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eval_kitti_noc_sf.py
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eval_kitti_noc_sf.py
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import os
import utils
import hydra
import shutil
import logging
import torch
import torch.optim
import torch.utils.data as data
import torch.backends.cudnn as cudnn
import numpy as np
from tqdm import tqdm
from omegaconf import DictConfig
from factory import model_factory
from utils import copy_to_device, size_of_batch, load_calib
class KITTIPointPWC(data.Dataset):
""" Non-occluded evaluation following PointPWC """
def __init__(self, remove_ground=True):
self.root = 'datasets/kitti_scene_flow/training/pointcloud'
self.remove_ground = remove_ground
self.DEPTH_THRESHOLD = 35.0
self.no_corr = True
self.num_points = 8192
self.allow_less_points = False
self.samples = self.make_dataset()
if len(self.samples) == 0:
raise (RuntimeError("Found 0 files in subfolders of: " + self.root + "\n"))
def __len__(self):
return len(self.samples)
def __getitem__(self, index):
data_dict = {'index': index}
pc1_loaded, pc2_loaded = self.pc_loader(self.samples[index])
pc1_transformed, pc2_transformed, sf_transformed = self.process_pc(pc1_loaded, pc2_loaded)
# pack
pc_pair = np.concatenate([pc1_transformed, pc2_transformed], axis=1)
data_dict['pcs'] = pc_pair.transpose()
data_dict['flow_3d'] = sf_transformed.transpose()
# pass camera params for IDS
proj_mat = load_calib(os.path.join('datasets/kitti_scene_flow/training/calib_cam_to_cam', '%06d.txt' % index))
f, cx, cy = proj_mat[0, 0], proj_mat[0, 2], proj_mat[1, 2]
data_dict['intrinsics'] = np.float32([f, cx, cy]) # f, cx, cy
# adjust domain range according to mean and std
data_dict['src_mean'] = np.array([1.9823, -4.0814, 87.4855], dtype=np.float32) # kitti
data_dict['src_std'] = np.array([11.1490, 1.3005, 10.9335], dtype=np.float32)
data_dict['dst_mean'] = np.array([0.079332, 1.8988, 91.909], dtype=np.float32) # things
data_dict['dst_std'] = np.array([8.0472, 4.1851, 13.6923], dtype=np.float32)
return data_dict
def make_dataset(self):
do_mapping = True
root = os.path.realpath(os.path.expanduser(self.root))
all_paths = sorted(os.walk(root))
useful_paths = [item[0] for item in all_paths if len(item[1]) == 0]
try:
assert (len(useful_paths) == 200)
except AssertionError:
print('assert (len(useful_paths) == 200) failed!', len(useful_paths))
if do_mapping:
mapping_path = os.path.join(self.root, 'KITTI_mapping.txt')
with open(mapping_path) as fd:
lines = fd.readlines()
lines = [line.strip() for line in lines]
useful_paths = [path for path in useful_paths if lines[int(os.path.split(path)[-1])] != '']
res_paths = useful_paths
return res_paths
def pc_loader(self, path):
pc1 = np.load(os.path.join(path, 'pc1.npy')) #.astype(np.float32)
pc2 = np.load(os.path.join(path, 'pc2.npy')) #.astype(np.float32)
if self.remove_ground:
is_ground = np.logical_and(pc1[:,1] < -1.4, pc2[:,1] < -1.4)
not_ground = np.logical_not(is_ground)
pc1 = pc1[not_ground]
pc2 = pc2[not_ground]
return pc1, pc2
def process_pc(self, pc1, pc2):
np.random.seed(1)
if pc1 is None:
return None, None, None,
sf = pc2[:, :3] - pc1[:, :3]
if self.DEPTH_THRESHOLD > 0:
near_mask = np.logical_and(pc1[:, 2] < self.DEPTH_THRESHOLD, pc2[:, 2] < self.DEPTH_THRESHOLD)
else:
near_mask = np.ones(pc1.shape[0], dtype=np.bool)
indices = np.where(near_mask)[0]
assert len(indices) > 0
if self.num_points > 0:
try:
sampled_indices1 = np.random.choice(indices, size=self.num_points, replace=False, p=None)
if self.no_corr:
