def load(self):
"""Start loading data from the latest summary file to the loader."""
self.status = _LoaderStatus.LOADING.value
filenames = []
for filename in FileHandler.list_dir(self._loader_info['summary_dir']):
if FileHandler.is_file(
FileHandler.join(self._loader_info['summary_dir'],
filename)):
filenames.append(filename)
filenames = ExplainLoader._filter_files(filenames)
if not filenames:
raise TrainJobNotExistError(
'No summary file found in %s, explain job will be delete.' %
self._loader_info['summary_dir'])
is_end = False
while not is_end and self.status != _LoaderStatus.STOP.value:
try:
file_changed, is_end, event_dict = self._parser.list_events(
filenames)
except UnknownError:
break
if file_changed:
logger.info(
'Summary file in %s update, reload the data in the summary.',
self._loader_info['summary_dir'])
self._clear_job()
if event_dict:
self._import_data_from_event(event_dict)
def _parse_pb_file(self, filename):
"""
Parse pb file and write content to `EventsData`.
Args:
filename (str): The file path of pb file.
"""
file_path = FileHandler.join(self._summary_dir, filename)
logger.info("Start to load graph from pb file, file path: %s.",
file_path)
filehandler = FileHandler(file_path)
model_proto = anf_ir_pb2.ModelProto()
try:
model_proto.ParseFromString(filehandler.read())
except ParseError:
logger.warning(
"The given file is not a valid pb file, file path: %s.",
file_path)
return
graph = MSGraph()
graph.build_graph(model_proto.graph)
tensor_event = TensorEvent(wall_time=FileHandler.file_stat(file_path),
step=0,
tag=filename,
plugin_name=PluginNameEnum.GRAPH.value,
value=graph)
self._events_data.add_tensor_event(tensor_event)
def sort_files(self, filenames):
"""Sort by modify time increments and filenames increments."""
filenames = sorted(
filenames,
key=lambda file: (FileHandler.file_stat(
FileHandler.join(self._summary_dir, file)).mtime, file))
return filenames
def parse(self):
"""Load summary file and parse file content."""
summary_file_handler = FileHandler(self.summary_file, 'rb')
self._file_size = os.path.getsize(self.summary_file)
# when current parsed size bigger than self._process_info, print process
self._process_info = self._file_size // INFO_INTERVAL
parse_summary_logger.info("Loading %s.", self.summary_file)
result = self._load(summary_file_handler)
if result:
warning = ''
scalar_path = FileHandler.join(self._output, "scalar.csv")
image_path = FileHandler.join(self._output, IMAGE)
if not self._image_check:
warning = warning + " The summary file contains no image."
else:
parse_summary_logger.info("Images are written in %s.", image_path)
if not self._scalar_check:
warning = warning + " The summary file contains no scalar value."
else:
parse_summary_logger.info("Writing scalar data into %s.", scalar_path)
self._scalar_writer.write()
if warning:
parse_summary_logger.warning(warning)
parse_summary_logger.info("Finished loading %s.", self.summary_file)
def load(self):
"""Start loading data from the latest summary file to the loader."""
filenames = []
for filename in FileHandler.list_dir(self._loader_info['summary_dir']):
if FileHandler.is_file(
FileHandler.join(self._loader_info['summary_dir'],
filename)):
filenames.append(filename)
filenames = ExplainLoader._filter_files(filenames)
if not filenames:
raise TrainJobNotExistError(
'No summary file found in %s, explain job will be delete.' %
self._loader_info['summary_dir'])
is_end = False
while not is_end:
is_clean, is_end, event_dict = self._parser.parse_explain(
filenames)
if is_clean:
logger.info(
'Summary file in %s update, reload the data in the summary.',
self._loader_info['summary_dir'])
self._clear_job()
if event_dict:
self._import_data_from_event(event_dict)
def parse_files(self, filenames, events_data):
"""
Load summary file and parse file content.
Args:
filenames (list[str]): File name list.
events_data (EventsData): The container of event data.
