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test_scpnet_comp.py
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test_scpnet_comp.py
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# -*- coding:utf-8 -*-
# author: Xinge, Xzy
# @file: train_cylinder_asym.py
import os
import time
import argparse
import sys
import numpy as np
import torch
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")
import yaml
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']
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)
model_load_path += 'iou26.6891_epoch19.pth'
if os.path.exists(model_load_path):
print('Load model from: %s' % model_load_path)
my_model = load_checkpoint(model_load_path, my_model)
else:
print('No existing model, training model from scratch...')
my_model.to(pytorch_device)
_, 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
dataset_name = val_dataloader_config["imageset"]
output_path = 'out_scpnet/' + dataset_name
if True:
print('Generate predictions for test split')
pbar = tqdm(total=len(test_dataset_loader))
time.sleep(10)
### learning map
with open("config/label_mapping/semantic-kitti.yaml", 'r') as stream:
semkittiyaml = yaml.safe_load(stream)
# make lookup table for mapping
learning_map_inv = semkittiyaml["learning_map_inv"]
maxkey = max(learning_map_inv.keys())
# +100 hack making lut bigger just in case there are unknown labels
remap_lut_First = np.zeros((maxkey + 100), dtype=np.int32)
remap_lut_First[list(learning_map_inv.keys())] = list(learning_map_inv.values())
if True:
if True:
my_model.eval()
with torch.no_grad():
for i_iter_test, (_, _, test_grid, _, test_pt_fea, test_index, origin_len) in enumerate(
test_dataset_loader):
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, use_tta=False)
predict_labels = torch.argmax(predict_labels, dim=1)
predict_labels = predict_labels.cpu().detach().numpy()
if True:
test_pred_label = np.squeeze(predict_labels)
### save prediction after remapping
pred = test_pred_label
pred = pred.astype(np.uint32)
pred = pred.reshape((-1))
upper_half = pred >> 16 # get upper half for instances
lower_half = pred & 0xFFFF # get lower half for semantics
lower_half = remap_lut_First[lower_half] # do the remapping of semantics
pred = (upper_half << 16) + lower_half # reconstruct full label
pred = pred.astype(np.uint32)
final_preds = pred.astype(np.uint16)
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
final_preds.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)