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eval_things.py
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eval_things.py
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import os
import utils
import hydra
import shutil
import logging
import torch
import torch.optim
import torch.backends.cudnn as cudnn
import numpy as np
from tqdm import tqdm
from omegaconf import DictConfig
from factory import model_factory, dataset_factory
from utils import copy_to_device, size_of_batch, save_flow_png
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 = dataset_factory(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)
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...')
self.model.eval()
metrics_2d = {'counts': 0, 'EPE2d': 0.0, '1px': 0.0, 'Fl': 0.0}
metrics_3d = {'counts': 0, 'EPE3d': 0.0, '5cm': 0.0, '10cm': 0.0}
metrics_3d_noc = {'counts': 0, 'EPE3d': 0.0, '5cm': 0.0, '10cm': 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)):
if 'flow_2d' in outputs:
flow_2d_pred = outputs['flow_2d'][batch_id]
flow_2d_target = inputs['flow_2d'][batch_id]
if flow_2d_target.shape[0] > 2:
flow_2d_mask = flow_2d_target[2] > 0
flow_2d_target = flow_2d_target[:2]
else:
flow_2d_mask = torch.ones(flow_2d_target.shape[1:], dtype=torch.int64, device=self.device)
epe2d_map = torch.sqrt(torch.sum((flow_2d_pred - flow_2d_target) ** 2, dim=0))
flow_2d_mask = torch.logical_and(flow_2d_mask, torch.logical_not(torch.isnan(epe2d_map)))
flow_2d_target_mag = torch.linalg.norm(flow_2d_target, dim=0)
fl_err_map = torch.logical_and(epe2d_map > 3.0, epe2d_map / flow_2d_target_mag > 0.05)
metrics_2d['counts'] += epe2d_map[flow_2d_mask].shape[0]
metrics_2d['EPE2d'] += epe2d_map[flow_2d_mask].sum().item()
metrics_2d['1px'] += torch.count_nonzero(epe2d_map[flow_2d_mask] < 1.0).item()
metrics_2d['Fl'] += fl_err_map[flow_2d_mask].float().sum().item()
if self.cfgs.save_results:
test_id = inputs['index'][batch_id]
os.makedirs('prediction/things/flow_2d', exist_ok=True)
flow_2d_pred = flow_2d_pred.clamp(-500, 500).permute(1, 2, 0).cpu().numpy()
save_flow_png('prediction/things/flow_2d/%07d.png' % test_id, flow_2d_pred)
if 'flow_3d' in outputs:
flow_3d_pred = outputs['flow_3d'][batch_id]
flow_3d_target = inputs['flow_3d'][batch_id]
if flow_3d_target.shape[0] > 3:
flow_3d_mask = flow_3d_target[3] > 0
flow_3d_target = flow_3d_target[:3]
else:
flow_3d_mask = torch.ones(flow_3d_target.shape[1], dtype=torch.int64, device=self.device)
epe3d_map = torch.sqrt(torch.sum((flow_3d_pred - flow_3d_target) ** 2, dim=0))
flow_3d_mask = torch.logical_and(flow_3d_mask, torch.logical_not(torch.isnan(epe3d_map)))
metrics_3d['counts'] += epe3d_map[flow_3d_mask].shape[0]
metrics_3d['EPE3d'] += epe3d_map[flow_3d_mask].sum().item()
metrics_3d['5cm'] += torch.count_nonzero(epe3d_map[flow_3d_mask] < 0.05).item()
metrics_3d['10cm'] += torch.count_nonzero(epe3d_map[flow_3d_mask] < 0.1).item()
# evaluate on non-occluded points
if 'occ_mask_3d' in inputs:
occ_mask_3d = inputs['occ_mask_3d'][batch_id]
epe3d_map_noc = epe3d_map[torch.logical_and(occ_mask_3d == 0, flow_3d_mask)]
metrics_3d_noc['counts'] += epe3d_map_noc.shape[0]
metrics_3d_noc['EPE3d'] += epe3d_map_noc.sum().item()
metrics_3d_noc['5cm'] += torch.count_nonzero(epe3d_map_noc < 0.05).item()
metrics_3d_noc['10cm'] += torch.count_nonzero(epe3d_map_noc < 0.1).item()
if self.cfgs.save_results:
test_id = inputs['index'][batch_id]
os.makedirs('prediction/things/flow_3d', exist_ok=True)
flow_3d_pred = flow_3d_pred.transpose(0, 1).cpu().numpy()
np.save('prediction/things/flow_3d/%07d.npy' % test_id, flow_3d_pred)
if metrics_2d['counts'] > 0:
logging.info('#### 2D Metrics ####')
logging.info('EPE: %.3f' % (metrics_2d['EPE2d'] / metrics_2d['counts']))
logging.info('1px: %.2f%%' % (metrics_2d['1px'] / metrics_2d['counts'] * 100.0))
logging.info('Fl: %.2f%%' % (metrics_2d['Fl'] / metrics_2d['counts'] * 100.0))
if metrics_3d['counts'] > 0:
logging.info('#### 3D Metrics ####')
logging.info('EPE: %.3f' % (metrics_3d['EPE3d'] / metrics_3d['counts']))
logging.info('5cm: %.2f%%' % (metrics_3d['5cm'] / metrics_3d['counts'] * 100.0))
logging.info('10cm: %.2f%%' % (metrics_3d['10cm'] / metrics_3d['counts'] * 100.0))
if metrics_3d_noc['counts'] > 0:
logging.info('#### 3D Metrics (Non-occluded) ####')
logging.info('EPE: %.3f' % (metrics_3d_noc['EPE3d'] / metrics_3d_noc['counts']))
logging.info('5cm: %.2f%%' % (metrics_3d_noc['5cm'] / metrics_3d_noc['counts'] * 100.0))
logging.info('10cm: %.2f%%' % (metrics_3d_noc['10cm'] / metrics_3d_noc['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()