From 7abf45f673a180077aacf43fa728e84217cbb901 Mon Sep 17 00:00:00 2001 From: N00MKRAD Date: Tue, 15 Jun 2021 15:41:15 +0200 Subject: [PATCH] Updated RIFE-CUDA to support new 3.2-3.5 models with fallback for 3.0-3.1 --- .gitignore | 1 + Pkgs/rife-cuda/model/IFNet_HD.py | 11 +- Pkgs/rife-cuda/model/IFNet_HDv2.py | 14 +- Pkgs/rife-cuda/model/IFNet_HDv3.py | 142 +++++++---- Pkgs/rife-cuda/model/RIFE_HD.py | 12 +- Pkgs/rife-cuda/model/RIFE_HDv2.py | 12 +- Pkgs/rife-cuda/model/RIFE_HDv3.py | 229 +++-------------- Pkgs/rife-cuda/model/refine.py | 91 +++++++ Pkgs/rife-cuda/model_v3_legacy/IFNet_HDv3.py | 81 ++++++ Pkgs/rife-cuda/model_v3_legacy/RIFE_HDv3.py | 249 +++++++++++++++++++ Pkgs/rife-cuda/model_v3_legacy/loss.py | 128 ++++++++++ Pkgs/rife-cuda/model_v3_legacy/warplayer.py | 22 ++ Pkgs/rife-cuda/models.json | 20 +- Pkgs/rife-cuda/rife.py | 26 +- 14 files changed, 753 insertions(+), 285 deletions(-) create mode 100644 Pkgs/rife-cuda/model/refine.py create mode 100644 Pkgs/rife-cuda/model_v3_legacy/IFNet_HDv3.py create mode 100644 Pkgs/rife-cuda/model_v3_legacy/RIFE_HDv3.py create mode 100644 Pkgs/rife-cuda/model_v3_legacy/loss.py create mode 100644 Pkgs/rife-cuda/model_v3_legacy/warplayer.py diff --git a/.gitignore b/.gitignore index 0be6dc9..b6259fb 100644 --- a/.gitignore +++ b/.gitignore @@ -32,6 +32,7 @@ bld/ [Ll]ogs/ Flowframes*.7z FF*.7z +Build/WebInstaller # NMKD Python Redist Pkg [Pp]y*/ diff --git a/Pkgs/rife-cuda/model/IFNet_HD.py b/Pkgs/rife-cuda/model/IFNet_HD.py index fe315b2..6975679 100644 --- a/Pkgs/rife-cuda/model/IFNet_HD.py +++ b/Pkgs/rife-cuda/model/IFNet_HD.py @@ -91,12 +91,9 @@ class IFNet(nn.Module): self.block2 = IFBlock(8, scale=2, c=96) self.block3 = IFBlock(8, scale=1, c=48) - def forward(self, x, UHD=False): - if UHD: - x = F.interpolate(x, scale_factor=0.25, mode="bilinear", align_corners=False) - else: - x = F.interpolate(x, scale_factor=0.5, mode="bilinear", - align_corners=False) + def forward(self, x, scale=1.0): + x = F.interpolate(x, scale_factor=0.5 * scale, mode="bilinear", + align_corners=False) flow0 = self.block0(x) F1 = flow0 warped_img0 = warp(x[:, :3], F1) @@ -111,6 +108,8 @@ class IFNet(nn.Module): warped_img1 = warp(x[:, 3:], -F3) flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3), 1)) F4 = (flow0 + flow1 + flow2 + flow3) + F4 = F.interpolate(F4, scale_factor=1 / scale, mode="bilinear", + align_corners=False) / scale return F4, [F1, F2, F3, F4] if __name__ == '__main__': diff --git a/Pkgs/rife-cuda/model/IFNet_HDv2.py b/Pkgs/rife-cuda/model/IFNet_HDv2.py index f9b18cf..c7002d3 100644 --- a/Pkgs/rife-cuda/model/IFNet_HDv2.py +++ b/Pkgs/rife-cuda/model/IFNet_HDv2.py @@ -61,26 +61,28 @@ class IFNet(nn.Module): self.block2 = IFBlock(10, scale=2, c=96) self.block3 = IFBlock(10, scale=1, c=48) - def forward(self, x, UHD=False): - if UHD: - x = F.interpolate(x, scale_factor=0.5, mode="bilinear", align_corners=False) + def forward(self, x, scale=1.0): + if scale != 1.0: + x = F.interpolate(x, scale_factor=scale, mode="bilinear", align_corners=False) flow0 = self.block0(x) F1 = flow0 - F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0 + F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0 warped_img0 = warp(x[:, :3], F1_large[:, :2]) warped_img1 = warp(x[:, 3:], F1_large[:, 2:4]) flow1 = self.block1(torch.cat((warped_img0, warped_img1, F1_large), 1)) F2 = (flow0 + flow1) - F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0 + F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0 warped_img0 = warp(x[:, :3], F2_large[:, :2]) warped_img1 = warp(x[:, 3:], F2_large[:, 2:4]) flow2 = self.block2(torch.cat((warped_img0, warped_img1, F2_large), 1)) F3 = (flow0 + flow1 + flow2) - F3_large = F.interpolate(F3, scale_factor=2.0, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 2.0 + F3_large = F.interpolate(F3, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0 warped_img0 = warp(x[:, :3], F3_large[:, :2]) warped_img1 = warp(x[:, 3:], F3_large[:, 2:4]) flow3 = self.block3(torch.cat((warped_img0, warped_img1, F3_large), 1)) F4 = (flow0 + flow1 + flow2 + flow3) + if scale != 1.0: + F4 = F.interpolate(F4, scale_factor=1 / scale, mode="bilinear", align_corners=False) / scale return F4, [F1, F2, F3, F4] if __name__ == '__main__': diff --git a/Pkgs/rife-cuda/model/IFNet_HDv3.py b/Pkgs/rife-cuda/model/IFNet_HDv3.py index 9e2b8e0..4cf9077 100644 --- a/Pkgs/rife-cuda/model/IFNet_HDv3.py +++ b/Pkgs/rife-cuda/model/IFNet_HDv3.py @@ -2,23 +2,22 @@ import torch import torch.nn as nn import torch.nn.functional as F from model.warplayer import warp +from model.refine import * -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), - nn.PReLU(out_planes) - ) - -def conv_wo_act(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), - ) +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), + padding=padding, dilation=dilation, bias=True), + nn.PReLU(out_planes) + ) + +def conv_bn(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=False), + nn.BatchNorm2d(out_planes), nn.PReLU(out_planes) ) @@ -26,56 +25,93 @@ class IFBlock(nn.Module): def __init__(self, in_planes, c=64): super(IFBlock, self).__init__() self.conv0 = nn.Sequential( - conv(in_planes, c, 3, 2, 1), - conv(c, 2*c, 3, 2, 1), + conv(in_planes, c//2, 3, 2, 1), + conv(c//2, c, 3, 2, 1), ) self.convblock0 = nn.Sequential( - conv(2*c, 2*c), - conv(2*c, 2*c), + conv(c, c), + conv(c, c) ) self.convblock1 = nn.Sequential( - conv(2*c, 2*c), - conv(2*c, 2*c), + conv(c, c), + conv(c, c) ) self.convblock2 = nn.Sequential( - conv(2*c, 2*c), - conv(2*c, 2*c), + conv(c, c), + conv(c, c) ) - self.conv1 = nn.ConvTranspose2d(2*c, 4, 4, 2, 1) + self.convblock3 = nn.Sequential( + conv(c, c), + conv(c, c) + ) + self.conv1 = nn.Sequential( + nn.ConvTranspose2d(c, 4, 4, 2, 1), + ) + self.conv2 = nn.ConvTranspose2d(c, 1, 4, 2, 1) - def forward(self, x, flow=None, scale=1): - x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False) - if flow != None: - flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False) * (1. / scale) - x = torch.cat((x, flow), 1) - x = self.conv0(x) - x = self.convblock0(x) + x - x = self.convblock1(x) + x - x = self.convblock2(x) + x - x = self.conv1(x) - flow = x - if scale != 1: - flow = F.interpolate(flow, scale_factor= scale, mode="bilinear", align_corners=False) * scale - return flow - + def forward(self, x, flow, scale=1): + x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) + flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False, recompute_scale_factor=False) * 1. / scale + feat = self.conv0(torch.cat((x, flow), 1)) + feat = self.convblock0(feat) + feat + feat = self.convblock1(feat) + feat + feat = self.convblock2(feat) + feat + feat = self.convblock3(feat) + feat + flow = self.conv1(feat) + mask = self.conv2(feat) + flow = F.interpolate(flow, scale_factor=scale*2, mode="bilinear", align_corners=False, recompute_scale_factor=False) * scale*2 + mask = F.interpolate(mask, scale_factor=scale*2, mode="bilinear", align_corners=False, recompute_scale_factor=False) + return flow, mask + class IFNet(nn.Module): def __init__(self): super(IFNet, self).