mirror of
https://github.com/hzwer/ECCV2022-RIFE.git
synced 2025-12-16 00:17:46 +01:00
Add privileged training arXiv v6
This commit is contained in:
hzwer
@@ -2,6 +2,7 @@ import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from model.warplayer import warp
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from model.refine import *
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def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
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return nn.Sequential(
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@@ -9,12 +10,6 @@ def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
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nn.PReLU(out_planes)
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)
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def conv_wo_act(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
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return nn.Sequential(
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nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
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padding=padding, dilation=dilation, bias=True),
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)
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def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
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return nn.Sequential(
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nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
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@@ -23,9 +18,8 @@ def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
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)
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class IFBlock(nn.Module):
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def __init__(self, in_planes, scale=1, c=64):
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def __init__(self, in_planes, c=64):
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super(IFBlock, self).__init__()
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self.scale = scale
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self.conv0 = nn.Sequential(
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conv(in_planes, c//2, 3, 2, 1),
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conv(c//2, c, 3, 2, 1),
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@@ -40,37 +34,72 @@ class IFBlock(nn.Module):
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conv(c, c),
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conv(c, c),
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)
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self.conv1 = nn.ConvTranspose2d(c, 4, 4, 2, 1)
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self.lastconv = nn.ConvTranspose2d(c, 5, 4, 2, 1)
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def forward(self, x):
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if self.scale != 1:
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x = F.interpolate(x, scale_factor= 1. / self.scale, mode="bilinear", align_corners=False)
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def forward(self, x, flow, scale):
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if scale != 1:
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x = F.interpolate(x, scale_factor = 1. / scale, mode="bilinear", align_corners=False)
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if flow != None:
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flow = F.interpolate(flow, scale_factor = 1. / scale, mode="bilinear", align_corners=False) * 1. / scale
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x = torch.cat((x, flow), 1)
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x = self.conv0(x)
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x = self.convblock(x) + x
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x = self.conv1(x)
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flow = x
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if self.scale != 1:
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flow = F.interpolate(flow, scale_factor= self.scale, mode="bilinear", align_corners=False)
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return flow
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tmp = self.lastconv(x)
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tmp = F.interpolate(tmp, scale_factor = scale * 2, mode="bilinear", align_corners=False)
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flow = tmp[:, :4] * scale * 2
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mask = tmp[:, 4:5]
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return flow, mask
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class IFNet(nn.Module):
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def __init__(self):
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super(IFNet, self).__init__()
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self.block0 = IFBlock(6, scale=4, c=240)
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self.block1 = IFBlock(10, scale=2, c=150)
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self.block2 = IFBlock(10, scale=1, c=90)
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self.block0 = IFBlock(6, c=240)
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self.block1 = IFBlock(13+4, c=150)
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self.block2 = IFBlock(13+4, c=90)
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self.block_tea = IFBlock(16+4, c=90)
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self.contextnet = Contextnet()
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self.unet = Unet()
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def forward(self, x):
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flow0 = self.block0(x)
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F1 = flow0
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F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
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warped_img0 = warp(x[:, :3], F1_large[:, :2])
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warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
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flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
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F2 = (flow0 + flow1)
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F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
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warped_img0 = warp(x[:, :3], F2_large[:, :2])
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warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
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flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
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F3 = (flow0 + flow1 + flow2)
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return F3, [F1, F2, F3]
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def forward(self, x, scale=[4,2,1]):
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img0 = x[:, :3]
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img1 = x[:, 3:6]
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gt = x[:, 6:] # In inference time, gt is None
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flow_list = []
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merged = []
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mask_list = []
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warped_img0 = img0
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warped_img1 = img1
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flow = None
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loss_distill = 0
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stu = [self.block0, self.block1, self.block2]
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for i in range(3):
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if flow != None:
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flow_d, mask_d = stu[i](torch.cat((img0, img1, warped_img0, warped_img1, mask), 1), flow, scale=scale[i])
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flow = flow + flow_d
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mask = mask + mask_d
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else:
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flow, mask = stu[i](torch.cat((img0, img1), 1), None, scale=scale[i])
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mask_list.append(torch.sigmoid(mask))
