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validate_pgd.py
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import argparse
import csv
from timm.utils import setup_default_logging
import src.models as models # Import needed to register the extra models that are not in timm
from validate import validate, write_results
parser = argparse.ArgumentParser(description='PyTorch ImageNet Validation')
parser.add_argument('data', metavar='DIR', help='path to dataset')
parser.add_argument('--dataset',
'-d',
metavar='NAME',
default='',
help='dataset type (default: ImageFolder/ImageTar if empty)')
parser.add_argument('--split',
metavar='NAME',
default='validation',
help='dataset split (default: validation)')
parser.add_argument('--dataset-download',
action='store_true',
default=False,
help='Allow download of dataset for torch/ '
'and tfds/ datasets that support it.')
parser.add_argument('--model',
'-m',
metavar='NAME',
default='resnet50',
help='model architecture (default: resnet50)')
parser.add_argument('-j',
'--workers',
default=4,
type=int,
metavar='N',
help='number of data loading workers (default: 2)')
parser.add_argument('-b',
'--batch-size',
default=256,
type=int,
metavar='N',
help='mini-batch size (default: 256)')
parser.add_argument('--img-size',
default=None,
type=int,
metavar='N',
help='Input image dimension, uses model default if empty')
parser.add_argument('--input-size',
default=None,
nargs=3,
type=int,
metavar='N N N',
help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), '
'uses model default if empty')
parser.add_argument('--crop-pct', default=None, type=float, metavar='N', help='Input image center crop pct')
parser.add_argument('--mean',
type=float,
nargs='+',
default=None,
metavar='MEAN',
help='Override mean pixel value of dataset')
parser.add_argument('--std',
type=float,
nargs='+',
default=None,
metavar='STD',
help='Override std deviation of of dataset')
parser.add_argument('--interpolation',
default='',
type=str,
metavar='NAME',
help='Image resize interpolation type (overrides model)')
parser.add_argument('-nn',
'--no-normalize',
action='store_true',
default=False,
help='Avoids normalizing inputs (but it scales them in [0, 1]')
parser.add_argument('--normalize-model',
action='store_true',
default=False,
help='Performs normalization as part of the model')
parser.add_argument('--num-classes', type=int, default=None, help='Number classes in dataset')
parser.add_argument('--class-map',
default='',
type=str,
metavar='FILENAME',
help='path to class to idx mapping file (default: "")')
parser.add_argument('--gp',
default=None,
type=str,
metavar='POOL',
help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.')
parser.add_argument('--log-freq',
default=1,
type=int,
metavar='N',
help='batch logging frequency (default: 10)')
parser.add_argument('--checkpoint',
default='',
type=str,
metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--pretrained', dest='pretrained', action='store_true', help='use pre-trained model')
# parser.add_argument('--num-gpu', type=int, default=1,
# help='Number of GPUS to use')
parser.add_argument('--test-pool', dest='test_pool', action='store_true', help='enable test time pool')
parser.add_argument('--pin-mem',
action='store_true',
default=False,
help='Pin CPU memory in DataLoader for more'
'efficient (sometimes) transfer to GPU.')
parser.add_argument('--channels-last',
action='store_true',
default=False,
help='Use channels_last memory layout')
parser.add_argument('--amp',
action='store_true',
default=False,
help='Use AMP mixed precision. Defaults to Apex, fallback to native Torch AMP.')
parser.add_argument('--tf-preprocessing',
action='store_true',
default=False,
help='Use Tensorflow preprocessing pipeline (require CPU TF installed')
parser.add_argument('--use-ema',
dest='use_ema',
action='store_true',
help='use ema version of weights if present')
parser.add_argument('--torchscript',
dest='torchscript',
action='store_true',
help='convert model torchscript for inference')
parser.add_argument('--results-file',
default='',
type=str,
metavar='FILENAME',
help='Output csv file for validation results (summary)')
parser.add_argument('--real-labels',
default='',
type=str,
metavar='FILENAME',
help='Real labels JSON file for imagenet evaluation')
parser.add_argument('--valid-labels',
default='',
type=str,
metavar='FILENAME',
help='Valid label indices txt file for validation of partial label space')
parser.add_argument('--force-cpu',
action='store_true',
default=False,
help='Force CPU to be used even if HW accelerator exists.')
parser.add_argument('--seed', type=int, default=0, metavar='S', help='random seed (default: 0)')
parser.add_argument('--attack',
default='',
type=str,
metavar='ATTACK',
help='What attack to use (default: "pgd")')
parser.add_argument('--attack-eps',
default=4,
type=float,
metavar='EPS',
help='The epsilon to use for the attack (default 4/255)')
parser.add_argument('--attack-lr',
default=None,
type=float,
metavar='ATTACK_LR',
help='Learning rate for the attack (default 1e-4)')
parser.add_argument('--attack-steps',
default=10,
type=int,
nargs='+',
metavar='ATTACK_STEPS',
help='Number of steps to run attack for (default 10)')
parser.add_argument('--attack-norm',
default='linf',
type=str,
metavar='NORM',
help='The norm to use for the attack (default linf)')
parser.add_argument('--attack-boundaries',
default=(0, 1),
nargs=2,
type=int,
metavar='L H',
help='Boundaries of projection')
parser.add_argument('--log-wandb',
action='store_true',
default=False,
help='Log results to wandb using the run stored in the bucket')
parser.add_argument('--use-mp-loader', action='store_true', default=False, help='Use Torch XLA\'s MP Loader')
parser.add_argument('--num-examples',
default=None,
type=int,
metavar='EXAMPLES',
help='Number of examples to use for the evaluation (default the entire dataset)')
parser.add_argument('--patch-size', default=None, type=int, metavar='N', help='The patch size to use')
parser.add_argument('--verbose', action='store_true', default=False, help='Runs autoattack in verbose mode')
def main():
setup_default_logging()
args = parser.parse_args()
steps_to_try = args.attack_steps
results_file = args.results_file or './results-all.csv'
all_results = []
for steps in steps_to_try:
args.attack_steps = steps
steps_results = validate(args)
steps_results["attack_steps"] = steps
all_results.append(steps_results)
write_results(results_file, all_results)