def main(_config, seed):
global EPOCH, weights
if _config['weight'] is not None:
weights = _config['weight']
dataset_class = DatasetVisibilityKittiSingle
img_shape = (384, 1280)
split = 'test'
if _config['random_initial_pose']:
split = 'test_random'
maps_folder = 'local_maps'
if _config['maps_folder'] is not None:
maps_folder = _config['maps_folder']
if _config['test_sequence'] is None:
raise TypeError('test_sequences cannot be None')
else:
if isinstance(_config['test_sequence'], int):
_config['test_sequence'] = f"{_config['test_sequence']:02d}"
dataset_val = dataset_class(_config['data_folder'],
max_r=_config['max_r'],
max_t=_config['max_t'],
split=split,
use_reflectance=_config['use_reflectance'],
maps_folder=maps_folder,
test_sequence=_config['test_sequence'])
np.random.seed(seed)
torch.random.manual_seed(seed)
def init_fn(x):
return _init_fn(x, seed)
num_worker = 6
batch_size = 1
TestImgLoader = torch.utils.data.DataLoader(dataset=dataset_val,
shuffle=False,
batch_size=batch_size,
num_workers=num_worker,
worker_init_fn=init_fn,
collate_fn=merge_inputs,
drop_last=False,
pin_memory=False)
print(len(TestImgLoader))
models = []
for i in range(len(weights)):
if _config['network'].startswith('PWC'):
feat = 1
md = 4
split = _config['network'].split('_')
for item in split[1:]:
if item.startswith('f'):
feat = int(item[-1])
elif item.startswith('md'):
md = int(item[2:])
assert 0 < feat < 7, "Feature Number from PWC have to be between 1 and 6"
assert 0 < md, "md must be positive"
model = CMRNet(img_shape,
use_feat_from=feat,
md=md,
use_reflectance=_config['use_reflectance'])
else:
raise TypeError("Network unknown")
checkpoint = torch.load(weights[i], map_location='cpu')
saved_state_dict = checkpoint['state_dict']
model.load_state_dict(saved_state_dict)
model = model.to(device)
model.eval()
models.append(model)
if i == 0:
_config['occlusion_threshold'] = checkpoint['config'][
'occlusion_threshold']
_config['occlusion_kernel'] = checkpoint['config'][
'occlusion_kernel']
else:
assert _config['occlusion_threshold'] == checkpoint['config'][
'occlusion_threshold']
assert _config['occlusion_kernel'] == checkpoint['config'][
'occlusion_kernel']
if _config['save_log']:
log_file = f'./results_for_paper/log_seq{_config["test_sequence"]}.csv'
log_file = open(log_file, 'w')
log_file = csv.writer(log_file)
header = ['frame']
for i in range(len(weights) + 1):
header += [
f'iter{i}_error_t', f'iter{i}_error_r', f'iter{i}_error_x',
f'iter{i}_error_y', f'iter{i}_error_z', f'iter{i}_error_r',
f'iter{i}_error_p', f'iter{i}_error_y'
]
log_file.writerow(header)
show = _config['show']
show = True
errors_r = []
errors_t = []
errors_t2 = []
errors_rpy = []
all_RTs = []
prev_tr_error = None
prev_rot_error = None
for i in range(len(weights) + 1):
errors_r.append([])
errors_t.append([])
errors_t2.append([])
errors_rpy.append([])
for batch_idx, sample in enumerate(TestImgLoader):
log_string = [str(batch_idx)]
lidar_input = []
rgb_input = []
shape_pad = [0, 0, 0, 0]
if batch_idx == 0 or not _config['use_prev_output']:
# Qui dare posizione di input del frame corrente rispetto alla GT
sample['tr_error'] = sample['tr_error'].cuda()
sample['rot_error'] = sample['rot_error'].cuda()
else:
sample['tr_error'] = prev_tr_error
sample['rot_error'] = prev_rot_error
for idx in range(len(sample['rgb'])):
real_shape = [
sample['rgb'][idx].shape[1], sample['rgb'][idx].shape[2],
sample['rgb'][idx].shape[0]
]
# ProjectPointCloud in RT-pose
sample['point_cloud'][idx] = sample['point_cloud'][idx].cuda()
pc_rotated = sample['point_cloud'][idx].clone()
reflectance = None
if _config['use_reflectance']:
reflectance = sample['reflectance'][idx].cuda()
R = mathutils.Quaternion(sample['rot_error'][idx])
T = mathutils.Vector(sample['tr_error'][idx])
pc_rotated = rotate_back(pc_rotated, R, T)
cam_params = sample['calib'][idx].cuda()
cam_model = CameraModel()
cam_model.focal_length = cam_params[:2]
cam_model.principal_point = cam_params[2:]
uv, depth, points, refl = cam_model.project_pytorch(
pc_rotated, real_shape, reflectance)
uv = uv.t().int()
depth_img = torch.zeros(real_shape[:2],
device='cuda',
dtype=torch.float)
depth_img += 1000.
