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import math |
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import torch |
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import torch.nn as nn |
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import typing |
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from ..components import register |
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from .backwarp import * |
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from .softsplat import _FunctionSoftsplat |
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def forwarp_mframe_mask(tenIn1, tenFlow1, t1, tenIn2, tenFlow2, t2, tenMetric1=None, tenMetric2=None): |
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def one_fdir(tenIn, tenFlow, td, tenMetric): |
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tenIn = torch.cat([tenIn * td * (tenMetric).clip(-20.0, 20.0).exp(), td * (tenMetric).clip(-20.0, 20.0).exp()], |
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1) |
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tenOut = _FunctionSoftsplat.apply(tenIn, tenFlow) |
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return tenOut[:, :-1, :, :], tenOut[:, -1:, :, :] + 0.0000001 |
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flow_num = tenFlow1.shape[0] |
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tenOutF, tenOutB = 0, 0 |
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tenNormalizeF, tenNormalizeB = 0, 0 |
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for idx in range(flow_num): |
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tenOutF_, tenNormalizeF_ = one_fdir(tenIn1[idx], tenFlow1[idx], t1[idx], tenMetric1[idx]) |
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tenOutB_, tenNormalizeB_ = one_fdir(tenIn2[idx], tenFlow2[idx], t2[idx], tenMetric2[idx]) |
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tenOutF += tenOutF_ |
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tenOutB += tenOutB_ |
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tenNormalizeF += tenNormalizeF_ |
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tenNormalizeB += tenNormalizeB_ |
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return tenOutF / tenNormalizeF, tenNormalizeF < 0.00001, tenOutB / tenNormalizeB, tenNormalizeB < 0.00001 |
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c = 16 |
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def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): |
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return torch.nn.Sequential( |
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torch.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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torch.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 torch.nn.Sequential( |
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torch.torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, |
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kernel_size=kernel_size, stride=stride, padding=padding, bias=True), |
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torch.nn.PReLU(out_planes) |
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) |
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class Conv2(torch.nn.Module): |
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def __init__(self, in_planes, out_planes, stride=2): |
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super(Conv2, self).__init__() |
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self.conv1 = conv(in_planes, out_planes, 3, stride, 1) |
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self.conv2 = conv(out_planes, out_planes, 3, 1, 1) |
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def forward(self, x): |
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x = self.conv1(x) |
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x = self.conv2(x) |
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return x |
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class Conv2n(torch.nn.Module): |
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def __init__(self, in_planes, out_planes, stride=2): |
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super(Conv2n, self).__init__() |
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self.conv1 = conv(in_planes, in_planes, 3, stride, 1) |
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self.conv2 = conv(in_planes, in_planes, 3, 1, 1) |
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self.conv3 = conv(in_planes, in_planes, 1, 1, 0) |
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self.conv4 = conv(in_planes, out_planes, 1, 1, 0) |
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def forward(self, x): |
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x = self.conv1(x) |
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x = self.conv2(x) |
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x = self.conv3(x) |
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x = self.conv4(x) |
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return x |
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class ImgPyramid(torch.nn.Module): |
