initial commit

.gitignore

@@ -0,0 +1,3 @@
+*__pycache__*
+*.py[cod]
+checkpoints/

README.md

@@ -23,5 +23,8 @@ Authors of this work are affiliated with Graz University of Technology, Institut
 
 Parts of this code repository is based on the following works:
 
+* https://github.com/pralab/ImageNet-Patch
 * https://github.com/inspire-group/hydra
-* ...
+* https://github.com/allenai/hidden-networks
+* https://github.com/andyzoujm/pixmix
+* https://github.com/uoguelph-mlrg/Cutout

args.py

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+import argparse
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="PyTorch Training")
+
+    parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet50',
+                        help='model architecture (default: resnet50)')
+    parser.add_argument('--pretrained', dest='pretrained', action='store_true',
+                        help='use pre-trained model')
+    parser.add_argument('--test_all', action='store_true',
+                        help='Run all validation set (default: will run 5k test samples from RobustBench)')
+    parser.add_argument('-c', '--corruption', action='store_true',
+                        help='Corruptions to be used for test set evaluations')
+    parser.add_argument('--augmentation', default="standard", type=str,
+                        help='augmentation type')
+    parser.add_argument('--dir', default='/data/', type=str, metavar='DIR',
+                        help='Path to dataset ')
+    parser.add_argument('--mixing_set', default='/data/fractals_and_fvis/', type=str, metavar='DIR',
+                        help='Path to mixing set')
+    parser.add_argument('--batch_size', default=1, type=int, metavar='N',
+                        help='Evaluation mini-batch size (default: 1)')
+    parser.add_argument('--path', default=None, type=str, metavar='PATH',
+                        help='Path for trained model checkpoint to load')
+    parser.add_argument('--seed', default=42, type=int, metavar='N',
+                        help='Randomization seed number  (default: 42)')
+    parser.add_argument('--prune', action='store_true',
+                        help='Prune given model')
+    parser.add_argument('--pruning_ratio', default=0, type=float, metavar='N',
+                        help='Pruning Ratio to prune model')
+    parser.add_argument('--pruning_type', default='global', type=str,
+                        help='Pruning type: "global", "local"')
+    parser.add_argument('--model_name', default=None, type=str,
+                        help='Save Model filename location')
+    parser.add_argument('--lr', '--learning-rate', default=0.01, type=float,
+                        metavar='LR', help='initial learning rate', dest='lr')
+    parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
+                        help='momentum')
+    parser.add_argument('--wd', '--weight-decay', default=5e-4, type=float,
+                        metavar='W', help='weight decay (default: 1e-4)',
+                        dest='weight_decay')
+    parser.add_argument('--epochs', default=90, type=int, metavar='N',
+                        help='number of total epochs to run')
+    parser.add_argument('--print-freq', default=10, type=int,
+                        metavar='N', help='print frequency (default: 10)')
+    parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
+                        help='number of total epochs to run')
+    parser.add_argument("--optimizer", type=str, default="sgd", 
+                        choices=("sgd", "adam", "rmsprop"))
+    parser.add_argument("--warmup-epochs", type=int, default=0, 
+                        help="Number of warmup epochs")
+    parser.add_argument("--num-classes", type=int, default=1000, 
+                        help="Number of output classes in the model")
+    parser.add_argument("--resume", type=str, default="",
+                        help="path to latest checkpoint (default:None)")
+    parser.add_argument( "--lr-schedule", type=str, default="cosine", choices=("step", "cosine"),
+                        help="Learning rate schedule")
+
+    # PixMix arguments
+    parser.add_argument('--aug-severity', default=1, type=int,
+                        help='Severity of base augmentation operators')
+    parser.add_argument( '--beta', default=4, type=int, 
+                        help='Severity of mixing')
+    parser.add_argument('--k_mixing', default=4,type=int,
+                        help='Mixing iterations')
+    parser.add_argument('--all-ops', '--all', action='store_true', default=True,
+                        help='Turn on all augmentation operations (+brightness,contrast,color,sharpness).')
+    # CutOut arguments
+    parser.add_argument('--cutout_length', default=56, type=int, metavar='N',
+                        help='length of hole (usually half of dim)')
+
+
+    #Score Based Pruning
+    parser.add_argument(
+        "--layer-type", type=str, choices=("dense", "subnet"), help="dense | subnet"
+    )
+    parser.add_argument(
+        "--scaled-score-init",
+        action="store_true",
+        default=False,
+        help="Init importance scores proportaional to weights (default kaiming init)",
+    )
+    parser.add_argument(
+        "--exp-mode",
+        type=str,
+        choices=("pretrain", "prune", "finetune"),
+        default="prune",
+        help="Train networks following one of these methods.",
+    )
+    parser.add_argument(
+        "--freeze-bn",
+        action="store_true",
+        default=False,
+        help="freeze batch-norm parameters in pruning",
+    )
+    parser.add_argument(
+        "--scores_init_type",
+        choices=("kaiming_normal", "kaiming_uniform", "xavier_uniform", "xavier_normal", "weight_magnitude"),
+        help="Which init to use for relevance scores",
+    )
+    parser.add_argument(
+        "--init_type",
+        choices=("kaiming_normal", "kaiming_uniform", "signed_const"),
+        help="Which init to use for weight parameters: kaiming_normal | kaiming_uniform | signed_const",
+    )
+    parser.add_argument(
+        "--k",
+        type=float,
+        default=1.0,
+        help="Fraction of weight variables kept in subnet",
+    )
+    parser.add_argument(
+        "--save-dense",
+        action="store_true",
+        default=False,
+        help="Save dense model alongwith subnets.",
+    )
+
+    return parser.parse_args()