mirror of https://github.com/kritiksoman/GIMP-ML
Merge branch 'master' into dev
Brieuc de Voghel
4 years ago
commit
3dd7feee58
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MIT License
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Copyright (c) 2020 hzwer
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
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SOFTWARE.
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import torch
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import numpy as np
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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_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=False),
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nn.BatchNorm2d(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=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 ResBlock(nn.Module):
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def __init__(self, in_planes, out_planes, stride=1):
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super(ResBlock, self).__init__()
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if in_planes == out_planes and stride == 1:
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self.conv0 = nn.Identity()
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else:
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self.conv0 = nn.Conv2d(in_planes, out_planes,
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3, stride, 1, bias=False)
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self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
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self.conv2 = conv_wo_act(out_planes, out_planes, 3, 1, 1)
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self.relu1 = nn.PReLU(1)
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self.relu2 = nn.PReLU(out_planes)
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self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False)
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self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False)
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def forward(self, x):
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y = self.conv0(x)
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x = self.conv1(x)
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x = self.conv2(x)
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w = x.mean(3, True).mean(2, True)
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w = self.relu1(self.fc1(w))
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w = torch.sigmoid(self.fc2(w))
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x = self.relu2(x * w + y)
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return x
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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 = conv(in_planes, c, 3, 2, 1)
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self.res0 = ResBlock(c, c)
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self.res1 = ResBlock(c, c)
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self.res2 = ResBlock(c, c)
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self.res3 = ResBlock(c, c)
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self.res4 = ResBlock(c, c)
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self.res5 = ResBlock(c, c)
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self.conv1 = nn.Conv2d(c, 8, 3, 1, 1)
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self.up = nn.PixelShuffle(2)
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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",
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align_corners=False)
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x = self.conv0(x)
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x = self.res0(x)
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x = self.res1(x)
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x = self.res2(x)
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x = self.res3(x)
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x = self.res4(x)
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x = self.res5(x)
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x = self.conv1(x)
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flow = self.up(x)
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if self.scale != 1:
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flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
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align_corners=False)
