Add SGM-VFI (CVPR 2024) frame interpolation support

SGM-VFI combines local flow estimation with sparse global matching
(GMFlow) to handle large motion and occlusion-heavy scenes. Adds 3 new
nodes: Load SGM-VFI Model, SGM-VFI Interpolate, SGM-VFI Segment
Interpolate. Architecture files vendored from MCG-NJU/SGM-VFI with
device-awareness fixes (no hardcoded .cuda()), relative imports, and
debug code removed. README updated with model comparison table.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

sgm_vfi_arch/trident_conv.py

@@ -0,0 +1,90 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# https://github.com/facebookresearch/detectron2/blob/main/projects/TridentNet/tridentnet/trident_conv.py
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.modules.utils import _pair
+
+
+class MultiScaleTridentConv(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            strides=1,
+            paddings=0,
+            dilations=1,
+            dilation=1,
+            groups=1,
+            num_branch=1,
+            test_branch_idx=-1,
+            bias=False,
+            norm=None,
+            activation=None,
+    ):
+        super(MultiScaleTridentConv, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = _pair(kernel_size)
+        self.num_branch = num_branch
+        self.stride = _pair(stride)
+        self.groups = groups
+        self.with_bias = bias
+        self.dilation = dilation
+        if isinstance(paddings, int):
+            paddings = [paddings] * self.num_branch
+        if isinstance(dilations, int):
+            dilations = [dilations] * self.num_branch
+        if isinstance(strides, int):
+            strides = [strides] * self.num_branch
+        self.paddings = [_pair(padding) for padding in paddings]
+        self.dilations = [_pair(dilation) for dilation in dilations]
+        self.strides = [_pair(stride) for stride in strides]
+        self.test_branch_idx = test_branch_idx
+        self.norm = norm
+        self.activation = activation
+
+        assert len({self.num_branch, len(self.paddings), len(self.strides)}) == 1
+
+        self.weight = nn.Parameter(
+            torch.Tensor(out_channels, in_channels // groups, *self.kernel_size)
+        )
+        if bias:
+            self.bias = nn.Parameter(torch.Tensor(out_channels))
+        else:
+            self.bias = None
+
+        nn.init.kaiming_uniform_(self.weight, nonlinearity="relu")
+        if self.bias is not None:
+            nn.init.constant_(self.bias, 0)
+
+    def forward(self, inputs):
+        num_branch = self.num_branch if self.training or self.test_branch_idx == -1 else 1
+        assert len(inputs) == num_branch
+
+        if self.training or self.test_branch_idx == -1:
+            outputs = [
+                F.conv2d(input, self.weight, self.bias, stride, padding, self.dilation, self.groups)
+                for input, stride, padding in zip(inputs, self.strides, self.paddings)
+            ]
+        else:
+            outputs = [
+                F.conv2d(
+                    inputs[0],
+                    self.weight,
+                    self.bias,
+                    self.strides[self.test_branch_idx] if self.test_branch_idx == -1 else self.strides[-1],
+                    self.paddings[self.test_branch_idx] if self.test_branch_idx == -1 else self.paddings[-1],
+                    self.dilation,
+                    self.groups,
+                )
+            ]
+
+        if self.norm is not None:
+            outputs = [self.norm(x) for x in outputs]
+        if self.activation is not None:
+            outputs = [self.activation(x) for x in outputs]
+        return outputs