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test_pretrain.py
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test_pretrain.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import time
import argparse
import sys
import yaml
import numpy as np
import torch
import torch.optim as optim
from tqdm import tqdm
import errno
from network.BEV_Unet import BEV_Unet
from network.ptBEV import ptBEVnet
from dataloader.dataset import collate_fn_BEV,SemKITTI,SemKITTI_label_name,spherical_dataset,voxel_dataset,collate_fn_BEV_test
from network.instance_post_processing import get_panoptic_segmentation
from utils.eval_pq import PanopticEval
from utils.configs import merge_configs
#ignore weird np warning
import warnings
warnings.filterwarnings("ignore")
def SemKITTI2train(label):
if isinstance(label, list):
return [SemKITTI2train_single(a) for a in label]
else:
return SemKITTI2train_single(label)
def SemKITTI2train_single(label):
return label - 1 # uint8 trick
def main(args):
data_path = args['dataset']['path']
test_batch_size = args['model']['test_batch_size']
pretrained_model = args['model']['pretrained_model']
output_path = args['dataset']['output_path']
compression_model = args['dataset']['grid_size'][2]
grid_size = args['dataset']['grid_size']
visibility = args['model']['visibility']
pytorch_device = torch.device('cuda:0')
if args['model']['polar']:
fea_dim = 9
circular_padding = True
else:
fea_dim = 7
circular_padding = False
# prepare miou fun
unique_label=np.asarray(sorted(list(SemKITTI_label_name.keys())))[1:] - 1
unique_label_str=[SemKITTI_label_name[x] for x in unique_label+1]
# prepare model
my_BEV_model=BEV_Unet(n_class=len(unique_label), n_height = compression_model, input_batch_norm = True, dropout = 0.5, circular_padding = circular_padding, use_vis_fea=visibility)
my_model = ptBEVnet(my_BEV_model, pt_model = 'pointnet', grid_size = grid_size, fea_dim = fea_dim, max_pt_per_encode = 256,
out_pt_fea_dim = 512, kernal_size = 1, pt_selection = 'random', fea_compre = compression_model)
if os.path.exists(pretrained_model):
my_model.load_state_dict(torch.load(pretrained_model))
pytorch_total_params = sum(p.numel() for p in my_model.parameters())
print('params: ',pytorch_total_params)
my_model.to(pytorch_device)
my_model.eval()
# prepare dataset
test_pt_dataset = SemKITTI(data_path + '/sequences/', imageset = 'test', return_ref = True, instance_pkl_path=args['dataset']['instance_pkl_path'])
val_pt_dataset = SemKITTI(data_path + '/sequences/', imageset = 'val', return_ref = True, instance_pkl_path=args['dataset']['instance_pkl_path'])
if args['model']['polar']:
test_dataset=spherical_dataset(test_pt_dataset, args['dataset'], grid_size = grid_size, ignore_label = 0, return_test= True)
val_dataset=spherical_dataset(val_pt_dataset, args['dataset'], grid_size = grid_size, ignore_label = 0)
else:
test_dataset=voxel_dataset(test_pt_dataset, args['dataset'], grid_size = grid_size, ignore_label = 0, return_test= True)
val_dataset=voxel_dataset(val_pt_dataset, args['dataset'], grid_size = grid_size, ignore_label = 0)
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 = 4)
val_dataset_loader = torch.utils.data.DataLoader(dataset = val_dataset,
batch_size = test_batch_size,
collate_fn = collate_fn_BEV,
shuffle = False,
num_workers = 4)
# validation
print('*'*80)
print('Test network performance on validation split')
print('*'*80)
pbar = tqdm(total=len(val_dataset_loader))
time_list = []
pp_time_list = []
evaluator = PanopticEval(len(unique_label)+1, None, [0], min_points=50)
with torch.no_grad():
for i_iter_val,(val_vox_fea,val_vox_label,val_gt_center,val_gt_offset,val_grid,val_pt_labels,val_pt_ints,val_pt_fea) in enumerate(val_dataset_loader):
val_vox_fea_ten = val_vox_fea.to(pytorch_device)
val_vox_label = SemKITTI2train(val_vox_label)
val_pt_fea_ten = [torch.from_numpy(i).type(torch.FloatTensor).to(pytorch_device) for i in val_pt_fea]
val_grid_ten = [torch.from_numpy(i[:,:2]).to(pytorch_device) for i in val_grid]
val_label_tensor=val_vox_label.type(torch.LongTensor).to(pytorch_device)
val_gt_center_tensor = val_gt_center.to(pytorch_device)
val_gt_offset_tensor = val_gt_offset.to(pytorch_device)
torch.cuda.synchronize()
start_time = time.time()
if visibility:
predict_labels,center,offset = my_model(val_pt_fea_ten, val_grid_ten, val_vox_fea_ten)
else:
predict_labels,center,offset = my_model(val_pt_fea_ten, val_grid_ten)
torch.cuda.synchronize()
time_list.append(time.time()-start_time)
for count,i_val_grid in enumerate(val_grid):