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return flow
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class IFNet(nn.Module):
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def __init__(self, cFlag):
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super(IFNet, self).__init__()
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self.block0 = IFBlock(6, scale=4, c=192)
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self.block1 = IFBlock(8, scale=2, c=128)
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self.block2 = IFBlock(8, scale=1, c=64)
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self.cFlag = cFlag
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def forward(self, x):
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x = F.interpolate(x, scale_factor=0.5, mode="bilinear",
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align_corners=False)
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flow0 = self.block0(x)
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F1 = flow0
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warped_img0 = warp(x[:, :3], F1, self.cFlag)
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warped_img1 = warp(x[:, 3:], -F1, self.cFlag)
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flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1), 1))
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F2 = (flow0 + flow1)
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warped_img0 = warp(x[:, :3], F2, self.cFlag)
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warped_img1 = warp(x[:, 3:], -F2, self.cFlag)
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flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2), 1))
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F3 = (flow0 + flow1 + flow2)
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return F3, [F1, F2, F3]
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if __name__ == '__main__':
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img0 = torch.zeros(3, 3, 256, 256).float().to(device)
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img1 = torch.tensor(np.random.normal(
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0, 1, (3, 3, 256, 256))).float().to(device)
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imgs = torch.cat((img0, img1), 1)
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flownet = IFNet()
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flow, _ = flownet(imgs)
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print(flow.shape)
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import torch
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import numpy as np
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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_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=False),
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nn.BatchNorm2d(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=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 ResBlock(nn.Module):
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def __init__(self, in_planes, out_planes, stride=1):
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super(ResBlock, self).__init__()
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if in_planes == out_planes and stride == 1:
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self.conv0 = nn.Identity()
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else:
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self.conv0 = nn.Conv2d(in_planes, out_planes,
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3, stride, 1, bias=False)
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self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
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self.conv2 = conv_wo_act(out_planes, out_planes, 3, 1, 1)
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self.relu1 = nn.PReLU(1)
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self.relu2 = nn.PReLU(out_planes)
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self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False)
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self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False)
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def forward(self, x):
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y = self.conv0(x)
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x = self.conv1(x)
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x = self.conv2(x)
