def evaluate(opt):
"""Evaluates a pretrained model using a specified test set
"""
MIN_DEPTH = 1e-3
MAX_DEPTH = 80
K = np.array(
[[0.58, 0, 0.5, 0], [0, 1.92, 0.5, 0], [0, 0, 1, 0], [0, 0, 0, 1]],
dtype=np.float32)
assert sum((opt.eval_mono, opt.eval_stereo)) == 1, \
"Please choose mono or stereo evaluation by setting either --eval_mono or --eval_stereo"
if opt.ext_disp_to_eval is None:
opt.load_weights_folder = os.path.expanduser(opt.load_weights_folder)
assert os.path.isdir(opt.load_weights_folder), \
"Cannot find a folder at {}".format(opt.load_weights_folder)
print("-> Loading weights from {}".format(opt.load_weights_folder))
filenames = readlines(
os.path.join(splits_dir, opt.eval_split, "test_files.txt"))
encoder_path = os.path.join(opt.load_weights_folder, "encoder.pth")
decoder_path = os.path.join(opt.load_weights_folder, "depth.pth")
encoder_dict = torch.load(encoder_path)
img_ext = '.png' if opt.png else '.jpg'
dataset = datasets.KITTIRAWDataset(opt.data_path,
filenames,
encoder_dict['height'],
encoder_dict['width'], [0],
4,
is_train=False,
img_ext=img_ext)
dataloader = DataLoader(dataset,
16,
shuffle=False,
num_workers=opt.num_workers,
pin_memory=True,
drop_last=False)
encoder = networks.ResnetEncoder(opt.num_layers, False)
depth_decoder = networks.DepthDecoder(encoder.num_ch_enc)
model_dict = encoder.state_dict()
encoder.load_state_dict(
{k: v
for k, v in encoder_dict.items() if k in model_dict})
depth_decoder.load_state_dict(torch.load(decoder_path))
encoder.cuda()
encoder.eval()
depth_decoder.cuda()
depth_decoder.eval()
pred_disps = []
print("-> Computing predictions with size {}x{}".format(
encoder_dict['width'], encoder_dict['height']))
with torch.no_grad():
for data in dataloader:
input_color = data[("color", 0, 0)].cuda()
if opt.post_process:
# Post-processed results require each image to have two forward passes
input_color = torch.cat(
(input_color, torch.flip(input_color, [3])), 0)
output = depth_decoder(encoder(input_color))
pred_disp, _ = disp_to_depth(output[("disp", 0)],
opt.min_depth, opt.max_depth)
pred_disp = pred_disp.cpu()[:, 0].numpy()
if opt.post_process:
N = pred_disp.shape[0] // 2
pred_disp = batch_post_process_disparity(
pred_disp[:N], pred_disp[N:, :, ::-1])
pred_disps.append(pred_disp)
pred_disps = np.concatenate(pred_disps)
else:
# Load predictions from file
print("-> Loading predictions from {}".format(opt.ext_disp_to_eval))
pred_disps = np.load(opt.ext_disp_to_eval)
if opt.eval_eigen_to_benchmark:
eigen_to_benchmark_ids = np.load(
os.path.join(splits_dir, "benchmark",
"eigen_to_benchmark_ids.npy"))
pred_disps = pred_disps[eigen_to_benchmark_ids]
if opt.eval_object:
object_masks = []
for line in filenames:
line = line.split()
folder, frame_index = line[0], int(line[1])
object_mask_filename = os.path.join(
os.path.dirname(__file__), "object_masks", folder,
"{:010d}.npy".format(int(frame_index)))
object_mask = np.load(object_mask_filename)
object_masks.append(object_mask)
if opt.save_pred_disps:
output_path = os.path.join(opt.load_weights_folder,
"disps_{}_split.npy".format(opt.eval_split))
print("-> Saving predicted disparities to ", output_path)
np.save(output_path, pred_disps)
if opt.no_eval:
print("-> Evaluation disabled. Done.")
quit()
elif opt.eval_split == 'benchmark':
save_dir = os.path.join(opt.load_weights_folder,
"benchmark_predictions")
print("-> Saving out benchmark predictions to {}".format(save_dir))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
for idx in range(len(pred_disps)):
disp_resized = cv2.resize(pred_disps[idx], (1216, 352))
depth = STEREO_SCALE_FACTOR / disp_resized
depth = np.clip(depth, 0, 80)
depth = np.uint16(depth * 256)
save_path = os.path.join(save_dir, "{:010d}.png".format(idx))
cv2.imwrite(save_path, depth)
print(
"-> No ground truth is available for the KITTI benchmark, so not evaluating. Done."
