Update references to use the new Model Registration API (#6369)

* Expose on Hub the public methods of the registration API

* Limit methods and update docs.

* Update references to use the new Model Registration API

references/classification/train.py

@@ -221,7 +221,7 @@ def main(args):
     )
 
     print("Creating model")
-    model = torchvision.models.__dict__[args.model](weights=args.weights, num_classes=num_classes)
+    model = torchvision.models.get_model(args.model, weights=args.weights, num_classes=num_classes)
     model.to(device)
 
     if args.distributed and args.sync_bn:

references/classification/train_quantization.py

@@ -46,7 +46,11 @@ def main(args):
 
     print("Creating model", args.model)
     # when training quantized models, we always start from a pre-trained fp32 reference model
-    model = torchvision.models.quantization.__dict__[args.model](weights=args.weights, quantize=args.test_only)
+    prefix = "quantized_"
+    model_name = args.model
+    if not model_name.startswith(prefix):
+        model_name = prefix + model_name
+    model = torchvision.models.get_model(model_name, weights=args.weights, quantize=args.test_only)
     model.to(device)
 
     if not (args.test_only or args.post_training_quantize):

references/detection/train.py

@@ -216,8 +216,8 @@ def main(args):
     if "rcnn" in args.model:
         if args.rpn_score_thresh is not None:
             kwargs["rpn_score_thresh"] = args.rpn_score_thresh
-    model = torchvision.models.detection.__dict__[args.model](
-        weights=args.weights, weights_backbone=args.weights_backbone, num_classes=num_classes, **kwargs
+    model = torchvision.models.get_model(
+        args.model, weights=args.weights, weights_backbone=args.weights_backbone, num_classes=num_classes, **kwargs
     )
     model.to(device)
     if args.distributed and args.sync_bn:

references/optical_flow/train.py

@@ -215,7 +215,7 @@ def main(args):
     else:
         torch.backends.cudnn.benchmark = True
 
-    model = torchvision.models.optical_flow.__dict__[args.model](weights=args.weights)
+    model = torchvision.models.get_model(args.model, weights=args.weights)
 
     if args.distributed:
         model = model.to(args.local_rank)

references/segmentation/train.py

@@ -156,8 +156,12 @@ def main(args):
         dataset_test, batch_size=1, sampler=test_sampler, num_workers=args.workers, collate_fn=utils.collate_fn
     )
 
-    model = torchvision.models.segmentation.__dict__[args.model](
-        weights=args.weights, weights_backbone=args.weights_backbone, num_classes=num_classes, aux_loss=args.aux_loss
+    model = torchvision.models.get_model(
+        args.model,
+        weights=args.weights,
+        weights_backbone=args.weights_backbone,
+        num_classes=num_classes,
+        aux_loss=args.aux_loss,
     )
     model.to(device)
     if args.distributed:

references/video_classification/train.py

@@ -246,7 +246,7 @@ def main(args):
     )
 
     print("Creating model")
-    model = torchvision.models.video.__dict__[args.model](weights=args.weights)
+    model = torchvision.models.get_model(args.model, weights=args.weights)
     model.to(device)
     if args.distributed and args.sync_bn:
         model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)