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w = x.mean(3, True).mean(2, True)
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w = self.relu1(self.fc1(w))
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w = torch.sigmoid(self.fc2(w))
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x = self.relu2(x * w + y)
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return x
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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 = conv(in_planes, c, 3, 1, 1)
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self.res0 = ResBlock(c, c)
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self.res1 = ResBlock(c, c)
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self.res2 = ResBlock(c, c)
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self.res3 = ResBlock(c, c)
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self.res4 = ResBlock(c, c)
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self.res5 = ResBlock(c, c)
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self.conv1 = nn.Conv2d(c, 2, 3, 1, 1)
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self.up = nn.PixelShuffle(2)
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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",
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align_corners=False)
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x = self.conv0(x)
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x = self.res0(x)
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x = self.res1(x)
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x = self.res2(x)
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x = self.res3(x)
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x = self.res4(x)
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x = self.res5(x)
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x = self.conv1(x)
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flow = x # self.up(x)
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if self.scale != 1:
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flow = F.interpolate(flow, scale_factor=self.scale, mode="bilinear",
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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=192)
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self.block1 = IFBlock(8, scale=2, c=128)
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self.block2 = IFBlock(8, scale=1, c=64)
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def forward(self, x):
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x = F.interpolate(x, scale_factor=0.5, mode="bilinear",
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align_corners=False)
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flow0 = self.block0(x)
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F1 = flow0
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warped_img0 = warp(x[:, :3], F1)
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warped_img1 = warp(x[:, 3:], -F1)
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flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1), 1))
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F2 = (flow0 + flow1)
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warped_img0 = warp(x[:, :3], F2)
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warped_img1 = warp(x[:, 3:], -F2)
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flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2), 1))
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F3 = (flow0 + flow1 + flow2)
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return F3, [F1, F2, F3]
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if __name__ == '__main__':
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img0 = torch.zeros(3, 3, 256, 256).float().to(device)
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img1 = torch.tensor(np.random.normal(
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0, 1, (3, 3, 256, 256))).float().to(device)
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imgs = torch.cat((img0, img1), 1)
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flownet = IFNet()
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flow, _ = flownet(imgs)
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print(flow.shape)
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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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from model.IFNet import *
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import torch.nn.functional as F
