def forward(self, batch, return_logs=False, progress=0.0, **kwargs):
"""
Processes a batch.
Parameters
----------
batch : dict
Input batch
return_logs : bool
True if logs are stored
progress :
Training progress percentage
Returns
-------
output : dict
Dictionary containing a "loss" scalar and different metrics and predictions
for logging and downstream usage.
"""
if not self.training:
# If not training, no need for self-supervised loss
return SfmModel.forward(self, batch, return_logs=return_logs, **kwargs)
else:
if self.supervised_loss_weight == 1.:
# If no self-supervision, no need to calculate loss
self_sup_output = SfmModel.forward(self, batch, return_logs=return_logs, **kwargs)
loss = torch.tensor([0.]).type_as(batch['rgb'])
else:
# Otherwise, calculate and weight self-supervised loss
self_sup_output = SelfSupModel.forward(
self, batch, return_logs=return_logs, progress=progress, **kwargs)
loss = (1.0 - self.supervised_loss_weight) * self_sup_output['loss']
# Calculate and weight supervised loss
sup_output = self.supervised_loss(
self_sup_output['inv_depths'], depth2inv(batch['depth']),
return_logs=return_logs, progress=progress)
loss += self.supervised_loss_weight * sup_output['loss']
if 'inv_depths_rgbd' in self_sup_output:
sup_output2 = self.supervised_loss(
self_sup_output['inv_depths_rgbd'], depth2inv(batch['depth']),
return_logs=return_logs, progress=progress)
loss += self.weight_rgbd * self.supervised_loss_weight * sup_output2['loss']
if 'depth_loss' in self_sup_output:
loss += self_sup_output['depth_loss']
# Merge and return outputs
return {
'loss': loss,
**merge_outputs(self_sup_output, sup_output),
}
def forward(self, batch, return_logs=False, progress=0.0):
"""
Processes a batch.
Parameters
----------
batch : dict
Input batch
return_logs : bool
True if logs are stored
progress :
Training progress percentage
Returns
-------
output : dict
Dictionary containing a "loss" scalar and different metrics and predictions
for logging and downstream usage.
"""
if not self.training:
# If not training, no need for self-supervised loss
return SfmModel.forward(self, batch)
else:
# Calculate predicted depth and pose output
output = super().forward(batch, return_logs=return_logs)
# Introduce poses ground_truth
poses_gt = [[], []]
poses_gt[0], poses_gt[1] = torch.zeros((6, 4, 4)), torch.zeros((6, 4, 4))
for i in range(6):
poses_gt[0][i] = batch['pose_context'][0][i].inverse() @ batch['pose'][i]
poses_gt[1][i] = batch['pose_context'][1][i].inverse() @ batch['pose'][i]
poses_gt = [Pose(poses_gt[0]), Pose(poses_gt[1])]
multiview_loss = self.multiview_photometric_loss(
batch['rgb_original'], batch['rgb_context_original'],
output['inv_depths'], poses_gt, batch['intrinsics'], batch['extrinsics'],
return_logs=return_logs, progress=progress)
# loss = multiview_loss['loss']
loss = 0.
# Calculate supervised loss
supervision_loss = self.supervised_loss(output['inv_depths'], depth2inv(batch['depth']),
return_logs=return_logs, progress=progress)
loss += self.supervised_loss_weight * supervision_loss['loss']
# Return loss and metrics
return {
'loss': loss,
**merge_outputs(merge_outputs(multiview_loss, supervision_loss), output)
}
def forward(self,
image,
context,
inv_depths,
poses,
path_to_ego_mask,
path_to_ego_mask_context,
K,
ref_K,
extrinsics,
ref_extrinsics,
context_type,
return_logs=False,
progress=0.0):
"""
Calculates training photometric loss.
