def get_random_rotation_LAFs(patches, angle_mag=math.pi):
rot_LAFs = Variable(
torch.FloatTensor([[0.5, 0, 0.5],
[0, 0.5,
0.5]]).unsqueeze(0).repeat(patches.size(0), 1, 1))
phi = (Variable(2.0 * torch.rand(patches.size(0)) - 1.0)).view(-1, 1, 1)
if patches.is_cuda:
rot_LAFs = rot_LAFs.cuda()
phi = phi.cuda()
rotmat = get_rotation_matrix(angle_mag * phi)
inv_rotmat = get_rotation_matrix(-angle_mag * phi)
rot_LAFs[:, 0:2, 0:2] = torch.bmm(rotmat, rot_LAFs[:, 0:2, 0:2])
return rot_LAFs, inv_rotmat
def forward(self, input, return_A_matrix=False):
x = self.features(self.input_norm(input)).view(-1, 5)
rot = get_rotation_matrix(torch.atan2(x[:, 3], x[:, 4] + 1e-8))
if input.is_cuda:
return torch.bmm(
rot,
torch.cat([
torch.cat([
x[:, 0:1].view(-1, 1, 1),
torch.zeros(x.size(0), 1, 1).cuda()
],
dim=2), x[:, 1:3].view(-1, 1, 2).contiguous()
],
dim=1))
else:
return torch.bmm(
rot,
torch.cat([
torch.cat([
x[:, 0:1].view(-1, 1, 1),
torch.zeros(x.size(0), 1, 1)
],
dim=2), x[:, 1:3].view(-1, 1, 2).contiguous()
],
dim=1))
def forward(self, input, return_A_matrix=False):
x = self.features(self.input_norm(input)).view(-1, 3)
angle = torch.atan2(x[:, 1], x[:, 2] + 1e-8)
rot = get_rotation_matrix(angle)
tilt = torch.exp(1.8 * F.tanh(x[:, 0]))
tilt_matrix = torch.eye(2).unsqueeze(0).repeat(input.size(0), 1, 1)
if x.is_cuda:
tilt_matrix = tilt_matrix.cuda()
tilt_matrix[:, 0, 0] = torch.sqrt(tilt)
tilt_matrix[:, 1, 1] = 1.0 / torch.sqrt(tilt)
return rectifyAffineTransformationUpIsUp(torch.bmm(
rot, tilt_matrix)).contiguous()
def forward(self, input, return_A_matrix=False):
x = self.features(self.input_norm(input)).view(-1, 5)
angle = torch.atan2(x[:, 3], x[:, 4] + 1e-8)
rot = get_rotation_matrix(angle)
return torch.bmm(
rot,
torch.cat([
torch.cat([
x[:, 0:1].view(-1, 1, 1), x[:, 1:2].view(x.size(0), 1,
1).contiguous()
],
dim=2), x[:, 1:3].view(-1, 1, 2).contiguous()
],
dim=1))
def forward(self, input, return_rot_matrix = True):
xy = self.features(self.input_norm(input)).view(-1,2)
angle = torch.atan2(xy[:,0] + 1e-8, xy[:,1]+1e-8);
if return_rot_matrix:
return get_rotation_matrix(angle)
return angle
def forward(self, input, return_A_matrix = False):
xy = self.features(self.input_norm(input)).view(-1,5)
a1 = torch.cat([1.0 + xy[:,0].contiguous().view(-1,1,1), xy[:,1].contiguous().view(-1,1,1)], dim = 2).contiguous()
a2 = torch.cat([xy[:,1].contiguous().view(-1,1,1), 1.0 + xy[:,2].contiguous().view(-1,1,1)], dim = 2).contiguous()
return torch.bmm(get_rotation_matrix(torch.atan2(xy[:,3] + 1e-8, xy[:,4]+1e-8)), torch.cat([a1,a2], dim = 1).contiguous())
def forward(self, input, return_rot_matrix=False):
xy = self.features(self.input_norm(input))
angle = torch.atan2(xy[:, 0] + 1e-8, xy[:, 1] + 1e-8)
if return_rot_matrix:
return get_rotation_matrix(-angle)
return angle