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s3disvis.py
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s3disvis.py
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import argparse
import logging
import os
import sys
from pathlib import Path
import numpy as np
import torch
from tqdm import tqdm
from data.meta.indoor3d_util import g_label2color
from data.scannet import ScannetDatasetWholeScene
from models.encoder import DGCNNSemSeg
from models.model import ClusterNet
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = BASE_DIR
sys.path.append(os.path.join(ROOT_DIR, 'models'))
classes = ['ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door', 'table', 'chair',
'sofa', 'bookcase', 'board', 'clutter']
class2label = {cls: i for i, cls in enumerate(classes)}
seg_classes = class2label
seg_label_to_cat = {}
for i, cat in enumerate(seg_classes.keys()):
seg_label_to_cat[i] = cat
def parse_args():
'''PARAMETERS'''
parser = argparse.ArgumentParser('Model')
parser.add_argument('--batch_size', type=int, default=1, help='batch size in testing [default: 32]')
parser.add_argument('--gpu', type=str, default='0', help='specify gpu device')
parser.add_argument('--num_point', type=int, default=4096, help='point number [default: 4096]')
parser.add_argument('--pretrained_path', type=str, default='ckpt_epoch_200.pth', metavar='N',
help='Pretrained model path')
parser.add_argument('--log_dir', type=str, required=True, help='experiment root')
parser.add_argument('--visual', action='store_true', default=False, help='visualize result [default: False]')
parser.add_argument('--test_area', type=int, default=5, help='area for testing, option: 1-6 [default: 5]')
parser.add_argument('--num_votes', type=int, default=3,
help='aggregate segmentation scores with voting [default: 5]')
return parser.parse_args()
def add_vote(vote_label_pool, point_idx, pred_label, weight):
B = pred_label.shape[0]
N = pred_label.shape[1]
for b in range(B):
for n in range(N):
if weight[b, n] != 0 and not np.isinf(weight[b, n]):
vote_label_pool[int(point_idx[b, n]), int(pred_label[b, n])] += 1
return vote_label_pool
def main(args):
def log_string(str):
logger.info(str)
print(str)
'''HYPER PARAMETER'''
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
experiment_dir = 'log/sem_seg/' + args.log_dir
visual_dir = experiment_dir + '/visual/'
visual_dir = Path(visual_dir)
visual_dir.mkdir(exist_ok=True)
'''LOG'''
args = parse_args()
logger = logging.getLogger("Model")
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
file_handler = logging.FileHandler('%s/eval.txt' % experiment_dir)
file_handler.setLevel(logging.INFO)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
log_string('PARAMETER ...')
log_string(args)
NUM_CLASSES = 13
BATCH_SIZE = args.batch_size
NUM_POINT = args.num_point
root = '/data/gmei/data/stanford_indoor3d/'
test_dataset = ScannetDatasetWholeScene(root, split='test', test_area=args.test_area, block_points=NUM_POINT)
log_string("The number of test data is: %d" % len(test_dataset))
'''MODEL LOADING'''
net = DGCNNSemSeg(args.emb_dims, args.k, dropout=0.5, num_channel=9, num_class=40, pretrain=True).cuda()
# net = nn.DataParallel(net)
point_model = ClusterNet(net, dim=args.emb_dims, num_clus=args.num_clus)
model_path = os.path.join(f'checkpoints/{args.exp_name}/models/', args.pretrained_path)
point_model.load_state_dict(torch.load(model_path))
classifier = point_model.backbone
classifier = classifier.eval()
with torch.no_grad():
scene_id = test_dataset.file_list
scene_id = [x[:-4] for x in scene_id]
num_batches = len(test_dataset)
total_seen_class = [0 for _ in range(NUM_CLASSES)]
total_correct_class = [0 for _ in range(NUM_CLASSES)]
total_iou_deno_class = [0 for _ in range(NUM_CLASSES)]
log_string('---- EVALUATION WHOLE SCENE----')
for batch_idx in range(num_batches):
print("Inference [%d/%d] %s ..." % (batch_idx + 1, num_batches, scene_id[batch_idx]))
total_seen_class_tmp = [0 for _ in range(NUM_CLASSES)]
total_correct_class_tmp = [0 for _ in range(NUM_CLASSES)]
total_iou_deno_class_tmp = [0 for _ in range(NUM_CLASSES)]
if args.visual:
fout = open(os.path.join(visual_dir, scene_id[batch_idx] + '_pred.obj'), 'w')
fout_gt = open(os.path.join(visual_dir, scene_id[batch_idx] + '_gt.obj'), 'w')
whole_scene_data = test_dataset.scene_points_list[batch_idx]
whole_scene_label = test_dataset.semantic_labels_list[batch_idx]
vote_label_pool = np.zeros((whole_scene_label.shape[0], NUM_CLASSES))
for _ in tqdm(range(args.num_votes), total=args.num_votes):
scene_data, scene_label, scene_smpw, scene_point_index = test_dataset[batch_idx]
num_blocks = scene_data.shape[0]
s_batch_num = (num_blocks + BATCH_SIZE - 1) // BATCH_SIZE
batch_data = np.zeros((BATCH_SIZE, NUM_POINT, 9))
batch_label = np.zeros((BATCH_SIZE, NUM_POINT))
batch_point_index = np.zeros((BATCH_SIZE, NUM_POINT))
batch_smpw = np.zeros((BATCH_SIZE, NUM_POINT))
for sbatch in range(s_batch_num):
start_idx = sbatch * BATCH_SIZE
end_idx = min((sbatch + 1) * BATCH_SIZE, num_blocks)
real_batch_size = end_idx - start_idx
batch_data[0:real_batch_size, ...] = scene_data[start_idx:end_idx, ...]