"""
self._events_data = events_data
summary_files = self.filter_files(filenames)
summary_files = self.sort_files(summary_files)
for filename in summary_files:
if self._latest_filename and \
(self._compare_summary_file(self._latest_filename, filename)):
continue
file_path = FileHandler.join(self._summary_dir, filename)
if filename != self._latest_filename:
self._summary_file_handler = FileHandler(file_path, 'rb')
self._latest_filename = filename
self._latest_file_size = 0
new_size = FileHandler.file_stat(file_path).size
if new_size == self._latest_file_size:
continue
self._latest_file_size = new_size
try:
self._load_single_file(self._summary_file_handler)
except UnknownError as ex:
logger.warning("Parse summary file failed, detail: %r,"
"file path: %s.", str(ex), file_path)
def phase_pb_file(file_path: str) -> Union[MSGraph, None]:
"""
Parse pb file to graph
Args:
file_path (str): The file path of pb file.
Returns:
MSGraph, if load pb file and build graph success, will return the graph, else return None.
"""
if not CONFIG.VERBOSE:
logger.setLevel(logging.ERROR)
logger.info("Start to load graph from pb file, file path: %s.", file_path)
model_proto = anf_ir_pb2.ModelProto()
try:
model_proto.ParseFromString(FileHandler(file_path).read())
except ParseError:
logger.warning("The given file is not a valid pb file, file path: %s.",
file_path)
return None
graph = MSGraph()
try:
graph.build_graph(model_proto.graph)
except Exception as ex:
logger.error("Build graph failed, file path: %s.", file_path)
logger.exception(ex)
raise UnknownError(str(ex))
logger.info("Build graph success, file path: %s.", file_path)
return graph
def parse_files(self, executor, filenames, events_data):
"""
Load summary file and parse file content.
Args:
executor (Executor): The executor instance.
filenames (list[str]): File name list.
events_data (EventsData): The container of event data.
Returns:
bool, True if all the summary files are finished loading.
"""
summary_files = self.filter_files(filenames)
summary_files = self.sort_files(summary_files)
if self._latest_filename in summary_files:
index = summary_files.index(self._latest_filename)
summary_files = summary_files[index:]
for filename in summary_files:
file_path = FileHandler.join(self._summary_dir, filename)
if filename != self._latest_filename:
self._summary_file_handler = FileHandler(file_path, 'rb')
self._latest_filename = filename
self._latest_file_size = 0
new_size = FileHandler.file_stat(file_path).size
if new_size == self._latest_file_size:
continue
try:
if not self._load_single_file(self._summary_file_handler,
executor, events_data):
self._latest_file_size = self._summary_file_handler.offset
else:
self._latest_file_size = new_size
# Wait for data in this file to be processed to avoid loading multiple files at the same time.
logger.debug("Parse summary file offset %d, file path: %s.",
self._latest_file_size, file_path)
return False
except UnknownError as ex:
logger.warning(
"Parse summary file failed, detail: %r,"
"file path: %s.", str(ex), file_path)
return True
def filter_valid_files(self):
"""
Gets a list of valid files from the given file path.
Returns:
list[str], file name list.
"""
filenames = []
for filename in FileHandler.list_dir(self._summary_dir):
if FileHandler.is_file(FileHandler.join(self._summary_dir, filename)):
filenames.append(filename)
valid_filenames = []
for parser in self._parser_list:
valid_filenames.extend(parser.filter_files(filenames))
return list(set(valid_filenames))
def _generate_loader_from_relative_path(
self, relative_path: str) -> ExplainLoader:
"""Generate explain loader from the given relative path."""
self._check_summary_exist(relative_path)
current_dir = os.path.realpath(
FileHandler.join(self._summary_base_dir, relative_path))
loader_id = self._generate_loader_id(relative_path)
loader = ExplainLoader(loader_id=loader_id, summary_dir=current_dir)
return loader
def __init__(self, summary_file, output):
self.summary_file = summary_file
self._output = output
self._scalar_writer = ScalarWriter(self._output)
self._image_writer = ImageWriter(FileHandler.join(self._output, IMAGE))
self._file_size = 0
self._process_info = 0
self._image_check = False
self._scalar_check = False
def _parse_pb_file(summary_dir, filename):
"""
Parse pb file and write content to `EventsData`.
Args:
filename (str): The file path of pb file.
Returns:
TensorEvent, if load pb file and build graph success, will return tensor event, else return None.
