text: # -*- coding: utf-8 -*-
  # Copyright (c) Facebook, Inc. and its affiliates.
  
  import datetime
  import itertools
  import logging
  import os
  import tempfile
  import time
  from collections import Counter
  import torch
  from fvcore.common.checkpoint import PeriodicCheckpointer as _PeriodicCheckpointer
  from fvcore.common.param_scheduler import ParamScheduler
  from fvcore.common.timer import Timer
  from fvcore.nn.precise_bn import get_bn_modules, update_bn_stats
  
  import detectron2.utils.comm as comm
  from detectron2.evaluation.testing import flatten_results_dict
  from detectron2.solver import LRMultiplier
  from detectron2.utils.events import EventStorage, EventWriter
  from detectron2.utils.file_io import PathManager
  
  from .train_loop import HookBase
  
  __all__ = [
      "CallbackHook",
      "IterationTimer",
      "PeriodicWriter",
      "PeriodicCheckpointer",
      "LRScheduler",
      "AutogradProfiler",
      "EvalHook",
      "PreciseBN",
  ]
  
  
  """
  Implement some common hooks.
  """
  
  
  class CallbackHook(HookBase):
      """
      Create a hook using callback functions provided by the user.
      """
  
      def __init__(self, *, before_train=None, after_train=None, before_step=None, after_step=None):
          """
          Each argument is a function that takes one argument: the trainer.
          """
          self._before_train = before_train
          self._before_step = before_step
          self._after_step = after_step
          self._after_train = after_train
  
      def before_train(self):
          if self._before_train:
              self._before_train(self.trainer)
  
      def after_train(self):
          if self._after_train:
              self._after_train(self.trainer)
          # The functions may be closures that hold reference to the trainer
          # Therefore, delete them to avoid circular reference.
          del self._before_train, self._after_train
          del self._before_step, self._after_step
  
      def before_step(self):
          if self._before_step:
              self._before_step(self.trainer)
  
      def after_step(self):
          if self._after_step:
              self._after_step(self.trainer)
  
  
  class IterationTimer(HookBase):
      """
      Track the time spent for each iteration (each run_step call in the trainer).
      Print a summary in the end of training.
  
      This hook uses the time between the call to its :meth:`before_step`
      and :meth:`after_step` methods.
      Under the convention that :meth:`before_step` of all hooks should only
      take negligible amount of time, the :class:`IterationTimer` hook should be
      placed at the beginning of the list of hooks to obtain accurate timing.
      """
  
      def __init__(self, warmup_iter=3):
          """
          Args:
              warmup_iter (int): the number of iterations at the beginning to exclude
                  from timing.
          """
          self._warmup_iter = warmup_iter
          self._step_timer = Timer()
          self._start_time = time.perf_counter()
          self._total_timer = Timer()
  
      def before_train(self):
          self._start_time = time.perf_counter()
          self._total_timer.reset()
          self._total_timer.pause()
  
      def after_train(self):
          logger = logging.getLogger(__name__)
          total_time = time.perf_counter() - self._start_time
          total_time_minus_hooks = self._total_timer.seconds()
          hook_time = total_time - total_time_minus_hooks
  
          num_iter = self.trainer.iter + 1 - self.trainer.start_iter - self._warmup_iter
  
          if num_iter > 0 and total_time_minus_hooks > 0:
              # Speed is meaningful only after warmup
              # NOTE this format is parsed by grep in some scripts
              logger.info(
                  "Overall training speed: {} iterations in {} ({:.4f} s / it)".format(
                      num_iter,
                      str(datetime.timedelta(seconds=int(total_time_minus_hooks))),
                      total_time_minus_hooks / num_iter,
                  )
              )
  
          logger.info(
              "Total training time: {} ({} on hooks)".format(
                  str(datetime.timedelta(seconds=int(total_time))),
                  str(datetime.timedelta(seconds=int(hook_time))),
              )
          )
  
      def before_step(self):
          self._step_timer.reset()
          self._total_timer.resume()
  
      def after_step(self):
          # +1 because we're in after_step, the current step is done
          # but not yet counted
          iter_done = self.trainer.iter - self.trainer.start_iter + 1
          if iter_done >= self._warmup_iter:
              sec = self._step_timer.seconds()
              self.trainer.storage.put_scalars(time=sec)
          else:
              self._start_time = time.perf_counter()
              self._total_timer.reset()
  
          self._total_timer.pause()
  
  
  class PeriodicWriter(HookBase):
      """
      Write events to EventStorage (by calling ``writer.write()``) periodically.
  
