Source code for detectron2.utils.events

# 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 fvcore.common.file_io import PathManager
from fvcore.common.history_buffer import HistoryBuffer

__all__ = [
    "get_event_storage",
    "JSONWriter",
    "TensorboardXWriter",
    "CommonMetricPrinter",
    "EventStorage",
]

_CURRENT_STORAGE_STACK = []


[docs]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
[docs]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 ... """
[docs] 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
[docs] 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
[docs] def close(self): self._file_handle.close()
[docs]class TensorboardXWriter(EventWriter): """ Write all scalars to a tensorboard file. """
[docs] 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
[docs] 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()
[docs] def close(self): if hasattr(self, "_writer"): # doesn't exist when the code fails at import self._writer.close()
[docs]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. """
[docs] 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
[docs] 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 = "{:.6f}".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( [ "{}: {:.3f}".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 "", ) )
[docs]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. """
[docs] 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 = []
[docs] 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))
[docs] 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
[docs] 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)
[docs] 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)
[docs] 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
[docs] def histories(self): """ Returns: dict[name -> HistoryBuffer]: the HistoryBuffer for all scalars """ return self._history
[docs] 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
[docs] 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
[docs] 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
[docs] 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()
[docs] @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
[docs] def clear_images(self): """ Delete all the stored images for visualization. This should be called after images are written to tensorboard. """ self._vis_data = []
[docs] def clear_histograms(self): """ Delete all the stored histograms for visualization. This should be called after histograms are written to tensorboard. """ self._histograms = []