Commit 0ff4e488 authored by Benoit Martin's avatar Benoit Martin

fix parallel transport and update functional tests (better precision)

parent af9538d5
Pipeline #56324245 failed with stages
in 16 minutes and 13 seconds
......@@ -2,7 +2,7 @@ import torch
import logging
import math
from core import default, GpuMode
from core import default
from core.model_tools.deformations.exponential import Exponential
from core.model_tools.deformations.geodesic import Geodesic
from core.observations.deformable_objects.deformable_multi_object import DeformableMultiObject
......@@ -63,18 +63,24 @@ def compute_parallel_transport(template_specifications,
control_points_to_transport = read_2D_array(initial_control_points_to_transport)
need_to_project_initial_momenta = True
device, _ = utilities.get_best_device(GpuMode.NONE) # TODO: could this be done on gpu ?
device, _ = utilities.get_best_device(gpu_mode)
control_points = utilities.move_data(control_points, dtype=tensor_scalar_type, device=device)
control_points_to_transport = utilities.move_data(control_points_to_transport, dtype=tensor_scalar_type, device=device)
initial_momenta = utilities.move_data(initial_momenta, dtype=tensor_scalar_type, device=device)
initial_momenta_to_transport = utilities.move_data(initial_momenta_to_transport, dtype=tensor_scalar_type, device=device)
# We start by projecting the initial momenta if they are not carried at the reference progression control points.
if need_to_project_initial_momenta:
control_points_to_transport = utilities.move_data(control_points_to_transport, dtype=tensor_scalar_type, device=device)
velocity = deformation_kernel.convolve(control_points, control_points_to_transport,
initial_momenta_to_transport)
velocity = deformation_kernel.convolve(control_points, control_points_to_transport, initial_momenta_to_transport)
kernel_matrix = deformation_kernel.get_kernel_matrix(control_points)
"""
The following code block needs to be done on cpu due to the high memory usage of the matrix inversion.
TODO: maybe use Keops Inv ?
"""
kernel_matrix = utilities.move_data(kernel_matrix, dtype=tensor_scalar_type, device='cpu') # TODO: could this be done on gpu ?
cholesky_kernel_matrix = torch.potrf(kernel_matrix)
# cholesky_kernel_matrix = torch.Tensor(np.linalg.cholesky(kernel_matrix.data.numpy()).type_as(kernel_matrix))#Dirty fix if pytorch fails.
projected_momenta = torch.potrs(velocity, cholesky_kernel_matrix).squeeze().contiguous()
......@@ -82,12 +88,17 @@ def compute_parallel_transport(template_specifications,
else:
projected_momenta = initial_momenta_to_transport
"""
Re-send data to device depending on gpu_mode
"""
device, _ = utilities.get_best_device(gpu_mode)
projected_momenta = utilities.move_data(projected_momenta, dtype=tensor_scalar_type, device=device)
"""
Second half of the code.
"""
objects_list, objects_name, objects_name_extension, _, _ = create_template_metadata(template_specifications,
dimension)
objects_list, objects_name, objects_name_extension, _, _ = create_template_metadata(template_specifications, dimension)
template = DeformableMultiObject(objects_list)
template_points = template.get_points()
......
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......
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