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pointpillars.py
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pointpillars.py
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import numpy as np
import pdb
import torch
import torch.nn as nn
import torch.nn.functional as F
from model.anchors import Anchors, anchor_target, anchors2bboxes
from ops import Voxelization, nms_cuda
from utils import limit_period
class PillarLayer(nn.Module):
def __init__(self, voxel_size, point_cloud_range, max_num_points, max_voxels):
super().__init__()
self.voxel_layer = Voxelization(voxel_size=voxel_size,
point_cloud_range=point_cloud_range,
max_num_points=max_num_points,
max_voxels=max_voxels)
@torch.no_grad()
def forward(self, batched_pts):
'''
batched_pts: list[tensor], len(batched_pts) = bs
return:
pillars: (p1 + p2 + ... + pb, num_points, c),
coors_batch: (p1 + p2 + ... + pb, 1 + 3),
num_points_per_pillar: (p1 + p2 + ... + pb, ), (b: batch size)
'''
pillars, coors, npoints_per_pillar = [], [], []
for i, pts in enumerate(batched_pts):
voxels_out, coors_out, num_points_per_voxel_out = self.voxel_layer(pts)
# voxels_out: (max_voxel, num_points, c), coors_out: (max_voxel, 3)
# num_points_per_voxel_out: (max_voxel, )
pillars.append(voxels_out)
coors.append(coors_out.long())
npoints_per_pillar.append(num_points_per_voxel_out)
pillars = torch.cat(pillars, dim=0) # (p1 + p2 + ... + pb, num_points, c)
npoints_per_pillar = torch.cat(npoints_per_pillar, dim=0) # (p1 + p2 + ... + pb, )
coors_batch = []
for i, cur_coors in enumerate(coors):
coors_batch.append(F.pad(cur_coors, (1, 0), value=i))
coors_batch = torch.cat(coors_batch, dim=0) # (p1 + p2 + ... + pb, 1 + 3)
return pillars, coors_batch, npoints_per_pillar
class PillarEncoder(nn.Module):
def __init__(self, voxel_size, point_cloud_range, in_channel, out_channel):
super().__init__()
self.out_channel = out_channel
self.vx, self.vy = voxel_size[0], voxel_size[1]
self.x_offset = voxel_size[0] / 2 + point_cloud_range[0]
self.y_offset = voxel_size[1] / 2 + point_cloud_range[1]
self.x_l = int((point_cloud_range[3] - point_cloud_range[0]) / voxel_size[0])
self.y_l = int((point_cloud_range[4] - point_cloud_range[1]) / voxel_size[1])
self.conv = nn.Conv1d(in_channel, out_channel, 1, bias=False)
self.bn = nn.BatchNorm1d(out_channel, eps=1e-3, momentum=0.01)
def forward(self, pillars, coors_batch, npoints_per_pillar):
'''
pillars: (p1 + p2 + ... + pb, num_points, c), c = 4
coors_batch: (p1 + p2 + ... + pb, 1 + 3)
npoints_per_pillar: (p1 + p2 + ... + pb, )
return: (bs, out_channel, y_l, x_l)
'''
device = pillars.device
# 1. calculate offset to the points center (in each pillar)
offset_pt_center = pillars[:, :, :3] - torch.sum(pillars[:, :, :3], dim=1, keepdim=True) / npoints_per_pillar[:, None, None] # (p1 + p2 + ... + pb, num_points, 3)
# 2. calculate offset to the pillar center
x_offset_pi_center = pillars[:, :, :1] - (coors_batch[:, None, 1:2] * self.vx + self.x_offset) # (p1 + p2 + ... + pb, num_points, 1)
y_offset_pi_center = pillars[:, :, 1:2] - (coors_batch[:, None, 2:3] * self.vy + self.y_offset) # (p1 + p2 + ... + pb, num_points, 1)
# 3. encoder
features = torch.cat([pillars, offset_pt_center, x_offset_pi_center, y_offset_pi_center], dim=-1) # (p1 + p2 + ... + pb, num_points, 9)
