def build_hooks(self):
"""
Build a list of default hooks, including timing, evaluation,
checkpointing, lr scheduling, precise BN, writing events.
Returns:
list[HookBase]:
"""
cfg = self.cfg.clone()
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
ret = [
hooks.IterationTimer(),
]
# Do PreciseBN before checkpointer, because it updates the model and need to
# be saved by checkpointer.
# This is not always the best: if checkpointing has a different frequency,
# some checkpoints may have more precise statistics than others.
def test_and_save_results():
self._last_eval_results = self.test(self.cfg, self.model)
return self._last_eval_results
# Do evaluation after checkpointer, because then if it fails,
# we can use the saved checkpoint to debug.
return ret
def benchmark_train(args):
cfg = setup(args)
model = build_model(cfg)
logger.info("Model:\n{}".format(model))
if comm.get_world_size() > 1:
model = DistributedDataParallel(
model, device_ids=[comm.get_local_rank()], broadcast_buffers=False
)
optimizer = build_optimizer(cfg, model)
checkpointer = DetectionCheckpointer(model, optimizer=optimizer)
checkpointer.load(cfg.MODEL.WEIGHTS)
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0
data_loader = build_detection_train_loader(cfg)
dummy_data = list(itertools.islice(data_loader, 100))
def f():
while True:
yield from DatasetFromList(dummy_data, copy=False)
max_iter = 400
trainer = SimpleTrainer(model, f(), optimizer)
trainer.register_hooks(
[hooks.IterationTimer(), hooks.PeriodicWriter([CommonMetricPrinter(max_iter)])]
)
trainer.train(1, max_iter)
def do_train(cfg):
model = instantiate(cfg.model)
logger = logging.getLogger("detectron2")
logger.info("Model:\n{}".format(model))
model.to(cfg.train.device)
cfg.optimizer.params.model = model
optim = instantiate(cfg.optimizer)
train_loader = instantiate(cfg.dataloader.train)
model = create_ddp_model(model, **cfg.train.ddp)
trainer = (AMPTrainer if cfg.train.amp.enabled else SimpleTrainer)(
model, train_loader, optim)
checkpointer = DetectionCheckpointer(
model,
cfg.train.output_dir,
optimizer=optim,
trainer=trainer,
)
trainer.register_hooks([
hooks.IterationTimer(),
hooks.LRScheduler(scheduler=instantiate(cfg.lr_multiplier)),
hooks.PeriodicCheckpointer(checkpointer, **cfg.train.checkpointer)
if comm.is_main_process() else None,
hooks.EvalHook(cfg.train.eval_period, lambda: do_test(cfg, model)),
hooks.PeriodicWriter(
default_writers(cfg.train.output_dir, cfg.train.max_iter),
period=cfg.train.log_period,
) if comm.is_main_process() else None,
])
checkpointer.resume_or_load(cfg.train.init_checkpoint, resume=True)
start_iter = 0
trainer.train(start_iter, cfg.train.max_iter)
def benchmark_train(args):
cfg = setup(args)
model = build_model(cfg)
logger.info("Model:\n{}".format(model))
if comm.get_world_size() > 1:
model = DistributedDataParallel(model,
device_ids=[comm.get_local_rank()],
broadcast_buffers=False)
optimizer = build_optimizer(cfg, model)
checkpointer = DetectionCheckpointer(model, optimizer=optimizer)
checkpointer.load(cfg.MODEL.WEIGHTS)
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 2
data_loader = build_detection_train_loader(cfg)
dummy_data = list(itertools.islice(data_loader, 100))
def f():
data = DatasetFromList(dummy_data, copy=False, serialize=False)
while True:
yield from data
max_iter = 400
trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)(
model, f(), optimizer)
trainer.register_hooks([
hooks.IterationTimer(),
hooks.PeriodicWriter([CommonMetricPrinter(max_iter)]),
hooks.TorchProfiler(lambda trainer: trainer.iter == max_iter - 1,
cfg.OUTPUT_DIR,
save_tensorboard=True),
])
trainer.train(1, max_iter)
def build_hooks(self):
r"""Build hooks
We use: timing, lr scheduling, checkpointing, lr scheduling, ValidationLoss, writing events
"""
cfg = self.cfg.clone()
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
ret = [
hooks.IterationTimer(),
hooks.LRScheduler(self.optimizer, self.scheduler),
]