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flow_list.append(flow)
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warped_img0 = warp(img0, flow[:, :2])
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warped_img1 = warp(img1, flow[:, 2:4])
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merged_student = (warped_img0, warped_img1)
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merged.append(merged_student)
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if gt.shape[1] == 3:
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flow_d, mask_d = self.block_tea(torch.cat((img0, img1, warped_img0, warped_img1, mask, gt), 1), flow, scale=1)
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flow_teacher = flow + flow_d
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warped_img0_teacher = warp(img0, flow_teacher[:, :2])
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warped_img1_teacher = warp(img1, flow_teacher[:, 2:4])
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mask_teacher = torch.sigmoid(mask + mask_d)
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merged_teacher = warped_img0_teacher * mask_teacher + warped_img1_teacher * (1 - mask_teacher)
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for i in range(3):
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merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])
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if gt.shape[1] == 3:
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loss_mask = ((merged[i] - gt).abs().mean(1, True) > (merged_teacher - gt).abs().mean(1, True) + 0.01).float().detach()
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loss_distill += ((flow_teacher.detach() - flow_list[i]).abs() * loss_mask).mean()
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c0 = self.contextnet(img0, flow[:, :2])
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c1 = self.contextnet(img1, flow[:, 2:4])
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tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
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res = tmp[:, :3] * 2 - 1
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merged[2] = torch.clamp(merged[2] + res, 0, 1)
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return flow_list, mask_list[2], merged, flow_teacher, merged_teacher, loss_distill
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@@ -1,76 +0,0 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from model.warplayer import warp
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def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
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return nn.Sequential(
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torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
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nn.PReLU(out_planes)
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)
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def conv_wo_act(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
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return nn.Sequential(
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nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
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padding=padding, dilation=dilation, bias=True),
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)
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def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
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return nn.Sequential(
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nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
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padding=padding, dilation=dilation, bias=True),
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nn.PReLU(out_planes)
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)
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class IFBlock(nn.Module):
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def __init__(self, in_planes, scale=1, c=64):
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super(IFBlock, self).__init__()
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self.scale = scale
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self.conv0 = nn.Sequential(
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conv(in_planes, c//2, 3, 2, 1),
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conv(c//2, c, 3, 2, 1),
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)
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self.convblock = nn.Sequential(
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conv(c, c),
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conv(c, c),
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conv(c, c),
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conv(c, c),
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conv(c, c),
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conv(c, c),
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conv(c, c),
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conv(c, c),
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)
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self.conv1 = nn.ConvTranspose2d(c, 4, 4, 2, 1)
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def forward(self, x):
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if self.scale != 1:
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x = F.interpolate(x, scale_factor= 1. / self.scale, mode="bilinear", align_corners=False)
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x = self.conv0(x)
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x = self.convblock(x) + x
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x = self.conv1(x)
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flow = x
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if self.scale != 1:
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flow = F.interpolate(flow, scale_factor= self.scale, mode="bilinear", align_corners=False)
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return flow
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class IFNet(nn.Module):
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def __init__(self):
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super(IFNet, self).__init__()
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self.block0 = IFBlock(6, scale=4, c=320)
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self.block1 = IFBlock(10, scale=2, c=225)
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self.block2 = IFBlock(10, scale=1, c=135)
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def forward(self, x):
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flow0 = self.block0(x)
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F1 = flow0
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F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
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warped_img0 = warp(x[:, :3], F1_large[:, :2])
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warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
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flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
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F2 = (flow0 + flow1)
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F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
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warped_img0 = warp(x[:, :3], F2_large[:, :2])
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warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
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flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
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F3 = (flow0 + flow1 + flow2)
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return F3, [F1, F2, F3]
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@@ -1,75 +0,0 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from model.warplayer import warp
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def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
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return nn.Sequential(
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torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
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nn.PReLU(out_planes)
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)
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def conv_wo_act(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
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return nn.Sequential(