sampled_indices2 = np.random.choice(indices, size=self.num_points, replace=False, p=None)
else:
sampled_indices2 = sampled_indices1
except ValueError:
if not self.allow_less_points:
#replicate some points
sampled_indices1 = np.random.choice(indices, size=self.num_points, replace=True, p=None)
if self.no_corr:
sampled_indices2 = np.random.choice(indices, size=self.num_points, replace=True, p=None)
else:
sampled_indices2 = sampled_indices1
else:
sampled_indices1 = indices
sampled_indices2 = indices
else:
sampled_indices1 = indices
sampled_indices2 = indices
pc1 = pc1[sampled_indices1]
sf = sf[sampled_indices1]
pc2 = pc2[sampled_indices2]
return pc1, pc2, sf
class Evaluator:
def __init__(self, device: torch.device, cfgs: DictConfig):
self.cfgs = cfgs
self.device = device
logging.info('Loading test set from %s' % self.cfgs.testset.root_dir)
self.test_dataset = KITTIPointPWC()
self.test_loader = utils.FastDataLoader(
dataset=self.test_dataset,
batch_size=8,
num_workers=self.cfgs.testset.n_workers
)
logging.info('Creating model: %s' % self.cfgs.model.name)
self.model = model_factory(self.cfgs.model).to(device=self.device)
self.model.eval()
logging.info('Loading checkpoint from %s' % self.cfgs.ckpt.path)
checkpoint = torch.load(self.cfgs.ckpt.path, self.device)
self.model.load_state_dict(checkpoint['state_dict'], strict=self.cfgs.ckpt.strict)
@torch.no_grad()
def run(self):
logging.info('Running evaluation...')
metrics_3d = {'counts': 0, 'EPE3d': 0.0, 'AccS': 0.0, 'AccR': 0.0, 'Outlier': 0.0}
for inputs in tqdm(self.test_loader):
inputs = copy_to_device(inputs, self.device)
with torch.cuda.amp.autocast(enabled=False):
outputs = self.model.forward(inputs)
for batch_id in range(size_of_batch(inputs)):
flow_3d_pred = outputs['flow_3d'][batch_id]
flow_3d_target = inputs['flow_3d'][batch_id]
epe3d_map = torch.sqrt(torch.sum((flow_3d_pred - flow_3d_target) ** 2, dim=0))
gt_norm = torch.linalg.norm(flow_3d_target, axis=0)
relative_err = epe3d_map / (gt_norm + 1e-4)
acc3d_strict = torch.logical_or(epe3d_map < 0.05, relative_err < 0.05)
acc3d_relax = torch.logical_or(epe3d_map < 0.1, relative_err < 0.1)
outlier = torch.logical_or(epe3d_map > 0.3, relative_err > 0.1)
metrics_3d['counts'] += 1 # averaged over batch (following PointPWC)
metrics_3d['EPE3d'] += epe3d_map.sum().item() / epe3d_map.shape[0]
metrics_3d['AccS'] += torch.count_nonzero(acc3d_strict).item() / epe3d_map.shape[0]
metrics_3d['AccR'] += torch.count_nonzero(acc3d_relax).item() / epe3d_map.shape[0]
metrics_3d['Outlier'] += torch.count_nonzero(outlier).item() / epe3d_map.shape[0]
logging.info('#### 3D Metrics ####')
logging.info('EPE: %.3f' % (metrics_3d['EPE3d'] / metrics_3d['counts']))
logging.info('AccS: %.2f%%' % (metrics_3d['AccS'] / metrics_3d['counts'] * 100.0))
logging.info('AccR: %.2f%%' % (metrics_3d['AccR'] / metrics_3d['counts'] * 100.0))
logging.info('Outlier: %.2f%%' % (metrics_3d['Outlier'] / metrics_3d['counts'] * 100.0))
@hydra.main(config_path='conf', config_name='evaluator')
def main(cfgs: DictConfig):
utils.init_logging()
# change working directory
shutil.rmtree(os.getcwd(), ignore_errors=True)
os.chdir(hydra.utils.get_original_cwd())
if torch.cuda.device_count() == 0:
device = torch.device('cpu')
logging.info('No CUDA device detected, using CPU for evaluation')
elif torch.cuda.device_count() == 1:
device = torch.device('cuda:0')
logging.info('Using GPU: %s' % torch.cuda.get_device_name(device))
cudnn.benchmark = True
else:
raise RuntimeError('Evaluation script does not support multi-GPU systems.')
evaluator = Evaluator(device, cfgs)
evaluator.run()
if __name__ == '__main__':
main()