def rotate_and_flip(tensor,device,p=0.5):
for i in range(tensor.shape[0]):
random_number=np.random.uniform()
if random_number < p:
center = torch.ones(tensor.shape[1], 2).to(device)
center[:, 0] = tensor.shape[3] / 2 # x
center[:, 1] = tensor.shape[2] / 2 # y
#scale: torch.tensor = torch.ones(1)#*np.random.uniform(0.8,1.2)
angle = torch.tensor([np.random.randint(-90,90,)*np.ones(tensor.shape[1])]).squeeze().to(device).float()
#print(angle.shape)
#print(tensor[i].shape)
#M = kornia.get_rotation_matrix2d(center, angle, scale)#.to(device)
#Mt = torch.ones((tensor.shape[0],2,3))
#Mt[:] = M
#Mt=Mt.to(device)
#tensor[:,j]=kornia.warp_affine(tensor[:,j], Mt, dsize=(tensor.shape[3], tensor.shape[4]))
#print(tensor.dtype)
tensor[i]=kornia.rotate(tensor[i],angle,center)
random_number=np.random.uniform()
if random_number < p:
tensor[i,:]=kornia.hflip(tensor[i,:])
random_number=np.random.uniform()
if random_number < p:
tensor[i,:]=kornia.vflip(tensor[i,:])
return tensor
def hflip(tensor,p=1):
for i in range(tensor.shape[0]):
random_number=np.random.uniform()
if random_number < p:
tensor[i,:]=kornia.vflip(tensor[i,:])
return tensor
def op_script(data: torch.Tensor) -> torch.Tensor:
return kornia.vflip(data)