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nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
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padding=padding, dilation=dilation, bias=True),
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)
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def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
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return nn.Sequential(
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nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
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padding=padding, dilation=dilation, bias=True),
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nn.PReLU(out_planes)
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)
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class IFBlock(nn.Module):
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def __init__(self, in_planes, scale=1, c=64):
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super(IFBlock, self).__init__()
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self.scale = scale
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self.conv0 = nn.Sequential(
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conv(in_planes, c, 3, 2, 1),
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)
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self.convblock = nn.Sequential(
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conv(c, c),
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conv(c, c),
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conv(c, c),
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conv(c, c),
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conv(c, c),
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conv(c, c),
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conv(c, c),
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conv(c, c),
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)
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self.conv1 = nn.Conv2d(c, 4, 3, 1, 1)
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def forward(self, x):
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if self.scale != 1:
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x = F.interpolate(x, scale_factor= 1. / self.scale, mode="bilinear", align_corners=False)
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x = self.conv0(x)
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x = self.convblock(x) + x
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x = self.conv1(x)
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flow = x
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if self.scale != 1:
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flow = F.interpolate(flow, scale_factor= self.scale, mode="bilinear", align_corners=False)
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return flow
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class IFNet(nn.Module):
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def __init__(self):
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super(IFNet, self).__init__()
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self.block0 = IFBlock(6, scale=4, c=240)
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self.block1 = IFBlock(10, scale=2, c=150)
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self.block2 = IFBlock(10, scale=1, c=90)
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def forward(self, x):
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flow0 = self.block0(x)
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F1 = flow0
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F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
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warped_img0 = warp(x[:, :3], F1_large[:, :2])
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warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
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flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
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F2 = (flow0 + flow1)
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F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
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warped_img0 = warp(x[:, :3], F2_large[:, :2])
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warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
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flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
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F3 = (flow0 + flow1 + flow2)
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return F3, [F1, F2, F3]
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@@ -1,75 +0,0 @@
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from model.warplayer import warp
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|
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def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
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return nn.Sequential(
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torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
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nn.PReLU(out_planes)
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)
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def conv_wo_act(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
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return nn.Sequential(
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nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
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padding=padding, dilation=dilation, bias=True),
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)
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def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
|
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return nn.Sequential(
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nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
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padding=padding, dilation=dilation, bias=True),
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nn.PReLU(out_planes)
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)
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|
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class IFBlock(nn.Module):
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def __init__(self, in_planes, scale=1, c=64):
|
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super(IFBlock, self).__init__()
|
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self.scale = scale
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self.conv0 = nn.Sequential(
|
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conv(in_planes, c, 3, 2, 1),
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)
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self.convblock = nn.Sequential(
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conv(c, c),
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conv(c, c),
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conv(c, c),
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conv(c, c),
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conv(c, c),
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conv(c, c),
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conv(c, c),
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conv(c, c),
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)
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self.conv1 = nn.Conv2d(c, 4, 3, 1, 1)
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def forward(self, x):
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if self.scale != 1:
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x = F.interpolate(x, scale_factor= 1. / self.scale, mode="bilinear", align_corners=False)
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x = self.conv0(x)
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x = self.convblock(x) + x
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x = self.conv1(x)
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flow = x
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if self.scale != 1:
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flow = F.interpolate(flow, scale_factor= self.scale, mode="bilinear", align_corners=False)
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return flow
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class IFNet(nn.Module):
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def __init__(self):
