load checkpoint file based on sparsezoo recipe in pytorch_vision script

scripts/pytorch_vision.py

@@ -80,7 +80,9 @@
   --checkpoint-path CHECKPOINT_PATH
                         A path to a previous checkpoint to load the state from
                         and resume the state for. If provided, pretrained will
-                        be ignored
+                        be ignored. If using a SparseZoo recipe, can also
+                        provide 'zoo' to load the base weights associated with
+                        that recipe
   --model-kwargs MODEL_KWARGS
                         kew word arguments to be passed to model constructor,
                         should be given as a json object
@@ -112,9 +114,9 @@
                         in as a json object
   --recipe-path RECIPE_PATH
                         The path to the yaml file containing the modifiers and
-                        schedule to apply them with. If set to
-                        'transfer_learning', then will create a schedule to
-                        enable sparse transfer learning
+                        schedule to apply them with.  Can also provide a
+                        SparseZoo stub prefixed with 'zoo:' with an optional
+                        '?recipe_type=' argument"
   --sparse-transfer-learn
                         Enable sparse transfer learning modifiers to enforce
                         the sparsity for already sparse layers. The modifiers
@@ -462,6 +464,7 @@
     torch_distributed_zero_first,
 )
 from sparseml.utils import convert_to_bool, create_dirs
+from sparsezoo import Zoo
 
 
 LOGGER = get_main_logger()
@@ -540,12 +543,20 @@ def parse_args():
             "Default is None which will load the default dataset for the architecture."
             " Ex can be set to imagenet, cifar10, etc",
         )
+        checkpoint_path_help = (
+            "A path to a previous checkpoint to load the state from and "
+            "resume the state for. If provided, pretrained will be ignored"
+        )
+        if par == train_parser:
+            checkpoint_path_help += (
+                ". If using a SparseZoo recipe, can also provide 'zoo' to load "
+                "the base weights associated with that recipe"
+            )
         par.add_argument(
             "--checkpoint-path",
             type=str,
             default=None,
-            help="A path to a previous checkpoint to load the state from and "
-            "resume the state for. If provided, pretrained will be ignored",
+            help=checkpoint_path_help,
         )
         par.add_argument(
             "--model-kwargs",
@@ -664,8 +675,8 @@ def parse_args():
                 type=str,
                 default=None,
                 help="The path to the yaml file containing the modifiers and "
-                "schedule to apply them with. If set to 'transfer_learning', "
-                "then will create a schedule to enable sparse transfer learning",
+                "schedule to apply them with. Can also provide a SparseZoo stub "
+                "prefixed with 'zoo:' with an optional '?recipe_type=' argument",
             )
             par.add_argument(
                 "--sparse-transfer-learn",
@@ -1337,6 +1348,17 @@ def main(args):
         num_classes = dataset_attributes["num_classes"]
 
     with torch_distributed_zero_first(args.local_rank):  # only download once locally
+        if args.checkpoint_path == "zoo":
+            if args.recipe_path and args.recipe_path.startswith("zoo:"):
+                args.checkpoint_path = Zoo.download_recipe_base_framework_files(
+                    args.recipe_path, extensions=[".pth"]
+                )[0]
+            else:
+                raise ValueError(
+                    "'zoo' provided as --checkpoint-path but a SparseZoo stub"
+                    " prefixed by 'zoo:' not provided as --recipe-path"
+                )
+
         model = ModelRegistry.create(
             args.arch_key,
             args.pretrained,