"""
file_path = FileHandler.join(summary_dir, filename)
logger.info("Start to load graph from pb file, file path: %s.",
file_path)
filehandler = FileHandler(file_path)
model_proto = anf_ir_pb2.ModelProto()
try:
model_proto.ParseFromString(filehandler.read())
except ParseError:
logger.warning(
"The given file is not a valid pb file, file path: %s.",
file_path)
return None
graph = MSGraph()
try:
graph.build_graph(model_proto.graph)
except Exception as ex:
# Normally, there are no exceptions, and it is only possible for users on the MindSpore side
# to dump other non-default graphs.
logger.error("Build graph failed, file path: %s.", file_path)
logger.exception(ex)
raise UnknownError(str(ex))
tensor_event = TensorEvent(
wall_time=FileHandler.file_stat(file_path).mtime,
step=0,
tag=filename,
plugin_name=PluginNameEnum.GRAPH.value,
value=graph,
filename=filename)
logger.info("Build graph success, file path: %s.", file_path)
return tensor_event
def _generate_loader_by_relative_path(self, relative_path):
"""
Generate loader by relative path.
Args:
relative_path (str): Relative path of a summary directory, e.g. './log1'.
Returns:
dict[str, LoaderStruct], a dict of `Loader`.
"""
current_dir = os.path.realpath(FileHandler.join(self._summary_path, relative_path))
data_loader = DataLoader(current_dir)
loader_id = self._generate_loader_id(relative_path)
loader = LoaderStruct(loader_id=loader_id,
name=self._generate_loader_name(relative_path),
path=current_dir,
latest_update_time=FileHandler.file_stat(current_dir).mtime,
data_loader=data_loader)
return loader
def _set_latest_file(self, filename):
"""
Check if the file's modification time is newer than the last time it was loaded, and if so, set the time.
Args:
filename (str): The file name that needs to be checked and set.
Returns:
bool, Returns True if the file was modified earlier than the last time it was loaded, or False.
"""
mtime = FileHandler.file_stat(FileHandler.join(self._summary_dir, filename)).mtime
if mtime < self._latest_mtime or \
(mtime == self._latest_mtime and filename <= self._latest_filename):
return False
self._latest_mtime = mtime
self._latest_filename = filename
return True
def run(self, args):
"""
Execute for start command.
Args:
args (Namespace): Parsed arguments to hold customized parameters.
"""
try:
date_time = datetime.datetime.now().strftime(
'output_%Y%m%d_%H%M%S_%f')
output_path = os.path.join(args.output, date_time)
summary_dir = args.summary_dir
if not self._check_dirpath(summary_dir):
return
summary_parser = _SummaryParser(summary_dir)
summary_files = summary_parser.filter_files(
os.listdir(summary_dir))
if not summary_files:
parse_summary_logger.error('Path %s has no summary file.',
summary_dir)
return
summary_files = summary_parser.sort_files(summary_files)
filename = summary_files[-1]
summary_file = FileHandler.join(summary_dir, filename)
if not (self._check_filepath(summary_file)
and self._check_create_filepath(output_path)
and self._check_create_filepath(
FileHandler.join(output_path, 'image'))):
return
eventparser = EventParser(summary_file, output_path)
eventparser.parse()
except Exception as ex:
parse_summary_logger.error(
"Parse summary file failed, detail: %r.", str(ex))
raise UnknownError(str(ex))
def _load_pb_files(self, filenames):
"""
Load and parse the pb files.
Args:
filenames (list[str]): File name list, like [filename1, filename2].
Returns:
list[str], filename list.
"""
pb_filenames = self._filter_pb_files(filenames)
pb_filenames = sorted(
pb_filenames,
key=lambda file: FileHandler.file_stat(
FileHandler.join(self._summary_dir, file)).mtime)
for filename in pb_filenames:
mtime = FileHandler.file_stat(
FileHandler.join(self._summary_dir, filename)).mtime
if mtime <= self._latest_pb_file_mtime:
continue
self._latest_pb_file_mtime = mtime
self._parse_pb_file(filename)
def parse_files(self, executor, filenames, events_data):
"""
Load summary file and parse file content.
