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test_kitti_ssc.py
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test_kitti_ssc.py
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# *_*coding:utf-8 *_*
"""
Author: Xu Yan
File: test_kitti_ssc.py
Date: 2020/4/9 14:40
"""
import sys
import os
import json
import spconv
import time
import yaml
import torch
import importlib
import argparse
import numpy as np
from tqdm import tqdm
import torch.nn as nn
from models import model_utils
import sparseconvnet as scn
from datetime import datetime
import kitti_dataset
'''Inference'''
def parse_args():
'''PARAMETERS'''
parser = argparse.ArgumentParser('Model')
parser.add_argument('--gpu', type=str, default='0', help='specify gpu device')
parser.add_argument('--log_dir', type=str, default='log/JS3C-Net-kitti/', help='Experiment root')
parser.add_argument('--dataset', type=str, default='valid', help='[valid/test]')
return parser.parse_args()
args = parse_args()
print('Load Model...')
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
model_path = args.log_dir
output_dir = model_path + '/dump/'
if not os.path.exists(output_dir): os.mkdir(output_dir)
output_dir = output_dir + 'completion'
if not os.path.exists(output_dir): os.mkdir(output_dir)
submit_dir = output_dir + '/submit_' + args.dataset + datetime.now().strftime('%Y_%m_%d')
if not os.path.exists(submit_dir): os.mkdir(submit_dir)
use_cuda = torch.cuda.is_available()
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = BASE_DIR
sys.path.append(model_path)
with open(os.path.join(model_path, 'args.txt'), 'r') as f:
config = json.load(f)
config['GENERAL']['debug'] = False
nClasses = 20
config_file = os.path.join('opt', 'semantic-kitti.yaml')
kitti_config = yaml.safe_load(open(config_file, 'r'))
valid_labels = np.zeros((20), dtype=np.int32)
learning_map_inv = kitti_config['learning_map_inv']
for key,value in learning_map_inv.items():
valid_labels[key] = value
seg_head = importlib.import_module('models.' + config['Segmentation']['model_name'])
seg_model = seg_head.get_model
complet_head = importlib.import_module('models.' + config['Completion']['model_name'])
complet_model = complet_head.get_model
class J3SC_Net(nn.Module):
def __init__(self):
super().__init__()
self.args = config
self.seg_head = seg_model(config)
self.complet_head = complet_model(config)
self.voxelpool = model_utils.VoxelPooling(config)
self.seg_sigmas_sq = nn.Parameter(torch.Tensor(1).uniform_(0.2, 1), requires_grad=True)
self.complet_sigmas_sq = nn.Parameter(torch.Tensor(1).uniform_(0.2, 1), requires_grad=True)
def forward(self, x):
seg_inputs, complet_inputs, _ = x
'''Segmentation Head'''
seg_output, feat = self.seg_head(seg_inputs)
torch.cuda.empty_cache()
'''Completion Head'''
coords = complet_inputs['complet_coords']
coords = coords[:, [0, 3, 2, 1]]
if self.args['DATA']['dataset'] == 'SemanticKITTI':
coords[:, 3] += 1 # TODO SemanticKITTI will generate [256,256,31]
elif self.args['DATA']['dataset'] == 'SemanticPOSS':
coords[:, 3][coords[:, 3] > 31] = 31
if self.args['Completion']['feeding'] == 'both':
feeding = torch.cat([seg_output, feat], 1)
elif self.args['Completion']['feeding'] == 'feat':
feeding = feat
else:
feeding = seg_output
features = self.voxelpool(invoxel_xyz=complet_inputs['complet_invoxel_features'][:, :, :-1],
invoxel_map=complet_inputs['complet_invoxel_features'][:, :, -1].long(),
src_feat=feeding,
voxel_center=complet_inputs['voxel_centers'])
if self.args['Completion']['no_fuse_feat']:
features[...] = 1
features = features.detach()
batch_complet = spconv.SparseConvTensor(features.float(), coords.int(), self.args['Completion']['full_scale'],
self.args['TRAIN']['batch_size'])
batch_complet = dataset.sparse_tensor_augmentation(batch_complet, complet_inputs['state'])
if self.args['GENERAL']['debug']:
model_utils.check_occupation(complet_inputs['complet_input'], batch_complet.dense())
complet_output = self.complet_head(batch_complet)
torch.cuda.empty_cache()
return seg_output, complet_output
classifier = J3SC_Net()
if use_cuda:
classifier = classifier.cuda()
classifier = classifier.eval()
scn.checkpoint_restore(classifier, model_path, use_cuda)
print('#classifer parameters %d' % sum([x.nelement() for x in classifier.parameters()]))
dataset = importlib.import_module('kitti_dataset')
input_data = kitti_dataset.get_dataset(config, split = args.dataset)
data_loader = torch.utils.data.DataLoader(
input_data,
batch_size=1,
collate_fn=seg_head.Merge,
num_workers=config['TRAIN']['train_workers'],
pin_memory=True,
shuffle=False,
drop_last=False,
worker_init_fn=lambda x: np.random.seed(x + int(time.time()))
)
num_sample = len(data_loader)
print("# files: {}".format(num_sample))
with torch.no_grad():
for idx, batch in tqdm(enumerate(data_loader), total=len(data_loader)):
start = time.time()
sequence, filename = batch[2][0]
os.makedirs(os.path.join(submit_dir, 'sequences', sequence, 'predictions'), exist_ok=True)
full_save_dir = os.path.join(submit_dir, 'sequences', sequence, 'predictions', filename + '.label')
if os.path.exists(full_save_dir):
print('%s already exsist...' % (full_save_dir))
continue
seg_pred, complet_pred= classifier(batch)
pred_choice_complet = complet_pred[-1].data.max(1)[1]
pred = pred_choice_complet.cpu().long().data.numpy()
# make lookup table for mapping
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())
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)
# Save
final_preds = pred.astype(np.uint16)
final_preds.tofile(full_save_dir)