def kitti_eval():
if float(sys.version[:3]) < 3.6:
print("KITTI mAP evaluation can only run with python3.6+")
sys.exit(1)
args = parse_args()
label_dir = os.path.join(args.data_dir, 'KITTI/object/training', 'label_2')
split_file = os.path.join(args.data_dir, 'KITTI/ImageSets',
'{}.txt'.format(args.split))
final_output_dir = os.path.join(args.result_dir, 'final_result', 'data')
name_to_class = {'Car': 0, 'Pedestrian': 1, 'Cyclist': 2}
from tools.kitti_object_eval_python.evaluate import evaluate as kitti_evaluate
ap_result_str, ap_dict = kitti_evaluate(
label_dir, final_output_dir, label_split_file=split_file,
current_class=name_to_class[args.class_name])
print("KITTI evaluate: ", ap_result_str, ap_dict)
def eval_one_epoch_joint(model, dataloader, epoch_id, result_dir, logger):
np.random.seed(666)
MEAN_SIZE = torch.from_numpy(cfg.CLS_MEAN_SIZE[0]).cuda()
mode = 'TEST' if args.test else 'EVAL'
final_output_dir = os.path.join(result_dir, 'final_result', 'data')
os.makedirs(final_output_dir, exist_ok=True)
if args.save_result:
roi_output_dir = os.path.join(result_dir, 'roi_result', 'data')
refine_output_dir = os.path.join(result_dir, 'refine_result', 'data')
rpn_output_dir = os.path.join(result_dir, 'rpn_result', 'data')
os.makedirs(rpn_output_dir, exist_ok=True)
os.makedirs(roi_output_dir, exist_ok=True)
os.makedirs(refine_output_dir, exist_ok=True)
logger.info('---- EPOCH %s JOINT EVALUATION ----' % epoch_id)
logger.info('==> Output file: %s' % result_dir)
model.eval()
thresh_list = [0.1, 0.3, 0.5, 0.7, 0.9]
total_recalled_bbox_list, total_gt_bbox = [0] * 5, 0
total_roi_recalled_bbox_list = [0] * 5
dataset = dataloader.dataset
cnt = final_total = total_cls_acc = total_cls_acc_refined = total_rpn_iou = 0
progress_bar = tqdm.tqdm(total=len(dataloader), leave=True, desc='eval')
for data in dataloader:
cnt += 1
sample_id, pts_rect, pts_features, pts_input = \
data['sample_id'], data['pts_rect'], data['pts_features'], data['pts_input']
batch_size = len(sample_id)
inputs = torch.from_numpy(pts_input).cuda(non_blocking=True).float()
input_data = {'pts_input': inputs}
# model inference
ret_dict = model(input_data)
roi_scores_raw = ret_dict['roi_scores_raw'] # (B, M)
roi_boxes3d = ret_dict['rois'] # (B, M, 7)
seg_result = ret_dict['seg_result'].long() # (B, N)
rcnn_cls = ret_dict['rcnn_cls'].view(batch_size, -1,
ret_dict['rcnn_cls'].shape[1])
rcnn_reg = ret_dict['rcnn_reg'].view(
batch_size, -1, ret_dict['rcnn_reg'].shape[1]) # (B, M, C)
# bounding box regression
anchor_size = MEAN_SIZE
if cfg.RCNN.SIZE_RES_ON_ROI:
assert False
pred_boxes3d = decode_bbox_target(
roi_boxes3d.view(-1, 7),
rcnn_reg.view(-1, rcnn_reg.shape[-1]),
anchor_size=anchor_size,
loc_scope=cfg.RCNN.LOC_SCOPE,
loc_bin_size=cfg.RCNN.LOC_BIN_SIZE,
num_head_bin=cfg.RCNN.NUM_HEAD_BIN,
get_xz_fine=True,
get_y_by_bin=cfg.RCNN.LOC_Y_BY_BIN,
loc_y_scope=cfg.RCNN.LOC_Y_SCOPE,
loc_y_bin_size=cfg.RCNN.LOC_Y_BIN_SIZE,
get_ry_fine=True).view(batch_size, -1, 7)
# scoring
if rcnn_cls.shape[2] == 1:
raw_scores = rcnn_cls # (B, M, 1)
norm_scores = torch.sigmoid(raw_scores)
pred_classes = (norm_scores > cfg.RCNN.SCORE_THRESH).long()
else:
pred_classes = torch.argmax(rcnn_cls, dim=1).view(-1)
cls_norm_scores = F.softmax(rcnn_cls, dim=1)
raw_scores = rcnn_cls[:, pred_classes]
norm_scores = cls_norm_scores[:, pred_classes]
# evaluation
recalled_num = gt_num = rpn_iou = 0
if not args.test:
if not cfg.RPN.FIXED:
rpn_cls_label, rpn_reg_label = data['rpn_cls_label'], data[
'rpn_reg_label']
rpn_cls_label = torch.from_numpy(rpn_cls_label).cuda(
non_blocking=True).long()
gt_boxes3d = data['gt_boxes3d']
for k in range(batch_size):
# calculate recall
cur_gt_boxes3d = gt_boxes3d[k]
tmp_idx = cur_gt_boxes3d.__len__() - 1
while tmp_idx >= 0 and cur_gt_boxes3d[tmp_idx].sum() == 0:
tmp_idx -= 1
if tmp_idx >= 0:
cur_gt_boxes3d = cur_gt_boxes3d[:tmp_idx + 1]
cur_gt_boxes3d = torch.from_numpy(cur_gt_boxes3d).cuda(
non_blocking=True).float()
iou3d = iou3d_utils.boxes_iou3d_gpu(
pred_boxes3d[k], cur_gt_boxes3d)
gt_max_iou, _ = iou3d.max(dim=0)
refined_iou, _ = iou3d.max(dim=1)
for idx, thresh in enumerate(thresh_list):
total_recalled_bbox_list[idx] += (gt_max_iou >
thresh).sum().item()
recalled_num += (gt_max_iou > 0.7).sum().item()
gt_num += cur_gt_boxes3d.shape[0]
total_gt_bbox += cur_gt_boxes3d.shape[0]
# original recall
iou3d_in = iou3d_utils.boxes_iou3d_gpu(
roi_boxes3d[k], cur_gt_boxes3d)
gt_max_iou_in, _ = iou3d_in.max(dim=0)
for idx, thresh in enumerate(thresh_list):
total_roi_recalled_bbox_list[idx] += (
gt_max_iou_in > thresh).sum().item()
if not cfg.RPN.FIXED:
fg_mask = rpn_cls_label > 0
correct = ((seg_result == rpn_cls_label)
& fg_mask).sum().float()
union = fg_mask.sum().float() + (seg_result >
0).sum().float() - correct
rpn_iou = correct / torch.clamp(union, min=1.0)
total_rpn_iou += rpn_iou.item()
disp_dict = {
'mode': mode,
'recall': '%d/%d' % (total_recalled_bbox_list[3], total_gt_bbox)
}
progress_bar.set_postfix(disp_dict)
progress_bar.update()
if args.save_result:
# save roi and refine results
roi_boxes3d_np = roi_boxes3d.cpu().numpy()
pred_boxes3d_np = pred_boxes3d.cpu().numpy()
roi_scores_raw_np = roi_scores_raw.cpu().numpy()
raw_scores_np = raw_scores.cpu().numpy()
rpn_cls_np = ret_dict['rpn_cls'].cpu().numpy()
rpn_xyz_np = ret_dict['backbone_xyz'].cpu().numpy()
seg_result_np = seg_result.cpu().numpy()
output_data = np.concatenate(
(rpn_xyz_np, rpn_cls_np.reshape(batch_size, -1, 1),
seg_result_np.reshape(batch_size, -1, 1)),
axis=2)
for k in range(batch_size):
cur_sample_id = sample_id[k]
calib = dataset.get_calib(cur_sample_id)
image_shape = dataset.get_image_shape(cur_sample_id)
save_kitti_format(cur_sample_id, calib, roi_boxes3d_np[k],
roi_output_dir, roi_scores_raw_np[k],
image_shape)
save_kitti_format(cur_sample_id, calib, pred_boxes3d_np[k],
refine_output_dir, raw_scores_np[k],
image_shape)
output_file = os.path.join(rpn_output_dir,
'%06d.npy' % cur_sample_id)
np.save(output_file, output_data.astype(np.float32))
