from models import vgg19_model
from algorithms import neural_best_buddies as NBBs
from util import util
from tqdm import tqdm
from util import MLS
vgg19 = vgg19_model.define_Vgg19(opt)
img_nums, images = get_imgs()
for imgnum in tqdm(range(start_imgnum, start_imgnum + N)):
save_dir = os.path.join(opt.results_dir, str(img_nums[imgnum]))
print('Working on', images[imgnum])
try:
source_path = os.path.join(opt.source, images[imgnum])
A = util.read_image(source_path, opt.imageSize)
B = util.read_image(opt.target, opt.imageSize)
nbbs = NBBs.sparse_semantic_correspondence(vgg19, opt.gpu_ids, opt.tau,
opt.border_size, save_dir,
opt.k_per_level, opt.k_final,
opt.fast)
points = nbbs.run(A, B)
mls = MLS.MLS(v_class=np.int32)
mls.run_MLS_in_folder(root_folder=save_dir)
except Exception as e:
print(e)
if __name__ == "__main__":
pidfile.exit_if_job_done(pid_file)
main()
pidfile.mark_job_done(pid_file)
def main():
# Training settings
def strpair(arg):
p = tuple(arg.split(':'))
if len(p) == 1:
p = p + p
return p
def intpair(arg):
p = arg.split(',')
if len(p) == 1:
p = p + p
return tuple(int(v) for v in p)
parser = argparse.ArgumentParser(
description='Net dissect utility',
prog='python -m netdissect',
epilog=textwrap.dedent(help_epilog),
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('--model',
type=str,
default=None,
help='constructor for the model to test')
parser.add_argument('--pthfile',
type=str,
default=None,
help='filename of .pth file for the model')
parser.add_argument('--unstrict',
action='store_true',
default=False,
help='ignore unexpected pth parameters')
parser.add_argument('--submodule',
type=str,
default=None,
help='submodule to load from pthfile')
parser.add_argument('--outdir',
type=str,
default='dissect',
help='directory for dissection output')
parser.add_argument('--layers',
type=strpair,
nargs='+',
help='space-separated list of layer names to dissect' +
', in the form layername[:reportedname]')
parser.add_argument('--segments',
type=str,
default='dataset/broden',
help='directory containing segmentation dataset')
parser.add_argument('--segmenter',
type=str,
default=None,
help='constructor for asegmenter class')
parser.add_argument('--download',
action='store_true',
default=False,
help='downloads Broden dataset if needed')
parser.add_argument('--imagedir',
type=str,
default=None,
help='directory containing image-only dataset')
parser.add_argument('--imgsize',
type=intpair,
default=(227, 227),
help='input image size to use')
parser.add_argument('--netname',
type=str,
default=None,
help='name for network in generated reports')
parser.add_argument('--meta',
type=str,
nargs='+',
help='json files of metadata to add to report')
parser.add_argument('--merge',
type=str,
help='json file of unit data to merge in report')
parser.add_argument('--examples',
type=int,
default=20,
help='number of image examples per unit')
parser.add_argument('--size',
type=int,
default=10000,
help='dataset subset size to use')
parser.add_argument('--batch_size',
type=int,
default=100,
help='batch size for forward pass')
parser.add_argument('--num_workers',
type=int,
default=24,
help='number of DataLoader workers')
parser.add_argument('--quantile_threshold',
type=strfloat,
default=None,
choices=[FloatRange(0.0, 1.0), 'iqr'],
help='quantile to use for masks')
parser.add_argument('--no-labels',
action='store_true',
default=False,
help='disables labeling of units')
parser.add_argument('--maxiou',
action='store_true',
default=False,
help='enables maxiou calculation')
parser.add_argument('--covariance',
action='store_true',
default=False,
help='enables covariance calculation')
parser.add_argument('--rank_all_labels',
action='store_true',
default=False,
help='include low-information labels in rankings')
parser.add_argument('--no-images',
action='store_true',
default=False,
help='disables generation of unit images')
parser.add_argument('--no-report',
action='store_true',
default=False,
help='disables generation report summary')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='disables CUDA usage')
parser.add_argument('--gen',
action='store_true',
default=False,
help='test a generator model (e.g., a GAN)')
parser.add_argument('--gan',
action='store_true',
default=False,
help='synonym for --gen')
parser.add_argument('--perturbation',
default=None,
help='filename of perturbation attack to apply')
parser.add_argument('--add_scale_offset',
action='store_true',
default=None,
help='offsets masks according to stride and padding')
parser.add_argument('--quiet',
action='store_true',
default=False,
help='silences console output')
if len(sys.argv) == 1:
parser.print_usage(sys.stderr)
sys.exit(1)
args = parser.parse_args()
args.images = not args.no_images
args.report = not args.no_report
args.labels = not args.no_labels
if args.gan:
args.gen = args.gan
# Set up console output
verbose_progress(not args.quiet)