depth_img = visibility.depth_image(uv, depth, depth_img,
uv.shape[0], real_shape[1],
real_shape[0])
depth_img[depth_img == 1000.] = 0.
projected_points = torch.zeros_like(depth_img, device='cuda')
projected_points = visibility.visibility2(
depth_img, cam_params, projected_points, depth_img.shape[1],
depth_img.shape[0], _config['occlusion_threshold'],
_config['occlusion_kernel'])
if _config['use_reflectance']:
uv = uv.long()
indexes = projected_points[uv[:, 1], uv[:, 0]] == depth
refl_img = torch.zeros(real_shape[:2],
device='cuda',
dtype=torch.float)
refl_img[uv[indexes, 1], uv[indexes, 0]] = refl[0, indexes]
projected_points /= 100.
if not _config['use_reflectance']:
projected_points = projected_points.unsqueeze(0)
else:
projected_points = torch.stack((projected_points, refl_img))
rgb = sample['rgb'][idx].cuda()
shape_pad[3] = (img_shape[0] - rgb.shape[1])
shape_pad[1] = (img_shape[1] - rgb.shape[2])
rgb = F.pad(rgb, shape_pad)
projected_points = F.pad(projected_points, shape_pad)
rgb_input.append(rgb)
lidar_input.append(projected_points)
lidar_input = torch.stack(lidar_input)
rgb_input = torch.stack(rgb_input)
if show:
out0 = overlay_imgs(rgb, lidar_input)
cv2.imshow("INPUT", out0[:, :, [2, 1, 0]])
cv2.waitKey(1)
pc_GT = sample['point_cloud'][idx].clone()
uv, depth, _, refl = cam_model.project_pytorch(pc_GT, real_shape)
uv = uv.t().int()
depth_img = torch.zeros(real_shape[:2],
device='cuda',
dtype=torch.float)
depth_img += 1000.
depth_img = visibility.depth_image(uv, depth, depth_img,
uv.shape[0], real_shape[1],
real_shape[0])
depth_img[depth_img == 1000.] = 0.
projected_points = torch.zeros_like(depth_img, device='cuda')
projected_points = visibility.visibility2(
depth_img, cam_params, projected_points, depth_img.shape[1],
depth_img.shape[0], _config['occlusion_threshold'],
_config['occlusion_kernel'])
projected_points /= 100.
