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
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