      It is executed every ``period`` iterations and after the last iteration.
      Note that ``period`` does not affect how data is smoothed by each writer.
      """
  
      def __init__(self, writers, period=20):
          """
          Args:
              writers (list[EventWriter]): a list of EventWriter objects
              period (int):
          """
          self._writers = writers
          for w in writers:
              assert isinstance(w, EventWriter), w
          self._period = period
  
      def after_step(self):
          if (self.trainer.iter + 1) % self._period == 0 or (
              self.trainer.iter == self.trainer.max_iter - 1
          ):
              for writer in self._writers:
                  writer.write()
  
      def after_train(self):
          for writer in self._writers:
              # If any new data is found (e.g. produced by other after_train),
              # write them before closing
              writer.write()
              writer.close()
  
  
  class PeriodicCheckpointer(_PeriodicCheckpointer, HookBase):
      """
      Same as :class:`detectron2.checkpoint.PeriodicCheckpointer`, but as a hook.
  
      Note that when used as a hook,
      it is unable to save additional data other than what's defined
      by the given `checkpointer`.
  
      It is executed every ``period`` iterations and after the last iteration.
      """
  
      def before_train(self):
          self.max_iter = self.trainer.max_iter
  
      def after_step(self):
          # No way to use **kwargs
          self.step(self.trainer.iter)
  
  
  class LRScheduler(HookBase):
      """
      A hook which executes a torch builtin LR scheduler and summarizes the LR.
      It is executed after every iteration.
      """
  
      def __init__(self, optimizer=None, scheduler=None):
          """
          Args:
              optimizer (torch.optim.Optimizer):
              scheduler (torch.optim.LRScheduler or fvcore.common.param_scheduler.ParamScheduler):
                  if a :class:`ParamScheduler` object, it defines the multiplier over the base LR
                  in the optimizer.
  
          If any argument is not given, will try to obtain it from the trainer.
          """
          self._optimizer = optimizer
          self._scheduler = scheduler
  
      def before_train(self):
          self._optimizer = self._optimizer or self.trainer.optimizer
          self._scheduler = self._scheduler or self.trainer.scheduler
          if isinstance(self._scheduler, ParamScheduler):
              self._scheduler = LRMultiplier(
                  self._optimizer,
                  self._scheduler,
                  self.trainer.max_iter,
                  last_iter=self.trainer.iter - 1,
              )
  
          # NOTE: some heuristics on what LR to summarize
          # summarize the param group with most parameters
          largest_group = max(len(g["params"]) for g in self._optimizer.param_groups)
  
          if largest_group == 1:
              # If all groups have one parameter,
              # then find the most common initial LR, and use it for summary
              lr_count = Counter([g["lr"] for g in self._optimizer.param_groups])
              lr = lr_count.most_common()[0][0]
              for i, g in enumerate(self._optimizer.param_groups):
                  if g["lr"] == lr:
                      self._best_param_group_id = i
                      break
          else:
              for i, g in enumerate(self._optimizer.param_groups):
                  if len(g["params"]) == largest_group:
                      self._best_param_group_id = i
                      break
  
      def after_step(self):
          lr = self._optimizer.param_groups[self._best_param_group_id]["lr"]
          self.trainer.storage.put_scalar("lr", lr, smoothing_hint=False)
          self._scheduler.step()
  
  
  class AutogradProfiler(HookBase):
      """
      A hook which runs `torch.autograd.profiler.profile`.
  
      Examples:
      ::
          hooks.AutogradProfiler(
               lambda trainer: trainer.iter > 10 and trainer.iter < 20, self.cfg.OUTPUT_DIR
          )
  
      The above example will run the profiler for iteration 10~20 and dump
      results to ``OUTPUT_DIR``. We did not profile the first few iterations
      because they are typically slower than the rest.
      The result files can be loaded in the ``chrome://tracing`` page in chrome browser.
  
      Note:
          When used together with NCCL on older version of GPUs,
          autograd profiler may cause deadlock because it unnecessarily allocates
          memory on every device it sees. The memory management calls, if
          interleaved with NCCL calls, lead to deadlock on GPUs that do not
          support ``cudaLaunchCooperativeKernelMultiDevice``.
      """
  
      def __init__(self, enable_predicate, output_dir, *, use_cuda=True):
          """
          Args:
              enable_predicate (callable[trainer -> bool]): a function which takes a trainer,
                  and returns whether to enable the profiler.
                  It will be called once every step, and can be used to select which steps to profile.
              output_dir (str): the output directory to dump tracing files.
              use_cuda (bool): same as in `torch.autograd.profiler.profile`.
          """
          self._enable_predicate = enable_predicate
          self._use_cuda = use_cuda
          self._output_dir = output_dir
  
      def before_step(self):
          if self._enable_predicate(self.trainer):
              self._profiler = torch.autograd.profiler.profile(use_cuda=self._use_cuda)
              self._profiler.__enter__()
          else:
              self._profiler = None
  
