File size: 9,442 Bytes
da2e2ac |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 |
from __future__ import annotations
from typing import Any, List, Dict
import torch
import torch.optim as optim
import copy
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LRScheduler
import torch.nn as nn
from det_map.data.datasets.dataclasses import SensorConfig, Scene
from det_map.data.datasets.feature_builders import LiDARCameraFeatureBuilder
from navsim.agents.abstract_agent import AbstractAgent
from navsim.planning.training.abstract_feature_target_builder import AbstractFeatureBuilder, AbstractTargetBuilder
from det_map.det.dal.mmdet3d.models.utils.grid_mask import GridMask
import torch.nn.functional as F
from det_map.det.dal.mmdet3d.ops import Voxelization, DynamicScatter
from det_map.det.dal.mmdet3d.models import builder
from mmcv.utils import TORCH_VERSION, digit_version
class MapModel(nn.Module):
def __init__(
self,
use_grid_mask=False,
pts_voxel_layer=None,
pts_voxel_encoder=None,
pts_middle_encoder=None,
pts_fusion_layer=None,
img_backbone=None,
pts_backbone=None,
img_neck=None,
pts_neck=None,
pts_bbox_head=None,
img_roi_head=None,
img_rpn_head=None,
train_cfg=None,
test_cfg=None,
pretrained=None,
video_test_mode=False,
modality='vision',
lidar_encoder=None,
lr=None,
):
super().__init__()
# self.pipelines = pipelines
self.grid_mask = GridMask(
True, True, rotate=1, offset=False, ratio=0.5, mode=1, prob=0.7)
if pts_voxel_layer:
self.pts_voxel_layer = Voxelization(**pts_voxel_layer)
if pts_voxel_encoder:
self.pts_voxel_encoder = builder.build_voxel_encoder(
pts_voxel_encoder)
if pts_middle_encoder:
self.pts_middle_encoder = builder.build_middle_encoder(
pts_middle_encoder)
if pts_backbone:
self.pts_backbone = builder.build_backbone(pts_backbone)
if pts_fusion_layer:
self.pts_fusion_layer = builder.build_fusion_layer(
pts_fusion_layer)
if pts_neck is not None:
self.pts_neck = builder.build_neck(pts_neck)
if pts_bbox_head:
pts_train_cfg = None
pts_bbox_head.update(train_cfg=pts_train_cfg)
pts_test_cfg = None
pts_bbox_head.update(test_cfg=pts_test_cfg)
self.pts_bbox_head = builder.build_head(pts_bbox_head)
if img_backbone:
self.img_backbone = builder.build_backbone(img_backbone)
if img_neck is not None:
self.img_neck = builder.build_neck(img_neck)
if img_rpn_head is not None:
self.img_rpn_head = builder.build_head(img_rpn_head)
if img_roi_head is not None:
self.img_roi_head = builder.build_head(img_roi_head)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
if pretrained is None:
img_pretrained = None
pts_pretrained = None
elif isinstance(pretrained, dict):
img_pretrained = pretrained.get('img', None)
pts_pretrained = pretrained.get('pts', None)
else:
raise ValueError(
f'pretrained should be a dict, got {type(pretrained)}')
self.use_grid_mask = use_grid_mask
self.fp16_enabled = False
# temporal
self.video_test_mode = video_test_mode
self.prev_frame_info = {
'prev_bev': None,
'scene_token': None,
'prev_pos': 0,
'prev_angle': 0,
}
self.modality = modality
if self.modality == 'fusion' and lidar_encoder is not None:
if lidar_encoder["voxelize"].get("max_num_points", -1) > 0:
voxelize_module = Voxelization(**lidar_encoder["voxelize"])
else:
voxelize_module = DynamicScatter(**lidar_encoder["voxelize"])
self.lidar_modal_extractor = nn.ModuleDict(
{
"voxelize": voxelize_module,
"backbone": builder.build_middle_encoder(lidar_encoder["backbone"]),
}
)
self.voxelize_reduce = lidar_encoder.get("voxelize_reduce", True)
self._lr = lr
def extract_img_feat(self, img, img_metas=None, len_queue=None):
"""Extract features of images."""
