# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Record the summary event."""
import atexit
import os
import re
import threading
import time
from collections import defaultdict
from mindspore import log as logger
from mindspore.nn import Cell
from ..._c_expression import Tensor, security
from ..._checkparam import Validator
from ...common.api import _cell_graph_executor
from .._utils import _check_lineage_value, _check_to_numpy, _make_directory, check_value_type
from ._summary_adapter import get_event_file_name, package_graph_event
from ._writer_pool import WriterPool
from .enums import PluginEnum
# for the moment, this lock is for caution's sake,
# there are actually no any concurrences happening.
_summary_lock = threading.Lock()
# cache the summary data
_summary_tensor_cache = {}
_DEFAULT_EXPORT_OPTIONS = {
'tensor_format': {'npy', None},
}
def _cache_summary_tensor_data(summary):
"""
Get the time of ms.
Args:
summary (list): [{"name": tag_name, "data": tensor}, {"name": tag_name, "data": tensor},...].
"""
with _summary_lock:
for item in summary:
_summary_tensor_cache[item['name']] = item['data']
return True
def _get_summary_tensor_data():
global _summary_tensor_cache
with _summary_lock:
data = _summary_tensor_cache
_summary_tensor_cache = {}
return data
def process_export_options(export_options):
"""Check specified data type and value."""
if export_options is None:
return None
check_value_type('export_options', export_options, [dict, type(None)])
for export_option, export_format in export_options.items():
check_value_type('export_option', export_option, [str])
check_value_type('export_format', export_format, [str, type(None)])
unexpected_params = set(export_options) - set(_DEFAULT_EXPORT_OPTIONS)
if unexpected_params:
raise ValueError(f'For "SummaryRecord", the keys {unexpected_params} of "export_options" are unsupported, '
f'expect the follow keys: {list(_DEFAULT_EXPORT_OPTIONS.keys())}')
for export_option, export_format in export_options.items():
unexpected_format = {export_format} - _DEFAULT_EXPORT_OPTIONS.get(export_option)
if unexpected_format:
raise ValueError(
f'For "SummaryRecord", the export_format {unexpected_format} of "export_options" are unsupported '
f'for {export_option}, expect the follow values: {list(_DEFAULT_EXPORT_OPTIONS.get(export_option))}')
for item in set(export_options):
check_value_type(item, export_options.get(item), [str, type(None)])
return export_options
[文档]class SummaryRecord:
"""
SummaryRecord is used to record the summary data and lineage data.
The API will create a summary file and lineage files lazily in a given directory and writes data to them.
It writes the data to files by executing the 'record' method. In addition to recording the data bubbled up from
the network by defining the summary operators, SummaryRecord also supports to record extra data which
can be added by calling add_value.
Note:
1. When using SummaryRecord, you need to run the code in `if __name__ == "__main__"` .
2. Make sure to close the SummaryRecord at the end, otherwise the process will not exit.
Please see the Example section below to learn how to close properly in two ways.
3. Only one SummaryRecord instance is allowed at a time, otherwise it will cause data writing problems.
4. SummaryRecord only supports Linux systems.
5. The Summary is not supported when compile source with `-s on` option.
Args:
log_dir (str): The log_dir is a directory location to save the summary.
file_prefix (str): The prefix of file. Default: "events".
file_suffix (str): The suffix of file. Default: "_MS".
network (Cell): Obtain a pipeline through network for saving graph summary. Default: None.
max_file_size (int, optional): The maximum size of each file that can be written to disk (in bytes).
For example, to write not larger than 4GB, specify `max_file_size=4*1024**3`.
Default: None, which means no limit.
raise_exception (bool, optional): Sets whether to throw an exception when a RuntimeError or OSError exception
occurs in recording data. Default: False, this means that error logs are printed and no exception is thrown.
export_options (Union[None, dict]): Perform custom operations on the export data.
Note that the size of export files is not limited by the max_file_size.
You can customize the export data with a dictionary. For example, you can set {'tensor_format': 'npy'}
to export tensor as npy file. The data that supports control is shown below. Default: None, it means that
the data is not exported.
- tensor_format (Union[str, None]): Customize the export tensor format. Supports ["npy", None].
Default: None, it means that the tensor is not exported.
- npy: export tensor as npy file.
Raises:
TypeError: `max_file_size` is not int or `file_prefix` and `file_suffix` is not string.
ValueError: The Summary is not supported, please without `-s on` and recompile source.
