Update the dataset cache to factor input parameters (#6234)

* Update the dataset cache to factor in parameters from the args.

* Fix linter

references/video_classification/train.py

@@ -98,10 +98,11 @@ def evaluate(model, criterion, data_loader, device):
     return metric_logger.acc1.global_avg
 
 
-def _get_cache_path(filepath):
+def _get_cache_path(filepath, args):
     import hashlib
 
-    h = hashlib.sha1(filepath.encode()).hexdigest()
+    value = f"{filepath}-{args.clip_len}-{args.kinetics_version}-{args.frame_rate}"
+    h = hashlib.sha1(value.encode()).hexdigest()
     cache_path = os.path.join("~", ".torch", "vision", "datasets", "kinetics", h[:10] + ".pt")
     cache_path = os.path.expanduser(cache_path)
     return cache_path
@@ -135,7 +136,7 @@ def main(args):
 
     print("Loading training data")
     st = time.time()
-    cache_path = _get_cache_path(traindir)
+    cache_path = _get_cache_path(traindir, args)
     transform_train = presets.VideoClassificationPresetTrain(crop_size=(112, 112), resize_size=(128, 171))
 
     if args.cache_dataset and os.path.exists(cache_path):
@@ -167,7 +168,7 @@ def main(args):
     print("Took", time.time() - st)
 
     print("Loading validation data")
-    cache_path = _get_cache_path(valdir)
+    cache_path = _get_cache_path(valdir, args)
 
     if args.weights and args.test_only:
         weights = torchvision.models.get_weight(args.weights)