Parameters
----------
image : torch.Tensor [B,3,H,W]
Original image
context : list of torch.Tensor [B,3,H,W]
Context containing a list of reference images
inv_depths : list of torch.Tensor [B,1,H,W]
Predicted depth maps for the original image, in all scales
K : torch.Tensor [B,3,3]
Original camera intrinsics
ref_K : torch.Tensor [B,3,3]
Reference camera intrinsics
poses : list of Pose
Camera transformation between original and context
return_logs : bool
True if logs are saved for visualization
progress : float
Training percentage
Returns
-------
losses_and_metrics : dict
Output dictionary
"""
# If using progressive scaling
self.n = self.progressive_scaling(progress)
# Loop over all reference images
photometric_losses = [[] for _ in range(self.n)]
images = match_scales(image, inv_depths, self.n)
inv_depths2 = []
for k in range(len(inv_depths)):
Btmp, C, H, W = inv_depths[k].shape
depths2 = torch.zeros_like(inv_depths[k])
for i in range(H):
for j in range(W):
depths2[
0, 0, i,
j] = 2.0 #(2/H)**4 * (2/W)**4 * i**2 * (H - i)**2 * j**2 * (W - j)**2 * 20
inv_depths2.append(depth2inv(depths2))
if is_list(context_type[0][0]):
n_context = len(context_type[0])
else:
n_context = len(context_type)
#n_context = len(context)
device = image.get_device()
B = len(path_to_ego_mask)
if is_list(path_to_ego_mask[0]):
H_full, W_full = np.load(path_to_ego_mask[0][0]).shape
else:
H_full, W_full = np.load(path_to_ego_mask[0]).shape
# getting ego masks for target and source cameras
# fullsize mask
ego_mask_tensor = torch.ones(B, 1, H_full, W_full).to(device)
ref_ego_mask_tensor = []
for i_context in range(n_context):
ref_ego_mask_tensor.append(
torch.ones(B, 1, H_full, W_full).to(device))
for b in range(B):
if self.mask_ego:
if is_list(path_to_ego_mask[b]):
ego_mask_tensor[b, 0] = torch.from_numpy(
np.load(path_to_ego_mask[b][0])).float()
else:
ego_mask_tensor[b, 0] = torch.from_numpy(
np.load(path_to_ego_mask[b])).float()
for i_context in range(n_context):
if is_list(path_to_ego_mask_context[0][0]):
paths_context_ego = [
p[i_context][0] for p in path_to_ego_mask_context
]
else:
paths_context_ego = path_to_ego_mask_context[i_context]
ref_ego_mask_tensor[i_context][b, 0] = torch.from_numpy(
np.load(paths_context_ego[b])).float()
# resized masks
ego_mask_tensors = []
ref_ego_mask_tensors = []
for i_context in range(n_context):
ref_ego_mask_tensors.append([])
for i in range(self.n):
Btmp, C, H, W = images[i].shape
if W < W_full:
#inv_scale_factor = int(W_full / W)
#print(W_full / W)
#ego_mask_tensors.append(-nn.MaxPool2d(inv_scale_factor, inv_scale_factor)(-ego_mask_tensor))
ego_mask_tensors.append(
interpolate_image(ego_mask_tensor,
shape=(Btmp, 1, H, W),
mode='nearest',
align_corners=None))
for i_context in range(n_context):
#ref_ego_mask_tensors[i_context].append(-nn.MaxPool2d(inv_scale_factor, inv_scale_factor)(-ref_ego_mask_tensor[i_context]))
ref_ego_mask_tensors[i_context].append(
interpolate_image(ref_ego_mask_tensor[i_context],
shape=(Btmp, 1, H, W),
mode='nearest',
align_corners=None))
else:
ego_mask_tensors.append(ego_mask_tensor)
for i_context in range(n_context):
ref_ego_mask_tensors[i_context].append(
ref_ego_mask_tensor[i_context])
for i_context in range(n_context):
_, C, H, W = context[i_context].shape
if W < W_full:
inv_scale_factor = int(W_full / W)
#ref_ego_mask_tensor[i_context] = -nn.MaxPool2d(inv_scale_factor, inv_scale_factor)(-ref_ego_mask_tensor[i_context])
ref_ego_mask_tensor[i_context] = interpolate_image(
ref_ego_mask_tensor[i_context],
shape=(Btmp, 1, H, W),
mode='nearest',
align_corners=None)
print(ref_extrinsics)
for j, (ref_image, pose) in enumerate(zip(context, poses)):
print(ref_extrinsics[j])
ref_context_type = [c[j][0] for c in context_type] if is_list(
context_type[0][0]) else context_type[j]
print(ref_context_type)
print(pose.mat)
# Calculate warped images
ref_warped, ref_ego_mask_tensors_warped = self.warp_ref_image(
inv_depths2, ref_image, ref_ego_mask_tensor[j], K,
ref_K[:, j, :, :], pose, ref_extrinsics[j], ref_context_type)
print(pose.mat)
# Calculate and store image loss
photometric_loss = self.calc_photometric_loss(ref_warped, images)
tt = str(int(time.time() % 10000))
