Adding prototype flag on reference scripts (#5248)

* Adding prototype flag on reference scripts.

* Import prototype instead of models/transforms.

* Correcting exception type.

* fixing none referencing

Author: datumbox

references/classification/train.py

@@ -16,9 +16,9 @@
 
 
 try:
-    from torchvision.prototype import models as PM
+    from torchvision import prototype
 except ImportError:
-    PM = None
+    prototype = None
 
 
 def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args, model_ema=None, scaler=None):
@@ -154,13 +154,18 @@ def load_data(traindir, valdir, args):
         print(f"Loading dataset_test from {cache_path}")
         dataset_test, _ = torch.load(cache_path)
     else:
-        if not args.weights:
+        if not args.prototype:
             preprocessing = presets.ClassificationPresetEval(
                 crop_size=val_crop_size, resize_size=val_resize_size, interpolation=interpolation
             )
         else:
-            weights = PM.get_weight(args.weights)
-            preprocessing = weights.transforms()
+            if args.weights:
+                weights = prototype.models.get_weight(args.weights)
+                preprocessing = weights.transforms()
+            else:
+                preprocessing = prototype.transforms.ImageNetEval(
+                    crop_size=val_crop_size, resize_size=val_resize_size, interpolation=interpolation
+                )
 
         dataset_test = torchvision.datasets.ImageFolder(
             valdir,
@@ -186,8 +191,10 @@ def load_data(traindir, valdir, args):
 
 
 def main(args):
-    if args.weights and PM is None:
+    if args.prototype and prototype is None:
         raise ImportError("The prototype module couldn't be found. Please install the latest torchvision nightly.")
+    if not args.prototype and args.weights:
+        raise ValueError("The weights parameter works only in prototype mode. Please pass the --prototype argument.")
     if args.output_dir:
         utils.mkdir(args.output_dir)
 
@@ -229,10 +236,10 @@ def main(args):
     )
 
     print("Creating model")
-    if not args.weights:
+    if not args.prototype:
         model = torchvision.models.__dict__[args.model](pretrained=args.pretrained, num_classes=num_classes)
     else:
-        model = PM.__dict__[args.model](weights=args.weights, num_classes=num_classes)
+        model = prototype.models.__dict__[args.model](weights=args.weights, num_classes=num_classes)
     model.to(device)
 
     if args.distributed and args.sync_bn:
@@ -491,6 +498,12 @@ def get_args_parser(add_help=True):
     )
 
     # Prototype models only
+    parser.add_argument(
+        "--prototype",
+        dest="prototype",
+        help="Use prototype model builders instead those from main area",
+        action="store_true",
+    )
     parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")
 
     return parser

references/detection/train.py

@@ -34,9 +34,9 @@
 
 
 try:
-    from torchvision.prototype import models as PM
+    from torchvision import prototype
 except ImportError:
-    PM = None
+    prototype = None
 
 
 def get_dataset(name, image_set, transform, data_path):
@@ -50,11 +50,14 @@ def get_dataset(name, image_set, transform, data_path):
 def get_transform(train, args):
     if train:
         return presets.DetectionPresetTrain(args.data_augmentation)
-    elif not args.weights:
+    elif not args.prototype:
         return presets.DetectionPresetEval()
     else:
-        weights = PM.get_weight(args.weights)
-        return weights.transforms()
+        if args.weights:
+            weights = prototype.models.get_weight(args.weights)
+            return weights.transforms()
+        else:
+            return prototype.transforms.CocoEval()
 
 
 def get_args_parser(add_help=True):
@@ -141,6 +144,12 @@ def get_args_parser(add_help=True):
     parser.add_argument("--dist-url", default="env://", type=str, help="url used to set up distributed training")
 
     # Prototype models only
+    parser.add_argument(
+        "--prototype",
+        dest="prototype",
+        help="Use prototype model builders instead those from main area",
+        action="store_true",
+    )
     parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")
 
     # Mixed precision training parameters
@@ -150,8 +159,10 @@ def get_args_parser(add_help=True):
 
 
 def main(args):
-    if args.weights and PM is None:
+    if args.prototype and prototype is None:
         raise ImportError("The prototype module couldn't be found. Please install the latest torchvision nightly.")
+    if not args.prototype and args.weights:
+        raise ValueError("The weights parameter works only in prototype mode. Please pass the --prototype argument.")
     if args.output_dir:
         utils.mkdir(args.output_dir)
 