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def __init__(self): |
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super(ImgPyramid, self).__init__() |
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self.conv1 = Conv2(3, c) |
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self.conv2 = Conv2(c, 2 * c) |
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self.conv3 = Conv2(2 * c, 4 * c) |
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self.conv4 = Conv2(4 * c, 8 * c) |
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def forward(self, x): |
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x1 = self.conv1(x) |
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x2 = self.conv2(x1) |
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x3 = self.conv3(x2) |
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x4 = self.conv4(x3) |
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return [x1, x2, x3, x4] |
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class EncDec(torch.nn.Module): |
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def __init__(self, branch): |
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super(EncDec, self).__init__() |
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self.branch = branch |
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self.down0 = Conv2(8, 2 * c) |
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self.down1 = Conv2(6 * c, 4 * c) |
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self.down2 = Conv2(12 * c, 8 * c) |
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self.down3 = Conv2(24 * c, 16 * c) |
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self.up0 = deconv(48 * c, 8 * c) |
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self.up1 = deconv(16 * c, 4 * c) |
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self.up2 = deconv(8 * c, 2 * c) |
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self.up3 = deconv(4 * c, c) |
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self.conv = torch.nn.Conv2d(c, 2 * self.branch, 3, 1, 1) |
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self.conv_m = torch.nn.Conv2d(c, self.branch, 3, 1, 1) |
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self.conv_C = torch.nn.Sequential( |
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torch.nn.AdaptiveAvgPool2d(1), |
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torch.nn.Conv2d(16 * c, 16 * 16 * c, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), bias=True), |
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torch.nn.Sigmoid() |
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) |
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self.conv_H = torch.nn.Sequential( |
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torch.nn.AdaptiveAvgPool2d((None, 1)), |
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torch.nn.Conv2d(16 * c, 16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), bias=True), |
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torch.nn.Sigmoid() |
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) |
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self.conv_W = torch.nn.Sequential( |
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torch.nn.AdaptiveAvgPool2d((1, None)), |
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torch.nn.Conv2d(16 * c, 16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), bias=True), |
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torch.nn.Sigmoid() |
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) |
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self.sigmoid = torch.nn.Sigmoid() |
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def forward(self, flow0, flow1, im0, im1, c0, c1): |
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N_, C_, H_, W_ = im0.shape |
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wim1 = backwarp(im1, flow0) |
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wim0 = backwarp(im0, flow1) |
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s0_0 = self.down0(torch.cat((flow0, im0, wim1), 1)) |
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s1_0 = self.down0(torch.cat((flow1, im1, wim0), 1)) |
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flow0 = torch.nn.functional.interpolate(flow0, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5 |
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flow1 = torch.nn.functional.interpolate(flow1, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5 |
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wf0 = backwarp(torch.cat((s0_0, c0[0]), 1), flow1) |
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wf1 = backwarp(torch.cat((s1_0, c1[0]), 1), flow0) |
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s0_1 = self.down1(torch.cat((s0_0, c0[0], wf1), 1)) |
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s1_1 = self.down1(torch.cat((s1_0, c1[0], wf0), 1)) |
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flow0 = torch.nn.functional.interpolate(flow0, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5 |
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flow1 = torch.nn.functional.interpolate(flow1, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5 |