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from model.loss import *
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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 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,
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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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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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class ResBlock(nn.Module):
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def __init__(self, in_planes, out_planes, stride=2):
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super(ResBlock, self).__init__()
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if in_planes == out_planes and stride == 1:
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self.conv0 = nn.Identity()
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else:
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self.conv0 = nn.Conv2d(in_planes, out_planes,
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3, stride, 1, bias=False)
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self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
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self.conv2 = conv_woact(out_planes, out_planes, 3, 1, 1)
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self.relu1 = nn.PReLU(1)
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self.relu2 = nn.PReLU(out_planes)
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self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False)
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self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False)
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def forward(self, x):
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y = self.conv0(x)
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x = self.conv1(x)
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x = self.conv2(x)
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w = x.mean(3, True).mean(2, True)
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w = self.relu1(self.fc1(w))
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w = torch.sigmoid(self.fc2(w))
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x = self.relu2(x * w + y)
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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, cFlag):
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super(ContextNet, self).__init__()
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self.conv1 = ResBlock(3, c)
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self.conv2 = ResBlock(c, 2 * c)
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self.conv3 = ResBlock(2 * c, 4 * c)
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self.conv4 = ResBlock(4 * c, 8 * c)
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self.cFlag = cFlag
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def forward(self, x, flow):
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x = self.conv1(x)
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f1 = warp(x, flow, self.cFlag)
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x = self.conv2(x)
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flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
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align_corners=False) * 0.5
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f2 = warp(x, flow, self.cFlag)
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x = self.conv3(x)
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flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
|
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align_corners=False) * 0.5
|
||||
f3 = warp(x, flow, self.cFlag)
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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) * 0.5
|
||||
f4 = warp(x, flow, self.cFlag)
|
||||
return [f1, f2, f3, f4]
|
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|
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class FusionNet(nn.Module):
|
||||
def __init__(self, cFlag):
|
||||
super(FusionNet, self).__init__()
|
||||
self.down0 = ResBlock(8, 2 * c)
|
||||
self.down1 = ResBlock(4 * c, 4 * c)
|
||||
self.down2 = ResBlock(8 * c, 8 * c)
|
||||
self.down3 = ResBlock(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, 4, 3, 1, 1)
|
||||
self.cFlag = cFlag
|
||||
|
||||
def forward(self, img0, img1, flow, c0, c1, flow_gt):
|
||||
