lab3 impl
This commit is contained in:
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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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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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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(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, c=64):
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super(IFBlock, self).__init__()
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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.lastconv = nn.ConvTranspose2d(c, 5, 4, 2, 1)
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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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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, 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, scale=[4,2,1], timestep=0.5):
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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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else:
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flow_teacher = None
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merged_teacher = None
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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]) ** 2).mean(1, True) ** 0.5 * 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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@@ -0,0 +1,108 @@
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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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from model.refine_2R 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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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(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, c=64):
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super(IFBlock, self).__init__()
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self.conv0 = nn.Sequential(
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conv(in_planes, c//2, 3, 1, 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.lastconv = nn.ConvTranspose2d(c, 5, 4, 2, 1)
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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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tmp = self.lastconv(x)
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tmp = F.interpolate(tmp, scale_factor = scale, mode="bilinear", align_corners=False)
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flow = tmp[:, :4] * scale
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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, 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, scale=[4,2,1], timestep=0.5):
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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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else:
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flow_teacher = None
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merged_teacher = None
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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]) ** 2).mean(1, True) ** 0.5 * 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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@@ -0,0 +1,115 @@
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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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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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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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def conv_bn(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=False),
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nn.BatchNorm2d(out_planes),
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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, c=64):
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super(IFBlock, self).__init__()
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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.convblock0 = nn.Sequential(
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conv(c, c),
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conv(c, c)
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)
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self.convblock1 = nn.Sequential(
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conv(c, c),
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conv(c, c)
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)