__init__() - self.block0 = IFBlock(6, c=80) - self.block1 = IFBlock(10, c=80) - self.block2 = IFBlock(10, c=80) + self.block0 = IFBlock(7+4, c=90) + self.block1 = IFBlock(7+4, c=90) + self.block2 = IFBlock(7+4, c=90) + self.block_tea = IFBlock(10+4, c=90) + # self.contextnet = Contextnet() + # self.unet = Unet() - def forward(self, x, scale_list=[4,2,1]): - flow0 = self.block0(x, scale=scale_list[0]) - F1 = flow0 - F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0 - warped_img0 = warp(x[:, :3], F1_large[:, :2]) - warped_img1 = warp(x[:, 3:], F1_large[:, 2:4]) - flow1 = self.block1(torch.cat((warped_img0, warped_img1), 1), F1_large, scale=scale_list[1]) - F2 = (flow0 + flow1) - F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0 - warped_img0 = warp(x[:, :3], F2_large[:, :2]) - warped_img1 = warp(x[:, 3:], F2_large[:, 2:4]) - flow2 = self.block2(torch.cat((warped_img0, warped_img1), 1), F2_large, scale=scale_list[2]) - F3 = (flow0 + flow1 + flow2) - return F3, [F1, F2, F3] \ No newline at end of file + def forward(self, x, scale_list=[4, 2, 1], scale=1.0, training=False): + x = F.interpolate(x, scale_factor=scale, mode="bilinear", align_corners=False) + if training == False: + channel = x.shape[1] // 2 + img0 = x[:, :channel] + img1 = x[:, channel:] + flow_list = [] + merged = [] + mask_list = [] + warped_img0 = img0 + warped_img1 = img1 + flow = torch.zeros_like(x[:, :4]).to(device) + mask = torch.zeros_like(x[:, :1]).to(device) + loss_cons = 0 + block = [self.block0, self.block1, self.block2] + for i in range(3): + f0, m0 = block[i](torch.cat((warped_img0[:, :3], warped_img1[:, :3], mask), 1), flow, scale=scale_list[i]) + f1, m1 = block[i](torch.cat((warped_img1[:, :3], warped_img0[:, :3], -mask), 1), torch.cat((flow[:, 2:4], flow[:, :2]), 1), scale=scale_list[i]) + flow = flow + (f0 + torch.cat((f1[:, 2:4], f1[:, :2]), 1)) / 2 + mask = mask + (m0 + (-m1)) / 2 + mask_list.append(mask) + flow_list.append(flow) + warped_img0 = warp(img0, flow[:, :2]) + warped_img1 = warp(img1, flow[:, 2:4]) + merged.append((warped_img0, warped_img1)) + if scale != 1.0: + flow = F.interpolate(flow, scale_factor=1 / scale, mode="bilinear", align_corners=False) / scale + mask_list[2] = F.interpolate(mask_list[2], scale_factor=1 / scale, mode="bilinear", align_corners=False) + warped_img0 = warp(img0, flow[:, :2]) + warped_img1 = warp(img1, flow[:, 2:4]) + merged[2] = (warped_img0, warped_img1) + ''' + c0 = self.contextnet(img0, flow[:, :2]) + c1 = self.contextnet(img1, flow[:, 2:4]) + tmp = self.unet(img0, img1, warped_img0, warped_img1, mask, flow, c0, c1) + res = tmp[:, 1:4] * 2 - 1 + ''' + for i in range(3): + mask_list[i] = torch.sigmoid(mask_list[i]) + merged[i] = merged[i][0] * mask_list[i] + merged[i][1] * (1 - mask_list[i]) + # merged[i] = torch.clamp(merged[i] + res, 0, 1) + return flow_list, mask_list[2], merged diff --git a/Pkgs/rife-cuda/model/RIFE_HD.py b/Pkgs/rife-cuda/model/RIFE_HD.py index b96576f..47df49a 100644 --- a/Pkgs/rife-cuda/model/RIFE_HD.py +++ b/Pkgs/rife-cuda/model/RIFE_HD.py @@ -135,7 +135,7 @@ class Model: self.optimG = AdamW(itertools.chain( self.flownet.parameters(), self.contextnet.parameters(), - self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-5) + self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-4) 