for i in range(self.n):
B, C, H, W = images[i].shape
for b in range(B):
orig_PIL_0 = torch.transpose(
(ref_image[b, :, :, :]).unsqueeze(0).unsqueeze(4), 1,
4).squeeze().detach().cpu().numpy()
orig_PIL = torch.transpose(
(ref_image[b, :, :, :] *
ref_ego_mask_tensor[j][b, :, :, :]
).unsqueeze(0).unsqueeze(4), 1,
4).squeeze().detach().cpu().numpy()
warped_PIL_0 = torch.transpose(
(ref_warped[i][b, :, :, :]).unsqueeze(0).unsqueeze(4),
1, 4).squeeze().detach().cpu().numpy()
warped_PIL = torch.transpose(
(ref_warped[i][b, :, :, :] *
ego_mask_tensors[i][b, :, :, :]
).unsqueeze(0).unsqueeze(4), 1,
4).squeeze().detach().cpu().numpy()
target_PIL_0 = torch.transpose(
(images[i][b, :, :, :]).unsqueeze(0).unsqueeze(4), 1,
4).squeeze().detach().cpu().numpy()
target_PIL = torch.transpose(
(images[i][b, :, :, :] *
ego_mask_tensors[i][b, :, :, :]
).unsqueeze(0).unsqueeze(4), 1,
4).squeeze().detach().cpu().numpy()
cv2.imwrite(
'/home/users/vbelissen/test' + '_' + str(j) + '_' +
tt + '_' + str(b) + '_' + str(i) + '_orig_PIL_0.png',
orig_PIL_0 * 255)
cv2.imwrite(
'/home/users/vbelissen/test' + '_' + str(j) + '_' +
tt + '_' + str(b) + '_' + str(i) + '_orig_PIL.png',
orig_PIL * 255)
cv2.imwrite(
'/home/users/vbelissen/test' + '_' + str(j) + '_' +
tt + '_' + str(b) + '_' + str(i) + '_warped_PIL_0.png',
warped_PIL_0 * 255)
cv2.imwrite(
'/home/users/vbelissen/test' + '_' + str(j) + '_' +
tt + '_' + str(b) + '_' + str(i) + '_warped_PIL.png',
warped_PIL * 255)
cv2.imwrite(
'/home/users/vbelissen/test' + '_' + str(j) + '_' +
tt + '_' + str(b) + '_' + str(i) + '_target_PIL_0.png',
target_PIL_0 * 255)
cv2.imwrite(
'/home/users/vbelissen/test' + '_' + str(j) + '_' +
tt + '_' + str(b) + '_' + str(i) + '_target_PIL.png',
target_PIL * 255)
for i in range(self.n):
photometric_losses[i].append(photometric_loss[i] *
ego_mask_tensors[i] *
ref_ego_mask_tensors_warped[i])
# If using automask
if self.automask_loss:
# Calculate and store unwarped image loss
ref_images = match_scales(ref_image, inv_depths, self.n)
unwarped_image_loss = self.calc_photometric_loss(
ref_images, images)
for i in range(self.n):
photometric_losses[i].append(unwarped_image_loss[i] *
ego_mask_tensors[i] *
ref_ego_mask_tensors[j][i])
# Calculate reduced photometric loss
loss = self.nonzero_reduce_photometric_loss(photometric_losses)
# Include smoothness loss if requested
if self.smooth_loss_weight > 0.0:
loss += self.calc_smoothness_loss(
[a * b for a, b in zip(inv_depths, ego_mask_tensors)],
[a * b for a, b in zip(images, ego_mask_tensors)])
# Return losses and metrics
return {
'loss': loss.unsqueeze(0),
'metrics': self.metrics,
}
mode='bilinear',
padding_mode='zeros',
align_corners=True)
warped_right_front_PIL = torch.transpose(warped_right_front.unsqueeze(4), 1,
4).squeeze().cpu().numpy()
cv2.imwrite('/home/users/vbelissen/test' + tt + '_right_front.png',
warped_right_front_PIL[:, :, ::-1])
simulated_depth_right_to_front = funct.grid_sample(simulated_depth_right,
ref_coords_right,
mode='bilinear',
padding_mode='zeros',
align_corners=True)
viz_pred_inv_depth_front = viz_inv_depth(depth2inv(simulated_depth)[0],
normalizer=1.0) * 255
viz_pred_inv_depth_right = viz_inv_depth(depth2inv(simulated_depth_right)[0],
normalizer=1.0) * 255
viz_pred_inv_depth_right_to_front = viz_inv_depth(
depth2inv(simulated_depth_right_to_front)[0], normalizer=1.0) * 255
world_points_right_in_front_coords = cam_front.Tcw @ world_points_right
simulated_depth_right_in_front_coords = torch.norm(
world_points_right_in_front_coords, dim=1, keepdim=True)
simulated_depth_right_in_front_coords[~not_masked_right] = 0
simulated_depth_right_to_front_in_front_coords = funct.grid_sample(
simulated_depth_right_in_front_coords,
ref_coords_right,
mode='bilinear',
padding_mode='zeros',
def warp_ref(self, inv_depths, ref_image, ref_tensor, ref_inv_depths,
path_to_theta_lut, path_to_ego_mask, poly_coeffs, principal_point, scale_factors,
ref_path_to_theta_lut, ref_path_to_ego_mask, ref_poly_coeffs, ref_principal_point,
ref_scale_factors,
same_timestamp_as_origin,
pose_matrix_context,
pose,
warp_ref_depth,
allow_context_rotation):
"""
Warps a reference image to produce a reconstruction of the original one.
