Add pure-PyTorch fallback for SGM-VFI softsplat forward warp

Make cupy import optional so the module loads without cupy installed.
Replace @cupy.memoize decorator with a simple dict cache to avoid
crash at import time. Add _pytorch_softsplat() using scatter_add_
as a fallback when cupy is unavailable or tensors are on CPU.

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

sgm_vfi_arch/softsplat.py

@@ -1,7 +1,10 @@
 #!/usr/bin/env python
 
 import collections
-import cupy
+try:
+    import cupy
+except ImportError:
+    cupy = None
 import os
 import re
 import torch
@@ -216,20 +219,70 @@ def cuda_kernel(strFunction:str, strKernel:str, objVariables:typing.Dict):
 # end
 
 
-@cupy.memoize(for_each_device=True)
+_cuda_launch_cache = {}
-def cuda_launch(strKey:str):
-    if 'CUDA_HOME' not in os.environ:
-        os.environ['CUDA_HOME'] = cupy.cuda.get_cuda_path()
-    # end
 
-    return cupy.RawKernel(objCudacache[strKey]['strKernel'], objCudacache[strKey]['strFunction'],
+def cuda_launch(strKey:str):
-                          options=tuple(['-I ' + os.environ['CUDA_HOME'], '-I ' + os.environ['CUDA_HOME'] + '/include']))
+    if strKey not in _cuda_launch_cache:
+        if 'CUDA_HOME' not in os.environ:
+            os.environ['CUDA_HOME'] = cupy.cuda.get_cuda_path()
+        _cuda_launch_cache[strKey] = cupy.RawKernel(
+            objCudacache[strKey]['strKernel'],
+            objCudacache[strKey]['strFunction'],
+            options=tuple(['-I ' + os.environ['CUDA_HOME'],
+                           '-I ' + os.environ['CUDA_HOME'] + '/include'])
+        )
+    return _cuda_launch_cache[strKey]
 # end
 
 
 ##########################################################
 
 
+def _pytorch_softsplat(tenIn, tenFlow):
+    """Pure-PyTorch forward warp via bilinear splatting (scatter_add)."""
+    B, C, H, W = tenIn.shape
+    tenOut = tenIn.new_zeros(B, C, H, W)
+
+    grid_y, grid_x = torch.meshgrid(
+        torch.arange(H, device=tenIn.device, dtype=tenIn.dtype),
+        torch.arange(W, device=tenIn.device, dtype=tenIn.dtype),
+        indexing='ij',
+    )
+
+    flt_x = grid_x.unsqueeze(0) + tenFlow[:, 0, :, :]
+    flt_y = grid_y.unsqueeze(0) + tenFlow[:, 1, :, :]
+
+    valid = torch.isfinite(flt_x) & torch.isfinite(flt_y)
+    flt_x = torch.where(valid, flt_x, torch.zeros_like(flt_x))
+    flt_y = torch.where(valid, flt_y, torch.zeros_like(flt_y))
+
+    nw_x = flt_x.floor().long()
+    nw_y = flt_y.floor().long()
+    frac_x = flt_x - nw_x.float()
+    frac_y = flt_y - nw_y.float()
+
+    w_nw = (1.0 - frac_x) * (1.0 - frac_y) * valid
+    w_ne = frac_x * (1.0 - frac_y) * valid
+    w_sw = (1.0 - frac_x) * frac_y * valid
+    w_se = frac_x * frac_y * valid
+
+    out_flat = tenOut.view(B, C, -1)
+    in_flat = tenIn
+
+    for dx, dy, w in [(0, 0, w_nw), (1, 0, w_ne), (0, 1, w_sw), (1, 1, w_se)]:
+        tx = nw_x + dx
+        ty = nw_y + dy
+        in_bounds = (tx >= 0) & (tx < W) & (ty >= 0) & (ty < H)
+        w_masked = w * in_bounds
+        idx = (ty.clamp(0, H - 1) * W + tx.clamp(0, W - 1))
+        idx = idx.unsqueeze(1).expand_as(in_flat)
+        weighted = in_flat * w_masked.unsqueeze(1)
+        out_flat.scatter_add_(2, idx.reshape(B, C, -1), weighted.reshape(B, C, -1))
+
+    return tenOut
+# end
+
+
 def softsplat(tenIn:torch.Tensor, tenFlow:torch.Tensor, tenMetric:torch.Tensor, strMode:str):
     assert(strMode.split('-')[0] in ['sum', 'avg', 'linear', 'soft'])
 
@@ -281,7 +334,7 @@ class softsplat_func(torch.autograd.Function):
     def forward(self, tenIn, tenFlow):
         tenOut = tenIn.new_zeros([tenIn.shape[0], tenIn.shape[1], tenIn.shape[2], tenIn.shape[3]])
 
-        if tenIn.is_cuda == True:
+        if tenIn.is_cuda and cupy is not None:
             cuda_launch(cuda_kernel('softsplat_out', '''
                 extern "C" __global__ void __launch_bounds__(512) softsplat_out(
                     const int n,
@@ -345,8 +398,8 @@ class softsplat_func(torch.autograd.Function):
                 stream=collections.namedtuple('Stream', 'ptr')(torch.cuda.current_stream().cuda_stream)
             )
 
-        elif tenIn.is_cuda != True:
+        else:
-            assert(False)
+            tenOut = _pytorch_softsplat(tenIn, tenFlow)
 
         # end