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infer_SemanticKITTI.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Developed by Jiapeng Xie
import os
import time
import numpy as np
import torch
from tqdm import tqdm
from network.CA_BEV_Unet import CA_Unet
from network.A_BEV_Unet import BEV_Unet
from network.ptBEVnet import ptBEVnet
from dataloader.dataset import collate_fn_BEV, collate_fn_BEV_test, SemKITTI, spherical_dataset, \
voxel_dataset, get_SemKITTI_label_name
from config.config import load_config_data
# ignore weird np warning
import warnings
warnings.filterwarnings("ignore")
def fast_hist(pred, label, n):
k = (label >= 0) & (label < n)
bin_count = np.bincount(
n * label[k].astype(int) + pred[k], minlength=n ** 2)
return bin_count[:n ** 2].reshape(n, n)
def per_class_iu(hist):
return np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))
def fast_hist_crop(output, target, unique_label):
hist = fast_hist(output.flatten(), target.flatten(), np.max(unique_label) + 1)
hist = hist[unique_label, :]
hist = hist[:, unique_label]
return hist
def main(arch_config, data_config):
print("arch_config: ", arch_config)
print("data_config: ", data_config)
# parameters
configs = load_config_data(arch_config)
data_cfg = configs['data_loader']
model_cfg = configs['model_params']
train_cfg = configs['train_params']
fea_compre = model_cfg['grid_size'][2]
pytorch_device = torch.device('cuda:0')
test_batch_size = 1
prediction_save_dir = './prediction_save_dir_KITTI'
val = True # False #True
test = True # True #False
if data_cfg['dataset_type'] == 'polar':
fea_dim = 9
circular_padding = True
elif data_cfg['dataset_type'] == 'traditional':
fea_dim = 7
circular_padding = False
else:
raise NotImplementedError
# prepare miou fun
unique_label, unique_label_str, inv_learning_map = get_SemKITTI_label_name(data_config)
# prepare model
if model_cfg['use_co_attention']:
my_BEV_model = CA_Unet(n_class=len(unique_label),
n_height=fea_compre,
residual=data_cfg['residual'],
input_batch_norm=model_cfg['use_norm'],
dropout=model_cfg['dropout'],
circular_padding=circular_padding)
else:
my_BEV_model = BEV_Unet(n_class=len(unique_label),
n_height=fea_compre,
residual=data_cfg['residual'],
input_batch_norm=model_cfg['use_norm'],
dropout=model_cfg['dropout'],
circular_padding=circular_padding)
my_model = ptBEVnet(my_BEV_model,
grid_size=model_cfg['grid_size'],
fea_dim=fea_dim,
ppmodel_init_dim=model_cfg['ppmodel_init_dim'],
kernal_size=1,
fea_compre=fea_compre)
model_load_path = train_cfg['model_load_path']
if os.path.exists(model_load_path):
print("Load model from: " + model_load_path)
my_model.load_state_dict(torch.load(model_load_path))
else:
print(model_load_path, " : not exist!")
exit()
my_model.to(pytorch_device)
# prepare dataset
test_pt_dataset = SemKITTI(data_config_path=data_config,
data_path=data_cfg['data_path'] + '/sequences/',
imageset='test',
return_ref=data_cfg['return_ref'],
residual=data_cfg['residual'],
residual_path=data_cfg['residual_path'],
drop_few_static_frames=False)
val_pt_dataset = SemKITTI(data_config_path=data_config,
data_path=data_cfg['data_path'] + '/sequences/',
imageset='val',
return_ref=data_cfg['return_ref'],
residual=data_cfg['residual'],
residual_path=data_cfg['residual_path'],
drop_few_static_frames=False)
if data_cfg['dataset_type'] == 'polar':
test_dataset = spherical_dataset(test_pt_dataset,
grid_size=model_cfg['grid_size'],
fixed_volume_space=data_cfg['fixed_volume_space'],
return_test=True)
val_dataset = spherical_dataset(val_pt_dataset,
grid_size=model_cfg['grid_size'],
fixed_volume_space=data_cfg['fixed_volume_space'],
return_test=True)
elif data_cfg['dataset_type'] == 'traditional':
test_dataset = voxel_dataset(test_pt_dataset,
grid_size=model_cfg['grid_size'],
fixed_volume_space=data_cfg['fixed_volume_space'],
return_test=True)
val_dataset = voxel_dataset(val_pt_dataset,
grid_size=model_cfg['grid_size'],
fixed_volume_space=data_cfg['fixed_volume_space'],