projected_points = F.pad(projected_points, shape_pad)
lidar_GT = projected_points.unsqueeze(0).unsqueeze(0)
out1 = overlay_imgs(rgb_input[0], lidar_GT)
cv2.imshow("GT", out1[:, :, [2, 1, 0]])
# plt.figure()
# plt.imshow(out1)
# if batch_idx == 0:
# # import ipdb; ipdb.set_trace()
# out2 = overlay_imgs(sample['rgb'][0], lidar_input[:,:,:,1241])
# plt.figure()
# plt.imshow(out2)
# io.imshow(lidar_input[0][0].cpu().numpy(), cmap='jet')
# io.show()
rgb = rgb_input.to(device)
lidar = lidar_input.to(device)
target_transl = sample['tr_error'].to(device)
target_rot = sample['rot_error'].to(device)
point_cloud = sample['point_cloud'][0].to(device)
reflectance = None
if _config['use_reflectance']:
reflectance = sample['reflectance'][0].to(device)
camera_model = cam_model
R = quat2mat(target_rot[0])
T = tvector2mat(target_transl[0])
RT1_inv = torch.mm(T, R)
RT1 = RT1_inv.clone().inverse()
rotated_point_cloud = rotate_forward(point_cloud, RT1)
RTs = [RT1]
T_composed = RT1[:3, 3]
R_composed = quaternion_from_matrix(RT1)
errors_t[0].append(T_composed.norm().item())
errors_t2[0].append(T_composed)
errors_r[0].append(
quaternion_distance(
R_composed.unsqueeze(0),
torch.tensor([1., 0., 0., 0.],
device=R_composed.device).unsqueeze(0),
R_composed.device))
# rpy_error = quaternion_to_tait_bryan(R_composed)
rpy_error = mat2xyzrpy(RT1)[3:]
rpy_error *= (180.0 / 3.141592)
errors_rpy[0].append(rpy_error)
log_string += [
str(errors_t[0][-1]),
str(errors_r[0][-1]),
str(errors_t2[0][-1][0].item()),
str(errors_t2[0][-1][1].item()),
str(errors_t2[0][-1][2].item()),
str(errors_rpy[0][-1][0].item()),
str(errors_rpy[0][-1][1].item()),
str(errors_rpy[0][-1][2].item())
]
if batch_idx == 0.:
print(f'Initial T_erorr: {errors_t[0]}')
print(f'Initial R_erorr: {errors_r[0]}')
start = 0
# Run model
with torch.no_grad():
for iteration in range(start, len(weights)):
# Run the i-th network
T_predicted, R_predicted = models[iteration](rgb, lidar)
if _config['rot_transl_separated'] and iteration == 0:
T_predicted = torch.tensor([[0., 0., 0.]], device='cuda')
if _config['rot_transl_separated'] and iteration == 1:
R_predicted = torch.tensor([[1., 0., 0., 0.]],
device='cuda')
# Project the points in the new pose predicted by the i-th network
R_predicted = quat2mat(R_predicted[0])
T_predicted = tvector2mat(T_predicted[0])
RT_predicted = torch.mm(T_predicted, R_predicted)
RTs.append(torch.mm(RTs[iteration], RT_predicted))
rotated_point_cloud = rotate_forward(rotated_point_cloud,
RT_predicted)
uv2, depth2, _, refl = camera_model.project_pytorch(
rotated_point_cloud, real_shape, reflectance)
uv2 = uv2.t().int()
depth_img2 = torch.zeros(real_shape[:2], device=device)
depth_img2 += 1000.
depth_img2 = visibility.depth_image(uv2, depth2, depth_img2,
uv2.shape[0],
real_shape[1],
real_shape[0])
depth_img2[depth_img2 == 1000.] = 0.
out_cuda2 = torch.zeros_like(depth_img2, device=device)
out_cuda2 = visibility.visibility2(
depth_img2, cam_params, out_cuda2, depth_img2.shape[1],
depth_img2.shape[0], _config['occlusion_threshold'],
_config['occlusion_kernel'])
if _config['use_reflectance']:
uv = uv.long()
indexes = projected_points[uv[:, 1], uv[:, 0]] == depth
refl_img = torch.zeros(real_shape[:2],
device='cuda',
dtype=torch.float)
refl_img[uv[indexes, 1], uv[indexes, 0]] = refl[0, indexes]
refl_img = F.pad(refl_img, shape_pad)
out_cuda2 = F.pad(out_cuda2, shape_pad)
lidar = out_cuda2.clone()
lidar /= 100.