# Do PreciseBN before checkpointer, because it updates the model and need to
# be saved by checkpointer.
# This is not always the best: if checkpointing has a different frequency,
# some checkpoints may have more precise statistics than others.
if comm.is_main_process():
ret.append(
hooks.PeriodicCheckpointer(self.checkpointer,
cfg.SOLVER.CHECKPOINT_PERIOD))
ret.append(ValidationLoss(cfg, VAL_TRANSF, cfg.VAL_LOG_PERIOD))
if comm.is_main_process():
# run writers in the end, so that evaluation metrics are written
ret.append(
hooks.PeriodicWriter(self.build_writers(),
period=cfg.VAL_LOG_PERIOD))
return ret
def do_train(args, cfg):
"""
Args:
cfg: an object with the following attributes:
model: instantiate to a module
dataloader.{train,test}: instantiate to dataloaders
dataloader.evaluator: instantiate to evaluator for test set
optimizer: instantaite to an optimizer
lr_multiplier: instantiate to a fvcore scheduler
train: other misc config defined in `configs/common/train.py`, including:
output_dir (str)
init_checkpoint (str)
amp.enabled (bool)
max_iter (int)
eval_period, log_period (int)
device (str)
checkpointer (dict)
ddp (dict)
"""
model = instantiate(cfg.model)
logger = logging.getLogger("detectron2")
logger.info("Model:\n{}".format(model))
model.to(cfg.train.device)
cfg.optimizer.params.model = model
optim = instantiate(cfg.optimizer)
train_loader = instantiate(cfg.dataloader.train)
model = create_ddp_model(model, **cfg.train.ddp)
trainer = (AMPTrainer if cfg.train.amp.enabled else SimpleTrainer)(
model, train_loader, optim)
checkpointer = DetectionCheckpointer(
model,
cfg.train.output_dir,
optimizer=optim,
trainer=trainer,
)
trainer.register_hooks([
hooks.IterationTimer(),
hooks.LRScheduler(scheduler=instantiate(cfg.lr_multiplier)),
hooks.PeriodicCheckpointer(checkpointer, **cfg.train.checkpointer)
if comm.is_main_process() else None,
hooks.EvalHook(cfg.train.eval_period, lambda: do_test(cfg, model)),
hooks.PeriodicWriter(
default_writers(cfg.train.output_dir, cfg.train.max_iter),
period=cfg.train.log_period,
) if comm.is_main_process() else None,
])
checkpointer.resume_or_load(cfg.train.init_checkpoint, resume=args.resume)
if args.resume and checkpointer.has_checkpoint():
# The checkpoint stores the training iteration that just finished, thus we start
# at the next iteration
start_iter = trainer.iter + 1
else:
start_iter = 0
trainer.train(start_iter, cfg.train.max_iter)
def build_hooks(self):
"""
Build a list of default hooks, including timing, evaluation,
checkpointing, lr scheduling, precise BN, writing events.
Returns:
list[HookBase]:
"""
logger = logging.getLogger(__name__)
cfg = self.cfg.clone()
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
ret = [
hooks.IterationTimer(),
hooks.LRScheduler(self.optimizer, self.scheduler),
]
if cfg.SOLVER.SWA.ENABLED:
ret.append(
additional_hooks.SWA(
cfg.SOLVER.MAX_ITER,
cfg.SOLVER.SWA.PERIOD,
cfg.SOLVER.SWA.LR_START,
cfg.SOLVER.SWA.ETA_MIN_LR,
cfg.SOLVER.SWA.LR_SCHED,
)
)
if cfg.TEST.PRECISE_BN.ENABLED and hooks.get_bn_modules(self.model):
logger.info("Prepare precise BN dataset")
ret.append(hooks.PreciseBN(
# Run at the same freq as (but before) evaluation.
cfg.TEST.EVAL_PERIOD,
self.model,
# Build a new data loader to not affect training
self.build_train_loader(cfg),
cfg.TEST.PRECISE_BN.NUM_ITER,
))
