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test_SemanticKITTI.py
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test_SemanticKITTI.py
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# Common
import os
import time
import yaml
import logging
import warnings
import argparse
import numpy as np
from tqdm import tqdm
# torch
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
# my module
from dataset.semkitti_testset import SemanticKITTI
import pickle
from help_utils import get_logger
np.random.seed(0)
# os.environ["CUDA_VISIBLE_DEVICES"] = '1'
torch.backends.cudnn.enabled = False
warnings.filterwarnings("ignore")
parser = argparse.ArgumentParser()
parser.add_argument('--infer_type', default='all', type=str, choices=['all', 'sub'], help='Infer ALL or just infer Subsample')
parser.add_argument('--sampling', type=str, default='random', choices=['random', 'polar'], help='Polar sample or not')
parser.add_argument('--backbone', type=str, default='randla', choices=['randla', 'baflac', 'baaf'])
parser.add_argument('--checkpoint_path', default=None, help='Model checkpoint path [default: None]')
parser.add_argument('--test_id', default='08', type=str, help='Predicted sequence id [default: 08]')
parser.add_argument('--result_dir', default='test_pred', help='Dump dir to save prediction [default: result/]')
parser.add_argument('--yaml_config', default='utils/semantic-kitti.yaml', help='semantic-kitti.yaml path')
parser.add_argument('--batch_size', type=int, default=12, help='Batch Size during training [default: 30]')
parser.add_argument('--step', type=int, default=0, help='sub dataset size')
parser.add_argument('--grid', nargs='+', type=int, default=[64, 64, 16], help='grid size of BEV representation')
FLAGS = parser.parse_args()
if FLAGS.backbone == 'baflac':
from config import ConfigSemanticKITTI_BAF as cfg
else:
from config import ConfigSemanticKITTI as cfg
class Tester:
def __init__(self):
# Init Logging
os.makedirs(FLAGS.result_dir, exist_ok=True)
log_fname = os.path.join(FLAGS.result_dir, 'log_test.txt')
self.logger = get_logger(log_fname, name="Tester")
argsDict = FLAGS.__dict__
for eachArg, value in argsDict.items():
self.logger.info(eachArg + ' : ' + str(value))
self.logger.info(vars(cfg))
# load yaml file
self.remap_lut = self.load_yaml(FLAGS.yaml_config)
test_dataset = SemanticKITTI('test', test_id=FLAGS.test_id, batch_size=FLAGS.batch_size,
sampling_way=FLAGS.sampling, step=FLAGS.step, grid=FLAGS.grid)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if FLAGS.backbone == 'baflac':
from network.BAF_LAC import BAF_LAC
self.logger.info("Use Baseline: BAF-LAC")
self.net = BAF_LAC(cfg)
self.net.to(device)
collate_fn = test_dataset.collate_fn_baf_lac
elif FLAGS.backbone == 'randla':
from network.RandLANet import Network
self.logger.info("Use Baseline: Rand-LA")
self.net = Network(cfg)
self.net.to(device)
collate_fn = test_dataset.collate_fn
elif FLAGS.backbone == 'baaf':
from network.BAAF import Network
self.logger.info("Use Baseline: BAAF")
self.net = Network(cfg)
self.net.to(device)
collate_fn = test_dataset.collate_fn
else:
raise TypeError("1~5!! can can need !!!")
