ychenhq's picture
Upload folder using huggingface_hub
c62dd62 verified
raw
history blame
3.21 kB
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)