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() @@ -188,11 +188,9 @@ class Model: 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, UHD=False): + def predict(self, imgs, flow, training=True, flow_gt=None): img0 = imgs[:, :3] img1 = imgs[:, 3:] - if UHD: - 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) flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear", @@ -209,10 +207,10 @@ class Model: else: return pred - def inference(self, img0, img1, UHD=False): + def inference(self, img0, img1, scale=1.0): imgs = torch.cat((img0, img1), 1) - flow, _ = self.flownet(imgs, UHD) - return self.predict(imgs, flow, training=False, UHD=UHD) + flow, _ = self.flownet(imgs, scale) + return self.predict(imgs, flow, training=False) def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None): for param_group in self.optimG.param_groups: diff --git a/Pkgs/rife-cuda/model/RIFE_HDv2.py b/Pkgs/rife-cuda/model/RIFE_HDv2.py index 9f19ae2..ce5cd56 100644 --- a/Pkgs/rife-cuda/model/RIFE_HDv2.py +++ b/Pkgs/rife-cuda/model/RIFE_HDv2.py @@ -120,7 +120,7 @@ class Model: self.optimG = AdamW(itertools.chain( self.flownet.parameters(), self.contextnet.parameters(), - self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-5) + self.fusionnet.parameters()), lr=1e-6, weight_decay=1e-4) 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() @@ -173,11 +173,9 @@ class Model: 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, UHD=False): + def predict(self, imgs, flow, training=True, flow_gt=None): img0 = imgs[:, :3] img1 = imgs[:, 3:] - if UHD: - flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0 c0 = self.contextnet(img0, flow[:, :2]) c1 = self.contextnet(img1, flow[:, 2:4]) flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear", @@ -194,10 +192,10 @@ class Model: else: return pred - def inference(self, img0, img1, UHD=False): + def inference(self, img0, img1, scale=1.0): imgs = torch.cat((img0, img1), 1) - flow, _ = self.flownet(imgs, UHD) - return self.predict(imgs, flow, training=False, UHD=UHD) + flow, _ = self.flownet(imgs, scale) + return self.predict(imgs, flow, training=False) def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None): for param_group in self.optimG.param_groups: diff --git a/Pkgs/rife-cuda/model/RIFE_HDv3.py b/Pkgs/rife-cuda/model/RIFE_HDv3.py index b217e83..6978043 100644 --- a/Pkgs/rife-cuda/model/RIFE_HDv3.py +++ b/Pkgs/rife-cuda/model/RIFE_HDv3.py @@ -11,145 +11,28 @@ 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 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 = 32 - -class ContextNet(nn.Module): - def __init__(self): - super(ContextNet, self).__init__() - self.conv0 = Conv2(3, c) - self.conv1 = Conv2(c, 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.conv0(x) - x = self.conv1(x) - flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5 - 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.conv0 = Conv2(10, c) - self.down0 = Conv2(c, 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.ConvTranspose2d(c, 4, 4, 2, 1) - - def forward(self, img0, img1, flow, c0, c1, flow_gt): - warped_img0 = warp(img0, flow[:, :2]) - warped_img1 = warp(img1, flow[:, 2:4]) - 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]) - x = self.conv0(torch.cat((warped_img0, warped_img1, flow), 1)) - s0 = self.down0(x) - 