warped_img0 = warp(img0, flow, self.cFlag)
|
||||
warped_img1 = warp(img1, -flow, self.cFlag)
|
||||
if flow_gt == None:
|
||||
warped_img0_gt, warped_img1_gt = None, None
|
||||
else:
|
||||
warped_img0_gt = warp(img0, flow_gt[:, :2])
|
||||
warped_img1_gt = warp(img1, flow_gt[:, 2:4])
|
||||
s0 = self.down0(torch.cat((warped_img0, warped_img1, 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 x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
|
||||
|
||||
|
||||
class Model:
|
||||
def __init__(self, c_flag, local_rank=-1):
|
||||
self.flownet = IFNet(c_flag)
|
||||
self.contextnet = ContextNet(c_flag)
|
||||
self.fusionnet = FusionNet(c_flag)
|
||||
self.device(c_flag)
|
||||
self.optimG = AdamW(itertools.chain(
|
||||
self.flownet.parameters(),
|
||||
self.contextnet.parameters(),
|
||||
self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-5)
|
||||
self.schedulerG = optim.lr_scheduler.CyclicLR(
|
||||
self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
|
||||
self.epe = EPE()
|
||||
self.ter = Ternary(c_flag)
|
||||
self.sobel = SOBEL(c_flag)
|
||||
if local_rank != -1:
|
||||
self.flownet = DDP(self.flownet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
self.contextnet = DDP(self.contextnet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
self.fusionnet = DDP(self.fusionnet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
|
||||
def train(self):
|
||||
self.flownet.train()
|
||||
self.contextnet.train()
|
||||
self.fusionnet.train()
|
||||
|
||||
def eval(self):
|
||||
self.flownet.eval()
|
||||
self.contextnet.eval()
|
||||
self.fusionnet.eval()
|
||||
|
||||
def device(self, c_flag):
|
||||
if torch.cuda.is_available() and not c_flag:
|
||||
device = torch.device("cuda")
|
||||
else:
|
||||
device = torch.device("cpu")
|
||||
self.flownet.to(device)
|
||||
self.contextnet.to(device)
|
||||
self.fusionnet.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), map_location=torch.device("cpu"))))
|
||||
self.contextnet.load_state_dict(
|
||||
convert(torch.load('{}/contextnet.pkl'.format(path), map_location=torch.device("cpu"))))
|
||||
self.fusionnet.load_state_dict(
|
||||
convert(torch.load('{}/unet.pkl'.format(path), map_location=torch.device("cpu"))))
|
||||
|
||||
def save_model(self, path, rank=0):
|
||||
if rank == 0:
|
||||
torch.save(self.flownet.state_dict(),
|
||||
'{}/flownet.pkl'.format(path))
|
||||
torch.save(self.contextnet.state_dict(),
|
||||
'{}/contextnet.pkl'.format(path))
|
||||
torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
|
||||
|
||||
def predict(self, imgs, flow, training=True, flow_gt=None):
|
||||
img0 = imgs[:, :3]
|
||||
img1 = imgs[:, 3:]
|
||||
c0 = self.contextnet(img0, flow)
|
||||
c1 = self.contextnet(img1, -flow)
|
||||
flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
|
||||
align_corners=False) * 2.0
|
||||
refine_output, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.fusionnet(
|
||||
img0, img1, flow, c0, c1, flow_gt)
|
||||
res = torch.sigmoid(refine_output[:, :3]) * 2 - 1
|
||||
mask = torch.sigmoid(refine_output[:, 3:4])
|
||||
merged_img = warped_img0 * mask + warped_img1 * (1 - mask)
|
||||
pred = merged_img + res
|
||||
pred = torch.clamp(pred, 0, 1)
|
||||
if training:
|
||||
return pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
|
||||
else:
|
||||
return pred
|
||||
|
||||
def inference(self, img0, img1):
|
||||
imgs = torch.cat((img0, img1), 1)
|
||||
flow, _ = self.flownet(imgs)
|
||||
return self.predict(imgs, flow, training=False).detach()
|
||||
|
||||
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
|
||||
if training:
|
||||
self.train()
|
||||
else:
|
||||
self.eval()
|
||||
flow, flow_list = self.flownet(imgs)
|
||||
pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.predict(
|
||||
imgs, flow, flow_gt=flow_gt)
|
||||
loss_ter = self.ter(pred, gt).mean()
|
||||
if training:
|
||||
with torch.no_grad():
|
||||
loss_flow = torch.abs(warped_img0_gt - gt).mean()
|
||||
loss_mask = torch.abs(
|
||||
merged_img - gt).sum(1, True).float().detach()
|
||||
loss_mask = F.interpolate(loss_mask, scale_factor=0.5, mode="bilinear",
|
||||
align_corners=False).detach()
|
||||
flow_gt = (F.interpolate(flow_gt, scale_factor=0.5, mode="bilinear",
|
||||
align_corners=False) * 0.5).detach()
|
||||
loss_cons = 0
|
||||
for i in range(3):
|
||||
loss_cons += self.epe(flow_list[i], flow_gt[:, :2], 1)
|
||||
loss_cons += self.epe(-flow_list[i], flow_gt[:, 2:4], 1)
|
||||
loss_cons = loss_cons.mean() * 0.01
|
||||
else:
|
||||
loss_cons = torch.tensor([0])
|
||||
loss_flow = torch.abs(warped_img0 - gt).mean()
|
||||
loss_mask = 1
|
||||
