def evaluate(self, eval_dataset, eval_hooks=[]):
if not isinstance(eval_dataset, Dataset):
raise ValueError(
'expect dataset to be instance of Dataset, got %s' %
repr(eval_dataset))
program, model_spec = self.build_for_eval(eval_dataset)
single_card_place = F.cuda_places()[0]
eval_executor = F.Executor(single_card_place)
eval_hooks = [
hooks.StopAtStepHook(self.run_config.eval_max_steps,
self.run_config.eval_max_steps),
hooks.EvalHook(model_spec.metrics, )
]
mon_exe = MonitoredExecutor(eval_executor,
program,
run_config=self.run_config,
run_hooks=eval_hooks)
mon_exe.init_or_restore_variables()
try:
with mon_exe:
for data in eval_dataset.start(places=[single_card_place]):
mon_exe.run(feed=data)
except (StopException, F.core.EOFException) as e:
pass
_, eval_result = mon_exe.result
summary_writer = get_summary_writer(
os.path.join(self.run_config.model_dir, 'eval_history'))
log_eval_result('eval', eval_result, summary_writer, mon_exe.state)
return mon_exe.result
def train(self, train_ds, train_hooks=[]):
"""train on a `Dataset`"""
if not isinstance(train_ds, Dataset):
raise ValueError(
'expect dataset to be instance of Dataset, got %s' %
repr(train_ds))
train_program, model_spec, summary_record = self._build_for_train(
train_ds)
train_run_hooks = [
hooks.StopAtStepHook(self.run_config.max_steps,
self.run_config.run_steps),
hooks.LoggingHook(
model_spec.loss,
summary_record=summary_record,
summary_writer=_get_summary_writer(
os.path.join(self.run_config.model_dir,
'train_history%s' % self.run_config.log_id)),
per_step=self.run_config.log_steps,
prefix=self.run_config.log_prefix or 'training',
skip_step=self.run_config.skip_steps),
]
if model_spec.train_hooks is not None:
train_run_hooks.extend(model_spec.train_hooks)
train_run_hooks.extend(train_hooks)
train_executor = F.Executor(_get_one_place())
mon_exe = MonitoredExecutor(train_executor,
train_program,
loss=model_spec.loss,
run_config=self.run_config,
run_hooks=train_run_hooks,
warm_start_setting=self.warm_start_setting)
distribution.init_distribuition_env(
train_program) #only initialize distribute training with
mon_exe.init_or_restore_variables()
if distribution.status.is_master:
mon_exe._hooks.append(
hooks.CheckpointSaverHook(
mon_exe._saver,
per_step=mon_exe._save_steps,
skip_step=mon_exe._skip_steps,
))
try:
with mon_exe:
for data in train_ds.start():
mon_exe.run(feed=data)
except (StopException, F.core.EOFException) as e:
pass
return mon_exe.result
def predict(self,
predict_dataset,
ckpt=-1,
ckpt_path=None,
steps=-1,
split_batch=True):
"""
Perform predictoin
will call `model_fn` and initiate user-specifed model in `propeller.RunMode.PREDICT` mode
Args:
infer_dataset (propeller.data.Dataset): should not `shuffle` or `repeat`
steps (int): steps to predict, if None is specifed,
will stop when `StopException` is raised in `infer_dataset`
ckpt_path (None|str): Path of a specific checkpoint to predict.
If None, the latest checkpoint in model_dir is used.
If there are no checkpoints in model_dir,
prediction is run with newly initialized Variables instead of ones restored from checkpoint.
ckpt (int): deprecated args
split_batch (bool): if True, prediction of each example in a batch is returned.
Yields:
Evaluated values of predictions tensors.
