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# 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
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