events.py

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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import datetime
import json
import logging
import os
import time
from collections import defaultdict
from contextlib import contextmanager
import torch
from .file_io import PathManager
from .history_buffer import HistoryBuffer
 
__all__ = [
    "get_event_storage",
    "JSONWriter",
    "TensorboardXWriter",
    "CommonMetricPrinter",
    "EventStorage",
]
 
_CURRENT_STORAGE_STACK = []
 
 
def get_event_storage():
    """
    Returns:
        The :class:`EventStorage` object that's currently being used.
        Throws an error if no :class:`EventStorage` is currently enabled.
    """
    assert len(
        _CURRENT_STORAGE_STACK
    ), "get_event_storage() has to be called inside a 'with EventStorage(...)' context!"
    return _CURRENT_STORAGE_STACK[-1]
 
 
class EventWriter:
    """
    Base class for writers that obtain events from :class:`EventStorage` and process them.
    """
 
    def write(self):
        raise NotImplementedError
 
    def close(self):
        pass
 
 
class JSONWriter(EventWriter):
    """
    Write scalars to a json file.
    It saves scalars as one json per line (instead of a big json) for easy parsing.
    Examples parsing such a json file:
    ::
        $ cat metrics.json | jq -s '.[0:2]'
        [
          {
            "data_time": 0.008433341979980469,
            "iteration": 20,
            "loss": 1.9228371381759644,
            "loss_box_reg": 0.050025828182697296,
            "loss_classifier": 0.5316952466964722,
            "loss_mask": 0.7236229181289673,
            "loss_rpn_box": 0.0856662318110466,
            "loss_rpn_cls": 0.48198649287223816,
            "lr": 0.007173333333333333,
            "time": 0.25401854515075684
          },
          {
            "data_time": 0.007216215133666992,
            "iteration": 40,
            "loss": 1.282649278640747,
            "loss_box_reg": 0.06222952902317047,
            "loss_classifier": 0.30682939291000366,
            "loss_mask": 0.6970193982124329,
            "loss_rpn_box": 0.038663312792778015,
            "loss_rpn_cls": 0.1471673548221588,
            "lr": 0.007706666666666667,
            "time": 0.2490077018737793
          }
        ]
        $ cat metrics.json | jq '.loss_mask'
        0.7126231789588928
        0.689423680305481
        0.6776131987571716
        ...
    """
 
    def __init__(self, json_file, window_size=20):
        """
        Args:
            json_file (str): path to the json file. New data will be appended if the file exists.
            window_size (int): the window size of median smoothing for the scalars whose
                `smoothing_hint` are True.
        """
        self._file_handle = PathManager.open(json_file, "a")
        self._window_size = window_size
        self._last_write = -1
 
    def write(self):
        storage = get_event_storage()
        to_save = defaultdict(dict)
 
        for k, (v, iter) in storage.latest_with_smoothing_hint(self._window_size).items():
            # keep scalars that have not been written
            if iter <= self._last_write:
                continue
            to_save[iter][k] = v
        all_iters = sorted(to_save.keys())
        self._last_write = max(all_iters)
 
        for itr, scalars_per_iter in to_save.items():
            scalars_per_iter["iteration"] = itr
            self._file_handle.write(json.dumps(scalars_per_iter, sort_keys=True) + "\n")
        self._file_handle.flush()
        try:
            os.fsync(self._file_handle.fileno())
        except AttributeError:
            pass
 
    def close(self):
        self._file_handle.close()
 
 
class TensorboardXWriter(EventWriter):
    """
    Write all scalars to a tensorboard file.
    """
 
    def __init__(self, log_dir: str, window_size: int = 20, **kwargs):
        """
        Args:
            log_dir (str): the directory to save the output events
            window_size (int): the scalars will be median-smoothed by this window size
            kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)`
        """
        self._window_size = window_size
        from torch.utils.tensorboard import SummaryWriter
 
        self._writer = SummaryWriter(log_dir, **kwargs)
        self._last_write = -1
 
    def write(self):
        storage = get_event_storage()
        new_last_write = self._last_write
        for k, (v, iter) in storage.latest_with_smoothing_hint(self._window_size).items():
            if iter > self._last_write:
                self._writer.add_scalar(k, v, iter)
                new_last_write = max(new_last_write, iter)
        self._last_write = new_last_write
 
