mindspore-mindinsight/mindinsight/explainer/manager/event_parse.py

# 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.
# ============================================================================
"""EventParser for summary event."""
from collections import namedtuple, defaultdict
from typing import Dict, List, Optional, Tuple

from mindinsight.explainer.common.enums import PluginNameEnum
from mindinsight.explainer.common.log import logger
from mindinsight.utils.exceptions import UnknownError

_IMAGE_DATA_TAGS = {
    'sample_id': PluginNameEnum.SAMPLE_ID.value,
    'ground_truth_label': PluginNameEnum.GROUND_TRUTH_LABEL.value,
    'inference': PluginNameEnum.INFERENCE.value,
    'explanation': PluginNameEnum.EXPLANATION.value
}

_NUM_DIGIT = 7


class EventParser:
    """Parser for event data."""

    def __init__(self, job):
        self._job = job
        self._sample_pool = {}

    @staticmethod
    def parse_metadata(metadata) -> Tuple[List, List, List]:
        """Parse the metadata event."""
        explainers = list(metadata.explain_method)
        metrics = list(metadata.benchmark_method)
        labels = list(metadata.label)
        return explainers, metrics, labels

    @staticmethod
    def parse_benchmark(benchmarks) -> Tuple[Dict, Dict]:
        """Parse the benchmark event."""
        explainer_score_dict = defaultdict(list)
        label_score_dict = defaultdict(dict)

        for benchmark in benchmarks:
            explainer = benchmark.explain_method
            metric = benchmark.benchmark_method
            metric_score = benchmark.total_score
            label_score_event = benchmark.label_score

            explainer_score_dict[explainer].append({
                'metric': metric,
                'score': round(metric_score, _NUM_DIGIT)})
            new_label_score_dict = EventParser._score_event_to_dict(label_score_event, metric)
            for label, label_scores in new_label_score_dict.items():
                label_score_dict[explainer][label] = label_score_dict[explainer].get(label, []) + label_scores

        return explainer_score_dict, label_score_dict

    def parse_sample(self, sample: namedtuple) -> Optional[namedtuple]:
        """Parse the sample event."""
        sample_id = sample.sample_id

        if sample_id not in self._sample_pool:
            self._sample_pool[sample_id] = sample
            return None

        for tag in _IMAGE_DATA_TAGS:
            try:
                if tag == PluginNameEnum.INFERENCE.value:
                    self._parse_inference(sample, sample_id)
                elif tag == PluginNameEnum.EXPLANATION.value:
                    self._parse_explanation(sample, sample_id)
                else:
                    self._parse_sample_info(sample, sample_id, tag)
            except UnknownError as ex:
                logger.warning("Parse %s data failed within image related data, detail: %r", tag, str(ex))
                continue

        if EventParser._is_ready_for_display(self._sample_pool[sample_id]):
            return self._sample_pool[sample_id]
        return None

    def clear(self):
        """Clear the loaded data."""
        self._sample_pool.clear()

    @staticmethod
    def _is_ready_for_display(image_container: namedtuple) -> bool:
        """
        Check whether the image_container is ready for frontend display.

        Args:
            image_container (namedtuple): container consists of sample data

        Return:
            bool: whether the image_container if ready for display
        """
        required_attrs = ['image_path', 'ground_truth_label', 'inference']
        for attr in required_attrs:
            if not EventParser.is_attr_ready(image_container, attr):
                return False
        return True

    @staticmethod
    def is_attr_ready(image_container: namedtuple, attr: str) -> bool:
        """
        Check whether the given attribute is ready in image_container.

        Args:
            image_container (namedtuple): container consist of sample data
            attr (str): attribute to check

        Returns:
            bool, whether the attr is ready
        """
        if getattr(image_container, attr, False):
            return True
        return False

    @staticmethod
    def _score_event_to_dict(label_score_event, metric):
        """Transfer metric scores per label to pre-defined structure."""
        new_label_score_dict = defaultdict(list)
        for label_id, label_score in enumerate(label_score_event):
            new_label_score_dict[label_id].append({
                'metric': metric,
                'score': round(label_score, _NUM_DIGIT),
            })
        return new_label_score_dict

    def _parse_inference(self, event, sample_id):
        """Parse the inference event."""
        self._sample_pool[sample_id].inference.ground_truth_prob.extend(event.inference.ground_truth_prob)
        self._sample_pool[sample_id].inference.ground_truth_prob_sd.extend(event.inference.ground_truth_prob_sd)
        self._sample_pool[sample_id].inference.ground_truth_prob_itl95_low.\
            extend(event.inference.ground_truth_prob_itl95_low)
        self._sample_pool[sample_id].inference.ground_truth_prob_itl95_hi.\
            extend(event.inference.ground_truth_prob_itl95_hi)

        self._sample_pool[sample_id].inference.predicted_label.extend(event.inference.predicted_label)
        self._sample_pool[sample_id].inference.predicted_prob.extend(event.inference.predicted_prob)
        self._sample_pool[sample_id].inference.predicted_prob_sd.extend(event.inference.predicted_prob_sd)
        self._sample_pool[sample_id].inference.predicted_prob_itl95_low.extend(event.inference.predicted_prob_itl95_low)
        self._sample_pool[sample_id].inference.predicted_prob_itl95_hi.extend(event.inference.predicted_prob_itl95_hi)

    def _parse_explanation(self, event, sample_id):
        """Parse the explanation event."""
        if event.explanation:
            for explanation_item in event.explanation:
                new_explanation = self._sample_pool[sample_id].explanation.add()
                new_explanation.explain_method = explanation_item.explain_method
                new_explanation.label = explanation_item.label
                new_explanation.heatmap_path = explanation_item.heatmap_path

    def _parse_sample_info(self, event, sample_id, tag):
        """Parse the event containing image info."""
        if not getattr(self._sample_pool[sample_id], tag):
            setattr(self._sample_pool[sample_id], tag, getattr(event, tag))