# scores thresh
inds = norm_scores > cfg.RCNN.SCORE_THRESH
for k in range(batch_size):
cur_inds = inds[k].view(-1)
if cur_inds.sum() == 0:
continue
pred_boxes3d_selected = pred_boxes3d[k, cur_inds]
raw_scores_selected = raw_scores[k, cur_inds]
norm_scores_selected = norm_scores[k, cur_inds]
# NMS thresh
# rotated nms
boxes_bev_selected = kitti_utils.boxes3d_to_bev_torch(
pred_boxes3d_selected)
keep_idx = iou3d_utils.nms_gpu(boxes_bev_selected,
raw_scores_selected,
cfg.RCNN.NMS_THRESH).view(-1)
pred_boxes3d_selected = pred_boxes3d_selected[keep_idx]
scores_selected = raw_scores_selected[keep_idx]
pred_boxes3d_selected, scores_selected = pred_boxes3d_selected.cpu(
).numpy(), scores_selected.cpu().numpy()
cur_sample_id = sample_id[k]
calib = dataset.get_calib(cur_sample_id)
final_total += pred_boxes3d_selected.shape[0]
image_shape = dataset.get_image_shape(cur_sample_id)
save_kitti_format(cur_sample_id, calib, pred_boxes3d_selected,
final_output_dir, scores_selected, image_shape)
progress_bar.close()
# dump empty files
split_file = os.path.join(dataset.imageset_dir, '..', '..', 'ImageSets',
dataset.split + '.txt')
split_file = os.path.abspath(split_file)
image_idx_list = [x.strip() for x in open(split_file).readlines()]
empty_cnt = 0
for k in range(image_idx_list.__len__()):
cur_file = os.path.join(final_output_dir, '%s.txt' % image_idx_list[k])
if not os.path.exists(cur_file):
with open(cur_file, 'w') as temp_f:
pass
empty_cnt += 1
logger.info('empty_cnt=%d: dump empty file %s' %
(empty_cnt, cur_file))
ret_dict = {'empty_cnt': empty_cnt}
logger.info(
'-------------------performance of epoch %s---------------------' %
epoch_id)
logger.info(str(datetime.now()))
avg_rpn_iou = (total_rpn_iou / max(cnt, 1.0))
avg_cls_acc = (total_cls_acc / max(cnt, 1.0))
avg_cls_acc_refined = (total_cls_acc_refined / max(cnt, 1.0))
avg_det_num = (final_total / max(len(dataset), 1.0))
logger.info('final average detections: %.3f' % avg_det_num)
logger.info('final average rpn_iou refined: %.3f' % avg_rpn_iou)
logger.info('final average cls acc: %.3f' % avg_cls_acc)
logger.info('final average cls acc refined: %.3f' % avg_cls_acc_refined)
ret_dict['rpn_iou'] = avg_rpn_iou
ret_dict['rcnn_cls_acc'] = avg_cls_acc
ret_dict['rcnn_cls_acc_refined'] = avg_cls_acc_refined
ret_dict['rcnn_avg_num'] = avg_det_num
for idx, thresh in enumerate(thresh_list):
cur_roi_recall = total_roi_recalled_bbox_list[idx] / max(
total_gt_bbox, 1.0)
logger.info('total roi bbox recall(thresh=%.3f): %d / %d = %f' %
(thresh, total_roi_recalled_bbox_list[idx], total_gt_bbox,
cur_roi_recall))
ret_dict['rpn_recall(thresh=%.2f)' % thresh] = cur_roi_recall
for idx, thresh in enumerate(thresh_list):
cur_recall = total_recalled_bbox_list[idx] / max(total_gt_bbox, 1.0)
logger.info(
'total bbox recall(thresh=%.3f): %d / %d = %f' %
(thresh, total_recalled_bbox_list[idx], total_gt_bbox, cur_recall))
ret_dict['rcnn_recall(thresh=%.2f)' % thresh] = cur_recall
if cfg.TEST.SPLIT != 'test':
logger.info('Averate Precision:')
name_to_class = {'Car': 0, 'Pedestrian': 1, 'Cyclist': 2}
ap_result_str, ap_dict = kitti_evaluate(
dataset.label_dir,
final_output_dir,
label_split_file=split_file,
current_class=name_to_class[cfg.CLASSES])
logger.info(ap_result_str)
ret_dict.update(ap_dict)
logger.info('result is saved to: %s' % result_dir)
return ret_dict
def eval_one_epoch_rcnn(model, dataloader, epoch_id, result_dir, logger):
np.random.seed(1024)
MEAN_SIZE = torch.from_numpy(cfg.CLS_MEAN_SIZE[0]).cuda()
mode = 'TEST' if args.test else 'EVAL'
final_output_dir = os.path.join(result_dir, 'final_result', 'data')
os.makedirs(final_output_dir, exist_ok=True)
if args.save_result:
roi_output_dir = os.path.join(result_dir, 'roi_result', 'data')
refine_output_dir = os.path.join(result_dir, 'refine_result', 'data')
os.makedirs(roi_output_dir, exist_ok=True)
os.makedirs(refine_output_dir, exist_ok=True)
logger.info('---- EPOCH %s RCNN EVALUATION ----' % epoch_id)
model.eval()
thresh_list = [0.1, 0.3, 0.5, 0.7, 0.9]
total_recalled_bbox_list, total_gt_bbox = [0] * 5, 0
total_roi_recalled_bbox_list = [0] * 5
dataset = dataloader.dataset
cnt = final_total = total_cls_acc = total_cls_acc_refined = 0
progress_bar = tqdm.tqdm(total=len(dataloader), leave=True, desc='eval')
for data in dataloader:
sample_id = data['sample_id']
cnt += 1
assert args.batch_size == 1, 'Only support bs=1 here'
input_data = {}
for key, val in data.items():
if key != 'sample_id':
input_data[key] = torch.from_numpy(val).contiguous().cuda(
non_blocking=True).float()
roi_boxes3d = input_data['roi_boxes3d']
roi_scores = input_data['roi_scores']
if cfg.RCNN.ROI_SAMPLE_JIT:
for key, val in input_data.items():
if key in ['gt_iou', 'gt_boxes3d']:
continue
input_data[key] = input_data[key].unsqueeze(dim=0)
else:
pts_input = torch.cat(
(input_data['pts_input'], input_data['pts_features']), dim=-1)
input_data['pts_input'] = pts_input
ret_dict = model(input_data)
rcnn_cls = ret_dict['rcnn_cls']
rcnn_reg = ret_dict['rcnn_reg']
# bounding box regression
anchor_size = MEAN_SIZE
if cfg.RCNN.SIZE_RES_ON_ROI:
roi_size = input_data['roi_size']
anchor_size = roi_size
pred_boxes3d = decode_bbox_target(
roi_boxes3d,
rcnn_reg,
anchor_size=anchor_size,
loc_scope=cfg.RCNN.LOC_SCOPE,
loc_bin_size=cfg.RCNN.LOC_BIN_SIZE,
num_head_bin=cfg.RCNN.NUM_HEAD_BIN,
get_xz_fine=True,
get_y_by_bin=cfg.RCNN.LOC_Y_BY_BIN,
loc_y_scope=cfg.RCNN.LOC_Y_SCOPE,
loc_y_bin_size=cfg.RCNN.LOC_Y_BIN_SIZE,
get_ry_fine=True)
# scoring
if rcnn_cls.shape[1] == 1:
raw_scores = rcnn_cls.view(-1)
norm_scores = torch.sigmoid(raw_scores)
pred_classes = (norm_scores > cfg.RCNN.SCORE_THRESH).long()
else:
pred_classes = torch.argmax(rcnn_cls, dim=1).view(-1)
cls_norm_scores = F.softmax(rcnn_cls, dim=1)
raw_scores = rcnn_cls[:, pred_classes]
norm_scores = cls_norm_scores[:, pred_classes]
# evaluation
disp_dict = {'mode': mode}
if not args.test:
gt_boxes3d = input_data['gt_boxes3d']
gt_iou = input_data['gt_iou']
# calculate recall
gt_num = gt_boxes3d.shape[0]
if gt_num > 0:
iou3d = iou3d_utils.boxes_iou3d_gpu(pred_boxes3d, gt_boxes3d)
gt_max_iou, _ = iou3d.max(dim=0)
refined_iou, _ = iou3d.max(dim=1)
for idx, thresh in enumerate(thresh_list):
total_recalled_bbox_list[idx] += (gt_max_iou >
thresh).sum().item()
recalled_num = (gt_max_iou > 0.7).sum().item()
total_gt_bbox += gt_num
iou3d_in = iou3d_utils.boxes_iou3d_gpu(roi_boxes3d, gt_boxes3d)
gt_max_iou_in, _ = iou3d_in.max(dim=0)
for idx, thresh in enumerate(thresh_list):
total_roi_recalled_bbox_list[idx] += (gt_max_iou_in >
thresh).sum().item()
# classification accuracy
cls_label = (gt_iou > cfg.RCNN.CLS_FG_THRESH).float()
cls_valid_mask = ((gt_iou >= cfg.RCNN.CLS_FG_THRESH) |
(gt_iou <= cfg.RCNN.CLS_BG_THRESH)).float()
cls_acc = ((pred_classes == cls_label.long()).float() *
cls_valid_mask).sum() / max(cls_valid_mask.sum(), 1.0)
iou_thresh = 0.7 if cfg.CLASSES == 'Car' else 0.5
cls_label_refined = (gt_iou >= iou_thresh).float()
cls_acc_refined = (
pred_classes == cls_label_refined.long()).float().sum() / max(
cls_label_refined.shape[0], 1.0)
total_cls_acc += cls_acc.item()
total_cls_acc_refined += cls_acc_refined.item()
disp_dict['recall'] = '%d/%d' % (total_recalled_bbox_list[3],
total_gt_bbox)
disp_dict['cls_acc_refined'] = '%.2f' % cls_acc_refined.item()
progress_bar.set_postfix(disp_dict)
progress_bar.update()
image_shape = dataset.get_image_shape(sample_id)
if args.save_result:
# save roi and refine results
roi_boxes3d_np = roi_boxes3d.cpu().numpy()
pred_boxes3d_np = pred_boxes3d.cpu().numpy()
calib = dataset.get_calib(sample_id)
save_kitti_format(sample_id, calib, roi_boxes3d_np, roi_output_dir,
roi_scores, image_shape)
save_kitti_format(sample_id, calib, pred_boxes3d_np,
refine_output_dir,
raw_scores.cpu().numpy(), image_shape)
# NMS and scoring
# scores thresh
inds = norm_scores > cfg.RCNN.SCORE_THRESH
if inds.sum() == 0:
continue
pred_boxes3d_selected = pred_boxes3d[inds]
raw_scores_selected = raw_scores[inds]
# NMS thresh
boxes_bev_selected = kitti_utils.boxes3d_to_bev_torch(
pred_boxes3d_selected)
keep_idx = iou3d_utils.nms_gpu(boxes_bev_selected, raw_scores_selected,
cfg.RCNN.NMS_THRESH)
pred_boxes3d_selected = pred_boxes3d_selected[keep_idx]
scores_selected = raw_scores_selected[keep_idx]
pred_boxes3d_selected, scores_selected = pred_boxes3d_selected.cpu(
).numpy(), scores_selected.cpu().numpy()
calib = dataset.get_calib(sample_id)
final_total += pred_boxes3d_selected.shape[0]
save_kitti_format(sample_id, calib, pred_boxes3d_selected,
final_output_dir, scores_selected, image_shape)
progress_bar.close()
# dump empty files
split_file = os.path.join(dataset.imageset_dir, '..', '..', 'ImageSets',
dataset.split + '.txt')
split_file = os.path.abspath(split_file)
image_idx_list = [x.strip() for x in open(split_file).readlines()]
empty_cnt = 0
for k in range(image_idx_list.__len__()):
cur_file = os.path.join(final_output_dir, '%s.txt' % image_idx_list[k])
if not os.path.exists(cur_file):
with open(cur_file, 'w') as temp_f:
pass
empty_cnt += 1
logger.info('empty_cnt=%d: dump empty file %s' %
(empty_cnt, cur_file))
ret_dict = {'empty_cnt': empty_cnt}
logger.info(
'-------------------performance of epoch %s---------------------' %
epoch_id)
logger.info(str(datetime.now()))
avg_cls_acc = (total_cls_acc / max(cnt, 1.0))
avg_cls_acc_refined = (total_cls_acc_refined / max(cnt, 1.0))
avg_det_num = (final_total / max(cnt, 1.0))
logger.info('final average detections: %.3f' % avg_det_num)
logger.info('final average cls acc: %.3f' % avg_cls_acc)
logger.info('final average cls acc refined: %.3f' % avg_cls_acc_refined)
ret_dict['rcnn_cls_acc'] = avg_cls_acc
ret_dict['rcnn_cls_acc_refined'] = avg_cls_acc_refined
ret_dict['rcnn_avg_num'] = avg_det_num
for idx, thresh in enumerate(thresh_list):
cur_roi_recall = total_roi_recalled_bbox_list[idx] / max(
total_gt_bbox, 1.0)
logger.info('total roi bbox recall(thresh=%.3f): %d / %d = %f' %
(thresh, total_roi_recalled_bbox_list[idx], total_gt_bbox,
cur_roi_recall))
ret_dict['rpn_recall(thresh=%.2f)' % thresh] = cur_roi_recall
for idx, thresh in enumerate(thresh_list):
cur_recall = total_recalled_bbox_list[idx] / max(total_gt_bbox, 1.0)
logger.info(
'total bbox recall(thresh=%.3f): %d / %d = %f' %
(thresh, total_recalled_bbox_list[idx], total_gt_bbox, cur_recall))
ret_dict['rcnn_recall(thresh=%.2f)' % thresh] = cur_recall
if cfg.TEST.SPLIT != 'test':
logger.info('Averate Precision:')
name_to_class = {'Car': 0, 'Pedestrian': 1, 'Cyclist': 2}
ap_result_str, ap_dict = kitti_evaluate(
dataset.label_dir,
final_output_dir,
label_split_file=split_file,
current_class=name_to_class[cfg.CLASSES])
logger.info(ap_result_str)
ret_dict.update(ap_dict)
logger.info('result is saved to: %s' % result_dir)
return ret_dict
ret_dict['rcnn_cls_acc_refined'] = avg_cls_acc_refined
ret_dict['rcnn_avg_num'] = avg_det_num
for idx, thresh in enumerate(thresh_list):
cur_roi_recall = total_roi_recalled_bbox_list[idx] / max(
total_gt_bbox, 1.0)
logger.info('total roi bbox recall(thresh=%.3f): %d / %d = %f' %
(thresh, total_roi_recalled_bbox_list[idx], total_gt_bbox,
cur_roi_recall))
ret_dict['rpn_recall(thresh=%.2f)' % thresh] = cur_roi_recall
for idx, thresh in enumerate(thresh_list):
cur_recall = total_recalled_bbox_list[idx] / max(total_gt_bbox, 1.0)
logger.info(
'total bbox recall(thresh=%.3f): %d / %d = %f' %
(thresh, total_recalled_bbox_list[idx], total_gt_bbox, cur_recall))
ret_dict['rcnn_recall(thresh=%.2f)' % thresh] = cur_recall
if cfg.TEST.SPLIT != 'test':
logger.info('Averate Precision:')
name_to_class = {'Car': 0, 'Pedestrian': 1, 'Cyclist': 2}
ap_result_str, ap_dict = kitti_evaluate(
dataset.label_dir,
final_output_dir,
label_split_file=split_file,
current_class=name_to_class[cfg.CLASSES])
logger.info(ap_result_str)
ret_dict.update(ap_dict)
logger.info('result is saved to: %s' % result_dir)
def eval():
args = parse_args()
print_arguments(args)
# check whether the installed paddle is compiled with GPU
# PointRCNN model can only run on GPU
check_gpu(True)
load_config(args.cfg)
if args.set_cfgs is not None:
set_config_from_list(args.set_cfgs)
if not os.path.isdir(args.output_dir):
os.makedirs(args.output_dir)
if args.eval_mode == 'rpn':
cfg.RPN.ENABLED = True
cfg.RCNN.ENABLED = False
elif args.eval_mode == 'rcnn':
cfg.RCNN.ENABLED = True
cfg.RPN.ENABLED = cfg.RPN.FIXED = True
assert args.batch_size, "batch size must be 1 in rcnn evaluation"
elif args.eval_mode == 'rcnn_offline':
cfg.RCNN.ENABLED = True
cfg.RPN.ENABLED = False
assert args.batch_size, "batch size must be 1 in rcnn_offline evaluation"
else:
raise NotImplementedError("unkown eval mode: {}".format(
args.eval_mode))
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
# build model
startup = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup):
with fluid.unique_name.guard():
eval_model = PointRCNN(cfg, args.batch_size, True, 'TEST')
eval_model.build()
eval_loader = eval_model.get_loader()
eval_feeds = eval_model.get_feeds()
eval_outputs = eval_model.get_outputs()
eval_prog = eval_prog.clone(True)
extra_keys = []
if args.eval_mode == 'rpn':
extra_keys.extend(['sample_id', 'rpn_cls_label', 'gt_boxes3d'])
if args.save_rpn_feature:
extra_keys.extend([
'pts_rect',
'pts_features',
'pts_input',
])
eval_keys, eval_values = parse_outputs(eval_outputs,
prog=eval_prog,
extra_keys=extra_keys)
eval_compile_prog = fluid.compiler.CompiledProgram(
eval_prog).with_data_parallel()
exe.run(startup)
# load weights
if not os.path.isdir(args.weights):
assert os.path.exists("{}.pdparams".format(args.weights)), \
"Given resume weight {}.pdparams not exist.".format(args.weights)
fluid.load(eval_prog, args.weights, exe)
kitti_feature_dir = os.path.join(args.output_dir, 'features')
kitti_output_dir = os.path.join(args.output_dir, 'detections', 'data')
seg_output_dir = os.path.join(args.output_dir, 'seg_result')
if args.save_rpn_feature:
if os.path.exists(kitti_feature_dir):
shutil.rmtree(kitti_feature_dir)
os.makedirs(kitti_feature_dir)
if os.path.exists(kitti_output_dir):
shutil.rmtree(kitti_output_dir)
os.makedirs(kitti_output_dir)
if os.path.exists(seg_output_dir):
shutil.rmtree(seg_output_dir)
os.makedirs(seg_output_dir)
# must make sure these dirs existing
roi_output_dir = os.path.join('./result_dir', 'roi_result', 'data')
refine_output_dir = os.path.join('./result_dir', 'refine_result', 'data')
final_output_dir = os.path.join("./result_dir", 'final_result', 'data')
if not os.path.exists(final_output_dir):
os.makedirs(final_output_dir)
if args.save_result:
if not os.path.exists(roi_output_dir):
os.makedirs(roi_output_dir)
if not os.path.exists(refine_output_dir):
os.makedirs(refine_output_dir)
# get reader
kitti_rcnn_reader = KittiRCNNReader(
data_dir=args.data_dir,
npoints=cfg.RPN.NUM_POINTS,
split=cfg.TEST.SPLIT,
mode='EVAL',
classes=cfg.CLASSES,
rcnn_eval_roi_dir=args.rcnn_eval_roi_dir,
rcnn_eval_feature_dir=args.rcnn_eval_feature_dir)
eval_reader = kitti_rcnn_reader.get_multiprocess_reader(
args.batch_size, eval_feeds)
eval_loader.set_sample_list_generator(eval_reader, place)
thresh_list = [0.1, 0.3, 0.5, 0.7, 0.9]
queue = multiprocessing.Queue(128)
mgr = multiprocessing.Manager()
lock = multiprocessing.Lock()
mdict = mgr.dict()
if cfg.RPN.ENABLED:
mdict['exit_proc'] = 0
mdict['total_gt_bbox'] = 0
mdict['total_cnt'] = 0
mdict['total_rpn_iou'] = 0
for i in range(len(thresh_list)):
mdict['total_recalled_bbox_list_{}'.format(i)] = 0
p_list = []
for i in range(METRIC_PROC_NUM):
p_list.append(
multiprocessing.Process(target=rpn_metric,
args=(queue, mdict, lock, thresh_list,
args.save_rpn_feature,
kitti_feature_dir,
seg_output_dir, kitti_output_dir,
kitti_rcnn_reader, cfg.CLASSES)))
p_list[-1].start()
if cfg.RCNN.ENABLED:
for i in range(len(thresh_list)):
mdict['total_recalled_bbox_list_{}'.format(i)] = 0
mdict['total_roi_recalled_bbox_list_{}'.format(i)] = 0
mdict['exit_proc'] = 0
mdict['total_cls_acc'] = 0
mdict['total_cls_acc_refined'] = 0
mdict['total_det_num'] = 0
mdict['total_gt_bbox'] = 0
p_list = []
for i in range(METRIC_PROC_NUM):
p_list.append(
multiprocessing.Process(
target=rcnn_metric,
args=(queue, mdict, lock, thresh_list, kitti_rcnn_reader,
roi_output_dir, refine_output_dir, final_output_dir,
args.save_result)))
p_list[-1].start()
try:
eval_loader.start()
eval_iter = 0
start_time = time.time()
cur_time = time.time()
while True:
eval_outs = exe.run(eval_compile_prog,
fetch_list=eval_values,
return_numpy=False)
rets_dict = {
k: (np.array(v), v.recursive_sequence_lengths())
for k, v in zip(eval_keys, eval_outs)
}
run_time = time.time() - cur_time
cur_time = time.time()
queue.put(rets_dict)
eval_iter += 1
logger.info("[EVAL] iter {}, time: {:.2f}".format(
eval_iter, run_time))
except fluid.core.EOFException:
# terminate metric process
for i in range(METRIC_PROC_NUM):
queue.put(None)
while mdict['exit_proc'] < METRIC_PROC_NUM:
time.sleep(1)
for p in p_list:
if p.is_alive():
p.join()
end_time = time.time()
logger.info(
"[EVAL] total {} iter finished, average time: {:.2f}".format(
eval_iter, (end_time - start_time) / float(eval_iter)))
if cfg.RPN.ENABLED:
avg_rpn_iou = mdict['total_rpn_iou'] / max(len(kitti_rcnn_reader),
1.)
logger.info("average rpn iou: {:.3f}".format(avg_rpn_iou))
total_gt_bbox = float(max(mdict['total_gt_bbox'], 1.0))
for idx, thresh in enumerate(thresh_list):
recall = mdict['total_recalled_bbox_list_{}'.format(
idx)] / total_gt_bbox
logger.info(
"total bbox recall(thresh={:.3f}): {} / {} = {:.3f}".
format(thresh,
mdict['total_recalled_bbox_list_{}'.format(idx)],
mdict['total_gt_bbox'], recall))
if cfg.RCNN.ENABLED:
cnt = float(max(eval_iter, 1.0))
avg_cls_acc = mdict['total_cls_acc'] / cnt
avg_cls_acc_refined = mdict['total_cls_acc_refined'] / cnt
avg_det_num = mdict['total_det_num'] / cnt
logger.info("avg_cls_acc: {}".format(avg_cls_acc))
logger.info("avg_cls_acc_refined: {}".format(avg_cls_acc_refined))
logger.info("avg_det_num: {}".format(avg_det_num))
total_gt_bbox = float(max(mdict['total_gt_bbox'], 1.0))
for idx, thresh in enumerate(thresh_list):
cur_roi_recall = mdict['total_roi_recalled_bbox_list_{}'.