# get_dataset & dataloader
self.test_loader = DataLoader(test_dataset, batch_size=None, collate_fn=collate_fn,
pin_memory=True, num_workers=0)
# Load module
CHECKPOINT_PATH = FLAGS.checkpoint_path
if CHECKPOINT_PATH is not None and os.path.isfile(CHECKPOINT_PATH):
self.logger.info("Loading from " + CHECKPOINT_PATH)
checkpoint = torch.load(CHECKPOINT_PATH)
try:
self.logger.info("Loading strict=True")
self.net.load_state_dict(checkpoint['model_state_dict'], strict=True)
except:
self.logger.info("Loading strict=False")
self.net.load_state_dict(checkpoint['model_state_dict'], strict=False)
self.test_dataset = test_dataset
# Initialize testing probability
self.test_dataset.init_prob()
self.test_probs = self.init_prob()
self.test_smooth = 0.98
def load_yaml(self, path):
DATA = yaml.safe_load(open(path, 'r'))
# get number of interest classes, and the label mappings
remapdict = DATA["learning_map_inv"]
# make lookup table for mapping
maxkey = max(remapdict.keys())
# +100 hack making lut bigger just in case there are unknown labels
remap_lut = np.zeros((maxkey + 100), dtype=np.int32)
remap_lut[list(remapdict.keys())] = list(remapdict.values())
return remap_lut
def init_prob(self):
probs = []
for item in self.test_dataset.possibility:
prob = np.zeros(shape=[len(item), self.test_dataset.num_classes], dtype=np.float32)
probs.append(prob)
return probs
def test(self):
self.logger.info("Start Testing")
self.rolling_predict()
# Merge Probability
self.merge_and_store()
def rolling_predict(self):
self.net.eval() # set model to eval mode (for bn and dp)
iter_loader = iter(self.test_loader)
start = time.time()
step_id = 0
with torch.no_grad():
min_possibility = self.test_dataset.min_possibility
while np.min(min_possibility) <= 0.5:
batch_data, input_inds, cloud_inds, min_possibility = next(iter_loader)
for key in batch_data:
if type(batch_data[key]) is list:
for i in range(cfg.num_layers):
batch_data[key][i] = batch_data[key][i].cuda(non_blocking=True)
else:
batch_data[key] = batch_data[key].cuda(non_blocking=True)
# Forward pass
with torch.cuda.amp.autocast():
semantic_out = self.net(batch_data)
semantic_out = semantic_out.transpose(1, 2)
# update prediction (multi-thread)
self.update_predict(semantic_out, batch_data, input_inds, cloud_inds)
if step_id % 10 == 0:
end = time.time()
self.logger.info('Step {:3d}, Min possibility = {:.6e}, Time = {:.3f}'.format(step_id, np.min(min_possibility), end-start))
start = time.time()
step_id += 1
def update_predict(self, semantic_out, batch_data, input_inds, cloud_inds):
# Store logits into list
B = semantic_out.size(0)
semantic_out = semantic_out.cpu().numpy()
for j in range(B):
probs = semantic_out[j]
inds = input_inds[j]
c_i = cloud_inds[j][0]
self.test_probs[c_i][inds] = \
self.test_smooth * self.test_probs[c_i][inds] + (1 - self.test_smooth) * probs
def merge_and_store(self):
# initialize result directory
root_dir = os.path.join(FLAGS.result_dir, self.test_dataset.test_scan_number, 'predictions')
os.makedirs(root_dir, exist_ok=True)
self.logger.info(f'mkdir {root_dir}')
N = len(self.test_probs)
for j in tqdm(range(N)):
if FLAGS.infer_type == 'all':
proj_path = os.path.join(self.test_dataset.dataset_path, self.test_dataset.test_scan_number, 'proj')
proj_file = os.path.join(proj_path, self.test_dataset.data_list[j][1] + '_proj.pkl')
if os.path.isfile(proj_file):
with open(proj_file, 'rb') as f:
proj_inds = pickle.load(f)
probs = self.test_probs[j][proj_inds[0], :]
pred = np.argmax(probs, 1).astype(np.uint32)
elif FLAGS.infer_type == 'sub':
pred = np.argmax(self.test_probs[j], 1).astype(np.uint32)
else:
raise TypeError("Choose what you want to infer")
# pred += 1
pred = self.remap(pred)
name = self.test_dataset.data_list[j][1] + '.label'
output_path = os.path.join(root_dir, name)
pred.tofile(output_path)
def remap(self, label):
upper_half = label >> 16 # get upper half for instances
lower_half = label & 0xFFFF # get lower half for semantics
lower_half = self.remap_lut[lower_half] # do the remapping of semantics
label = (upper_half << 16) + lower_half # reconstruct full label
label = label.astype(np.uint32)
return label
def main():
tester = Tester()
tester.test()
if __name__ == '__main__':
main()