      def after_step(self):
          if self._profiler is None:
              return
          self._profiler.__exit__(None, None, None)
          PathManager.mkdirs(self._output_dir)
          out_file = os.path.join(
              self._output_dir, "profiler-trace-iter{}.json".format(self.trainer.iter)
          )
          if "://" not in out_file:
              self._profiler.export_chrome_trace(out_file)
          else:
              # Support non-posix filesystems
              with tempfile.TemporaryDirectory(prefix="detectron2_profiler") as d:
                  tmp_file = os.path.join(d, "tmp.json")
                  self._profiler.export_chrome_trace(tmp_file)
                  with open(tmp_file) as f:
                      content = f.read()
              with PathManager.open(out_file, "w") as f:
                  f.write(content)
  
  
  class EvalHook(HookBase):
      """
      Run an evaluation function periodically, and at the end of training.
  
      It is executed every ``eval_period`` iterations and after the last iteration.
      """
  
      def __init__(self, eval_period, eval_function):
          """
          Args:
              eval_period (int): the period to run `eval_function`. Set to 0 to
                  not evaluate periodically (but still after the last iteration).
              eval_function (callable): a function which takes no arguments, and
                  returns a nested dict of evaluation metrics.
  
          Note:
              This hook must be enabled in all or none workers.
              If you would like only certain workers to perform evaluation,
              give other workers a no-op function (`eval_function=lambda: None`).
          """
          self._period = eval_period
          self._func = eval_function
  
      def _do_eval(self):
          results = self._func()
  
          if results:
              assert isinstance(
                  results, dict
              ), "Eval function must return a dict. Got {} instead.".format(results)
  
              flattened_results = flatten_results_dict(results)
              for k, v in flattened_results.items():
                  try:
                      v = float(v)
                  except Exception as e:
                      raise ValueError(
                          "[EvalHook] eval_function should return a nested dict of float. "
                          "Got '{}: {}' instead.".format(k, v)
                      ) from e
              self.trainer.storage.put_scalars(**flattened_results, smoothing_hint=False)
  
          # Evaluation may take different time among workers.
          # A barrier make them start the next iteration together.
          comm.synchronize()
  
      def after_step(self):
          next_iter = self.trainer.iter + 1
          if self._period > 0 and next_iter % self._period == 0:
              self._do_eval()
  
      def after_train(self):
          # This condition is to prevent the eval from running after a failed training
          if self.trainer.iter + 1 >= self.trainer.max_iter:
              self._do_eval()
          # func is likely a closure that holds reference to the trainer
          # therefore we clean it to avoid circular reference in the end
          del self._func
  
  
  class PreciseBN(HookBase):
      """
      The standard implementation of BatchNorm uses EMA in inference, which is
      sometimes suboptimal.
      This class computes the true average of statistics rather than the moving average,
      and put true averages to every BN layer in the given model.
  
      It is executed every ``period`` iterations and after the last iteration.
      """
  
      def __init__(self, period, model, data_loader, num_iter):
          """
          Args:
              period (int): the period this hook is run, or 0 to not run during training.
                  The hook will always run in the end of training.
              model (nn.Module): a module whose all BN layers in training mode will be
                  updated by precise BN.
                  Note that user is responsible for ensuring the BN layers to be
                  updated are in training mode when this hook is triggered.
              data_loader (iterable): it will produce data to be run by `model(data)`.
              num_iter (int): number of iterations used to compute the precise
                  statistics.
          """
          self._logger = logging.getLogger(__name__)
          if len(get_bn_modules(model)) == 0:
              self._logger.info(
                  "PreciseBN is disabled because model does not contain BN layers in training mode."
              )
              self._disabled = True
              return
  
          self._model = model
          self._data_loader = data_loader
          self._num_iter = num_iter
          self._period = period
          self._disabled = False
  
          self._data_iter = None
  
      def after_step(self):
          next_iter = self.trainer.iter + 1
          is_final = next_iter == self.trainer.max_iter
          if is_final or (self._period > 0 and next_iter % self._period == 0):
              self.update_stats()
  
      def update_stats(self):
          """
          Update the model with precise statistics. Users can manually call this method.
          """
          if self._disabled:
              return
  
          if self._data_iter is None:
              self._data_iter = iter(self._data_loader)
  
          def data_loader():
              for num_iter in itertools.count(1):
                  if num_iter % 100 == 0:
                      self._logger.info(
                          "Running precise-BN ... {}/{} iterations.".format(num_iter, self._num_iter)
                      )
                  # This way we can reuse the same iterator
                  yield next(self._data_iter)
  
          with EventStorage():  # capture events in a new storage to discard them
              self._logger.info(
                  "Running precise-BN for {} iterations...  ".format(self._num_iter)
                  + "Note that this could produce different statistics every time."
              )
              update_bn_stats(self._model, data_loader(), self._num_iter)
  