# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import datetime
import itertools
import logging
import math
import operator
import os
import tempfile
import time
import warnings
from collections import Counter
import torch
from fvcore.common.checkpoint import Checkpointer
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 annotator.oneformer.detectron2.utils.comm as comm
from annotator.oneformer.detectron2.evaluation.testing import flatten_results_dict
from annotator.oneformer.detectron2.solver import LRMultiplier
from annotator.oneformer.detectron2.solver import LRScheduler as _LRScheduler
from annotator.oneformer.detectron2.utils.events import EventStorage, EventWriter
from annotator.oneformer.detectron2.utils.file_io import PathManager
from .train_loop import HookBase
__all__ = [
"CallbackHook",
"IterationTimer",
"PeriodicWriter",
"PeriodicCheckpointer",
"BestCheckpointer",
"LRScheduler",
"AutogradProfiler",
"EvalHook",
"PreciseBN",
"TorchProfiler",
"TorchMemoryStats",
]
"""
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.storage.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.storage.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 BestCheckpointer(HookBase):
"""
Checkpoints best weights based off given metric.
This hook should be used in conjunction to and executed after the hook
that produces the metric, e.g. `EvalHook`.
"""
def __init__(
self,
eval_period: int,
checkpointer: Checkpointer,
val_metric: str,
mode: str = "max",
file_prefix: str = "model_best",
) -> None:
"""
Args:
eval_period (int): the period `EvalHook` is set to run.
checkpointer: the checkpointer object used to save checkpoints.
val_metric (str): validation metric to track for best checkpoint, e.g. "bbox/AP50"
mode (str): one of {'max', 'min'}. controls whether the chosen val metric should be
maximized or minimized, e.g. for "bbox/AP50" it should be "max"
file_prefix (str): the prefix of checkpoint's filename, defaults to "model_best"
"""
self._logger = logging.getLogger(__name__)
self._period = eval_period
self._val_metric = val_metric
assert mode in [
"max",
"min",
], f'Mode "{mode}" to `BestCheckpointer` is unknown. It should be one of {"max", "min"}.'
if mode == "max":
self._compare = operator.gt
else:
self._compare = operator.lt
self._checkpointer = checkpointer
self._file_prefix = file_prefix
self.best_metric = None
self.best_iter = None
def _update_best(self, val, iteration):
if math.isnan(val) or math.isinf(val):
return False
self.best_metric = val
self.best_iter = iteration
return True
def _best_checking(self):
metric_tuple = self.trainer.storage.latest().get(self._val_metric)
if metric_tuple is None:
self._logger.warning(
f"Given val metric {self._val_metric} does not seem to be computed/stored."
"Will not be checkpointing based on it."
)
return
else:
latest_metric, metric_iter = metric_tuple
if self.best_metric is None:
if self._update_best(latest_metric, metric_iter):
additional_state = {"iteration": metric_iter}
self._checkpointer.save(f"{self._file_prefix}", **additional_state)
self._logger.info(
f"Saved first model at {self.best_metric:0.5f} @ {self.best_iter} steps"
)
elif self._compare(latest_metric, self.best_metric):
additional_state = {"iteration": metric_iter}
self._checkpointer.save(f"{self._file_prefix}", **additional_state)
self._logger.info(
f"Saved best model as latest eval score for {self._val_metric} is "
f"{latest_metric:0.5f}, better than last best score "
f"{self.best_metric:0.5f} @ iteration {self.best_iter}."
)
self._update_best(latest_metric, metric_iter)
else:
self._logger.info(
f"Not saving as latest eval score for {self._val_metric} is {latest_metric:0.5f}, "
f"not better than best score {self.best_metric:0.5f} @ iteration {self.best_iter}."
)
def after_step(self):
# same conditions as `EvalHook`
next_iter = self.trainer.iter + 1
if (
self._period > 0
and next_iter % self._period == 0
and next_iter != self.trainer.max_iter
):
self._best_checking()
def after_train(self):
# same conditions as `EvalHook`
if self.trainer.iter + 1 >= self.trainer.max_iter:
self._best_checking()
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
if isinstance(self.scheduler, ParamScheduler):
self._scheduler = LRMultiplier(
self._optimizer,
self.scheduler,
self.trainer.max_iter,
last_iter=self.trainer.iter - 1,
)
self._best_param_group_id = LRScheduler.get_best_param_group_id(self._optimizer)
@staticmethod
def get_best_param_group_id(optimizer):
# NOTE: some heuristics on what LR to summarize
# summarize the param group with most parameters
largest_group = max(len(g["params"]) for g in 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 optimizer.param_groups])
lr = lr_count.most_common()[0][0]
for i, g in enumerate(optimizer.param_groups):
if g["lr"] == lr:
return i
else:
for i, g in enumerate(optimizer.param_groups):
if len(g["params"]) == largest_group:
return i
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()
@property
def scheduler(self):
return self._scheduler or self.trainer.scheduler
def state_dict(self):
if isinstance(self.scheduler, _LRScheduler):
return self.scheduler.state_dict()
return {}
def load_state_dict(self, state_dict):
if isinstance(self.scheduler, _LRScheduler):
logger = logging.getLogger(__name__)
logger.info("Loading scheduler from state_dict ...")