return_test=True)
else:
raise NotImplementedError
test_dataset_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=test_batch_size,
collate_fn=collate_fn_BEV_test,
shuffle=False,
num_workers=data_cfg['num_workers'],
pin_memory=True)
val_dataset_loader = torch.utils.data.DataLoader(dataset=val_dataset,
batch_size=test_batch_size,
collate_fn=collate_fn_BEV_test,
shuffle=False,
num_workers=data_cfg['num_workers'],
pin_memory=True)
# validation
if val:
print('*' * 80)
print('Test network performance on validation split')
print('*' * 80)
pbar = tqdm(total=len(val_dataset_loader))
my_model.eval()
hist_list = []
time_list = []
with torch.no_grad():
for i_iter_val, (val_vox_label, val_grid, val_pt_labs, val_pt_fea, val_index) in enumerate(
val_dataset_loader):
val_pt_fea_ten = [i.to(pytorch_device) for i in val_pt_fea]
val_grid_ten = [i.to(pytorch_device) for i in val_grid]
# val_vox_label_ten = val_vox_label.to(pytorch_device)
torch.cuda.synchronize()
start_time = time.time()
predict_labels, pt_out = my_model(val_pt_fea_ten, val_grid_ten, pytorch_device)
torch.cuda.synchronize()
time_list.append(time.time() - start_time)
predict_labels = torch.argmax(predict_labels, dim=1)
predict_labels = predict_labels.cpu().detach().numpy()
for count, i_val_grid in enumerate(val_grid):
hist_list.append(fast_hist_crop(
predict_labels[count, val_grid[count][:, 0], val_grid[count][:, 1], val_grid[count][:, 2]],
val_pt_labs[count], unique_label))
inv_labels = np.vectorize(inv_learning_map.__getitem__)(
predict_labels[count, val_grid[count][:, 0], val_grid[count][:, 1], val_grid[count][:, 2]])
inv_labels = inv_labels.astype('uint32')
# print(predict_labels)
save_dir = val_pt_dataset.scan_files[val_index[count]]
_, dir2 = save_dir.split('/sequences/', 1)
new_save_dir = prediction_save_dir + '/sequences/' + dir2.replace('velodyne', 'predictions')[
:-3] + 'label'
if not os.path.exists(os.path.dirname(new_save_dir)):
os.makedirs(os.path.dirname(new_save_dir))
inv_labels.tofile(new_save_dir)
pbar.update(1)
iou = per_class_iu(sum(hist_list))
print('Validation per class iou: ')
for class_name, class_iou in zip(unique_label_str, iou):
print('%s : %.2f%%' % (class_name, class_iou * 100))
val_miou = np.nanmean(iou) * 100
del val_vox_label, val_grid, val_pt_fea, val_grid_ten
pbar.close()
print('Current val miou is %.3f ' % val_miou)
print('Inference time per %d is %.4f seconds\n' % (test_batch_size, np.mean(time_list)))
# test
if test:
print('*' * 80)
print('Generate predictions for test split')
print('*' * 80)
pbar = tqdm(total=len(test_dataset_loader))
with torch.no_grad():
for i_iter_test, (_, test_grid, _, test_pt_fea, test_index) in enumerate(test_dataset_loader):
# predict
test_pt_fea_ten = [i.to(pytorch_device) for i in test_pt_fea]
test_grid_ten = [i.to(pytorch_device) for i in test_grid]
predict_labels, pt_out = my_model(test_pt_fea_ten, test_grid_ten, pytorch_device)
predict_labels = torch.argmax(predict_labels, dim=1)
predict_labels = predict_labels.cpu().detach().numpy()
# write to label file
for count, i_test_grid in enumerate(test_grid):
test_pred_label = np.vectorize(inv_learning_map.__getitem__)(
predict_labels[count, test_grid[count][:, 0], test_grid[count][:, 1], test_grid[count][:, 2]])
test_pred_label = test_pred_label.astype('uint32')
save_dir = test_pt_dataset.scan_files[test_index[count]]
_, dir2 = save_dir.split('/sequences/', 1)
new_save_dir = prediction_save_dir + '/sequences/' + dir2.replace('velodyne', 'predictions')[
:-3] + 'label'
if not os.path.exists(os.path.dirname(new_save_dir)):
os.makedirs(os.path.dirname(new_save_dir))
test_pred_label.tofile(new_save_dir)
pbar.update(1)
del test_grid, test_pt_fea, test_index
pbar.close()
print('Predicted test labels are saved in %s. ' % prediction_save_dir)
if __name__ == '__main__':
arch_config_path = "config/MotionBEV-semantickitti.yaml"
data_config_path = "config/semantic-kitti-MOS.yaml"
main(arch_config_path, data_config_path)