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eval_things_occ_sf.py
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eval_things_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
class FlyingThings3DSubsetFlowNet3D(data.Dataset):
"""Occuluded evaluation following FlowNet3D"""
def __init__(self, cfgs):
self.root_dir = cfgs.root_dir
self.n_points = cfgs.n_points
self.datapath = glob.glob(os.path.join(self.root_dir, 'TEST*.npz'))
def __len__(self):
return len(self.datapath)
def __getitem__(self, index):
data_dict = {'index': index}
with open(self.datapath[index], 'rb') as fp:
data = np.load(fp)
pc1 = data['points1'].astype(np.float32)
pc2 = data['points2'].astype(np.float32)
sf = data['flow'].astype(np.float32)
noc_mask = data['valid_mask1'].astype(np.bool)
indices1 = np.random.choice(pc1.shape[0], size=self.n_points, replace=False, p=None)
indices2 = np.random.choice(pc2.shape[0], size=self.n_points, replace=False, p=None)
pc1, pc2, sf, noc_mask = pc1[indices1], pc2[indices2], sf[indices1], noc_mask[indices1]
pc_pair = np.concatenate([pc1, pc2], axis=1)
data_dict['pcs'] = pc_pair.transpose()
data_dict['flow_3d'] = sf.transpose()
data_dict['intrinsics'] = np.float32([1050, 479.5, 269.5]) # f, cx, cy
data_dict['noc_mask_3d'] = noc_mask
return data_dict
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 = FlyingThings3DSubsetFlowNet3D(self.cfgs.testset)
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]
mask = inputs['noc_mask_3d'][batch_id].float()
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) * mask, (relative_err < 0.05) * mask)
acc3d_relax = torch.logical_or((epe3d_map < 0.1) * mask, (relative_err < 0.1) * mask)
outlier = torch.logical_or((epe3d_map > 0.3) * mask, (relative_err > 0.1) * mask)
if mask.sum() > 0:
metrics_3d['counts'] += 1 # averaged over batch (to follow FlowNet3D)
metrics_3d['EPE3d'] += (epe3d_map * mask).sum().item() / mask.sum()
metrics_3d['AccS'] += torch.count_nonzero(acc3d_strict).item() / mask.sum()
metrics_3d['AccR'] += torch.count_nonzero(acc3d_relax).item() / mask.sum()
metrics_3d['Outlier'] += torch.count_nonzero(outlier).item() / mask.sum()
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()