Parameters
----------
inv_depths : torch.Tensor [B,1,H,W]
Inverse depth map of the original image
ref_image : torch.Tensor [B,3,H,W]
Reference RGB image
K : torch.Tensor [B,3,3]
Original camera intrinsics
ref_K : torch.Tensor [B,3,3]
Reference camera intrinsics
pose : Pose
Original -> Reference camera transformation
Returns
-------
ref_warped : torch.Tensor [B,3,H,W]
Warped reference image (reconstructing the original one)
"""
B, _, H, W = ref_image.shape
device = ref_image.get_device()
# Generate cameras for all scales
cams, ref_cams = [], []
if allow_context_rotation:
pose_matrix = torch.zeros(B, 4, 4)
for b in range(B):
if same_timestamp_as_origin[b]:
pose_matrix[b, :3, 3] = pose.mat[b, :3, :3] @ pose_matrix_context[b, :3, 3]
pose_matrix[b, 3, 3] = 1
pose_matrix[b, :3, :3] = pose.mat[b, :3, :3] @ pose_matrix_context[b, :3, :3]
else:
pose_matrix[b, :, :] = pose.mat[b, :, :]
else:
for b in range(B):
if same_timestamp_as_origin[b]:
pose.mat[b, :, :] = pose_matrix_context[b, :, :]
for i in range(self.n):
_, _, DH, DW = inv_depths[i].shape
scale_factor = DW / float(W)
cams.append(CameraFisheye(path_to_theta_lut=path_to_theta_lut,
path_to_ego_mask=path_to_ego_mask,
poly_coeffs=poly_coeffs.float(),
principal_point=principal_point.float(),
scale_factors=scale_factors.float()).scaled(scale_factor).to(device))
ref_cams.append(CameraFisheye(path_to_theta_lut=ref_path_to_theta_lut,
path_to_ego_mask=ref_path_to_ego_mask,
poly_coeffs=ref_poly_coeffs.float(),
principal_point=ref_principal_point.float(),
scale_factors=ref_scale_factors.float(),
Tcw=pose_matrix if allow_context_rotation else pose).scaled(scale_factor).to(device))
# View synthesis
depths = [inv2depth(inv_depths[i]) for i in range(self.n)]
ref_images = match_scales(ref_image, inv_depths, self.n)
if warp_ref_depth:
ref_depths = [inv2depth(ref_inv_depths[i]) for i in range(self.n)]
ref_warped = []
depths_wrt_ref_cam = []
ref_depths_warped = []
for i in range(self.n):
view_i, depth_wrt_ref_cam_i, ref_depth_warped_i \
= view_depth_synthesis2(ref_images[i], depths[i], ref_depths[i], ref_cams[i], cams[i], padding_mode=self.padding_mode)
ref_warped.append(view_i)
depths_wrt_ref_cam.append(depth_wrt_ref_cam_i)
ref_depths_warped.append(ref_depth_warped_i)
inv_depths_wrt_ref_cam = [depth2inv(depths_wrt_ref_cam[i]) for i in range(self.n)]
ref_inv_depths_warped = [depth2inv(ref_depths_warped[i]) for i in range(self.n)]
else:
ref_warped = [view_synthesis(ref_images[i], depths[i], ref_cams[i], cams[i], padding_mode=self.padding_mode) for i in range(self.n)]
inv_depths_wrt_ref_cam = None
ref_inv_depths_warped = None
ref_tensors = match_scales(ref_tensor, inv_depths, self.n, mode='nearest', align_corners=None)
ref_tensors_warped = [view_synthesis(
ref_tensors[i], depths[i], ref_cams[i], cams[i],
padding_mode=self.padding_mode, mode='nearest', align_corners=None) for i in range(self.n)]
# Return warped reference image
return ref_warped, ref_tensors_warped, inv_depths_wrt_ref_cam, ref_inv_depths_warped
def infer_optimal_calib(input_files, model_wrappers, image_shape):
"""
Process a list of input files to infer correction in extrinsic calibration.
Files should all correspond to the same car.
Number of cameras is assumed to be 4 or 5.