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self.convblock2 = nn.Sequential(
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conv(c, c),
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conv(c, c)
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)
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self.convblock3 = nn.Sequential(
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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.Sequential(
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nn.ConvTranspose2d(c, c//2, 4, 2, 1),
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nn.PReLU(c//2),
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nn.ConvTranspose2d(c//2, 4, 4, 2, 1),
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)
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self.conv2 = nn.Sequential(
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nn.ConvTranspose2d(c, c//2, 4, 2, 1),
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nn.PReLU(c//2),
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nn.ConvTranspose2d(c//2, 1, 4, 2, 1),
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)
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def forward(self, x, flow, scale=1):
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x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False)
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flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 1. / scale
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feat = self.conv0(torch.cat((x, flow), 1))
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feat = self.convblock0(feat) + feat
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feat = self.convblock1(feat) + feat
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feat = self.convblock2(feat) + feat
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feat = self.convblock3(feat) + feat
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flow = self.conv1(feat)
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mask = self.conv2(feat)
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flow = F.interpolate(flow, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * scale
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mask = F.interpolate(mask, scale_factor=scale, mode="bilinear", align_corners=False, recompute_scale_factor=False)
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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(7+4, c=90)
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self.block1 = IFBlock(7+4, c=90)
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self.block2 = IFBlock(7+4, c=90)
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self.block_tea = IFBlock(10+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, scale_list=[4, 2, 1], training=False):
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if training == False:
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channel = x.shape[1] // 2
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img0 = x[:, :channel]
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img1 = x[:, channel:]
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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 = (x[:, :4]).detach() * 0
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mask = (x[:, :1]).detach() * 0
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loss_cons = 0
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block = [self.block0, self.block1, self.block2]
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for i in range(3):
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f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], mask), 1), flow, scale=scale_list[i])
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f1, m1 = block[i](torch.cat((warped_img1[:, :3], warped_img0[:, :3], -mask), 1), torch.cat((flow[:, 2:4], flow[:, :2]), 1), scale=scale_list[i])
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flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2
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mask = mask + (m0 + (-m1)) / 2
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mask_list.append(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.append((warped_img0, warped_img1))
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'''
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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[:, 1:4] * 2 - 1
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'''
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for i in range(3):
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mask_list[i] = torch.sigmoid(mask_list[i])
