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inter
2025-09-21 20:18:48 +08:00
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pcdet/models/model_utils/centernet_utils.py Normal file
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# This file is modified from https://github.com/tianweiy/CenterPoint
import torch
import torch.nn.functional as F
import numpy as np
import numba
def gaussian_radius(height, width, min_overlap=0.5):
"""
Args:
height: (N)
width: (N)
min_overlap:
Returns:
"""
a1 = 1
b1 = (height + width)
c1 = width * height * (1 - min_overlap) / (1 + min_overlap)
sq1 = (b1 ** 2 - 4 * a1 * c1).sqrt()
r1 = (b1 + sq1) / 2
a2 = 4
b2 = 2 * (height + width)
c2 = (1 - min_overlap) * width * height
sq2 = (b2 ** 2 - 4 * a2 * c2).sqrt()
r2 = (b2 + sq2) / 2
a3 = 4 * min_overlap
b3 = -2 * min_overlap * (height + width)
c3 = (min_overlap - 1) * width * height
sq3 = (b3 ** 2 - 4 * a3 * c3).sqrt()
r3 = (b3 + sq3) / 2
ret = torch.min(torch.min(r1, r2), r3)
return ret
def gaussian2D(shape, sigma=1):
m, n = [(ss - 1.) / 2. for ss in shape]
y, x = np.ogrid[-m:m + 1, -n:n + 1]
h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
h[h < np.finfo(h.dtype).eps * h.max()] = 0
return h
def draw_gaussian_to_heatmap(heatmap, center, radius, k=1, valid_mask=None):
diameter = 2 * radius + 1
gaussian = gaussian2D((diameter, diameter), sigma=diameter / 6)
x, y = int(center[0]), int(center[1])
height, width = heatmap.shape[0:2]
left, right = min(x, radius), min(width - x, radius + 1)
top, bottom = min(y, radius), min(height - y, radius + 1)
masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
masked_gaussian = torch.from_numpy(
gaussian[radius - top:radius + bottom, radius - left:radius + right]
).to(heatmap.device).float()
if min(masked_gaussian.shape) > 0 and min(masked_heatmap.shape) > 0: # TODO debug
if valid_mask is not None:
cur_valid_mask = valid_mask[y - top:y + bottom, x - left:x + right]
masked_gaussian = masked_gaussian * cur_valid_mask.float()
torch.max(masked_heatmap, masked_gaussian * k, out=masked_heatmap)
return heatmap
def _nms(heat, kernel=3):
pad = (kernel - 1) // 2
hmax = F.max_pool2d(heat, (kernel, kernel), stride=1, padding=pad)
keep = (hmax == heat).float()
return heat * keep
def gaussian3D(shape, sigma=1):
m, n = [(ss - 1.) / 2. for ss in shape]
y, x = np.ogrid[-m:m + 1, -n:n + 1]
h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
h[h < np.finfo(h.dtype).eps * h.max()] = 0
return h
def draw_gaussian_to_heatmap_voxels(heatmap, distances, radius, k=1):
diameter = 2 * radius + 1
sigma = diameter / 6
masked_gaussian = torch.exp(- distances / (2 * sigma * sigma))
torch.max(heatmap, masked_gaussian, out=heatmap)
return heatmap
@numba.jit(nopython=True)
def circle_nms(dets, thresh):
x1 = dets[:, 0]
y1 = dets[:, 1]
scores = dets[:, 2]
order = scores.argsort()[::-1].astype(np.int32) # highest->lowest
ndets = dets.shape[0]
suppressed = np.zeros((ndets), dtype=np.int32)
keep = []
for _i in range(ndets):
i = order[_i] # start with highest score box
if suppressed[i] == 1: # if any box have enough iou with this, remove it
continue
keep.append(i)
for _j in range(_i + 1, ndets):
j = order[_j]
if suppressed[j] == 1:
continue
# calculate center distance between i and j box
dist = (x1[i] - x1[j]) ** 2 + (y1[i] - y1[j]) ** 2
# ovr = inter / areas[j]
if dist <= thresh:
suppressed[j] = 1
return keep
def _circle_nms(boxes, min_radius, post_max_size=83):
"""
NMS according to center distance
"""