Parameters
----------
input_file : list (number of cameras) of lists (number of files) of str
Image file
model_wrappers : nn.Module
Model wrappers used for inference
image_shape : Image shape
Input image shape
"""
N_files = len(input_files[0])
N_cams = len(input_files)
image_area = image_shape[0] * image_shape[1]
camera_context_pairs = CAMERA_CONTEXT_PAIRS[N_cams]
# Rotation will be optimized if not all cams are frozen
optimize_rotation = (args.frozen_cams_rot != [i for i in range(N_cams)])
# Rotation will be optimized if not all cams are frozen
optimize_translation = (args.frozen_cams_trans !=
[i for i in range(N_cams)])
calib_data = {}
for i_cam in range(N_cams):
base_folder_str = get_base_folder(input_files[i_cam][0])
split_type_str = get_split_type(input_files[i_cam][0])
seq_name_str = get_sequence_name(input_files[i_cam][0])
camera_str = get_camera_name(input_files[i_cam][0])
calib_data[camera_str] = read_raw_calib_files_camera_valeo_with_suffix(
base_folder_str, split_type_str, seq_name_str, camera_str,
args.calibrations_suffix)
cams = []
cams_untouched = []
not_masked = []
# Assume all images are from the same sequence (thus same cameras)
for i_cam in range(N_cams):
path_to_ego_mask = get_path_to_ego_mask(input_files[i_cam][0])
poly_coeffs, principal_point, scale_factors, K, k, p = get_full_intrinsics(
input_files[i_cam][0], calib_data)
poly_coeffs_untouched = torch.from_numpy(poly_coeffs).unsqueeze(0)
principal_point_untouched = torch.from_numpy(
principal_point).unsqueeze(0)
scale_factors_untouched = torch.from_numpy(scale_factors).unsqueeze(0)
K_untouched = torch.from_numpy(K).unsqueeze(0)
k_untouched = torch.from_numpy(k).unsqueeze(0)
p_untouched = torch.from_numpy(p).unsqueeze(0)
pose_matrix_untouched = torch.from_numpy(
get_extrinsics_pose_matrix(input_files[i_cam][0],
calib_data)).unsqueeze(0)
pose_tensor_untouched = Pose(pose_matrix_untouched)
camera_type_untouched = get_camera_type(input_files[i_cam][0],
calib_data)
camera_type_int_untouched = torch.tensor(
[get_camera_type_int(camera_type_untouched)])
cams.append(
CameraMultifocal(poly_coeffs=poly_coeffs_untouched.float(),
principal_point=principal_point_untouched.float(),
scale_factors=scale_factors_untouched.float(),
K=K_untouched.float(),
k1=k_untouched[:, 0].float(),
k2=k_untouched[:, 1].float(),
k3=k_untouched[:, 2].float(),
p1=p_untouched[:, 0].float(),
p2=p_untouched[:, 1].float(),
camera_type=camera_type_int_untouched,
Tcw=pose_tensor_untouched))
cams_untouched.append(
CameraMultifocal(poly_coeffs=poly_coeffs_untouched.float(),
principal_point=principal_point_untouched.float(),
scale_factors=scale_factors_untouched.float(),
K=K_untouched.float(),
k1=k_untouched[:, 0].float(),
k2=k_untouched[:, 1].float(),
k3=k_untouched[:, 2].float(),
p1=p_untouched[:, 0].float(),
p2=p_untouched[:, 1].float(),
camera_type=camera_type_int_untouched,
Tcw=pose_tensor_untouched))
if torch.cuda.is_available():
cams[i_cam] = cams[i_cam].to('cuda:{}'.format(rank()))
cams_untouched[i_cam] = cams_untouched[i_cam].to('cuda:{}'.format(
rank()))
ego_mask = np.load(path_to_ego_mask)
not_masked.append(
torch.from_numpy(ego_mask.astype(float)).cuda().float())
# Learning variables
extra_trans_m = [
torch.autograd.Variable(torch.zeros(3).cuda(), requires_grad=True)
for _ in range(N_cams)
]
extra_rot_deg = [
torch.autograd.Variable(torch.zeros(3).cuda(), requires_grad=True)
for _ in range(N_cams)
]
# Constraints: translation
frozen_cams_trans = args.frozen_cams_trans
if frozen_cams_trans is not None:
for i_cam in frozen_cams_trans:
extra_trans_m[i_cam].requires_grad = False
# Constraints: rotation
frozen_cams_rot = args.frozen_cams_rot
if frozen_cams_rot is not None:
for i_cam in frozen_cams_rot:
extra_rot_deg[i_cam].requires_grad = False
# Parameters from argument parser
save_pictures = args.save_pictures
n_epochs = args.n_epochs
learning_rate = args.lr
step_size = args.scheduler_step_size
gamma = args.scheduler_gamma
# Table of loss
loss_tab = np.zeros(n_epochs)
# Table of extra rotation values
extra_rot_values_tab = np.zeros((N_cams * 3, N_files * n_epochs))
# Table of extra translation values
extra_trans_values_tab = np.zeros((N_cams * 3, N_files * n_epochs))
# Optimizer
optimizer = optim.Adam(extra_trans_m + extra_rot_deg, lr=learning_rate)
# Scheduler
scheduler = optim.lr_scheduler.StepLR(optimizer,
step_size=step_size,
gamma=gamma)
# Regularization weights
regul_weight_trans = torch.tensor(args.regul_weight_trans).cuda()
regul_weight_rot = torch.tensor(args.regul_weight_rot).cuda()
regul_weight_overlap = torch.tensor(args.regul_weight_overlap).cuda()