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super(IFNet, self).__init__()
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self.block0 = IFBlock(6, scale=4, c=360)
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self.block1 = IFBlock(10, scale=2, c=225)
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self.block2 = IFBlock(10, scale=1, c=135)
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def forward(self, x):
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flow0 = self.block0(x)
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F1 = flow0
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F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
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warped_img0 = warp(x[:, :3], F1_large[:, :2])
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warped_img1 = warp(x[:, 3:], F1_large[:, 2:4])
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flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1))
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F2 = (flow0 + flow1)
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F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0
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warped_img0 = warp(x[:, :3], F2_large[:, :2])
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warped_img1 = warp(x[:, 3:], F2_large[:, 2:4])
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flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1))
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F3 = (flow0 + flow1 + flow2)
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return F3, [F1, F2, F3]
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226
model/RIFE.py
226
model/RIFE.py
@@ -5,233 +5,87 @@ from torch.optim import AdamW
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import torch.optim as optim
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import itertools
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from model.warplayer import warp
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from torchstat import stat
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from torch.nn.parallel import DistributedDataParallel as DDP
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from model.IFNet import *
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import torch.nn.functional as F
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from model.loss import *
|
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from model.laplacian import *
|
||||
from model.refine import *
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
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)
|
||||
)
|
||||
|
||||
|
||||
def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
|
||||
return nn.Sequential(
|
||||
torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes,
|
||||
kernel_size=4, stride=2, padding=1, bias=True),
|
||||
nn.PReLU(out_planes)
|
||||
)
|
||||
|
||||
def conv_woact(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),
|
||||
)
|
||||
|
||||
class Conv2(nn.Module):
|
||||
def __init__(self, in_planes, out_planes, stride=2):
|
||||
super(Conv2, self).__init__()
|
||||
self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
|
||||
self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.conv2(x)
|
||||
return x
|
||||
|
||||
c = 16
|
||||
|
||||
class ContextNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(ContextNet, self).__init__()
|
||||
self.conv1 = Conv2(3, c)
|
||||
self.conv2 = Conv2(c, 2*c)
|
||||
self.conv3 = Conv2(2*c, 4*c)
|
||||
self.conv4 = Conv2(4*c, 8*c)
|
||||
|
||||
def forward(self, x, flow):
|
||||
x = self.conv1(x)
|
||||
f1 = warp(x, flow)
|
||||
x = self.conv2(x)
|
||||
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", 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) * 0.5
|
||||
f3 = warp(x, flow)
|
||||
x = self.conv4(x)
|
||||
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
|
||||
f4 = warp(x, flow)
|
||||
return [f1, f2, f3, f4]
|
||||
|
||||
class FusionNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(FusionNet, self).__init__()
|
||||
self.down0 = Conv2(12, 2*c)
|
||||
self.down1 = Conv2(4*c, 4*c)
|
||||
self.down2 = Conv2(8*c, 8*c)
|
||||
self.down3 = Conv2(16*c, 16*c)
|
||||
self.up0 = deconv(32*c, 8*c)
|
||||
self.up1 = deconv(16*c, 4*c)
|
||||
self.up2 = deconv(8*c, 2*c)
|
||||
self.up3 = deconv(4*c, c)
|
||||
self.conv = nn.Conv2d(c, 4, 3, 1, 1)
|
||||
|
||||
def forward(self, img0, img1, flow, c0, c1, flow_gt):
|
||||
warped_img0 = warp(img0, flow[:, :2])
|
||||
warped_img1 = warp(img1, flow[:, 2:4])
|
||||
if flow_gt == None:
|
||||
warped_img0_gt, warped_img1_gt = None, None
|
||||
else:
|
||||
warped_img0_gt = warp(img0, flow_gt[:, :2])
|
||||
warped_img1_gt = warp(img1, flow_gt[:, 2:4])
|
||||
s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1), 1))
|
||||
s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
|
||||
s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
|
||||
s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
|
||||
x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
|
||||
x = self.up1(torch.cat((x, s2), 1))
|
||||
x = self.up2(torch.cat((x, s1), 1))
|
||||
x = self.up3(torch.cat((x, s0), 1))
|
||||
x = self.conv(x)
|
||||
return x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
|
||||
|
||||
device = torch.device("cuda")
|
||||
|
||||
class Model:
|
||||
def __init__(self, local_rank=-1):
|
||||
self.flownet = IFNet()
|
||||
self.contextnet = ContextNet()
|
||||
self.fusionnet = FusionNet()
|
||||
self.device()
|
||||
self.optimG = AdamW(itertools.chain(
|
||||
self.flownet.parameters(),
|
||||
self.contextnet.parameters(),
|
||||
self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-4)
|
||||
self.schedulerG = optim.lr_scheduler.CyclicLR(
|
||||
self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
|
||||
self.optimG = AdamW(self.flownet.parameters(), lr=1e-6, weight_decay=1e-4)
|
||||
self.epe = EPE()
|
||||
self.ter = Ternary()
|
||||
self.lap = LapLoss()
|
||||
self.sobel = SOBEL()
|
||||
# self.vgg = VGGPerceptualLoss().to(device)
|
||||
if local_rank != -1:
|
||||
self.flownet = DDP(self.flownet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
self.contextnet = DDP(self.contextnet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
self.fusionnet = DDP(self.fusionnet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
self.flownet = DDP(self.flownet, device_ids=[local_rank], output_device=local_rank)
|
||||
|
||||
def train(self):
|
||||
self.flownet.train()
|
||||
self.contextnet.train()
|
||||
self.fusionnet.train()
|
||||
|
||||
def eval(self):
|
||||
self.flownet.eval()
|
||||
self.contextnet.eval()
|
||||
self.fusionnet.eval()
|
||||
|
||||
def device(self):
|
||||
self.flownet.to(device)
|
||||
self.contextnet.to(device)
|
||||
self.fusionnet.to(device)
|
||||
|
||||
def load_model(self, path, rank=-1):
|
||||
def convert(param):
|
||||
if rank == -1:
|
||||
return {
|
||||
k.replace("module.", ""): v
|
||||
for k, v in param.items()
|
||||
if "module." in k
|
||||
}
|
||||
else:
|
||||
return param
|
||||
if rank <= 0:
|
||||
self.flownet.load_state_dict(
|
||||
convert(torch.load('{}/flownet.pkl'.format(path), map_location=device)))
|
||||
self.contextnet.load_state_dict(
|
||||
convert(torch.load('{}/contextnet.pkl'.format(path), map_location=device)))
|
||||
self.fusionnet.load_state_dict(
|
||||
convert(torch.load('{}/unet.pkl'.format(path), map_location=device)))
|
||||
|
||||
def save_model(self, path, rank):
|
||||
def load_model(self, path, rank=0):
|
||||
if rank == 0:
|
||||
torch.save(self.flownet.state_dict(), '{}/flownet.pkl'.format(path))
|
||||
torch.save(self.contextnet.state_dict(), '{}/contextnet.pkl'.format(path))
|
||||
torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
|
||||
self.flownet.load_state_dict(torch.load('{}/flownet.pkl'.format(path)))
|
||||
|
||||
def save_model(self, path, rank=0):
|
||||
if rank == 0:
|
||||
torch.save(self.flownet.state_dict(),'{}/flownet.pkl'.format(path))
|
||||
|
||||
def predict(self, imgs, flow, training=True, flow_gt=None):
|
||||
'''
|
||||
def predict(self, imgs, flow, merged, training=True, flow_gt=None):
|
||||
img0 = imgs[:, :3]
|
||||
img1 = imgs[:, 3:]
|
||||
c0 = self.contextnet(img0, flow[:, :2])
|
||||
c1 = self.contextnet(img1, flow[:, 2:4])
|
||||
flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
|
||||
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)
|
||||
refine_output = self.unet(img0, img1, flow, merged, c0, c1, flow_gt)
|
||||
res = torch.sigmoid(refine_output[:, :3]) * 2 - 1
|
||||
mask = torch.sigmoid(refine_output[:, 3:4])
|
||||
