def forward(self, theta):
b = theta.size(0)
if theta.size(1) == 9:
H = theta
else:
H = homography_mat_from_4_pts(theta)
h0 = H[:, 0].unsqueeze(1).unsqueeze(2).unsqueeze(3)
h1 = H[:, 1].unsqueeze(1).unsqueeze(2).unsqueeze(3)
h2 = H[:, 2].unsqueeze(1).unsqueeze(2).unsqueeze(3)
h3 = H[:, 3].unsqueeze(1).unsqueeze(2).unsqueeze(3)
h4 = H[:, 4].unsqueeze(1).unsqueeze(2).unsqueeze(3)
h5 = H[:, 5].unsqueeze(1).unsqueeze(2).unsqueeze(3)
h6 = H[:, 6].unsqueeze(1).unsqueeze(2).unsqueeze(3)
h7 = H[:, 7].unsqueeze(1).unsqueeze(2).unsqueeze(3)
h8 = H[:, 8].unsqueeze(1).unsqueeze(2).unsqueeze(3)
grid_X = expand_dim(self.grid_X, 0, b)
grid_Y = expand_dim(self.grid_Y, 0, b)
grid_Xp = grid_X * h0 + grid_Y * h1 + h2
grid_Yp = grid_X * h3 + grid_Y * h4 + h5
k = grid_X * h6 + grid_Y * h7 + h8
grid_Xp /= k
grid_Yp /= k
return torch.cat((grid_Xp, grid_Yp), 3)
def forward(self, theta, matches, seg_mask=None, return_outliers=False):
if isinstance(theta,Variable): # handle normal batch transformations
batch_size=theta.size()[0]
theta=theta.clone()
mask = self.geometricTnf(expand_dim(self.mask_id,0,batch_size),theta)
if return_outliers:
mask_outliers = self.geometricTnf(expand_dim(1.0-self.mask_id,0,batch_size),theta)
if self.normalize:
epsilon=1e-5
mask = torch.div(mask,
torch.sum(torch.sum(torch.sum(mask+epsilon,3),2),1).unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(mask))
if return_outliers:
mask_outliers = torch.div(mask_outliers,
torch.sum(torch.sum(torch.sum(mask_outliers+epsilon,3),2),1).unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(mask_outliers))
if self.seg_mask:
score = torch.sum(torch.sum(torch.mul(torch.sum(torch.mul(mask,matches),1),seg_mask),2),1)
else:
score = torch.sum(torch.sum(torch.sum(torch.mul(mask,matches),3),2),1)
if return_outliers:
score_outliers = torch.sum(torch.sum(torch.sum(torch.mul(mask_outliers,matches),3),2),1)
#return (score,score_outliers)
return (score,score_outliers)
elif isinstance(theta,list): # handle multiple transformations per batch item, batch is in list format (used for RANSAC)
batch_size = len(theta)
score = []
for b in range(batch_size):
sample_size=theta[b].size(0)
s=self.forward(theta[b],expand_dim(matches[b,:,:,:].unsqueeze(0),0,sample_size))
score.append(s)
return score
def forward(self, theta_aff, theta_aff_tps, matches, seg_mask=None, return_outliers=False):
batch_size=theta_aff.size()[0]
mask = self.compGeometricTnf(image_batch=expand_dim(self.mask_id,0,batch_size),
theta_aff=theta_aff,
theta_aff_tps=theta_aff_tps)
if return_outliers:
mask_outliers = self.compGeometricTnf(image_batch=expand_dim(1.0-self.mask_id,0,batch_size),
theta_aff=theta_aff,
theta_aff_tps=theta_aff_tps)
if self.normalize:
epsilon=1e-5
mask = torch.div(mask,
torch.sum(torch.sum(torch.sum(mask+epsilon,3),2),1).unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(mask))
if return_outliers:
mask_outliers = torch.div(mask,
torch.sum(torch.sum(torch.sum(mask_outliers+epsilon,3),2),1).unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(mask_outliers))
if self.seg_mask:
score = torch.sum(torch.sum(torch.mul(torch.sum(torch.mul(mask,matches),1),seg_mask),2),1)
else:
score = torch.sum(torch.sum(torch.sum(torch.mul(mask,matches),3),2),1)
if return_outliers:
score_outliers = torch.sum(torch.sum(torch.sum(torch.mul(mask_outliers,matches),3),2),1)
return (score,score_outliers)
return score
