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| 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 |
|
|