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merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])
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# merged[i] = torch.clamp(merged[i] + res, 0, 1)
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return flow_list, mask_list[2], merged
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@@ -0,0 +1,112 @@
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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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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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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(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, c=64):
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super(IFBlock, self).__init__()
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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.lastconv = nn.ConvTranspose2d(c, 5, 4, 2, 1)
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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
|
||||
tmp = self.lastconv(x)
|
||||
tmp = F.interpolate(tmp, scale_factor = scale * 2, mode="bilinear", align_corners=False)
|
||||
flow = tmp[:, :4] * scale * 2
|
||||
mask = tmp[:, 4:5]
|
||||
return flow, mask
|
||||
|
||||
class IFNet_m(nn.Module):
|
||||
def __init__(self):
|
||||
super(IFNet_m, self).__init__()
|
||||
self.block0 = IFBlock(6+1, c=240)
|
||||
self.block1 = IFBlock(13+4+1, c=150)
|
||||
self.block2 = IFBlock(13+4+1, c=90)
|
||||
self.block_tea = IFBlock(16+4+1, c=90)
|
||||
self.contextnet = Contextnet()
|
||||
self.unet = Unet()
|
||||
|
||||
def forward(self, x, scale=[4,2,1], timestep=0.5, returnflow=False):
|
||||
timestep = (x[:, :1].clone() * 0 + 1) * timestep
|
||||
img0 = x[:, :3]
|
||||
img1 = x[:, 3:6]
|
||||
gt = x[:, 6:] # In inference time, gt is None
|
||||
flow_list = []
|
||||
merged = []
|
||||
mask_list = []
|
||||
warped_img0 = img0
|
||||
warped_img1 = img1
|
||||
flow = None
|
||||
loss_distill = 0
|
||||
stu = [self.block0, self.block1, self.block2]
|
||||
for i in range(3):
|
||||
if flow != None:
|
||||
flow_d, mask_d = stu[i](torch.cat((img0, img1, timestep, warped_img0, warped_img1, mask), 1), flow, scale=scale[i])
|
||||
flow = flow + flow_d
|
||||
mask = mask + mask_d
|
||||
else:
|
||||
flow, mask = stu[i](torch.cat((img0, img1, timestep), 1), None, scale=scale[i])
|
||||
mask_list.append(torch.sigmoid(mask))
|
||||
flow_list.append(flow)
|
||||
warped_img0 = warp(img0, flow[:, :2])
|
||||
warped_img1 = warp(img1, flow[:, 2:4])
|
||||
merged_student = (warped_img0, warped_img1)
|
||||
merged.append(merged_student)
|
||||
if gt.shape[1] == 3:
|
||||
flow_d, mask_d = self.block_tea(torch.cat((img0, img1, timestep, warped_img0, warped_img1, mask, gt), 1), flow, scale=1)
|
||||
flow_teacher = flow + flow_d
|
||||
warped_img0_teacher = warp(img0, flow_teacher[:, :2])
|
||||
warped_img1_teacher = warp(img1, flow_teacher[:, 2:4])
|
||||
mask_teacher = torch.sigmoid(mask + mask_d)
|
||||
merged_teacher = warped_img0_teacher * mask_teacher + warped_img1_teacher * (1 - mask_teacher)
|
||||
else:
|
||||
flow_teacher = None
|
||||
merged_teacher = None
|
||||
for i in range(3):
|
||||
merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i])
|
||||
if gt.shape[1] == 3:
|
||||
loss_mask = ((merged[i] - gt).abs().mean(1, True) > (merged_teacher - gt).abs().mean(1, True) + 0.01).float().detach()
|
||||
loss_distill += (((flow_teacher.detach() - flow_list[i]) ** 2).mean(1, True) ** 0.5 * loss_mask).mean()
|
||||
if returnflow:
|
||||
return flow
|
||||
else:
|
||||
c0 = self.contextnet(img0, flow[:, :2])
|
||||
c1 = self.contextnet(img1, flow[:, 2:4])
|
||||
tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1)
|
||||
res = tmp[:, :3] * 2 - 1
|
||||
merged[2] = torch.clamp(merged[2] + res, 0, 1)
|
||||
return flow_list, mask_list[2], merged, flow_teacher, merged_teacher, loss_distill
|
||||
@@ -0,0 +1,97 @@
|
||||
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.IFNet import *
|
||||
from model.IFNet_m import *
|
||||
import torch.nn.functional as F
|
||||
from model.loss import *
|
||||
from model.laplacian import *
|
||||
from model.refine import *
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
class Model:
|
||||
def __init__(self, local_rank=-1, arbitrary=False):
|
||||
if arbitrary == True:
|
||||
self.flownet = IFNet_m()
|
||||
else:
|
||||
self.flownet = IFNet()
|
||||
self.device()
|
||||
self.optimG = AdamW(self.flownet.parameters(), lr=1e-6, weight_decay=1e-3) # use large weight decay may avoid NaN loss
|
||||
self.epe = EPE()
|
||||
self.lap = LapLoss()
|
||||
self.sobel = SOBEL()
|
||||
if local_rank != -1:
|
||||
self.flownet = DDP(self.flownet, device_ids=[local_rank], output_device=local_rank)