def forward(self, theta, matches, return_outliers=False):
if isinstance(theta,Variable): # handle normal batch transformations
batch_size=theta.size()[0]
theta=theta.clone()
mask = self.geometricTnf(expand_dim(self.mask_id,0,batch_size),theta)
if return_outliers:
mask_outliers = self.geometricTnf(expand_dim(1.0-self.mask_id,0,batch_size),theta)
if self.normalize:
epsilon=1e-5
mask = torch.div(mask,
torch.sum(torch.sum(torch.sum(mask+epsilon,3),2),1).unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(mask))
if return_outliers:
mask_outliers = torch.div(mask_outliers,
torch.sum(torch.sum(torch.sum(mask_outliers+epsilon,3),2),1).unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(mask_outliers))
score = torch.sum(torch.sum(torch.sum(torch.mul(mask,matches),3),2),1)
if return_outliers:
score_outliers = torch.sum(torch.sum(torch.sum(torch.mul(mask_outliers,matches),3),2),1)
return (score,score_outliers)
elif isinstance(theta,list): # handle multiple transformations per batch item, batch is in list format (used for RANSAC)
batch_size = len(theta)
score = []
for b in range(batch_size):
sample_size=theta[b].size(0)
s=self.forward(theta[b],expand_dim(matches[b,:,:,:].unsqueeze(0),0,sample_size))
score.append(s)
return score
def forward(self, theta):
b = theta.size(0)
if not theta.size() == (b, 6):
theta = theta.view(b, 6)
theta = theta.contiguous()
t0 = theta[:, 0].unsqueeze(1).unsqueeze(2).unsqueeze(3)
t1 = theta[:, 1].unsqueeze(1).unsqueeze(2).unsqueeze(3)
t2 = theta[:, 2].unsqueeze(1).unsqueeze(2).unsqueeze(3)
t3 = theta[:, 3].unsqueeze(1).unsqueeze(2).unsqueeze(3)
t4 = theta[:, 4].unsqueeze(1).unsqueeze(2).unsqueeze(3)
t5 = theta[:, 5].unsqueeze(1).unsqueeze(2).unsqueeze(3)
X = expand_dim(self.grid_X, 0, b)
Y = expand_dim(self.grid_Y, 0, b)
Xp = X * t0 + Y * t1 + t2
Yp = X * t3 + Y * t4 + t5
return torch.cat((Xp, Yp), 3)
def forward(self, theta):
b=theta.size(0)
if not theta.size()==(b,6):
theta = theta.view(b,6)
theta = theta.contiguous()
t0=theta[:,0].unsqueeze(1).unsqueeze(2).unsqueeze(3)
t1=theta[:,1].unsqueeze(1).unsqueeze(2).unsqueeze(3)
t2=theta[:,2].unsqueeze(1).unsqueeze(2).unsqueeze(3)
t3=theta[:,3].unsqueeze(1).unsqueeze(2).unsqueeze(3)
t4=theta[:,4].unsqueeze(1).unsqueeze(2).unsqueeze(3)
t5=theta[:,5].unsqueeze(1).unsqueeze(2).unsqueeze(3)
X = expand_dim(self.grid_X,0,b)
Y = expand_dim(self.grid_Y,0,b)
Xp = X*t0 + Y*t1 + t2
Yp = X*t3 + Y*t4 + t5
return torch.cat((Xp,Yp),3)
def forward(self, theta_aff, theta_aff_tps, matches,return_outliers=False):
batch_size=theta_aff.size()[0]
mask = self.compGeometricTnf(image_batch=expand_dim(self.mask_id,0,batch_size),
theta_aff=theta_aff,
theta_aff_tps=theta_aff_tps)
if return_outliers:
mask_outliers = self.compGeometricTnf(image_batch=expand_dim(1.0-self.mask_id,0,batch_size),
theta_aff=theta_aff,
theta_aff_tps=theta_aff_tps)
if self.normalize:
epsilon=1e-5
mask = torch.div(mask,
torch.sum(torch.sum(torch.sum(mask+epsilon,3),2),1).unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(mask))
if return_outliers:
mask_outliers = torch.div(mask,
torch.sum(torch.sum(torch.sum(mask_outliers+epsilon,3),2),1).unsqueeze(1).unsqueeze(2).unsqueeze(3).expand_as(mask_outliers))
score = torch.sum(torch.sum(torch.sum(torch.mul(mask,matches),3),2),1)
if return_outliers:
score_outliers = torch.sum(torch.sum(torch.sum(torch.mul(mask_outliers,matches),3),2),1)
return (score,score_outliers)
return score