# Exit right away if job is already done or being done.
if args.outdir is not None:
exit_if_job_done(args.outdir)
# Speed up pytorch
torch.backends.cudnn.benchmark = True
# Special case: download flag without model to test.
if args.model is None and args.download:
from netdissect.broden import ensure_broden_downloaded
for resolution in [224, 227, 384]:
ensure_broden_downloaded(args.segments, resolution, 1)
from netdissect.segmenter import ensure_upp_segmenter_downloaded
ensure_upp_segmenter_downloaded('dataset/segmodel')
sys.exit(0)
# Help if broden is not present
if not args.gen and not args.imagedir and not os.path.isdir(args.segments):
print_progress('Segmentation dataset not found at %s.' % args.segments)
print_progress('Specify dataset directory using --segments [DIR]')
print_progress('To download Broden, run: netdissect --download')
sys.exit(1)
# Default segmenter class
if args.gen and args.segmenter is None:
args.segmenter = ("netdissect.segmenter.UnifiedParsingSegmenter(" +
"segsizes=[256], segdiv=None)")
# Default threshold
if args.quantile_threshold is None:
if args.gen:
args.quantile_threshold = 'iqr'
else:
args.quantile_threshold = 0.005
# Set up CUDA
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.cuda:
torch.backends.cudnn.benchmark = True
# Construct the network with specified layers instrumented
if args.model is None:
print_progress('No model specified')
sys.exit(1)
model = create_instrumented_model(args)
# Update any metadata from files, if any
meta = getattr(model, 'meta', {})
if args.meta:
for mfilename in args.meta:
with open(mfilename) as f:
meta.update(json.load(f))
# Load any merge data from files
mergedata = None
if args.merge:
with open(args.merge) as f:
mergedata = json.load(f)
# Set up the output directory, verify write access
if args.outdir is None:
args.outdir = os.path.join('dissect', type(model).__name__)
exit_if_job_done(args.outdir)
print_progress('Writing output into %s.' % args.outdir)
os.makedirs(args.outdir, exist_ok=True)
train_dataset = None
if not args.gen:
# Load dataset for classifier case.
# Load perturbation
perturbation = numpy.load(
args.perturbation) if args.perturbation else None
segrunner = None
# Load broden dataset
if args.imagedir is not None:
dataset = try_to_load_images(args.imagedir, args.imgsize,
perturbation, args.size)
segrunner = ImageOnlySegRunner(dataset)
else:
dataset = try_to_load_broden(args.segments, args.imgsize, 1,
perturbation, args.download,
args.size)
if dataset is None:
dataset = try_to_load_multiseg(args.segments, args.imgsize,
perturbation, args.size)
if dataset is None:
print_progress('No segmentation dataset found in %s',
args.segments)
print_progress('use --download to download Broden.')
sys.exit(1)
else:
# For segmenter case the dataset is just a random z
#dataset = z_dataset_for_model(model, args.size)
#train_dataset = z_dataset_for_model(model, args.size, seed=2)
dataset = datasets.ImageFolder('dataset/Adissect',
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
(0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))
]))
train_dataset = dataset
segrunner = GeneratorSegRunner(autoimport_eval(args.segmenter))
torch.no_grad()
# Run dissect
dissect(args.outdir,
model,
dataset,
train_dataset=train_dataset,
segrunner=segrunner,
examples_per_unit=args.examples,
netname=args.netname,
quantile_threshold=args.quantile_threshold,
meta=meta,
merge=mergedata,
make_images=args.images,
make_labels=args.labels,
make_maxiou=args.maxiou,
make_covariance=args.covariance,
make_report=args.report,
make_row_images=args.images,
make_single_images=True,
rank_all_labels=args.rank_all_labels,
batch_size=args.batch_size,
num_workers=args.num_workers,
settings=vars(args))
# Mark the directory so that it's not done again.
mark_job_done(args.outdir)