Add MViT architecture in TorchVision

docs/source/models.rst

@@ -465,6 +465,7 @@ pre-trained weights:
 .. toctree::
    :maxdepth: 1
 
+   models/video_mvit
    models/video_resnet
 
 |

docs/source/models/video_mvit.rst

@@ -0,0 +1,26 @@
+Video MViT
+==========
+
+.. currentmodule:: torchvision.models.video
+
+The MViT model is based on the
+`MViTv2: Improved Multiscale Vision Transformers for Classification and Detection
+<https://arxiv.org/abs/2112.01526>`__ and `Multiscale Vision Transformers
+<https://arxiv.org/abs/2104.11227>`__ papers.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instantiate a MViT model, with or
+without pre-trained weights. All the model builders internally rely on the
+``torchvision.models.video.MViT`` base class. Please refer to the `source
+code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/video/mvit.py>`_ for
+more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    mvit_v1_b

references/video_classification/train.py

@@ -152,7 +152,7 @@ def main(args):
             split="train",
             step_between_clips=1,
             transform=transform_train,
-            frame_rate=15,
+            frame_rate=args.frame_rate,
             extensions=(
                 "avi",
                 "mp4",
@@ -189,7 +189,7 @@ def main(args):
             split="val",
             step_between_clips=1,
             transform=transform_test,
-            frame_rate=15,
+            frame_rate=args.frame_rate,
             extensions=(
                 "avi",
                 "mp4",
@@ -324,6 +324,7 @@ def parse_args():
     parser.add_argument("--model", default="r2plus1d_18", type=str, help="model name")
     parser.add_argument("--device", default="cuda", type=str, help="device (Use cuda or cpu Default: cuda)")
     parser.add_argument("--clip-len", default=16, type=int, metavar="N", help="number of frames per clip")
+    parser.add_argument("--frame-rate", default=15, type=int, metavar="N", help="the frame rate")
     parser.add_argument(
         "--clips-per-video", default=5, type=int, metavar="N", help="maximum number of clips per video to consider"
     )

test/test_extended_models.py

@@ -87,6 +87,7 @@ def test_schema_meta_validation(model_fn):
         "license",
         "_metrics",
         "min_size",
+        "min_temporal_size",
         "num_params",
         "recipe",
         "unquantized",
@@ -180,7 +181,7 @@ def test_transforms_jit(model_fn):
             "input_shape": (1, 3, 520, 520),
         },
         "video": {
-            "input_shape": (1, 4, 3, 112, 112),
+            "input_shape": (1, 3, 4, 112, 112),
         },
         "optical_flow": {
             "input_shape": (1, 3, 128, 128),
@@ -194,6 +195,8 @@ def test_transforms_jit(model_fn):
     if module_name == "optical_flow":
         args = (x, x)
     else:
+        if module_name == "video":
+            x = x.permute(0, 2, 1, 3, 4)
         args = (x,)
 
     problematic_weights = []

test/test_models.py

@@ -309,6 +309,9 @@ def _check_input_backprop(model, inputs):
         "image_size": 56,
         "input_shape": (1, 3, 56, 56),
     },
+    "mvit_v1_b": {
+        "input_shape": (1, 3, 16, 224, 224),
+    },
 }
 # speeding up slow models:
 slow_models = [
@@ -830,6 +833,8 @@ def test_video_model(model_fn, dev):
         "num_classes": 50,
     }
     model_name = model_fn.__name__
+    if SKIP_BIG_MODEL and model_name in skipped_big_models:
+        pytest.skip("Skipped to reduce memory usage. Set env var SKIP_BIG_MODEL=0 to enable test for this model")
     kwargs = {**defaults, **_model_params.get(model_name, {})}
     num_classes = kwargs.get("num_classes")
     input_shape = kwargs.pop("input_shape")

torchvision/models/video/__init__.py

@@ -1 +1,2 @@
+from .mvit import *
 from .resnet import *