def initialize_model(args):
global model, voxel_generator
cfg = Config.fromfile(args.config)
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
if args.checkpoint is not None:
load_checkpoint(model, args.checkpoint, map_location="cpu")
# print(model)
if args.fp16:
print("cast model to fp16")
model = model.half()
model = model.cuda()
model.eval()
global device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
range = cfg.voxel_generator.range
voxel_size = cfg.voxel_generator.voxel_size
max_points_in_voxel = cfg.voxel_generator.max_points_in_voxel
max_voxel_num = cfg.voxel_generator.max_voxel_num[1]
voxel_generator = VoxelGenerator(voxel_size=voxel_size,
point_cloud_range=range,
max_num_points=max_points_in_voxel,
max_voxels=max_voxel_num)
return model
def main():
args = parse_args()
if len(args.shape) == 1:
input_shape = (3, args.shape[0], args.shape[0])
elif len(args.shape) == 2:
input_shape = (3, ) + tuple(args.shape)
else:
raise ValueError("invalid input shape")
cfg = Config.fromfile(args.config)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg).cuda()
model.eval()
if hasattr(model, "forward_dummy"):
model.forward = model.forward_dummy
else:
raise NotImplementedError(
"FLOPs counter is currently not currently supported with {}".
format(model.__class__.__name__))
flops, params = get_model_complexity_info(model, input_shape)
split_line = "=" * 30
print("{0}\nInput shape: {1}\nFlops: {2}\nParams: {3}\n{0}".format(
split_line, input_shape, flops, params))
def read_config(self):
config_path = self.config_path
cfg = Config.fromfile(self.config_path)
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.net = build_detector(
cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
self.net.load_state_dict(torch.load(self.model_path)["state_dict"])
self.net = self.net.to(self.device).eval()
self.range = cfg.voxel_generator.range
self.voxel_size = cfg.voxel_generator.voxel_size
self.max_points_in_voxel = cfg.voxel_generator.max_points_in_voxel
self.max_voxel_num = cfg.voxel_generator.max_voxel_num
self.voxel_generator = VoxelGenerator(
voxel_size=self.voxel_size,
point_cloud_range=self.range,
max_num_points=self.max_points_in_voxel,
max_voxels=self.max_voxel_num,
)
# nuscenes dataset
lidar2imu_t = np.array([0.985793, 0.0, 1.84019])
lidar2imu_r = Quaternion([0.706749235, -0.01530099378, 0.0173974518, -0.7070846])
## UDI dataset
# lidar2imu_t = np.array([1.50, 0., 1.42])
# lidar2imu_r = Quaternion([1., 0., 0., 0.])
self.lidar2imu = transform_matrix(lidar2imu_t, lidar2imu_r, inverse=True)
self.imu2lidar = transform_matrix(lidar2imu_t, lidar2imu_r, inverse=False)
def read_config(self):
config_path = self.config_path
cfg = Config.fromfile(self.config_path)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.net = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
self.net.load_state_dict(torch.load(self.model_path)["state_dict"])
self.net = self.net.to(self.device).eval()
self.range = cfg.voxel_generator.range
self.voxel_size = cfg.voxel_generator.voxel_size
self.max_points_in_voxel = cfg.voxel_generator.max_points_in_voxel
self.max_voxel_num = cfg.voxel_generator.max_voxel_num
self.voxel_generator = VoxelGenerator(
voxel_size=self.voxel_size,
point_cloud_range=self.range,
max_num_points=self.max_points_in_voxel,
max_voxels=self.max_voxel_num,
)
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
checkpoint = torch.load(args.checkpoint, map_location='cpu')
state_dict = checkpoint['state_dict']
if list(state_dict.keys())[0].startswith("module."):
state_dict = {k[7:]: v for k, v in checkpoint["state_dict"].items()}
convert_state_dict(model, state_dict)
save_checkpoint(model, osp.join(args.work_dir, 'voxelnet_converted.pth'))
def main():
# config
cfg = Config.fromfile(CONFIG_FILE)
# model loading
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
checkpoint = load_checkpoint(model, CHECK_POINT, map_location="cpu")
model = model.cuda()
model.eval()
# data loader
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(