B = img.size(0)
if img is not None:
# input_shape = img.shape[-2:]
# # update real input shape of each single img
# for img_meta in img_metas:
# img_meta.update(input_shape=input_shape)
if img.dim() == 5 and img.size(0) == 1:
img.squeeze_()
elif img.dim() == 5 and img.size(0) > 1:
B, N, C, H, W = img.size()
img = img.reshape(B * N, C, H, W)
if self.use_grid_mask:
img = self.grid_mask(img)
img_feats = self.img_backbone(img)
if isinstance(img_feats, dict):
img_feats = list(img_feats.values())
else:
return None
self.with_img_neck = True
if self.with_img_neck:
img_feats = self.img_neck(img_feats)
BN, C, H, W = img_feats[0].shape
return [tmp.view(B, BN // B, C, H , W) for tmp in img_feats]
@torch.no_grad()
def voxelize(self, points):
feats, coords, sizes = [], [], []
for k, res in enumerate(points):
ret = self.lidar_modal_extractor["voxelize"](res)
if len(ret) == 3:
# hard voxelize
f, c, n = ret
else:
assert len(ret) == 2
f, c = ret
n = None
feats.append(f)
coords.append(F.pad(c, (1, 0), mode="constant", value=k))
if n is not None:
sizes.append(n)
feats = torch.cat(feats, dim=0)
coords = torch.cat(coords, dim=0)
if len(sizes) > 0:
sizes = torch.cat(sizes, dim=0)
if self.voxelize_reduce:
feats = feats.sum(dim=1, keepdim=False) / sizes.type_as(feats).view(
-1, 1
)
feats = feats.contiguous()
return feats, coords, sizes
def extract_lidar_feat(self, points):
feats, coords, sizes = self.voxelize(points)
# voxel_features = self.lidar_modal_extractor["voxel_encoder"](feats, sizes, coords)
batch_size = coords[-1, 0] + 1
lidar_feat = self.lidar_modal_extractor["backbone"](feats, coords, batch_size)
return lidar_feat
def forward(self, feature_dict=None, points=None, img_metas=None) -> Dict[str, torch.Tensor]:
lidar_feat = None
# points = feature_dict['lidars_warped']
# points_input = []
# for tmp in points:
# points_input.append(torch.cat(tmp, 0))
if self.modality == 'fusion':
lidar_feat = self.extract_lidar_feat(points_input)
img = feature_dict['image']
len_queue = img.size(1)
img = img[:, -1, ...]
img_feats = self.extract_img_feat(img, img_metas, len_queue=len_queue)
outs = self.pts_bbox_head(
img_feats, lidar_feat, feature_dict, None)
return outs
# class MyLightningModule(pl.LightningModule):
# def __init__(
# self,
# agent: AbstractAgent,
# ):
# super().__init__()
# self.agent = agent
# def _step(
# self,
# batch: Tuple[Dict[str, Tensor], Dict[str, Tensor]],
# logging_prefix: str,
# ):
# features, targets = batch
# prediction = self.agent.forward(features)
# loss = self.agent.compute_loss(features, targets, prediction)
# self.log(f"{logging_prefix}/loss", loss, on_step=True, on_epoch=True, prog_bar=True, sync_dist=True)
# return loss
# def training_step(
# self,
# batch: Tuple[Dict[str, Tensor], Dict[str, Tensor]],
# batch_idx: int
# ):
# return self._step(batch, "train")
# def validation_step(
# self,
# batch: Tuple[Dict[str, Tensor], Dict[str, Tensor]],
# batch_idx: int
# ):
# return self._step(batch, "val")
# def configure_optimizers(self):
# optimizer = self.agent.get_optimizers()
# # 应用梯度裁剪
# if 'grad_clip' in self.optimizer_config:
# grad_clip = self.optimizer_config['grad_clip']
# max_norm = grad_clip.get('max_norm', 1.0)
# norm_type = grad_clip.get('norm_type', 2)
# optimizer = optim.Adam(self.parameters(), lr=1e-3)
# return {
# 'optimizer': optimizer,
# 'clip_grad_norm': max_norm,
# 'clip_grad_value': None, # 可以使用 'clip_grad_value' 来限制梯度的绝对值
# }
# else:
# return optimizerfrom __future__ import annotations
|