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bev_height_lss_r50_864_1536_128x128_102.py
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bev_height_lss_r50_864_1536_128x128_102.py
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# Copyright (c) Megvii Inc. All rights reserved.
from argparse import ArgumentParser, Namespace
import os
import mmcv
import pytorch_lightning as pl
import torch
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
import torchvision.models as models
from pytorch_lightning.core import LightningModule
from pytorch_lightning.callbacks import ModelCheckpoint
from torch.optim.lr_scheduler import MultiStepLR
from dataset.nusc_mv_det_dataset import NuscMVDetDataset, collate_fn
from evaluators.det_evaluators import RoadSideEvaluator
from models.bev_height import BEVHeight
from utils.torch_dist import all_gather_object, get_rank, synchronize
from utils.backup_files import backup_codebase
H = 1080
W = 1920
final_dim = (864, 1536)
img_conf = dict(img_mean=[123.675, 116.28, 103.53],
img_std=[58.395, 57.12, 57.375],
to_rgb=True)
data_root = "data/rope3d/"
gt_label_path = "data/rope3d-kitti/training/label_2"
backbone_conf = {
'x_bound': [0, 102.4, 0.8],
'y_bound': [-51.2, 51.2, 0.8],
'z_bound': [-5, 3, 8],
'd_bound': [-1.5, 3.0, 180],
'final_dim':
final_dim,
'output_channels':
80,
'downsample_factor':
16,
'img_backbone_conf':
dict(
type='ResNet',
depth=50,
frozen_stages=0,
out_indices=[0, 1, 2, 3],
norm_eval=False,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
),
'img_neck_conf':
dict(
type='SECONDFPN',
in_channels=[256, 512, 1024, 2048],
upsample_strides=[0.25, 0.5, 1, 2],
out_channels=[128, 128, 128, 128],
),
'height_net_conf':
dict(in_channels=512, mid_channels=512)
}
ida_aug_conf = {
'final_dim':
final_dim,
'H':
H,
'W':
W,
'bot_pct_lim': (0.0, 0.0),
'cams': ['CAM_FRONT'],
'Ncams': 1,
}
bev_backbone = dict(
type='ResNet',
in_channels=80,
depth=18,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=[0, 1, 2],
norm_eval=False,
base_channels=160,
)
bev_neck = dict(type='SECONDFPN',
in_channels=[80, 160, 320, 640],
upsample_strides=[1, 2, 4, 8],
out_channels=[64, 64, 64, 64])
CLASSES = [
'car',
'truck',
'construction_vehicle',
'bus',
'trailer',
'barrier',
'motorcycle',
'bicycle',
'pedestrian',
'traffic_cone',
]
TASKS = [
dict(num_class=1, class_names=['car']),
dict(num_class=2, class_names=['truck', 'construction_vehicle']),
dict(num_class=2, class_names=['bus', 'trailer']),
dict(num_class=1, class_names=['barrier']),
dict(num_class=2, class_names=['motorcycle', 'bicycle']),
dict(num_class=2, class_names=['pedestrian', 'traffic_cone']),
]
common_heads = dict(reg=(2, 2),
height=(1, 2),
dim=(3, 2),
rot=(2, 2),
vel=(2, 2))
bbox_coder = dict(
type='CenterPointBBoxCoder',
post_center_range=[0.0, -61.2, -10.0, 122.4, 61.2, 10.0],
max_num=500,
score_threshold=0.1,
out_size_factor=4,
voxel_size=[0.2, 0.2, 8],
pc_range=[0, -51.2, -5, 104.4, 51.2, 3],
code_size=9,
)
train_cfg = dict(
point_cloud_range=[0, -51.2, -5, 102.4, 51.2, 3],
grid_size=[512, 512, 1],
voxel_size=[0.2, 0.2, 8],
out_size_factor=4,
dense_reg=1,
gaussian_overlap=0.1,
max_objs=500,
min_radius=2,
code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.5, 0.5],
)
test_cfg = dict(
post_center_limit_range=[0.0, -61.2, -10.0, 122.4, 61.2, 10.0],
max_per_img=500,
max_pool_nms=False,
min_radius=[4, 12, 10, 1, 0.85, 0.175],
score_threshold=0.1,
out_size_factor=4,
voxel_size=[0.2, 0.2, 8],
nms_type='circle',
pre_max_size=1000,
post_max_size=83,
nms_thr=0.2,
)
head_conf = {
'bev_backbone_conf': bev_backbone,
'bev_neck_conf': bev_neck,
'tasks': TASKS,
'common_heads': common_heads,
'bbox_coder': bbox_coder,
'train_cfg': train_cfg,
'test_cfg': test_cfg,
'in_channels': 256, # Equal to bev_neck output_channels.