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wf0 = backwarp(torch.cat((s0_1, c0[1]), 1), flow1) |
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wf1 = backwarp(torch.cat((s1_1, c1[1]), 1), flow0) |
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s0_2 = self.down2(torch.cat((s0_1, c0[1], wf1), 1)) |
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s1_2 = self.down2(torch.cat((s1_1, c1[1], wf0), 1)) |
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flow0 = torch.nn.functional.interpolate(flow0, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5 |
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flow1 = torch.nn.functional.interpolate(flow1, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5 |
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wf0 = backwarp(torch.cat((s0_2, c0[2]), 1), flow1) |
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wf1 = backwarp(torch.cat((s1_2, c1[2]), 1), flow0) |
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s0_3 = self.down3(torch.cat((s0_2, c0[2], wf1), 1)) |
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s1_3 = self.down3(torch.cat((s1_2, c1[2], wf0), 1)) |
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s0_3_c = self.conv_C(s0_3) |
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s0_3_c = s0_3_c.view(N_, 16, -1, 1, 1) |
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s0_3_h = self.conv_H(s0_3) |
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s0_3_h = s0_3_h.view(N_, 16, 1, -1, 1) |
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s0_3_w = self.conv_W(s0_3) |
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s0_3_w = s0_3_w.view(N_, 16, 1, 1, -1) |
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cube0 = (s0_3_c * s0_3_h * s0_3_w).mean(1) |
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s0_3 = s0_3 * cube0 |
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s1_3_c = self.conv_C(s1_3) |
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s1_3_c = s1_3_c.view(N_, 16, -1, 1, 1) |
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s1_3_h = self.conv_H(s1_3) |
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s1_3_h = s1_3_h.view(N_, 16, 1, -1, 1) |
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s1_3_w = self.conv_W(s1_3) |
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s1_3_w = s1_3_w.view(N_, 16, 1, 1, -1) |
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cube1 = (s1_3_c * s1_3_h * s1_3_w).mean(1) |
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s1_3 = s1_3 * cube1 |
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flow0 = torch.nn.functional.interpolate(flow0, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5 |
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flow1 = torch.nn.functional.interpolate(flow1, scale_factor=0.5, mode="bilinear", align_corners=False) * 0.5 |
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wf0 = backwarp(torch.cat((s0_3, c0[3]), 1), flow1) |
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wf1 = backwarp(torch.cat((s1_3, c1[3]), 1), flow0) |
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x0 = self.up0(torch.cat((s0_3, c0[3], wf1), 1)) |
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x1 = self.up0(torch.cat((s1_3, c1[3], wf0), 1)) |
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x0 = self.up1(torch.cat((s0_2, x0), 1)) |
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x1 = self.up1(torch.cat((s1_2, x1), 1)) |
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x0 = self.up2(torch.cat((s0_1, x0), 1)) |
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x1 = self.up2(torch.cat((s1_1, x1), 1)) |
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x0 = self.up3(torch.cat((s0_0, x0), 1)) |
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x1 = self.up3(torch.cat((s1_0, x1), 1)) |
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m0 = self.sigmoid(self.conv_m(x0)) * 0.8 + 0.1 |
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m1 = self.sigmoid(self.conv_m(x1)) * 0.8 + 0.1 |
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x0 = self.conv(x0) |
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x1 = self.conv(x1) |
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return x0, x1, m0, m1 |
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@register('m2m_pwc') |
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class M2M_PWC(torch.nn.Module): |
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def __init__(self, ratio=4): |
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super(M2M_PWC, self).__init__() |
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self.branch = 4 |
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self.ratio = ratio |
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self.paramAlpha = torch.nn.Parameter(10.0 * torch.ones(1, 1, 1, 1)) |
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class MotionRefineNet(torch.nn.Module): |
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def __init__(self, branch): |
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super(MotionRefineNet, self).__init__() |
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self.branch = branch |