|
||||
|
||||
def train(self):
|
||||
self.flownet.train()
|
||||
|
||||
def eval(self):
|
||||
self.flownet.eval()
|
||||
|
||||
def device(self):
|
||||
self.flownet.to(device)
|
||||
|
||||
def load_model(self, path, rank=0):
|
||||
def convert(param):
|
||||
return {
|
||||
k.replace("module.", ""): v
|
||||
for k, v in param.items()
|
||||
if "module." in k
|
||||
}
|
||||
|
||||
if rank <= 0:
|
||||
self.flownet.load_state_dict(convert(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 inference(self, img0, img1, scale=1, scale_list=None, TTA=False, timestep=0.5):
|
||||
if scale_list is None:
|
||||
scale_list = [4, 2, 1]
|
||||
for i in range(3):
|
||||
scale_list[i] = scale_list[i] * 1.0 / scale
|
||||
imgs = torch.cat((img0, img1), 1)
|
||||
flow, mask, merged, flow_teacher, merged_teacher, loss_distill = self.flownet(imgs, scale_list, timestep=timestep)
|
||||
if TTA == False:
|
||||
return merged[2]
|
||||
else:
|
||||
flow2, mask2, merged2, flow_teacher2, merged_teacher2, loss_distill2 = self.flownet(imgs.flip(2).flip(3), scale_list, timestep=timestep)
|
||||
return (merged[2] + merged2[2].flip(2).flip(3)) / 2
|
||||
|
||||
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, 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_tea + loss_distill * 0.01 # when training RIFEm, the weight of loss_distill should be 0.005 or 0.002
|
||||
loss_G.backward()
|
||||
self.optimG.step()
|
||||
else:
|
||||
flow_teacher = flow[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,
|
||||
}
|
||||
@@ -0,0 +1,88 @@
|
||||
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 train_log.IFNet_HDv3 import *
|
||||
import torch.nn.functional as F
|
||||
from model.loss import *
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
class Model:
|
||||
def __init__(self, local_rank=-1):
|
||||
self.flownet = IFNet()
|
||||
self.device()
|
||||
self.optimG = AdamW(self.flownet.parameters(), lr=1e-6, weight_decay=1e-4)
|
||||
self.epe = EPE()
|
||||
# self.vgg = VGGPerceptualLoss().to(device)
|
||||
self.sobel = SOBEL()
|
||||
if local_rank != -1:
|
||||
self.flownet = DDP(self.flownet, device_ids=[local_rank], output_device=local_rank)
|
||||
|
||||
def train(self):
|
||||
self.flownet.train()
|
||||
|
||||
def eval(self):
|
||||
self.flownet.eval()
|
||||
|
||||
def device(self):
|
||||
self.flownet.to(device)
|
||||
|
||||
def load_model(self, path, rank=0):
|
||||
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:
|
||||
if torch.cuda.is_available():
|
||||
self.flownet.load_state_dict(convert(torch.load('{}/flownet.pkl'.format(path))))
|
||||
else:
|
||||
self.flownet.load_state_dict(convert(torch.load('{}/flownet.pkl'.format(path), map_location ='cpu')))
|
||||
|
||||
def save_model(self, path, rank=0):
|
||||
if rank == 0:
|
||||
torch.save(self.flownet.state_dict(),'{}/flownet.pkl'.format(path))
|
||||
|
||||
def inference(self, img0, img1, scale=1.0):
|
||||
imgs = torch.cat((img0, img1), 1)
|
||||
scale_list = [4/scale, 2/scale, 1/scale]
|
||||
flow, mask, merged = self.flownet(imgs, scale_list)
|
||||
return merged[2]
|
||||
|
||||
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()
|
||||
scale = [4, 2, 1]
|
||||
flow, mask, merged = self.flownet(torch.cat((imgs, gt), 1), scale=scale, training=training)
|
||||
loss_l1 = (merged[2] - gt).abs().mean()
|
||||
loss_smooth = self.sobel(flow[2], flow[2]*0).mean()
|
||||
# loss_vgg = self.vgg(merged[2], gt)
|
||||
if training:
|
||||
self.optimG.zero_grad()
|
||||
loss_G = loss_cons + loss_smooth * 0.1
|
||||
loss_G.backward()
|
||||
self.optimG.step()
|
||||
else:
|
||||
flow_teacher = flow[2]
|
||||
return merged[2], {
|
||||
'mask': mask,
|
||||
'flow': flow[2][:, :2],
|
||||
'loss_l1': loss_l1,
|
||||
'loss_cons': loss_cons,
|
||||
'loss_smooth': loss_smooth,
|
||||
}
|
||||
@@ -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))
|
||||
@@ -0,0 +1,128 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torchvision.models as models
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
class EPE(nn.Module):
|
||||
def __init__(self):
|
||||
super(EPE, self).__init__()
|
||||
|
||||
def forward(self, flow, gt, loss_mask):
|
||||
loss_map = (flow - gt.detach()) ** 2
|
||||
loss_map = (loss_map.sum(1, True) + 1e-6) ** 0.5
|
||||
return (loss_map * loss_mask)
|
||||
|
||||
|
||||
class Ternary(nn.Module):
|
||||
def __init__(self):
|
||||
super(Ternary, self).__init__()
|
||||
patch_size = 7
|
||||
out_channels = patch_size * patch_size
|
||||
self.w = np.eye(out_channels).reshape(
|
||||
(patch_size, patch_size, 1, out_channels))
|
||||
self.w = np.transpose(self.w, (3, 2, 0, 1))
|
||||
self.w = torch.tensor(self.w).float().to(device)
|
||||
|
||||
def transform(self, img):
|