@@ -193,12 +204,12 @@ def main(args):
     if "rcnn" in args.model:
         if args.rpn_score_thresh is not None:
             kwargs["rpn_score_thresh"] = args.rpn_score_thresh
-    if not args.weights:
+    if not args.prototype:
         model = torchvision.models.detection.__dict__[args.model](
             pretrained=args.pretrained, num_classes=num_classes, **kwargs
         )
     else:
-        model = PM.detection.__dict__[args.model](weights=args.weights, num_classes=num_classes, **kwargs)
+        model = prototype.models.detection.__dict__[args.model](weights=args.weights, num_classes=num_classes, **kwargs)
     model.to(device)
     if args.distributed and args.sync_bn:
         model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)

references/optical_flow/train.py

@@ -10,10 +10,9 @@
 from torchvision.datasets import KittiFlow, FlyingChairs, FlyingThings3D, Sintel, HD1K
 
 try:
-    from torchvision.prototype import models as PM
-    from torchvision.prototype.models import optical_flow as PMOF
+    from torchvision import prototype
 except ImportError:
-    PM = PMOF = None
+    prototype = None
 
 
 def get_train_dataset(stage, dataset_root):
@@ -133,9 +132,12 @@ def inner_loop(blob):
 def validate(model, args):
     val_datasets = args.val_dataset or []
 
-    if args.weights:
-        weights = PM.get_weight(args.weights)
-        preprocessing = weights.transforms()
+    if args.prototype:
+        if args.weights:
+            weights = prototype.models.get_weight(args.weights)
+            preprocessing = weights.transforms()
+        else:
+            preprocessing = prototype.transforms.RaftEval()
     else:
         preprocessing = OpticalFlowPresetEval()
 
@@ -192,10 +194,14 @@ def train_one_epoch(model, optimizer, scheduler, train_loader, logger, args):
 
 
 def main(args):
+    if args.prototype and prototype is None:
+        raise ImportError("The prototype module couldn't be found. Please install the latest torchvision nightly.")
+    if not args.prototype and args.weights:
+        raise ValueError("The weights parameter works only in prototype mode. Please pass the --prototype argument.")
     utils.setup_ddp(args)
 
-    if args.weights:
-        model = PMOF.__dict__[args.model](weights=args.weights)
+    if args.prototype:
+        model = prototype.models.optical_flow.__dict__[args.model](weights=args.weights)
     else:
         model = torchvision.models.optical_flow.__dict__[args.model](pretrained=args.pretrained)
 
@@ -317,7 +323,6 @@ def get_args_parser(add_help=True):
     )
     # TODO: resume, pretrained, and weights should be in an exclusive arg group
     parser.add_argument("--pretrained", action="store_true", help="Whether to use pretrained weights")
-    parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load.")
 
     parser.add_argument(
         "--num_flow_updates",
@@ -336,6 +341,15 @@ def get_args_parser(add_help=True):
         required=True,
     )
 
+    # Prototype models only
+    parser.add_argument(
+        "--prototype",
+        dest="prototype",
+        help="Use prototype model builders instead those from main area",
+        action="store_true",
+    )
+    parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load.")
+
     return parser
 
 

references/segmentation/train.py

@@ -12,9 +12,9 @@
 
 
 try:
-    from torchvision.prototype import models as PM
+    from torchvision import prototype
 except ImportError:
-    PM = None
+    prototype = None
 
 
 def get_dataset(dir_path, name, image_set, transform):
@@ -35,11 +35,14 @@ def sbd(*args, **kwargs):
 def get_transform(train, args):
     if train:
         return presets.SegmentationPresetTrain(base_size=520, crop_size=480)
-    elif not args.weights:
+    elif not args.prototype:
         return presets.SegmentationPresetEval(base_size=520)
     else:
-        weights = PM.get_weight(args.weights)
-        return weights.transforms()
+        if args.weights:
+            weights = prototype.models.get_weight(args.weights)
+            return weights.transforms()
+        else:
+            return prototype.transforms.VocEval(resize_size=520)
 
 
 def criterion(inputs, target):
@@ -97,8 +100,10 @@ def train_one_epoch(model, criterion, optimizer, data_loader, lr_scheduler, devi
 
 
 def main(args):
-    if args.weights and PM is None:
+    if args.prototype and prototype is None:
         raise ImportError("The prototype module couldn't be found. Please install the latest torchvision nightly.")
+    if not args.prototype and args.weights:
+        raise ValueError("The weights parameter works only in prototype mode. Please pass the --prototype argument.")
     if args.output_dir:
         utils.mkdir(args.output_dir)
 