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self.img_pyramid = ImgPyramid() |
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self.motion_encdec = EncDec(branch) |
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def forward(self, flow0, flow1, im0, im1, ratio): |
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flow0 = ratio * torch.nn.functional.interpolate(input=flow0, scale_factor=ratio, mode='bilinear', |
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align_corners=False) |
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flow1 = ratio * torch.nn.functional.interpolate(input=flow1, scale_factor=ratio, mode='bilinear', |
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align_corners=False) |
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c0 = self.img_pyramid(im0) |
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c1 = self.img_pyramid(im1) |
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flow_res = self.motion_encdec(flow0, flow1, im0, im1, c0, c1) |
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flow0 = flow0.repeat(1, self.branch, 1, 1) + flow_res[0] |
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flow1 = flow1.repeat(1, self.branch, 1, 1) + flow_res[1] |
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return flow0, flow1, flow_res[2], flow_res[3] |
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self.MRN = MotionRefineNet(self.branch) |
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def forward(self, img0, img1, time_step=[0.5], ratio=None, **kwargs): |
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if ratio is None: |
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ratio = self.ratio |
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intWidth = img0.shape[3] and img1.shape[3] |
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intHeight = img0.shape[2] and img1.shape[2] |
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intPadr = ((ratio * 16) - (intWidth % (ratio * 16))) % (ratio * 16) |
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intPadb = ((ratio * 16) - (intHeight % (ratio * 16))) % (ratio * 16) |
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img0 = torch.nn.functional.pad(input=img0, pad=[0, intPadr, 0, intPadb], mode='replicate') |
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img1 = torch.nn.functional.pad(input=img1, pad=[0, intPadr, 0, intPadb], mode='replicate') |
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N_, C_, H_, W_ = img0.shape |
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outputs = [] |
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result_dict = {} |
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with torch.set_grad_enabled(False): |
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tenStats = [img0, img1] |
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tenMean_ = sum([tenIn.mean([1, 2, 3], True) for tenIn in tenStats]) / len(tenStats) |
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tenStd_ = (sum([tenIn.std([1, 2, 3], False, True).square() + ( |
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tenMean_ - tenIn.mean([1, 2, 3], True)).square() for tenIn in tenStats]) / len(tenStats)).sqrt() |
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im0_o = (img0 - tenMean_) / (tenStd_ + 0.0000001) |
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im1_o = (img1 - tenMean_) / (tenStd_ + 0.0000001) |
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img0 = (img0 - tenMean_) / (tenStd_ + 0.0000001) |
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img1 = (img1 - tenMean_) / (tenStd_ + 0.0000001) |
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im0_ = torch.nn.functional.interpolate(input=img0, scale_factor=2.0 / ratio, mode='bilinear', |
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align_corners=False) |
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im1_ = torch.nn.functional.interpolate(input=img1, scale_factor=2.0 / ratio, mode='bilinear', |
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align_corners=False) |
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tenFwd, tenBwd = self.netFlow.bidir(im0_, im1_) |
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result_dict['flowfwd'] = torch.nn.functional.interpolate(tenFwd, scale_factor=ratio, mode='bilinear', align_corners=False)[:, :, |
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:intHeight, :intWidth].clone().detach() * ratio |
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result_dict['flowbwd'] = torch.nn.functional.interpolate(tenBwd, scale_factor=ratio, mode='bilinear', align_corners=False)[:, :, |
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:intHeight, :intWidth].clone().detach() * ratio |
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tenFwd, tenBwd, WeiMF, WeiMB = self.MRN(tenFwd, tenBwd, img0, img1, ratio) |
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img0 = im0_o.repeat(1, self.branch, 1, 1) |
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img1 = im1_o.repeat(1, self.branch, 1, 1) |
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tenStd = tenStd_.repeat(1, self.branch, 1, 1) |