Examples:
>>> from mindspore.train.summary import SummaryRecord
>>> if __name__ == '__main__':
... # use in with statement to auto close
... with SummaryRecord(log_dir="./summary_dir") as summary_record:
... pass
...
... # use in try .. finally .. to ensure closing
... try:
... summary_record = SummaryRecord(log_dir="./summary_dir")
... finally:
... summary_record.close()
"""
def __init__(self, log_dir, file_prefix="events", file_suffix="_MS",
network=None, max_file_size=None, raise_exception=False, export_options=None):
if security.enable_security():
raise ValueError('The Summary is not supported, please without `-s on` and recompile source.')
self._event_writer = None
self._mode, self._data_pool = 'train', defaultdict(list)
self._status = {
'closed': False,
'has_graph': False
}
self.file_info = {
'file_name': None,
'file_path': None
}
log_path = _make_directory(log_dir, "log_dir")
if not isinstance(max_file_size, (int, type(None))):
raise TypeError(f"For '{self.__class__.__name__}', the 'max_file_size' should be int type, "
f"but got type {type(max_file_size)}")
if not isinstance(file_prefix, str) or not isinstance(file_suffix, str):
raise TypeError(f"For '{self.__class__.__name__}', `file_prefix` and `file_suffix` should be str, "
f"but got type {type(file_prefix)}")
Validator.check_str_by_regular(file_prefix)
Validator.check_str_by_regular(file_suffix)
if max_file_size is not None and max_file_size < 0:
logger.warning(f"For '{self.__class__.__name__}', the 'max_file_size' should be greater than 0. "
f"but got value {max_file_size}.")
max_file_size = None
Validator.check_value_type(arg_name='raise_exception', arg_value=raise_exception, valid_types=bool)
self.network = network
time_second = str(int(time.time()))
# create the summary writer file
self.file_info['file_name'] = get_event_file_name(file_prefix, file_suffix, time_second)
self.file_info['file_path'] = os.path.join(log_path, self.file_info.get('file_name'))
self._export_options = process_export_options(export_options)
export_dir = ''
if self._export_options is not None:
export_dir = "export_{}".format(time_second)
filename_dict = dict(summary=self.file_info.get('file_name'),
lineage=get_event_file_name(file_prefix, '_lineage', time_second),
exporter=export_dir)
self._event_writer = WriterPool(log_dir,
max_file_size,
raise_exception,
**filename_dict)
_get_summary_tensor_data()
atexit.register(self.close)
def __enter__(self):
"""Enter the context manager."""
if self._status.get('closed'):
raise ValueError(f'For "{self.__class__.__name__}", SummaryRecord has been closed, '
f'please check if close() method is called')
return self
def __exit__(self, *err):
"""Exit the context manager."""
self.close()
[文档] def set_mode(self, mode):
"""
Set the model running phase. Different phases affect data recording.
Args:
mode (str): The mode to be set, which should be 'train' or 'eval'. When the mode is 'eval',
summary_record will not record the data of summary operators.
- train：the model running phase is train mode.
- eval：the model running phase is eval mode，When the mode is 'eval',
summary_record will not record the data of summary operators.
Raises:
ValueError: `mode` is not in the optional value.
Examples:
>>> from mindspore.train.summary import SummaryRecord
>>> if __name__ == '__main__':
... with SummaryRecord(log_dir="./summary_dir", file_prefix="xx_", file_suffix="_yy") as summary_record:
... summary_record.set_mode('eval')
"""
mode_spec = 'train', 'eval'
if mode not in mode_spec:
raise ValueError(f'For "{self.__class__.__name__}.set_mode", {repr(mode)} is not a '
f'recognized mode, expect the parameter "mode" is "train" or "eval"')
self._mode = mode
[文档] def add_value(self, plugin, name, value):
"""
Add value to be recorded later.
Args:
plugin (str): The plugin of the value.
- graph: the value is a computational graph.
- scalar: the value is a scalar.
- image: the value is an image.
- tensor: the value is a tensor.
- histogram: the value is a histogram.
- train_lineage: the value is a lineage data for the training phase.
- eval_lineage: the value is a lineage data for the evaluation phase.
- dataset_graph: the value is a dataset graph.
- custom_lineage_data: the value is a customized lineage data.
- LANDSCAPE: the value is a landscape.
name (str): The value of the name.
value (Union[Tensor, GraphProto, TrainLineage, EvaluationLineage, DatasetGraph, UserDefinedInfo, LossLandscape]): The value to store.
