mirror of https://github.com/n00mkrad/flowframes
77 lines
2.8 KiB
Python
77 lines
2.8 KiB
Python
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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