keep = np.array(circle_nms(boxes.cpu().numpy(), thresh=min_radius))[:post_max_size]
keep = torch.from_numpy(keep).long().to(boxes.device)
return keep
def _gather_feat(feat, ind, mask=None):
dim = feat.size(2)
ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
feat = feat.gather(1, ind)
if mask is not None:
mask = mask.unsqueeze(2).expand_as(feat)
feat = feat[mask]
feat = feat.view(-1, dim)
return feat
def _transpose_and_gather_feat(feat, ind):
feat = feat.permute(0, 2, 3, 1).contiguous()
feat = feat.view(feat.size(0), -1, feat.size(3))
feat = _gather_feat(feat, ind)
return feat
def _topk(scores, K=40):
batch, num_class, height, width = scores.size()
topk_scores, topk_inds = torch.topk(scores.flatten(2, 3), K)
topk_inds = topk_inds % (height * width)
topk_ys = (topk_inds // width).float()
topk_xs = (topk_inds % width).int().float()
topk_score, topk_ind = torch.topk(topk_scores.view(batch, -1), K)
topk_classes = (topk_ind // K).int()
topk_inds = _gather_feat(topk_inds.view(batch, -1, 1), topk_ind).view(batch, K)
topk_ys = _gather_feat(topk_ys.view(batch, -1, 1), topk_ind).view(batch, K)
topk_xs = _gather_feat(topk_xs.view(batch, -1, 1), topk_ind).view(batch, K)
return topk_score, topk_inds, topk_classes, topk_ys, topk_xs
def decode_bbox_from_heatmap(heatmap, rot_cos, rot_sin, center, center_z, dim,
point_cloud_range=None, voxel_size=None, feature_map_stride=None, vel=None, iou=None, K=100,
circle_nms=False, score_thresh=None, post_center_limit_range=None):
batch_size, num_class, _, _ = heatmap.size()
if circle_nms:
# TODO: not checked yet
assert False, 'not checked yet'
heatmap = _nms(heatmap)
scores, inds, class_ids, ys, xs = _topk(heatmap, K=K)
center = _transpose_and_gather_feat(center, inds).view(batch_size, K, 2)
rot_sin = _transpose_and_gather_feat(rot_sin, inds).view(batch_size, K, 1)
rot_cos = _transpose_and_gather_feat(rot_cos, inds).view(batch_size, K, 1)
center_z = _transpose_and_gather_feat(center_z, inds).view(batch_size, K, 1)
dim = _transpose_and_gather_feat(dim, inds).view(batch_size, K, 3)
angle = torch.atan2(rot_sin, rot_cos)
xs = xs.view(batch_size, K, 1) + center[:, :, 0:1]
ys = ys.view(batch_size, K, 1) + center[:, :, 1:2]
xs = xs * feature_map_stride * voxel_size[0] + point_cloud_range[0]
ys = ys * feature_map_stride * voxel_size[1] + point_cloud_range[1]
box_part_list = [xs, ys, center_z, dim, angle]
if vel is not None:
vel = _transpose_and_gather_feat(vel, inds).view(batch_size, K, 2)
box_part_list.append(vel)
if iou is not None:
iou = _transpose_and_gather_feat(iou, inds).view(batch_size, K)
final_box_preds = torch.cat((box_part_list), dim=-1)
final_scores = scores.view(batch_size, K)
final_class_ids = class_ids.view(batch_size, K)
assert post_center_limit_range is not None
mask = (final_box_preds[..., :3] >= post_center_limit_range[:3]).all(2)
mask &= (final_box_preds[..., :3] <= post_center_limit_range[3:]).all(2)
if score_thresh is not None:
mask &= (final_scores > score_thresh)
ret_pred_dicts = []
for k in range(batch_size):
cur_mask = mask[k]
cur_boxes = final_box_preds[k, cur_mask]
cur_scores = final_scores[k, cur_mask]
cur_labels = final_class_ids[k, cur_mask]
if circle_nms:
assert False, 'not checked yet'
centers = cur_boxes[:, [0, 1]]
boxes = torch.cat((centers, scores.view(-1, 1)), dim=1)
keep = _circle_nms(boxes, min_radius=min_radius, post_max_size=nms_post_max_size)
cur_boxes = cur_boxes[keep]
cur_scores = cur_scores[keep]
cur_labels = cur_labels[keep]
ret_pred_dicts.append({