dataset,
batch_size=cfg.data.samples_per_gpu,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=False,
shuffle=False,
)
# infer
detections = []
for i, data_batch in enumerate(data_loader):
print("step:", i)
with torch.no_grad():
outputs = batch_processor(
model,
data_batch,
train_mode=False,
local_rank=0,
)
for output in outputs:
token = output["metadata"]["token"]
for k, v in output.items():
if k not in [
"metadata",
]:
output[k] = v.to(cpu_device)
detections.update({
token: output,
})
all_predictions = all_gather(detections)
"data": image_str,
"datatype": "png",
}
return data
# todo: for debug
if __name__ == "__main__":
warnings.filterwarnings('ignore')
data_path = "/mnt/proj50/zhengwu/KITTI/object"
info_path = "/mnt/proj50/zhengwu/KITTI/object/kitti_infos_train.pkl"
from det3d.torchie import Config
from torch.utils.data import Dataset, DataLoader
cfg = Config.fromfile("../../../examples/second/configs/config.py")
pipeline = cfg.train_pipeline
kitti = KittiDataset(data_path, info_path, pipeline=pipeline)
data = kitti.get_sensor_data(99, by_index=True)
# for i in range(3000):
# print(i)
# data = kitti.get_sensor_data(i, by_index=False)
import ipdb; ipdb.set_trace()
trainloader = DataLoader(kitti, batch_size=4, shuffle=True)
dataitr = iter(trainloader)
data = next(dataitr)
import ipdb; ipdb.set_trace()
def main():
cfg = Config.fromfile(
'configs/nusc/pp/nusc_centerpoint_pp_02voxel_two_pfn_10sweep_demo.py')
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
dataset = build_dataset(cfg.data.val)
data_loader = DataLoader(
dataset,
batch_size=1,
sampler=None,
shuffle=False,
num_workers=8,
collate_fn=collate_kitti,
pin_memory=False,
)
checkpoint = load_checkpoint(
model,
'work_dirs/centerpoint_pillar_512_demo/latest.pth',
map_location="cpu")
model.eval()
model = model.cuda()
cpu_device = torch.device("cpu")
points_list = []
gt_annos = []
detections = []
for i, data_batch in enumerate(data_loader):
info = dataset._nusc_infos[i]
gt_annos.append(convert_box(info))
points = data_batch['points'][:, 1:4].cpu().numpy()
with torch.no_grad():
outputs = batch_processor(
model,
data_batch,
train_mode=False,
local_rank=0,
)
for output in outputs:
for k, v in output.items():
if k not in [
"metadata",
]:
output[k] = v.to(cpu_device)
detections.append(output)
points_list.append(points.T)
print(
'Done model inference. Please wait a minute, the matplotlib is a little slow...'
)
for i in range(len(points_list)):
visual(points_list[i], gt_annos[i], detections[i], i)
print("Rendered Image {}".format(i))
image_folder = 'demo'
video_name = 'video.avi'
images = [img for img in os.listdir(image_folder) if img.endswith(".png")]
images.sort(key=lambda img_name: int(img_name.split('.')[0][4:]))
frame = cv2.imread(os.path.join(image_folder, images[0]))
height, width, layers = frame.shape
video = cv2.VideoWriter(video_name, 0, 1, (width, height))
cv2_images = []
for image in images:
cv2_images.append(cv2.imread(os.path.join(image_folder, image)))
for img in cv2_images:
video.write(img)
cv2.destroyAllWindows()
video.release()
print("Successfully save video in the main folder")
def main():
# torch.manual_seed(0)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
# np.random.seed(0)
args = parse_args()
cfg = Config.fromfile(args.config)
cfg.local_rank = args.local_rank
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
distributed = False
if "WORLD_SIZE" in os.environ:
distributed = int(os.environ["WORLD_SIZE"]) > 1
if distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl", init_method="env://")
cfg.gpus = torch.distributed.get_world_size()
if args.autoscale_lr:
cfg.lr_config.lr_max = cfg.lr_config.lr_max * cfg.gpus
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info("Distributed training: {}".format(distributed))
logger.info(f"torch.backends.cudnn.benchmark: {torch.backends.cudnn.benchmark}")
if args.local_rank == 0:
# copy important files to backup
backup_dir = os.path.join(cfg.work_dir, "det3d")