Args:
executor (Executor): The executor instance.
filenames (list[str]): File name list.
events_data (EventsData): The container of event data.
"""
self._events_data = events_data
summary_files = self.filter_files(filenames)
summary_files = self.sort_files(summary_files)
for filename in summary_files:
if self._latest_filename and \
(self._compare_summary_file(self._latest_filename, filename)):
continue
file_path = FileHandler.join(self._summary_dir, filename)
if filename != self._latest_filename:
self._summary_file_handler = FileHandler(file_path, 'rb')
self._latest_filename = filename
self._latest_file_size = 0
new_size = FileHandler.file_stat(file_path).size
if new_size == self._latest_file_size:
continue
self._latest_file_size = new_size
try:
self._load_single_file(self._summary_file_handler, executor)
# Wait for data in this file to be processed to avoid loading multiple files at the same time.
executor.wait_all_tasks_finish()
except UnknownError as ex:
logger.warning(
"Parse summary file failed, detail: %r,"
"file path: %s.", str(ex), file_path)
def check_path(file_path):
"""
Check argument for file path.
Args:
file_path (str): File path.
"""
if file_path.startswith('~'):
file_path = os.path.realpath(os.path.expanduser(file_path))
if not file_path.startswith('/'):
file_path = os.path.realpath(
FileHandler.join(os.getcwd(), file_path))
return os.path.realpath(file_path)
def generate_loaders(self, loader_pool):
"""
Generate loader from summary path, if summary path is empty, will return empty list.
Args:
loader_pool (dict[str, LoaderStruct]): Current loader pool in data_manager.
Returns:
dict[str, LoaderStruct], a dict of `Loader`.
"""
loader_dict = {}
if not FileHandler.exists(self._summary_path):
logger.warning(
"Summary path does not exist. It will not start loading events data. "
"Current path is %r.", self._summary_path)
return loader_dict
dir_map_mtime_dict = {}
min_modify_time = None
summaries_info = self._summary_watcher.list_summary_directories(
self._summary_path)
for item in summaries_info:
relative_path = item.get("relative_path")
current_dir = FileHandler.join(self._summary_path, relative_path)
dataloader = DataLoader(current_dir)
if not dataloader.has_valid_files():
logger.debug(
"Can not find valid train log file in folder %s , "
"will ignore.", relative_path)
continue
modify_time = item.get("update_time").timestamp()
# if loader exists in loader pool and newer time, update its time
loader_id = self._generate_loader_id(relative_path)
loader = loader_pool.get(loader_id)
if loader is not None and loader.latest_update_time > modify_time:
modify_time = loader.latest_update_time
if not min_modify_time:
# The first load, init min modify time
min_modify_time = modify_time
# We need to find `MAX_DATA_LOADER_SIZE` newly modified folders.
if len(dir_map_mtime_dict) < MAX_DATA_LOADER_SIZE:
if modify_time < min_modify_time:
min_modify_time = modify_time
dir_map_mtime_dict.update({relative_path: modify_time})
else:
if modify_time >= min_modify_time:
dir_map_mtime_dict.update({relative_path: modify_time})
sorted_dir_tuple = sorted(dir_map_mtime_dict.items(),
key=lambda d: d[1])[-MAX_DATA_LOADER_SIZE:]
for relative_path, modify_time in sorted_dir_tuple:
loader_id = self._generate_loader_id(relative_path)
loader = self._generate_loader_by_relative_path(relative_path)
loader_dict.update({loader_id: loader})
return loader_dict
class ExplainParser(_SummaryParser):
"""The summary file parser."""
def __init__(self, summary_dir):
super(ExplainParser, self).__init__(summary_dir)
self._latest_filename = ''
def parse_explain(self, filenames):
"""
Load summary file and parse file content.
Args:
filenames (list[str]): File name list.
Returns:
bool, True if all the summary files are finished loading.