"""
if not isinstance(predict_dataset, Dataset):
raise ValueError(
'expect dataset to be instance of Dataset, got %s' %
repr(predict_dataset))
program, model_spec = self._build_for_predict(predict_dataset)
single_card_place = _get_one_place()
executor = F.Executor(single_card_place)
pred_run_config = RunConfig(run_steps=steps if steps == -1 else None,
model_dir=self.run_config.model_dir)
mon_exe = MonitoredExecutor(
executor,
program,
run_config=pred_run_config,
warm_start_setting=self.warm_start_setting,
)
mon_exe.init_or_restore_variables(
ckpt if ckpt_path is None else ckpt_path)
try:
with mon_exe:
log.info('Runining predict from dir: %s' % repr(mon_exe.state))
single_card_place = _get_one_place()
for data in predict_dataset.start(places=[single_card_place]):
res = mon_exe.run(fetch_list=model_spec.predictions,
feed=data)
if split_batch:
res = map(lambda i: i.tolist(), res)
res = zip(*res) # transpose
for r in res:
yield r
else:
yield list(map(lambda i: i.tolist(), res))
except (StopException, F.core.EOFException) as e:
pass
def evaluate(self, eval_dataset, eval_hooks=[]):
"""eval on a `Dataset`"""
if not isinstance(eval_dataset, Dataset):
raise ValueError(
'expect dataset to be instance of Dataset, got %s' %
repr(eval_dataset))
program, model_spec = self._build_for_eval(eval_dataset)
single_card_place = _get_one_place()
eval_executor = F.Executor(single_card_place)
eval_run_hooks = [
hooks.StopAtStepHook(self.run_config.eval_max_steps,
self.run_config.eval_max_steps),
hooks.EvalHook(model_spec.metrics, )
]
if model_spec.eval_hooks is not None:
eval_run_hooks.extend(model_spec.eval_hooks)
eval_run_hooks.extend(eval_hooks)
mon_exe = MonitoredExecutor(eval_executor,
program,
loss=model_spec.loss,
run_config=self.run_config,
run_hooks=eval_run_hooks,
warm_start_setting=self.warm_start_setting)
distribution.init_distribuition_env(
program) #only initialize distribute training with
mon_exe.init_or_restore_variables()
try:
with mon_exe:
for data in eval_dataset.start():
mon_exe.run(feed=data)
except (StopException, F.core.EOFException) as e:
pass
_, eval_result = mon_exe.result
summary_writer = _get_summary_writer(
os.path.join(self.run_config.model_dir,
'eval_history%s' % self.run_config.log_id))
_log_eval_result('eval', eval_result, summary_writer, mon_exe.state)
return eval_result
def after_run(self, _, state):
"""doc"""
if state.gstep > run_config.skip_steps and state.gstep % run_config.eval_steps == 0:
eval_results = {}
for name, ds in six.iteritems(eval_dataset):
ehooks = [
hooks.StopAtStepHook(est.run_config.eval_max_steps,
est.run_config.eval_max_steps),
hooks.EvalHook(
self.model_spec.metrics,
summary_writer=self.summary_writers[name],
)
]
single_card_place = _get_one_place()
eval_executor = F.Executor(single_card_place)
mon_exe = MonitoredExecutor(eval_executor,
self.program,
run_config=est.run_config,
run_hooks=ehooks + eval_hooks)
try:
with mon_exe:
for data in ds.start(places=[single_card_place]):
mon_exe.run(feed=data)
except (StopException, F.core.EOFException) as e:
pass
hook_results = mon_exe.result
eval_res = hook_results[
1] # hook_results: [StopAtStepHook, EvalHook, ...]
eval_results[name] = eval_res
_log_eval_result(name, eval_res,
self.summary_writers[name], state)
if distribution.status.is_master:
for exporter in exporters:
exporter.export(eval_executor, self.program,
self.model_spec, eval_results, state)
else:
eval_results = {}
return eval_results
def predict(self, predict_dataset, ckpt=None, steps=-1, split_batch=True):
'''
Perform predictoin
will call `model_fn` and initiate user-specifed model in `propeller.RunMode.PREDICT` mode
Args:
infer_dataset (propeller.data.Dataset): should not `shuffle` or `repeat`
steps (int): steps to predict, if -1 is specifed, will stop when `StopException` is raised in `infer_dataset`
split_batch (bool): if True, prediction of each example in a batch is returned.
Yields:
Evaluated values of predictions tensors.
'''
if not isinstance(predict_dataset, Dataset):
raise ValueError(
'expect dataset to be instance of Dataset, got %s' %
repr(predict_dataset))
program, model_spec = self.build_for_predict(predict_dataset)
single_card_place = F.cuda_places()[0]
executor = F.Executor(single_card_place)
pred_run_config = RunConfig(run_steps=steps if steps == -1 else None,
model_dir=self.run_config.model_dir)
mon_exe = MonitoredExecutor(
executor,
program,
run_config=pred_run_config,
)
mon_exe.init_or_restore_variables()
try:
with mon_exe:
log.info('Runining predict from dir: %s' % repr(mon_exe.state))
single_card_place = F.cuda_places()[0]
for data in predict_dataset.start(places=[single_card_place]):
res = mon_exe.run(fetch_list=model_spec.predictions,
feed=data)
if split_batch:
res = map(lambda i: i.tolist(), res)
res = zip(*res) # transpose
for r in res:
yield r
else:
yield list(map(lambda i: i.tolist(), res))
except (StopException, F.core.EOFException) as e:
pass