loss_l1 = (((pred - gt) ** 2 + 1e-6) ** 0.5).mean()
|
||||
if training:
|
||||
self.optimG.zero_grad()
|
||||
loss_G = loss_l1 + loss_cons + loss_ter
|
||||
loss_G.backward()
|
||||
self.optimG.step()
|
||||
return pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, loss_mask
|
||||
|
||||
|
||||
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)
|
||||
imgs = torch.cat((img0, img1), 1)
|
||||
model = Model()
|
||||
model.eval()
|
||||
print(model.inference(imgs).shape)
|
||||
@ -0,0 +1,250 @@
|
||||
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.IFNet2F import *
|
||||
import torch.nn.functional as F
|
||||
from model.loss import *
|
||||
|
||||
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)
|
||||
)
|
||||
|
||||
def conv_woact(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),
|
||||
)
|
||||
|
||||
class ResBlock(nn.Module):
|
||||
def __init__(self, in_planes, out_planes, stride=2):
|
||||
super(ResBlock, self).__init__()
|
||||
if in_planes == out_planes and stride == 1:
|
||||
self.conv0 = nn.Identity()
|
||||
else:
|
||||
self.conv0 = nn.Conv2d(in_planes, out_planes,
|
||||
3, stride, 1, bias=False)
|
||||
self.conv1 = conv(in_planes, out_planes, 3, stride, 1)
|
||||
self.conv2 = conv_woact(out_planes, out_planes, 3, 1, 1)
|
||||
self.relu1 = nn.PReLU(1)
|
||||
self.relu2 = nn.PReLU(out_planes)
|
||||
self.fc1 = nn.Conv2d(out_planes, 16, kernel_size=1, bias=False)
|
||||
self.fc2 = nn.Conv2d(16, out_planes, kernel_size=1, bias=False)
|
||||
|
||||
def forward(self, x):
|
||||
y = self.conv0(x)
|
||||
x = self.conv1(x)
|
||||
x = self.conv2(x)
|
||||
w = x.mean(3, True).mean(2, True)
|
||||
w = self.relu1(self.fc1(w))
|
||||
w = torch.sigmoid(self.fc2(w))
|
||||
x = self.relu2(x * w + y)
|
||||
return x
|
||||
|
||||
c = 16
|
||||
|
||||
class ContextNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(ContextNet, self).__init__()
|
||||
self.conv1 = ResBlock(3, c, 1)
|
||||
self.conv2 = ResBlock(c, 2*c)
|
||||
self.conv3 = ResBlock(2*c, 4*c)
|
||||
self.conv4 = ResBlock(4*c, 8*c)
|
||||
|
||||
def forward(self, x, flow):
|
||||
x = self.conv1(x)
|
||||
f1 = warp(x, flow)
|
||||
x = self.conv2(x)
|
||||
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
|
||||
align_corners=False) * 0.5
|
||||
f2 = warp(x, flow)
|
||||
x = self.conv3(x)
|
||||
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
|
||||
align_corners=False) * 0.5
|
||||
f3 = warp(x, flow)
|
||||
x = self.conv4(x)
|
||||
flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear",
|
||||
align_corners=False) * 0.5
|
||||
f4 = warp(x, flow)
|
||||
return [f1, f2, f3, f4]
|
||||
|
||||
|
||||
class FusionNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(FusionNet, self).__init__()
|
||||
self.down0 = ResBlock(8, 2*c, 1)
|
||||
self.down1 = ResBlock(4*c, 4*c)
|
||||
self.down2 = ResBlock(8*c, 8*c)
|
||||
self.down3 = ResBlock(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, 4, 3, 2, 1)
|
||||
|
||||
def forward(self, img0, img1, flow, c0, c1, flow_gt):
|
||||
warped_img0 = warp(img0, flow)
|
||||
warped_img1 = warp(img1, -flow)
|
||||
if flow_gt == None:
|
||||
warped_img0_gt, warped_img1_gt = None, None
|
||||
else:
|
||||
warped_img0_gt = warp(img0, flow_gt[:, :2])
|
||||
warped_img1_gt = warp(img1, flow_gt[:, 2:4])
|
||||
s0 = self.down0(torch.cat((warped_img0, warped_img1, 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 x, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
|
||||
|
||||
|
||||
class Model:
|
||||
def __init__(self, local_rank=-1):
|
||||
self.flownet = IFNet()
|
||||
self.contextnet = ContextNet()
|
||||
self.fusionnet = FusionNet()
|
||||
self.device()
|
||||
self.optimG = AdamW(itertools.chain(
|
||||
self.flownet.parameters(),
|
||||
self.contextnet.parameters(),
|
||||
self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-5)
|
||||
self.schedulerG = optim.lr_scheduler.CyclicLR(
|
||||
self.optimG, base_lr=1e-6, max_lr=1e-3, step_size_up=8000, cycle_momentum=False)
|
||||
self.epe = EPE()
|
||||
self.ter = Ternary()
|
||||
self.sobel = SOBEL()
|
||||
if local_rank != -1:
|
||||
self.flownet = DDP(self.flownet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
self.contextnet = DDP(self.contextnet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
self.fusionnet = DDP(self.fusionnet, device_ids=[
|
||||
local_rank], output_device=local_rank)
|
||||
|
||||
def train(self):
|
||||
self.flownet.train()
|
||||
self.contextnet.train()
|
||||
self.fusionnet.train()
|
||||
|
||||
def eval(self):
|
||||
self.flownet.eval()
|
||||
self.contextnet.eval()
|
||||
self.fusionnet.eval()
|
||||
|
||||
def device(self):
|
||||
