Adding logging calls for raft and vit (#5044)

* Adding logging calls for raft and vit

* Linter fix

torchvision/models/optical_flow/raft.py

@@ -8,6 +8,7 @@ from torch.nn.modules.batchnorm import BatchNorm2d
 from torch.nn.modules.instancenorm import InstanceNorm2d
 from torchvision.ops import ConvNormActivation
 
+from ...utils import _log_api_usage_once
 from ._utils import grid_sample, make_coords_grid, upsample_flow
 
 
@@ -432,6 +433,7 @@ class RAFT(nn.Module):
                 If ``None`` (default), the flow is upsampled using interpolation.
         """
         super().__init__()
+        _log_api_usage_once(self)
 
         self.feature_encoder = feature_encoder
         self.context_encoder = context_encoder

torchvision/prototype/models/vision_transformer.py

@@ -11,6 +11,7 @@ import torch
 import torch.nn as nn
 from torch import Tensor
 
+from ...utils import _log_api_usage_once
 from ._api import WeightsEnum
 from ._utils import handle_legacy_interface
 
@@ -139,6 +140,7 @@ class VisionTransformer(nn.Module):
         norm_layer: Callable[..., torch.nn.Module] = partial(nn.LayerNorm, eps=1e-6),
     ):
         super().__init__()
+        _log_api_usage_once(self)
         torch._assert(image_size % patch_size == 0, "Input shape indivisible by patch size!")
         self.image_size = image_size
         self.patch_size = patch_size