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test_cyl_sem_ms_tta.py
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test_cyl_sem_ms_tta.py
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# -*- coding:utf-8 -*-
# author: Xinge
# @file: train_cylinder_asym.py
import os
import time
import argparse
import sys
import numpy as np
import torch
import torch.optim as optim
from tqdm import tqdm
from utils.metric_util import per_class_iu, fast_hist_crop
from dataloader.pc_dataset import get_SemKITTI_label_name
from builder import data_builder, model_builder, loss_builder
from config.config import load_config_data
from utils.load_save_util import load_checkpoint
import warnings
warnings.filterwarnings("ignore")
def train2SemKITTI(input_label):
# delete 0 label (uses uint8 trick : 0 - 1 = 255 )
return input_label + 1
def main(args):
pytorch_device = torch.device('cuda:0')
config_path = args.config_path
configs = load_config_data(config_path)
dataset_config = configs['dataset_params']
train_dataloader_config = configs['train_data_loader']
val_dataloader_config = configs['val_data_loader']
val_batch_size = val_dataloader_config['batch_size']
train_batch_size = train_dataloader_config['batch_size']
model_config = configs['model_params']
train_hypers = configs['train_params']
grid_size = model_config['output_shape']
num_class = model_config['num_class']
ignore_label = dataset_config['ignore_label']
model_load_path = train_hypers['model_load_path']
model_save_path = train_hypers['model_save_path']
SemKITTI_label_name = get_SemKITTI_label_name(dataset_config["label_mapping"])
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]
my_model = model_builder.build(model_config)
if os.path.exists(model_load_path):
my_model = load_checkpoint(model_load_path, my_model)
my_model.to(pytorch_device)
optimizer = optim.Adam(my_model.parameters(), lr=train_hypers["learning_rate"])
loss_func, lovasz_softmax = loss_builder.build(wce=True, lovasz=True,
num_class=num_class, ignore_label=ignore_label)
train_dataset_loader, test_dataset_loader, test_pt_dataset = data_builder.build(dataset_config,
train_dataloader_config,
val_dataloader_config,
grid_size=grid_size,
use_tta=True,
use_multiscan=True)
# training
output_path = 'out_cyl_ms/test'
if True:
loss_list = []
print('*'*80)
print('Generate predictions for test split')
print('*'*80)
pbar = tqdm(total=len(test_dataset_loader))
time.sleep(10)
if True:
if True:
my_model.eval()
hist_list = []
val_loss_list = []
with torch.no_grad():
for i_iter_test, (_, _, test_grid, _, test_pt_fea, test_index, origin_len) in enumerate(
test_dataset_loader):
#print(f"{i_iter_test}/{total_len}")
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).to(pytorch_device) for i in test_grid]
predict_labels = my_model(test_pt_fea_ten, test_grid_ten, val_batch_size, test_grid, voting_num=4, use_tta=True)#val_batch_size)
# aux_loss = loss_fun(aux_outputs, point_label_tensor)
predict_labels = torch.argmax(predict_labels, dim=0).type(torch.uint8)
predict_labels = predict_labels.cpu().detach().numpy()
if True:
#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]][0:origin_len[count]]
#test_pred_label = train2SemKITTI(test_pred_label)
test_pred_label = np.expand_dims(predict_labels,axis=1)
save_dir = test_pt_dataset.im_idx[test_index[0]]
_,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.uint32)
test_pred_label2 = test_pred_label[0:origin_len[0]]
test_pred_label2.tofile(new_save_dir)
pbar.update(1)
del test_grid, test_pt_fea, test_grid_ten, 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__':
# Training settings
parser = argparse.ArgumentParser(description='')
parser.add_argument('-y', '--config_path', default='config/semantickitti-multiscan.yaml')
args = parser.parse_args()
print(' '.join(sys.argv))
print(args)
main(args)