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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.LeakyReLU(0.2, True)
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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.LeakyReLU(0.2, True)
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)
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class Head(nn.Module):
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def __init__(self):
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super(Head, self).__init__()
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self.cnn0 = nn.Conv2d(3, 16, 3, 2, 1)
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self.cnn1 = nn.Conv2d(16, 16, 3, 1, 1)
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self.cnn2 = nn.Conv2d(16, 16, 3, 1, 1)
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self.cnn3 = nn.ConvTranspose2d(16, 4, 4, 2, 1)
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self.relu = nn.LeakyReLU(0.2, True)
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def forward(self, x, feat=False):
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x0 = self.cnn0(x)
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x = self.relu(x0)
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x1 = self.cnn1(x)
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x = self.relu(x1)
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x2 = self.cnn2(x)
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x = self.relu(x2)
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x3 = self.cnn3(x)
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if feat:
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return [x0, x1, x2, x3]
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return x3
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class ResConv(nn.Module):
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def __init__(self, c, dilation=1):
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super(ResConv, self).__init__()
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self.conv = nn.Conv2d(c, c, 3, 1, dilation, dilation=dilation, groups=1\
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)
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self.beta = nn.Parameter(torch.ones((1, c, 1, 1)), requires_grad=True)
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self.relu = nn.LeakyReLU(0.2, True)
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def forward(self, x):
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return self.relu(self.conv(x) * self.beta + x)
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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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ResConv(c),
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ResConv(c),
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ResConv(c),
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ResConv(c),
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ResConv(c),
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ResConv(c),
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ResConv(c),
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ResConv(c),
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)
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self.lastconv = nn.Sequential(
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nn.ConvTranspose2d(c, 4*13, 4, 2, 1),
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nn.PixelShuffle(2)
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)
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def forward(self, x, flow=None, 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 is not 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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feat = self.conv0(x)
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feat = self.convblock(feat)
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tmp = self.lastconv(feat)
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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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feat = tmp[:, 5:]
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return flow, mask, feat
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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+8, c=192)
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self.block1 = IFBlock(8+4+8+8, c=128)
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self.block2 = IFBlock(8+4+8+8, c=96)
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self.block3 = IFBlock(8+4+8+8, c=64) |
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self.block4 = IFBlock(8+4+8+8, c=32)
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self.encode = Head()
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'''
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self.teacher = IFBlock(8+4+8+3+8, c=64)
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self.caltime = nn.Sequential(
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nn.Conv2d(16+9, 8, 3, 2, 1),
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nn.LeakyReLU(0.2, True),
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nn.Conv2d(32, 64, 3, 2, 1),
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nn.LeakyReLU(0.2, True),
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nn.Conv2d(64, 64, 3, 1, 1),
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nn.LeakyReLU(0.2, True),
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nn.Conv2d(64, 64, 3, 1, 1),
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nn.LeakyReLU(0.2, True),
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nn.Conv2d(64, 1, 3, 1, 1),
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nn.Sigmoid()
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) |
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'''
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def forward(self, x, timestep=0.5, scale_list=[8, 4, 2, 1], training=False, fastmode=True, ensemble=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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if not torch.is_tensor(timestep):
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timestep = (x[:, :1].clone() * 0 + 1) * timestep
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else:
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timestep = timestep.repeat(1, 1, img0.shape[2], img0.shape[3])
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f0 = self.encode(img0[:, :3])
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f1 = self.encode(img1[:, :3])
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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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mask = None
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loss_cons = 0
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block = [self.block0, self.block1, self.block2, self.block3, self.block4]
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for i in range(5):
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if flow is None:
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flow, mask, feat = block[i](torch.cat((img0[:, :3], img1[:, :3], f0, f1, timestep), 1), None, scale=scale_list[i])
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if ensemble:
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print("warning: ensemble is not supported since RIFEv4.21")
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else:
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wf0 = warp(f0, flow[:, :2])
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wf1 = warp(f1, flow[:, 2:4])
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fd, m0, feat = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], wf0, wf1, timestep, mask, feat), 1), flow, scale=scale_list[i])
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if ensemble:
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print("warning: ensemble is not supported since RIFEv4.21")
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else:
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mask = m0
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flow = flow + fd
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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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mask = torch.sigmoid(mask)
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merged[4] = (warped_img0 * mask + warped_img1 * (1 - mask))
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if not fastmode:
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print('contextnet is removed')
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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[:, :3] * 2 - 1
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merged[4] = torch.clamp(merged[4] + res, 0, 1)
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'''
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return flow_list, mask_list[4], merged
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