if not _config['use_reflectance']:
lidar = lidar.unsqueeze(0)
else:
lidar = torch.stack((lidar, refl_img))
lidar = lidar.unsqueeze(0)
if show:
out3 = overlay_imgs(rgb[0], lidar, idx=batch_idx)
cv2.imshow(f'Iter_{iteration}', out3[:, :, [2, 1, 0]])
cv2.waitKey(1)
# if iter == 1:
# plt.figure()
# plt.imshow(out3)
# io.imshow(lidar.cpu().numpy()[0,0], cmap='jet')
# io.show()
T_composed = RTs[iteration + 1][:3, 3]
R_composed = quaternion_from_matrix(RTs[iteration + 1])
errors_t[iteration + 1].append(T_composed.norm().item())
errors_t2[iteration + 1].append(T_composed)
errors_r[iteration + 1].append(
quaternion_distance(
R_composed.unsqueeze(0),
torch.tensor([1., 0., 0., 0.],
device=R_composed.device).unsqueeze(0),
R_composed.device))
# rpy_error = quaternion_to_tait_bryan(R_composed)
rpy_error = mat2xyzrpy(RTs[iteration + 1])[3:]
rpy_error *= (180.0 / 3.141592)
errors_rpy[iteration + 1].append(rpy_error)
log_string += [
str(errors_t[iteration + 1][-1]),
str(errors_r[iteration + 1][-1]),
str(errors_t2[iteration + 1][-1][0].item()),
str(errors_t2[iteration + 1][-1][1].item()),
str(errors_t2[iteration + 1][-1][2].item()),
str(errors_rpy[iteration + 1][-1][0].item()),
str(errors_rpy[iteration + 1][-1][1].item()),
str(errors_rpy[iteration + 1][-1][2].item())
]
all_RTs.append(RTs[-1])
prev_RT = RTs[-1].inverse()
prev_tr_error = prev_RT[:3, 3].unsqueeze(0)
prev_rot_error = quaternion_from_matrix(prev_RT).unsqueeze(0)
# Qui prev_rt è quanto si discosta l'output della rete rispetto alla GT
if _config['save_log']:
log_file.writerow(log_string)
if _config['save_log']:
log_file.close()
print("Iterative refinement: ")
for i in range(len(weights) + 1):
errors_r[i] = torch.tensor(errors_r[i]) * (180.0 / 3.141592)
errors_t[i] = torch.tensor(errors_t[i]) * 100
print(
f"Iteration {i}: \tMean Translation Error: {errors_t[i].mean():.4f} cm "
f" Mean Rotation Error: {errors_r[i].mean():.4f} °")
print(
f"Iteration {i}: \tMedian Translation Error: {errors_t[i].median():.4f} cm "
f" Median Rotation Error: {errors_r[i].median():.4f} °\n")
print("-------------------------------------------------------")
print("Timings:")
for i in range(len(errors_t2)):
errors_t2[i] = torch.stack(errors_t2[i])
errors_rpy[i] = torch.stack(errors_rpy[i])
plt.plot(errors_t2[-1][:, 0].cpu().numpy())
plt.show()
plt.plot(errors_t2[-1][:, 1].cpu().numpy())
plt.show()
plt.plot(errors_t2[-1][:, 2].cpu().numpy())
plt.show()
if _config["save_name"] is not None:
torch.save(
torch.stack(errors_t).cpu().numpy(),
f'./results_for_paper/{_config["save_name"]}_errors_t')
torch.save(
torch.stack(errors_r).cpu().numpy(),
f'./results_for_paper/{_config["save_name"]}_errors_r')
torch.save(
torch.stack(errors_t2).cpu().numpy(),
f'./results_for_paper/{_config["save_name"]}_errors_t2')
torch.save(
torch.stack(errors_rpy).cpu().numpy(),
f'./results_for_paper/{_config["save_name"]}_errors_rpy')
print("End!")