||||
patches = F.conv2d(img, self.w, padding=3, bias=None)
|
||||
transf = patches - img
|
||||
transf_norm = transf / torch.sqrt(0.81 + transf**2)
|
||||
return transf_norm
|
||||
|
||||
def rgb2gray(self, rgb):
|
||||
r, g, b = rgb[:, 0:1, :, :], rgb[:, 1:2, :, :], rgb[:, 2:3, :, :]
|
||||
gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
|
||||
return gray
|
||||
|
||||
def hamming(self, t1, t2):
|
||||
dist = (t1 - t2) ** 2
|
||||
dist_norm = torch.mean(dist / (0.1 + dist), 1, True)
|
||||
return dist_norm
|
||||
|
||||
def valid_mask(self, t, padding):
|
||||
n, _, h, w = t.size()
|
||||
inner = torch.ones(n, 1, h - 2 * padding, w - 2 * padding).type_as(t)
|
||||
mask = F.pad(inner, [padding] * 4)
|
||||
return mask
|
||||
|
||||
def forward(self, img0, img1):
|
||||
img0 = self.transform(self.rgb2gray(img0))
|
||||
img1 = self.transform(self.rgb2gray(img1))
|
||||
return self.hamming(img0, img1) * self.valid_mask(img0, 1)
|
||||
|
||||
|
||||
class SOBEL(nn.Module):
|
||||
def __init__(self):
|
||||
super(SOBEL, self).__init__()
|
||||
self.kernelX = torch.tensor([
|
||||
[1, 0, -1],
|
||||
[2, 0, -2],
|
||||
[1, 0, -1],
|
||||
]).float()
|
||||
self.kernelY = self.kernelX.clone().T
|
||||
self.kernelX = self.kernelX.unsqueeze(0).unsqueeze(0).to(device)
|
||||
self.kernelY = self.kernelY.unsqueeze(0).unsqueeze(0).to(device)
|
||||
|
||||
def forward(self, pred, gt):
|
||||
N, C, H, W = pred.shape[0], pred.shape[1], pred.shape[2], pred.shape[3]
|
||||
img_stack = torch.cat(
|
||||
[pred.reshape(N*C, 1, H, W), gt.reshape(N*C, 1, H, W)], 0)
|
||||
sobel_stack_x = F.conv2d(img_stack, self.kernelX, padding=1)
|
||||
sobel_stack_y = F.conv2d(img_stack, self.kernelY, padding=1)
|
||||
pred_X, gt_X = sobel_stack_x[:N*C], sobel_stack_x[N*C:]
|
||||
pred_Y, gt_Y = sobel_stack_y[:N*C], sobel_stack_y[N*C:]
|
||||
|
||||
L1X, L1Y = torch.abs(pred_X-gt_X), torch.abs(pred_Y-gt_Y)
|
||||
loss = (L1X+L1Y)
|
||||
return loss
|
||||
|
||||
class MeanShift(nn.Conv2d):
|
||||
def __init__(self, data_mean, data_std, data_range=1, norm=True):
|
||||
c = len(data_mean)
|
||||
super(MeanShift, self).__init__(c, c, kernel_size=1)
|
||||
std = torch.Tensor(data_std)
|
||||
self.weight.data = torch.eye(c).view(c, c, 1, 1)
|
||||
if norm:
|
||||
self.weight.data.div_(std.view(c, 1, 1, 1))
|
||||
self.bias.data = -1 * data_range * torch.Tensor(data_mean)
|
||||
self.bias.data.div_(std)
|
||||
else:
|
||||
self.weight.data.mul_(std.view(c, 1, 1, 1))
|
||||
self.bias.data = data_range * torch.Tensor(data_mean)
|
||||
self.requires_grad = False
|
||||
|
||||
class VGGPerceptualLoss(torch.nn.Module):
|
||||
def __init__(self, rank=0):
|
||||
super(VGGPerceptualLoss, self).__init__()
|
||||
blocks = []
|
||||
pretrained = True
|
||||
self.vgg_pretrained_features = models.vgg19(pretrained=pretrained).features
|
||||
self.normalize = MeanShift([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], norm=True).cuda()
|
||||
for param in self.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
def forward(self, X, Y, indices=None):
|
||||
X = self.normalize(X)
|
||||
Y = self.normalize(Y)
|
||||
indices = [2, 7, 12, 21, 30]
|
||||
weights = [1.0/2.6, 1.0/4.8, 1.0/3.7, 1.0/5.6, 10/1.5]
|
||||
k = 0
|
||||
loss = 0
|
||||
for i in range(indices[-1]):
|
||||
X = self.vgg_pretrained_features[i](X)
|
||||
Y = self.vgg_pretrained_features[i](Y)
|
||||
if (i+1) in indices:
|
||||
loss += weights[k] * (X - Y.detach()).abs().mean() * 0.1
|
||||
k += 1
|
||||
return loss
|
||||
|
||||
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)
|
||||
ternary_loss = Ternary()
|
||||
print(ternary_loss(img0, img1).shape)
|
||||
@@ -0,0 +1,200 @@
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from math import exp
|
||||
import numpy as np
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
def gaussian(window_size, sigma):
|
||||
gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
|
||||
return gauss/gauss.sum()
|
||||
|
||||
|
||||
def create_window(window_size, channel=1):
|
||||
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
|
||||
_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0).to(device)
|
||||
window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()
|
||||
return window
|
||||
|
||||
def create_window_3d(window_size, channel=1):
|
||||
_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
|
||||
_2D_window = _1D_window.mm(_1D_window.t())
|
||||
_3D_window = _2D_window.unsqueeze(2) @ (_1D_window.t())
|
||||
window = _3D_window.expand(1, channel, window_size, window_size, window_size).contiguous().to(device)
|
||||
return window
|
||||
|
||||
|
||||
def ssim(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None):
|
||||
# Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh).
