mindspore-mindinsight/mindinsight/backend/explainer/explainer_api.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.
# ============================================================================
"""Explainer restful api."""

import os
import json
import urllib.parse

from flask import Blueprint
from flask import jsonify
from flask import request

from mindinsight.conf import settings
from mindinsight.utils.exceptions import ParamMissError
from mindinsight.utils.exceptions import ParamValueError
from mindinsight.datavisual.common.exceptions import ImageNotExistError
from mindinsight.datavisual.common.validation import Validation
from mindinsight.datavisual.data_transform.summary_watcher import SummaryWatcher
from mindinsight.datavisual.utils.tools import get_train_id
from mindinsight.explainer.manager.explain_manager import EXPLAIN_MANAGER
from mindinsight.explainer.encapsulator.explain_job_encap import ExplainJobEncap
from mindinsight.explainer.encapsulator.datafile_encap import DatafileEncap
from mindinsight.explainer.encapsulator.saliency_encap import SaliencyEncap
from mindinsight.explainer.encapsulator.evaluation_encap import EvaluationEncap

URL_PREFIX = settings.URL_PATH_PREFIX + settings.API_PREFIX
BLUEPRINT = Blueprint("explainer", __name__, url_prefix=URL_PREFIX)


def _image_url_formatter(train_id, image_path, image_type):
    """Returns image url."""
    data = {
        "train_id": train_id,
        "path": image_path,
        "type": image_type
    }
    return f"{URL_PREFIX}/explainer/image?{urllib.parse.urlencode(data)}"


def _read_post_request(post_request):
    """
    Extract the body of post request.

    Args:
        post_request (object): The post request.

    Returns:
        dict, the deserialized body of request.
    """
    body = post_request.stream.read()
    try:
        body = json.loads(body if body else "{}")
    except json.decoder.JSONDecodeError:
        raise ParamValueError("Json data parse failed.")
    return body


@BLUEPRINT.route("/explainer/explain-jobs", methods=["GET"])
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,
    })


@BLUEPRINT.route("/explainer/explain-job", methods=["GET"])
def query_explain_job():
    """Query explain job meta-data."""
    train_id = get_train_id(request)
    if train_id is None:
        raise ParamMissError("train_id")
    encapsulator = ExplainJobEncap(EXPLAIN_MANAGER)
    metadata = encapsulator.query_meta(train_id)

    return jsonify(metadata)


@BLUEPRINT.route("/explainer/saliency", methods=["POST"])
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
    })


@BLUEPRINT.route("/explainer/evaluation", methods=["GET"])
def query_evaluation():
    """Query saliency explainer evaluation scores."""
    train_id = get_train_id(request)
    if train_id is None:
        raise ParamMissError("train_id")
    encapsulator = EvaluationEncap(EXPLAIN_MANAGER)
    scores = encapsulator.query_explainer_scores(train_id)
    return jsonify({
        "explainer_scores": scores,
    })


@BLUEPRINT.route("/explainer/image", methods=["GET"])
def query_image():
    """Query image."""
    train_id = get_train_id(request)
    if train_id is None:
        raise ParamMissError("train_id")
    image_path = request.args.get("path")
    if image_path is None:
        raise ParamMissError("path")
    image_type = request.args.get("type")
    if image_type is None:
        raise ParamMissError("type")
    if image_type not in ("original", "overlay"):
        raise ParamValueError(f"type:{image_type}, valid options: 'original' 'overlay'")

    encapsulator = DatafileEncap(EXPLAIN_MANAGER)
    image = encapsulator.query_image_binary(train_id, image_path, image_type)
    if image is None:
        raise ImageNotExistError(f"{image_path}")

    return image


def init_module(app):
    """
    Init module entry.

    Args:
        app: the application obj.

    """
    app.register_blueprint(BLUEPRINT)