)
quit()
gt_path = os.path.join(splits_dir, opt.eval_split, "gt_depths.npz")
gt_depths = np.load(gt_path,
fix_imports=True,
encoding='latin1',
allow_pickle=True)["data"]
print("-> Evaluating")
if opt.eval_stereo:
print(" Stereo evaluation - "
"disabling median scaling, scaling by {}".format(
STEREO_SCALE_FACTOR))
opt.scaling = "disable"
opt.pred_depth_scale_factor = STEREO_SCALE_FACTOR
else:
print(" Mono evaluation - using median scaling")
errors = []
ratios = []
for i in range(pred_disps.shape[0]):
gt_depth = gt_depths[i]
gt_height, gt_width = gt_depth.shape[:2]
pred_disp = pred_disps[i]
pred_disp = cv2.resize(pred_disp, (gt_width, gt_height))
pred_depth = 1 / pred_disp
if opt.eval_split == "eigen":
mask = np.logical_and(gt_depth > MIN_DEPTH, gt_depth < MAX_DEPTH)
crop = np.array([
0.40810811 * gt_height, 0.99189189 * gt_height,
0.03594771 * gt_width, 0.96405229 * gt_width
]).astype(np.int32)
crop_mask = np.zeros(mask.shape)
crop_mask[crop[0]:crop[1], crop[2]:crop[3]] = 1
mask = np.logical_and(mask, crop_mask)
if opt.eval_object:
object_mask = object_masks[i].astype(np.bool)
else:
mask = gt_depth > 0
if opt.scaling == "gt":
ratio = np.median(gt_depth[mask]) / np.median(pred_depth[mask])
if opt.eval_object:
mask = np.logical_and(mask, object_mask)
elif opt.scaling == "dgc":
tensor_K = K.copy()
tensor_K[0, :] *= gt_width
tensor_K[1, :] *= gt_height
tensor_K = torch.from_numpy(tensor_K).unsqueeze(0).cuda()
cam_height = torch.tensor([opt.cam_height]).cuda()
scale_recovery = ScaleRecovery(1, gt_height, gt_width).cuda()
pred_depth = torch.from_numpy(pred_depth).unsqueeze(0).cuda()
ratio = scale_recovery(pred_depth, tensor_K,
cam_height).cpu().item()
pred_depth = pred_depth[0].cpu().numpy()
else:
ratio = 1
pred_depth = pred_depth[mask]
gt_depth = gt_depth[mask]
pred_depth *= ratio
ratios.append(ratio)
pred_depth[pred_depth < MIN_DEPTH] = MIN_DEPTH
pred_depth[pred_depth > MAX_DEPTH] = MAX_DEPTH
if len(gt_depth) != 0:
errors.append(compute_errors(gt_depth, pred_depth))
ratios = np.array(ratios)
med = np.median(ratios)
print(" Scaling ratios | med: {:0.3f} | std: {:0.3f}".format(
med, np.std(ratios / med)))
mean_errors = np.array(errors).mean(0)
print("\n " +
("{:>8} | " * 7
).format("abs_rel", "sq_rel", "rmse", "rmse_log", "a1", "a2", "a3"))
print(("&{: 8.3f} " * 7).format(*mean_errors.tolist()) + "\\\\")
print("\n-> Done!")