|
||||
if val_range is None:
|
||||
if torch.max(img1) > 128:
|
||||
max_val = 255
|
||||
else:
|
||||
max_val = 1
|
||||
|
||||
if torch.min(img1) < -0.5:
|
||||
min_val = -1
|
||||
else:
|
||||
min_val = 0
|
||||
L = max_val - min_val
|
||||
else:
|
||||
L = val_range
|
||||
|
||||
padd = 0
|
||||
(_, channel, height, width) = img1.size()
|
||||
if window is None:
|
||||
real_size = min(window_size, height, width)
|
||||
window = create_window(real_size, channel=channel).to(img1.device)
|
||||
|
||||
# mu1 = F.conv2d(img1, window, padding=padd, groups=channel)
|
||||
# mu2 = F.conv2d(img2, window, padding=padd, groups=channel)
|
||||
mu1 = F.conv2d(F.pad(img1, (5, 5, 5, 5), mode='replicate'), window, padding=padd, groups=channel)
|
||||
mu2 = F.conv2d(F.pad(img2, (5, 5, 5, 5), mode='replicate'), window, padding=padd, groups=channel)
|
||||
|
||||
mu1_sq = mu1.pow(2)
|
||||
mu2_sq = mu2.pow(2)
|
||||
mu1_mu2 = mu1 * mu2
|
||||
|
||||
sigma1_sq = F.conv2d(F.pad(img1 * img1, (5, 5, 5, 5), 'replicate'), window, padding=padd, groups=channel) - mu1_sq
|
||||
sigma2_sq = F.conv2d(F.pad(img2 * img2, (5, 5, 5, 5), 'replicate'), window, padding=padd, groups=channel) - mu2_sq
|
||||
sigma12 = F.conv2d(F.pad(img1 * img2, (5, 5, 5, 5), 'replicate'), window, padding=padd, groups=channel) - mu1_mu2
|
||||
|
||||
C1 = (0.01 * L) ** 2
|
||||
C2 = (0.03 * L) ** 2
|
||||
|
||||
v1 = 2.0 * sigma12 + C2
|
||||
v2 = sigma1_sq + sigma2_sq + C2
|
||||
cs = torch.mean(v1 / v2) # contrast sensitivity
|
||||
|
||||
ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2)
|
||||
|
||||
if size_average:
|
||||
ret = ssim_map.mean()
|
||||
else:
|
||||
ret = ssim_map.mean(1).mean(1).mean(1)
|
||||
|
||||
if full:
|
||||
return ret, cs
|
||||
return ret
|
||||
|
||||
|
||||
def ssim_matlab(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None):
|
||||
# Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh).
|
||||
if val_range is None:
|
||||
if torch.max(img1) > 128:
|
||||
max_val = 255
|
||||
else:
|
||||
max_val = 1
|
||||
|
||||
if torch.min(img1) < -0.5:
|
||||
min_val = -1
|
||||
else:
|
||||
min_val = 0
|
||||
L = max_val - min_val
|
||||
else:
|
||||
L = val_range
|
||||
|
||||
padd = 0
|
||||
(_, _, height, width) = img1.size()
|
||||
if window is None:
|
||||
real_size = min(window_size, height, width)
|
||||
window = create_window_3d(real_size, channel=1).to(img1.device)
|
||||
# Channel is set to 1 since we consider color images as volumetric images
|
||||
|
||||
img1 = img1.unsqueeze(1)
|
||||
img2 = img2.unsqueeze(1)
|
||||
|
||||
mu1 = F.conv3d(F.pad(img1, (5, 5, 5, 5, 5, 5), mode='replicate'), window, padding=padd, groups=1)
|
||||
mu2 = F.conv3d(F.pad(img2, (5, 5, 5, 5, 5, 5), mode='replicate'), window, padding=padd, groups=1)
|
||||
|
||||
mu1_sq = mu1.pow(2)
|
||||
mu2_sq = mu2.pow(2)
|
||||
mu1_mu2 = mu1 * mu2
|
||||
|
||||
sigma1_sq = F.conv3d(F.pad(img1 * img1, (5, 5, 5, 5, 5, 5), 'replicate'), window, padding=padd, groups=1) - mu1_sq
|
||||
sigma2_sq = F.conv3d(F.pad(img2 * img2, (5, 5, 5, 5, 5, 5), 'replicate'), window, padding=padd, groups=1) - mu2_sq
|
||||
sigma12 = F.conv3d(F.pad(img1 * img2, (5, 5, 5, 5, 5, 5), 'replicate'), window, padding=padd, groups=1) - mu1_mu2
|
||||
|
||||
C1 = (0.01 * L) ** 2
|
||||
C2 = (0.03 * L) ** 2
|
||||
|
||||
v1 = 2.0 * sigma12 + C2
|
||||
v2 = sigma1_sq + sigma2_sq + C2
|
||||
cs = torch.mean(v1 / v2) # contrast sensitivity
|
||||
|
||||
ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2)
|
||||
|
||||
if size_average:
|
||||
ret = ssim_map.mean()
|
||||
else:
|
||||
ret = ssim_map.mean(1).mean(1).mean(1)
|
||||
|
||||
if full:
|
||||
return ret, cs
|
||||
return ret
|
||||
|
||||
|
||||
def msssim(img1, img2, window_size=11, size_average=True, val_range=None, normalize=False):
|
||||
device = img1.device
|
||||
weights = torch.FloatTensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333]).to(device)