def main(_config, _run, seed):
global EPOCH
print(_config['loss'])
if _config['test_sequence'] is None:
raise TypeError('test_sequences cannot be None')
else:
_config['test_sequence'] = f"{_config['test_sequence']:02d}"
print("Test Sequence: ", _config['test_sequence'])
dataset_class = DatasetVisibilityKittiSingle
occlusion_threshold = _config['occlusion_threshold']
img_shape = (384, 1280)
_config["savemodel"] = os.path.join(_config["savemodel"], _config['dataset'])
maps_folder = 'local_maps'
if _config['maps_folder'] is not None:
maps_folder = _config['maps_folder']
dataset = dataset_class(_config['data_folder'], max_r=_config['max_r'], max_t=_config['max_t'],
split='train', use_reflectance=_config['use_reflectance'], maps_folder=maps_folder,
test_sequence=_config['test_sequence'])
dataset_val = dataset_class(_config['data_folder'], max_r=_config['max_r'], max_t=_config['max_t'],
split='test', use_reflectance=_config['use_reflectance'], maps_folder=maps_folder,
test_sequence=_config['test_sequence'])
_config["savemodel"] = os.path.join(_config["savemodel"], _config['test_sequence'])
if not os.path.exists(_config["savemodel"]):
os.mkdir(_config["savemodel"])
np.random.seed(seed)
torch.random.manual_seed(seed)
def init_fn(x): return _init_fn(x, seed)
dataset_size = len(dataset)
# Training and test set creation
num_worker = _config['num_worker']
batch_size = _config['batch_size']
TrainImgLoader = torch.utils.data.DataLoader(dataset=dataset,
shuffle=True,
batch_size=batch_size,
num_workers=num_worker,
worker_init_fn=init_fn,
collate_fn=merge_inputs,
drop_last=False,
pin_memory=True)
TestImgLoader = torch.utils.data.DataLoader(dataset=dataset_val,
shuffle=False,
batch_size=batch_size,
num_workers=num_worker,
worker_init_fn=init_fn,
collate_fn=merge_inputs,
drop_last=False,
pin_memory=True)
print(len(TrainImgLoader))
print(len(TestImgLoader))
if _config['loss'] == 'simple':
loss_fn = ProposedLoss(_config['rescale_transl'], _config['rescale_rot'])
elif _config['loss'] == 'geometric':
loss_fn = GeometricLoss()
loss_fn = loss_fn.to(device)
elif _config['loss'] == 'points_distance':
loss_fn = DistancePoints3D()
elif _config['loss'] == 'L1':
loss_fn = L1Loss(_config['rescale_transl'], _config['rescale_rot'])
else:
raise ValueError("Unknown Loss Function")
#runs = datetime.now().strftime('%b%d_%H-%M-%S') + "/"
#train_writer = SummaryWriter('./logs/' + runs)
#ex.info["tensorflow"] = {}
#ex.info["tensorflow"]["logdirs"] = ['./logs/' + runs]
if _config['network'].startswith('PWC'):
feat = 1
md = 4
split = _config['network'].split('_')
for item in split[1:]:
if item.startswith('f'):
feat = int(item[-1])
elif item.startswith('md'):
md = int(item[2:])
assert 0 < feat < 7, "Feature Number from PWC have to be between 1 and 6"
assert 0 < md, "md must be positive"
model = CMRNet(img_shape, use_feat_from=feat, md=md,
use_reflectance=_config['use_reflectance'], dropout=_config['dropout'])
else:
raise TypeError("Network unknown")
if _config['weights'] is not None:
print(f"Loading weights from {_config['weights']}")
checkpoint = torch.load(_config['weights'], map_location='cpu')
saved_state_dict = checkpoint['state_dict']
model.load_state_dict(saved_state_dict)
model = model.to(device)
print(dataset_size)
print('Number of model parameters: {}'.format(sum([p.data.nelement() for p in model.parameters()])))
parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
if _config['loss'] == 'geometric':
parameters += list(loss_fn.parameters())
if _config['optimizer'] == 'adam':
optimizer = optim.Adam(parameters, lr=_config['BASE_LEARNING_RATE'], weight_decay=5e-6)
# Probably this scheduler is not used
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[20, 50, 70], gamma=0.5)
else:
optimizer = optim.SGD(parameters, lr=_config['BASE_LEARNING_RATE'], momentum=0.9,
weight_decay=5e-6, nesterov=True)
starting_epoch = 0
if _config['weights'] is not None and _config['resume']:
checkpoint = torch.load(_config['weights'], map_location='cpu')
opt_state_dict = checkpoint['optimizer']
optimizer.load_state_dict(opt_state_dict)
starting_epoch = checkpoint['epoch']
# Allow mixed-precision if needed
# model, optimizer = apex.amp.initialize(model, optimizer, opt_level=_config["precision"])
start_full_time = time.time()
BEST_VAL_LOSS = 10000.
