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evaluate_iterative_single_CALIB.py
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evaluate_iterative_single_CALIB.py
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# -------------------------------------------------------------------
# Copyright (C) 2020 Università degli studi di Milano-Bicocca, iralab
# Author: Daniele Cattaneo ([email protected])
# Released under Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License.
# http://creativecommons.org/licenses/by-nc-sa/4.0/
# -------------------------------------------------------------------
import csv
import random
import cv2
import mathutils
# import matplotlib
# matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
import torch.nn.parallel
import torch.utils.data
import visibility
from sacred import Experiment
from sacred.utils import apply_backspaces_and_linefeeds
from skimage import io
from tqdm import tqdm
from camera_model import CameraModel
from DatasetVisibilityKitti import DatasetVisibilityKittiSingle
from models.CMRNet.CMRNet import CMRNet
from quaternion_distances import quaternion_distance
from utils import (mat2xyzrpy, merge_inputs, overlay_imgs, quat2mat,
quaternion_from_matrix, rotate_back, rotate_forward,
tvector2mat)
ex = Experiment("CMRNet-evaluate-iterative")
ex.captured_out_filter = apply_backspaces_and_linefeeds
# noinspection PyUnusedLocal
@ex.config
def config():
dataset = 'kitti'
data_folder = './KITTI/sequences/'
test_sequence = 0
use_prev_output = False
max_t = 2.
max_r = 10.
occlusion_kernel = 5
occlusion_threshold = 3.0
network = 'PWC_f1'
norm = 'bn'
show = False
use_reflectance = False
weight = None # List of weights' path, for iterative refinement
save_name = None
# Set to True only if you use two network, the first for rotation and the second for translation
rot_transl_separated = False
random_initial_pose = False
save_log = False
maps_folder = None
weights = [
'/checkpoints/kitti/iterative_final_test/kitti_iter1.tar',
'/checkpoints/kitti/iterative_final_test/kitti_iter2.tar',
'/checkpoints/kitti/iterative_final_test/kitti_iter3.tar',
]
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
EPOCH = 1
def _init_fn(worker_id, seed):
seed = seed + worker_id + EPOCH * 100
print(f"Init worker {worker_id} with seed {seed}")
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
@ex.automain
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']
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(tqdm(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:
# out2 = overlay_imgs(sample['rgb'][0], lidar_input)
# 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(), 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!")