"""
summary_files = self.sort_files(filenames)
is_end = False
is_clean = False
event_data = {}
filename = summary_files[-1]
file_path = FileHandler.join(self._summary_dir, filename)
if filename != self._latest_filename:
self._summary_file_handler = FileHandler(file_path, 'rb')
self._latest_filename = filename
self._latest_file_size = 0
is_clean = True
new_size = FileHandler.file_stat(file_path).size
if new_size == self._latest_file_size:
is_end = True
return is_clean, is_end, event_data
while True:
start_offset = self._summary_file_handler.offset
try:
event_str = self.event_load(self._summary_file_handler)
if event_str is None:
self._summary_file_handler.reset_offset(start_offset)
is_end = True
return is_clean, is_end, event_data
if len(event_str) > MAX_EVENT_STRING:
logger.warning(
"file_path: %s, event string: %d exceeds %d and drop it.",
self._summary_file_handler.file_path, len(event_str),
MAX_EVENT_STRING)
continue
field_list, tensor_value_list = self._event_decode(event_str)
for field, tensor_value in zip(field_list, tensor_value_list):
event_data[field] = tensor_value
logger.info("Parse summary file offset %d, file path: %s.",
self._summary_file_handler.offset, file_path)
return is_clean, is_end, event_data
except (exceptions.CRCFailedError,
exceptions.CRCLengthFailedError) as ex:
self._summary_file_handler.reset_offset(start_offset)
is_end = True
logger.warning(
"Check crc failed and ignore this file, file_path=%s, offset=%s. Detail: %r.",
self._summary_file_handler.file_path,
self._summary_file_handler.offset, str(ex))
return is_clean, is_end, event_data
except (OSError, DecodeError,
exceptions.MindInsightException) as ex:
is_end = True
logger.warning(
"Parse log file fail, and ignore this file, detail: %r,"
"file path: %s.", str(ex),
self._summary_file_handler.file_path)
return is_clean, is_end, event_data
except Exception as ex:
logger.exception(ex)
raise UnknownError(str(ex))
@staticmethod
def _event_decode(event_str):
"""
Transform `Event` data to tensor_event and update it to EventsData.
Args:
event_str (str): Message event string in summary proto, data read from file handler.
"""
logger.debug("Start to parse event string. Event string len: %s.",
len(event_str))
event = summary_pb2.Event.FromString(event_str)
logger.debug("Deserialize event string completed.")
fields = {
'sample_id': ExplainFieldsEnum.SAMPLE_ID,
'benchmark': ExplainFieldsEnum.BENCHMARK,
'metadata': ExplainFieldsEnum.METADATA
}
tensor_event_value = getattr(event, 'explain')
field_list = []
tensor_value_list = []
for field in fields:
if not getattr(tensor_event_value, field, False):
continue
if ExplainFieldsEnum.METADATA.value == field and not tensor_event_value.metadata.label:
continue
tensor_value = None
if field == ExplainFieldsEnum.SAMPLE_ID.value:
tensor_value = ExplainParser._add_image_data(
tensor_event_value)
elif field == ExplainFieldsEnum.BENCHMARK.value:
tensor_value = ExplainParser._add_benchmark(tensor_event_value)
elif field == ExplainFieldsEnum.METADATA.value:
tensor_value = ExplainParser._add_metadata(tensor_event_value)
logger.debug("Event generated, label is %s, step is %s.", field,
event.step)
field_list.append(field)
tensor_value_list.append(tensor_value)
return field_list, tensor_value_list
@staticmethod
def _add_image_data(tensor_event_value):
"""
Parse image data based on sample_id in Explain message
Args:
tensor_event_value: the object of Explain message
"""
inference = InferfenceContainer(
ground_truth_prob=tensor_event_value.inference.ground_truth_prob,
ground_truth_prob_sd=tensor_event_value.inference.
ground_truth_prob_sd,
ground_truth_prob_itl95_low=tensor_event_value.inference.
ground_truth_prob_itl95_low,
ground_truth_prob_itl95_hi=tensor_event_value.inference.
ground_truth_prob_itl95_hi,
predicted_label=tensor_event_value.inference.predicted_label,
predicted_prob=tensor_event_value.inference.predicted_prob,
predicted_prob_sd=tensor_event_value.inference.predicted_prob_sd,
predicted_prob_itl95_low=tensor_event_value.inference.
predicted_prob_itl95_low,
predicted_prob_itl95_hi=tensor_event_value.inference.
predicted_prob_itl95_hi)
sample_data = SampleContainer(
sample_id=tensor_event_value.sample_id,
image_path=tensor_event_value.image_path,
ground_truth_label=tensor_event_value.ground_truth_label,
inference=inference,
explanation=tensor_event_value.explanation,
status=tensor_event_value.status)
return sample_data
@staticmethod
def _add_benchmark(tensor_event_value):
"""
Parse benchmark data from Explain message.