# get foreground_mask
for_mask = torch.zeros(1,grid_size[0],grid_size[1],grid_size[2],dtype=torch.bool).to(pytorch_device)
for_mask[0,val_grid[count][:,0],val_grid[count][:,1],val_grid[count][:,2]] = True
# post processing
torch.cuda.synchronize()
start_time = time.time()
panoptic_labels,center_points = get_panoptic_segmentation(torch.unsqueeze(predict_labels[count], 0),torch.unsqueeze(center[count], 0),torch.unsqueeze(offset[count], 0),val_pt_dataset.thing_list,\
threshold=args['model']['post_proc']['threshold'], nms_kernel=args['model']['post_proc']['nms_kernel'],\
top_k=args['model']['post_proc']['top_k'], polar=circular_padding,foreground_mask=for_mask)
torch.cuda.synchronize()
pp_time_list.append(time.time()-start_time)
panoptic_labels = panoptic_labels.cpu().detach().numpy().astype(np.uint32)
panoptic = panoptic_labels[0,val_grid[count][:,0],val_grid[count][:,1],val_grid[count][:,2]]
evaluator.addBatch(panoptic & 0xFFFF,panoptic,np.squeeze(val_pt_labels[count]),np.squeeze(val_pt_ints[count]))
del val_vox_label,val_pt_fea_ten,val_label_tensor,val_grid_ten,val_gt_center,val_gt_center_tensor,val_gt_offset,val_gt_offset_tensor,predict_labels,center,offset,panoptic_labels,center_points
pbar.update(1)
class_PQ, class_SQ, class_RQ, class_all_PQ, class_all_SQ, class_all_RQ = evaluator.getPQ()
miou,ious = evaluator.getSemIoU()
print('Validation per class PQ, SQ, RQ and IoU: ')
for class_name, class_pq, class_sq, class_rq, class_iou in zip(unique_label_str,class_all_PQ[1:],class_all_SQ[1:],class_all_RQ[1:],ious[1:]):
print('%15s : %6.2f%% %6.2f%% %6.2f%% %6.2f%%' % (class_name, class_pq*100, class_sq*100, class_rq*100, class_iou*100))
pbar.close()
print('Current val PQ is %.3f' %
(class_PQ*100))
print('Current val miou is %.3f'%
(miou*100))
print('Inference time per %d is %.4f seconds\n, postprocessing time is %.4f seconds per scan' %
(test_batch_size,np.mean(time_list),np.mean(pp_time_list)))
# 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_vox_fea,_,_,_,test_grid,_,_,test_pt_fea,test_index) in enumerate(test_dataset_loader):
# predict
test_vox_fea_ten = test_vox_fea.to(pytorch_device)
test_pt_fea_ten = [torch.from_numpy(i).type(torch.FloatTensor).to(pytorch_device) for i in test_pt_fea]
test_grid_ten = [torch.from_numpy(i[:,:2]).to(pytorch_device) for i in test_grid]
if visibility:
predict_labels,center,offset = my_model(test_pt_fea_ten,test_grid_ten,test_vox_fea_ten)
else:
predict_labels,center,offset = my_model(test_pt_fea_ten,test_grid_ten)
# write to label file
for count,i_test_grid in enumerate(test_grid):
# get foreground_mask
for_mask = torch.zeros(1,grid_size[0],grid_size[1],grid_size[2],dtype=torch.bool).to(pytorch_device)
for_mask[0,test_grid[count][:,0],test_grid[count][:,1],test_grid[count][:,2]] = True
# post processing
panoptic_labels,center_points = get_panoptic_segmentation(torch.unsqueeze(predict_labels[count], 0),torch.unsqueeze(center[count], 0),torch.unsqueeze(offset[count], 0),test_pt_dataset.thing_list,\
threshold=args['model']['post_proc']['threshold'], nms_kernel=args['model']['post_proc']['nms_kernel'],\
top_k=args['model']['post_proc']['top_k'], polar=circular_padding,foreground_mask=for_mask)
panoptic_labels = panoptic_labels.cpu().detach().numpy().astype(np.uint32)
panoptic = panoptic_labels[0,test_grid[count][:,0],test_grid[count][:,1],test_grid[count][:,2]]
save_dir = test_pt_dataset.im_idx[test_index[count]]
_,dir2 = save_dir.split('/sequences/',1)
new_save_dir = output_path + '/sequences/' +dir2.replace('velodyne','predictions')[:-3]+'label'
if not os.path.exists(os.path.dirname(new_save_dir)):
try:
os.makedirs(os.path.dirname(new_save_dir))
except OSError as exc:
if exc.errno != errno.EEXIST:
raise
panoptic.tofile(new_save_dir)
del test_pt_fea_ten,test_grid_ten,test_pt_fea,predict_labels,center,offset
pbar.update(1)
pbar.close()
print('Predicted test labels are saved in %s. Need to be shifted to original label format before submitting to the Competition website.' % output_path)
print('Remapping script can be found in semantic-kitti-api.')
if __name__ == '__main__':
# Testing settings
parser = argparse.ArgumentParser(description='')
parser.add_argument('-d', '--data_dir', default='data')
parser.add_argument('-p', '--pretrained_model', default='pretrained_weight/Panoptic_SemKITTI_PolarNet.pt')
parser.add_argument('-c', '--configs', default='configs/SemanticKITTI_model/Panoptic-PolarNet.yaml')
args = parser.parse_args()
with open(args.configs, 'r') as s:
new_args = yaml.safe_load(s)
args = merge_configs(args,new_args)
print(' '.join(sys.argv))
print(args)
main(args)