format(idx)] / total_gt_bbox
logger.info(
'total roi bbox recall(thresh=%.3f): %d / %d = %f' %
(thresh,
mdict['total_roi_recalled_bbox_list_{}'.format(idx)],
total_gt_bbox, cur_roi_recall))
for idx, thresh in enumerate(thresh_list):
cur_recall = mdict['total_recalled_bbox_list_{}'.format(
idx)] / total_gt_bbox
logger.info(
'total bbox recall(thresh=%.2f) %d / %.2f = %.4f' %
(thresh, mdict['total_recalled_bbox_list_{}'.format(idx)],
total_gt_bbox, cur_recall))
split_file = os.path.join('./data/KITTI', 'ImageSets', 'val.txt')
image_idx_list = [x.strip() for x in open(split_file).readlines()]
for k in range(image_idx_list.__len__()):
cur_file = os.path.join(final_output_dir,
'%s.txt' % image_idx_list[k])
if not os.path.exists(cur_file):
with open(cur_file, 'w') as temp_f:
pass
if float(sys.version[:3]) >= 3.6:
label_dir = os.path.join('./data/KITTI/object/training',
'label_2')
split_file = os.path.join('./data/KITTI', 'ImageSets',
'val.txt')
final_output_dir = os.path.join("./result_dir", 'final_result',
'data')
name_to_class = {'Car': 0, 'Pedestrian': 1, 'Cyclist': 2}
from tools.kitti_object_eval_python.evaluate import evaluate as kitti_evaluate
ap_result_str, ap_dict = kitti_evaluate(
label_dir,
final_output_dir,
label_split_file=split_file,
current_class=name_to_class["Car"])
logger.info("KITTI evaluate: {}, {}".format(
ap_result_str, ap_dict))
else:
logger.info(
"KITTI mAP only support python version >= 3.6, users can "
"run 'python3 tools/kitti_eval.py' to evaluate KITTI mAP.")
finally:
eval_loader.reset()
def eval_one_epoch_joint(model, dataloader, epoch_id, result_dir, logger):
np.random.seed(666)
MEAN_SIZE = torch.from_numpy(cfg.CLS_MEAN_SIZE[0]).cuda()
mode = 'TEST' if args.test else 'EVAL'
final_output_dir = os.path.join(result_dir, 'final_result', 'data')
if os.path.exists(final_output_dir): shutil.rmtree(final_output_dir)
os.makedirs(final_output_dir, exist_ok=True)
logger.info('---- EPOCH %s JOINT EVALUATION ----' % epoch_id)
logger.info('==> Output file: %s' % result_dir)
model.eval()
thresh_list = [0.1, 0.3, 0.5, 0.7, 0.9]
total_recalled_bbox_list, total_gt_bbox = [0] * 5, 0
total_roi_recalled_bbox_list = [0] * 5
dataset = dataloader.dataset
cnt = final_total = total_cls_acc = total_cls_acc_refined = total_rpn_iou = 0
obj_num = 0
progress_bar = tqdm.tqdm(total=len(dataloader), leave=True, desc='eval')
iou_list = []
iou_p_score_list = []
rcnn_p_score_list = []
prop_count = 0
for data in dataloader:
# Loading sample
sample_id_list, pts_input = data['sample_id'], data['pts_input']
sample_id = sample_id_list[0]
cnt += len(sample_id_list)
#if cnt < 118: continue
#load label
if not args.test:
gt_boxes3d = data['gt_boxes3d']
obj_num += gt_boxes3d.shape[1]
# print(obj_num)
if gt_boxes3d.shape[1] == 0: # (B, M, 7)
pass
else:
gt_boxes3d = gt_boxes3d
# rpn model inference
inputs = torch.from_numpy(pts_input).cuda(non_blocking=True).float()
#inputs = inputs[:,torch.argsort(-inputs[0,:,2])]
input_data = {'pts_input': inputs}
ret_dict = model.rpn_forward(input_data)
rpn_cls, rpn_reg = ret_dict['rpn_cls'], ret_dict['rpn_reg']
rpn_backbone_xyz, rpn_backbone_features = ret_dict[
'backbone_xyz'], ret_dict['backbone_features']
# stage score parsing
rpn_scores_raw = rpn_cls[:, :, 0]
rpn_scores_norm = torch.sigmoid(rpn_cls[:, :, 0])
rcnn_input_scores = rpn_scores_norm.view(-1).clone()
inputs = inputs.view(-1, inputs.shape[-1])
rpn_backbone_features = rpn_backbone_features.view(
-1, rpn_backbone_features.shape[-2])
rpn_backbone_xyz = rpn_backbone_xyz.view(-1,
rpn_backbone_xyz.shape[-1])
# if VISUAL:
# order = torch.argsort(-rpn_scores_norm).view(-1)
# inputs = inputs.view(-1,inputs.shape[-1])[order]
# rpn_scores_norm = rpn_scores_norm.view(-1)[order]
# rpn_backbone_features = rpn_backbone_features.view(-1,rpn_backbone_features.shape[-1])[order]
#
# norm_feature = F.normalize(rpn_backbone_features)
# similarity = norm_feature.mm(norm_feature.t())
#
# inputs_plt = inputs.detach().cpu().numpy()
# scores_plt = rpn_scores_norm.detach().cpu().numpy()
# similarity_plt = similarity.detach().cpu().numpy()
#
#
# fig = plt.figure(figsize=(10, 10))
# plt.axes(facecolor='silver')
# plt.axis([-30,30,0,70])
# plt.title('point_regressed_center %06d'%sample_id)
# plt.scatter(inputs_plt[:, 0], inputs_plt[:, 2], s=15, c=scores_plt[:], edgecolor='none',
# cmap=plt.get_cmap('rainbow'), alpha=1, marker='.', vmin=0, vmax=1)
# if args.test==False:
# gt_boxes3d = gt_boxes3d.reshape(-1,7)
# plt.scatter(gt_boxes3d[:, 0], gt_boxes3d[:, 2], s=200, c='blue',
# alpha=0.5, marker='+', vmin=-1, vmax=1)
# plt.show()
#
# for i in range(similarity_plt.shape[0]):
# fig = plt.figure(figsize=(10, 10))
# plt.axes(facecolor='silver')
# plt.axis([-30, 30, 0, 70])
# sm_plt = similarity_plt[i]
# plt.scatter(inputs_plt[i, 0].reshape(-1), inputs_plt[i, 2].reshape(-1), s=400, c='blue',
# alpha=0.5, marker='+', vmin=-1, vmax=1)
# plt.scatter(inputs_plt[:, 0], inputs_plt[:, 2], s=15, c=(sm_plt[:]+scores_plt[:])/2, edgecolor='none',
# cmap=plt.get_cmap('rainbow'), alpha=1, marker='.', vmin=0, vmax=1)
# plt.show()
# thresh select and jump out
# rpn_mask = rpn_scores_norm.view(-1) > cfg.RPN.SCORE_THRESH
# if rpn_mask.float().sum() == 0: continue
# rpn_scores_raw = rpn_scores_raw.view(-1)[rpn_mask]
# rpn_scores_norm = rpn_scores_norm.view(-1)[rpn_mask]
# rpn_reg = rpn_reg.view(-1, rpn_reg.shape[-1])[rpn_mask]
# rpn_backbone_xyz = rpn_backbone_xyz.view(-1, rpn_backbone_xyz.shape[-1])[rpn_mask]
# generate rois
rpn_rois = decode_center_target(
rpn_backbone_xyz,
rpn_reg.view(-1, rpn_reg.shape[-1]),
loc_scope=cfg.RPN.LOC_SCOPE,
loc_bin_size=cfg.RPN.LOC_BIN_SIZE,
).view(-1, 3)
rpn_reg_dist = (rpn_rois - rpn_backbone_xyz).clone()
#similarity = torch.cosine_similarity(rpn_backbone_xyz[:, [0, 2]], rpn_reg_dist[:, [0, 2]], dim=1)
# # thresh select and jump out
rpn_mask = (rpn_scores_norm.view(-1) > cfg.RPN.SCORE_THRESH) & (
rpn_reg_dist[:, [0, 2]].pow(2).sum(-1).sqrt() > 0.2) #\