old_save_filename = None
total_iter = 0
for epoch in range(starting_epoch, _config['epochs'] + 1):
EPOCH = epoch
print('This is %d-th epoch' % epoch)
epoch_start_time = time.time()
total_train_loss = 0
local_loss = 0.
if _config['optimizer'] != 'adam':
_run.log_scalar("LR", _config['BASE_LEARNING_RATE'] *
math.exp((1 - epoch) * 4e-2), epoch)
for param_group in optimizer.param_groups:
param_group['lr'] = _config['BASE_LEARNING_RATE'] * \
math.exp((1 - epoch) * 4e-2)
else:
#scheduler.step(epoch%100)
_run.log_scalar("LR", scheduler.get_lr()[0])
## Training ##
time_for_50ep = time.time()
for batch_idx, sample in enumerate(TrainImgLoader):
#print(f'batch {batch_idx+1}/{len(TrainImgLoader)}', end='\r')
start_time = time.time()
lidar_input = []
rgb_input = []
sample['tr_error'] = sample['tr_error'].cuda()
sample['rot_error'] = sample['rot_error'].cuda()
start_preprocess = time.time()
for idx in range(len(sample['rgb'])):
# ProjectPointCloud in RT-pose
real_shape = [sample['rgb'][idx].shape[1], sample['rgb'][idx].shape[2], sample['rgb'][idx].shape[0]]
sample['point_cloud'][idx] = sample['point_cloud'][idx].cuda()
pc_rotated = sample['point_cloud'][idx].clone()
reflectance = None
if _config['use_reflectance']:
reflectance = sample['reflectance'][idx].cuda()
R = mathutils.Quaternion(sample['rot_error'][idx]).to_matrix()
R.resize_4x4()
T = mathutils.Matrix.Translation(sample['tr_error'][idx])
RT = T * R
pc_rotated = rotate_back(pc_rotated, RT)
if _config['max_depth'] < 100.:
pc_rotated = pc_rotated[:, pc_rotated[0, :] < _config['max_depth']].clone()
cam_params = sample['calib'][idx].cuda()
cam_model = CameraModel()
cam_model.focal_length = cam_params[:2]
cam_model.principal_point = cam_params[2:]
uv, depth, _, refl = cam_model.project_pytorch(pc_rotated, real_shape, reflectance)
uv = uv.t().int()
depth_img = torch.zeros(real_shape[:2], device='cuda', dtype=torch.float)
depth_img += 1000.
depth_img = visibility.depth_image(uv, depth, depth_img, uv.shape[0], real_shape[1], real_shape[0])
depth_img[depth_img == 1000.] = 0.
