| 51 | return flow, mask |
| 52 | |
| 53 | class IFNet(nn.Module): |
| 54 | def __init__(self): |
| 55 | super(IFNet, self).__init__() |
| 56 | self.block0 = IFBlock(6, c=240) |
| 57 | self.block1 = IFBlock(13+4, c=150) |
| 58 | self.block2 = IFBlock(13+4, c=90) |
| 59 | self.block_tea = IFBlock(16+4, c=90) |
| 60 | self.contextnet = Contextnet() |
| 61 | self.unet = Unet() |
| 62 | |
| 63 | def forward(self, x, scale=[4,2,1], timestep=0.5): |
| 64 | img0 = x[:, :3] |
| 65 | img1 = x[:, 3:6] |
| 66 | gt = x[:, 6:] # In inference time, gt is None |
| 67 | flow_list = [] |
| 68 | merged = [] |
| 69 | mask_list = [] |
| 70 | warped_img0 = img0 |
| 71 | warped_img1 = img1 |
| 72 | flow = None |
| 73 | loss_distill = 0 |
| 74 | stu = [self.block0, self.block1, self.block2] |
| 75 | for i in range(3): |
| 76 | if flow != None: |
| 77 | flow_d, mask_d = stu[i](torch.cat((img0, img1, warped_img0, warped_img1, mask), 1), flow, scale=scale[i]) |
| 78 | flow = flow + flow_d |
| 79 | mask = mask + mask_d |
| 80 | else: |
| 81 | flow, mask = stu[i](torch.cat((img0, img1), 1), None, scale=scale[i]) |
| 82 | mask_list.append(torch.sigmoid(mask)) |
| 83 | flow_list.append(flow) |
| 84 | warped_img0 = warp(img0, flow[:, :2]) |
| 85 | warped_img1 = warp(img1, flow[:, 2:4]) |
| 86 | merged_student = (warped_img0, warped_img1) |
| 87 | merged.append(merged_student) |
| 88 | if gt.shape[1] == 3: |
| 89 | flow_d, mask_d = self.block_tea(torch.cat((img0, img1, warped_img0, warped_img1, mask, gt), 1), flow, scale=1) |
| 90 | flow_teacher = flow + flow_d |
| 91 | warped_img0_teacher = warp(img0, flow_teacher[:, :2]) |
| 92 | warped_img1_teacher = warp(img1, flow_teacher[:, 2:4]) |
| 93 | mask_teacher = torch.sigmoid(mask + mask_d) |
| 94 | merged_teacher = warped_img0_teacher * mask_teacher + warped_img1_teacher * (1 - mask_teacher) |
| 95 | else: |
| 96 | flow_teacher = None |
| 97 | merged_teacher = None |
| 98 | for i in range(3): |
| 99 | merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i]) |
| 100 | if gt.shape[1] == 3: |
| 101 | loss_mask = ((merged[i] - gt).abs().mean(1, True) > (merged_teacher - gt).abs().mean(1, True) + 0.01).float().detach() |
| 102 | loss_distill += (((flow_teacher.detach() - flow_list[i]) ** 2).mean(1, True) ** 0.5 * loss_mask).mean() |
| 103 | c0 = self.contextnet(img0, flow[:, :2]) |
| 104 | c1 = self.contextnet(img1, flow[:, 2:4]) |
| 105 | tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1) |
| 106 | res = tmp[:, :3] * 2 - 1 |
| 107 | merged[2] = torch.clamp(merged[2] + res, 0, 1) |
| 108 | return flow_list, mask_list[2], merged, flow_teacher, merged_teacher, loss_distill |