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tenMean = tenMean_.repeat(1, self.branch, 1, 1) |
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fltTime = time_step.repeat(1, self.branch, 1, 1) |
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tenFwd = tenFwd.reshape(N_, self.branch, 2, H_, W_).view(N_ * self.branch, 2, H_, W_) |
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tenBwd = tenBwd.reshape(N_, self.branch, 2, H_, W_).view(N_ * self.branch, 2, H_, W_) |
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WeiMF = WeiMF.reshape(N_, self.branch, 1, H_, W_).view(N_ * self.branch, 1, H_, W_) |
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WeiMB = WeiMB.reshape(N_, self.branch, 1, H_, W_).view(N_ * self.branch, 1, H_, W_) |
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img0 = img0.reshape(N_, self.branch, 3, H_, W_).view(N_ * self.branch, 3, H_, W_) |
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img1 = img1.reshape(N_, self.branch, 3, H_, W_).view(N_ * self.branch, 3, H_, W_) |
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tenStd = tenStd.reshape(N_, self.branch, 1, 1, 1).view(N_ * self.branch, 1, 1, 1) |
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tenMean = tenMean.reshape(N_, self.branch, 1, 1, 1).view(N_ * self.branch, 1, 1, 1) |
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fltTime = fltTime.reshape(N_, self.branch, 1, 1, 1).view(N_ * self.branch, 1, 1, 1) |
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tenPhotoone = (1.0 - (WeiMF * (img0 - backwarp(img1, tenFwd).detach()).abs().mean([1], True))).clip( |
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0.001, None).square() |
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tenPhototwo = (1.0 - (WeiMB * (img1 - backwarp(img0, tenBwd).detach()).abs().mean([1], True))).clip( |
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0.001, None).square() |
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t0 = fltTime |
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flow0 = tenFwd * t0 |
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metric0 = self.paramAlpha * tenPhotoone |
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t1 = 1.0 - fltTime |
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flow1 = tenBwd * t1 |
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metric1 = self.paramAlpha * tenPhototwo |
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flow0 = flow0.reshape(N_, self.branch, 2, H_, W_).permute(1, 0, 2, 3, 4) |
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flow1 = flow1.reshape(N_, self.branch, 2, H_, W_).permute(1, 0, 2, 3, 4) |
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metric0 = metric0.reshape(N_, self.branch, 1, H_, W_).permute(1, 0, 2, 3, 4) |
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metric1 = metric1.reshape(N_, self.branch, 1, H_, W_).permute(1, 0, 2, 3, 4) |
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img0 = img0.reshape(N_, self.branch, 3, H_, W_).permute(1, 0, 2, 3, 4) |
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img1 = img1.reshape(N_, self.branch, 3, H_, W_).permute(1, 0, 2, 3, 4) |
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t0 = t0.reshape(N_, self.branch, 1, 1, 1).permute(1, 0, 2, 3, 4) |
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t1 = t1.reshape(N_, self.branch, 1, 1, 1).permute(1, 0, 2, 3, 4) |
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tenOutput, mask = forwarp_mframe_mask(img0, flow0, t1, img1, flow1, t0, metric0, metric1) |
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tenOutput = tenOutput + mask * (t1.mean(0) * im0_o + t0.mean(0) * im1_o) |
|
|
|
|
|
output = (tenOutput * (tenStd_ + 0.0000001)) + tenMean_ |
|
|
result_dict['imgt_pred'] = output[:, :, :intHeight, :intWidth] |
|
|
|
|
|
return result_dict |
|
|
|
|
|
class ResBlock(nn.Module): |
|
|
def __init__(self, in_channels, side_channels, bias=True): |
|
|
super(ResBlock, self).__init__() |
|
|
self.side_channels = side_channels |
|
|
self.conv1 = nn.Sequential( |
|
|
nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=bias), |
|
|
nn.PReLU(in_channels) |
|
|
) |
|
|
self.conv2 = nn.Sequential( |
|
|
nn.Conv2d(side_channels, side_channels, kernel_size=3, stride=1, padding=1, bias=bias), |
|
|
nn.PReLU(side_channels) |
|
|
) |
|
|
self.conv3 = nn.Sequential( |
|
|
nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=bias), |
|
|
nn.PReLU(in_channels) |
|
|
) |
|
|
self.conv4 = nn.Sequential( |
|
|
nn.Conv2d(side_channels, side_channels, kernel_size=3, stride=1, padding=1, bias=bias), |
|
|
nn.PReLU(side_channels) |
|
|
) |
|
|
self.conv5 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=bias) |
|
|
self.prelu = nn.PReLU(in_channels) |
|
|
|
|
|
def forward(self, x): |
|
|
out = self.conv1(x) |
|
|
|
|
|
res_feat = out[:, :-self.side_channels, ...] |
|
|
side_feat = out[:, -self.side_channels:, :, :] |
|
|
side_feat = self.conv2(side_feat) |
|
|
out = self.conv3(torch.cat([res_feat, side_feat], 1)) |
|
|
|
|
|
res_feat = out[:, :-self.side_channels, ...] |
|
|
side_feat = out[:, -self.side_channels:, :, :] |
|
|
side_feat = self.conv4(side_feat) |
|
|
out = self.conv5(torch.cat([res_feat, side_feat], 1)) |
|
|
|
|
|
out = self.prelu(x + out) |
|
|
return out |