#& (similarity > -0.7)
if rpn_mask.float().sum() == 0: continue
rpn_scores_raw = rpn_scores_raw.view(-1)[rpn_mask]
rpn_scores_norm = rpn_scores_norm.view(-1)[rpn_mask]
rpn_rois = rpn_rois[rpn_mask]
rpn_backbone_xyz = rpn_backbone_xyz.view(
-1, rpn_backbone_xyz.shape[-1])[rpn_mask]
# radius NMS
# sort by center score
sort_points = torch.argsort(-rpn_scores_raw)
rpn_rois = rpn_rois[sort_points]
rpn_scores_norm = rpn_scores_norm[sort_points]
rpn_scores_raw = rpn_scores_raw[sort_points]
if rpn_rois.shape[0] > 1:
keep_id = [0]
prop_prop_distance = distance_2(rpn_rois[:, [0, 2]],
rpn_rois[:, [0, 2]])
for i in range(1, rpn_rois.shape[0]):
#if torch.min(prop_prop_distance[:i, i], dim=-1)[0] > 0.3:
if torch.min(prop_prop_distance[keep_id, i], dim=-1)[0] > 0.3:
keep_id.append(i)
rpn_center = rpn_rois[keep_id][:, [0, 2]]
rpn_scores_norm = rpn_scores_norm[keep_id]
rpn_scores_raw = rpn_scores_raw[keep_id]
else:
rpn_center = rpn_rois[:, [0, 2]]
rpn_scores_norm = rpn_scores_norm
rpn_scores_raw = rpn_scores_raw
# #rcnn input select:
point_center_distance = distance_2(rpn_center, inputs[:, [0, 2]])
cur_proposal_points_index = (torch.min(point_center_distance,
dim=-1)[0] < 4.0)
point_center_distance = point_center_distance[
cur_proposal_points_index]
inputs = inputs[cur_proposal_points_index]
rcnn_input_scores = rcnn_input_scores.view(
-1)[cur_proposal_points_index]
if VISUAL:
inputs_plt = inputs.detach().cpu().numpy()
scores_plt = rcnn_input_scores.detach().cpu().numpy()
# point_center= rpn_center[rpn_scores_norm > 0.5]
# point_center_score = rpn_scores_norm[rpn_scores_norm > 0.5]
point_center = rpn_center
point_center_score = rpn_scores_norm
fig = plt.figure(figsize=(10, 10))
plt.axes(facecolor='silver')
plt.axis([-30, 30, 0, 70])
point_center_plt = point_center.cpu().numpy()
plt.title('point_regressed_center %06d' % sample_id)
plt.scatter(inputs_plt[:, 0],
inputs_plt[:, 2],
s=15,
c=scores_plt[:],
edgecolor='none',
cmap=plt.get_cmap('rainbow'),
alpha=1,
marker='.',
vmin=0,
vmax=1)
if point_center.shape[0] > 0:
plt.scatter(point_center_plt[:, 0],
point_center_plt[:, 1],
s=200,
c='white',
alpha=0.5,
marker='x',
vmin=-1,
vmax=1)
if args.test == False:
gt_boxes3d = gt_boxes3d.reshape(-1, 7)
plt.scatter(gt_boxes3d[:, 0],
gt_boxes3d[:, 2],
s=200,
c='blue',
alpha=0.5,
marker='+',
vmin=-1,
vmax=1)
plt.savefig('../visual/rpn.jpg')
# RCNN stage
box_list = []
raw_score_list = []
iou_score_list = []
inputs[:, 1] -= 1.65
point_center_distance = distance_2(rpn_center[:, :], inputs[:, [0, 2]])
#for c in range(min(rpn_center.shape[0],100)):
prop_count += rpn_center.shape[0]
print('num %d' % (prop_count / float(cnt)))
for c in range(rpn_center.shape[0]):
# rcnn input generate
cur_input = inputs.clone()
cur_input_score = rcnn_input_scores.clone()
# if COSINE_DISTANCE:
# cur_center_points_index = ((point_center_distance[:, c] < 4.0) & \
# (point_prop_cos_matrix[:, c] > COS_THRESH) | \
# (point_center_distance[:, c].view(-1) < 0.7)).view(-1)
# else:
cur_center_points_index = (point_center_distance[:, c] <
4.0).view(-1)
if cur_center_points_index.long().sum() == 0: continue
cur_center_points_xyz = cur_input[cur_center_points_index, :3]
cur_center_points_xyz[:, 0] -= rpn_center[c, 0]
cur_center_points_xyz[:, 2] -= rpn_center[c, 1]
cur_center_points_r = cur_input[cur_center_points_index,
3].view(-1, 1)
cur_center_points_mask = (cur_input_score[cur_center_points_index]
> 0.5).view(-1, 1).float()
# # easy sample sampling
# if pts_input.shape[0]>512:
# cur_input = torch.cat((cur_center_points_xyz, cur_center_points_r,
# (cur_input_score[cur_center_points_index] > 0.5).view(-1, 1).float()), dim=-1)
# pts_input = cur_input
# pts_input = pts_input[:min(pts_input.shape[0], 2000), :]
# pts_input = pts_input[:, :]
# sample_index = fps(pts_input[:, 0:3].contiguous(), ratio=min(512 / pts_input.shape[0], 0.99),
# random_start=False)
# perm = sample_index
# while sample_index.shape[0] < 512:
# sample_index = torch.cat(
# (sample_index, perm[:min(perm.shape[0], 512 - sample_index.shape[0])]), dim=0)
#
# cur_center_points_xyz = pts_input[sample_index, 0:3]
# cur_center_points_r = pts_input[sample_index, 3].reshape(-1, 1)
# cur_center_points_mask = pts_input[sample_index, 4].reshape(-1, 1)
cur_center_points_xyz = cur_center_points_xyz.unsqueeze(0).float()
cur_center_points_r = cur_center_points_r.unsqueeze(0).float()
cur_center_points_mask = cur_center_points_mask.unsqueeze(
0).float() - 0.5
input_data = {
'cur_box_point': cur_center_points_xyz,
'cur_box_reflect': cur_center_points_r,
'train_mask': cur_center_points_mask,
}
# # globaly random sampling
# pts_input = pts_input[:min(pts_input.shape[0], self.npoints), :]
# sample_index = np.arange(0, pts_input.shape[0], 1).astype(np.int)
# perm = np.copy(sample_index)
# while sample_index.shape[0] < self.npoints:
# sample_index = np.concatenate(
# (sample_index, perm[:min(perm.shape[0], self.npoints - sample_index.shape[0])]))
#
# cur_box_point = pts_input[sample_index, 0:3]
# cur_box_reflect = pts_input[sample_index, 3].reshape(-1, 1)
# cur_prob_mask = pts_input[sample_index, 4].reshape(-1, 1)
# gt_mask = pts_input[sample_index, 5].reshape(-1, 1)
# rcnn model inference
ret_dict = model.rcnn_forward(input_data)
rcnn_cls = ret_dict['rcnn_cls']
ioun_cls = ret_dict['ioun_cls']
rcnn_reg = ret_dict['rcnn_reg']
rcnn_iou = ret_dict['rcnn_iou']
rcnn_ref = ret_dict['rcnn_ref'].view(1, 1, -1)
rcnn_box3d = ret_dict['pred_boxes3d']
refined_box = ret_dict['refined_box']
rcnn_box3d = refined_box
rcnn_box3d[:, :, 6] = rcnn_box3d[:, :, 6] % (np.pi * 2)
if rcnn_box3d[:, :, 6] > np.pi: rcnn_box3d[:, :, 6] -= np.pi * 2
rcnn_box3d[:, :, 0] += rpn_center[c][0]
rcnn_box3d[:, :, 2] += rpn_center[c][1]
rcnn_box3d[:, :, 1] += 1.65
box_list.append(rcnn_box3d)
raw_score_list.append(rcnn_cls.view(1, 1))
#raw_score_list.append(ioun_cls.view(1,1))
iou_score_list.append(rcnn_iou.view(1, 1))
rcnn_box3d = torch.cat((box_list), dim=1)
