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rep_flow_layer.py
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rep_flow_layer.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class FlowLayer(nn.Module):
def __init__(self, channels=1, params=[1,1,1,1,1], n_iter=10):
super(FlowLayer, self).__init__()
self.n_iter = n_iter
if params[0]:
self.img_grad = nn.Parameter(torch.FloatTensor([[[[-0.5,0,0.5]]]]).repeat(channels,1,1,1))
self.img_grad2 = nn.Parameter(torch.FloatTensor([[[[-0.5,0,0.5]]]]).transpose(3,2).repeat(channels,1,1,1))
else:
self.img_grad = nn.Parameter(torch.FloatTensor([[[[-0.5,0,0.5]]]]).repeat(channels,1,1,1), requires_grad=False)
self.img_grad2 = nn.Parameter(torch.FloatTensor([[[[-0.5,0,0.5]]]]).transpose(3,2).repeat(channels,1,1,1), requires_grad=False)
if params[1]:
self.f_grad = nn.Parameter(torch.FloatTensor([[[[-1,1]]]]).repeat(channels,1,1,1))
self.f_grad2 = nn.Parameter(torch.FloatTensor([[[[-1],[1]]]]).repeat(channels,1,1,1))
self.div = nn.Parameter(torch.FloatTensor([[[[-1,1]]]]).repeat(channels,1,1,1))
self.div2 = nn.Parameter(torch.FloatTensor([[[[-1],[1]]]]).repeat(channels,1,1,1))
else:
self.f_grad = nn.Parameter(torch.FloatTensor([[[[-1,1]]]]).repeat(channels,1,1,1), requires_grad=False)
self.f_grad2 = nn.Parameter(torch.FloatTensor([[[[-1],[1]]]]).repeat(channels,1,1,1), requires_grad=False)
self.div = nn.Parameter(torch.FloatTensor([[[[-1,1]]]]).repeat(channels,1,1,1), requires_grad=False)
self.div2 = nn.Parameter(torch.FloatTensor([[[[-1],[1]]]]).repeat(channels,1,1,1), requires_grad=False)
self.channels = channels
self.t = 0.3
self.l = 0.15
self.a = 0.25
if params[2]:
self.t = nn.Parameter(torch.FloatTensor([self.t]))
if params[3]:
self.l = nn.Parameter(torch.FloatTensor([self.l]))
if params[4]:
self.a = nn.Parameter(torch.FloatTensor([self.a]))
def norm_img(self, x):
mx = torch.max(x)
mn = torch.min(x)
x = 255*(x-mn)/(mn-mx)
return x
def forward_grad(self, x):
grad_x = F.conv2d(F.pad(x, (0,1,0,0)), self.f_grad, groups=self.channels)
grad_x[:,:,:,-1] = 0
grad_y = F.conv2d(F.pad(x, (0,0,0,1)), self.f_grad2, groups=self.channels)
grad_y[:,:,-1,:] = 0
return grad_x, grad_y
def divergence(self, x, y):
tx = F.pad(x[:,:,:,:-1], (1,0,0,0))
ty = F.pad(y[:,:,:-1,:], (0,0,1,0))
grad_x = F.conv2d(F.pad(tx, (0,1,0,0)), self.div, groups=self.channels)
grad_y = F.conv2d(F.pad(ty, (0,0,0,1)), self.div2, groups=self.channels)
return grad_x + grad_y
def forward(self, x, y):
u1 = torch.zeros_like(x)
u2 = torch.zeros_like(x)
l_t = self.l * self.t
taut = self.a/self.t
grad2_x = F.conv2d(F.pad(y,(1,1,0,0)), self.img_grad, groups=self.channels, padding=0, stride=1)
grad2_x[:,:,:,0] = 0.5 * (x[:,:,:,1] - x[:,:,:,0])
grad2_x[:,:,:,-1] = 0.5 * (x[:,:,:,-1] - x[:,:,:,-2])
grad2_y = F.conv2d(F.pad(y, (0,0,1,1)), self.img_grad2, groups=self.channels, padding=0, stride=1)
grad2_y[:,:,0,:] = 0.5 * (x[:,:,1,:] - x[:,:,0,:])
grad2_y[:,:,-1,:] = 0.5 * (x[:,:,-1,:] - x[:,:,-2,:])
p11 = torch.zeros_like(x.data)
p12 = torch.zeros_like(x.data)
p21 = torch.zeros_like(x.data)
p22 = torch.zeros_like(x.data)
gsqx = grad2_x**2
gsqy = grad2_y**2
grad = gsqx + gsqy + 1e-12
rho_c = y - grad2_x * u1 - grad2_y * u2 - x
for i in range(self.n_iter):
rho = rho_c + grad2_x * u1 + grad2_y * u2 + 1e-12
v1 = torch.zeros_like(x.data)
v2 = torch.zeros_like(x.data)
mask1 = (rho < -l_t*grad).detach()
v1[mask1] = (l_t * grad2_x)[mask1]
v2[mask1] = (l_t * grad2_y)[mask1]
mask2 = (rho > l_t*grad).detach()
v1[mask2] = (-l_t * grad2_x)[mask2]
v2[mask2] = (-l_t * grad2_y)[mask2]
mask3 = ((mask1^1) & (mask2^1) & (grad > 1e-12)).detach()
v1[mask3] = ((-rho/grad) * grad2_x)[mask3]
v2[mask3] = ((-rho/grad) * grad2_y)[mask3]
del rho
del mask1
del mask2
del mask3
v1 += u1
v2 += u2
u1 = v1 + self.t * self.divergence(p11, p12)
u2 = v2 + self.t * self.divergence(p21, p22)
del v1
del v2
u1 = u1
u2 = u2
u1x, u1y = self.forward_grad(u1)
u2x, u2y = self.forward_grad(u2)
p11 = (p11 + taut * u1x) / (1. + taut * torch.sqrt(u1x**2 + u1y**2 + 1e-12))
p12 = (p12 + taut * u1y) / (1. + taut * torch.sqrt(u1x**2 + u1y**2 + 1e-12))
p21 = (p21 + taut * u2x) / (1. + taut * torch.sqrt(u2x**2 + u2y**2 + 1e-12))
p22 = (p22 + taut * u2y) / (1. + taut * torch.sqrt(u2x**2 + u2y**2 + 1e-12))
del u1x
del u1y
del u2x
del u2y
return u1, u2