| 98 | # self.unet = Unet() |
| 99 | |
| 100 | def forward(self, x, scale_list=[4, 2, 1], training=False): |
| 101 | if training == False: |
| 102 | channel = x.shape[1] // 2 |
| 103 | img0 = x[:, :channel] |
| 104 | img1 = x[:, channel:] |
| 105 | flow_list = [] |
| 106 | merged = [] |
| 107 | mask_list = [] |
| 108 | warped_img0 = img0 |
| 109 | warped_img1 = img1 |
| 110 | flow = (x[:, :4]).detach() * 0 |
| 111 | mask = (x[:, :1]).detach() * 0 |
| 112 | loss_cons = 0 |
| 113 | block = [self.block0, self.block1, self.block2] |
| 114 | for i in range(3): |
| 115 | f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], mask), 1), flow, scale=scale_list[i]) |
| 116 | f1, m1 = block[i]( |
| 117 | torch.cat((warped_img1[:, :3], warped_img0[:, :3], -mask), 1), |
| 118 | torch.cat((flow[:, 2:4], flow[:, :2]), 1), |
| 119 | scale=scale_list[i], |
| 120 | ) |
| 121 | flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2 |
| 122 | mask = mask + (m0 + (-m1)) / 2 |
| 123 | mask_list.append(mask) |
| 124 | flow_list.append(flow) |
| 125 | warped_img0 = warp(img0, flow[:, :2]) |
| 126 | warped_img1 = warp(img1, flow[:, 2:4]) |
| 127 | merged.append((warped_img0, warped_img1)) |
| 128 | """ |
| 129 | c0 = self.contextnet(img0, flow[:, :2]) |
| 130 | c1 = self.contextnet(img1, flow[:, 2:4]) |
| 131 | tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1) |
| 132 | res = tmp[:, 1:4] * 2 - 1 |
| 133 | """ |
| 134 | for i in range(3): |
| 135 | mask_list[i] = torch.sigmoid(mask_list[i]) |
| 136 | merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i]) |
| 137 | # merged[i] = torch.clamp(merged[i] + res, 0, 1) |
| 138 | return flow_list, mask_list[2], merged |