self.scheduler.load_state_dict(state_dict)
class TorchProfiler(HookBase):
"""
A hook which runs `torch.profiler.profile`.
Examples:
::
hooks.TorchProfiler(
lambda trainer: 10 < 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,
and the tensorboard visualizations can be visualized using
``tensorboard --logdir OUTPUT_DIR/log``
"""
def __init__(self, enable_predicate, output_dir, *, activities=None, save_tensorboard=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.
activities (iterable): same as in `torch.profiler.profile`.
save_tensorboard (bool): whether to save tensorboard visualizations at (output_dir)/log/
"""
self._enable_predicate = enable_predicate
self._activities = activities
self._output_dir = output_dir
self._save_tensorboard = save_tensorboard
def before_step(self):
if self._enable_predicate(self.trainer):
if self._save_tensorboard:
on_trace_ready = torch.profiler.tensorboard_trace_handler(
os.path.join(
self._output_dir,
"log",
"profiler-tensorboard-iter{}".format(self.trainer.iter),
),
f"worker{comm.get_rank()}",
)
else:
on_trace_ready = None
self._profiler = torch.profiler.profile(
activities=self._activities,
on_trace_ready=on_trace_ready,
record_shapes=True,
profile_memory=True,
with_stack=True,
with_flops=True,
)
self._profiler.__enter__()
else:
self._profiler = None
def after_step(self):
if self._profiler is None:
return
self._profiler.__exit__(None, None, None)
if not self._save_tensorboard:
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 AutogradProfiler(TorchProfiler):
"""
A hook which runs `torch.autograd.profiler.profile`.
Examples:
::
hooks.AutogradProfiler(
lambda trainer: 10 < 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`.
"""
warnings.warn("AutogradProfiler has been deprecated in favor of TorchProfiler.")
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
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, eval_after_train=True):
"""
Args:
eval_period (int): the period to run `eval_function`. Set to 0 to
not evaluate periodically (but still evaluate after the last iteration
if `eval_after_train` is True).
eval_function (callable): a function which takes no arguments, and
returns a nested dict of evaluation metrics.
eval_after_train (bool): whether to evaluate after the last iteration
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
self._eval_after_train = eval_after_train
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:
# do the last eval in after_train
if next_iter != self.trainer.max_iter:
self._do_eval()
def after_train(self):
# This condition is to prevent the eval from running after a failed training
if self._eval_after_train and 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)
class TorchMemoryStats(HookBase):
"""
Writes pytorch's cuda memory statistics periodically.
"""
def __init__(self, period=20, max_runs=10):
"""
Args:
period (int): Output stats each 'period' iterations
max_runs (int): Stop the logging after 'max_runs'
"""
self._logger = logging.getLogger(__name__)
self._period = period
self._max_runs = max_runs
self._runs = 0
def after_step(self):
if self._runs > self._max_runs:
return
if (self.trainer.iter + 1) % self._period == 0 or (
self.trainer.iter == self.trainer.max_iter - 1
):
if torch.cuda.is_available():
max_reserved_mb = torch.cuda.max_memory_reserved() / 1024.0 / 1024.0
reserved_mb = torch.cuda.memory_reserved() / 1024.0 / 1024.0
max_allocated_mb = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0
allocated_mb = torch.cuda.memory_allocated() / 1024.0 / 1024.0
self._logger.info(
(
" iter: {} "
" max_reserved_mem: {:.0f}MB "
" reserved_mem: {:.0f}MB "
" max_allocated_mem: {:.0f}MB "
" allocated_mem: {:.0f}MB "
).format(
self.trainer.iter,
max_reserved_mb,
reserved_mb,
max_allocated_mb,
allocated_mb,
)
)
self._runs += 1
if self._runs == self._max_runs:
mem_summary = torch.cuda.memory_summary()
self._logger.info("\n" + mem_summary)
torch.cuda.reset_peak_memory_stats()