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loss.py
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loss.py
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import torch
import numpy as np
import CFOG as des
import torch.nn.functional as F
import torch.nn as nn
from torch.autograd import Variable
def ComputeLoss(reference, sensed_tran, sensed, reference_inv_tran):
loss_1 = CFOG_SSD(reference, sensed_tran)
loss_2 = CFOG_SSD(sensed, reference_inv_tran)
loss = loss_1 + loss_2
return loss
def gradient_loss(s, penalty='l2'):
dy = torch.abs(s[:, :, 1:, :] - s[:, :, :-1, :])
dx = torch.abs(s[:, :, :, 1:] - s[:, :, :, :-1])
if (penalty == 'l2'):
dy = dy * dy
dx = dx * dx
d = torch.mean(dx) + torch.mean(dy)
return d / 2.0
def mse_loss(x, y):
return torch.mean((x - y) ** 2)
def DSC(pred, target):
smooth = 1e-5
m1 = pred.flatten()
m2 = target.flatten()
intersection = (m1 * m2).sum()
return (2. * intersection + smooth) / (m1.sum() + m2.sum() + smooth)
def CFOG_NCC(i, j): # sar_flow, optical
x = torch.ge(i.squeeze(0).squeeze(0), 1)
x = torch.tensor(x, dtype=torch.float32)
y = torch.ge(j.squeeze(0).squeeze(0), 1)
y = torch.tensor(y, dtype=torch.float32)
z = torch.mul(x, y)
num = z[z.ge(1)].size()[0]
i = torch.mul(i, z)
j = torch.mul(j, z)
CFOG_sar = torch.mul(des.CFOG(i), z)
CFOG_optical = torch.mul(des.CFOG(j), z)
# loss = gncc_loss(MIND_sar, MIND_optical)
loss = gncc_loss(CFOG_sar, CFOG_optical)*512*512/num
return loss
def gncc_loss(I, J, eps=1e-5):
I2 = I.pow(2)
J2 = J.pow(2)
IJ = I*J
I_ave, J_ave = I.mean(), J.mean()
I2_ave, J2_ave = I2.mean(), J2.mean()
IJ_ave = IJ.mean()
cross = IJ_ave - I_ave * J_ave
I_var = I2_ave - I_ave.pow(2)
J_var = J2_ave - J_ave.pow(2)
cc = cross / (I_var.sqrt() * J_var.sqrt() + eps) # 1e-5
return -1.0 * cc + 1
def CFOG_SSD(i, j):
x = torch.ge(i.squeeze(0).squeeze(0), 1)
x = torch.tensor(x, dtype=torch.float32)
y = torch.ge(j.squeeze(0).squeeze(0), 1)
y = torch.tensor(y, dtype=torch.float32)
z = torch.mul(x, y)
num = z[z.ge(1)].size()[0]
i = torch.mul(i, z)
j = torch.mul(j, z)
CFOG_sar = torch.mul(des.CFOG(i), z)
CFOG_optical = torch.mul(des.CFOG(j), z)
SSD_loss = nn.MSELoss(reduction='sum')
loss = SSD_loss(CFOG_sar, CFOG_optical)/num
return loss
def compute_local_sums(I, J, filt, stride, padding, win):
I2, J2, IJ = I * I, J * J, I * J
I_sum = F.conv2d(I, filt, stride=stride, padding=padding)
J_sum = F.conv2d(J, filt, stride=stride, padding=padding)
I2_sum = F.conv2d(I2, filt, stride=stride, padding=padding)
J2_sum = F.conv2d(J2, filt, stride=stride, padding=padding)
IJ_sum = F.conv2d(IJ, filt, stride=stride, padding=padding)
win_size = np.prod(win)
u_I = I_sum / win_size
u_J = J_sum / win_size
cross = IJ_sum - u_J * I_sum - u_I * J_sum + u_I * u_J * win_size
I_var = I2_sum - 2 * u_I * I_sum + u_I * u_I * win_size
J_var = J2_sum - 2 * u_J * J_sum + u_J * u_J * win_size
return I_var, J_var, cross
def cc_loss(x, y):
dim = [2, 3, 4]
mean_x = torch.mean(x, dim, keepdim=True)
mean_y = torch.mean(y, dim, keepdim=True)
mean_x2 = torch.mean(x ** 2, dim, keepdim=True)
mean_y2 = torch.mean(y ** 2, dim, keepdim=True)
stddev_x = torch.sum(torch.sqrt(mean_x2 - mean_x ** 2), dim, keepdim=True)
stddev_y = torch.sum(torch.sqrt(mean_y2 - mean_y ** 2), dim, keepdim=True)
return -torch.mean((x - mean_x) * (y - mean_y) / (stddev_x * stddev_y))
def Get_Ja(flow):
D_y = (flow[:, 1:, :-1, :-1, :] - flow[:, :-1, :-1, :-1, :])
D_x = (flow[:, :-1, 1:, :-1, :] - flow[:, :-1, :-1, :-1, :])
D_z = (flow[:, :-1, :-1, 1:, :] - flow[:, :-1, :-1, :-1, :])
D1 = (D_x[..., 0] + 1) * ((D_y[..., 1] + 1) * (D_z[..., 2] + 1) - D_z[..., 1] * D_y[..., 2])
D2 = (D_x[..., 1]) * (D_y[..., 0] * (D_z[..., 2] + 1) - D_y[..., 2] * D_x[..., 0])
D3 = (D_x[..., 2]) * (D_y[..., 0] * D_z[..., 1] - (D_y[..., 1] + 1) * D_z[..., 0])
return D1 - D2 + D3
def NJ_loss(ypred):
Neg_Jac = 0.5 * (torch.abs(Get_Ja(ypred)) - Get_Ja(ypred))
return torch.sum(Neg_Jac)
def lncc_loss(i, j, win=[9, 9], eps=1e-5):
I = i
J = j
I2 = I.pow(2)
J2 = J.pow(2)
IJ = I*J
filters = Variable(torch.ones(1, 1, win[0], win[1])).cuda()
padding = (win[0]//2, win[1]//2)
I_sum = F.conv2d(I, filters, stride=1, padding=padding)
J_sum = F.conv2d(J, filters, stride=1, padding=padding)
I2_sum = F.conv2d(I2, filters, stride=1, padding=padding)
J2_sum = F.conv2d(J2, filters, stride=1, padding=padding)
IJ_sum = F.conv2d(IJ, filters, stride=1, padding=padding)
win_size = win[0] * win[1]
u_I = I_sum / win_size
u_J = J_sum / win_size
cross = IJ_sum - u_J * I_sum - u_I * J_sum + u_I * u_J * win_size
I_var = I2_sum - 2 * u_I * I_sum + u_I * u_I * win_size
J_var = J2_sum - 2 * u_J * J_sum + u_J * u_J * win_size
cc = cross * cross / (I_var * J_var + eps)
lcc = -1.0 * torch.mean(cc) + 1
return lcc