self.flownet.to(device)
|
||||
self.contextnet.to(device)
|
||||
self.fusionnet.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), map_location=device)))
|
||||
self.contextnet.load_state_dict(
|
||||
convert(torch.load('{}/contextnet.pkl'.format(path), map_location=device)))
|
||||
self.fusionnet.load_state_dict(
|
||||
convert(torch.load('{}/unet.pkl'.format(path), map_location=device)))
|
||||
|
||||
def save_model(self, path, rank=0):
|
||||
if rank == 0:
|
||||
torch.save(self.flownet.state_dict(),
|
||||
'{}/flownet.pkl'.format(path))
|
||||
torch.save(self.contextnet.state_dict(),
|
||||
'{}/contextnet.pkl'.format(path))
|
||||
torch.save(self.fusionnet.state_dict(), '{}/unet.pkl'.format(path))
|
||||
|
||||
def predict(self, imgs, flow, training=True, flow_gt=None):
|
||||
img0 = imgs[:, :3]
|
||||
img1 = imgs[:, 3:]
|
||||
flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear",
|
||||
align_corners=False) * 2.0
|
||||
c0 = self.contextnet(img0, flow)
|
||||
c1 = self.contextnet(img1, -flow)
|
||||
refine_output, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.fusionnet(
|
||||
img0, img1, flow, c0, c1, flow_gt)
|
||||
res = torch.sigmoid(refine_output[:, :3]) * 2 - 1
|
||||
mask = torch.sigmoid(refine_output[:, 3:4])
|
||||
merged_img = warped_img0 * mask + warped_img1 * (1 - mask)
|
||||
pred = merged_img + res
|
||||
pred = torch.clamp(pred, 0, 1)
|
||||
if training:
|
||||
return pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt
|
||||
else:
|
||||
return pred
|
||||
|
||||
def inference(self, img0, img1):
|
||||
with torch.no_grad():
|
||||
imgs = torch.cat((img0, img1), 1)
|
||||
flow, _ = self.flownet(imgs)
|
||||
return self.predict(imgs, flow, training=False).detach()
|
||||
|
||||
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
|
||||
if training:
|
||||
self.train()
|
||||
else:
|
||||
self.eval()
|
||||
flow, flow_list = self.flownet(imgs)
|
||||
pred, mask, merged_img, warped_img0, warped_img1, warped_img0_gt, warped_img1_gt = self.predict(
|
||||
imgs, flow, flow_gt=flow_gt)
|
||||
loss_ter = self.ter(pred, gt).mean()
|
||||
if training:
|
||||
with torch.no_grad():
|
||||
loss_flow = torch.abs(warped_img0_gt - gt).mean()
|
||||
loss_mask = torch.abs(
|
||||
merged_img - gt).sum(1, True).float().detach()
|
||||
loss_cons = 0
|
||||
for i in range(3):
|
||||
loss_cons += self.epe(flow_list[i], flow_gt[:, :2], 1)
|
||||
loss_cons += self.epe(-flow_list[i], flow_gt[:, 2:4], 1)
|
||||
loss_cons = loss_cons.mean() * 0.01
|
||||
else:
|
||||
loss_cons = torch.tensor([0])
|
||||
loss_flow = torch.abs(warped_img0 - gt).mean()
|
||||
loss_mask = 1
|
||||
loss_l1 = (((pred - gt) ** 2 + 1e-6) ** 0.5).mean()
|
||||
if training:
|
||||
self.optimG.zero_grad()
|
||||
loss_G = loss_l1 + loss_cons + loss_ter
|
||||
loss_G.backward()
|
||||
self.optimG.step()
|
||||
return pred, merged_img, flow, loss_l1, loss_flow, loss_cons, loss_ter, loss_mask
|
||||
|
||||
|
||||
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)
|
||||
imgs = torch.cat((img0, img1), 1)
|
||||
model = Model()
|
||||
model.eval()
|
||||
print(model.inference(imgs).shape)
|
||||
@ -0,0 +1,90 @@
|
||||
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")
|
||||
|
||||
|
||||
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, cFlag):
|
||||
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.device = torch.device("cuda" if torch.cuda.is_available() and not cFlag else "cpu")
|
||||
self.w = torch.tensor(self.w).float().to(self.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, cFlag):
|
||||
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.device = torch.device("cuda" if torch.cuda.is_available() and not cFlag else "cpu")
|
||||
self.kernelX = self.kernelX.unsqueeze(0).unsqueeze(0).to(self.device)
|
||||
self.kernelY = self.kernelY.unsqueeze(0).unsqueeze(0).to(self.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
|
||||
|
||||
|
||||
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 +1,23 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
backwarp_tenGrid = {}
|
||||
|
||||
|
||||
def warp(tenInput, tenFlow, cFlag):
|
||||
device = torch.device("cuda" if torch.cuda.is_available() and not cFlag else "cpu")
|
||||
k = (str(tenFlow.device), str(tenFlow.size()))
|
||||
if k not in backwarp_tenGrid:
|
||||
tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3]).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]).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=torch.clamp(g, -1, 1), mode='bilinear',
|
||||
padding_mode='zeros', align_corners=True)
|
||||
@ -0,0 +1,130 @@
|
||||
import os
|
||||
|
||||