merged_img = warped_img0 * mask + warped_img1 * (1 - mask)
|
||||
pred = merged_img + res
|
||||
pred = merged + res
|
||||
pred = torch.clamp(pred, 0, 1)
|
||||
if training:
|
||||
return pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
|
||||
if training:
|
||||
return pred, merged
|
||||
else:
|
||||
return pred
|
||||
|
||||
def inference(self, img0, img1):
|
||||
imgs = torch.cat((img0, img1), 1)
|
||||
flow, _ = self.flownet(torch.cat((img0, img1), 1))
|
||||
return self.predict(imgs, flow, training=False)
|
||||
'''
|
||||
|
||||
def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
|
||||
for param_group in self.optimG.param_groups:
|
||||
param_group['lr'] = learning_rate
|
||||
img0 = imgs[:, :3]
|
||||
img1 = imgs[:, 3:]
|
||||
if training:
|
||||
self.train()
|
||||
else:
|
||||
self.eval()
|
||||
flow, flow_list = self.flownet(imgs)
|
||||
pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.predict(
|
||||
imgs, flow, flow_gt=flow_gt)
|
||||
loss_ter = self.ter(pred, gt).mean()
|
||||
if training:
|
||||
with torch.no_grad():
|
||||
loss_flow = torch.abs(warped_img0_gt - gt).mean()
|
||||
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).detach()
|
||||
flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear",
|
||||
align_corners=False) * 0.5).detach()
|
||||
loss_cons = 0
|
||||
for i in range(3):
|
||||
loss_cons += self.epe(flow_list[i][:, :2], flow_gt[:, :2], 1)
|
||||
loss_cons += self.epe(flow_list[i][:, 2:4], flow_gt[:, 2:4], 1)
|
||||
loss_cons = loss_cons.mean() * 0.01
|
||||
else:
|
||||
loss_cons = torch.tensor([0])
|
||||
loss_flow = torch.abs(warped_img0 - gt).mean()
|
||||
loss_mask = 1
|
||||
loss_l1 = (((pred - gt) ** 2 + 1e-6) ** 0.5).mean()
|
||||
flow, mask, merged, flow_teacher, merged_teacher, loss_distill = self.flownet(torch.cat((imgs, gt), 1), scale=[4, 2, 1])
|
||||
loss_l1 = (self.lap(merged[2], gt)).mean()
|
||||
loss_tea = (self.lap(merged_teacher, gt)).mean()
|
||||
if training:
|
||||
self.optimG.zero_grad()
|
||||
loss_G = loss_l1 + loss_cons + loss_ter
|
||||
# loss_G = self.vgg(pred, gt) + loss_cons + loss_ter
|
||||
loss_G = loss_l1 + loss_tea + loss_distill * 0.01
|
||||
loss_G.backward()
|
||||
self.optimG.step()
|
||||
return pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, loss_mask
|
||||
|
||||
|
||||
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)
|
||||
model = Model()
|
||||
model.eval()
|
||||
print(model.inference(imgs).shape)
|
||||
else:
|
||||
flow_teacher = flow[2]
|
||||
merged_teacher = merged[2]
|
||||
return merged[2], {
|
||||
'merged_tea': merged_teacher,
|
||||
'mask': mask,
|
||||
'mask_tea': mask,
|
||||
'flow': flow[2][:, :2],
|
||||
'flow_tea': flow_teacher,
|
||||
'loss_l1': loss_l1,
|
||||
'loss_tea': loss_tea,
|
||||
'loss_distill': loss_distill,
|
||||
}
|
||||
|
||||
235
model/RIFE15C.py
235
model/RIFE15C.py
@@ -1,235 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from torch.optim import AdamW
|
||||
import torch.optim as optim
|
||||
import itertools
|
||||
from model.warplayer import warp
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from model.IFNet15C import *
|
||||
import torch.nn.functional as F
|
||||
from model.loss import *
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
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)
|
||||
)
|
||||
|
||||
|
||||
def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
|
||||
return nn.Sequential(
|
||||
torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes,
|
||||
kernel_size=4, stride=2, padding=1, bias=True),
|
||||
nn.PReLU(out_planes)
|
||||
)
|
||||
|
||||
def conv_woact(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),
|
||||
)
|
||||
|
||||
class Conv2(nn.Module):
|
||||
def __init__(self, in_planes, out_planes, stride=2):
|
||||
super(Conv2, self).__init__()
|
||||
self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
|
||||
self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.conv2(x)
|
||||
return x
|
||||
|
||||
c = 24
|
||||
|
||||
class ContextNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(ContextNet, self).__init__()
|
||||
self.conv1 = Conv2(3, c)
|
||||
self.conv2 = Conv2(c, 2*c)
|
||||
self.conv3 = Conv2(2*c, 4*c)
|
||||
self.conv4 = Conv2(4*c, 8*c)
|
||||
|
||||
def forward(self, x, flow):
|
||||
x = self.conv1(x)
|
||||
f1 = warp(x, flow)
|
||||
x = self.conv2(x)
|
||||
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", 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) * 0.5
|
||||
f3 = warp(x, flow)
|
||||
x = self.conv4(x)
|
||||
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
|
||||
f4 = warp(x, flow)
|
||||
return [f1, f2, f3, f4]
|
||||
|
||||
class FusionNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(FusionNet, self).__init__()
|
||||
self.down0 = Conv2(12, 2*c)
|
||||
self.down1 = Conv2(4*c, 4*c)
|
||||
self.down2 = Conv2(8*c, 8*c)
|
||||
self.down3 = Conv2(16*c, 16*c)
|
||||
self.up0 = deconv(32*c, 8*c)
|
||||
self.up1 = deconv(16*c, 4*c)
|
||||
self.up2 = deconv(8*c, 2*c)
|
||||
self.up3 = deconv(4*c, c)
|
||||
self.conv = nn.Conv2d(c, 4, 3, 1, 1)
|
||||
|
||||
def forward(self, img0, img1, flow, c0, c1, flow_gt):
|
||||
warped_img0 = warp(img0, flow[:, :2])
|
||||
warped_img1 = warp(img1, flow[:, 2:4])
|
||||
if flow_gt == None:
|
||||
warped_img0_gt, warped_img1_gt = None, None
|
||||
else:
|
||||
warped_img0_gt = warp(img0, flow_gt[:, :2])
|
||||
warped_img1_gt = warp(img1, flow_gt[:, 2:4])
|
||||
s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1), 1))
|
||||
s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
|
||||
s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
|
||||
s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
|
||||
x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
|
||||
x = self.up1(torch.cat((x, s2), 1))
|
||||
x = self.up2(torch.cat((x, s1), 1))
|
||||
x = self.up3(torch.cat((x, s0), 1))
|
||||
x = self.conv(x)
|
||||
return x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
|
||||
|
||||
class Model:
|
||||
def __init__(self, local_rank=-1):
|
||||
self.flownet = IFNet()
|
||||
self.contextnet = ContextNet()
|
||||
self.fusionnet = FusionNet()
|
||||
self.device()
|
||||
self.optimG = AdamW(itertools.chain(
|
||||
self.flownet.parameters(),
|
||||
self.contextnet.parameters(),
|
||||
self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-4)
|
||||
self.schedulerG = optim.lr_scheduler.CyclicLR(
|
||||
self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
|
||||
self.epe = EPE()
|
||||
self.ter = Ternary()
|
||||
self.sobel = SOBEL()
|
||||
if local_rank != -1:
|
||||
self.flownet = DDP(self.flownet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
self.contextnet = DDP(self.contextnet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
self.fusionnet = DDP(self.fusionnet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
|
||||
def train(self):
|
||||
self.flownet.train()
|
||||
self.contextnet.train()
|
||||
self.fusionnet.train()
|
||||
|
||||
def eval(self):
|
||||
self.flownet.eval()
|
||||
self.contextnet.eval()
|
||||
self.fusionnet.eval()
|
||||
|
||||
def device(self):
|
||||
self.flownet.to(device)
|
||||
self.contextnet.to(device)
|
||||
self.fusionnet.to(device)
|
||||
|
||||
def load_model(self, path, rank=-1):
|
||||
def convert(param):
|
||||
if rank == -1:
|
||||
return {
|
||||
k.replace("module.", ""): v
|
||||
for k, v in param.items()
|
||||
if "module." in k
|
||||
}
|
||||
else:
|
||||
return param
|
||||
if rank <= 0:
|
||||
self.flownet.load_state_dict(
|
||||
convert(torch.load('{}/flownet.pkl'.format(path), map_location=device)))
|
||||
self.contextnet.load_state_dict(
|
||||
convert(torch.load('{}/contextnet.pkl'.format(path), map_location=device)))
|
||||
self.fusionnet.load_state_dict(
|
||||
convert(torch.load('{}/unet.pkl'.format(path), map_location=device)))
|
||||
|
||||
def save_model(self, path, rank):
|
||||
if rank == 0:
|
||||
torch.save(self.flownet.state_dict(), '{}/flownet.pkl'.format(path))
|
||||
torch.save(self.contextnet.state_dict(), '{}/contextnet.pkl'.format(path))
|
||||
torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
|
||||
|
||||
def predict(self, imgs, flow, training=True, flow_gt=None):
|
||||
img0 = imgs[:, :3]
|
||||
img1 = imgs[:, 3:]
|
||||
c0 = self.contextnet(img0, flow[:, :2])
|
||||
c1 = self.contextnet(img1, flow[:, 2:4])
|
||||
flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
|
||||
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
|
||||
mask = torch.sigmoid(refine_output[:, 3:4])
|
||||
merged_img = warped_img0 * mask + warped_img1 * (1 - mask)
|
||||
pred = merged_img + res
|
||||
pred = torch.clamp(pred, 0, 1)
|
||||
if training:
|
||||
return pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
|
||||
else:
|