'pred_boxes': cur_boxes,
'pred_scores': cur_scores,
'pred_labels': cur_labels
})
if iou is not None:
ret_pred_dicts[-1]['pred_iou'] = iou[k, cur_mask]
return ret_pred_dicts
def _topk_1d(scores, batch_size, batch_idx, obj, K=40, nuscenes=False):
# scores: (N, num_classes)
topk_score_list = []
topk_inds_list = []
topk_classes_list = []
for bs_idx in range(batch_size):
batch_inds = batch_idx==bs_idx
if obj.shape[-1] == 1 and not nuscenes:
score = scores[batch_inds].permute(1, 0)
topk_scores, topk_inds = torch.topk(score, K)
topk_score, topk_ind = torch.topk(obj[topk_inds.view(-1)].squeeze(-1), K) #torch.topk(topk_scores.view(-1), K)
else:
score = obj[batch_inds].permute(1, 0)
topk_scores, topk_inds = torch.topk(score, min(K, score.shape[-1]))
topk_score, topk_ind = torch.topk(topk_scores.view(-1), min(K, topk_scores.view(-1).shape[-1]))
#topk_score, topk_ind = torch.topk(score.reshape(-1), K)
topk_classes = (topk_ind // K).int()
topk_inds = topk_inds.view(-1).gather(0, topk_ind)
#print('topk_inds', topk_inds)
if not obj is None and obj.shape[-1] == 1:
topk_score_list.append(obj[batch_inds][topk_inds])
else:
topk_score_list.append(topk_score)
topk_inds_list.append(topk_inds)
topk_classes_list.append(topk_classes)
topk_score = torch.stack(topk_score_list)
topk_inds = torch.stack(topk_inds_list)
topk_classes = torch.stack(topk_classes_list)
return topk_score, topk_inds, topk_classes
def gather_feat_idx(feats, inds, batch_size, batch_idx):
feats_list = []
dim = feats.size(-1)
_inds = inds.unsqueeze(-1).expand(inds.size(0), inds.size(1), dim)
for bs_idx in range(batch_size):
batch_inds = batch_idx==bs_idx
feat = feats[batch_inds]
feats_list.append(feat.gather(0, _inds[bs_idx]))
feats = torch.stack(feats_list)
return feats
def decode_bbox_from_voxels_nuscenes(batch_size, indices, obj, rot_cos, rot_sin,
center, center_z, dim, vel=None, iou=None, point_cloud_range=None, voxel_size=None, voxels_3d=None,
feature_map_stride=None, K=100, score_thresh=None, post_center_limit_range=None, add_features=None):
batch_idx = indices[:, 0]
spatial_indices = indices[:, 1:]
scores, inds, class_ids = _topk_1d(None, batch_size, batch_idx, obj, K=K, nuscenes=True)
center = gather_feat_idx(center, inds, batch_size, batch_idx)
rot_sin = gather_feat_idx(rot_sin, inds, batch_size, batch_idx)
rot_cos = gather_feat_idx(rot_cos, inds, batch_size, batch_idx)
center_z = gather_feat_idx(center_z, inds, batch_size, batch_idx)
dim = gather_feat_idx(dim, inds, batch_size, batch_idx)
spatial_indices = gather_feat_idx(spatial_indices, inds, batch_size, batch_idx)
if not add_features is None:
add_features = [gather_feat_idx(add_feature, inds, batch_size, batch_idx) for add_feature in add_features]
if not isinstance(feature_map_stride, int):
feature_map_stride = gather_feat_idx(feature_map_stride.unsqueeze(-1), inds, batch_size, batch_idx)
angle = torch.atan2(rot_sin, rot_cos)
xs = (spatial_indices[:, :, -1:] + center[:, :, 0:1]) * feature_map_stride * voxel_size[0] + point_cloud_range[0]
ys = (spatial_indices[:, :, -2:-1] + center[:, :, 1:2]) * feature_map_stride * voxel_size[1] + point_cloud_range[1]
#zs = (spatial_indices[:, :, 0:1]) * feature_map_stride * voxel_size[2] + point_cloud_range[2] + center_z
box_part_list = [xs, ys, center_z, dim, angle]
if not vel is None:
vel = gather_feat_idx(vel, inds, batch_size, batch_idx)
box_part_list.append(vel)
if not iou is None:
iou = gather_feat_idx(iou, inds, batch_size, batch_idx)
iou = torch.clamp(iou, min=0, max=1.)
