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test.py
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test.py
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'''
PointGroup test.py
Written by Li Jiang
'''
import torch
import time
import numpy as np
import random
import os
from util.config import cfg
cfg.task = 'test'
from util.log import logger
import util.utils as utils
import util.eval as eval
def init():
global result_dir
result_dir = os.path.join(cfg.exp_path, 'result', 'epoch{}_nmst{}_scoret{}_npointt{}'.format(cfg.test_epoch, cfg.TEST_NMS_THRESH, cfg.TEST_SCORE_THRESH, cfg.TEST_NPOINT_THRESH), cfg.split)
backup_dir = os.path.join(result_dir, 'backup_files')
os.makedirs(backup_dir, exist_ok=True)
os.makedirs(os.path.join(result_dir, 'predicted_masks'), exist_ok=True)
os.system('cp test.py {}'.format(backup_dir))
os.system('cp {} {}'.format(cfg.model_dir, backup_dir))
os.system('cp {} {}'.format(cfg.dataset_dir, backup_dir))
os.system('cp {} {}'.format(cfg.config, backup_dir))
global semantic_label_idx
semantic_label_idx = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39]
logger.info(cfg)
random.seed(cfg.test_seed)
np.random.seed(cfg.test_seed)
torch.manual_seed(cfg.test_seed)
torch.cuda.manual_seed_all(cfg.test_seed)
def test(model, model_fn, data_name, epoch):
logger.info('>>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>')
if cfg.dataset == 'scannetv2':
if data_name == 'scannet':
from data.scannetv2_inst import Dataset
dataset = Dataset(test=True)
dataset.testLoader()
else:
print("Error: no data loader - " + data_name)
exit(0)
dataloader = dataset.test_data_loader
with torch.no_grad():
model = model.eval()
start = time.time()
matches = {}
for i, batch in enumerate(dataloader):
N = batch['feats'].shape[0]
test_scene_name = dataset.test_file_names[int(batch['id'][0])].split('/')[-1][:12]
start1 = time.time()
preds = model_fn(batch, model, epoch)
end1 = time.time() - start1
##### get predictions (#1 semantic_pred, pt_offsets; #2 scores, proposals_pred)
semantic_scores = preds['semantic'] # (N, nClass=20) float32, cuda
semantic_pred = semantic_scores.max(1)[1] # (N) long, cuda
pt_offsets = preds['pt_offsets'] # (N, 3), float32, cuda
if (epoch > cfg.prepare_epochs):
scores = preds['score'] # (nProposal, 1) float, cuda
scores_pred = torch.sigmoid(scores.view(-1))
proposals_idx, proposals_offset = preds['proposals']
# proposals_idx: (sumNPoint, 2), int, cpu, dim 0 for cluster_id, dim 1 for corresponding point idxs in N
# proposals_offset: (nProposal + 1), int, cpu
proposals_pred = torch.zeros((proposals_offset.shape[0] - 1, N), dtype=torch.int, device=scores_pred.device) # (nProposal, N), int, cuda
proposals_pred[proposals_idx[:, 0].long(), proposals_idx[:, 1].long()] = 1
semantic_id = torch.tensor(semantic_label_idx, device=scores_pred.device)[semantic_pred[proposals_idx[:, 1][proposals_offset[:-1].long()].long()]] # (nProposal), long
##### score threshold
score_mask = (scores_pred > cfg.TEST_SCORE_THRESH)
scores_pred = scores_pred[score_mask]
proposals_pred = proposals_pred[score_mask]
semantic_id = semantic_id[score_mask]
##### npoint threshold
proposals_pointnum = proposals_pred.sum(1)
npoint_mask = (proposals_pointnum > cfg.TEST_NPOINT_THRESH)
scores_pred = scores_pred[npoint_mask]
proposals_pred = proposals_pred[npoint_mask]
semantic_id = semantic_id[npoint_mask]
##### nms
if semantic_id.shape[0] == 0:
pick_idxs = np.empty(0)
else:
proposals_pred_f = proposals_pred.float() # (nProposal, N), float, cuda
intersection = torch.mm(proposals_pred_f, proposals_pred_f.t()) # (nProposal, nProposal), float, cuda
proposals_pointnum = proposals_pred_f.sum(1) # (nProposal), float, cuda
proposals_pn_h = proposals_pointnum.unsqueeze(-1).repeat(1, proposals_pointnum.shape[0])
proposals_pn_v = proposals_pointnum.unsqueeze(0).repeat(proposals_pointnum.shape[0], 1)
cross_ious = intersection / (proposals_pn_h + proposals_pn_v - intersection)