# Loop on the number of epochs
count = 0
for epoch in range(n_epochs):
print('Epoch ' + str(epoch) + '/' + str(n_epochs))
# Initialize loss
loss_sum = 0
# Loop on the number of files
for i_file in range(N_files):
print('')
# Filename for camera 0
base_0, ext_0 = os.path.splitext(
os.path.basename(input_files[0][i_file]))
print(base_0)
# Initialize list of tensors: images, predicted inverse depths and predicted depths
images, pred_inv_depths, pred_depths = [], [], []
input_depth_files, has_gt_depth, gt_depth, gt_inv_depth = [], [], [], []
nb_gt_depths = 0
# Reset camera poses Twc
CameraMultifocal.Twc.fget.cache_clear()
# Loop on cams and predict depth
for i_cam in range(N_cams):
images.append(
load_image(input_files[i_cam][i_file]).convert('RGB'))
images[i_cam] = resize_image(images[i_cam], image_shape)
images[i_cam] = to_tensor(images[i_cam]).unsqueeze(0)
if torch.cuda.is_available():
images[i_cam] = images[i_cam].to('cuda:{}'.format(rank()))
with torch.no_grad():
pred_inv_depths.append(model_wrappers[i_cam].depth(
images[i_cam]))
pred_depths.append(inv2depth(pred_inv_depths[i_cam]))
if args.use_lidar:
input_depth_files.append(
get_depth_file(input_files[i_cam][i_file],
args.depth_suffix))
has_gt_depth.append(
os.path.exists(input_depth_files[i_cam]))
if has_gt_depth[i_cam]:
nb_gt_depths += 1
gt_depth.append(
np.load(input_depth_files[i_cam])
['velodyne_depth'].astype(np.float32))
gt_depth[i_cam] = torch.from_numpy(
gt_depth[i_cam]).unsqueeze(0).unsqueeze(0)
gt_inv_depth.append(depth2inv(gt_depth[i_cam]))
if torch.cuda.is_available():
gt_depth[i_cam] = gt_depth[i_cam].to(
'cuda:{}'.format(rank()))
gt_inv_depth[i_cam] = gt_inv_depth[i_cam].to(
'cuda:{}'.format(rank()))
else:
gt_depth.append(0)
gt_inv_depth.append(0)
# Apply correction on cams
pose_matrix = get_extrinsics_pose_matrix_extra_trans_rot_torch(
input_files[i_cam][i_file], calib_data,
extra_trans_m[i_cam], extra_rot_deg[i_cam]).unsqueeze(0)
pose_tensor = Pose(pose_matrix).to('cuda:{}'.format(rank()))
cams[i_cam].Tcw = pose_tensor
# Define a loss function between 2 images
def photo_loss_2imgs(i_cam1, i_cam2, save_pictures):
# Computes the photometric loss between 2 images of adjacent cameras
# It reconstructs each image from the adjacent one, applying correction in rotation and translation
# Reconstruct 3D points for each cam
world_points1 = cams[i_cam1].reconstruct(pred_depths[i_cam1],
frame='w')
world_points2 = cams[i_cam2].reconstruct(pred_depths[i_cam2],
frame='w')
# Get coordinates of projected points on other cam
ref_coords1to2 = cams[i_cam2].project(world_points1, frame='w')
ref_coords2to1 = cams[i_cam1].project(world_points2, frame='w')
# Reconstruct each image from the adjacent camera
reconstructedImg2to1 = funct.grid_sample(images[i_cam2] *
not_masked[i_cam2],
ref_coords1to2,
mode='bilinear',
padding_mode='zeros',
align_corners=True)
reconstructedImg1to2 = funct.grid_sample(images[i_cam1] *
not_masked[i_cam1],
ref_coords2to1,
mode='bilinear',
padding_mode='zeros',
align_corners=True)
# Save pictures if requested
if save_pictures:
# Save original files if first epoch
if epoch == 0:
cv2.imwrite(
args.save_folder + '/cam_' + str(i_cam1) +
'_file_' + str(i_file) + '_orig.png',
(images[i_cam1][0].permute(1, 2, 0)
)[:, :, [2, 1, 0]].detach().cpu().numpy() * 255)
cv2.imwrite(
args.save_folder + '/cam_' + str(i_cam2) +
'_file_' + str(i_file) + '_orig.png',
(images[i_cam2][0].permute(1, 2, 0)
)[:, :, [2, 1, 0]].detach().cpu().numpy() * 255)
# Save reconstructed images
cv2.imwrite(
args.save_folder + '/epoch_' + str(epoch) + '_file_' +
str(i_file) + '_cam_' + str(i_cam1) + '_recons_from_' +
str(i_cam2) + '.png',
((reconstructedImg2to1 *
not_masked[i_cam1])[0].permute(1, 2, 0)
)[:, :, [2, 1, 0]].detach().cpu().numpy() * 255)
cv2.imwrite(
args.save_folder + '/epoch_' + str(epoch) + '_file_' +
str(i_file) + '_cam_' + str(i_cam2) + '_recons_from_' +
str(i_cam1) + '.png',
((reconstructedImg1to2 *
not_masked[i_cam2])[0].permute(1, 2, 0)
)[:, :, [2, 1, 0]].detach().cpu().numpy() * 255)
# L1 loss
l1_loss_1 = torch.abs(images[i_cam1] * not_masked[i_cam1] -
reconstructedImg2to1 *
not_masked[i_cam1])
l1_loss_2 = torch.abs(images[i_cam2] * not_masked[i_cam2] -
reconstructedImg1to2 *
not_masked[i_cam2])
# SSIM loss
ssim_loss_weight = 0.85
ssim_loss_1 = SSIM(images[i_cam1] * not_masked[i_cam1],
reconstructedImg2to1 * not_masked[i_cam1],
C1=1e-4,
C2=9e-4,
kernel_size=3)
ssim_loss_2 = SSIM(images[i_cam2] * not_masked[i_cam2],
reconstructedImg1to2 * not_masked[i_cam2],
C1=1e-4,
C2=9e-4,
kernel_size=3)
ssim_loss_1 = torch.clamp((1. - ssim_loss_1) / 2., 0., 1.)
