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Author: sniklaus

run.py

@@ -41,8 +41,8 @@
 
 def backwarp(tenInput, tenFlow):
     if str(tenFlow.shape) not in backwarp_tenGrid:
-        tenHor = torch.linspace(-1.0 + (1.0 / tenFlow.shape[3]), 1.0 - (1.0 / tenFlow.shape[3]), tenFlow.shape[3]).view(1, 1, 1, -1).repeat(1, 1, tenFlow.shape[2], 1)
-        tenVer = torch.linspace(-1.0 + (1.0 / tenFlow.shape[2]), 1.0 - (1.0 / tenFlow.shape[2]), tenFlow.shape[2]).view(1, 1, -1, 1).repeat(1, 1, 1, tenFlow.shape[3])
+        tenHor = torch.linspace(-1.0, 1.0, tenFlow.shape[3]).view(1, 1, 1, -1).repeat(1, 1, tenFlow.shape[2], 1)
+        tenVer = torch.linspace(-1.0, 1.0, tenFlow.shape[2]).view(1, 1, -1, 1).repeat(1, 1, 1, tenFlow.shape[3])
 
         backwarp_tenGrid[str(tenFlow.shape)] = torch.cat([ tenHor, tenVer ], 1).cuda()
     # end
@@ -51,10 +51,10 @@ def backwarp(tenInput, tenFlow):
         backwarp_tenPartial[str(tenFlow.shape)] = tenFlow.new_ones([ tenFlow.shape[0], 1, tenFlow.shape[2], tenFlow.shape[3] ])
     # end
 
-    tenFlow = torch.cat([ tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0), tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0) ], 1)
+    tenFlow = torch.cat([ tenFlow[:, 0:1, :, :] * (2.0 / (tenInput.shape[3] - 1.0)), tenFlow[:, 1:2, :, :] * (2.0 / (tenInput.shape[2] - 1.0)) ], 1)
     tenInput = torch.cat([ tenInput, backwarp_tenPartial[str(tenFlow.shape)] ], 1)
 
-    tenOutput = torch.nn.functional.grid_sample(input=tenInput, grid=(backwarp_tenGrid[str(tenFlow.shape)] + tenFlow).permute(0, 2, 3, 1), mode='bilinear', padding_mode='zeros', align_corners=False)
+    tenOutput = torch.nn.functional.grid_sample(input=tenInput, grid=(backwarp_tenGrid[str(tenFlow.shape)] + tenFlow).permute(0, 2, 3, 1), mode='bilinear', padding_mode='zeros', align_corners=True)
 
     tenMask = tenOutput[:, -1:, :, :]; tenMask[tenMask > 0.999] = 1.0; tenMask[tenMask < 1.0] = 0.0