From 382904d0b78167f6aa4e8125cf820ebe7a9e0c52 Mon Sep 17 00:00:00 2001
From: hzwer <598460606@163.com>
Date: Tue, 17 Nov 2020 10:26:11 +0800
Subject: [PATCH] Support old Pytorch version

---
 model/IFNet.py |  4 ++--
 model/RIFE.py  | 12 ++++++------
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/model/IFNet.py b/model/IFNet.py
index ec74aeb..4270b2a 100644
--- a/model/IFNet.py
+++ b/model/IFNet.py
@@ -79,7 +79,7 @@ class IFBlock(nn.Module):
         flow = self.up(x)
         if self.scale != 1:
             flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
-                                 align_corners=False, recompute_scale_factor=False)
+                                 align_corners=False)
         return flow
 
 
@@ -92,7 +92,7 @@ class IFNet(nn.Module):
 
     def forward(self, x):
         x = F.interpolate(x, scale_factor=0.5, mode="bilinear",
-                          align_corners=False, recompute_scale_factor=False)
+                          align_corners=False)
         flow0 = self.block0(x)
         F1 = flow0
         warped_img0 = warp(x[:, :3], F1)
diff --git a/model/RIFE.py b/model/RIFE.py
index 2f54c30..1b9399a 100644
--- a/model/RIFE.py
+++ b/model/RIFE.py
@@ -74,15 +74,15 @@ class ContextNet(nn.Module):
         f1 = warp(x, flow)
         x = self.conv2(x)
         flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
-                             align_corners=False, recompute_scale_factor=False) * 0.5
+                             align_corners=False) * 0.5
         f2 = warp(x, flow)
         x = self.conv3(x)
         flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
-                             align_corners=False, recompute_scale_factor=False) * 0.5
+                             align_corners=False) * 0.5
         f3 = warp(x, flow)
         x = self.conv4(x)
         flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
-                             align_corners=False, recompute_scale_factor=False) * 0.5
+                             align_corners=False) * 0.5
         f4 = warp(x, flow)
         return [f1, f2, f3, f4]
 
@@ -187,7 +187,7 @@ class Model:
         c0 = self.contextnet(img0, flow)
         c1 = self.contextnet(img1, -flow)
         flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
-                             align_corners=False, recompute_scale_factor=False) * 2.0
+                             align_corners=False) * 2.0
         refine_output, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.fusionnet(
             img0, img1, flow, c0, c1, flow_gt)
         res = torch.sigmoid(refine_output[:, :3]) * 2 - 1
@@ -222,9 +222,9 @@ class Model:
                 loss_mask = torch.abs(
                     merged_img - gt).sum(1, True).float().detach()
                 loss_mask = F.interpolate(loss_mask, scale_factor=0.5, mode="bilinear",
-                                          align_corners=False, recompute_scale_factor=False).detach()
+                                          align_corners=False).detach()
                 flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear",
-                                         align_corners=False, recompute_scale_factor=False) * 0.5).detach()
+                                         align_corners=False) * 0.5).detach()
             loss_cons = 0
             for i in range(3):
                 loss_cons += self.epe(flow_list[i], flow_gt[:, :2], 1)