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eval_kitti_occ_sf.py
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eval_kitti_occ_sf.py
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import os
import glob
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 KITTIFlowNet3d(data.Dataset):
""" Occluded evaluation following FlowNet3D """
def __init__(self, root='datasets/kitti_scene_flow/training/kitti_rm_ground', npoints=8192):
self.npoints = npoints
self.root = root
self.datapath = glob.glob(os.path.join(self.root, '*.npz'))
self.cache = {}
self.cache_size = 30000
def __getitem__(self, index):
np.random.seed(1)
data_dict = {'index': index}
if index in self.cache:
pos1, pos2, flow = self.cache[index]
else:
fn = self.datapath[index]
with open(fn, 'rb') as fp:
data = np.load(fp)
pos1 = data['pos1']
pos2 = data['pos2']
flow = data['gt']
if len(self.cache) < self.cache_size:
self.cache[index] = (pos1, pos2, flow)
n1 = pos1.shape[0]
n2 = pos2.shape[0]
if n1 >= self.npoints:
sample_idx1 = np.random.choice(n1, self.npoints, replace=False)
else:
sample_idx1 = np.concatenate((np.arange(n1), np.random.choice(n1, self.npoints - n1, replace=True)), axis=-1)
if n2 >= self.npoints:
sample_idx2 = np.random.choice(n2, self.npoints, replace=False)
else:
sample_idx2 = np.concatenate((np.arange(n2), np.random.choice(n2, self.npoints - n2, replace=True)), axis=-1)
pos1_ = np.copy(pos1)[sample_idx1, :]
pos2_ = np.copy(pos2)[sample_idx2, :]
flow_ = np.copy(flow)[sample_idx1, :]
# pack
xyz_order = [1, 2, 0]
pos1_ = pos1_[:, xyz_order]
pos2_ = pos2_[:, xyz_order]
flow_ = flow_[:, xyz_order]
pc_pair = np.concatenate([pos1_, pos2_], axis=1)
data_dict['pcs'] = pc_pair.transpose().astype(np.float32)
data_dict['flow_3d'] = flow_.transpose().astype(np.float32)
# 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([3.8450, -3.6596, 86.1627], dtype=np.float32) # kitti
data_dict['src_std'] = np.array([10.1774, 1.2327, 13.5970], 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 __len__(self):
return len(self.datapath)
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 = KITTIFlowNet3d()
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 FlowNet3D)
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()