batch_label[0:real_batch_size, ...] = scene_label[start_idx:end_idx, ...]
batch_point_index[0:real_batch_size, ...] = scene_point_index[start_idx:end_idx, ...]
batch_smpw[0:real_batch_size, ...] = scene_smpw[start_idx:end_idx, ...]
batch_data[:, :, 3:6] /= 1.0
torch_data = torch.Tensor(batch_data)
torch_data = torch_data.float().cuda()
torch_data = torch_data.transpose(2, 1)
seg_pred, _ = classifier(torch_data)
batch_pred_label = seg_pred.contiguous().cpu().data.max(2)[1].numpy()
vote_label_pool = add_vote(vote_label_pool, batch_point_index[0:real_batch_size, ...],
batch_pred_label[0:real_batch_size, ...],
batch_smpw[0:real_batch_size, ...])
pred_label = np.argmax(vote_label_pool, 1)
for l in range(NUM_CLASSES):
total_seen_class_tmp[l] += np.sum((whole_scene_label == l))
total_correct_class_tmp[l] += np.sum((pred_label == l) & (whole_scene_label == l))
total_iou_deno_class_tmp[l] += np.sum(((pred_label == l) | (whole_scene_label == l)))
total_seen_class[l] += total_seen_class_tmp[l]
total_correct_class[l] += total_correct_class_tmp[l]
total_iou_deno_class[l] += total_iou_deno_class_tmp[l]
iou_map = np.array(total_correct_class_tmp) / (np.array(total_iou_deno_class_tmp, dtype=np.float) + 1e-6)
print(iou_map)
arr = np.array(total_seen_class_tmp)
tmp_iou = np.mean(iou_map[arr != 0])
log_string('Mean IoU of %s: %.4f' % (scene_id[batch_idx], tmp_iou))
print('----------------------------')
filename = os.path.join(visual_dir, scene_id[batch_idx] + '.txt')
with open(filename, 'w') as pl_save:
for i in pred_label:
pl_save.write(str(int(i)) + '\n')
pl_save.close()
for i in range(whole_scene_label.shape[0]):
color = g_label2color[pred_label[i]]
color_gt = g_label2color[whole_scene_label[i]]
if args.visual:
fout.write('v %f %f %f %d %d %d\n' % (
whole_scene_data[i, 0], whole_scene_data[i, 1], whole_scene_data[i, 2], color[0], color[1],
color[2]))
fout_gt.write(
'v %f %f %f %d %d %d\n' % (
whole_scene_data[i, 0], whole_scene_data[i, 1], whole_scene_data[i, 2], color_gt[0],
color_gt[1], color_gt[2]))
if args.visual:
fout.close()
fout_gt.close()
IoU = np.array(total_correct_class) / (np.array(total_iou_deno_class, dtype=np.float) + 1e-6)
iou_per_class_str = '------- IoU --------\n'
for l in range(NUM_CLASSES):
iou_per_class_str += 'class %s, IoU: %.3f \n' % (
seg_label_to_cat[l] + ' ' * (14 - len(seg_label_to_cat[l])),
total_correct_class[l] / float(total_iou_deno_class[l]))
log_string(iou_per_class_str)
log_string('eval point avg class IoU: %f' % np.mean(IoU))
log_string('eval whole scene point avg class acc: %f' % (
np.mean(np.array(total_correct_class) / (np.array(total_seen_class, dtype=np.float) + 1e-6))))
log_string('eval whole scene point accuracy: %f' % (
np.sum(total_correct_class) / float(np.sum(total_seen_class) + 1e-6)))
print("Done!")
if __name__ == '__main__':
args = parse_args()
main(args)