os.makedirs(backup_dir, exist_ok=True)
os.system("cp -r ./det3d %s/" % backup_dir)
os.system("cp -r ./tools %s/" % backup_dir)
os.system("cp -r ./examples %s/" % backup_dir)
logger.info(f"Backup source files to {cfg.work_dir}/det3d")
# set random seeds
if args.seed is not None:
logger.info("Set random seed to {}".format(args.seed))
set_random_seed(args.seed)
model = build_detector(cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
datasets.append(build_dataset(cfg.data.val))
if cfg.checkpoint_config is not None:
# save det3d version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(
det3d_version=__version__, config=cfg.text, CLASSES=datasets[0].CLASSES
)
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
train_detector(
model,
datasets,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger,
)
def main():
# torch.manual_seed(0)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
# np.random.seed(0)
args = parse_args()
cfg = Config.fromfile(args.config)
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
if args.resume_from is not None:
cfg.resume_from = args.resume_from
distributed = torch.cuda.device_count() > 1
if distributed:
if args.launcher == "pytorch":
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
cfg.local_rank = args.local_rank
elif args.launcher == "slurm":
proc_id = int(os.environ["SLURM_PROCID"])
ntasks = int(os.environ["SLURM_NTASKS"])
node_list = os.environ["SLURM_NODELIST"]
num_gpus = torch.cuda.device_count()
cfg.gpus = num_gpus
torch.cuda.set_device(proc_id % num_gpus)
addr = subprocess.getoutput(
f"scontrol show hostname {node_list} | head -n1")
# specify master port
port = None
if port is not None:
os.environ["MASTER_PORT"] = str(port)
elif "MASTER_PORT" in os.environ:
pass # use MASTER_PORT in the environment variable
else:
# 29500 is torch.distributed default port
os.environ["MASTER_PORT"] = "29501"
# use MASTER_ADDR in the environment variable if it already exists
if "MASTER_ADDR" not in os.environ:
os.environ["MASTER_ADDR"] = addr
os.environ["WORLD_SIZE"] = str(ntasks)
os.environ["LOCAL_RANK"] = str(proc_id % num_gpus)
os.environ["RANK"] = str(proc_id)
dist.init_process_group(backend="nccl")
cfg.local_rank = int(os.environ["LOCAL_RANK"])
cfg.gpus = dist.get_world_size()
else:
cfg.local_rank = 0
cfg.gpus = 1
if args.autoscale_lr:
cfg.lr_config.lr_max = cfg.lr_config.lr_max * cfg.gpus
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info("Distributed training: {}".format(distributed))
logger.info(
f"torch.backends.cudnn.benchmark: {torch.backends.cudnn.benchmark}")
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
datasets = [build_dataset(cfg.data.train)]
if len(cfg.workflow) == 2:
datasets.append(build_dataset(cfg.data.val))
if cfg.checkpoint_config is not None:
# save det3d version, config file content and class names in
# checkpoints as meta data
cfg.checkpoint_config.meta = dict(det3d_version=__version__,
config=cfg.text,
CLASSES=datasets[0].CLASSES)
# add an attribute for visualization convenience
model.CLASSES = datasets[0].CLASSES
train_detector(
model,
datasets,
cfg,
distributed=distributed,
validate=args.validate,
logger=logger,
)
def main():
# torch.manual_seed(0)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
# np.random.seed(0)
args = parse_args()
cfg = Config.fromfile(args.config)
cfg.local_rank = args.local_rank
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
distributed = False
if "WORLD_SIZE" in os.environ:
distributed = int(os.environ["WORLD_SIZE"]) > 1
if distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
cfg.gpus = torch.distributed.get_world_size()
else:
cfg.gpus = args.gpus
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info("Distributed testing: {}".format(distributed))
logger.info(
f"torch.backends.cudnn.benchmark: {torch.backends.cudnn.benchmark}")
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
dataset = build_dataset(cfg.data.val)
data_loader = build_dataloader(