Args:
tensor_event_value: the object of Explain message
Returns:
benchmark_data: An object containing benchmark.
"""
benchmark_data = BenchmarkContainer(
benchmark=tensor_event_value.benchmark,
status=tensor_event_value.status)
return benchmark_data
@staticmethod
def _add_metadata(tensor_event_value):
"""
Parse metadata from Explain message.
Args:
tensor_event_value: the object of Explain message
Returns:
benchmark_data: An object containing metadata.
"""
metadata_value = MetadataContainer(
metadata=tensor_event_value.metadata,
status=tensor_event_value.status)
return metadata_value
def parse_explain(self, filenames):
"""
Load summary file and parse file content.
Args:
filenames (list[str]): File name list.
Returns:
bool, True if all the summary files are finished loading.
"""
summary_files = self.sort_files(filenames)
is_end = False
is_clean = False
event_data = {}
filename = summary_files[-1]
file_path = FileHandler.join(self._summary_dir, filename)
if filename != self._latest_filename:
self._summary_file_handler = FileHandler(file_path, 'rb')
self._latest_filename = filename
self._latest_file_size = 0
is_clean = True
new_size = FileHandler.file_stat(file_path).size
if new_size == self._latest_file_size:
is_end = True
return is_clean, is_end, event_data
while True:
start_offset = self._summary_file_handler.offset
try:
event_str = self.event_load(self._summary_file_handler)
if event_str is None:
self._summary_file_handler.reset_offset(start_offset)
is_end = True
return is_clean, is_end, event_data
if len(event_str) > MAX_EVENT_STRING:
logger.warning(
"file_path: %s, event string: %d exceeds %d and drop it.",
self._summary_file_handler.file_path, len(event_str),
MAX_EVENT_STRING)
continue
field_list, tensor_value_list = self._event_decode(event_str)
for field, tensor_value in zip(field_list, tensor_value_list):
event_data[field] = tensor_value
logger.info("Parse summary file offset %d, file path: %s.",
self._summary_file_handler.offset, file_path)
return is_clean, is_end, event_data
except (exceptions.CRCFailedError,
exceptions.CRCLengthFailedError) as ex:
self._summary_file_handler.reset_offset(start_offset)
is_end = True
logger.warning(
"Check crc failed and ignore this file, file_path=%s, offset=%s. Detail: %r.",
self._summary_file_handler.file_path,
self._summary_file_handler.offset, str(ex))
return is_clean, is_end, event_data
except (OSError, DecodeError,
exceptions.MindInsightException) as ex:
is_end = True
logger.warning(
"Parse log file fail, and ignore this file, detail: %r,"
"file path: %s.", str(ex),
self._summary_file_handler.file_path)
return is_clean, is_end, event_data
except Exception as ex:
logger.exception(ex)
raise UnknownError(str(ex))
def list_events(self, filenames):
"""
Load summary file and parse file content.
Args:
filenames (list[str]): File name list.
Returns:
tuple, the elements of the tuple are:
- file_changed (bool): True if the latest file is changed.
- is_end (bool): True if all the summary files are finished loading.
- event_data (dict): Event data where keys are explanation field.