def evaluate(opt):
"""Evaluates a pretrained model using a specified test set
"""
MIN_DEPTH = 1e-3
MAX_DEPTH = 80
K = np.array([[0.58, 0, 0.5, 0],
[0, 1.92, 0.5, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]], dtype=np.float32)
assert sum((opt.eval_mono, opt.eval_stereo)) == 1, \
"Please choose mono or stereo evaluation by setting either --eval_mono or --eval_stereo"
if opt.ext_disp_to_eval is None:
opt.load_weights_folder = os.path.expanduser(opt.load_weights_folder)
assert os.path.isdir(opt.load_weights_folder), \
"Cannot find a folder at {}".format(opt.load_weights_folder)
print("-> Loading weights from {}".format(opt.load_weights_folder))
filenames = readlines(os.path.join(splits_dir, opt.eval_split, "test_files.txt"))
encoder_path = os.path.join(opt.load_weights_folder, "encoder.pth")
decoder_path = os.path.join(opt.load_weights_folder, "depth.pth")
encoder_dict = torch.load(encoder_path)
dataset = datasets.KITTIRAWDataset(
opt.data_path, filenames,
encoder_dict['height'], encoder_dict['width'],
[0], 4, is_train=False)
dataloader = DataLoader(
dataset, 16, shuffle=False, num_workers=opt.num_workers,
pin_memory=True, drop_last=False)
encoder = networks.ResnetEncoder(opt.num_layers, False)
depth_decoder = networks.DepthDecoder(encoder.num_ch_enc)
model_dict = encoder.state_dict()
encoder.load_state_dict({k: v for k, v in encoder_dict.items() if k in model_dict})
depth_decoder.load_state_dict(torch.load(decoder_path))
encoder.cuda()
encoder.eval()
depth_decoder.cuda()
depth_decoder.eval()
pred_disps = []
print("-> Computing predictions with size {}x{}".format(
encoder_dict['width'], encoder_dict['height']))
with torch.no_grad():
for data in dataloader:
input_color = data[("color", 0, 0)].cuda()
if opt.post_process:
# Post-processed results require each image to have two forward passes
input_color = torch.cat((input_color, torch.flip(input_color, [3])), 0)
output = depth_decoder(encoder(input_color))
pred_disp, _ = disp_to_depth(output[("disp", 0)], opt.min_depth, opt.max_depth)
pred_disp = pred_disp.cpu()[:, 0].numpy()
if opt.post_process:
N = pred_disp.shape[0] // 2
pred_disp = batch_post_process_disparity(pred_disp[:N], pred_disp[N:, :, ::-1])
pred_disps.append(pred_disp)
pred_disps = np.concatenate(pred_disps)
else:
# Load predictions from file
print("-> Loading predictions from {}".format(opt.ext_disp_to_eval))
pred_disps = np.load(opt.ext_disp_to_eval)
if opt.eval_eigen_to_benchmark:
eigen_to_benchmark_ids = np.load(
os.path.join(splits_dir, "benchmark", "eigen_to_benchmark_ids.npy"))
pred_disps = pred_disps[eigen_to_benchmark_ids]
if opt.eval_object:
object_masks = []
for line in filenames:
line = line.split()
folder, frame_index = line[0], int(line[1])
object_mask_filename = os.path.join(
os.path.dirname(__file__),
"object_masks",
folder,
"{:010d}.npy".format(int(frame_index)))
object_mask = np.load(object_mask_filename)
object_masks.append(object_mask)
if opt.save_pred_disps:
output_path = os.path.join(
opt.load_weights_folder, "disps_{}_split.npy".format(opt.eval_split))
print("-> Saving predicted disparities to ", output_path)
np.save(output_path, pred_disps)
if opt.no_eval:
print("-> Evaluation disabled. Done.")
quit()
elif opt.eval_split == 'benchmark':
save_dir = os.path.join(opt.load_weights_folder, "benchmark_predictions")
print("-> Saving out benchmark predictions to {}".format(save_dir))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
for idx in range(len(pred_disps)):
disp_resized = cv2.resize(pred_disps[idx], (1216, 352))
depth = STEREO_SCALE_FACTOR / disp_resized
depth = np.clip(depth, 0, 80)
depth = np.uint16(depth * 256)
save_path = os.path.join(save_dir, "{:010d}.png".format(idx))
cv2.imwrite(save_path, depth)
print("-> No ground truth is available for the KITTI benchmark, so not evaluating. Done.")
