.
            For example, if the input ``our_dir`` is ``./tmp`` and
            ``runner.work_dir`` is ``./work_dir/cur_exp``, then the log will be
            saved in ``./tmp/cur_exp``. Defaults to None.
        out_suffix (Tuple[str] or str): Those files in ``runner._log_dir``
            ending with ``out_suffix`` will be copied to ``out_dir``. Defaults
            to ('json', '.log', '.py').
        keep_local (bool): Whether to keep local logs in the local machine
            when :attr:`out_dir` is specified. If False, the local log will be
            removed. Defaults to True.
        file_client_args (dict, optional): Arguments to instantiate a
            FileClient. See :class:`mmengine.fileio.FileClient` for details.
            Defaults to None. It will be deprecated in future. Please use
            `backend_args` instead.
        log_metric_by_epoch (bool): Whether to output metric in validation step
            by epoch. It can be true when running in epoch based runner.
            If set to True, `after_val_epoch` will set `step` to self.epoch in
            `runner.visualizer.add_scalars`. Otherwise `step` will be
            self.iter. Defaults to True.
        backend_args (dict, optional): Arguments to instantiate the
            prefix of uri corresponding backend. Defaults to None.
            New in v0.2.0.

    Examples:
        >>> # The simplest LoggerHook config.
        >>> logger_hook_cfg = dict(interval=20)
    """
    priority = 'BELOW_NORMAL'

    def __init__(self,
                 interval: int = 10,
                 ignore_last: bool = True,
                 interval_exp_name: int = 1000,
                 out_dir: Optional[Union[str, Path]] = None,
                 out_suffix: SUFFIX_TYPE = ('.json', '.log', '.py', 'yaml'),
                 keep_local: bool = True,
                 file_client_args: Optional[dict] = None,
                 log_metric_by_epoch: bool = True,
                 backend_args: Optional[dict] = None):

        if not isinstance(interval, int):
            raise TypeError('interval must be an integer')
        if interval <= 0:
            raise ValueError('interval must be greater than 0')

        if not isinstance(ignore_last, bool):
            raise TypeError('ignore_last must be a boolean')

        if not isinstance(interval_exp_name, int):
            raise TypeError('interval_exp_name must be an integer')
        if interval_exp_name <= 0:
            raise ValueError('interval_exp_name must be greater than 0')

        if out_dir is not None and not isinstance(out_dir, (str, Path)):
            raise TypeError('out_dir must be a str or Path object')

        if not isinstance(keep_local, bool):
            raise TypeError('keep_local must be a boolean')

        if out_dir is None and file_client_args is not None:
            raise ValueError(
                'file_client_args should be "None" when `out_dir` is not'
                'specified.')

        if file_client_args is not None:
            print_log(
                '"file_client_args" will be deprecated in future. '
                'Please use "backend_args" instead',
                logger='current',
                level=logging.WARNING)
            if backend_args is not None:
                raise ValueError(
                    '"file_client_args" and "backend_args" cannot be set '
                    'at the same time.')

        if not (isinstance(out_suffix, str) or is_seq_of(out_suffix, str)):
            raise TypeError('out_suffix should be a string or a sequence of '
                            f'string, but got {type(out_suffix)}')

        self.out_suffix = out_suffix
        self.out_dir = out_dir
        self.interval = interval
        self.ignore_last = ignore_last
        self.interval_exp_name = interval_exp_name
        self.keep_local = keep_local
        self.file_client_args = file_client_args
        self.json_log_path: Optional[str] = None

        if self.out_dir is not None:
            self.file_client = FileClient.infer_client(file_client_args,
                                                       self.out_dir)
            if file_client_args is None:
                self.file_backend = get_file_backend(
                    self.out_dir, backend_args=backend_args)
            else:
                self.file_backend = self.file_client

        self.log_metric_by_epoch = log_metric_by_epoch

    def before_run(self, runner) -> None:
        """Infer ``self.file_client`` from ``self.out_dir``. Initialize the
        ``self.start_iter`` and record the meta information.

        Args:
            runner (Runner): The runner of the training process.
        """
        if self.out_dir is not None:
            # The final `self.out_dir` is the concatenation of `self.out_dir`
            # and the last level directory of `runner.work_dir`
            basename = osp.basename(runner.work_dir.rstrip(osp.sep))
            self.out_dir = self.file_backend.join_path(self.out_dir, basename)
            runner.logger.info(
                f'Text logs will be saved to {self.out_dir} after the '
                'training process.')

        self.json_log_path = f'{runner.timestamp}.json'

    def after_train_iter(self,
                         runner,
                         batch_idx: int,
                         data_batch: DATA_BATCH = None,
                         outputs: Optional[dict] = None) -> None:
        """Record logs after training iteration.

        Args:
            runner (Runner): The runner of the training process.
            batch_idx (int): The index of the current batch in the train loop.
            data_batch (dict tuple or list, optional): Data from dataloader.
            outputs (dict, optional): Outputs from model.
        """
        # Print experiment name every n iterations.
        if self.every_n_train_iters(
                runner, self.interval_exp_name) or (self.end_of_epoch(
                    runner.train_dataloader, batch_idx)):
            exp_info = f'Exp name: {runner.experiment_name}'
            runner.logger.info(exp_info)
        if self.every_n_inner_iters(batch_idx, self.interval):
            tag, log_str = runner.log_processor.get_log_after_iter(runner, batch_idx, 'train')
        elif (self.end_of_epoch(runner.train_dataloader, batch_idx)
              and (not self.ignore_last
                   or len(runner.train_dataloader) <= self.interval)):
            # `runner.max_iters` may not be divisible by `self.interval`. if
            # `self.ignore_last==True`, the log of remaining iterations will
            # be recorded (Epoch [4][1000/1007], the logs of 998-1007
            # iterations will be recorded).
            tag, log_str = runner.log_processor.get_log_after_iter(
                runner, batch_idx, 'train')
        else:
            return
        runner.logger.info(log_str)
        runner.visualizer.add_scalars(
            tag, step=runner.iter + 1, file_path=self.json_log_path)

    def after_val_iter(self,
                       runner,
                       batch_idx: int,
                       data_batch: DATA_BATCH = None,
                       outputs: Optional[Sequence] = None) -> None:
        """Record logs after validation iteration.