# Do PreciseBN before checkpointer, because it updates the model and need to
# be saved by checkpointer.
# This is not always the best: if checkpointing has a different frequency,
# some checkpoints may have more precise statistics than others.
if comm.is_main_process():
ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD))
def test_and_save_results():
self._last_eval_results = self.test(self.cfg, self.model)
return self._last_eval_results
# Do evaluation after checkpointer, because then if it fails,
# we can use the saved checkpoint to debug.
ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))
if comm.is_main_process():
# run writers in the end, so that evaluation metrics are written
ret.append(hooks.PeriodicWriter(self.build_writers(), 20))
return ret
def build_hooks(self):
"""
Build a list of default hooks, including timing, evaluation,
checkpointing, lr scheduling, precise BN, writing events.
Returns:
list[HookBase]:
"""
cfg = self.cfg.clone()
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
ret = [
hooks.IterationTimer(),
hooks.LRScheduler(self.optimizer, self.scheduler),
hooks.PreciseBN(
# Run at the same freq as (but before) evaluation.
cfg.TEST.EVAL_PERIOD,
self.model,
# Build a new data loader to not affect training
self.build_train_loader(cfg),
cfg.TEST.PRECISE_BN.NUM_ITER,
) if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)
else None,
]
# Do PreciseBN before checkpointer, because it updates the model and need to
# be saved by checkpointer.
# This is not always the best: if checkpointing has a different frequency,
# some checkpoints may have more precise statistics than others.
if comm.is_main_process():
if cfg.SOLVER.CHECKPOINT_BY_EPOCH:
ckpt_period = cfg.SOLVER.CHECKPOINT_PERIOD * self.iters_per_epoch
else:
ckpt_period = cfg.SOLVER.CHECKPOINT_PERIOD
ret.append(
MyPeriodicCheckpointer(self.checkpointer,
ckpt_period,
max_to_keep=cfg.SOLVER.get(
"NUM_CKPT_KEEP", 5),
iters_per_epoch=self.iters_per_epoch))
def test_and_save_results():
self._last_eval_results = self.test(self.cfg, self.model)
return self._last_eval_results
# Do evaluation after checkpointer, because then if it fails,
# we can use the saved checkpoint to debug.
ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))
if comm.is_main_process():
# run writers in the end, so that evaluation metrics are written
ret.append(
hooks.PeriodicWriter(self.build_writers(),
period=cfg.TRAIN.get("PRINT_FREQ", 100)))
return ret
def build_hooks(self):
cfg = self.cfg.clone()
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
ret = [
hooks.IterationTimer(),
hooks.LRScheduler(self.optimizer, self.scheduler),
hooks.PreciseBN(
# Run at the same freq as (but before) evaluation.
cfg.TEST.EVAL_PERIOD,
self.model,
# Build a new data loader to not affect training
self.build_train_loader(cfg),
cfg.TEST.PRECISE_BN.NUM_ITER,
)
if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)
else None,
]