||||
return pred
|
||||
|
||||
def inference(self, img0, img1):
|
||||
imgs = torch.cat((img0, img1), 1)
|
||||
flow, _ = self.flownet(imgs)
|
||||
return self.predict(imgs, flow, training=False)
|
||||
|
||||
def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
|
||||
for param_group in self.optimG.param_groups:
|
||||
param_group['lr'] = learning_rate
|
||||
if training:
|
||||
self.train()
|
||||
else:
|
||||
self.eval()
|
||||
flow, flow_list = self.flownet(imgs)
|
||||
pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.predict(
|
||||
imgs, flow, flow_gt=flow_gt)
|
||||
loss_ter = self.ter(pred, gt).mean()
|
||||
if training:
|
||||
with torch.no_grad():
|
||||
loss_flow = torch.abs(warped_img0_gt - gt).mean()
|
||||
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).detach()
|
||||
flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear",
|
||||
align_corners=False) * 0.5).detach()
|
||||
loss_cons = 0
|
||||
for i in range(3):
|
||||
loss_cons += self.epe(flow_list[i][:, :2], flow_gt[:, :2], 1)
|
||||
loss_cons += self.epe(flow_list[i][:, 2:4], flow_gt[:, 2:4], 1)
|
||||
loss_cons = loss_cons.mean() * 0.01
|
||||
else:
|
||||
loss_cons = torch.tensor([0])
|
||||
loss_flow = torch.abs(warped_img0 - gt).mean()
|
||||
loss_mask = 1
|
||||
loss_l1 = (((pred - gt) ** 2 + 1e-6) ** 0.5).mean()
|
||||
if training:
|
||||
self.optimG.zero_grad()
|
||||
loss_G = loss_l1 + loss_cons + loss_ter
|
||||
loss_G.backward()
|
||||
self.optimG.step()
|
||||
return pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, loss_mask
|
||||
|
||||
|
||||
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)
|
||||
model = Model()
|
||||
model.eval()
|
||||
print(model.inference(imgs).shape)
|
||||
235
model/RIFE2F.py
235
model/RIFE2F.py
@@ -1,235 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from torch.optim import AdamW
|
||||
import torch.optim as optim
|
||||
import itertools
|
||||
from model.warplayer import warp
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from model.IFNet2F import *
|
||||
import torch.nn.functional as F
|
||||
from model.loss import *
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
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)
|
||||
)
|
||||
|
||||
|
||||
def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
|
||||
return nn.Sequential(
|
||||
torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes,
|
||||
kernel_size=4, stride=2, padding=1, bias=True),
|
||||
nn.PReLU(out_planes)
|
||||
)
|
||||
|
||||
def conv_woact(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),
|
||||
)
|
||||
|
||||
class Conv2(nn.Module):
|
||||
def __init__(self, in_planes, out_planes, stride=2):
|
||||
super(Conv2, self).__init__()
|
||||
self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
|
||||
self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.conv2(x)
|
||||
return x
|
||||
|
||||
c = 16
|
||||
|
||||
class ContextNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(ContextNet, self).__init__()
|
||||
self.conv1 = Conv2(3, c, 1)
|
||||
self.conv2 = Conv2(c, 2*c)
|
||||
self.conv3 = Conv2(2*c, 4*c)
|
||||
self.conv4 = Conv2(4*c, 8*c)
|
||||
|
||||
def forward(self, x, flow):
|
||||
x = self.conv1(x)
|
||||
f1 = warp(x, flow)
|
||||
x = self.conv2(x)
|
||||
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", 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) * 0.5
|
||||
f3 = warp(x, flow)
|
||||
x = self.conv4(x)
|
||||
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
|
||||
f4 = warp(x, flow)
|
||||
return [f1, f2, f3, f4]
|
||||
|
||||
class FusionNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(FusionNet, self).__init__()
|
||||
self.down0 = Conv2(12, 2*c, 1)
|
||||
self.down1 = Conv2(4*c, 4*c)
|
||||
self.down2 = Conv2(8*c, 8*c)
|
||||
self.down3 = Conv2(16*c, 16*c)
|
||||
self.up0 = deconv(32*c, 8*c)
|
||||
self.up1 = deconv(16*c, 4*c)
|
||||
self.up2 = deconv(8*c, 2*c)
|
||||
self.up3 = deconv(4*c, c)
|
||||
self.conv = nn.Conv2d(c, 4, 3, 2, 1)
|
||||
|
||||
def forward(self, img0, img1, flow, c0, c1, flow_gt):
|
||||
warped_img0 = warp(img0, flow[:, :2])
|
||||
warped_img1 = warp(img1, flow[:, 2:4])
|
||||
if flow_gt == None:
|
||||
warped_img0_gt, warped_img1_gt = None, None
|
||||
else:
|
||||
warped_img0_gt = warp(img0, flow_gt[:, :2])
|
||||
warped_img1_gt = warp(img1, flow_gt[:, 2:4])
|
||||
s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1), 1))
|
||||
s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
|
||||
s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
|
||||
s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
|
||||
x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
|
||||
x = self.up1(torch.cat((x, s2), 1))
|
||||
x = self.up2(torch.cat((x, s1), 1))
|
||||
x = self.up3(torch.cat((x, s0), 1))
|
||||
x = self.conv(x)
|
||||
return x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
|
||||
|
||||
class Model:
|
||||
def __init__(self, local_rank=-1):
|
||||
self.flownet = IFNet()
|
||||
self.contextnet = ContextNet()
|
||||
self.fusionnet = FusionNet()
|
||||
self.device()
|
||||
self.optimG = AdamW(itertools.chain(
|
||||
self.flownet.parameters(),
|
||||
self.contextnet.parameters(),
|
||||
self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-4)
|
||||
self.schedulerG = optim.lr_scheduler.CyclicLR(
|
||||
self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
|
||||
self.epe = EPE()
|
||||
self.ter = Ternary()
|
||||
self.sobel = SOBEL()
|
||||
if local_rank != -1:
|
||||
self.flownet = DDP(self.flownet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
self.contextnet = DDP(self.contextnet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
self.fusionnet = DDP(self.fusionnet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
|
||||
def train(self):
|
||||
self.flownet.train()
|
||||
self.contextnet.train()
|
||||
self.fusionnet.train()
|
||||
|
||||
def eval(self):
|
||||
self.flownet.eval()
|
||||
self.contextnet.eval()
|
||||
self.fusionnet.eval()
|
||||
|
||||
def device(self):
|
||||
self.flownet.to(device)
|
||||
self.contextnet.to(device)
|
||||
self.fusionnet.to(device)
|
||||
|
||||
def load_model(self, path, rank=-1):
|
||||
def convert(param):
|
||||
if rank == -1:
|
||||
return {
|
||||
k.replace("module.", ""): v
|
||||
for k, v in param.items()
|
||||
if "module." in k
|
||||
}
|
||||
else:
|
||||
return param
|
||||
if rank <= 0:
|
||||
self.flownet.load_state_dict(
|
||||
convert(torch.load('{}/flownet.pkl'.format(path), map_location=device)))
|
||||
self.contextnet.load_state_dict(
|
||||
convert(torch.load('{}/contextnet.pkl'.format(path), map_location=device)))
|
||||
self.fusionnet.load_state_dict(
|
||||
convert(torch.load('{}/unet.pkl'.format(path), map_location=device)))
|
||||
|
||||
def save_model(self, path, rank):
|
||||
if rank == 0:
|
||||
torch.save(self.flownet.state_dict(), '{}/flownet.pkl'.format(path))
|
||||
torch.save(self.contextnet.state_dict(), '{}/contextnet.pkl'.format(path))
|
||||
torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
|
||||
|
||||
def predict(self, imgs, flow, training=True, flow_gt=None):
|
||||
img0 = imgs[:, :3]
|
||||
img1 = imgs[:, 3:]
|
||||
flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
|
||||
align_corners=False) * 2.0
|
||||
c0 = self.contextnet(img0, flow[:, :2])
|
||||
c1 = self.contextnet(img1, flow[:, 2:4])
|
||||
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
|
||||
mask = torch.sigmoid(refine_output[:, 3:4])
|
||||
merged_img = warped_img0 * mask + warped_img1 * (1 - mask)
|
||||
pred = merged_img + res
|
||||
pred = torch.clamp(pred, 0, 1)
|
||||
if training:
|
||||
return pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
|
||||
else:
|
||||
return pred
|
||||
|
||||
def inference(self, img0, img1):
|
||||
imgs = torch.cat((img0, img1), 1)
|
||||
flow, _ = self.flownet(imgs)
|
||||
return self.predict(imgs, flow, training=False)
|
||||
|
||||
def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
|
||||
for param_group in self.optimG.param_groups:
|
||||
param_group['lr'] = learning_rate
|
||||
if training:
|
||||
self.train()
|
||||
else:
|
||||
self.eval()
|
||||
flow, flow_list = self.flownet(imgs)
|
||||
pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.predict(
|
||||
imgs, flow, flow_gt=flow_gt)
|
||||
loss_ter = self.ter(pred, gt).mean()
|
||||
if training:
|
||||
with torch.no_grad():
|
||||
loss_flow = torch.abs(warped_img0_gt - gt).mean()
|
||||
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).detach()
|
||||
flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear",
|
||||
align_corners=False) * 0.5).detach()
|
||||
loss_cons = 0
|
||||
for i in range(3):
|
||||
loss_cons += self.epe(flow_list[i][:, :2], flow_gt[:, :2], 1)
|