|
||||
levels = weights.size()[0]
|
||||
mssim = []
|
||||
mcs = []
|
||||
for _ in range(levels):
|
||||
sim, cs = ssim(img1, img2, window_size=window_size, size_average=size_average, full=True, val_range=val_range)
|
||||
mssim.append(sim)
|
||||
mcs.append(cs)
|
||||
|
||||
img1 = F.avg_pool2d(img1, (2, 2))
|
||||
img2 = F.avg_pool2d(img2, (2, 2))
|
||||
|
||||
mssim = torch.stack(mssim)
|
||||
mcs = torch.stack(mcs)
|
||||
|
||||
# Normalize (to avoid NaNs during training unstable models, not compliant with original definition)
|
||||
if normalize:
|
||||
mssim = (mssim + 1) / 2
|
||||
mcs = (mcs + 1) / 2
|
||||
|
||||
pow1 = mcs ** weights
|
||||
pow2 = mssim ** weights
|
||||
# From Matlab implementation https://ece.uwaterloo.ca/~z70wang/research/iwssim/
|
||||
output = torch.prod(pow1[:-1] * pow2[-1])
|
||||
return output
|
||||
|
||||
|
||||
# Classes to re-use window
|
||||
class SSIM(torch.nn.Module):
|
||||
def __init__(self, window_size=11, size_average=True, val_range=None):
|
||||
super(SSIM, self).__init__()
|
||||
self.window_size = window_size
|
||||
self.size_average = size_average
|
||||
self.val_range = val_range
|
||||
|
||||
# Assume 3 channel for SSIM
|
||||
self.channel = 3
|
||||
self.window = create_window(window_size, channel=self.channel)
|
||||
|
||||
def forward(self, img1, img2):
|
||||
(_, channel, _, _) = img1.size()
|
||||
|
||||
if channel == self.channel and self.window.dtype == img1.dtype:
|
||||
window = self.window
|
||||
else:
|
||||
window = create_window(self.window_size, channel).to(img1.device).type(img1.dtype)
|
||||
self.window = window
|
||||
self.channel = channel
|
||||
|
||||
_ssim = ssim(img1, img2, window=window, window_size=self.window_size, size_average=self.size_average)
|
||||
dssim = (1 - _ssim) / 2
|
||||
return dssim
|
||||
|
||||
class MSSSIM(torch.nn.Module):
|
||||
def __init__(self, window_size=11, size_average=True, channel=3):
|
||||
super(MSSSIM, self).__init__()
|
||||
self.window_size = window_size
|
||||
self.size_average = size_average
|
||||
self.channel = channel
|
||||
|
||||
def forward(self, img1, img2):
|
||||
return msssim(img1, img2, window_size=self.window_size, size_average=self.size_average)
|
||||
@@ -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)
|
||||
@@ -0,0 +1,83 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import numpy as np
|
||||
import torch.optim as optim
|
||||
import itertools
|
||||
from model.warplayer import warp
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
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, 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)
|
||||
# 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, 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, 3, 3, 2, 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)
|
||||
@@ -0,0 +1,22 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
backwarp_tenGrid = {}
|
||||
|
||||
|
||||
def warp(tenInput, tenFlow):
|
||||
k = (str(tenFlow.device), str(tenFlow.size()))
|
||||
if k not in backwarp_tenGrid:
|
||||
tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).view(
|
||||
1, 1, 1, tenFlow.shape[3]).expand(tenFlow.shape[0], -1, tenFlow.shape[2], -1)
|
||||
tenVertical = torch.linspace(-1.0, 1.0, tenFlow.shape[2], device=device).view(
|
||||
1, 1, tenFlow.shape[2], 1).expand(tenFlow.shape[0], -1, -1, tenFlow.shape[3])
|
||||
backwarp_tenGrid[k] = torch.cat(
|
||||
[tenHorizontal, tenVertical], 1).to(device)
|
||||
|
||||
tenFlow = torch.cat([tenFlow[:, 0:1, :, :] / ((tenInput.shape[3] - 1.0) / 2.0),
|
||||
tenFlow[:, 1:2, :, :] / ((tenInput.shape[2] - 1.0) / 2.0)], 1)
|
||||
|
||||
g = (backwarp_tenGrid[k] + tenFlow).permute(0, 2, 3, 1)
|
||||
return torch.nn.functional.grid_sample(input=tenInput, grid=g, mode='bilinear', padding_mode='border', align_corners=True)
|
||||
Reference in New Issue
Block a user