        # storage.put_{image,histogram} is only meant to be used by
        # tensorboard writer. So we access its internal fields directly from here.
        if len(storage._vis_data) >= 1:
            for img_name, img, step_num in storage._vis_data:
                self._writer.add_image(img_name, img, step_num)
            # Storage stores all image data and rely on this writer to clear them.
            # As a result it assumes only one writer will use its image data.
            # An alternative design is to let storage store limited recent
            # data (e.g. only the most recent image) that all writers can access.
            # In that case a writer may not see all image data if its period is long.
            storage.clear_images()
 
        if len(storage._histograms) >= 1:
            for params in storage._histograms:
                self._writer.add_histogram_raw(**params)
            storage.clear_histograms()
 
    def close(self):
        if hasattr(self, "_writer"):  # doesn't exist when the code fails at import
            self._writer.close()
 
 
class CommonMetricPrinter(EventWriter):
    """
    Print **common** metrics to the terminal, including
    iteration time, ETA, memory, all losses, and the learning rate.
    It also applies smoothing using a window of 20 elements.
    It's meant to print common metrics in common ways.
    To print something in more customized ways, please implement a similar printer by yourself.
    """
 
    def __init__(self, max_iter):
        """
        Args:
            max_iter (int): the maximum number of iterations to train.
                Used to compute ETA.
        """
        self.logger = logging.getLogger(__name__)
        self._max_iter = max_iter
        self._last_write = None
 
    def write(self):
        storage = get_event_storage()
        iteration = storage.iter
 
        try:
            data_time = storage.history("data_time").avg(20)
        except KeyError:
            # they may not exist in the first few iterations (due to warmup)
            # or when SimpleTrainer is not used
            data_time = None
 
        eta_string = None
        try:
            iter_time = storage.history("time").global_avg()
            eta_seconds = storage.history("time").median(1000) * (self._max_iter - iteration)
            storage.put_scalar("eta_seconds", eta_seconds, smoothing_hint=False)
            eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
        except KeyError:
            iter_time = None
            # estimate eta on our own - more noisy
            if self._last_write is not None:
                estimate_iter_time = (time.perf_counter() - self._last_write[1]) / (
                    iteration - self._last_write[0]
                )
                eta_seconds = estimate_iter_time * (self._max_iter - iteration)
                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
            self._last_write = (iteration, time.perf_counter())
 
        try:
            lr = "{:.2e}".format(storage.history("lr").latest())
        except KeyError:
            lr = "N/A"
 
        if torch.cuda.is_available():
            max_mem_mb = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0
        else:
            max_mem_mb = None
 
        # NOTE: max_mem is parsed by grep in "dev/parse_results.sh"
        self.logger.info(
            " {eta}iter: {iter}  {losses}  {time}{data_time}lr: {lr}  {memory}".format(
                eta=f"eta: {eta_string}  " if eta_string else "",
                iter=iteration,
                losses="  ".join(
                    [
                        "{}: {:.4g}".format(k, v.median(20))
                        for k, v in storage.histories().items()
                        if "loss" in k
                    ]
                ),
                time="time: {:.4f}  ".format(iter_time) if iter_time is not None else "",
                data_time="data_time: {:.4f}  ".format(data_time) if data_time is not None else "",
                lr=lr,
                memory="max_mem: {:.0f}M".format(max_mem_mb) if max_mem_mb is not None else "",
            )
        )
 
 
class EventStorage:
    """
    The user-facing class that provides metric storage functionalities.
    In the future we may add support for storing / logging other types of data if needed.
    """
 
    def __init__(self, start_iter=0):
        """
        Args:
            start_iter (int): the iteration number to start with
        """
        self._history = defaultdict(HistoryBuffer)
        self._smoothing_hints = {}
        self._latest_scalars = {}
        self._iter = start_iter
        self._current_prefix = ""
        self._vis_data = []
        self._histograms = []
 
    def put_image(self, img_name, img_tensor):
        """
        Add an `img_tensor` associated with `img_name`, to be shown on
        tensorboard.
        Args:
            img_name (str): The name of the image to put into tensorboard.
            img_tensor (torch.Tensor or numpy.array): An `uint8` or `float`
                Tensor of shape `[channel, height, width]` where `channel` is
                3. The image format should be RGB. The elements in img_tensor
                can either have values in [0, 1] (float32) or [0, 255] (uint8).
                The `img_tensor` will be visualized in tensorboard.
        """
        self._vis_data.append((img_name, img_tensor, self._iter))
 
    def put_scalar(self, name, value, smoothing_hint=True):
        """
        Add a scalar `value` to the `HistoryBuffer` associated with `name`.
        Args:
            smoothing_hint (bool): a 'hint' on whether this scalar is noisy and should be
                smoothed when logged. The hint will be accessible through
                :meth:`EventStorage.smoothing_hints`.  A writer may ignore the hint
                and apply custom smoothing rule.
                It defaults to True because most scalars we save need to be smoothed to
                provide any useful signal.
        """
        name = self._current_prefix + name
        history = self._history[name]
        value = float(value)
        history.update(value, self._iter)
        self._latest_scalars[name] = (value, self._iter)
 
