Add 2F model

model/IFNet2F.py

@@ -0,0 +1,115 @@
+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=False),
+        nn.BatchNorm2d(out_planes),
+    )
+
+
+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=False),
+        nn.BatchNorm2d(out_planes),
+        nn.PReLU(out_planes)
+    )
+
+
+class ResBlock(nn.Module):
+    def __init__(self, in_planes, out_planes, stride=1):
+        super(ResBlock, self).__init__()
+        if in_planes == out_planes and stride == 1:
+            self.conv0 = nn.Identity()
+        else:
+            self.conv0 = nn.Conv2d(in_planes, out_planes,
+                                   3, stride, 1, bias=False)
+        self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
+        self.conv2 = conv_wo_act(out_planes, out_planes, 3, 1, 1)
+        self.relu1 = nn.PReLU(1)
+        self.relu2 = nn.PReLU(out_planes)
+        self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False)
+        self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False)
+
+    def forward(self, x):
+        y = self.conv0(x)
+        x = self.conv1(x)
+        x = self.conv2(x)
+        w = x.mean(3, True).mean(2, True)
+        w = self.relu1(self.fc1(w))
+        w = torch.sigmoid(self.fc2(w))
+        x = self.relu2(x * w + y)
+        return x
+
+
+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, 3, 1, 1)
+        self.res0 = ResBlock(c, c)
+        self.res1 = ResBlock(c, c)
+        self.res2 = ResBlock(c, c)
+        self.res3 = ResBlock(c, c)
+        self.res4 = ResBlock(c, c)
+        self.res5 = ResBlock(c, c)
+        self.conv1 = nn.Conv2d(c, 2, 3, 1, 1)
+        self.up = nn.PixelShuffle(2)
+
+    def forward(self, x):
+        if self.scale != 1:
+            x = F.interpolate(x, scale_factor=1. / self.scale, mode="bilinear",
+                              align_corners=False, recompute_scale_factor=False)
+        x = self.conv0(x)
+        x = self.res0(x)
+        x = self.res1(x)
+        x = self.res2(x)
+        x = self.res3(x)
+        x = self.res4(x)
+        x = self.res5(x)
+        x = self.conv1(x)
+        flow = x # self.up(x)
+        if self.scale != 1:
+            flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
+                                 align_corners=False, recompute_scale_factor=False)
+        return flow
+
+
+class IFNet(nn.Module):
+    def __init__(self):
+        super(IFNet, self).__init__()
+        self.block0 = IFBlock(6, scale=4, c=192)
+        self.block1 = IFBlock(8, scale=2, c=128)
+        self.block2 = IFBlock(8, scale=1, c=64)
+
+    def forward(self, x):
+        x = F.interpolate(x, scale_factor=0.5, mode="bilinear",
+                          align_corners=False, recompute_scale_factor=False)
+        flow0 = self.block0(x)
+        F1 = flow0
+        warped_img0 = warp(x[:, :3], F1)
+        warped_img1 = warp(x[:, 3:], -F1)
+        flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1), 1))
+        F2 = (flow0 + flow1)
+        warped_img0 = warp(x[:, :3], F2)
+        warped_img1 = warp(x[:, 3:], -F2)
+        flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2), 1))
+        F3 = (flow0 + flow1 + flow2)
+        return F3, [F1, F2, F3]
+
+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)