Add HDv2

model/IFNet_HDv2.py

@@ -0,0 +1,92 @@
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from model.warplayer import warp
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def conv_wo_act(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+    )
+
+
+def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
+    return nn.Sequential(
+        nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
+                  padding=padding, dilation=dilation, bias=True),
+        nn.PReLU(out_planes)
+    )
+
+class IFBlock(nn.Module):
+    def __init__(self, in_planes, scale=1, c=64):
+        super(IFBlock, self).__init__()
+        self.scale = scale
+        self.conv0 = conv(in_planes, c, 5, 2, 2)
+        self.convblock = nn.Sequential(
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+            conv(c, c),
+        )        
+        self.conv1 = nn.Conv2d(c, 4, 3, 1, 1)
+
+    def forward(self, x):
+        if self.scale != 1:
+            x = F.interpolate(x, scale_factor=1. / self.scale, mode="bilinear",
+                              align_corners=False)
+        x = self.conv0(x)
+        x = self.convblock(x)
+        x = self.conv1(x)
+        flow = x
+        if self.scale != 1:
+            flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
+                                 align_corners=False) * self.scale
+        return flow
+
+
+class IFNet(nn.Module):
+    def __init__(self):
+        super(IFNet, self).__init__()
+        self.block0 = IFBlock(6, scale=8, c=192)
+        self.block1 = IFBlock(10, scale=4, c=128)
+        self.block2 = IFBlock(10, scale=2, c=96)
+        self.block3 = IFBlock(10, scale=1, c=48)
+
+    def forward(self, x, UHD=False):
+        if UHD:
+            x = F.interpolate(x, scale_factor=0.5, mode="bilinear", align_corners=False)
+        flow0 = self.block0(x)
+        F1 = flow0
+        F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0
+        warped_img0 = warp(x[:, :3], F1_large[:, :2])
+        warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
+        flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
+        F2 = (flow0 + flow1)
+        F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0
+        warped_img0 = warp(x[:, :3], F2_large[:, :2])
+        warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
+        flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
+        F3 = (flow0 + flow1 + flow2)
+        F3_large = F.interpolate(F3, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0
+        warped_img0 = warp(x[:, :3], F3_large[:, :2])
+        warped_img1 = warp(x[:, 3:], F3_large[:, 2:4])
+        flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3_large), 1))
+        F4 = (flow0 + flow1 + flow2 + flow3)
+        return F4, [F1, F2, F3, F4]
+
+if __name__ == '__main__':
+    img0 = torch.zeros(3, 3, 256, 256).float().to(device)
+    img1 = torch.tensor(np.random.normal(
+        0, 1, (3, 3, 256, 256))).float().to(device)
+    imgs = torch.cat((img0, img1), 1)
+    flownet = IFNet()
+    flow, _ = flownet(imgs)
+    print(flow.shape)