depth_img_no_occlusion = torch.zeros_like(depth_img, device='cuda')
depth_img_no_occlusion = visibility.visibility2(depth_img, cam_params, depth_img_no_occlusion,
depth_img.shape[1], depth_img.shape[0],
occlusion_threshold, _config['occlusion_kernel'])
if _config['use_reflectance']:
# This need to be checked
uv = uv.long()
indexes = depth_img_no_occlusion[uv[:,1], uv[:,0]] == depth
refl_img = torch.zeros(real_shape[:2], device='cuda', dtype=torch.float)
refl_img[uv[indexes,1], uv[indexes,0]] = refl[0, indexes]
depth_img_no_occlusion /= _config['max_depth']
if not _config['use_reflectance']:
depth_img_no_occlusion = depth_img_no_occlusion.unsqueeze(0)
else:
depth_img_no_occlusion = torch.stack((depth_img_no_occlusion, refl_img))
# PAD ONLY ON RIGHT AND BOTTOM SIDE
rgb = sample['rgb'][idx].cuda()
shape_pad = [0, 0, 0, 0]
shape_pad[3] = (img_shape[0] - rgb.shape[1]) # // 2
shape_pad[1] = (img_shape[1] - rgb.shape[2]) # // 2 + 1
rgb = F.pad(rgb, shape_pad)
depth_img_no_occlusion = F.pad(depth_img_no_occlusion, shape_pad)
rgb_input.append(rgb)
lidar_input.append(depth_img_no_occlusion)
lidar_input = torch.stack(lidar_input)
rgb_input = torch.stack(rgb_input)
end_preprocess = time.time()
loss = train(model, optimizer, rgb_input, lidar_input, sample['tr_error'],
sample['rot_error'], loss_fn, sample['point_cloud'])
if loss != loss:
raise ValueError("Loss is NaN")
#train_writer.add_scalar("Loss", loss, total_iter)
local_loss += loss
if batch_idx % 50 == 0 and batch_idx != 0:
print(f'Iter {batch_idx}/{len(TrainImgLoader)} training loss = {local_loss/50:.3f}, '
f'time = {(time.time() - start_time)/lidar_input.shape[0]:.4f}, '
#f'time_preprocess = {(end_preprocess-start_preprocess)/lidar_input.shape[0]:.4f}, '
f'time for 50 iter: {time.time()-time_for_50ep:.4f}')
time_for_50ep = time.time()
_run.log_scalar("Loss", local_loss/50, total_iter)
local_loss = 0.
total_train_loss += loss * len(sample['rgb'])
total_iter += len(sample['rgb'])
print("------------------------------------")
print('epoch %d total training loss = %.3f' % (epoch, total_train_loss / len(dataset)))
print('Total epoch time = %.2f' % (time.time() - epoch_start_time))
print("------------------------------------")
_run.log_scalar("Total training loss", total_train_loss / len(dataset), epoch)
## Test ##
total_test_loss = 0.
total_test_t = 0.
total_test_r = 0.
local_loss = 0.0
for batch_idx, sample in enumerate(TestImgLoader):
# print(f'batch {batch_idx + 1}/{len(TestImgLoader)}', end='\r')
start_time = time.time()
lidar_input = []
rgb_input = []
#sample['rgb'] = sample['rgb'].cuda()
sample['tr_error'] = sample['tr_error'].cuda()
sample['rot_error'] = sample['rot_error'].cuda()
for idx in range(len(sample['rgb'])):
# ProjectPointCloud in RT-pose
real_shape = [sample['rgb'][idx].shape[1], sample['rgb'][idx].shape[2], sample['rgb'][idx].shape[0]]
sample['point_cloud'][idx] = sample['point_cloud'][idx].cuda()
pc_rotated = sample['point_cloud'][idx].clone()
reflectance = None
if _config['use_reflectance']:
reflectance = sample['reflectance'][idx].cuda()
R = mathutils.Quaternion(sample['rot_error'][idx]).to_matrix()
R.resize_4x4()
T = mathutils.Matrix.Translation(sample['tr_error'][idx])
RT = T * R
pc_rotated = rotate_back(pc_rotated, RT)
if _config['max_depth'] < 100.:
pc_rotated = pc_rotated[:, pc_rotated[0, :] < _config['max_depth']].clone()
cam_params = sample['calib'][idx].cuda()
cam_model = CameraModel()
cam_model.focal_length = cam_params[:2]
cam_model.principal_point = cam_params[2:]
uv, depth, _, refl = cam_model.project_pytorch(pc_rotated, real_shape, reflectance)
uv = uv.t().int()
depth_img = torch.zeros(real_shape[:2], device='cuda', dtype=torch.float)
depth_img += 1000.
depth_img = visibility.depth_image(uv, depth, depth_img, uv.shape[0], real_shape[1], real_shape[0])
depth_img[depth_img == 1000.] = 0.