'loss_cls': dict(type='GaussianFocalLoss', reduction='mean'),
'loss_bbox': dict(type='L1Loss', reduction='mean', loss_weight=0.25),
'gaussian_overlap': 0.1,
'min_radius': 2,
}
class BEVHeightLightningModel(LightningModule):
MODEL_NAMES = sorted(name for name in models.__dict__
if name.islower() and not name.startswith('__')
and callable(models.__dict__[name]))
def __init__(self,
gpus: int = 1,
data_root=data_root,
eval_interval=1,
batch_size_per_device=8,
class_names=CLASSES,
backbone_conf=backbone_conf,
head_conf=head_conf,
ida_aug_conf=ida_aug_conf,
default_root_dir='outputs/',
**kwargs):
super().__init__()
self.save_hyperparameters()
self.gpus = gpus
self.eval_interval = eval_interval
self.batch_size_per_device = batch_size_per_device
self.data_root = data_root
self.basic_lr_per_img = 2e-4 / 64
self.class_names = class_names
self.backbone_conf = backbone_conf
self.head_conf = head_conf
self.ida_aug_conf = ida_aug_conf
mmcv.mkdir_or_exist(default_root_dir)
self.default_root_dir = default_root_dir
self.evaluator = RoadSideEvaluator(class_names=self.class_names,
current_classes=["Car", "Bus"],
data_root=data_root,
gt_label_path=gt_label_path,
output_dir=self.default_root_dir)
self.model = BEVHeight(self.backbone_conf, self.head_conf)
self.mode = 'valid'
self.img_conf = img_conf
self.data_use_cbgs = False
self.num_sweeps = 1
self.sweep_idxes = list()
self.key_idxes = list()
self.up_stride = 8
self.downsample_factor = self.backbone_conf['downsample_factor'] // self.up_stride
self.dbound = self.backbone_conf['d_bound']
self.height_channels = int(self.dbound[2])
def forward(self, sweep_imgs, mats):
return self.model(sweep_imgs, mats)
def training_step(self, batch):
(sweep_imgs, mats, _, _, gt_boxes, gt_labels) = batch
if torch.cuda.is_available():
for key, value in mats.items():
mats[key] = value.cuda()
sweep_imgs = sweep_imgs.cuda()
gt_boxes = [gt_box.cuda() for gt_box in gt_boxes]
gt_labels = [gt_label.cuda() for gt_label in gt_labels]
preds = self(sweep_imgs, mats)
if isinstance(self.model, torch.nn.parallel.DistributedDataParallel):
targets = self.model.module.get_targets(gt_boxes, gt_labels)
detection_loss = self.model.module.loss(targets, preds)
else:
targets = self.model.get_targets(gt_boxes, gt_labels)
detection_loss = self.model.loss(targets, preds)
self.log('detection_loss', detection_loss)
return detection_loss
def eval_step(self, batch, batch_idx, prefix: str):
(sweep_imgs, mats, _, img_metas, _, _) = batch
if torch.cuda.is_available():
for key, value in mats.items():
mats[key] = value.cuda()
sweep_imgs = sweep_imgs.cuda()
preds = self.model(sweep_imgs, mats)
if isinstance(self.model, torch.nn.parallel.DistributedDataParallel):
results = self.model.module.get_bboxes(preds, img_metas)
else:
results = self.model.get_bboxes(preds, img_metas)
for i in range(len(results)):
results[i][0] = results[i][0].tensor.detach().cpu().numpy()
results[i][1] = results[i][1].detach().cpu().numpy()
results[i][2] = results[i][2].detach().cpu().numpy()
results[i].append(img_metas[i])
return results
def validation_step(self, batch, batch_idx):
return self.eval_step(batch, batch_idx, 'val')
def validation_epoch_end(self, validation_step_outputs):
all_pred_results = list()
all_img_metas = list()
for validation_step_output in validation_step_outputs:
for i in range(len(validation_step_output)):
all_pred_results.append(validation_step_output[i][:3])
all_img_metas.append(validation_step_output[i][3])
synchronize()
len_dataset = len(self.val_dataloader().dataset)
all_pred_results = sum(
map(list, zip(*all_gather_object(all_pred_results))),
[])[:len_dataset]
all_img_metas = sum(map(list, zip(*all_gather_object(all_img_metas))),
[])[:len_dataset]
if get_rank() == 0:
self.evaluator.evaluate(all_pred_results, all_img_metas)
def test_epoch_end(self, test_step_outputs):
all_pred_results = list()
all_img_metas = list()
for test_step_output in test_step_outputs:
for i in range(len(test_step_output)):
all_pred_results.append(test_step_output[i][:3])
all_img_metas.append(test_step_output[i][3])
synchronize()