dataset,
batch_size=cfg.data.samples_per_gpu,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False,
)
checkpoint = load_checkpoint(model, args.checkpoint, map_location="cpu")
# put model on gpus
if distributed:
model = apex.parallel.convert_syncbn_model(model)
model = DistributedDataParallel(
model.cuda(cfg.local_rank),
device_ids=[cfg.local_rank],
output_device=cfg.local_rank,
# broadcast_buffers=False,
find_unused_parameters=True,
)
else:
model = model.cuda()
model.eval()
mode = "val"
logger.info(f"work dir: {args.work_dir}")
if cfg.local_rank == 0:
prog_bar = torchie.ProgressBar(len(data_loader.dataset) // cfg.gpus)
detections = {}
cpu_device = torch.device("cpu")
for i, data_batch in enumerate(data_loader):
with torch.no_grad():
outputs = batch_processor(
model,
data_batch,
train_mode=False,
local_rank=args.local_rank,
)
for output in outputs:
token = output["metadata"]["token"]
for k, v in output.items():
if k not in [
"metadata",
]:
output[k] = v.to(cpu_device)
detections.update({
token: output,
})
if args.local_rank == 0:
prog_bar.update()
synchronize()
all_predictions = all_gather(detections)
if args.local_rank != 0:
return
predictions = {}
for p in all_predictions:
predictions.update(p)
result_dict, _ = dataset.evaluation(predictions, output_dir=args.work_dir)
for k, v in result_dict["results"].items():
print(f"Evaluation {k}: {v}")
if args.txt_result:
res_dir = os.path.join(os.getcwd(), "predictions")
for k, dt in predictions.items():
with open(
os.path.join(res_dir,
"%06d.txt" % int(dt["metadata"]["token"])),
"w") as fout:
lines = kitti.annos_to_kitti_label(dt)
for line in lines:
fout.write(line + "\n")
ap_result_str, ap_dict = kitti_evaluate(
"/data/Datasets/KITTI/Kitti/object/training/label_2",
res_dir,
label_split_file="/data/Datasets/KITTI/Kitti/ImageSets/val.txt",
current_class=0,
)
print(ap_result_str)
def main():
# torch.manual_seed(0)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
# np.random.seed(0)
args = parse_args()
cfg = Config.fromfile(args.config)
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
distributed = torch.cuda.device_count() > 1
if distributed:
if args.launcher == "pytorch":
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl", init_method="env://")
cfg.local_rank = args.local_rank
elif args.launcher == "slurm":
proc_id = int(os.environ["SLURM_PROCID"])
ntasks = int(os.environ["SLURM_NTASKS"])
node_list = os.environ["SLURM_NODELIST"]
num_gpus = torch.cuda.device_count()
cfg.gpus = num_gpus
torch.cuda.set_device(proc_id % num_gpus)
addr = subprocess.getoutput(
f"scontrol show hostname {node_list} | head -n1")
# specify master port
port = None
if port is not None:
os.environ["MASTER_PORT"] = str(port)
elif "MASTER_PORT" in os.environ:
pass # use MASTER_PORT in the environment variable
else:
# 29500 is torch.distributed default port
os.environ["MASTER_PORT"] = "29501"
# use MASTER_ADDR in the environment variable if it already exists
if "MASTER_ADDR" not in os.environ:
os.environ["MASTER_ADDR"] = addr
os.environ["WORLD_SIZE"] = str(ntasks)
os.environ["LOCAL_RANK"] = str(proc_id % num_gpus)
os.environ["RANK"] = str(proc_id)
dist.init_process_group(backend="nccl")
cfg.local_rank = int(os.environ["LOCAL_RANK"])
cfg.gpus = torch.distributed.get_world_size()
else:
cfg.gpus = args.gpus
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info("Distributed testing: {}".format(distributed))
logger.info(f"torch.backends.cudnn.benchmark: {torch.backends.cudnn.benchmark}")
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
if args.testset:
print("Use Test Set")
dataset = build_dataset(cfg.data.test)
else:
print("Use Val Set")
dataset = build_dataset(cfg.data.val)
data_loader = build_dataloader(
dataset,
batch_size=cfg.data.samples_per_gpu if not args.speed_test else 1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False,
)
checkpoint = load_checkpoint(model, args.checkpoint, map_location="cpu")
# put model on gpus
if distributed:
# model = apex.parallel.convert_syncbn_model(model)
model = DistributedDataParallel(
model.cuda(cfg.local_rank),
device_ids=[cfg.local_rank],
output_device=cfg.local_rank,
# broadcast_buffers=False,
find_unused_parameters=True,
)
else:
# model = fuse_bn_recursively(model)
model = model.cuda()
model.eval()
mode = "val"
prog_bar = None
logger.info(f"work dir: {args.work_dir}")
if cfg.local_rank == 0:
prog_bar = torchie.ProgressBar(len(data_loader.dataset) // cfg.gpus)
detections = {}
cpu_device = torch.device("cpu")
start = time.time()
start = int(len(dataset) / 3)
end = int(len(dataset) * 2 /3)
time_start = 0
time_end = 0
for i, data_batch in enumerate(data_loader):
if i == start:
torch.cuda.synchronize()
time_start = time.time()
if i == end:
torch.cuda.synchronize()
time_end = time.time()
with torch.no_grad():
outputs = batch_processor(
model, data_batch, train_mode=False, local_rank=args.local_rank,
)
for output in outputs:
token = output["metadata"]["token"]
for k, v in output.items():
if k not in [
"metadata",
]:
output[k] = v.to(cpu_device)
detections.update(
{token: output,}
)
if args.local_rank == 0:
if prog_bar is not None:
prog_bar.update()
synchronize()
all_predictions = all_gather(detections)
print("\n Total time per frame: ", (time_end - time_start) / (end - start))
if args.local_rank != 0:
return
predictions = {}
for p in all_predictions:
predictions.update(p)
if not os.path.exists(args.work_dir):
os.makedirs(args.work_dir)
save_pred(predictions, args.work_dir)
result_dict, _ = dataset.evaluation(copy.deepcopy(predictions), output_dir=args.work_dir, testset=args.testset)
if result_dict is not None:
for k, v in result_dict["results"].items():
print(f"Evaluation {k}: {v}")
if args.txt_result:
assert False, "No longer support kitti"
def main():
# torch.manual_seed(0)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
# np.random.seed(0)
args = parse_args()
cfg = Config.fromfile(args.config)
cfg.local_rank = args.local_rank
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
distributed = False
if "WORLD_SIZE" in os.environ:
distributed = int(os.environ["WORLD_SIZE"]) > 1
if distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
cfg.gpus = torch.distributed.get_world_size()
else:
cfg.gpus = args.gpus
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info("Distributed testing: {}".format(distributed))
logger.info(
f"torch.backends.cudnn.benchmark: {torch.backends.cudnn.benchmark}")
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
if args.testset:
print("Use Test Set")
dataset = build_dataset(cfg.data.test)
else:
print("Use Val Set")
dataset = build_dataset(cfg.data.val)
data_loader = build_dataloader(
dataset,
batch_size=cfg.data.samples_per_gpu if not args.speed_test else 1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False,
)
checkpoint = load_checkpoint(model, args.checkpoint, map_location="cpu")
# put model on gpus
if distributed:
model = apex.parallel.convert_syncbn_model(model)
model = DistributedDataParallel(
model.cuda(cfg.local_rank),
device_ids=[cfg.local_rank],
output_device=cfg.local_rank,
# broadcast_buffers=False,
find_unused_parameters=True,
)
else:
# model = fuse_bn_recursively(model)
model = model.cuda()
model.eval()
mode = "val"
logger.info(f"work dir: {args.work_dir}")
if cfg.local_rank == 0:
prog_bar = torchie.ProgressBar(len(data_loader.dataset) // cfg.gpus)
detections = {}
cpu_device = torch.device("cpu")
start = time.time()
start = int(len(dataset) / 3)
end = int(len(dataset) * 2 / 3)
time_start = 0
time_end = 0
for i, data_batch in enumerate(data_loader):
if i == start:
torch.cuda.synchronize()
time_start = time.time()
if i == end:
torch.cuda.synchronize()
time_end = time.time()
with torch.no_grad():
outputs = batch_processor(
model,
data_batch,
train_mode=False,
local_rank=args.local_rank,
)
for output in outputs:
token = output["metadata"]["token"]
for k, v in output.items():
if k not in [
"metadata",
]:
output[k] = v.to(cpu_device)