depth_img_no_occlusion = torch.zeros_like(depth_img, device='cuda')
depth_img_no_occlusion = visibility.visibility2(depth_img,
cam_params, depth_img_no_occlusion,
depth_img.shape[1], depth_img.shape[0],
occlusion_threshold, _config['occlusion_kernel'])
if _config['use_reflectance']:
uv = uv.long()
indexes = depth_img_no_occlusion[uv[:,1], uv[:,0]] == depth
refl_img = torch.zeros(real_shape[:2], device='cuda', dtype=torch.float)
refl_img[uv[indexes,1], uv[indexes,0]] = refl[0, indexes]
depth_img_no_occlusion /= _config['max_depth']
if not _config['use_reflectance']:
depth_img_no_occlusion = depth_img_no_occlusion.unsqueeze(0)
else:
depth_img_no_occlusion = torch.stack((depth_img_no_occlusion, refl_img))
rgb = sample['rgb'][idx].cuda()
shape_pad = [0, 0, 0, 0]
shape_pad[3] = (img_shape[0] - rgb.shape[1])
shape_pad[1] = (img_shape[1] - rgb.shape[2])
rgb = F.pad(rgb, shape_pad)
depth_img_no_occlusion = F.pad(depth_img_no_occlusion, shape_pad)
rgb_input.append(rgb)
lidar_input.append(depth_img_no_occlusion)
lidar_input = torch.stack(lidar_input)
rgb_input = torch.stack(rgb_input)
loss, trasl_e, rot_e = test(model, rgb_input, lidar_input, sample['tr_error'],
sample['rot_error'], loss_fn, dataset_val.model, sample['point_cloud'])
if loss != loss:
raise ValueError("Loss is NaN")
total_test_t += trasl_e
total_test_r += rot_e
local_loss += loss
if batch_idx % 50 == 0 and batch_idx != 0:
print('Iter %d test loss = %.3f , time = %.2f' % (batch_idx, local_loss/50.,
(time.time() - start_time)/lidar_input.shape[0]))
local_loss = 0.0
total_test_loss += loss * len(sample['rgb'])
print("------------------------------------")
print('total test loss = %.3f' % (total_test_loss / len(dataset_val)))
print(f'total traslation error: {total_test_t / len(dataset_val)} cm')
print(f'total rotation error: {total_test_r / len(dataset_val)} °')
print("------------------------------------")
#train_writer.add_scalar("Val_Loss", total_test_loss / len(dataset_val), epoch)
#train_writer.add_scalar("Val_t_error", total_test_t / len(dataset_val), epoch)
#train_writer.add_scalar("Val_r_error", total_test_r / len(dataset_val), epoch)
_run.log_scalar("Val_Loss", total_test_loss / len(dataset_val), epoch)
_run.log_scalar("Val_t_error", total_test_t / len(dataset_val), epoch)
_run.log_scalar("Val_r_error", total_test_r / len(dataset_val), epoch)
# SAVE
val_loss = total_test_loss / len(dataset_val)
if val_loss < BEST_VAL_LOSS:
BEST_VAL_LOSS = val_loss
#_run.result = BEST_VAL_LOSS
if _config['rescale_transl'] > 0:
_run.result = total_test_t / len(dataset_val)
else:
_run.result = total_test_r / len(dataset_val)
savefilename = f'{_config["savemodel"]}/checkpoint_r{_config["max_r"]:.2f}_t{_config["max_t"]:.2f}_e{epoch}_{val_loss:.3f}.tar'
torch.save({
'config': _config,
'epoch': epoch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'train_loss': total_train_loss / len(dataset),
'test_loss': total_test_loss / len(dataset_val),
}, savefilename)
print(f'Model saved as {savefilename}')
if old_save_filename is not None:
if os.path.exists(old_save_filename):
os.remove(old_save_filename)
old_save_filename = savefilename
print('full training time = %.2f HR' % ((time.time() - start_full_time) / 3600))
return _run.result