quit()
gt_path = os.path.join(splits_dir, opt.eval_split, "gt_depths.npz")
gt_depths = np.load(gt_path, fix_imports=True, encoding='latin1', allow_pickle=True)["data"]
print("-> Evaluating")
if opt.eval_stereo:
print(" Stereo evaluation - "
"disabling median scaling, scaling by {}".format(STEREO_SCALE_FACTOR))
opt.scaling = "disable"
opt.pred_depth_scale_factor = STEREO_SCALE_FACTOR
else:
print(" Mono evaluation - using median scaling")
errors = []
ratios = []
ex_logs = []
mean_scale = []
side_map = {"2": 2, "3": 3, "l": 2, "r": 3}
#resize_ori = transforms.Resize((pred_disps.shape[1],pred_disps.shape[2]),interpolation=Image.ANTIALIAS)
for i in range(pred_disps.shape[0]):
gt_depth = gt_depths[i]
gt_height, gt_width = gt_depth.shape[:2]
line = filenames[i].split()
folder = line[0]
frame_index = line[1]
side = side_map[line[2]]
color = pil_loader(get_image_path(folder,int(frame_index),side))
#color = pil_loader('/mnt/sdb/xuefeng_data/dkit_dataset/20200629_mechanical_fast/images/{:006d}.png'.format(i))
#color = color.crop((0,191,640,383))
pred_disp = pred_disps[i]
pred_disp = cv2.resize(pred_disp, (gt_width, gt_height))
pred_depth = 1 / pred_disp
if opt.eval_split == "eigen":
mask = np.logical_and(gt_depth > MIN_DEPTH, gt_depth < MAX_DEPTH)
crop = np.array(
[0.40810811 * gt_height, 0.99189189 * gt_height,
0.03594771 * gt_width, 0.96405229 * gt_width]).astype(np.int32)
crop_mask = np.zeros(mask.shape)
crop_mask[crop[0]:crop[1], crop[2]:crop[3]] = 1
mask = np.logical_and(mask, crop_mask)
if opt.eval_object:
object_mask = object_masks[i].astype(np.bool)
else:
mask = gt_depth > 0
if opt.scaling == "gt":
ratio = np.median(gt_depth[mask]) / np.median(pred_depth[mask])
if opt.eval_object:
mask = np.logical_and(mask, object_mask)
elif opt.scaling == "dgc":
scale_recovery = ScaleRecovery(1, gt_height, gt_width, K).cuda()
#scale_recovery = ScaleRecovery(1, 192, 640, K).cuda()
pred_depth = torch.from_numpy(pred_depth).unsqueeze(0).cuda()
ratio1,surface_normal1,ground_mask1,cam_points1 = scale_recovery(pred_depth)
#ratio = ratio1.cpu().item()
surface_normal = surface_normal1.cpu()[0,0,:,:].numpy()
ground_mask = ground_mask1.cpu()[0,0,:,:].numpy()
pred_depth = pred_depth[0].cpu().numpy()
cam_points=cam_points1.cpu().numpy()
cam_points2=cam_points.transpose(1,2,0)
cam_points_masked = cam_points2[np.where(ground_mask==1)]
np.random.shuffle(cam_points_masked)
cam_points4 = np.array(cam_points_masked)
print(cam_points4.shape)
cam_points4 = cam_points4[:2000,:]
cam_points3 = np.concatenate((cam_points4, np.ones((cam_points4.shape[0], 1))), axis=1)
print(cam_points3.shape)
plane,inliers = fit_plane_LSE_RANSAC(cam_points3)
#print(plane)
ratio_rans = abs(1.65 / plane[-1])
else:
ratio = 1
#print(ratio)
#print(max(pred_depth))
#print(min(pred_depth))
pred_depth_ori = pred_depth*mask
gt_depth_ori = gt_depth*mask
pred_depth_ori = np.where(mask==1,pred_depth_ori,1)
pred_depth = pred_depth[mask]
gt_depth = gt_depth[mask]
#mean_scale.append(np.mean(gt_depth/pred_depth))
'''
error_try = 100
scale_abs = 0
for ratio_try in np.arange(0.1,50,step=0.1):
pred_depth1=pred_depth * ratio_try
error_tmp = compute_errors(gt_depth, pred_depth1)[0]
#print(error_tmp)
if error_tmp < error_try:
error_try = error_tmp
scale_abs = ratio_try
div_scale = gt_depth_ori / pred_depth_ori
#print(div_scale.shape)
div_values1 = div_scale[mask]
div_scale = (div_scale-scale_abs)/scale_abs
div_values = div_scale[mask]
div_rmse = sqrt(sum((div_values1-scale_abs)*(div_values1-scale_abs))/len(div_values1))
