def list_explain_directories(self, summary_base_dir, offset=0, limit=None):
"""
List explain directories within base directory.
Args:
summary_base_dir (str): Path of summary base directory.
offset (int): An offset for page. Ex, offset is 0, mean current page is 1. Default value is 0.
limit (int): The max data items for per page. Default value is 10.
Returns:
tuple[total, directories], total indicates the overall number of explain directories and directories
indicate list of summary directory info including the following attributes.
- relative_path (str): Relative path of summary directory, referring to settings.SUMMARY_BASE_DIR,
starting with "./".
- create_time (datetime): Creation time of summary file.
- update_time (datetime): Modification time of summary file.
Raises:
ParamValueError, if offset < 0 or limit is out of valid value range.
ParamTypeError, if offset or limit is not valid integer.
Examples:
>>> from mindinsight.datavisual.data_transform.summary_watcher import SummaryWatcher
>>> summary_watcher = SummaryWatcher()
>>> total, directories = summary_watcher.list_explain_directories('/summary/base/dir', offset=0, limit=10)
"""
offset = Validation.check_offset(offset=offset)
limit = Validation.check_limit(limit, min_value=1, max_value=999, default_value=None)
directories = self.list_summary_directories(summary_base_dir, overall=False, list_explain=True)
if limit is None:
return len(directories), directories
return len(directories), directories[offset * limit:(offset + 1) * limit]
def query_explain_jobs():
"""
Query explain jobs.
Returns:
Response, contains dict that stores base directory, total number of jobs and their detailed job metadata.
Raises:
ParamMissError: If train_id info is not in the request.
ParamTypeError: If one of (offset, limit) is not integer in the request.
ParamValueError: If one of (offset, limit) does not have the expected value in the request.
"""
offset = request.args.get("offset", default=0)
limit = request.args.get("limit", default=10)
offset = Validation.check_offset(offset=offset)
limit = Validation.check_limit(
limit, min_value=1, max_value=SummaryWatcher.MAX_SUMMARY_DIR_COUNT)
encapsulator = ExplainJobEncap(EXPLAIN_MANAGER)
total, jobs = encapsulator.query_explain_jobs(offset, limit)
return jsonify({
'name':
os.path.basename(os.path.realpath(settings.SUMMARY_BASE_DIR)),
'total':
total,
'explain_jobs':
jobs,
})
def _get_query_sample_parameters(data):
"""
Get parameter for query.
Args:
data (dict): Dict that contains request info.
Returns:
dict, key-value pairs to call backend query functions.
Raises:
ParamMissError: If train_id info is not in the request.
ParamTypeError: If certain key is not in the expected type in the request.
ParamValueError: If certain key does not have the expected value in the request.
"""
train_id = data.get("train_id")
if train_id is None:
raise ParamMissError('train_id')
labels = data.get("labels")
if labels is not None:
_validate_type(labels, "labels", list)
if labels:
for item in labels:
_validate_type(item, "element of labels", str)
limit = data.get("limit", 10)
limit = Validation.check_limit(limit, min_value=1, max_value=100)
offset = data.get("offset", 0)
offset = Validation.check_offset(offset=offset)
sorted_name = data.get("sorted_name", "")
_validate_value(sorted_name, "sorted_name",
('', 'confidence', 'uncertainty'))
sorted_type = data.get("sorted_type", "descending")
_validate_value(sorted_type, "sorted_type", ("ascending", "descending"))
prediction_types = data.get("prediction_types")
if prediction_types is not None:
_validate_type(prediction_types, "element of labels", list)
if prediction_types:
for item in prediction_types:
_validate_value(item, "element of prediction_types",
('TP', 'FN', 'FP'))
query_kwarg = {
"train_id": train_id,
"labels": labels,
"limit": limit,
"offset": offset,
"sorted_name": sorted_name,
"sorted_type": sorted_type,
"prediction_types": prediction_types
}
return query_kwarg
def search_node_names(self, search_content, offset, limit):
"""
Search node names by search content.
Args:
search_content (Any): This content can be the key content of the node to search.
offset (int): An offset for page. Ex, offset is 0, mean current page is 1.
limit (int): The max data items for per page.
Returns:
TypedDict('Names', {'names': list[str]}), {"names": ["node_names"]}.
"""
offset = Validation.check_offset(offset=offset)
limit = Validation.check_limit(limit, min_value=1, max_value=1000)
names = self._graph.search_node_names(search_content, offset, limit)
return {"names": names}
def query_train_jobs():
"""Query train jobs."""
offset = request.args.get("offset", default=0)
limit = request.args.get("limit", default=10)
offset = Validation.check_offset(offset=offset)
limit = Validation.check_limit(limit, min_value=1, max_value=SummaryWatcher.MAX_SUMMARY_DIR_COUNT)
processor = TrainTaskManager(DATA_MANAGER)
total, train_jobs = processor.query_train_jobs(offset, limit)
return jsonify({
'name': os.path.basename(os.path.realpath(settings.SUMMARY_BASE_DIR)),
'total': total,
'train_jobs': train_jobs,
})
def query_explain_jobs():
"""Query explain jobs."""
offset = request.args.get("offset", default=0)
limit = request.args.get("limit", default=10)
offset = Validation.check_offset(offset=offset)
limit = Validation.check_limit(
limit, min_value=1, max_value=SummaryWatcher.MAX_SUMMARY_DIR_COUNT)
encapsulator = ExplainJobEncap(EXPLAIN_MANAGER)
total, jobs = encapsulator.query_explain_jobs(offset, limit)
return jsonify({
'name':
os.path.basename(os.path.realpath(settings.SUMMARY_BASE_DIR)),
'total':
total,
'explain_jobs':
jobs,
})
def search_node_names(self, search_content, offset, limit):
"""
Search node names by search content.
Args:
search_content (Any): This content can be the key content of the node to search.
offset (int): An offset for page. Ex, offset is 0, mean current page is 1.
limit (int): The max data items for per page.
Returns:
Dict, the searched nodes.
"""
offset = Validation.check_offset(offset=offset)
limit = Validation.check_limit(limit, min_value=1, max_value=1000)
nodes = self._graph.search_nodes_by_pattern(search_content)
real_offset = offset * limit
search_nodes = self._graph.get_nodes(nodes[real_offset:real_offset +
limit])
return {"nodes": search_nodes}
def query_saliency():
"""Query saliency map related results."""
data = _read_post_request(request)
train_id = data.get("train_id")
if train_id is None:
raise ParamMissError('train_id')
labels = data.get("labels")
explainers = data.get("explainers")
limit = data.get("limit", 10)
limit = Validation.check_limit(limit, min_value=1, max_value=100)
offset = data.get("offset", 0)
offset = Validation.check_offset(offset=offset)
sorted_name = data.get("sorted_name", "")
sorted_type = data.get("sorted_type", "descending")
if sorted_name not in ("", "confidence", "uncertainty"):
raise ParamValueError(
f"sorted_name: {sorted_name}, valid options: '' 'confidence' 'uncertainty'"
)
if sorted_type not in ("ascending", "descending"):
raise ParamValueError(
f"sorted_type: {sorted_type}, valid options: 'confidence' 'uncertainty'"
)
encapsulator = SaliencyEncap(_image_url_formatter, EXPLAIN_MANAGER)
count, samples = encapsulator.query_saliency_maps(train_id=train_id,
labels=labels,
explainers=explainers,
limit=limit,
offset=offset,
sorted_name=sorted_name,
sorted_type=sorted_type)
return jsonify({"count": count, "samples": samples})