        existing_hint = self._smoothing_hints.get(name)
        if existing_hint is not None:
            assert (
                existing_hint == smoothing_hint
            ), "Scalar {} was put with a different smoothing_hint!".format(name)
        else:
            self._smoothing_hints[name] = smoothing_hint
 
    def put_scalars(self, *, smoothing_hint=True, **kwargs):
        """
        Put multiple scalars from keyword arguments.
        Examples:
            storage.put_scalars(loss=my_loss, accuracy=my_accuracy, smoothing_hint=True)
        """
        for k, v in kwargs.items():
            self.put_scalar(k, v, smoothing_hint=smoothing_hint)
 
    def put_histogram(self, hist_name, hist_tensor, bins=1000):
        """
        Create a histogram from a tensor.
        Args:
            hist_name (str): The name of the histogram to put into tensorboard.
            hist_tensor (torch.Tensor): A Tensor of arbitrary shape to be converted
                into a histogram.
            bins (int): Number of histogram bins.
        """
        ht_min, ht_max = hist_tensor.min().item(), hist_tensor.max().item()
 
        # Create a histogram with PyTorch
        hist_counts = torch.histc(hist_tensor, bins=bins)
        hist_edges = torch.linspace(start=ht_min, end=ht_max, steps=bins + 1, dtype=torch.float32)
 
        # Parameter for the add_histogram_raw function of SummaryWriter
        hist_params = dict(
            tag=hist_name,
            min=ht_min,
            max=ht_max,
            num=len(hist_tensor),
            sum=float(hist_tensor.sum()),
            sum_squares=float(torch.sum(hist_tensor ** 2)),
            bucket_limits=hist_edges[1:].tolist(),
            bucket_counts=hist_counts.tolist(),
            global_step=self._iter,
        )
        self._histograms.append(hist_params)
 
    def history(self, name):
        """
        Returns:
            HistoryBuffer: the scalar history for name
        """
        ret = self._history.get(name, None)
        if ret is None:
            raise KeyError("No history metric available for {}!".format(name))
        return ret
 
    def histories(self):
        """
        Returns:
            dict[name -> HistoryBuffer]: the HistoryBuffer for all scalars
        """
        return self._history
 
    def latest(self):
        """
        Returns:
            dict[str -> (float, int)]: mapping from the name of each scalar to the most
                recent value and the iteration number its added.
        """
        return self._latest_scalars
 
    def latest_with_smoothing_hint(self, window_size=20):
        """
        Similar to :meth:`latest`, but the returned values
        are either the un-smoothed original latest value,
        or a median of the given window_size,
        depend on whether the smoothing_hint is True.
        This provides a default behavior that other writers can use.
        """
        result = {}
        for k, (v, itr) in self._latest_scalars.items():
            result[k] = (
                self._history[k].median(window_size) if self._smoothing_hints[k] else v,
                itr,
            )
        return result
 
    def smoothing_hints(self):
        """
        Returns:
            dict[name -> bool]: the user-provided hint on whether the scalar
                is noisy and needs smoothing.
        """
        return self._smoothing_hints
 
    def step(self):
        """
        User should call this function at the beginning of each iteration, to
        notify the storage of the start of a new iteration.
        The storage will then be able to associate the new data with the
        correct iteration number.
        """
        self._iter += 1
 
    @property
    def iter(self):
        return self._iter
 
    @property
    def iteration(self):
        # for backward compatibility
        return self._iter
 
    def __enter__(self):
        _CURRENT_STORAGE_STACK.append(self)
        return self
 
    def __exit__(self, exc_type, exc_val, exc_tb):
        assert _CURRENT_STORAGE_STACK[-1] == self
        _CURRENT_STORAGE_STACK.pop()
 
    @contextmanager
    def name_scope(self, name):
        """
        Yields:
            A context within which all the events added to this storage
            will be prefixed by the name scope.
        """
        old_prefix = self._current_prefix
        self._current_prefix = name.rstrip("/") + "/"
        yield
        self._current_prefix = old_prefix
 
    def clear_images(self):
        """
        Delete all the stored images for visualization. This should be called
        after images are written to tensorboard.
        """
        self._vis_data = []
 
    def clear_histograms(self):
        """
        Delete all the stored histograms for visualization.
        This should be called after histograms are written to tensorboard.
        """
        self._histograms = []