raw_rcnn_score = torch.cat((raw_score_list),
dim=0).unsqueeze(0).float()
norm_ioun_score = torch.cat((iou_score_list),
dim=0).unsqueeze(0).float()
# scoring
pred_boxes3d = rcnn_box3d
norm_ioun_score = norm_ioun_score
raw_rcnn_score = raw_rcnn_score
norm_rcnn_score = torch.sigmoid(raw_rcnn_score)
# scores thresh
pred_h = pred_boxes3d[:, :, 3].view(-1)
pred_w = pred_boxes3d[:, :, 4].view(-1)
pred_l = pred_boxes3d[:, :, 5].view(-1)
inds = (norm_rcnn_score > cfg.RCNN.SCORE_THRESH) & (
norm_ioun_score > cfg.IOUN.SCORE_THRESH)
inds = inds.view(-1)
#size filiter
# inds = inds & \
# (pred_h > 1.2) & (pred_h < 2.2) & \
# (pred_w > 1.3) & (pred_w < 2.0) & \
# (pred_l > 2.2) & (pred_l < 5.0)
inds = inds & \
(pred_h > 1.1) & (pred_h < 2.3) & \
(pred_w > 1.2) & (pred_w < 2.1) & \
(pred_l > 2.1) & (pred_l < 5.1)
pred_boxes3d = pred_boxes3d[:, inds]
norm_rcnn_score = norm_rcnn_score[:, inds]
norm_ioun_score = norm_ioun_score[:, inds]
raw_rcnn_score = raw_rcnn_score[:, inds]
if pred_boxes3d.shape[1] == 0: continue
# evaluation
recalled_num = gt_num = 0
if not args.test:
gt_boxes3d = data['gt_boxes3d']
for k in range(1):
# calculate recall
cur_gt_boxes3d = gt_boxes3d[k]
tmp_idx = cur_gt_boxes3d.__len__() - 1
while tmp_idx >= 0 and cur_gt_boxes3d[tmp_idx].sum() == 0:
tmp_idx -= 1
if tmp_idx >= 0:
cur_gt_boxes3d = cur_gt_boxes3d[:tmp_idx + 1]
cur_gt_boxes3d = torch.from_numpy(cur_gt_boxes3d).cuda(
non_blocking=True).float()
_, iou3d = iou3d_utils.boxes_iou3d_gpu(
pred_boxes3d[k], cur_gt_boxes3d)
gt_max_iou, _ = iou3d.max(dim=0)
refined_iou, _ = iou3d.max(dim=1)
iou_list.append(refined_iou.view(-1, 1))
iou_p_score_list.append(norm_ioun_score.view(-1, 1))
rcnn_p_score_list.append(norm_rcnn_score.view(-1, 1))
for idx, thresh in enumerate(thresh_list):
total_recalled_bbox_list[idx] += (gt_max_iou >
thresh).sum().item()
recalled_num += (gt_max_iou > 0.7).sum().item()
gt_num += cur_gt_boxes3d.shape[0]
total_gt_bbox += cur_gt_boxes3d.shape[0]
if cnt == 1000:
iou_clloe = torch.cat(iou_list, dim=0).detach().cpu().numpy()
iou_score_clloe = torch.cat(iou_p_score_list,
dim=0).detach().cpu().numpy()
plt.axis([-.1, 1.1, -.1, 1.1])
plt.scatter(iou_clloe,
iou_score_clloe,
s=20,
c='blue',
edgecolor='none',
cmap=plt.get_cmap('YlOrRd'),
alpha=1,
marker='.')
plt.savefig(os.path.join(result_dir, 'distributercnn.png'))
disp_dict = {
'mode': mode,
'recall': '%d/%d' % (total_recalled_bbox_list[3], total_gt_bbox)
}
progress_bar.set_postfix(disp_dict)
progress_bar.update()
if VISUAL:
fig, ax = plt.subplots(figsize=(10, 10))
inputs_plt = inputs.detach().cpu().numpy()
#plt.axes(facecolor='silver')
plt.axis([-35, 35, 0, 70])
plt.scatter(inputs_plt[:, 0],
inputs_plt[:, 2],
s=15,
c=inputs_plt[:, 1],
edgecolor='none',
cmap=plt.get_cmap('Blues'),
alpha=1,
marker='.',
vmin=-1,
vmax=2)
pred_boxes3d_numpy = pred_boxes3d[0].detach().cpu().numpy()
pred_boxes3d_corner = kitti_utils.boxes3d_to_corners3d(
pred_boxes3d_numpy, rotate=True)
for o in range(pred_boxes3d_corner.shape[0]):
print_box_corner = pred_boxes3d_corner[o]
x1, x2, x3, x4 = print_box_corner[0:4, 0]
z1, z2, z3, z4 = print_box_corner[0:4, 2]
polygon = np.zeros([5, 2], dtype=np.float32)
polygon[0, 0] = x1
polygon[1, 0] = x2
polygon[2, 0] = x3
polygon[3, 0] = x4
polygon[4, 0] = x1
polygon[0, 1] = z1
polygon[1, 1] = z2
polygon[2, 1] = z3
polygon[3, 1] = z4
polygon[4, 1] = z1
line1 = [(x1, z1), (x2, z2)]
line2 = [(x2, z2), (x3, z3)]
line3 = [(x3, z3), (x4, z4)]
line4 = [(x4, z4), (x1, z1)]
(line1_xs, line1_ys) = zip(*line1)
(line2_xs, line2_ys) = zip(*line2)
(line3_xs, line3_ys) = zip(*line3)
(line4_xs, line4_ys) = zip(*line4)
ax.add_line(
Line2D(line1_xs, line1_ys, linewidth=1, color='green'))
ax.add_line(
Line2D(line2_xs, line2_ys, linewidth=1, color='red'))
ax.add_line(
Line2D(line3_xs, line3_ys, linewidth=1, color='red'))
ax.add_line(
Line2D(line4_xs, line4_ys, linewidth=1, color='red'))
# gt visualize
if args.test == False and data['gt_boxes3d'].shape[1] > 0:
gt_boxes3d_corner = kitti_utils.boxes3d_to_corners3d(
data['gt_boxes3d'].reshape(-1, 7), rotate=True)
for o in range(gt_boxes3d_corner.shape[0]):
print_box_corner = gt_boxes3d_corner[o]
x1, x2, x3, x4 = print_box_corner[0:4, 0]
z1, z2, z3, z4 = print_box_corner[0:4, 2]
polygon = np.zeros([5, 2], dtype=np.float32)
polygon[0, 0] = x1
polygon[1, 0] = x2
polygon[2, 0] = x3
polygon[3, 0] = x4
polygon[4, 0] = x1
polygon[0, 1] = z1
polygon[1, 1] = z2
polygon[2, 1] = z3
polygon[3, 1] = z4
polygon[4, 1] = z1
line1 = [(x1, z1), (x2, z2)]
line2 = [(x2, z2), (x3, z3)]
line3 = [(x3, z3), (x4, z4)]
line4 = [(x4, z4), (x1, z1)]
(line1_xs, line1_ys) = zip(*line1)
(line2_xs, line2_ys) = zip(*line2)
(line3_xs, line3_ys) = zip(*line3)
(line4_xs, line4_ys) = zip(*line4)
ax.add_line(
Line2D(line1_xs, line1_ys, linewidth=1,
color='yellow'))
ax.add_line(
Line2D(line2_xs, line2_ys, linewidth=1,
color='purple'))
ax.add_line(
Line2D(line3_xs, line3_ys, linewidth=1,
color='purple'))
ax.add_line(
Line2D(line4_xs, line4_ys, linewidth=1,
color='purple'))
plt.savefig('../visual/rcnn.jpg')
# scores thresh
inds = (norm_rcnn_score > cfg.RCNN.SCORE_THRESH) & (
norm_ioun_score > cfg.IOUN.SCORE_THRESH)
#inds = (norm_ioun_score > cfg.IOUN.SCORE_THRESH)
for k in range(1):
cur_inds = inds[k].view(-1)
if cur_inds.sum() == 0:
continue
pred_boxes3d_selected = pred_boxes3d[k, cur_inds]
norm_iou_scores_selected = norm_ioun_score[k, cur_inds]
raw_rcnn_score_selected = raw_rcnn_score[k, cur_inds]
#traditional nms
# NMS thresh rotated nms
# boxes_bev_selected = kitti_utils.boxes3d_to_bev_torch(pred_boxes3d_selected)
# #score NMS
# # boxes_bev_selected[:,-1] += np.pi/2
# keep_idx = iou3d_utils.nms_normal_gpu(boxes_bev_selected, norm_iou_scores_selected, cfg.RCNN.NMS_THRESH).view(-1)
# pred_boxes3d_selected = pred_boxes3d_selected[keep_idx]
# norm_iou_scores_selected = norm_iou_scores_selected[keep_idx]
# raw_rcnn_score_selected = raw_rcnn_score_selected[keep_idx]
#self NMS