print(min(div_values),max(div_values))
ex_logs.append([i,min(div_values), max(div_values), div_rmse,scale_abs])
#print(div_scale.shape)
#div_scale = div_scale/np.max(div_scale)
mu = np.mean(div_values1)
sigma = np.std(div_values1)
print(min(div_values1),max(div_values1))
fig,ax=plt.subplots()
n, bins, patches = ax.hist(div_values1,150,range=(3,130),density = True)
y = norm.pdf(bins, mu, 0.8*sigma)
ax.plot(bins, y, 'r')
plt.xlabel('Scale')
plt.ylabel('Density')
plt.savefig(os.path.join(os.path.dirname(__file__), "hist_imgs2","{:010d}.jpg".format(i)))
plt.close()
#blend_img = blending_imgs(div_scale, color,i)
#blend_img.save(os.path.join(os.path.dirname(__file__), "blend_imgs","{:010d}.jpg".format(i)))
blending_imgs(surface_normal,color,i,'surface_normals')
blending_imgs(ground_mask,color,i,'ground_masks')
'''
blending_imgs(ground_mask,color,i,ground_mask)
pred_depth *= ratio_rans
ratios.append(ratio_rans)
pred_depth[pred_depth < MIN_DEPTH] = MIN_DEPTH
pred_depth[pred_depth > MAX_DEPTH] = MAX_DEPTH
#blending_imgs(div_scale, color,i,mask)
if len(gt_depth) != 0:
errors.append(compute_errors(gt_depth, pred_depth))
'''
fl = open('ex.txt','w')
fl.writelines(str(ex_logs))
fl.close()
'''
#np.save('mean_scale.npy', mean_scale)
ratios = np.array(ratios)
med = np.median(ratios)
print(" Scaling ratios | med: {:0.3f} | std: {:0.3f}".format(med, np.std(ratios / med)))
mean_errors = np.array(errors).mean(0)
print("\n " + ("{:>8} | " * 7).format("abs_rel", "sq_rel", "rmse", "rmse_log", "a1", "a2", "a3"))
print(("&{: 8.3f} " * 7).format(*mean_errors.tolist()) + "\\\\")
print("\n-> Done!")
def evaluate(opt):
"""Evaluate odometry on the KITTI dataset
"""
MIN_DEPTH = 1e-3
MAX_DEPTH = 80
K = np.array(
[[0.58, 0, 0.5, 0], [0, 1.92, 0.5, 0], [0, 0, 1, 0], [0, 0, 0, 1]],
dtype=np.float32)
assert os.path.isdir(opt.load_weights_folder), \
"Cannot find a folder at {}".format(opt.load_weights_folder)
assert opt.eval_split == "odom_9" or opt.eval_split == "odom_10" or opt.eval_split == "odom_0", \
"eval_split should be either odom_9 or odom_10"
sequence_id = int(opt.eval_split.split("_")[1])
filenames = readlines(
os.path.join(os.path.dirname(__file__), "splits", "odom",
"test_files_{:02d}.txt".format(sequence_id)))
dataset = KITTIOdomDataset(opt.data_path,
filenames,
opt.height,
opt.width, [0, 1],
4,
is_train=False)
dataloader = DataLoader(dataset,
opt.batch_size,
shuffle=False,
num_workers=opt.num_workers,
pin_memory=True,
drop_last=False)
pose_encoder_path = os.path.join(opt.load_weights_folder,
"pose_encoder.pth")
pose_decoder_path = os.path.join(opt.load_weights_folder, "pose.pth")
depth_encoder_path = os.path.join(opt.load_weights_folder, "encoder.pth")
depth_decoder_path = os.path.join(opt.load_weights_folder, "depth.pth")
pose_encoder = networks.ResnetEncoder(opt.num_layers, False, 2)
pose_encoder.load_state_dict(torch.load(pose_encoder_path))
depth_encoder = networks.ResnetEncoder(opt.num_layers, False)
depth_encoder_dict = torch.load(depth_encoder_path)
model_dict = depth_encoder.state_dict()
depth_encoder.load_state_dict(
{k: v
for k, v in depth_encoder_dict.items() if k in model_dict})
pose_decoder = networks.PoseDecoder(pose_encoder.num_ch_enc, 1, 2)
pose_decoder.load_state_dict(torch.load(pose_decoder_path))
depth_decoder = networks.DepthDecoder(depth_encoder.num_ch_enc)
depth_decoder.load_state_dict(torch.load(depth_decoder_path))
pose_encoder.cuda()
pose_encoder.eval()
pose_decoder.cuda()