"""
summary_files = self.sort_files(filenames)
is_end = False
file_changed = False
event_data = {}
filename = summary_files[-1]
file_path = FileHandler.join(self._summary_dir, filename)
if filename != self._latest_filename:
self._summary_file_handler = FileHandler(file_path, 'rb')
self._latest_filename = filename
self._latest_offset = 0
file_changed = True
new_size = FileHandler.file_stat(file_path).size
if new_size == self._latest_offset:
is_end = True
return file_changed, is_end, event_data
while True:
start_offset = self._summary_file_handler.offset
try:
event_str = self.event_load(self._summary_file_handler)
if event_str is None:
self._summary_file_handler.reset_offset(start_offset)
is_end = True
return file_changed, is_end, event_data
if len(event_str) > MAX_EVENT_STRING:
logger.warning(
"file_path: %s, event string: %d exceeds %d and drop it.",
self._summary_file_handler.file_path, len(event_str),
MAX_EVENT_STRING)
continue
field_list, tensor_value_list = self._event_decode(event_str)
for field, tensor_value in zip(field_list, tensor_value_list):
event_data[field] = tensor_value
logger.debug("Parse summary file offset %d, file path: %s.",
self._summary_file_handler.offset, file_path)
return file_changed, is_end, event_data
except exceptions.CRCLengthFailedError as ex:
self._summary_file_handler.reset_offset(start_offset)
is_end = True
logger.warning(
"Check crc failed and reset offset, file_path=%s, offset=%s. Detail: %r.",
self._summary_file_handler.file_path,
self._summary_file_handler.offset, str(ex))
return file_changed, is_end, event_data
except Exception as ex:
# Note: If an unknown error occurs, we will set the offset to the end of this file,
# which is equivalent to stopping parsing this file. We do not delete the current job
# and retain the data that has been successfully parsed.
self._summary_file_handler.reset_offset(new_size)
# Notice: If the current job is the latest one in the loader pool and the job is deleted,
# the job goes into an infinite cycle of load-fail-delete-reload-load-fail-delete.
# We need to prevent this infinite loop.
logger.error(
"Parse summary file failed, will set offset to the file end. file_path: %s, "
"offset: %d, detail: %s.", file_path,
self._summary_file_handler.offset, str(ex))
logger.exception(ex)
raise UnknownError(str(ex))
finally:
self._latest_offset = self._summary_file_handler.offset
class ExplainParser(_SummaryParser):
"""The summary file parser."""
def __init__(self, summary_dir):
super(ExplainParser, self).__init__(summary_dir)
self._latest_offset = 0
def list_events(self, filenames):
"""
Load summary file and parse file content.
Args:
filenames (list[str]): File name list.
Returns:
tuple, the elements of the tuple are:
- file_changed (bool): True if the latest file is changed.
- is_end (bool): True if all the summary files are finished loading.
- event_data (dict): Event data where keys are explanation field.
"""
summary_files = self.sort_files(filenames)
is_end = False
file_changed = False
event_data = {}
filename = summary_files[-1]
file_path = FileHandler.join(self._summary_dir, filename)
if filename != self._latest_filename:
self._summary_file_handler = FileHandler(file_path, 'rb')
self._latest_filename = filename
self._latest_offset = 0
file_changed = True
new_size = FileHandler.file_stat(file_path).size
if new_size == self._latest_offset:
is_end = True
return file_changed, is_end, event_data
while True:
start_offset = self._summary_file_handler.offset
try:
event_str = self.event_load(self._summary_file_handler)
if event_str is None:
self._summary_file_handler.reset_offset(start_offset)
is_end = True
return file_changed, is_end, event_data
if len(event_str) > MAX_EVENT_STRING:
logger.warning(
"file_path: %s, event string: %d exceeds %d and drop it.",
self._summary_file_handler.file_path, len(event_str),
MAX_EVENT_STRING)
continue
field_list, tensor_value_list = self._event_decode(event_str)
for field, tensor_value in zip(field_list, tensor_value_list):
event_data[field] = tensor_value
logger.debug("Parse summary file offset %d, file path: %s.",
self._summary_file_handler.offset, file_path)
return file_changed, is_end, event_data
except exceptions.CRCLengthFailedError as ex:
self._summary_file_handler.reset_offset(start_offset)
is_end = True
logger.warning(
"Check crc failed and reset offset, file_path=%s, offset=%s. Detail: %r.",
self._summary_file_handler.file_path,
self._summary_file_handler.offset, str(ex))
return file_changed, is_end, event_data
except Exception as ex:
# Note: If an unknown error occurs, we will set the offset to the end of this file,
# which is equivalent to stopping parsing this file. We do not delete the current job
# and retain the data that has been successfully parsed.
self._summary_file_handler.reset_offset(new_size)
# Notice: If the current job is the latest one in the loader pool and the job is deleted,
# the job goes into an infinite cycle of load-fail-delete-reload-load-fail-delete.
# We need to prevent this infinite loop.
logger.error(
"Parse summary file failed, will set offset to the file end. file_path: %s, "
"offset: %d, detail: %s.", file_path,
self._summary_file_handler.offset, str(ex))
logger.exception(ex)
raise UnknownError(str(ex))
finally:
self._latest_offset = self._summary_file_handler.offset
@staticmethod
def _event_decode(event_str):
"""
Transform `Event` data to tensor_event and update it to EventsData.
