# Copyright (C) 2019-2020 Intel Corporation # # SPDX-License-Identifier: MIT import argparse import logging as log import os import os.path as osp from datumaro.components.project import Project from datumaro.util.command_targets import (TargetKinds, target_selector, ProjectTarget, SourceTarget, ImageTarget, is_project_path) from datumaro.util.image import load_image, save_image from ..util import MultilineFormatter from ..util.project import load_project def build_parser(parser_ctor=argparse.ArgumentParser): parser = parser_ctor(help="Run Explainable AI algorithm", description="Runs an explainable AI algorithm for a model.") parser.add_argument('-m', '--model', required=True, help="Model to use for inference") parser.add_argument('-t', '--target', default=None, help="Inference target - image, source, project " "(default: current dir)") parser.add_argument('-o', '--output-dir', dest='save_dir', default=None, help="Directory to save output (default: display only)") method_sp = parser.add_subparsers(dest='algorithm') rise_parser = method_sp.add_parser('rise', description=""" RISE: Randomized Input Sampling for Explanation of Black-box Models algorithm|n |n See explanations at: https://arxiv.org/pdf/1806.07421.pdf """, formatter_class=MultilineFormatter) rise_parser.add_argument('-s', '--max-samples', default=None, type=int, help="Number of algorithm iterations (default: mask size ^ 2)") rise_parser.add_argument('--mw', '--mask-width', dest='mask_width', default=7, type=int, help="Mask width (default: %(default)s)") rise_parser.add_argument('--mh', '--mask-height', dest='mask_height', default=7, type=int, help="Mask height (default: %(default)s)") rise_parser.add_argument('--prob', default=0.5, type=float, help="Mask pixel inclusion probability (default: %(default)s)") rise_parser.add_argument('--iou', '--iou-thresh', dest='iou_thresh', default=0.9, type=float, help="IoU match threshold for detections (default: %(default)s)") rise_parser.add_argument('--nms', '--nms-iou-thresh', dest='nms_iou_thresh', default=0.0, type=float, help="IoU match threshold in Non-maxima suppression (default: no NMS)") rise_parser.add_argument('--conf', '--det-conf-thresh', dest='det_conf_thresh', default=0.0, type=float, help="Confidence threshold for detections (default: include all)") rise_parser.add_argument('-b', '--batch-size', default=1, type=int, help="Inference batch size (default: %(default)s)") rise_parser.add_argument('--display', action='store_true', help="Visualize results during computations") parser.add_argument('-p', '--project', dest='project_dir', default='.', help="Directory of the project to operate on (default: current dir)") parser.set_defaults(command=explain_command) return parser def explain_command(args): project_path = args.project_dir if is_project_path(project_path): project = Project.load(project_path) else: project = None args.target = target_selector( ProjectTarget(is_default=True, project=project), SourceTarget(project=project), ImageTarget() )(args.target) if args.target[0] == TargetKinds.project: if is_project_path(args.target[1]): args.project_dir = osp.dirname(osp.abspath(args.target[1])) import cv2 from matplotlib import cm project = load_project(args.project_dir) model = project.make_executable_model(args.model) if str(args.algorithm).lower() != 'rise': raise NotImplementedError() from datumaro.components.algorithms.rise import RISE rise = RISE(model, max_samples=args.max_samples, mask_width=args.mask_width, mask_height=args.mask_height, prob=args.prob, iou_thresh=args.iou_thresh, nms_thresh=args.nms_iou_thresh, det_conf_thresh=args.det_conf_thresh, batch_size=args.batch_size) if args.target[0] == TargetKinds.image: image_path = args.target[1] image = load_image(image_path) log.info("Running inference explanation for '%s'" % image_path) heatmap_iter = rise.apply(image, progressive=args.display) image = image / 255.0 file_name = osp.splitext(osp.basename(image_path))[0] if args.display: for i, heatmaps in enumerate(heatmap_iter): for j, heatmap in enumerate(heatmaps): hm_painted = cm.jet(heatmap)[:, :, 2::-1] disp = (image + hm_painted) / 2 cv2.imshow('heatmap-%s' % j, hm_painted) cv2.imshow(file_name + '-heatmap-%s' % j, disp) cv2.waitKey(10) print("Iter", i, "of", args.max_samples, end='\r') else: heatmaps = next(heatmap_iter) if args.save_dir is not None: log.info("Saving inference heatmaps at '%s'" % args.save_dir) os.makedirs(args.save_dir, exist_ok=True) for j, heatmap in enumerate(heatmaps): save_path = osp.join(args.save_dir, file_name + '-heatmap-%s.png' % j) save_image(save_path, heatmap * 255.0) else: for j, heatmap in enumerate(heatmaps): disp = (image + cm.jet(heatmap)[:, :, 2::-1]) / 2 cv2.imshow(file_name + '-heatmap-%s' % j, disp) cv2.waitKey(0) elif args.target[0] == TargetKinds.source or \ args.target[0] == TargetKinds.project: if args.target[0] == TargetKinds.source: source_name = args.target[1] dataset = project.make_source_project(source_name).make_dataset() log.info("Running inference explanation for '%s'" % source_name) else: project_name = project.config.project_name dataset = project.make_dataset() log.info("Running inference explanation for '%s'" % project_name) for item in dataset: image = item.image.data if image is None: log.warn( "Dataset item %s does not have image data. Skipping." % \ (item.id)) continue heatmap_iter = rise.apply(image) image = image / 255.0 heatmaps = next(heatmap_iter) if args.save_dir is not None: log.info("Saving inference heatmaps to '%s'" % args.save_dir) os.makedirs(args.save_dir, exist_ok=True) for j, heatmap in enumerate(heatmaps): save_image(osp.join(args.save_dir, item.id + '-heatmap-%s.png' % j), heatmap * 255.0, create_dir=True) if not args.save_dir or args.display: for j, heatmap in enumerate(heatmaps): disp = (image + cm.jet(heatmap)[:, :, 2::-1]) / 2 cv2.imshow(item.id + '-heatmap-%s' % j, disp) cv2.waitKey(0) else: raise NotImplementedError() return 0