# Copyright 2020 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. # ============================================================================ """Explain job list encapsulator.""" import copy from datetime import datetime from mindinsight.utils.exceptions import ParamValueError from mindinsight.explainer.encapsulator.explain_data_encap import ExplainDataEncap class ExplainJobEncap(ExplainDataEncap): """Explain job list encapsulator.""" DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S" DEFAULT_MIN_CONFIDENCE = 0.5 def query_explain_jobs(self, offset, limit): """ Query explain job list. Args: offset (int): Page offset. limit (int): Max. no. of items to be returned. Returns: Tuple[int, List[Dict]], total no. of jobs and job list. """ total, dir_infos = self.job_manager.get_job_list(offset=offset, limit=limit) job_infos = [self._dir_2_info(dir_info) for dir_info in dir_infos] return total, job_infos def query_meta(self, train_id): """ Query explain job meta-data. Args: train_id (str): Job ID. Returns: Dict, the metadata. """ job = self.job_manager.get_job(train_id) if job is None: return None return self._job_2_meta(job) def query_image_binary(self, train_id, image_id, image_type): """ Query image binary content. Args: train_id (str): Job ID. image_id (str): Image ID. image_type (str): Image type, 'original' or 'overlay'. Returns: bytes, image binary. """ job = self.job_manager.get_job(train_id) if job is None: return None if image_type == "original": binary = job.retrieve_image(image_id) elif image_type == "overlay": binary = job.retrieve_overlay(image_id) else: raise ParamValueError(f"image_type:{image_type}") return binary @classmethod def _dir_2_info(cls, dir_info): """Convert ExplainJob object to jsonable info object.""" info = dict() info["train_id"] = dir_info["relative_path"] info["create_time"] = dir_info["create_time"].strftime(cls.DATETIME_FORMAT) info["update_time"] = dir_info["update_time"].strftime(cls.DATETIME_FORMAT) return info @classmethod def _job_2_info(cls, job): """Convert ExplainJob object to jsonable info object.""" info = dict() info["train_id"] = job.train_id info["create_time"] = datetime.fromtimestamp(job.create_time)\ .strftime(cls.DATETIME_FORMAT) info["update_time"] = datetime.fromtimestamp(job.latest_update_time)\ .strftime(cls.DATETIME_FORMAT) return info @classmethod def _job_2_meta(cls, job): """Convert ExplainJob's meta-data to jsonable info object.""" info = cls._job_2_info(job) info["sample_count"] = job.sample_count info["classes"] = copy.deepcopy(job.all_classes) saliency_info = dict() if job.min_confidence is None: saliency_info["min_confidence"] = cls.DEFAULT_MIN_CONFIDENCE else: saliency_info["min_confidence"] = job.min_confidence saliency_info["explainers"] = list(job.explainers) saliency_info["metrics"] = list(job.metrics) info["saliency"] = saliency_info info["uncertainty"] = {"enabled": False} return info