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.optimG = AdamW(self.flownet.parameters(), lr=1e-6, weight_decay=1e-4) self.epe = EPE() - self.ter = Ternary() + # self.vgg = VGGPerceptualLoss().to(device) 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) + self.flownet = DDP(self.flownet, 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): + def load_model(self, path, rank=0): def convert(param): if rank == -1: return { @@ -160,90 +43,46 @@ class Model: else: return param 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): + if torch.cuda.is_available(): + self.flownet.load_state_dict(convert(torch.load('{}/flownet.pkl'.format(path)))) + else: + self.flownet.load_state_dict(convert(torch.load('{}/flownet.pkl'.format(path), map_location ='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)) + torch.save(self.flownet.state_dict(),'{}/flownet.pkl'.format(path)) - def predict(self, imgs, flow, training=True, flow_gt=None, UHD=False): - img0 = imgs[:, :3] - img1 = imgs[:, 3:] - if UHD: - flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0 - c0 = self.contextnet(img0, flow[:, :2]) - c1 = self.contextnet(img1, flow[:, 2:4]) - 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, UHD=False): + def inference(self, img0, img1, scale=1.0): imgs = torch.cat((img0, img1), 1) - scale_list = [8, 4, 2] - flow, _ = self.flownet(imgs, scale_list) - res = self.predict(imgs, flow, training=False, UHD=False) - return res - + scale_list = [4, 2, 1] + flow, mask, merged = self.flownet(imgs, scale_list, scale=scale) + return merged[2] + 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 + img0 = imgs[:, :3] + img1 = imgs[:, 3:] 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(4): - loss_cons += self.epe(flow_list[i][:, :2], flow_gt[:, :2], 1) - loss_cons += self.epe(flow_list[i][:, 2:4], 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() + scale = [4, 2, 1] + flow, mask, merged = self.flownet(torch.cat((imgs, gt), 1), scale=scale, training=training) + loss_l1 = (merged[2] - gt).abs().mean() + loss_smooth = self.sobel(flow[2], flow[2]*0).mean() + # loss_vgg = self.vgg(merged[2], gt) if training: self.optimG.zero_grad() - loss_G = loss_l1 + loss_cons + loss_ter + loss_G = loss_cons + loss_smooth * 0.1 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) \ No newline at end of file + else: + flow_teacher = flow[2] + return merged[2], { + 'mask': mask, + 'flow': flow[2][:, :2], + 'loss_l1': loss_l1, + 'loss_cons': loss_cons, + 'loss_smooth': loss_smooth, + } diff --git a/Pkgs/rife-cuda/model/refine.py b/Pkgs/rife-cuda/model/refine.py new file mode 100644 index 0000000..c3abacb --- /dev/null +++ b/Pkgs/rife-cuda/model/refine.py @@ -0,0 +1,91 @@ +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 +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 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), + ) + +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, 4, 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) diff --git a/Pkgs/rife-cuda/model_v3_legacy/IFNet_HDv3.py b/Pkgs/rife-cuda/model_v3_legacy/IFNet_HDv3.py new file mode 100644 index 0000000..e1a5fac --- /dev/null +++ b/Pkgs/rife-cuda/model_v3_legacy/IFNet_HDv3.py @@ -0,0 +1,81 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from model_v3_legacy.warplayer import warp + +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), + nn.PReLU(out_planes) + ) + +def conv_wo_act(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), + ) + +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) + ) + +class IFBlock(nn.Module): + def __init__(self, in_planes, c=64): + super(IFBlock, self).