- The data type of value should be 'GraphProto' (see `mindspore/ccsrc/anf_ir.proto
<https://gitee.com/mindspore/mindspore/blob/master/mindspore/ccsrc/utils/anf_ir.proto>`_) object
when the plugin is 'graph'.
- The data type of value should be 'Tensor' object when the plugin is 'scalar', 'image', 'tensor'
or 'histogram'.
- The data type of value should be a 'TrainLineage' object when the plugin is 'train_lineage',
see `mindspore/ccsrc/lineage.proto
<https://gitee.com/mindspore/mindspore/blob/master/mindspore/ccsrc/utils/lineage.proto>`_.
- The data type of value should be a 'EvaluationLineage' object when the plugin is 'eval_lineage',
see `mindspore/ccsrc/lineage.proto
<https://gitee.com/mindspore/mindspore/blob/master/mindspore/ccsrc/utils/lineage.proto>`_.
- The data type of value should be a 'DatasetGraph' object when the plugin is 'dataset_graph',
see `mindspore/ccsrc/lineage.proto
<https://gitee.com/mindspore/mindspore/blob/master/mindspore/ccsrc/utils/lineage.proto>`_.
- The data type of value should be a 'UserDefinedInfo' object when the plugin is 'custom_lineage_data',
see `mindspore/ccsrc/lineage.proto
<https://gitee.com/mindspore/mindspore/blob/master/mindspore/ccsrc/utils/lineage.proto>`_.
- The data type of value should be a 'LossLandscape' object when the plugin is 'LANDSCAPE',
see `mindspore/ccsrc/summary.proto
<https://gitee.com/mindspore/mindspore/blob/master/mindspore/ccsrc/utils/summary.proto>`_.
Raises:
ValueError: `plugin` is not in the optional value.
TypeError: `name` is not non-empty string, or the data type of value is not 'Tensor' object when the plugin
is 'scalar', 'image', 'tensor' or 'histogram'.
Examples:
>>> from mindspore import Tensor
>>> from mindspore.train.summary import SummaryRecord
>>> if __name__ == '__main__':
... with SummaryRecord(log_dir="./summary_dir", file_prefix="xx_", file_suffix="_yy") as summary_record:
... summary_record.add_value('scalar', 'loss', Tensor(0.1))
"""
if plugin in ('tensor', 'scalar', 'image', 'histogram'):
if not name or not isinstance(name, str):
raise ValueError(f'For "{self.__class__.__name__}", the parameter "name" type should be str, '
f'but got {type(name)}.')
if not isinstance(value, Tensor):
raise TypeError(f'For "{self.__class__.__name__}", the parameter "value" expect to be Tensor, '
f'but got {type(value).__name__}')
np_value = _check_to_numpy(plugin, value)
if name in {item['tag'] for item in self._data_pool[plugin]}:
entry = repr(f'{name}/{plugin}')
logger.warning(f'For "{self.__class__.__name__}.add_value", {entry} has duplicate values. '
f'Only the newest one will be recorded.')
data = dict(tag=name, value=np_value)
export_plugin = '{}_format'.format(plugin)
if self._export_options is not None and export_plugin in self._export_options:
data['export_option'] = self._export_options.get(export_plugin)
self._data_pool[plugin].append(data)
elif plugin in ('train_lineage', 'eval_lineage', 'dataset_graph', 'custom_lineage_data'):
_check_lineage_value(plugin, value)
self._data_pool[plugin].append(dict(value=value.SerializeToString()))
elif plugin == 'graph':
package_graph_event(value)
self._data_pool[plugin].append(dict(value=value))
elif plugin == PluginEnum.LANDSCAPE.value:
self._data_pool[plugin].append(dict(tag=name, value=value.SerializeToString()))
else:
raise ValueError(f'For "{self.__class__.__name__}.add_value", no such "plugin" of {repr(plugin)} '
f', expect value is one of [tensor, scalar, image, histogram, train_lineage, '
f'eval_lineage, dataset_graph, custom_lineage_data, graph, landscape]')
[文档] def record(self, step, train_network=None, plugin_filter=None):
"""
Record the summary.
Args:
step (int): Represents training step number.
train_network (Cell): The spare network for saving graph.
Default: None, it means just do not save the graph summary when the original network graph is None.
plugin_filter (Callable[[str], bool], optional): The filter function, \
which is used to filter out which plugin should be written. Default: None.