||||
loss_cons += self.epe(flow_list[i][:, 2:4], flow_gt[:, 2:4], 1)
|
||||
loss_cons = loss_cons.mean() * 0.01
|
||||
else:
|
||||
loss_cons = torch.tensor([0])
|
||||
loss_flow = torch.abs(warped_img0 - gt).mean()
|
||||
loss_mask = 1
|
||||
loss_l1 = (((pred - gt) ** 2 + 1e-6) ** 0.5).mean()
|
||||
if training:
|
||||
self.optimG.zero_grad()
|
||||
loss_G = loss_l1 + loss_cons + loss_ter
|
||||
loss_G.backward()
|
||||
self.optimG.step()
|
||||
return pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, loss_mask
|
||||
|
||||
|
||||
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)
|
||||
model = Model()
|
||||
model.eval()
|
||||
print(model.inference(imgs).shape)
|
||||
@@ -1,235 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
from torch.optim import AdamW
|
||||
import torch.optim as optim
|
||||
import itertools
|
||||
from model.warplayer import warp
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from model.IFNet2F15C import *
|
||||
import torch.nn.functional as F
|
||||
from model.loss import *
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
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)
|
||||
)
|
||||
|
||||
|
||||
def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
|
||||
return nn.Sequential(
|
||||
torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes,
|
||||
kernel_size=4, stride=2, padding=1, bias=True),
|
||||
nn.PReLU(out_planes)
|
||||
)
|
||||
|
||||
def conv_woact(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),
|
||||
)
|
||||
|
||||
class Conv2(nn.Module):
|
||||
def __init__(self, in_planes, out_planes, stride=2):
|
||||
super(Conv2, self).__init__()
|
||||
self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
|
||||
self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.conv2(x)
|
||||
return x
|
||||
|
||||
c = 24
|
||||
|
||||
class ContextNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(ContextNet, self).__init__()
|
||||
self.conv1 = Conv2(3, c, 1)
|
||||
self.conv2 = Conv2(c, 2*c)
|
||||
self.conv3 = Conv2(2*c, 4*c)
|
||||
self.conv4 = Conv2(4*c, 8*c)
|
||||
|
||||
def forward(self, x, flow):
|
||||
x = self.conv1(x)
|
||||
f1 = warp(x, flow)
|
||||
x = self.conv2(x)
|
||||
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", 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) * 0.5
|
||||
f3 = warp(x, flow)
|
||||
x = self.conv4(x)
|
||||
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5
|
||||
f4 = warp(x, flow)
|
||||
return [f1, f2, f3, f4]
|
||||
|
||||
class FusionNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(FusionNet, self).__init__()
|
||||
self.down0 = Conv2(12, 2*c, 1)
|
||||
self.down1 = Conv2(4*c, 4*c)
|
||||
self.down2 = Conv2(8*c, 8*c)
|
||||
self.down3 = Conv2(16*c, 16*c)
|
||||
self.up0 = deconv(32*c, 8*c)
|
||||
self.up1 = deconv(16*c, 4*c)
|
||||
self.up2 = deconv(8*c, 2*c)
|
||||
self.up3 = deconv(4*c, c)
|
||||
self.conv = nn.Conv2d(c, 4, 3, 2, 1)
|
||||
|
||||
def forward(self, img0, img1, flow, c0, c1, flow_gt):
|
||||
warped_img0 = warp(img0, flow[:, :2])
|
||||
warped_img1 = warp(img1, flow[:, 2:4])
|
||||
if flow_gt == None:
|
||||
warped_img0_gt, warped_img1_gt = None, None
|
||||
else:
|
||||
warped_img0_gt = warp(img0, flow_gt[:, :2])
|
||||
warped_img1_gt = warp(img1, flow_gt[:, 2:4])
|
||||
s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1), 1))
|
||||
s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
|
||||
s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
|
||||
s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
|
||||
x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
|
||||
x = self.up1(torch.cat((x, s2), 1))
|
||||
x = self.up2(torch.cat((x, s1), 1))
|
||||
x = self.up3(torch.cat((x, s0), 1))
|
||||
x = self.conv(x)
|
||||
return x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
|
||||
|
||||
class Model:
|
||||
def __init__(self, local_rank=-1):
|
||||
self.flownet = IFNet()
|
||||
self.contextnet = ContextNet()
|
||||
self.fusionnet = FusionNet()
|
||||
self.device()
|
||||
self.optimG = AdamW(itertools.chain(
|
||||
self.flownet.parameters(),
|
||||
self.contextnet.parameters(),
|
||||
self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-4)
|
||||
self.schedulerG = optim.lr_scheduler.CyclicLR(
|
||||
self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
|
||||
self.epe = EPE()
|
||||
self.ter = Ternary()
|
||||
self.sobel = SOBEL()
|
||||
if local_rank != -1:
|
||||
self.flownet = DDP(self.flownet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
self.contextnet = DDP(self.contextnet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
self.fusionnet = DDP(self.fusionnet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
|
||||
def train(self):
|
||||
self.flownet.train()
|
||||
self.contextnet.train()
|
||||
self.fusionnet.train()
|
||||
|
||||
def eval(self):
|
||||
self.flownet.eval()
|
||||
self.contextnet.eval()
|
||||
self.fusionnet.eval()
|
||||
|
||||
def device(self):
|
||||
self.flownet.to(device)
|
||||
self.contextnet.to(device)
|
||||
self.fusionnet.to(device)
|
||||
|
||||
def load_model(self, path, rank=-1):
|
||||
def convert(param):
|
||||
if rank == -1:
|
||||
return {
|
||||
k.replace("module.", ""): v
|
||||
for k, v in param.items()
|
||||
if "module." in k
|
||||
}
|
||||
else:
|
||||
return param
|
||||
if rank <= 0:
|
||||
self.flownet.load_state_dict(
|
||||
convert(torch.load('{}/flownet.pkl'.format(path), map_location=device)))
|
||||
self.contextnet.load_state_dict(
|
||||
convert(torch.load('{}/contextnet.pkl'.format(path), map_location=device)))
|
||||
self.fusionnet.load_state_dict(
|
||||
convert(torch.load('{}/unet.pkl'.format(path), map_location=device)))
|
||||
|
||||
def save_model(self, path, rank):
|
||||
if rank == 0:
|
||||
torch.save(self.flownet.state_dict(), '{}/flownet.pkl'.format(path))
|
||||
torch.save(self.contextnet.state_dict(), '{}/contextnet.pkl'.format(path))
|
||||
torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
|
||||
|
||||
def predict(self, imgs, flow, training=True, flow_gt=None):
|
||||
img0 = imgs[:, :3]
|
||||
img1 = imgs[:, 3:]
|
||||
flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
|
||||
align_corners=False) * 2.0
|
||||
c0 = self.contextnet(img0, flow[:, :2])
|
||||
c1 = self.contextnet(img1, flow[:, 2:4])
|
||||
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
|
||||
mask = torch.sigmoid(refine_output[:, 3:4])
|
||||
merged_img = warped_img0 * mask + warped_img1 * (1 - mask)
|
||||
pred = merged_img + res
|
||||
pred = torch.clamp(pred, 0, 1)
|
||||
if training:
|
||||
return pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
|
||||
else:
|
||||
return pred
|
||||
|
||||
def inference(self, img0, img1):
|
||||
imgs = torch.cat((img0, img1), 1)
|
||||
flow, _ = self.flownet(imgs)
|
||||
return self.predict(imgs, flow, training=False)
|
||||
|
||||
def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
|
||||
for param_group in self.optimG.param_groups:
|
||||
param_group['lr'] = learning_rate
|
||||
if training:
|
||||
self.train()
|
||||
else:
|
||||
self.eval()
|
||||
flow, flow_list = self.flownet(imgs)
|
||||
pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.predict(
|
||||
imgs, flow, flow_gt=flow_gt)
|
||||
loss_ter = self.ter(pred, gt).mean()
|
||||
if training:
|
||||
with torch.no_grad():
|
||||
loss_flow = torch.abs(warped_img0_gt - gt).mean()
|
||||
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).detach()
|
||||
flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear",
|
||||
align_corners=False) * 0.5).detach()
|
||||
loss_cons = 0
|
||||
for i in range(3):
|
||||
loss_cons += self.epe(flow_list[i][:, :2], flow_gt[:, :2], 1)
|
||||
loss_cons += self.epe(flow_list[i][:, 2:4], flow_gt[:, 2:4], 1)
|
||||
loss_cons = loss_cons.mean() * 0.01
|
||||
else:
|
||||
loss_cons = torch.tensor([0])
|
||||
loss_flow = torch.abs(warped_img0 - gt).mean()
|
||||
loss_mask = 1
|
||||
loss_l1 = (((pred - gt) ** 2 + 1e-6) ** 0.5).mean()
|
||||
if training:
|
||||
self.optimG.zero_grad()
|
||||
loss_G = loss_l1 + loss_cons + loss_ter
|
||||
loss_G.backward()
|
||||
self.optimG.step()
|
||||
return pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, loss_mask
|
||||
|
||||
|
||||
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)
|
||||
model = Model()
|
||||
model.eval()
|
||||
print(model.inference(imgs).shape)
|
||||
59
model/laplacian.py
Normal file
59
model/laplacian.py
Normal file
@@ -0,0 +1,59 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
import torch
|
||||
|
||||
def gauss_kernel(size=5, channels=3):
|
||||
kernel = torch.tensor([[1., 4., 6., 4., 1],
|
||||
[4., 16., 24., 16., 4.],
|
||||
[6., 24., 36., 24., 6.],
|
||||
[4., 16., 24., 16., 4.],
|
||||
[1., 4., 6., 4., 1.]])
