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test_pretrain_SemanticKITTI.py
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test_pretrain_SemanticKITTI.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import time
import argparse
import sys
import numpy as np
import torch
import torch.optim as optim
from tqdm import tqdm
import yaml
from network.BEV_Unet import BEV_Unet
from network.ptBEV import ptBEVnet
from dataloader.dataset import collate_fn_BEV,collate_fn_BEV_test,SemKITTI,SemKITTI_label_name,spherical_dataset,voxel_dataset
#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)+2)
hist=hist[unique_label+1,:]
hist=hist[:,unique_label+1]
return hist
# 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 train2SemKITTI(input_label):
# # delete 0 label (uses uint8 trick : 0 - 1 = 255 )
# return input_label + 1
def main(args):
data_path = args.data_dir
test_batch_size = args.test_batch_size
model_save_path = args.model_save_path
output_path = args.test_output_path
compression_model = args.grid_size[2]
grid_size = args.grid_size
pytorch_device = torch.device('cuda:0')
model = args.model
if model == 'polar':
fea_dim = 9
circular_padding = True
elif model == 'traditional':
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)+1, n_height = compression_model, input_batch_norm = True, dropout = 0.5, circular_padding = circular_padding)
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(model_save_path):
my_model.load_state_dict(torch.load(model_save_path))
print('Load pretrained model')
my_model.to(pytorch_device)
# prepare dataset
test_pt_dataset = SemKITTI(data_path + '/sequences/', imageset = 'test', return_ref = True)
val_pt_dataset = SemKITTI(data_path + '/sequences/', imageset = 'val', return_ref = True)
if model == 'polar':
test_dataset=spherical_dataset(test_pt_dataset, grid_size = grid_size, ignore_label = 0, fixed_volume_space = True, return_test= True)
val_dataset=spherical_dataset(val_pt_dataset, grid_size = grid_size, ignore_label = 0, fixed_volume_space = True)
elif model == 'traditional':
test_dataset=voxel_dataset(test_pt_dataset, grid_size = grid_size, ignore_label = 0, fixed_volume_space = True, return_test= True)
val_dataset=voxel_dataset(val_pt_dataset, grid_size = grid_size, ignore_label = 0, fixed_volume_space = True)
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_test,
shuffle = False,
num_workers = 4)
# validation
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_grid,val_pt_labs,val_pt_fea,index, label_path) in enumerate(val_dataset_loader):
# if i_iter_val == 5:
# break
# val_vox_label = SemKITTI2train(val_vox_label)
# val_pt_labs = SemKITTI2train(val_pt_labs)
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)
torch.cuda.synchronize()
start_time = time.time()
predict_labels,_ = my_model(val_pt_fea_ten, val_grid_ten)
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))
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_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
print('*'*80)
print('Generate predictions for test split')
print('*'*80)
with open("semantic-kitti.yaml", 'r') as stream:
semkittiyaml = yaml.safe_load(stream)
learning_map_inv = semkittiyaml['learning_map_inv']
# my_model.eval()
pbar = tqdm(total=len(test_dataset_loader))
with torch.no_grad():
for i_iter_test,(_,_,test_grid,_,test_pt_fea,test_index,label_path) in enumerate(test_dataset_loader):
# predict
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]
predict_labels,_ = my_model(test_pt_fea_ten,test_grid_ten)
predict_labels = torch.argmax(predict_labels,1)#.type(torch.uint8)
predict_labels = predict_labels.cpu().detach().numpy()
# write to label file
for count,i_test_grid in enumerate(test_grid):
test_pred_label = predict_labels[count,test_grid[count][:,0],test_grid[count][:,1],test_grid[count][:,2]]
# test_pred_label = train2SemKITTI(test_pred_label)
test_pred_label = np.expand_dims(test_pred_label,axis=1)
test_pred_label = np.vectorize(learning_map_inv.__getitem__)(test_pred_label)
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
test_pred_label = test_pred_label.astype(np.int32).reshape(-1)
# print(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. 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='your_data_path')
parser.add_argument('-p', '--model_save_path', default='your_checkpoint.pt')
parser.add_argument('-o', '--test_output_path', default='your_path_to_save_predict_label')
parser.add_argument('-m', '--model', choices=['polar','traditional'], default='polar', help='training model: polar or traditional (default: polar)')
parser.add_argument('-s', '--grid_size', nargs='+', type=int, default = [480,360,32], help='grid size of BEV representation (default: [480,360,32])')
parser.add_argument('--test_batch_size', type=int, default=2, help='batch size for training (default: 1)')
args = parser.parse_args()
if not len(args.grid_size) == 3:
raise Exception('Invalid grid size! Grid size should have 3 dimensions.')
print(' '.join(sys.argv))
print(args)
main(args)