final_box_preds = torch.cat((box_part_list), dim=-1)
final_scores = scores.view(batch_size, K)
final_class_ids = class_ids.view(batch_size, K)
if not add_features is None:
add_features = [add_feature.view(batch_size, K, add_feature.shape[-1]) for add_feature in add_features]
assert post_center_limit_range is not None
mask = (final_box_preds[..., :3] >= post_center_limit_range[:3]).all(2)
mask &= (final_box_preds[..., :3] <= post_center_limit_range[3:]).all(2)
if score_thresh is not None:
mask &= (final_scores > score_thresh)
ret_pred_dicts = []
for k in range(batch_size):
cur_mask = mask[k]
cur_boxes = final_box_preds[k, cur_mask]
cur_scores = final_scores[k, cur_mask]
cur_labels = final_class_ids[k, cur_mask]
cur_add_features = [add_feature[k, cur_mask] for add_feature in add_features] if not add_features is None else None
cur_iou = iou[k, cur_mask] if not iou is None else None
ret_pred_dicts.append({
'pred_boxes': cur_boxes,
'pred_scores': cur_scores,
'pred_labels': cur_labels,
'pred_ious': cur_iou,
'add_features': cur_add_features,
})
return ret_pred_dicts
def decode_bbox_from_pred_dicts(pred_dict, point_cloud_range=None, voxel_size=None, feature_map_stride=None):
batch_size, _, H, W = pred_dict['center'].shape
batch_center = pred_dict['center'].permute(0, 2, 3, 1).contiguous().view(batch_size, H*W, 2) # (B, H, W, 2)
batch_center_z = pred_dict['center_z'].permute(0, 2, 3, 1).contiguous().view(batch_size, H*W, 1) # (B, H, W, 1)
batch_dim = pred_dict['dim'].exp().permute(0, 2, 3, 1).contiguous().view(batch_size, H*W, 3) # (B, H, W, 3)
batch_rot_cos = pred_dict['rot'][:, 0].unsqueeze(dim=1).permute(0, 2, 3, 1).contiguous().view(batch_size, H*W, 1) # (B, H, W, 1)
batch_rot_sin = pred_dict['rot'][:, 1].unsqueeze(dim=1).permute(0, 2, 3, 1).contiguous().view(batch_size, H*W, 1) # (B, H, W, 1)
batch_vel = pred_dict['vel'].permute(0, 2, 3, 1).contiguous().view(batch_size, H*W, 2) if 'vel' in pred_dict.keys() else None
angle = torch.atan2(batch_rot_sin, batch_rot_cos) # (B, H*W, 1)
ys, xs = torch.meshgrid([torch.arange(0, H, device=batch_center.device, dtype=batch_center.dtype),
torch.arange(0, W, device=batch_center.device, dtype=batch_center.dtype)])
ys = ys.view(1, H, W).repeat(batch_size, 1, 1)
xs = xs.view(1, H, W).repeat(batch_size, 1, 1)
xs = xs.view(batch_size, -1, 1) + batch_center[:, :, 0:1]
ys = ys.view(batch_size, -1, 1) + batch_center[:, :, 1:2]
xs = xs * feature_map_stride * voxel_size[0] + point_cloud_range[0]
ys = ys * feature_map_stride * voxel_size[1] + point_cloud_range[1]
box_part_list = [xs, ys, batch_center_z, batch_dim, angle]
if batch_vel is not None:
box_part_list.append(batch_vel)
box_preds = torch.cat((box_part_list), dim=-1).view(batch_size, H, W, -1)
return box_preds