pick_idxs = non_max_suppression(cross_ious.cpu().numpy(), scores_pred.cpu().numpy(), cfg.TEST_NMS_THRESH) # int, (nCluster, N)
clusters = proposals_pred[pick_idxs]
cluster_scores = scores_pred[pick_idxs]
cluster_semantic_id = semantic_id[pick_idxs]
nclusters = clusters.shape[0]
##### prepare for evaluation
if cfg.eval:
pred_info = {}
pred_info['conf'] = cluster_scores.cpu().numpy()
pred_info['label_id'] = cluster_semantic_id.cpu().numpy()
pred_info['mask'] = clusters.cpu().numpy()
gt_file = os.path.join(cfg.data_root, cfg.dataset, cfg.split + '_gt', test_scene_name + '.txt')
gt2pred, pred2gt = eval.assign_instances_for_scan(test_scene_name, pred_info, gt_file)
matches[test_scene_name] = {}
matches[test_scene_name]['gt'] = gt2pred
matches[test_scene_name]['pred'] = pred2gt
##### save files
start3 = time.time()
if cfg.save_semantic:
os.makedirs(os.path.join(result_dir, 'semantic'), exist_ok=True)
semantic_np = semantic_pred.cpu().numpy()
np.save(os.path.join(result_dir, 'semantic', test_scene_name + '.npy'), semantic_np)
if cfg.save_pt_offsets:
os.makedirs(os.path.join(result_dir, 'coords_offsets'), exist_ok=True)
pt_offsets_np = pt_offsets.cpu().numpy()
coords_np = batch['locs_float'].numpy()
coords_offsets = np.concatenate((coords_np, pt_offsets_np), 1) # (N, 6)
np.save(os.path.join(result_dir, 'coords_offsets', test_scene_name + '.npy'), coords_offsets)
if(epoch > cfg.prepare_epochs and cfg.save_instance):
f = open(os.path.join(result_dir, test_scene_name + '.txt'), 'w')
for proposal_id in range(nclusters):
clusters_i = clusters[proposal_id].cpu().numpy() # (N)
semantic_label = np.argmax(np.bincount(semantic_pred[np.where(clusters_i == 1)[0]].cpu()))
score = cluster_scores[proposal_id]
f.write('predicted_masks/{}_{:03d}.txt {} {:.4f}'.format(test_scene_name, proposal_id, semantic_label_idx[semantic_label], score))
if proposal_id < nclusters - 1:
f.write('\n')
np.savetxt(os.path.join(result_dir, 'predicted_masks', test_scene_name + '_%03d.txt' % (proposal_id)), clusters_i, fmt='%d')
f.close()
end3 = time.time() - start3
end = time.time() - start
start = time.time()
##### print
logger.info("instance iter: {}/{} point_num: {} ncluster: {} time: total {:.2f}s inference {:.2f}s save {:.2f}s".format(batch['id'][0] + 1, len(dataset.test_files), N, nclusters, end, end1, end3))
##### evaluation
if cfg.eval:
ap_scores = eval.evaluate_matches(matches)
avgs = eval.compute_averages(ap_scores)
eval.print_results(avgs)
def non_max_suppression(ious, scores, threshold):
ixs = scores.argsort()[::-1]
pick = []
while len(ixs) > 0:
i = ixs[0]
pick.append(i)
iou = ious[i, ixs[1:]]
remove_ixs = np.where(iou > threshold)[0] + 1
ixs = np.delete(ixs, remove_ixs)
ixs = np.delete(ixs, 0)
return np.array(pick, dtype=np.int32)
if __name__ == '__main__':
init()
##### get model version and data version
exp_name = cfg.config.split('/')[-1][:-5]
model_name = exp_name.split('_')[0]
data_name = exp_name.split('_')[-1]
##### model
logger.info('=> creating model ...')
logger.info('Classes: {}'.format(cfg.classes))
if model_name == 'pointgroup':
from model.pointgroup.pointgroup import PointGroup as Network
from model.pointgroup.pointgroup import model_fn_decorator
else:
print("Error: no model version " + model_name)
exit(0)
model = Network(cfg)
use_cuda = torch.cuda.is_available()
logger.info('cuda available: {}'.format(use_cuda))
assert use_cuda
model = model.cuda()
# logger.info(model)
logger.info('#classifier parameters (model): {}'.format(sum([x.nelement() for x in model.parameters()])))
##### model_fn (criterion)
model_fn = model_fn_decorator(test=True)
##### load model
utils.checkpoint_restore(model, cfg.exp_path, cfg.config.split('/')[-1][:-5], use_cuda, cfg.test_epoch, dist=False, f=cfg.pretrain) # resume from the latest epoch, or specify the epoch to restore
##### evaluate
test(model, model_fn, data_name, cfg.test_epoch)