features[:, :, 0:1] = x_offset_pi_center # tmp
features[:, :, 1:2] = y_offset_pi_center # tmp
# In consitent with mmdet3d.
# The reason can be referenced to https://github.com/open-mmlab/mmdetection3d/issues/1150
# 4. find mask for (0, 0, 0) and update the encoded features
# a very beautiful implementation
voxel_ids = torch.arange(0, pillars.size(1)).to(device) # (num_points, )
mask = voxel_ids[:, None] < npoints_per_pillar[None, :] # (num_points, p1 + p2 + ... + pb)
mask = mask.permute(1, 0).contiguous() # (p1 + p2 + ... + pb, num_points)
features *= mask[:, :, None]
# 5. embedding
features = features.permute(0, 2, 1).contiguous() # (p1 + p2 + ... + pb, 9, num_points)
features = F.relu(self.bn(self.conv(features))) # (p1 + p2 + ... + pb, out_channels, num_points)
pooling_features = torch.max(features, dim=-1)[0] # (p1 + p2 + ... + pb, out_channels)
# 6. pillar scatter
batched_canvas = []
bs = coors_batch[-1, 0] + 1
for i in range(bs):
cur_coors_idx = coors_batch[:, 0] == i
cur_coors = coors_batch[cur_coors_idx, :]
cur_features = pooling_features[cur_coors_idx]
canvas = torch.zeros((self.x_l, self.y_l, self.out_channel), dtype=torch.float32, device=device)
canvas[cur_coors[:, 1], cur_coors[:, 2]] = cur_features
canvas = canvas.permute(2, 1, 0).contiguous()
batched_canvas.append(canvas)
batched_canvas = torch.stack(batched_canvas, dim=0) # (bs, in_channel, self.y_l, self.x_l)
return batched_canvas
class Backbone(nn.Module):
def __init__(self, in_channel, out_channels, layer_nums, layer_strides=[2, 2, 2]):
super().__init__()
assert len(out_channels) == len(layer_nums)
assert len(out_channels) == len(layer_strides)
self.multi_blocks = nn.ModuleList()
for i in range(len(layer_strides)):
blocks = []
blocks.append(nn.Conv2d(in_channel, out_channels[i], 3, stride=layer_strides[i], bias=False, padding=1))
blocks.append(nn.BatchNorm2d(out_channels[i], eps=1e-3, momentum=0.01))
blocks.append(nn.ReLU(inplace=True))
for _ in range(layer_nums[i]):
blocks.append(nn.Conv2d(out_channels[i], out_channels[i], 3, bias=False, padding=1))
blocks.append(nn.BatchNorm2d(out_channels[i], eps=1e-3, momentum=0.01))
blocks.append(nn.ReLU(inplace=True))
in_channel = out_channels[i]
self.multi_blocks.append(nn.Sequential(*blocks))
# in consitent with mmdet3d
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
def forward(self, x):
'''
x: (b, c, y_l, x_l). Default: (6, 64, 496, 432)
return: list[]. Default: [(6, 64, 248, 216), (6, 128, 124, 108), (6, 256, 62, 54)]
'''
outs = []
for i in range(len(self.multi_blocks)):
x = self.multi_blocks[i](x)
outs.append(x)
return outs
class Neck(nn.Module):
def __init__(self, in_channels, upsample_strides, out_channels):
super().__init__()
assert len(in_channels) == len(upsample_strides)
assert len(upsample_strides) == len(out_channels)
self.decoder_blocks = nn.ModuleList()