Args:
event_str (str): Message event string in summary proto, data read from file handler.
Returns:
tuple, the elements of the result tuple are:
- field_list (list): Explain fields to be parsed.
- tensor_value_list (list): Parsed data with respect to the field list.
"""
logger.debug("Start to parse event string. Event string len: %s.",
len(event_str))
event = xai_pb2.Event.FromString(event_str)
logger.debug("Deserialize event string completed.")
fields = {
'sample_id': ExplainFieldsEnum.SAMPLE_ID,
'benchmark': ExplainFieldsEnum.BENCHMARK,
'metadata': ExplainFieldsEnum.METADATA
}
tensor_event_value = getattr(event, 'explain')
field_list = []
tensor_value_list = []
for field in fields:
if getattr(tensor_event_value, field, None) is None:
continue
if ExplainFieldsEnum.METADATA.value == field and not tensor_event_value.metadata.label:
continue
tensor_value = None
if field == ExplainFieldsEnum.SAMPLE_ID.value:
tensor_value = ExplainParser._add_image_data(
tensor_event_value)
elif field == ExplainFieldsEnum.BENCHMARK.value:
tensor_value = ExplainParser._add_benchmark(tensor_event_value)
elif field == ExplainFieldsEnum.METADATA.value:
tensor_value = ExplainParser._add_metadata(tensor_event_value)
logger.debug("Event generated, label is %s, step is %s.", field,
event.step)
field_list.append(field)
tensor_value_list.append(tensor_value)
return field_list, tensor_value_list
@staticmethod
def _add_image_data(tensor_event_value):
"""
Parse image data based on sample_id in Explain message.
Args:
tensor_event_value (Event): The object of Explain message.
Returns:
SampleContainer, a named tuple containing sample data.
"""
inference = InferfenceContainer(
ground_truth_prob=tensor_event_value.inference.ground_truth_prob,
ground_truth_prob_sd=tensor_event_value.inference.
ground_truth_prob_sd,
ground_truth_prob_itl95_low=tensor_event_value.inference.
ground_truth_prob_itl95_low,
ground_truth_prob_itl95_hi=tensor_event_value.inference.
ground_truth_prob_itl95_hi,
predicted_label=tensor_event_value.inference.predicted_label,
predicted_prob=tensor_event_value.inference.predicted_prob,
predicted_prob_sd=tensor_event_value.inference.predicted_prob_sd,
predicted_prob_itl95_low=tensor_event_value.inference.
predicted_prob_itl95_low,
predicted_prob_itl95_hi=tensor_event_value.inference.
predicted_prob_itl95_hi)
sample_data = SampleContainer(
sample_id=tensor_event_value.sample_id,
image_path=tensor_event_value.image_path,
ground_truth_label=tensor_event_value.ground_truth_label,
inference=inference,
explanation=tensor_event_value.explanation,
hierarchical_occlusion=tensor_event_value.hoc,
status=tensor_event_value.status)
return sample_data
@staticmethod
def _add_benchmark(tensor_event_value):
"""
Parse benchmark data from Explain message.
Args:
tensor_event_value (Event): The object of Explain message.
Returns:
BenchmarkContainer, a named tuple containing benchmark data.
"""
benchmark_data = BenchmarkContainer(
benchmark=tensor_event_value.benchmark,
status=tensor_event_value.status)
return benchmark_data
@staticmethod
def _add_metadata(tensor_event_value):
"""
Parse metadata from Explain message.
Args:
tensor_event_value (Event): The object of Explain message.
Returns:
MetadataContainer, a named tuple containing benchmark data.
"""
metadata_value = MetadataContainer(
metadata=tensor_event_value.metadata,
status=tensor_event_value.status)
return metadata_value
def abc():
FileHandler.is_file('aaa')
print('after')