__init__() + self.conv0 = nn.Sequential( + conv(in_planes, c, 3, 2, 1), + conv(c, 2*c, 3, 2, 1), + ) + self.convblock0 = nn.Sequential( + conv(2*c, 2*c), + conv(2*c, 2*c), + ) + self.convblock1 = nn.Sequential( + conv(2*c, 2*c), + conv(2*c, 2*c), + ) + self.convblock2 = nn.Sequential( + conv(2*c, 2*c), + conv(2*c, 2*c), + ) + self.conv1 = nn.ConvTranspose2d(2*c, 4, 4, 2, 1) + + def forward(self, x, flow=None, scale=1): + x = F.interpolate(x, scale_factor= 1. / scale, mode="bilinear", align_corners=False) + if flow != None: + flow = F.interpolate(flow, scale_factor= 1. / scale, mode="bilinear", align_corners=False) * (1. / scale) + x = torch.cat((x, flow), 1) + x = self.conv0(x) + x = self.convblock0(x) + x + x = self.convblock1(x) + x + x = self.convblock2(x) + x + x = self.conv1(x) + flow = x + if scale != 1: + flow = F.interpolate(flow, scale_factor= scale, mode="bilinear", align_corners=False) * scale + return flow + +class IFNet(nn.Module): + def __init__(self): + super(IFNet, self).__init__() + self.block0 = IFBlock(6, c=80) + self.block1 = IFBlock(10, c=80) + self.block2 = IFBlock(10, c=80) + + def forward(self, x, scale_list=[4,2,1]): + flow0 = self.block0(x, scale=scale_list[0]) + F1 = flow0 + F1_large = F.interpolate(F1, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0 + warped_img0 = warp(x[:, :3], F1_large[:, :2]) + warped_img1 = warp(x[:, 3:], F1_large[:, 2:4]) + flow1 = self.block1(torch.cat((warped_img0, warped_img1), 1), F1_large, scale=scale_list[1]) + F2 = (flow0 + flow1) + F2_large = F.interpolate(F2, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0 + warped_img0 = warp(x[:, :3], F2_large[:, :2]) + warped_img1 = warp(x[:, 3:], F2_large[:, 2:4]) + flow2 = self.block2(torch.cat((warped_img0, warped_img1), 1), F2_large, scale=scale_list[2]) + F3 = (flow0 + flow1 + flow2) + return F3, [F1, F2, F3] \ No newline at end of file diff --git a/Pkgs/rife-cuda/model_v3_legacy/RIFE_HDv3.py b/Pkgs/rife-cuda/model_v3_legacy/RIFE_HDv3.py new file mode 100644 index 0000000..751aa03 --- /dev/null +++ b/Pkgs/rife-cuda/model_v3_legacy/RIFE_HDv3.py @@ -0,0 +1,249 @@ +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_v3_legacy.warplayer import warp +from torch.nn.parallel import DistributedDataParallel as DDP +from model_v3_legacy.IFNet_HDv3 import * +import torch.nn.functional as F +from model_v3_legacy.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 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 = 32 + +class ContextNet(nn.Module): + def __init__(self): + super(ContextNet, self).__init__() + self.conv0 = Conv2(3, c) + self.conv1 = Conv2(c, 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.conv0(x) + x = self.conv1(x) + flow = F.interpolate(flow, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5 + 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.conv0 = Conv2(10, c) + self.down0 = Conv2(c, 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.ConvTranspose2d(c, 4, 4, 2, 1) + + def forward(self, img0, img1, flow, c0, c1, flow_gt): + warped_img0 = warp(img0, flow[:, :2]) + warped_img1 = warp(img1, flow[:, 2:4]) + 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]) + x = self.conv0(torch.cat((warped_img0, warped_img1, flow), 1)) + s0 = self.down0(x) + 