@@ -130,14 +135,14 @@ def main(args):
         dataset_test, batch_size=1, sampler=test_sampler, num_workers=args.workers, collate_fn=utils.collate_fn
     )
 
-    if not args.weights:
+    if not args.prototype:
         model = torchvision.models.segmentation.__dict__[args.model](
             pretrained=args.pretrained,
             num_classes=num_classes,
             aux_loss=args.aux_loss,
         )
     else:
-        model = PM.segmentation.__dict__[args.model](
+        model = prototype.models.segmentation.__dict__[args.model](
             weights=args.weights, num_classes=num_classes, aux_loss=args.aux_loss
         )
     model.to(device)
@@ -278,6 +283,12 @@ def get_args_parser(add_help=True):
     parser.add_argument("--dist-url", default="env://", type=str, help="url used to set up distributed training")
 
     # Prototype models only
+    parser.add_argument(
+        "--prototype",
+        dest="prototype",
+        help="Use prototype model builders instead those from main area",
+        action="store_true",
+    )
     parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")
 
     # Mixed precision training parameters

references/video_classification/train.py

@@ -13,9 +13,9 @@
 from torchvision.datasets.samplers import DistributedSampler, UniformClipSampler, RandomClipSampler
 
 try:
-    from torchvision.prototype import models as PM
+    from torchvision import prototype
 except ImportError:
-    PM = None
+    prototype = None
 
 
 def train_one_epoch(model, criterion, optimizer, lr_scheduler, data_loader, device, epoch, print_freq, scaler=None):
@@ -96,9 +96,10 @@ def collate_fn(batch):
 
 
 def main(args):
-    if args.weights and PM is None:
+    if args.prototype and prototype is None:
         raise ImportError("The prototype module couldn't be found. Please install the latest torchvision nightly.")
-
+    if not args.prototype and args.weights:
+        raise ValueError("The weights parameter works only in prototype mode. Please pass the --prototype argument.")
     if args.output_dir:
         utils.mkdir(args.output_dir)
 
@@ -149,11 +150,14 @@ def main(args):
     print("Loading validation data")
     cache_path = _get_cache_path(valdir)
 
-    if not args.weights:
-        transform_test = presets.VideoClassificationPresetEval((128, 171), (112, 112))
+    if not args.prototype:
+        transform_test = presets.VideoClassificationPresetEval(resize_size=(128, 171), crop_size=(112, 112))
     else:
-        weights = PM.get_weight(args.weights)
-        transform_test = weights.transforms()
+        if args.weights:
+            weights = prototype.models.get_weight(args.weights)
+            transform_test = weights.transforms()
+        else:
+            transform_test = prototype.transforms.Kinect400Eval(crop_size=(112, 112), resize_size=(128, 171))
 
     if args.cache_dataset and os.path.exists(cache_path):
         print(f"Loading dataset_test from {cache_path}")
@@ -204,10 +208,10 @@ def main(args):
     )
 
     print("Creating model")
-    if not args.weights:
+    if not args.prototype:
         model = torchvision.models.video.__dict__[args.model](pretrained=args.pretrained)
     else:
-        model = PM.video.__dict__[args.model](weights=args.weights)
+        model = prototype.models.video.__dict__[args.model](weights=args.weights)
     model.to(device)
     if args.distributed and args.sync_bn:
         model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
@@ -360,6 +364,12 @@ def parse_args():
     parser.add_argument("--dist-url", default="env://", type=str, help="url used to set up distributed training")
 
     # Prototype models only
+    parser.add_argument(
+        "--prototype",
+        dest="prototype",
+        help="Use prototype model builders instead those from main area",
+        action="store_true",
+    )
     parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")
 
     # Mixed precision training parameters