|
||||
kernel /= 256.
|
||||
kernel = kernel.repeat(channels, 1, 1, 1)
|
||||
kernel = kernel.to(device)
|
||||
return kernel
|
||||
|
||||
def downsample(x):
|
||||
return x[:, :, ::2, ::2]
|
||||
|
||||
def upsample(x):
|
||||
cc = torch.cat([x, torch.zeros(x.shape[0], x.shape[1], x.shape[2], x.shape[3]).to(device)], dim=3)
|
||||
cc = cc.view(x.shape[0], x.shape[1], x.shape[2]*2, x.shape[3])
|
||||
cc = cc.permute(0,1,3,2)
|
||||
cc = torch.cat([cc, torch.zeros(x.shape[0], x.shape[1], x.shape[3], x.shape[2]*2).to(device)], dim=3)
|
||||
cc = cc.view(x.shape[0], x.shape[1], x.shape[3]*2, x.shape[2]*2)
|
||||
x_up = cc.permute(0,1,3,2)
|
||||
return conv_gauss(x_up, 4*gauss_kernel(channels=x.shape[1]))
|
||||
|
||||
def conv_gauss(img, kernel):
|
||||
img = torch.nn.functional.pad(img, (2, 2, 2, 2), mode='reflect')
|
||||
out = torch.nn.functional.conv2d(img, kernel, groups=img.shape[1])
|
||||
return out
|
||||
|
||||
def laplacian_pyramid(img, kernel, max_levels=3):
|
||||
current = img
|
||||
pyr = []
|
||||
for level in range(max_levels):
|
||||
filtered = conv_gauss(current, kernel)
|
||||
down = downsample(filtered)
|
||||
up = upsample(down)
|
||||
diff = current-up
|
||||
pyr.append(diff)
|
||||
current = down
|
||||
return pyr
|
||||
|
||||
class LapLoss(torch.nn.Module):
|
||||
def __init__(self, max_levels=5, channels=3):
|
||||
super(LapLoss, self).__init__()
|
||||
self.max_levels = max_levels
|
||||
self.gauss_kernel = gauss_kernel(channels=channels)
|
||||
|
||||
def forward(self, input, target):
|
||||
pyr_input = laplacian_pyramid(img=input, kernel=self.gauss_kernel, max_levels=self.max_levels)
|
||||
pyr_target = laplacian_pyramid(img=target, kernel=self.gauss_kernel, max_levels=self.max_levels)
|
||||
return sum(torch.nn.functional.l1_loss(a, b) for a, b in zip(pyr_input, pyr_target))
|
||||
82
model/refine.py
Normal file
82
model/refine.py
Normal file
@@ -0,0 +1,82 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
import torch.optim as optim
|
||||
import itertools
|
||||
from model.warplayer import warp
|
||||
import torch.nn.functional as F
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
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)
|
||||
)
|
||||
|
||||
def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
|
||||
return nn.Sequential(
|
||||
torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1, bias=True),
|
||||
nn.PReLU(out_planes)
|
||||
)
|
||||
|
||||
class Conv2(nn.Module):
|
||||
def __init__(self, in_planes, out_planes, stride=2):
|
||||
super(Conv2, self).__init__()
|
||||
self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
|
||||
self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.conv2(x)
|
||||
return x
|
||||
|
||||
c = 16
|
||||
class Contextnet(nn.Module):
|
||||
def __init__(self):
|
||||
super(Contextnet, self).__init__()
|
||||
self.conv1 = Conv2(3, c)
|
||||
self.conv2 = Conv2(c, 2*c)
|
||||
self.conv3 = Conv2(2*c, 4*c)
|
||||
self.conv4 = Conv2(4*c, 8*c)
|
||||
|
||||
def forward(self, x, flow):
|
||||
x = self.conv1(x)
|
||||
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5
|
||||
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
|
||||
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
|
||||
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
|
||||
f4 = warp(x, flow)
|
||||
return [f1, f2, f3, f4]
|
||||
|
||||
class Unet(nn.Module):
|
||||
def __init__(self):
|
||||
super(Unet, self).__init__()
|
||||
self.down0 = Conv2(17, 2*c)
|
||||
self.down1 = Conv2(4*c, 4*c)
|
||||
self.down2 = Conv2(8*c, 8*c)
|
||||
self.down3 = Conv2(16*c, 16*c)
|
||||
self.up0 = deconv(32*c, 8*c)
|
||||
self.up1 = deconv(16*c, 4*c)
|
||||
self.up2 = deconv(8*c, 2*c)
|
||||
self.up3 = deconv(4*c, c)
|
||||
self.conv = nn.Conv2d(c, 3, 3, 1, 1)
|
||||
|
||||
def forward(self, img0, img1, warped_img0, warped_img1, mask, flow, c0, c1):
|
||||
s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1, mask, flow), 1))
|
||||
s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
|
||||
s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
|
||||
s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
|
||||
x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
|
||||
x = self.up1(torch.cat((x, s2), 1))
|
||||
x = self.up2(torch.cat((x, s1), 1))
|
||||
x = self.up3(torch.cat((x, s0), 1))
|
||||
x = self.conv(x)
|
||||
return torch.sigmoid(x)
|
||||
Reference in New Issue
Block a user