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): + def convert(param): + if rank == -1: + return { + k.replace("module.", ""): v + for k, v in param.items() + if "module." in k + } + else: + return param + 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): + 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, UHD=False): + img0 = imgs[:, :3] + img1 = imgs[:, 3:] + if UHD: + flow = F.interpolate(flow, scale_factor=2.0, mode="bilinear", align_corners=False) * 2.0 + c0 = self.contextnet(img0, flow[:, :2]) + c1 = self.contextnet(img1, flow[:, 2:4]) + 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, UHD=False): + imgs = torch.cat((img0, img1), 1) + scale_list = [8, 4, 2] + flow, _ = self.flownet(imgs, scale_list) + res = self.predict(imgs, flow, training=False, UHD=False) + return res + + 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(4): + loss_cons += self.epe(flow_list[i][:, :2], flow_gt[:, :2], 1) + loss_cons += self.epe(flow_list[i][:, 2:4], 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) \ No newline at end of file diff --git a/Pkgs/rife-cuda/model_v3_legacy/loss.py b/Pkgs/rife-cuda/model_v3_legacy/loss.py new file mode 100644 index 0000000..72e5de6 --- /dev/null +++ b/Pkgs/rife-cuda/model_v3_legacy/loss.py @@ -0,0 +1,128 @@ +import torch +import numpy as np +import torch.nn as nn +import torch.nn.functional as F +import torchvision.models as models + +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): + 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.w = torch.tensor(self.w).float().to(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): + 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.kernelX = self.kernelX.unsqueeze(0).unsqueeze(0).to(device) + self.kernelY = self.kernelY.unsqueeze(0).unsqueeze(0).to(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 + +class MeanShift(nn.Conv2d): + def __init__(self, data_mean, data_std, data_range=1, norm=True): + c = len(data_mean) + super(MeanShift, self).__init__(c, c, kernel_size=1) + std = torch.Tensor(data_std) + self.weight.data = torch.eye(c).view(c, c, 1, 1) + if norm: + self.weight.data.div_(std.view(c, 1, 1, 1)) + self.bias.data = -1 * data_range * torch.Tensor(data_mean) + self.bias.data.div_(std) + else: + self.weight.data.mul_(std.view(c, 1, 1, 1)) + self.bias.data = data_range * torch.Tensor(data_mean) + self.requires_grad = False + +class VGGPerceptualLoss(torch.nn.Module): + def __init__(self, rank=0): + super(VGGPerceptualLoss, self).__init__() + blocks = [] + pretrained = True + self.vgg_pretrained_features = models.vgg19(pretrained=pretrained).features + self.normalize = MeanShift([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], norm=True).cuda() + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X, Y, indices=None): + X = self.normalize(X) + Y = self.normalize(Y) + indices = [2, 7, 12, 21, 30] + weights = [1.0/2.6, 1.0/4.8, 1.0/3.7, 1.0/5.6, 10/1.5] + k = 0 + loss = 0 + for i in range(indices[-1]): + X = self.vgg_pretrained_features[i](X) + Y = self.vgg_pretrained_features[i](Y) + if (i+1) in indices: + loss += weights[k] * (X - Y.detach()).abs().mean() * 0.1 + k += 1 + 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) diff --git a/Pkgs/rife-cuda/model_v3_legacy/warplayer.py b/Pkgs/rife-cuda/model_v3_legacy/warplayer.py new file mode 100644 index 0000000..21b0b90 --- /dev/null +++ b/Pkgs/rife-cuda/model_v3_legacy/warplayer.py @@ -0,0 +1,22 @@ +import torch +import torch.nn as nn + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +backwarp_tenGrid = {} + + +def warp(tenInput, tenFlow): + k = (str(tenFlow.device), str(tenFlow.size())) + if k not in backwarp_tenGrid: + tenHorizontal = torch.linspace(-1.0, 1.0, tenFlow.shape[3], device=device).