baseLoc = os.path.dirname(os.path.realpath(__file__)) + '/'
|
||||
savePath = '/'.join(baseLoc.split('/')[:-2]) + '/output/interpolateframes'
|
||||
from gimpfu import *
|
||||
import sys
|
||||
|
||||
sys.path.extend([baseLoc + 'gimpenv/lib/python2.7', baseLoc + 'gimpenv/lib/python2.7/site-packages',
|
||||
baseLoc + 'gimpenv/lib/python2.7/site-packages/setuptools', baseLoc + 'RIFE'])
|
||||
|
||||
import cv2
|
||||
import torch
|
||||
from torch.nn import functional as F
|
||||
from model import RIFE
|
||||
import numpy as np
|
||||
|
||||
|
||||
|
||||
def channelData(layer): # convert gimp image to numpy
|
||||
region = layer.get_pixel_rgn(0, 0, layer.width, layer.height)
|
||||
pixChars = region[:, :] # Take whole layer
|
||||
bpp = region.bpp
|
||||
# return np.frombuffer(pixChars,dtype=np.uint8).reshape(len(pixChars)/bpp,bpp)
|
||||
return np.frombuffer(pixChars, dtype=np.uint8).reshape(layer.height, layer.width, bpp)
|
||||
|
||||
|
||||
def createResultLayer(image, name, result):
|
||||
rlBytes = np.uint8(result).tobytes();
|
||||
rl = gimp.Layer(image, name, image.width, image.height, 0, 100, NORMAL_MODE)
|
||||
region = rl.get_pixel_rgn(0, 0, rl.width, rl.height, True)
|
||||
region[:, :] = rlBytes
|
||||
image.add_layer(rl, 0)
|
||||
gimp.displays_flush()
|
||||
|
||||
|
||||
def getinter(img_s, img_e, c_flag, string_path):
|
||||
exp = 4
|
||||
out_path = string_path
|
||||
|
||||
model = RIFE.Model(c_flag)
|
||||
model.load_model(baseLoc + 'weights' + '/interpolateframes')
|
||||
model.eval()
|
||||
model.device(c_flag)
|
||||
|
||||
img0 = img_s
|
||||
img1 = img_e
|
||||
|
||||
img0 = (torch.tensor(img0.transpose(2, 0, 1)) / 255.).unsqueeze(0)
|
||||
img1 = (torch.tensor(img1.transpose(2, 0, 1))/ 255.).unsqueeze(0)
|
||||
if torch.cuda.is_available() and not c_flag:
|
||||
device = torch.device("cuda")
|
||||
else:
|
||||
device = torch.device("cpu")
|
||||
|
||||
img0 = img0.to(device)
|
||||
img1 = img1.to(device)
|
||||
|
||||
n, c, h, w = img0.shape
|
||||
ph = ((h - 1) // 32 + 1) * 32
|
||||
pw = ((w - 1) // 32 + 1) * 32
|
||||
padding = (0, pw - w, 0, ph - h)
|
||||
img0 = F.pad(img0, padding)
|
||||
img1 = F.pad(img1, padding)
|
||||
|
||||
img_list = [img0, img1]
|
||||
idx=0
|
||||
t=exp * (len(img_list) - 1)
|
||||
for i in range(exp):
|
||||
tmp = []
|
||||
for j in range(len(img_list) - 1):
|
||||
mid = model.inference(img_list[j], img_list[j + 1])
|
||||
tmp.append(img_list[j])
|
||||
tmp.append(mid)
|
||||
idx=idx+1
|
||||
gimp.progress_update(float(idx)/float(t))
|
||||
gimp.displays_flush()
|
||||
tmp.append(img1)
|
||||
img_list = tmp
|
||||
|
||||
if not os.path.exists(out_path):
|
||||
os.makedirs(out_path)
|
||||
for i in range(len(img_list)):
|
||||
cv2.imwrite(out_path + '/img{}.png'.format(i),
|
||||
(img_list[i][0] * 255).byte().cpu().numpy().transpose(1, 2, 0)[:h, :w, ::-1])
|
||||
|
||||
|
||||
|
||||
|
||||
def interpolateframes(imggimp, curlayer, string_path, layer_s, layer_e, c_flag):
|
||||
layer_1 = channelData(layer_s)
|
||||
layer_2 = channelData(layer_e)
|
||||
|
||||
if layer_1.shape[0] != imggimp.height or layer_1.shape[1] != imggimp.width or layer_2.shape[0] != imggimp.height or layer_2.shape[1] != imggimp.width:
|
||||
pdb.gimp_message(" Do (Layer -> Layer to Image Size) for both layers and try again.")
|
||||
else:
|
||||
if torch.cuda.is_available() and not c_flag:
|
||||
gimp.progress_init("(Using GPU) Running slomo and saving frames in "+string_path)
|
||||
# device = torch.device("cuda")
|
||||
else:
|
||||
gimp.progress_init("(Using CPU) Running slomo and saving frames in "+string_path)
|
||||
# device = torch.device("cpu")
|
||||
|
||||
if layer_1.shape[2] == 4: # get rid of alpha channel
|
||||
layer_1 = layer_1[:, :, 0:3]
|
||||
if layer_2.shape[2] == 4: # get rid of alpha channel
|
||||
layer_2 = layer_2[:, :, 0:3]
|
||||
getinter(layer_1, layer_2, c_flag, string_path)
|
||||
# pdb.gimp_message("Saved")
|
||||
|
||||
|
||||
register(
|
||||
"interpolate-frames",
|
||||
"interpolate-frames",
|
||||
"Running slomo...",
|
||||
"Kritik Soman",
|
||||
"Your",
|
||||
"2020",
|
||||
"interpolate-frames...",
|
||||
"*", # Alternately use RGB, RGB*, GRAY*, INDEXED etc.
|
||||
[(PF_IMAGE, "image", "Input image", None),
|
||||
(PF_DRAWABLE, "drawable", "Input drawable", None),
|
||||
(PF_STRING, "string", "Output Location", savePath),
|
||||
(PF_LAYER, "drawinglayer", "Start Frame:", None),
|
||||
(PF_LAYER, "drawinglayer", "End Frame:", None),
|
||||
(PF_BOOL, "fcpu", "Force CPU", False)
|
||||
],
|
||||
[],
|
||||
interpolateframes, menu="<Image>/Layer/GIML-ML")
|
||||
|
||||
main()
|
||||
Loading…
Reference in New Issue