        Args:
            runner (Runner): The runner of the validation process.
            batch_idx (int): The index of the current batch in the validation
                loop.
            data_batch (dict or tuple or list, optional): Data from dataloader.
                Defaults to None.
            outputs (sequence, optional): Outputs from model.
        """
        if self.every_n_inner_iters(batch_idx, self.interval):
            _, log_str = runner.log_processor.get_log_after_iter(
                runner, batch_idx, 'val')
            runner.logger.info(log_str)

    def after_test_iter(self,
                        runner,
                        batch_idx: int,
                        data_batch: DATA_BATCH = None,
                        outputs: Optional[Sequence] = None) -> None:
        """Record logs after testing iteration.

        Args:
            runner (Runner): The runner of the testing process.
            batch_idx (int): The index of the current batch in the test loop.
            data_batch (dict or tuple or list, optional): Data from dataloader.
            outputs (sequence, optional): Outputs from model.
        """
        if self.every_n_inner_iters(batch_idx, self.interval):
            _, log_str = runner.log_processor.get_log_after_iter(
                runner, batch_idx, 'test')
            runner.logger.info(log_str)

    def after_val_epoch(self,
                        runner,
                        metrics: Optional[Dict[str, float]] = None) -> None:
        """All subclasses should override this method, if they need any
        operations after each validation epoch.

        Args:
            runner (Runner): The runner of the validation process.
            metrics (Dict[str, float], optional): Evaluation results of all
                metrics on validation dataset. The keys are the names of the
                metrics, and the values are corresponding results.
        """
        tag, log_str = runner.log_processor.get_log_after_epoch(
            runner, len(runner.val_dataloader), 'val')
        runner.logger.info(log_str)
        if self.log_metric_by_epoch:
            # Accessing the epoch attribute of the runner will trigger
            # the construction of the train_loop. Therefore, to avoid
            # triggering the construction of the train_loop during
            # validation, check before accessing the epoch.
            if (isinstance(runner._train_loop, dict)
                    or runner._train_loop is None):
                epoch = 0
            else:
                epoch = runner.epoch
            runner.visualizer.add_scalars(
                tag, step=epoch, file_path=self.json_log_path)
        else:
            if (isinstance(runner._train_loop, dict)
                    or runner._train_loop is None):
                iter = 0
            else:
                iter = runner.iter
            runner.visualizer.add_scalars(
                tag, step=iter, file_path=self.json_log_path)

    def after_test_epoch(self,
                         runner,
                         metrics: Optional[Dict[str, float]] = None) -> None:
        """All subclasses should override this method, if they need any
        operations after each test epoch.

        Args:
            runner (Runner): The runner of the testing process.
            metrics (Dict[str, float], optional): Evaluation results of all
                metrics on test dataset. The keys are the names of the
                metrics, and the values are corresponding results.
        """
        tag, log_str = runner.log_processor.get_log_after_epoch(
            runner, len(runner.test_dataloader), 'test', with_non_scalar=True)
        runner.logger.info(log_str)
        dump(
            self._process_tags(tag),
            osp.join(runner.log_dir, self.json_log_path))  # type: ignore

    @staticmethod
    def _process_tags(tags: dict):
        """Convert tag values to json-friendly type."""

        def process_val(value):
            if isinstance(value, (list, tuple)):
                # Array type of json
                return [process_val(item) for item in value]
            elif isinstance(value, dict):
                # Object type of json
                return {k: process_val(v) for k, v in value.items()}
            elif isinstance(value, (str, int, float, bool)) or value is None:
                # Other supported type of json
                return value
            elif isinstance(value, (torch.Tensor, np.ndarray)):
                return value.tolist()
            # Drop unsupported values.

        processed_tags = OrderedDict(process_val(tags))

        return processed_tags

    def after_run(self, runner) -> None:
        """Copy logs to ``self.out_dir`` if ``self.out_dir is not None``

        Args:
            runner (Runner): The runner of the training/testing/validation
                process.
        """
        # close the visualizer
        runner.visualizer.close()

        # copy or upload logs to self.out_dir
        if self.out_dir is None:
            return

        removed_files = []
        for filename in scandir(runner._log_dir, self.out_suffix, True):
            local_filepath = osp.join(runner._log_dir, filename)
            removed_files.append(local_filepath)
            out_filepath = self.file_backend.join_path(self.out_dir, filename)
            with open(local_filepath) as f:
                self.file_backend.put_text(f.read(), out_filepath)

            runner.logger.info(
                f'The file {local_filepath} has been uploaded to '
                f'{out_filepath}.')

            if not self.keep_local:
                runner.logger.info(f'{local_filepath} was removed due to the '
                                   '`self.keep_local=False`. You can check '
                                   f'the running logs in {out_filepath}')

        if not self.keep_local:
            # Close file handler to avoid PermissionError on Windows.
            for handler in runner.logger.handlers:
                if isinstance(handler, logging.FileHandler):
                    handler.close()

            for file in removed_files:
                os.remove(file)