for i in range(len(in_channels)):
decoder_block = []
decoder_block.append(nn.ConvTranspose2d(in_channels[i],
out_channels[i],
upsample_strides[i],
stride=upsample_strides[i],
bias=False))
decoder_block.append(nn.BatchNorm2d(out_channels[i], eps=1e-3, momentum=0.01))
decoder_block.append(nn.ReLU(inplace=True))
self.decoder_blocks.append(nn.Sequential(*decoder_block))
# in consitent with mmdet3d
for m in self.modules():
if isinstance(m, nn.ConvTranspose2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
def forward(self, x):
'''
x: [(bs, 64, 248, 216), (bs, 128, 124, 108), (bs, 256, 62, 54)]
return: (bs, 384, 248, 216)
'''
outs = []
for i in range(len(self.decoder_blocks)):
xi = self.decoder_blocks[i](x[i]) # (bs, 128, 248, 216)
outs.append(xi)
out = torch.cat(outs, dim=1)
return out
class Head(nn.Module):
def __init__(self, in_channel, n_anchors=6, n_classes=3):
super().__init__()
self.conv_cls = nn.Conv2d(in_channel, n_anchors*n_classes, 1)
self.conv_reg = nn.Conv2d(in_channel, n_anchors*7, 1)
self.conv_dir_cls = nn.Conv2d(in_channel, n_anchors*2, 1)
# in consitent with mmdet3d
conv_layer_id = 0
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.normal_(m.weight, mean=0, std=0.01)
if conv_layer_id == 0:
prior_prob = 0.01
bias_init = float(-np.log((1 - prior_prob) / prior_prob))
nn.init.constant_(m.bias, bias_init)
else:
nn.init.constant_(m.bias, 0)
conv_layer_id += 1
def forward(self, x):
'''
x: (bs, 384, 248, 216)
return:
bbox_cls_pred: (bs, n_anchors*3, 248, 216)
bbox_pred: (bs, n_anchors*7, 248, 216)
bbox_dir_cls_pred: (bs, n_anchors*2, 248, 216)
'''
bbox_cls_pred = self.conv_cls(x)
bbox_pred = self.conv_reg(x)
bbox_dir_cls_pred = self.conv_dir_cls(x)
return bbox_cls_pred, bbox_pred, bbox_dir_cls_pred
class PointPillars(nn.Module):
def __init__(self,
nclasses=3,
voxel_size=[0.16, 0.16, 4],
point_cloud_range=[0, -39.68, -3, 69.12, 39.68, 1],
max_num_points=32,
max_voxels=(16000, 40000)):
super().__init__()
self.nclasses = nclasses
self.pillar_layer = PillarLayer(voxel_size=voxel_size,
point_cloud_range=point_cloud_range,
max_num_points=max_num_points,
max_voxels=max_voxels)
self.pillar_encoder = PillarEncoder(voxel_size=voxel_size,
point_cloud_range=point_cloud_range,
in_channel=9,
out_channel=64)
self.backbone = Backbone(in_channel=64,
out_channels=[64, 128, 256],
layer_nums=[3, 5, 5])
self.neck = Neck(in_channels=[64, 128, 256],
upsample_strides=[1, 2, 4],
out_channels=[128, 128, 128])
self.head = Head(in_channel=384)
# anchors
ranges = [[0, -39.68, -0.6, 69.12, 39.68, -0.6],
[0, -39.68, -0.6, 69.12, 39.68, -0.6],
[0, -39.68, -1.78, 69.12, 39.68, -1.78]]
sizes = [[0.6, 0.8, 1.73], [0.6, 1.76, 1.73], [1.6, 3.9, 1.56]]
rotations=[0, 1.57]
self.anchors_generator = Anchors(ranges=ranges,
sizes=sizes,
rotations=rotations)
# train
self.assigners = [
{'pos_iou_thr': 0.5, 'neg_iou_thr': 0.35, 'min_iou_thr': 0.35},