sort_boxes = torch.argsort(-norm_iou_scores_selected.view(-1))
pred_boxes3d_selected = pred_boxes3d_selected[sort_boxes]
norm_iou_scores_selected = norm_iou_scores_selected[sort_boxes]
if pred_boxes3d_selected.shape[0] > 1:
keep_id = [0]
iou2d, iou3d = iou3d_utils.boxes_iou3d_gpu(
pred_boxes3d_selected, pred_boxes3d_selected)
for i in range(1, pred_boxes3d_selected.shape[0]):
# if torch.min(prop_prop_distance[:i, i], dim=-1)[0] > 0.3:
if torch.max(iou2d[keep_id, i], dim=-1)[0] < 0.01:
keep_id.append(i)
pred_boxes3d_selected = pred_boxes3d_selected[keep_id]
norm_iou_scores_selected = norm_iou_scores_selected[keep_id]
else:
pred_boxes3d_selected = pred_boxes3d_selected
norm_iou_scores_selected = norm_iou_scores_selected
pred_boxes3d_selected, norm_iou_scores_selected = pred_boxes3d_selected.cpu(
).numpy(), norm_iou_scores_selected.cpu().numpy()
cur_sample_id = sample_id
calib = dataset.get_calib(cur_sample_id)
final_total += pred_boxes3d_selected.shape[0]
image_shape = dataset.get_image_shape(cur_sample_id)
save_kitti_format(cur_sample_id, calib, pred_boxes3d_selected,
final_output_dir, norm_iou_scores_selected,
image_shape)
if VISUAL:
fig, ax = plt.subplots(figsize=(10, 10))
inputs_plt = inputs.detach().cpu().numpy()
# plt.axes(facecolor='silver')
plt.axis([-35, 35, 0, 70])
plt.scatter(inputs_plt[:, 0],
inputs_plt[:, 2],
s=15,
c=inputs_plt[:, 1],
edgecolor='none',
cmap=plt.get_cmap('Blues'),
alpha=1,
marker='.',
vmin=-1,
vmax=2)
pred_boxes3d_numpy = pred_boxes3d_selected
pred_boxes3d_corner = kitti_utils.boxes3d_to_corners3d(
pred_boxes3d_numpy, rotate=True)
for o in range(pred_boxes3d_corner.shape[0]):
print_box_corner = pred_boxes3d_corner[o]
x1, x2, x3, x4 = print_box_corner[0:4, 0]
z1, z2, z3, z4 = print_box_corner[0:4, 2]
polygon = np.zeros([5, 2], dtype=np.float32)
polygon[0, 0] = x1
polygon[1, 0] = x2
polygon[2, 0] = x3
polygon[3, 0] = x4
polygon[4, 0] = x1
polygon[0, 1] = z1
polygon[1, 1] = z2
polygon[2, 1] = z3
polygon[3, 1] = z4
polygon[4, 1] = z1
line1 = [(x1, z1), (x2, z2)]
line2 = [(x2, z2), (x3, z3)]
line3 = [(x3, z3), (x4, z4)]
line4 = [(x4, z4), (x1, z1)]
(line1_xs, line1_ys) = zip(*line1)
(line2_xs, line2_ys) = zip(*line2)
(line3_xs, line3_ys) = zip(*line3)
(line4_xs, line4_ys) = zip(*line4)
ax.add_line(
Line2D(line1_xs, line1_ys, linewidth=1, color='green'))
ax.add_line(
Line2D(line2_xs, line2_ys, linewidth=1, color='red'))
ax.add_line(
Line2D(line3_xs, line3_ys, linewidth=1, color='red'))
ax.add_line(
Line2D(line4_xs, line4_ys, linewidth=1, color='red'))
# gt visualize
if args.test == False and data['gt_boxes3d'].shape[1] > 0:
gt_boxes3d_corner = kitti_utils.boxes3d_to_corners3d(
data['gt_boxes3d'].reshape(-1, 7), rotate=True)
for o in range(gt_boxes3d_corner.shape[0]):
print_box_corner = gt_boxes3d_corner[o]
x1, x2, x3, x4 = print_box_corner[0:4, 0]
z1, z2, z3, z4 = print_box_corner[0:4, 2]
polygon = np.zeros([5, 2], dtype=np.float32)
polygon[0, 0] = x1
polygon[1, 0] = x2
polygon[2, 0] = x3
polygon[3, 0] = x4
polygon[4, 0] = x1
polygon[0, 1] = z1
polygon[1, 1] = z2
polygon[2, 1] = z3
polygon[3, 1] = z4
polygon[4, 1] = z1
line1 = [(x1, z1), (x2, z2)]
line2 = [(x2, z2), (x3, z3)]
line3 = [(x3, z3), (x4, z4)]
line4 = [(x4, z4), (x1, z1)]
(line1_xs, line1_ys) = zip(*line1)
(line2_xs, line2_ys) = zip(*line2)
(line3_xs, line3_ys) = zip(*line3)
(line4_xs, line4_ys) = zip(*line4)
ax.add_line(
Line2D(line1_xs,
line1_ys,
linewidth=1,
color='yellow'))
ax.add_line(
Line2D(line2_xs,
line2_ys,
linewidth=1,
color='purple'))
ax.add_line(
Line2D(line3_xs,
line3_ys,
linewidth=1,
color='purple'))
ax.add_line(
Line2D(line4_xs,
line4_ys,
linewidth=1,
color='purple'))
plt.savefig('../visual/ioun.jpg')
progress_bar.close()
# dump empty files
split_file = os.path.join(dataset.imageset_dir, '..', 'ImageSets',
dataset.split + '.txt')
split_file = os.path.abspath(split_file)
image_idx_list = [x.strip() for x in open(split_file).readlines()]
empty_cnt = 0
for k in range(image_idx_list.__len__()):
cur_file = os.path.join(final_output_dir, '%s.txt' % image_idx_list[k])
if not os.path.exists(cur_file):
with open(cur_file, 'w') as temp_f:
pass
empty_cnt += 1
logger.info('empty_cnt=%d: dump empty file %s' %
(empty_cnt, cur_file))
ret_dict = {'empty_cnt': empty_cnt}
if not args.eval_all:
logger.info(
'-------------------performance of epoch %s---------------------' %
epoch_id)
logger.info(str(datetime.now()))
avg_rpn_iou = (total_rpn_iou / max(cnt, 1.0))
avg_cls_acc = (total_cls_acc / max(cnt, 1.0))
avg_cls_acc_refined = (total_cls_acc_refined / max(cnt, 1.0))
avg_det_num = (final_total / max(len(dataset), 1.0))
logger.info('final average detections: %.3f' % avg_det_num)
logger.info('final average rpn_iou refined: %.3f' % avg_rpn_iou)
logger.info('final average cls acc: %.3f' % avg_cls_acc)
logger.info('final average cls acc refined: %.3f' %
avg_cls_acc_refined)
ret_dict['rpn_iou'] = avg_rpn_iou
ret_dict['rcnn_cls_acc'] = avg_cls_acc
ret_dict['rcnn_cls_acc_refined'] = avg_cls_acc_refined
ret_dict['rcnn_avg_num'] = avg_det_num
for idx, thresh in enumerate(thresh_list):
cur_recall = total_recalled_bbox_list[idx] / max(
total_gt_bbox, 1.0)
logger.info('total bbox recall(thresh=%.3f): %d / %d = %f' %
(thresh, total_recalled_bbox_list[idx], total_gt_bbox,
cur_recall))
ret_dict['rcnn_recall(thresh=%.2f)' % thresh] = cur_recall
if thresh == 0.7:
recall = cur_recall
if cfg.TEST.SPLIT != 'test':
logger.info('Averate Precision:')
name_to_class = {'Car': 0, 'Pedestrian': 1, 'Cyclist': 2}
ap_result_str, ap_dict = kitti_evaluate(
dataset.label_dir,
final_output_dir,
label_split_file=split_file,
current_class=name_to_class[cfg.CLASSES])
if not args.eval_all:
logger.info(ap_result_str)
ret_dict.update(ap_dict)
logger.info('result is saved to: %s' % result_dir)
precision = ap_dict['Car_3d_easy'] + ap_dict['Car_3d_moderate'] + ap_dict[
'Car_3d_hard']
recall = total_recalled_bbox_list[3] / max(total_gt_bbox, 1.0)
F2_score = 0
return precision, recall, F2_score