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], device=device).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=g, mode='bilinear', padding_mode='border', align_corners=True) diff --git a/Pkgs/rife-cuda/models.json b/Pkgs/rife-cuda/models.json index a06fd83..5bb0ac2 100644 --- a/Pkgs/rife-cuda/models.json +++ b/Pkgs/rife-cuda/models.json @@ -6,23 +6,33 @@ }, { "name": "RIFE 2.3", - "desc": "Updated General Model", + "desc": "General Model", "dir": "RIFE23" }, { "name": "RIFE 2.4", - "desc": "Updated General Model (Sometimes worse than 2.3)", + "desc": "Latest v2 General Model (Sometimes worse than 2.3)", "dir": "RIFE24" }, { "name": "RIFE 3.0", - "desc": "Updated General Model", + "desc": "v3 General Model", "dir": "RIFE30", }, { "name": "RIFE 3.1", - "desc": "Latest General Model", - "dir": "RIFE31", + "desc": "Updated v3 General Model", + "dir": "RIFE31" +}, +{ + "name": "RIFE 3.4", + "desc": "Updated v3 General/Animation Model", + "dir": "RIFE34" +}, +{ + "name": "RIFE 3.5", + "desc": "Latest v3 General/Animation Model", + "dir": "RIFE35", "isDefault": "true" } ] \ No newline at end of file diff --git a/Pkgs/rife-cuda/rife.py b/Pkgs/rife-cuda/rife.py index 9ac9875..317140d 100644 --- a/Pkgs/rife-cuda/rife.py +++ b/Pkgs/rife-cuda/rife.py @@ -35,6 +35,9 @@ parser.add_argument('--scale', dest='scale', type=float, default=1.0, help='Try parser.add_argument('--exp', dest='exp', type=int, default=1) args = parser.parse_args() assert (not args.input is None) +if args.UHD and args.scale==1.0: + args.scale = 0.5 +assert args.scale in [0.25, 0.5, 1.0, 2.0, 4.0] device = torch.device("cuda" if torch.cuda.is_available() else "cpu") torch.set_grad_enabled(False) @@ -56,20 +59,31 @@ except: try: try: - from model.RIFE_HDv2 import Model - model = Model() - model.load_model(os.path.join(dname, args.model), -1) - print("Loaded v2.x HD model.") - except: + print(f"Trying to load v3 (new) model from {os.path.join(dname, args.model)}") from model.RIFE_HDv3 import Model model = Model() model.load_model(os.path.join(dname, args.model), -1) print("Loaded v3.x HD model.") + except: + try: + print(f"Trying to load v3 (legacy) model from {os.path.join(dname, args.model)}") + from model_v3_legacy.RIFE_HDv3 import Model + model = Model() + model.load_model(os.path.join(dname, args.model), -1) + print("Loaded v3.x HD model.") + except: + print(f"Trying to load v2 model from {os.path.join(dname, args.model)}") + from model.RIFE_HDv2 import Model + model = Model() + model.load_model(os.path.join(dname, args.model), -1) + print("Loaded v2.x HD model.") except: + print(f"Trying to load v1 model from {os.path.join(dname, args.model)}") from model.RIFE_HD import Model model = Model() model.load_model(os.path.join(dname, args.model), -1) print("Loaded v1.x HD model") + model.eval() model.device() @@ -118,7 +132,7 @@ def build_read_buffer(user_args, read_buffer, videogen): def make_inference(I0, I1, exp): global model - middle = model.inference(I0, I1, args.UHD) + middle = model.inference(I0, I1, args.scale) if exp == 1: return [middle] first_half = make_inference(I0, middle, exp=exp - 1)