pose_decoder.eval()
depth_encoder.cuda()
depth_encoder.eval()
depth_decoder.cuda()
depth_decoder.eval()
pred_poses = []
pred_disps = []
print("-> Computing pose predictions")
opt.frame_ids = [0, 1] # pose network only takes two frames as input
with torch.no_grad():
for inputs in dataloader:
input_color = inputs[("color", 0, 0)].cuda()
depth_output = depth_decoder(depth_encoder(input_color))
pred_disp, _ = disp_to_depth(depth_output[("disp", 0)],
opt.min_depth, opt.max_depth)
pred_disp = pred_disp.cpu()[:, 0].numpy()
pred_disps.append(pred_disp)
for key, ipt in inputs.items():
inputs[key] = ipt.cuda()
all_color_aug = torch.cat(
[inputs[("color_aug", i, 0)] for i in opt.frame_ids], 1)
features = [pose_encoder(all_color_aug)]
axisangle, translation = pose_decoder(features)
pred_poses.append(
transformation_from_parameters(axisangle[:, 0],
translation[:,
0]).cpu().numpy())
pred_poses = np.concatenate(pred_poses)
pred_disps = np.concatenate(pred_disps)
pred_poses_scaled = []
ratios_d = []
gt_norms_div = []
gt_norms = []
pred_norms = []
td_divs_dgc = []
poses_pred = []
for i in range(pred_poses.shape[0]):
pred_pose = pred_poses[i]
pred_disp = pred_disps[i + 1]
pred_depth = 1 / pred_disp
scale_recovery = ScaleRecovery(1, 192, 640, K).cuda()
pred_depth = torch.from_numpy(pred_depth).unsqueeze(0).cuda()
ratio = scale_recovery(pred_depth).cpu().item()
pred_pose_scaled = pred_pose[:3, 3] * ratio
poses_pred.append(pred_pose[:3, 3])
pred_poses_scaled.append(pred_pose_scaled)
ratios_d.append(ratio)
gt_poses_path = os.path.join(opt.data_path, "poses",
"{:02d}.txt".format(sequence_id))
gt_global_poses = np.loadtxt(gt_poses_path).reshape(-1, 3, 4)
gt_global_poses = np.concatenate(
(gt_global_poses, np.zeros((gt_global_poses.shape[0], 1, 4))), 1)
gt_global_poses[:, 3, 3] = 1
gt_xyzs = gt_global_poses[:, :3, 3]
gt_local_poses = []
for i in range(1, len(gt_global_poses)):
gt_local_poses.append(
np.linalg.inv(
np.dot(np.linalg.inv(gt_global_poses[i - 1]),
gt_global_poses[i])))
ates = []
num_frames = gt_xyzs.shape[0]
track_length = 5
for i in range(0, num_frames - 1):
local_xyzs = np.array(
dump_xyz(pred_poses_scaled[i:i + track_length - 1]))
gt_local_xyzs = np.array(
dump_xyz(gt_local_poses[i:i + track_length - 1]))
gt_norm_div = np.linalg.norm(gt_local_xyzs) / np.linalg.norm(
local_xyzs)
ates.append(compute_ate(gt_local_xyzs, local_xyzs))
gt_norms_div.append(gt_norm_div)
gt_norms.append(np.linalg.norm(gt_local_xyzs))
print("\n Trajectory error: {:0.3f}, std: {:0.3f}\n".format(
np.mean(ates), np.std(ates)))
save_path = os.path.join(os.path.dirname(__file__),
"poses_scaled{:02d}.npy".format(sequence_id))
np.save(save_path, pred_poses)
save_path = os.path.join(os.path.dirname(__file__),
"poses_gt{:02d}.npy".format(sequence_id))
np.save(save_path, pred_poses)
save_path = os.path.join(os.path.dirname(__file__),
"poses_pred{:02d}.npy".format(sequence_id))
np.save(save_path, gt_xyzs)
save_path = os.path.join(os.path.dirname(__file__),
"gt_norms{:02d}.npy".format(sequence_id))
np.save(save_path, gt_norms)
save_path = os.path.join(os.path.dirname(__file__),
"gt_norms_div{:02d}.npy".format(sequence_id))
np.save(save_path, gt_norms_div)
save_path = os.path.join(os.path.dirname(__file__),
"ratios_d{:02d}.npy".format(sequence_id))
np.save(save_path, ratios_d)
save_path = os.path.join(os.path.dirname(__file__),
"pred_norms{:02d}.npy".format(sequence_id))
np.save(save_path, pred_norms)
print("-> Predictions saved to", save_path)