use non-persistent buffers for pixel_mean/std

Summary:
supported since pytorch 1.6: pytorch/pytorch#37191

This will not save pixel_mean/std in checkpoints. This is consistent with our current model zoo format.

Differential Revision: D26576833

fbshipit-source-id: 65472efcfd71f9383bb47a192e095315037390b5

detectron2/modeling/meta_arch/rcnn.py

@@ -64,8 +64,8 @@ def __init__(
         if vis_period > 0:
             assert input_format is not None, "input_format is required for visualization!"
 
-        self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1))
-        self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1))
+        self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
+        self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
         assert (
             self.pixel_mean.shape == self.pixel_std.shape
         ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!"

detectron2/modeling/meta_arch/retinanet.py

@@ -137,8 +137,8 @@ def __init__(
         self.vis_period = vis_period
         self.input_format = input_format
 
-        self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1))
-        self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1))
+        self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
+        self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
 
         """
         In Detectron1, loss is normalized by number of foreground samples in the batch.

detectron2/modeling/meta_arch/semantic_seg.py

@@ -35,8 +35,8 @@ def __init__(self, cfg):
         super().__init__()
         self.backbone = build_backbone(cfg)
         self.sem_seg_head = build_sem_seg_head(cfg, self.backbone.output_shape())
-        self.register_buffer("pixel_mean", torch.Tensor(cfg.MODEL.PIXEL_MEAN).view(-1, 1, 1))
-        self.register_buffer("pixel_std", torch.Tensor(cfg.MODEL.PIXEL_STD).view(-1, 1, 1))
+        self.register_buffer("pixel_mean", torch.Tensor(cfg.MODEL.PIXEL_MEAN).view(-1, 1, 1), False)
+        self.register_buffer("pixel_std", torch.Tensor(cfg.MODEL.PIXEL_STD).view(-1, 1, 1), False)
 
     @property
     def device(self):

projects/Panoptic-DeepLab/panoptic_deeplab/panoptic_seg.py

@@ -44,8 +44,8 @@ def __init__(self, cfg):
         self.backbone = build_backbone(cfg)
         self.sem_seg_head = build_sem_seg_head(cfg, self.backbone.output_shape())
         self.ins_embed_head = build_ins_embed_branch(cfg, self.backbone.output_shape())
-        self.register_buffer("pixel_mean", torch.Tensor(cfg.MODEL.PIXEL_MEAN).view(-1, 1, 1))
-        self.register_buffer("pixel_std", torch.Tensor(cfg.MODEL.PIXEL_STD).view(-1, 1, 1))
+        self.register_buffer("pixel_mean", torch.Tensor(cfg.MODEL.PIXEL_MEAN).view(-1, 1, 1), False)
+        self.register_buffer("pixel_std", torch.Tensor(cfg.MODEL.PIXEL_STD).view(-1, 1, 1), False)
         self.meta = MetadataCatalog.get(cfg.DATASETS.TRAIN[0])
         self.stuff_area = cfg.MODEL.PANOPTIC_DEEPLAB.STUFF_AREA
         self.threshold = cfg.MODEL.PANOPTIC_DEEPLAB.CENTER_THRESHOLD

projects/TensorMask/tensormask/arch.py

@@ -348,8 +348,8 @@ def __init__(self, cfg):
         )
         # box transform
         self.box2box_transform = Box2BoxTransform(weights=cfg.MODEL.TENSOR_MASK.BBOX_REG_WEIGHTS)
-        self.register_buffer("pixel_mean", torch.Tensor(cfg.MODEL.PIXEL_MEAN).view(-1, 1, 1))
-        self.register_buffer("pixel_std", torch.Tensor(cfg.MODEL.PIXEL_STD).view(-1, 1, 1))
+        self.register_buffer("pixel_mean", torch.Tensor(cfg.MODEL.PIXEL_MEAN).view(-1, 1, 1), False)
+        self.register_buffer("pixel_std", torch.Tensor(cfg.MODEL.PIXEL_STD).view(-1, 1, 1), False)
 
     @property
     def device(self):