detections.update({
token: output,
})
if args.local_rank == 0:
prog_bar.update()
synchronize()
all_predictions = all_gather(detections)
print("\n Total time per frame: ", (time_end - time_start) / (end - start))
if args.local_rank != 0:
return
predictions = {}
for p in all_predictions:
predictions.update(p)
if not os.path.exists(args.work_dir):
os.makedirs(args.work_dir)
save_pred(predictions, args.work_dir)
with open(os.path.join(args.work_dir, 'prediction.pkl'), 'rb') as f:
predictions = pickle.load(f)
result_dict, _ = dataset.evaluation(copy.deepcopy(predictions),
output_dir=args.work_dir,
testset=args.testset)
if result_dict is not None:
for k, v in result_dict["results"].items():
print(f"Evaluation {k}: {v}")
if args.txt_result:
assert False, "No longer support kitti"
import copy
from pathlib import Path
import pickle
import fire
from det3d.datasets.kitti import kitti_common as kitti_ds
from det3d.datasets.utils.create_gt_database import create_groundtruth_database
from det3d.torchie import Config
cfg = Config.fromfile(
"../examples/second/configs/kitti_car_vfev3_spmiddlefhd_rpn1_mghead_syncbn.py"
)
def kitti_data_prep(root_path):
# compress info of image(path), velodyne(path), label (all info but dc removed),
# calib (all) into pkl file; DontCare has been removed
# root_path: "/mnt/proj50/zhengwu/KITTI/object"
#
# kitti_infos_train/trainval/val/test for each sample in train.txt/trainval.txt/val.txt/test.txt
# info{
# "point_cloud": {
# "num_featuers": 4,
# "velodyne_path": "/training/velodyne/000000.bin",
# }
# "image": {
# "image_idx": index, (eg. element in ImageSets/trainval.txt)
# "image_path": "/training/image_2/000000.png",
# "image_shape": [H(370, y), W(1224, x)], (eg. in image coord)
# }
from det3d.torchie.trainer import load_checkpoint
from det3d.torchie.parallel import collate, collate_kitti
from torch.utils.data import DataLoader
import matplotlib.cm as cm
import subprocess
import cv2
from tools.demo_utils import visual
from collections import defaultdict
import mayavi.mlab as mlab
import cv2 as cv
from tools.visualize import showPoints, createRotation
from tools.fileLoader import *
if __name__ == "__main__":
cfg = Config.fromfile('configs/centerpoint/myconfig.py')
for data in getBatchFromDepth(cfg):
points = data['points']
voxels = data['voxels']
# static of all points
xs = points[:, 0]
ys = points[:, 1]
zs = points[:, 2]
print('X: max {}, min {}'.format(xs.max(), xs.min()))
print('Y: max {}, min {}'.format(ys.max(), ys.min()))
print('Z: max {}, min {}'.format(zs.max(), zs.min()))
# # DO NOT display them in the for loop!
# # display all points
def main():
# torch.manual_seed(0)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
# np.random.seed(0)
args = parse_args()
cfg = Config.fromfile(args.config)
cfg.local_rank = args.local_rank
# update configs according to CLI args
if args.work_dir is not None:
cfg.work_dir = args.work_dir
distributed = False
if "WORLD_SIZE" in os.environ:
distributed = int(os.environ["WORLD_SIZE"]) > 1
if distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
cfg.gpus = torch.distributed.get_world_size()
else:
cfg.gpus = args.gpus
# init logger before other steps
logger = get_root_logger(cfg.log_level)
logger.info("Distributed testing: {}".format(distributed))
logger.info(
f"torch.backends.cudnn.benchmark: {torch.backends.cudnn.benchmark}")
torch.cuda.empty_cache()
model = build_detector(cfg.nohead_model,
train_cfg=None,
test_cfg=cfg.test_cfg)
if args.testset:
print("Use Test Set")
dataset = build_dataset(cfg.data.test)
else:
print("Use Val Set")
dataset = build_dataset(cfg.data.val)
data_loader = build_dataloader(
dataset,
batch_size=cfg.data.samples_per_gpu if not args.speed_test else 1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False,
)
checkpoint = load_checkpoint(model, args.checkpoint, map_location="cpu")
# put model on gpus
# model = fuse_bn_recursively(model)
model = model.cuda()
model.eval()
mode = "val"
logger.info(f"work dir: {args.work_dir}")