# Do PreciseBN before checkpointer, because it updates the model and need to
# be saved by checkpointer.
# This is not always the best: if checkpointing has a different frequency,
# some checkpoints may have more precise statistics than others.
if comm.is_main_process():
ret.append(
hooks.PeriodicCheckpointer(
self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD
)
)
def test_and_save_results_student():
self._last_eval_results_student = self.test(self.cfg, self.model)
_last_eval_results_student = {
k + "_student": self._last_eval_results_student[k]
for k in self._last_eval_results_student.keys()
}
return _last_eval_results_student
def test_and_save_results_teacher():
self._last_eval_results_teacher = self.test(
self.cfg, self.model_teacher)
return self._last_eval_results_teacher
ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD,
test_and_save_results_student))
ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD,
test_and_save_results_teacher))
if comm.is_main_process():
# run writers in the end, so that evaluation metrics are written
ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))
return ret
def setup(self, stage: str):
if self.cfg.MODEL.WEIGHTS:
self.checkpointer = DetectionCheckpointer(
# Assume you want to save checkpoints together with logs/statistics
self.model,
self.cfg.OUTPUT_DIR,
)
logger.info(f"Load model weights from checkpoint: {self.cfg.MODEL.WEIGHTS}.")
# Only load weights, use lightning checkpointing if you want to resume
self.checkpointer.load(self.cfg.MODEL.WEIGHTS)
self.iteration_timer = hooks.IterationTimer()
self.iteration_timer.before_train()
self.data_start = time.perf_counter()
self.writers = None
def _get_trainer_hooks(self, cfg, model, optimizer, scheduler,
periodic_checkpointer, trainer):
return [
hooks.IterationTimer(),
model_ema.EMAHook(cfg, model) if cfg.MODEL_EMA.ENABLED else None,
self._create_data_loader_hook(cfg),
self._create_after_step_hook(cfg, model, optimizer, scheduler,
periodic_checkpointer),
hooks.EvalHook(
cfg.TEST.EVAL_PERIOD,
lambda: self.do_test(cfg, model, train_iter=trainer.iter),
eval_after_train=
False, # done by a separate do_test call in tools/train_net.py
),
kmeans_anchors.compute_kmeans_anchors_hook(self, cfg),
self._create_qat_hook(cfg)
if cfg.QUANTIZATION.QAT.ENABLED else None,
]
def build_hooks(self):
"""
Only delete the LRScheduler hook
"""
cfg = self.cfg.clone()
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
ret = [
hooks.IterationTimer(),
hooks.PreciseBN(
# Run at the same freq as (but before) evaluation.
cfg.TEST.EVAL_PERIOD,
self.model,
# Build a new data loader to not affect training
self.build_train_loader(cfg),
cfg.TEST.PRECISE_BN.NUM_ITER,
) if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)
else None,
]
# Do PreciseBN before checkpointer, because it updates the model and need to
# be saved by checkpointer.
# This is not always the best: if checkpointing has a different frequency,
# some checkpoints may have more precise statistics than others.
if comm.is_main_process():
ret.append(
hooks.PeriodicCheckpointer(self.checkpointer,
cfg.SOLVER.CHECKPOINT_PERIOD))
def test_and_save_results():
self._last_eval_results = self.test(self.cfg, self.model)
return self._last_eval_results
# Do evaluation after checkpointer, because then if it fails,
# we can use the saved checkpoint to debug.
ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))
if comm.is_main_process():
# run writers in the end, so that evaluation metrics are written
ret.append(hooks.PeriodicWriter(self.build_writers()))
return ret
def build_hooks(self):
"""
Build a list of default hooks, including timing, evaluation,
checkpointing, lr scheduling, precise BN, writing events.
Returns:
list[HookBase]:
"""
cfg = self.cfg.clone()
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0
ret = [
hooks.IterationTimer(),
hooks.LRScheduler(self.optimizer, self.scheduler),
hooks.PreciseBN(
cfg.TEST.EVAL_PERIOD,
self.model,
self.build_train_loader(cfg),
cfg.TEST.PRECISE_BN.NUM_ITER,
)
if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)
else None,
]
if comm.is_main_process():
ret.append(
hooks.PeriodicCheckpointer(
self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD
)
)
def test_and_save_results():
self._last_eval_results = self.test(self.cfg, self.model)
return self._last_eval_results
ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))
if comm.is_main_process():
ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))
return ret
def build_hooks(self):
cfg = self.cfg.clone()
