mirror of https://github.com/n00mkrad/flowframes
92 lines
3.5 KiB
Python
92 lines
3.5 KiB
Python
import torch
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import torch.nn as nn
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import numpy as np
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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 torch.nn.parallel import DistributedDataParallel as DDP
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import torch.nn.functional as F
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from model.loss import *
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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_woact(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 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, bias=True),
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nn.PReLU(out_planes)
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)
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class Conv2(nn.Module):
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def __init__(self, in_planes, out_planes, stride=2):
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super(Conv2, self).__init__()
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self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
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self.conv2 = conv(out_planes, out_planes, 3, 1, 1)
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def forward(self, x):
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x = self.conv1(x)
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x = self.conv2(x)
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return x
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c = 16
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class Contextnet(nn.Module):
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def __init__(self):
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super(Contextnet, self).__init__()
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self.conv1 = Conv2(3, c)
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self.conv2 = Conv2(c, 2*c)
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self.conv3 = Conv2(2*c, 4*c)
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self.conv4 = Conv2(4*c, 8*c)
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def forward(self, x, flow):
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x = self.conv1(x)
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flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5
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f1 = warp(x, flow)
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x = self.conv2(x)
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flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5
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f2 = warp(x, flow)
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x = self.conv3(x)
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flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5
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f3 = warp(x, flow)
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x = self.conv4(x)
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flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 0.5
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f4 = warp(x, flow)
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return [f1, f2, f3, f4]
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class Unet(nn.Module):
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def __init__(self):
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super(Unet, self).__init__()
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self.down0 = Conv2(17, 2*c)
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self.down1 = Conv2(4*c, 4*c)
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self.down2 = Conv2(8*c, 8*c)
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self.down3 = Conv2(16*c, 16*c)
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self.up0 = deconv(32*c, 8*c)
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self.up1 = deconv(16*c, 4*c)
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self.up2 = deconv(8*c, 2*c)
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self.up3 = deconv(4*c, c)
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self.conv = nn.Conv2d(c, 4, 3, 1, 1)
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def forward(self, img0, img1, warped_img0, warped_img1, mask, flow, c0, c1):
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s0 = self.down0(torch.cat((img0, img1, warped_img0, warped_img1, mask, flow), 1))
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s1 = self.down1(torch.cat((s0, c0[0], c1[0]), 1))
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s2 = self.down2(torch.cat((s1, c0[1], c1[1]), 1))
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s3 = self.down3(torch.cat((s2, c0[2], c1[2]), 1))
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x = self.up0(torch.cat((s3, c0[3], c1[3]), 1))
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x = self.up1(torch.cat((x, s2), 1))
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x = self.up2(torch.cat((x, s1), 1))
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x = self.up3(torch.cat((x, s0), 1))
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x = self.conv(x)
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return torch.sigmoid(x)
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