ssim_loss_2 = torch.clamp((1. - ssim_loss_2) / 2., 0., 1.)
# Photometric loss: alpha * ssim + (1 - alpha) * l1
photometric_loss_1 = ssim_loss_weight * ssim_loss_1.mean(
1, True) + (1 - ssim_loss_weight) * l1_loss_1.mean(
1, True)
photometric_loss_2 = ssim_loss_weight * ssim_loss_2.mean(
1, True) + (1 - ssim_loss_weight) * l1_loss_2.mean(
1, True)
# Compute the number of valid pixels
mask1 = (reconstructedImg2to1 * not_masked[i_cam1]).sum(
axis=1, keepdim=True) != 0
s1 = mask1.sum().float()
mask2 = (reconstructedImg1to2 * not_masked[i_cam2]).sum(
axis=1, keepdim=True) != 0
s2 = mask2.sum().float()
# Compute the photometric losses weighed by the number of valid pixels
loss_1 = (photometric_loss_1 *
mask1).sum() / s1 if s1 > 0 else 0
loss_2 = (photometric_loss_2 *
mask2).sum() / s2 if s2 > 0 else 0
# The final loss can be regularized to encourage a similar overlap between images
if s1 > 0 and s2 > 0:
return loss_1 + loss_2 + regul_weight_overlap * image_area * (
1 / s1 + 1 / s2)
else:
return 0.
def lidar_loss(i_cam1, save_pictures):
if args.use_lidar and has_gt_depth[i_cam1]:
mask_zeros_lidar = (
gt_depth[i_cam1][0, 0, :, :] == 0).detach()
# Ground truth sparse depth maps were generated using the untouched camera extrinsics
world_points_gt_oldCalib = cams_untouched[
i_cam1].reconstruct(gt_depth[i_cam1], frame='w')
world_points_gt_oldCalib[0, 0, mask_zeros_lidar] = 0.
# Get coordinates of projected points on new cam
ref_coords = cams[i_cam1].project(world_points_gt_oldCalib,
frame='w')
ref_coords[0, mask_zeros_lidar, :] = 0.
# Reconstruct projected lidar from the new camera
reprojected_gt_inv_depth = funct.grid_sample(
gt_inv_depth[i_cam1],
ref_coords,
mode='nearest',
padding_mode='zeros',
align_corners=True)
reprojected_gt_inv_depth[0, 0, mask_zeros_lidar] = 0.
mask_reprojected = (reprojected_gt_inv_depth > 0.).detach()
if save_pictures:
mask_reprojected_numpy = mask_reprojected[
0, 0, :, :].cpu().numpy()
u = np.where(mask_reprojected_numpy)[0]
v = np.where(mask_reprojected_numpy)[1]
n_lidar = u.size
reprojected_gt_depth_numpy = inv2depth(
reprojected_gt_inv_depth)[
0, 0, :, :].detach().cpu().numpy()
im = (images[i_cam1][0].permute(
1, 2, 0))[:, :,
[2, 1, 0]].detach().cpu().numpy() * 255
dmax = 100.
for i_l in range(n_lidar):
d = reprojected_gt_depth_numpy[u[i_l], v[i_l]]
s = int((8 / d)) + 1
im[u[i_l] - s:u[i_l] + s, v[i_l] - s:v[i_l] + s,
0] = np.clip(
np.power(d / dmax, .7) * 255, 10, 245)
im[u[i_l] - s:u[i_l] + s, v[i_l] - s:v[i_l] + s,
1] = np.clip(
np.power((dmax - d) / dmax, 4.0) * 255, 10,
245)
im[u[i_l] - s:u[i_l] + s, v[i_l] - s:v[i_l] + s,
2] = np.clip(
np.power(np.abs(2 * (d - .5 * dmax) / dmax),
3.0) * 255, 10, 245)
cv2.imwrite(
args.save_folder + '/epoch_' + str(epoch) +
'_file_' + str(i_file) + '_cam_' + str(i_cam1) +
'_lidar.png', im)
if mask_reprojected.sum() > 0:
return l1_lidar_loss(
pred_inv_depths[i_cam1] * not_masked[i_cam1],
reprojected_gt_inv_depth * not_masked[i_cam1])
else:
return 0.
else:
return 0.
if nb_gt_depths > 0:
final_lidar_weight = (N_cams /
nb_gt_depths) * args.lidar_weight
else:
final_lidar_weight = 0.