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
ret = [
hooks.IterationTimer(),
hooks.LRScheduler(self.optimizer, self.scheduler),
hooks.PreciseBN(
# Run at the same freq as (but before) evaluation.
cfg.TEST.EVAL_PERIOD,
self.model,
# Build a new data loader to not affect training
self.build_train_loader(cfg),
cfg.TEST.PRECISE_BN.NUM_ITER,
)
if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)
else None,
]
if comm.is_main_process():
ret.append(hooks.PeriodicCheckpointer(self.checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD))
def test_and_save_results():
res = self._last_eval_results = self.test(self.cfg, self.model)
eval_dir = os.path.join(self.cfg.OUTPUT_DIR, 'evals')
os.makedirs(eval_dir, exist_ok=True)
pd.DataFrame(res).to_csv(os.path.join(eval_dir, f'{self.round}.csv'))
return self._last_eval_results
ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))
if comm.is_main_process():
# run writers in the end, so that evaluation metrics are written
ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))
return ret
def build_hooks(self):
cfg = self.cfg.clone()
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
ret = [
hooks.IterationTimer(),
hooks.LRScheduler(self.optimizer, self.scheduler),
hooks.PreciseBN(
# Run at the same freq as (but before) evaluation.
cfg.TEST.EVAL_PERIOD,
self.model,
# Build a new data loader to not affect training
self.build_train_loader(cfg),
cfg.TEST.PRECISE_BN.NUM_ITER,
) if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)
else None,
]
if comm.is_main_process():
ret.append(
hooks.PeriodicCheckpointer(self.checkpointer,
cfg.SOLVER.CHECKPOINT_PERIOD))
# def test_and_save_results():
# self._last_eval_results = self.test(self.cfg, self.model)
# return self._last_eval_results
# Do evaluation after checkpointer, because then if it fails,
# we can use the saved checkpoint to debug.
# ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))
if comm.is_main_process():
# run writers in the end, so that evaluation metrics are written
ret.append(hooks.PeriodicWriter(self.build_writers(), period=20))
return ret
def do_train(self, cfg, model, resume):
add_print_flops_callback(cfg, model, disable_after_callback=True)
optimizer = self.build_optimizer(cfg, model)
scheduler = self.build_lr_scheduler(cfg, optimizer)
checkpointer = self.build_checkpointer(
cfg,
model,
save_dir=cfg.OUTPUT_DIR,
optimizer=optimizer,
scheduler=scheduler,
)
checkpoint = checkpointer.resume_or_load(cfg.MODEL.WEIGHTS,
resume=resume)
start_iter = (checkpoint.get("iteration", -1)
if resume and checkpointer.has_checkpoint() else -1)
# The checkpoint stores the training iteration that just finished, thus we start
# at the next iteration (or iter zero if there's no checkpoint).
start_iter += 1
max_iter = cfg.SOLVER.MAX_ITER
periodic_checkpointer = PeriodicCheckpointer(
checkpointer, cfg.SOLVER.CHECKPOINT_PERIOD, max_iter=max_iter)
data_loader = self.build_detection_train_loader(cfg)
def _get_model_with_abnormal_checker(model):
if not cfg.ABNORMAL_CHECKER.ENABLED:
return model
tbx_writer = _get_tbx_writer(
get_tensorboard_log_dir(cfg.OUTPUT_DIR))
writers = abnormal_checker.get_writers(cfg, tbx_writer)
checker = abnormal_checker.AbnormalLossChecker(start_iter, writers)
ret = abnormal_checker.AbnormalLossCheckerWrapper(model, checker)
return ret
trainer = (AMPTrainer if cfg.SOLVER.AMP.ENABLED else SimpleTrainer)(