Returns:
bool, whether the record process is successful or not.
Raises:
TypeError: `step` is not int，or `train_network` is not `mindspore.nn.Cell \
<https://www.mindspore.cn/docs/en/r1.7/api_python/nn/mindspore.nn.Cell.html#mindspore-nn-cell>`_ 。
Examples:
>>> from mindspore.train.summary import SummaryRecord
>>> if __name__ == '__main__':
... with SummaryRecord(log_dir="./summary_dir", file_prefix="xx_", file_suffix="_yy") as summary_record:
... result = summary_record.record(step=2)
... print(result)
...
True
"""
logger.debug("SummaryRecord step is %r.", step)
Validator.check_value_type(arg_name='step', arg_value=step, valid_types=int)
Validator.check_value_type(arg_name='train_network', arg_value=train_network, valid_types=[Cell, type(None)])
if self._status.get('closed'):
logger.error(f"For '{self.__class__.__name__}', The record writer is closed, "
f"please check if close() method is called")
return False
# Set the current summary of train step
if self.network is not None and not self._status.get('has_graph'):
graph_proto = _cell_graph_executor.get_optimize_graph_proto(self.network)
if graph_proto is None and train_network is not None:
graph_proto = _cell_graph_executor.get_optimize_graph_proto(train_network)
if graph_proto is None:
logger.error("Failed to get proto for graph")
else:
self._event_writer.write({'graph': [{'step': step, 'value': graph_proto}]})
self._status['has_graph'] = True
if not _summary_tensor_cache:
return True
if self._mode == 'train':
self._add_summary_tensor_data()
if not plugin_filter:
self._event_writer.write(self._consume_data_pool(step))
else:
filtered = {}
for plugin, datalist in self._consume_data_pool(step).items():
if plugin_filter(plugin):
filtered[plugin] = datalist
self._event_writer.write(filtered)
return True
def _add_summary_tensor_data(self):
summary_data = _get_summary_tensor_data()
if not summary_data:
logger.debug(f'No summary data bubbled from the network.')
for name, tensor in summary_data.items():
tag, plugin = SummaryRecord._parse_from(name)
if (tag, plugin) == (None, None):
logger.warning("The name(%r) is invalid, expected 'TAG[:TYPE]'.", name)
else:
self.add_value(plugin.lower(), tag, tensor)
def _consume_data_pool(self, step):
try:
for values in self._data_pool.values():
for value in values:
value['step'] = step
return self._data_pool
finally:
self._data_pool = defaultdict(list)
@property
def log_dir(self):
"""
Get the full path of the log file.
Returns:
str, the full path of log file.
Examples:
>>> from mindspore.train.summary import SummaryRecord
>>> if __name__ == '__main__':
... with SummaryRecord(log_dir="./summary_dir", file_prefix="xx_", file_suffix="_yy") as summary_record:
... log_dir = summary_record.log_dir
"""
return self.file_info['file_path']
[文档] def flush(self):
"""
Flush the buffer and write buffer data to disk.
Call it to make sure that all pending events have been written to disk.
Examples:
>>> from mindspore.train.summary import SummaryRecord
>>> if __name__ == '__main__':
... with SummaryRecord(log_dir="./summary_dir", file_prefix="xx_", file_suffix="_yy") as summary_record:
... summary_record.flush()
"""
if self._status.get('closed'):
logger.error(f"For '{self.__class__.__name__}', the record writer is closed and can not flush, "
f"please check if close() method is called")
elif self._event_writer:
self._event_writer.flush()
[文档] def close(self):
"""
Flush the buffer and write files to disk and close summary records. Please use the statement to autoclose.
Examples:
>>> from mindspore.train.summary import SummaryRecord
>>> if __name__ == '__main__':
... try:
... summary_record = SummaryRecord(log_dir="./summary_dir")
... finally:
... summary_record.close()
"""
if not self._status.get('closed') and self._event_writer:
# event writer flush and close
logger.info('Please wait it may take quite some time to finish writing and closing.')
atexit.unregister(self.close)
self._event_writer.close()
self._event_writer.join()
self._status['closed'] = True
@staticmethod
def _parse_from(name: str = None):
"""Parse the tag and type from name."""
if not isinstance(name, str):
return None, None
match = re.match(r'(.+)\[:(.+)\]', name)
if match:
return match.groups()
return None, None