if cfg.local_rank == 0:
prog_bar = torchie.ProgressBar(len(data_loader.dataset) // cfg.gpus)
detections = {}
cpu_device = torch.device("cpu")
start = time.time()
start = int(len(dataset) / 3)
end = int(len(dataset) * 2 / 3)
time_start = 0
time_end = 0
device = torch.device(args.local_rank)
POINTS_NUM = 2
for i, data_batch in enumerate(data_loader):
if i == start:
torch.cuda.synchronize()
time_start = time.time()
if i == end:
torch.cuda.synchronize()
time_end = time.time()
with torch.no_grad():
sample = example_to_device(data_batch, device=device)
for i in range(len(sample["metadata"])):
sample["metadata"][i]['image_prefix'] = None
del sample["metadata"]
del sample["points"]
#del sample["shape"]
sample["shape"] = torch.tensor(sample["shape"])
sample["voxels"] = sample["voxels"][0:POINTS_NUM, :, :]
sample["num_points"] = sample["num_points"][0:POINTS_NUM]
sample["coordinates"] = sample["coordinates"][0:POINTS_NUM, :]
outputs = model(sample, return_loss=False)
#outputs = batch_processor(
# model, data_batch, train_mode=False, local_rank=args.local_rank,
#)
for k, t in sample.items():
print("====", k)
print(t.shape)
print("============== start =============")
register_custom_op_symbolic("spconv::get_indice_pairs_3d",
symbolic_get_indice_pairs_3d, 11)
torch.onnx.export(
model, # model being run
sample, # model input (or a tuple for multiple inputs)
"/workspace/data/center_point.onnx", # where to save the model (can be a file or file-like object)
export_params=
True, # store the trained parameter weights inside the model file
opset_version=11, # the ONNX version to export the model to
do_constant_folding=True
) # whether to execute constant folding for optimization
print("============== finish =============")
break
for output in outputs:
token = output["metadata"]["token"]
for k, v in output.items():
if k not in [
"metadata",
]:
output[k] = v.to(cpu_device)
detections.update({
token: output,
})
if args.local_rank == 0:
prog_bar.update()
synchronize()
all_predictions = all_gather(detections)
print("\n Total time per frame: ", (time_end - time_start) / (end - start))
if args.local_rank != 0:
return
predictions = {}
for p in all_predictions:
predictions.update(p)
if not os.path.exists(args.work_dir):
os.makedirs(args.work_dir)
save_pred(predictions, args.work_dir)
result_dict, _ = dataset.evaluation(copy.deepcopy(predictions),
output_dir=args.work_dir,
testset=args.testset)
if result_dict is not None:
for k, v in result_dict["results"].items():
print(f"Evaluation {k}: {v}")
if args.txt_result:
assert False, "No longer support kitti"
def main():
cfg = Config.fromfile(
'configs/nusc/pp/nusc_centerpoint_pp_02voxel_two_pfn_10sweep_demo_export_onnx.py'
)
model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
dataset = build_dataset(cfg.data.val)
data_loader = DataLoader(
dataset,
batch_size=1,
sampler=None,
shuffle=False,
num_workers=8,
collate_fn=collate_kitti,
pin_memory=False,
)
checkpoint = load_checkpoint(model, './latest.pth', map_location="cpu")
model.eval()
model = model.cuda()
gpu_device = torch.device("cuda")
points_list = []
gt_annos = []
detections = []
data_iter = iter(data_loader)
data_batch = next(data_iter)
pp_model = PointPillars(model)
points = data_batch['points'][:, 1:4].cpu().numpy()
with torch.no_grad():
example = example_to_device(data_batch, gpu_device, non_blocking=False)
example["voxels"] = torch.zeros(
(example["voxels"].shape[0], example["voxels"].shape[1], 10),
dtype=torch.float32,
device=gpu_device)
example.pop("metadata")
example.pop("points")
example["shape"] = torch.tensor(example["shape"],
dtype=torch.int32,
device=gpu_device)
model(example)
torch.onnx.export(
model.reader,
(example["voxels"], example["num_voxels"], example["coordinates"]),
"onnx_model/pfe.onnx",
opset_version=11)
rpn_input = torch.zeros((1, 64, 512, 512),
dtype=torch.float32,
device=gpu_device)
torch.onnx.export(pp_model,
rpn_input,
"onnx_model/rpn.onnx",
opset_version=11)
print("Done")