{'pos_iou_thr': 0.5, 'neg_iou_thr': 0.35, 'min_iou_thr': 0.35},
{'pos_iou_thr': 0.6, 'neg_iou_thr': 0.45, 'min_iou_thr': 0.45},
]
# val and test
self.nms_pre = 100
self.nms_thr = 0.01
self.score_thr = 0.1
self.max_num = 50
def get_predicted_bboxes_single(self, bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, anchors):
'''
bbox_cls_pred: (n_anchors*3, 248, 216)
bbox_pred: (n_anchors*7, 248, 216)
bbox_dir_cls_pred: (n_anchors*2, 248, 216)
anchors: (y_l, x_l, 3, 2, 7)
return:
bboxes: (k, 7)
labels: (k, )
scores: (k, )
'''
# 0. pre-process
bbox_cls_pred = bbox_cls_pred.permute(1, 2, 0).reshape(-1, self.nclasses)
bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 7)
bbox_dir_cls_pred = bbox_dir_cls_pred.permute(1, 2, 0).reshape(-1, 2)
anchors = anchors.reshape(-1, 7)
bbox_cls_pred = torch.sigmoid(bbox_cls_pred)
bbox_dir_cls_pred = torch.max(bbox_dir_cls_pred, dim=1)[1]
# 1. obtain self.nms_pre bboxes based on scores
inds = bbox_cls_pred.max(1)[0].topk(self.nms_pre)[1]
bbox_cls_pred = bbox_cls_pred[inds]
bbox_pred = bbox_pred[inds]
bbox_dir_cls_pred = bbox_dir_cls_pred[inds]
anchors = anchors[inds]
# 2. decode predicted offsets to bboxes
bbox_pred = anchors2bboxes(anchors, bbox_pred)
# 3. nms
bbox_pred2d_xy = bbox_pred[:, [0, 1]]
bbox_pred2d_lw = bbox_pred[:, [3, 4]]
bbox_pred2d = torch.cat([bbox_pred2d_xy - bbox_pred2d_lw / 2,
bbox_pred2d_xy + bbox_pred2d_lw / 2,
bbox_pred[:, 6:]], dim=-1) # (n_anchors, 5)
ret_bboxes, ret_labels, ret_scores = [], [], []
for i in range(self.nclasses):
# 3.1 filter bboxes with scores below self.score_thr
cur_bbox_cls_pred = bbox_cls_pred[:, i]
score_inds = cur_bbox_cls_pred > self.score_thr
if score_inds.sum() == 0:
continue
cur_bbox_cls_pred = cur_bbox_cls_pred[score_inds]
cur_bbox_pred2d = bbox_pred2d[score_inds]
cur_bbox_pred = bbox_pred[score_inds]
cur_bbox_dir_cls_pred = bbox_dir_cls_pred[score_inds]
# 3.2 nms core
keep_inds = nms_cuda(boxes=cur_bbox_pred2d,
scores=cur_bbox_cls_pred,
thresh=self.nms_thr,
pre_maxsize=None,
post_max_size=None)
cur_bbox_cls_pred = cur_bbox_cls_pred[keep_inds]
cur_bbox_pred = cur_bbox_pred[keep_inds]
cur_bbox_dir_cls_pred = cur_bbox_dir_cls_pred[keep_inds]
cur_bbox_pred[:, -1] = limit_period(cur_bbox_pred[:, -1].detach().cpu(), 1, np.pi).to(cur_bbox_pred) # [-pi, 0]
cur_bbox_pred[:, -1] += (1 - cur_bbox_dir_cls_pred) * np.pi
ret_bboxes.append(cur_bbox_pred)
ret_labels.append(torch.zeros_like(cur_bbox_pred[:, 0], dtype=torch.long) + i)
ret_scores.append(cur_bbox_cls_pred)
# 4. filter some bboxes if bboxes number is above self.max_num
if len(ret_bboxes) == 0:
return [], [], []
ret_bboxes = torch.cat(ret_bboxes, 0)
ret_labels = torch.cat(ret_labels, 0)
ret_scores = torch.cat(ret_scores, 0)
if ret_bboxes.size(0) > self.max_num:
final_inds = ret_scores.topk(self.max_num)[1]