_get_model_with_abnormal_checker(model), data_loader, optimizer)
trainer_hooks = [
hooks.IterationTimer(),
model_ema.EMAHook(cfg, model) if cfg.MODEL_EMA.ENABLED else None,
self._create_after_step_hook(cfg, model, optimizer, scheduler,
periodic_checkpointer),
hooks.EvalHook(
cfg.TEST.EVAL_PERIOD,
lambda: self.do_test(cfg, model, train_iter=trainer.iter),
),
kmeans_anchors.compute_kmeans_anchors_hook(self, cfg),
self._create_qat_hook(cfg)
if cfg.QUANTIZATION.QAT.ENABLED else None,
]
if comm.is_main_process():
tbx_writer = _get_tbx_writer(
get_tensorboard_log_dir(cfg.OUTPUT_DIR))
writers = [
CommonMetricPrinter(max_iter),
JSONWriter(os.path.join(cfg.OUTPUT_DIR, "metrics.json")),
tbx_writer,
]
trainer_hooks.append(hooks.PeriodicWriter(writers))
trainer.register_hooks(trainer_hooks)
trainer.train(start_iter, max_iter)
if hasattr(self, 'original_cfg'):
table = get_cfg_diff_table(cfg, self.original_cfg)
logger.info(
"GeneralizeRCNN Runner ignoring training config change: \n" +
table)
trained_cfg = self.original_cfg.clone()
else:
trained_cfg = cfg.clone()
with temp_defrost(trained_cfg):
trained_cfg.MODEL.WEIGHTS = checkpointer.get_checkpoint_file()
return {"model_final": trained_cfg}
def build_hooks(self):
"""
Build a list of default hooks, including timing, evaluation,
checkpointing, lr scheduling, precise BN, writing events.
Returns:
list[HookBase]:
"""
cfg = self.cfg.clone()
cfg.defrost()
cfg.DATALOADER.NUM_WORKERS = 0 # save some memory and time for PreciseBN
ret = \
[
hooks.IterationTimer(),
hooks.LRScheduler(self.optimizer, self.scheduler),
hooks.PreciseBN(
# Run at the same freq as (but before) evaluation.
cfg.TEST.EVAL_PERIOD,
self.model,
# Build a new data loader to not affect training
self.build_train_loader(cfg,self.mapper_object,self.isShuffleData),
cfg.TEST.PRECISE_BN.NUM_ITER,
)
if cfg.TEST.PRECISE_BN.ENABLED and get_bn_modules(self.model)
else None,
]
# Do PreciseBN before checkpointer, because it updates the model and need to
# be saved by checkpointer.
# This is not always the best: if checkpointing has a different frequency,
# some checkpoints may have more precise statistics than others.
if comm.is_main_process():
ret.append(
hooks.PeriodicCheckpointer(self.checkpointer,
cfg.SOLVER.CHECKPOINT_PERIOD))
def test_and_save_results():
# self._last_eval_results = self.test(self.cfg, self.model, self.isTrackAccuracy, self.getter, self.dataset_used)
self._last_eval_results = self.test(self.cfg,
self.model,
self.getter,
self.dataset_used,
self.mapper_object,
evaluators=None)
return self._last_eval_results
# Do evaluation after checkpointer, because then if it fails,
# we can use the saved checkpoint to debug.
ret.append(hooks.EvalHook(cfg.TEST.EVAL_PERIOD, test_and_save_results))
if comm.is_main_process():
numberOfSamples = 25
step = -1
if (self.max_iter <= numberOfSamples):
# Eg, maxiter = 20, so step = 20/2 = 10, take a sample every 10
step = int(round(float(self.max_iter) / float(2), 2))
else:
# Eg 10000/20 = 500, so will take a sample every 500 iterations
step = float(self.max_iter) / float(numberOfSamples)
step = int(round(step, 0))
if (step < 1): step = 1
# print("!!!!!!!!!!!!!!STEPS: ", step)
# ret.append(hooks.PeriodicWriter(self.build_writers()))
# run writers in the end, so that evaluation metrics are written
ret.append(hooks.PeriodicWriter(self.build_writers(), period=step))
# ret.append(hooks.PeriodicWriter(self.build_writers(),period=(self.max_iter-1)))
return ret