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!4061 Fix several minor issues

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Merge pull request !4061 from LiHongzhang/fix_summary
pull/4061/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent daefafbee6 de43c11e2e
commit 8333aea5f5
3 changed files with 32 additions and 14 deletions
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37
mindspore/train/callback/_summary_collector.py
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@ -111,10 +111,10 @@ class SummaryCollector(Callback):
Default: None, it means there is no custom data.
collect_tensor_freq (Optional[int]): Same semantic as the `collect_freq`, but controls TensorSummary only.
Because TensorSummary data is too large compared to other summary data, this parameter is used to reduce
its collection. By default, TensorSummary data will be collected at most 21 steps, but not more than how
its collection. By default, TensorSummary data will be collected at most 20 steps, but not more than how
many steps other summary data will be collected.
Default: None, which means to follow the behavior as described above. For example, given `collect_freq=10`,
when the total steps is 600, TensorSummary will be collected 21 steps, while other summary data 61 steps,
when the total steps is 600, TensorSummary will be collected 20 steps, while other summary data 61 steps,
but when the total steps is 20, both TensorSummary and other summary will be collected 3 steps.
Also note that when in parallel mode, the total steps will be splitted evenly, which will
affect how many steps TensorSummary will be collected.
@ -176,6 +176,7 @@ class SummaryCollector(Callback):
self._check_positive('collect_tensor_freq', collect_tensor_freq, allow_none=True)
self._collect_tensor_freq = collect_tensor_freq
self._tensor_collect_range = None
self._check_positive('max_file_size', max_file_size, allow_none=True)
self._max_file_size = max_file_size
@ -296,12 +297,6 @@ class SummaryCollector(Callback):
self._record.set_mode(cb_params.mode)
if cb_params.mode == ModeEnum.TRAIN.value:
if self._collect_tensor_freq is None:
default_tensor_summary_limit = 20
total_step = cb_params.epoch_num * cb_params.batch_num
self._collect_tensor_freq = max(self._collect_freq, total_step // default_tensor_summary_limit)
def step_end(self, run_context):
cb_params = run_context.original_args()
if cb_params.mode != ModeEnum.TRAIN.value:
@ -322,17 +317,36 @@ class SummaryCollector(Callback):
if self._first_step:
# Notice: This way of determining whether dataset sink mode is True does not work in the eval scenario
self._dataset_sink_mode = cb_params.cur_step_num == cb_params.batch_num
self._tensor_collect_range = self._get_tensor_collect_range(cb_params, self._dataset_sink_mode)
self._collect_at_step_end(cb_params, plugin_filter=None)
self._first_step = False
else:
current = cb_params.cur_epoch_num if self._dataset_sink_mode else cb_params.cur_step_num
if current % self._collect_freq == 0 and current % self._collect_tensor_freq == 0:
if current % self._collect_freq == 0 and current in self._tensor_collect_range:
self._collect_at_step_end(cb_params, plugin_filter=None)
elif current % self._collect_tensor_freq == 0:
elif current in self._tensor_collect_range:
self._collect_at_step_end(cb_params, lambda plugin: plugin == PluginEnum.TENSOR.value)
elif current % self._collect_freq == 0:
self._collect_at_step_end(cb_params, lambda plugin: plugin != PluginEnum.TENSOR.value)
def _get_tensor_collect_range(self, cb_params, dataset_sink_mode):
"""Get tensor collect range."""
total_step = cb_params.epoch_num
if not dataset_sink_mode:
total_step *= cb_params.batch_num
if self._collect_tensor_freq is not None:
# `total_step + 1`: `total_step` would be a value of `cb_params.cur_step_num`.
return range(0, total_step + 1, self._collect_tensor_freq)
summary_to_collect = len(range(0, total_step + 1, self._collect_freq))
default_tensor_summary_limit = 20
if summary_to_collect > default_tensor_summary_limit:
tensor_freq = total_step // (default_tensor_summary_limit - 1)
if tensor_freq > 1:
return range(0, total_step + 1, tensor_freq)[:default_tensor_summary_limit]
# `cb_params.cur_step_num` counting from `1`, when `1` is in the range, take `1` more steps.
return range(0, total_step + 1)[:default_tensor_summary_limit + 1]
return range(0, total_step + 1, self._collect_freq)
def _collect_at_step_end(self, cb_params, plugin_filter):
self._collect_input_data(cb_params)
self._collect_metric(cb_params)
@ -577,7 +591,8 @@ class SummaryCollector(Callback):
"""
learning_rate = optimizer.learning_rate
if not isinstance(learning_rate, Parameter):
logger.info("The learning rate detected in the optimizer is not a Parameter type, so it is not recorded.")
logger.warning("The learning rate detected in the optimizer "
"is not a Parameter type, so it is not recorded.")
return None
return learning_rate.data

8
mindspore/train/summary/_summary_writer.py
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@ -20,6 +20,8 @@ from shutil import disk_usage
from ..._c_expression import EventWriter_
from ._summary_adapter import package_init_event
FREE_DISK_SPACE_TIMES = 32
class BaseWriter:
"""BaseWriter to be subclass."""
@ -45,13 +47,13 @@ class BaseWriter:
def write(self, plugin, data):
"""Write data to file."""
if self.writer and disk_usage(self._filepath).free < len(data) * 32:
raise RuntimeError(f"The disk space may be soon exhausted by the '{self._filepath}'.")
# 8: data length
# 4: crc32 of data length
# 4: crc32 of data
metadata_length = 8 + 4 + 4
required_length = len(data) + metadata_length
if self.writer and disk_usage(self._filepath).free < required_length * FREE_DISK_SPACE_TIMES:
raise RuntimeError(f"The disk space may be soon exhausted by the '{self._filepath}'.")
if self._max_file_size is None:
self.writer.Write(data)
elif self._max_file_size >= required_length:
@ -77,7 +79,7 @@ class SummaryWriter(BaseWriter):
def init_writer(self):
"""Write some metadata etc."""
self.writer.Write(package_init_event().SerializeToString())
self.write('summary', package_init_event().SerializeToString())
def write(self, plugin, data):
"""Write data to file."""

1
mindspore/train/summary/summary_record.py
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@ -156,6 +156,7 @@ class SummaryRecord:
max_file_size,
summary=self.full_file_name,
lineage=get_event_file_name('events', '_lineage'))
_get_summary_tensor_data()
atexit.register(self.close)
def __enter__(self):

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