ret_bboxes = ret_bboxes[final_inds]
ret_labels = ret_labels[final_inds]
ret_scores = ret_scores[final_inds]
result = {
'lidar_bboxes': ret_bboxes.detach().cpu().numpy(),
'labels': ret_labels.detach().cpu().numpy(),
'scores': ret_scores.detach().cpu().numpy()
}
return result
def get_predicted_bboxes(self, bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, batched_anchors):
'''
bbox_cls_pred: (bs, n_anchors*3, 248, 216)
bbox_pred: (bs, n_anchors*7, 248, 216)
bbox_dir_cls_pred: (bs, n_anchors*2, 248, 216)
batched_anchors: (bs, y_l, x_l, 3, 2, 7)
return:
bboxes: [(k1, 7), (k2, 7), ... ]
labels: [(k1, ), (k2, ), ... ]
scores: [(k1, ), (k2, ), ... ]
'''
results = []
bs = bbox_cls_pred.size(0)
for i in range(bs):
result = self.get_predicted_bboxes_single(bbox_cls_pred=bbox_cls_pred[i],
bbox_pred=bbox_pred[i],
bbox_dir_cls_pred=bbox_dir_cls_pred[i],
anchors=batched_anchors[i])
results.append(result)
return results
def forward(self, batched_pts, mode='test', batched_gt_bboxes=None, batched_gt_labels=None):
batch_size = len(batched_pts)
# batched_pts: list[tensor] -> pillars: (p1 + p2 + ... + pb, num_points, c),
# coors_batch: (p1 + p2 + ... + pb, 1 + 3),
# num_points_per_pillar: (p1 + p2 + ... + pb, ), (b: batch size)
pillars, coors_batch, npoints_per_pillar = self.pillar_layer(batched_pts)
# pillars: (p1 + p2 + ... + pb, num_points, c), c = 4
# coors_batch: (p1 + p2 + ... + pb, 1 + 3)
# npoints_per_pillar: (p1 + p2 + ... + pb, )
# -> pillar_features: (bs, out_channel, y_l, x_l)
pillar_features = self.pillar_encoder(pillars, coors_batch, npoints_per_pillar)
# xs: [(bs, 64, 248, 216), (bs, 128, 124, 108), (bs, 256, 62, 54)]
xs = self.backbone(pillar_features)
# x: (bs, 384, 248, 216)
x = self.neck(xs)
# bbox_cls_pred: (bs, n_anchors*3, 248, 216)
# bbox_pred: (bs, n_anchors*7, 248, 216)
# bbox_dir_cls_pred: (bs, n_anchors*2, 248, 216)
bbox_cls_pred, bbox_pred, bbox_dir_cls_pred = self.head(x)
# anchors
device = bbox_cls_pred.device
feature_map_size = torch.tensor(list(bbox_cls_pred.size()[-2:]), device=device)
anchors = self.anchors_generator.get_multi_anchors(feature_map_size)
batched_anchors = [anchors for _ in range(batch_size)]
if mode == 'train':
anchor_target_dict = anchor_target(batched_anchors=batched_anchors,
batched_gt_bboxes=batched_gt_bboxes,
batched_gt_labels=batched_gt_labels,
assigners=self.assigners,
nclasses=self.nclasses)
return bbox_cls_pred, bbox_pred, bbox_dir_cls_pred, anchor_target_dict
elif mode == 'val':
results = self.get_predicted_bboxes(bbox_cls_pred=bbox_cls_pred,
bbox_pred=bbox_pred,
bbox_dir_cls_pred=bbox_dir_cls_pred,
batched_anchors=batched_anchors)
return results
elif mode == 'test':
results = self.get_predicted_bboxes(bbox_cls_pred=bbox_cls_pred,
bbox_pred=bbox_pred,
bbox_dir_cls_pred=bbox_dir_cls_pred,
batched_anchors=batched_anchors)
return results
else:
raise ValueError