# The final loss consists of summing the photometric loss of all pairs of adjacent cameras
# and is regularized to prevent weights from exploding
photo_loss = 1.0 * sum([
photo_loss_2imgs(p[0], p[1], save_pictures)
for p in camera_context_pairs
])
regul_rot_loss = regul_weight_rot * sum(
[(extra_rot_deg[i]**2).sum() for i in range(N_cams)])
regul_trans_loss = regul_weight_trans * sum(
[(extra_trans_m[i]**2).sum() for i in range(N_cams)])
lidar_gt_loss = final_lidar_weight * sum(
[lidar_loss(i, save_pictures) for i in range(N_cams)])
loss = photo_loss + regul_rot_loss + regul_trans_loss + lidar_gt_loss
with torch.no_grad():
extra_rot_deg_before = []
extra_trans_m_before = []
for i_cam in range(N_cams):
extra_rot_deg_before.append(extra_rot_deg[i_cam].clone())
extra_trans_m_before.append(extra_trans_m[i_cam].clone())
# Optimization steps
optimizer.zero_grad()
loss.backward()
optimizer.step()
with torch.no_grad():
extra_rot_deg_after = []
extra_trans_m_after = []
for i_cam in range(N_cams):
extra_rot_deg_after.append(extra_rot_deg[i_cam].clone())
extra_trans_m_after.append(extra_trans_m[i_cam].clone())
rot_change_file = 0.
trans_change_file = 0.
for i_cam in range(N_cams):
rot_change_file += torch.abs(
extra_rot_deg_after[i_cam] -
extra_rot_deg_before[i_cam]).mean().item()
trans_change_file += torch.abs(
extra_trans_m_after[i_cam] -
extra_trans_m_before[i_cam]).mean().item()
rot_change_file /= N_cams
trans_change_file /= N_cams
print('Average rotation change (deg.): ' +
"{:.4f}".format(rot_change_file))
print('Average translation change (m.): ' +
"{:.4f}".format(trans_change_file))
# Save correction values and print loss
with torch.no_grad():
loss_sum += loss.item()
for i_cam in range(N_cams):
for j in range(3):
if optimize_rotation:
extra_rot_values_tab[
3 * i_cam + j,
count] = extra_rot_deg[i_cam][j].item()
if optimize_translation:
extra_trans_values_tab[
3 * i_cam + j,
count] = extra_trans_m[i_cam][j].item()
print('Loss: ' + "{:.3f}".format(loss.item()) \
+ ' (photometric: ' + "{:.3f}".format(photo_loss.item()) \
+ ', rotation reg.: ' + "{:.4f}".format(regul_rot_loss.item()) \
+ ', translation reg.: ' + "{:.4f}".format(regul_trans_loss.item())
+ ', lidar: ' + "{:.3f}".format(lidar_gt_loss) +')')
if nb_gt_depths > 0:
print('Number of ground truth lidar maps: ' +
str(nb_gt_depths))
count += 1
# Update scheduler
print('Epoch:', epoch, 'LR:', scheduler.get_lr())
scheduler.step()
with torch.no_grad():
print('End of epoch')
if optimize_translation:
print('New translation correction values: ')
print(extra_trans_m)
if optimize_rotation:
print('New rotation correction values: ')
print(extra_rot_deg)
print('Average rotation change in epoch:')
loss_tab[epoch] = loss_sum / N_files
# Plot/save loss if requested
plt.figure()
plt.plot(loss_tab)
if args.show_plots:
plt.show()
if args.save_plots:
plt.savefig(
os.path.join(args.save_folder,
get_sequence_name(input_files[0][0]) + '_loss.png'))
# Plot/save correction values if requested
if optimize_rotation:
plt.figure()
for j in range(N_cams * 3):
plt.plot(extra_rot_values_tab[j])
if args.show_plots:
plt.show()
if args.save_plots:
plt.savefig(
os.path.join(
args.save_folder,
get_sequence_name(input_files[0][0]) + '_extra_rot.png'))
if optimize_translation:
plt.figure()
for j in range(N_cams * 3):
plt.plot(extra_trans_values_tab[j])
if args.show_plots:
plt.show()
if args.save_plots:
plt.savefig(
os.path.join(
args.save_folder,
get_sequence_name(input_files[0][0]) + '_extra_trans.png'))
# Save correction values table if requested
if args.save_rot_tab:
np.save(
os.path.join(args.save_folder,
get_sequence_name(input_files[0][0]) +
'_rot_tab.npy'), extra_rot_values_tab)