# TODO: Change another way.
dataset_length = len(self.val_dataloader().dataset)
all_pred_results = sum(
map(list, zip(*all_gather_object(all_pred_results))),
[])[:dataset_length]
all_img_metas = sum(map(list, zip(*all_gather_object(all_img_metas))),
[])[:dataset_length]
if get_rank() == 0:
self.evaluator.evaluate(all_pred_results, all_img_metas)
def configure_optimizers(self):
lr = self.basic_lr_per_img * \
self.batch_size_per_device * self.gpus
optimizer = torch.optim.AdamW(self.model.parameters(),
lr=lr,
weight_decay=1e-7)
scheduler = MultiStepLR(optimizer, [19, 23])
return [[optimizer], [scheduler]]
def train_dataloader(self):
train_dataset = NuscMVDetDataset(
ida_aug_conf=self.ida_aug_conf,
classes=self.class_names,
data_root=self.data_root,
info_path=os.path.join(data_root, 'rope3d_12hz_infos_hom_train.pkl'),
is_train=True,
use_cbgs=self.data_use_cbgs,
img_conf=self.img_conf,
num_sweeps=self.num_sweeps,
sweep_idxes=self.sweep_idxes,
key_idxes=self.key_idxes,
return_depth=False,
)
from functools import partial
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=self.batch_size_per_device,
num_workers=4,
drop_last=True,
shuffle=False,
collate_fn=partial(collate_fn,
is_return_depth=False),
sampler=None,
)
return train_loader
def val_dataloader(self):
val_dataset = NuscMVDetDataset(
ida_aug_conf=self.ida_aug_conf,
classes=self.class_names,
data_root=self.data_root,
info_path=os.path.join(data_root, 'rope3d_12hz_infos_hom_val.pkl'),
is_train=False,
img_conf=self.img_conf,
num_sweeps=self.num_sweeps,
sweep_idxes=self.sweep_idxes,
key_idxes=self.key_idxes,
return_depth=False,
)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=self.batch_size_per_device,
shuffle=False,
collate_fn=collate_fn,
num_workers=4,
sampler=None,
)
return val_loader
def test_dataloader(self):
return self.val_dataloader()
def test_step(self, batch, batch_idx):
return self.eval_step(batch, batch_idx, 'test')
@staticmethod
def add_model_specific_args(parent_parser): # pragma: no-cover
return parent_parser
def main(args: Namespace) -> None:
if args.seed is not None:
pl.seed_everything(args.seed)
print(args)
model = BEVHeightLightningModel(**vars(args))
checkpoint_callback = ModelCheckpoint(dirpath='./outputs/bev_height_lss_r50_864_1536_128x128/checkpoints', filename='{epoch}', every_n_epochs=5, save_last=True, save_top_k=-1)
trainer = pl.Trainer.from_argparse_args(args, callbacks=[checkpoint_callback])
if args.evaluate:
for ckpt_name in os.listdir(args.ckpt_path):
model_pth = os.path.join(args.ckpt_path, ckpt_name)
trainer.test(model, ckpt_path=model_pth)
else:
backup_codebase(os.path.join('./outputs/bev_height_lss_r50_864_1536_128x128', 'backup'))
trainer.fit(model)
def run_cli():
parent_parser = ArgumentParser(add_help=False)
parent_parser = pl.Trainer.add_argparse_args(parent_parser)
parent_parser.add_argument('-e',
'--evaluate',
dest='evaluate',
action='store_true',
help='evaluate model on validation set')
parent_parser.add_argument('-b', '--batch_size_per_device', type=int)
parent_parser.add_argument('--seed',
type=int,
default=0,
help='seed for initializing training.')
parent_parser.add_argument('--ckpt_path', type=str)
parser = BEVHeightLightningModel.add_model_specific_args(parent_parser)
parser.set_defaults(
profiler='simple',
deterministic=False,
max_epochs=20,
accelerator='ddp',
num_sanity_val_steps=0,
gradient_clip_val=5,
limit_val_batches=0,
enable_checkpointing=True,
precision=32,
default_root_dir='./outputs/bev_height_lss_r50_864_1536_128x128')
args = parser.parse_args()
main(args)
if __name__ == '__main__':
run_cli()