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eval.py
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import os, time, argparse, os.path as osp, numpy as np
import torch
import torch.distributed as dist
from utils.metric_util import MeanIoU
from utils.load_save_util import revise_ckpt
from dataloader.dataset import get_nuScenes_label_name
from builder import loss_builder
from mmcv import Config
from mmseg.utils import get_root_logger
import warnings
warnings.filterwarnings("ignore")
def pass_print(*args, **kwargs):
pass
def main(local_rank, args):
# global settings
torch.backends.cudnn.benchmark = True
# load config
cfg = Config.fromfile(args.py_config)
# check label_mapping, fill_label, ignore_label, pc_dataset_type
dataset_config = cfg.dataset_params
ignore_label = dataset_config['ignore_label']
version = dataset_config['version']
# check num_workers, imageset
train_dataloader_config = cfg.train_data_loader
val_dataloader_config = cfg.val_data_loader
grid_size = cfg.grid_size
# init DDP
distributed = True
ip = os.environ.get("MASTER_ADDR", "127.0.0.1")
port = os.environ.get("MASTER_PORT", "20506")
hosts = int(os.environ.get("WORLD_SIZE", 1)) # number of nodes
rank = int(os.environ.get("RANK", 0)) # node id
gpus = torch.cuda.device_count() # gpus per node
print(f"tcp://{ip}:{port}")
dist.init_process_group(
backend="nccl", init_method=f"tcp://{ip}:{port}",
world_size=hosts * gpus, rank=rank * gpus + local_rank
)
world_size = dist.get_world_size()
cfg.gpu_ids = range(world_size)
torch.cuda.set_device(local_rank)
if dist.get_rank() != 0:
import builtins
builtins.print = pass_print
logger = get_root_logger(log_file=None, log_level='INFO')
logger.info(f'Config:\n{cfg.pretty_text}')
# build model
if cfg.get('occupancy', False):
from builder import tpv_occupancy_builder as model_builder
else:
from builder import tpv_lidarseg_builder as model_builder
my_model = model_builder.build(cfg.model)
n_parameters = sum(p.numel() for p in my_model.parameters() if p.requires_grad)
logger.info(f'Number of params: {n_parameters}')
if distributed:
find_unused_parameters = cfg.get('find_unused_parameters', False)
ddp_model_module = torch.nn.parallel.DistributedDataParallel
my_model = ddp_model_module(
my_model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
else:
my_model = my_model.cuda()
print('done ddp model')
# generate datasets
SemKITTI_label_name = get_nuScenes_label_name(dataset_config["label_mapping"])
unique_label = np.asarray(cfg.unique_label)
unique_label_str = [SemKITTI_label_name[x] for x in unique_label]
from builder import data_builder
train_dataset_loader, val_dataset_loader = \
data_builder.build(
dataset_config,
train_dataloader_config,
val_dataloader_config,
grid_size=grid_size,
version=version,
dist=distributed,
scale_rate=cfg.get('scale_rate', 1)
)
# get optimizer, loss, scheduler
loss_func, lovasz_softmax = \
loss_builder.build(
ignore_label=ignore_label)
CalMeanIou_vox = MeanIoU(unique_label, ignore_label, unique_label_str, 'vox')
CalMeanIou_pts = MeanIoU(unique_label, ignore_label, unique_label_str, 'pts')
# resume and load
assert osp.isfile(args.ckpt_path)
cfg.resume_from = args.ckpt_path
print('ckpt path:', cfg.resume_from)
map_location = 'cpu'
ckpt = torch.load(cfg.resume_from, map_location=map_location)
if 'state_dict' in ckpt:
ckpt = ckpt['state_dict']
print(my_model.load_state_dict(revise_ckpt(ckpt), strict=False))
print(f'successfully loaded ckpt')
print_freq = cfg.print_freq
# eval
my_model.eval()
val_loss_list = []
CalMeanIou_pts.reset()
CalMeanIou_vox.reset()
with torch.no_grad():
for i_iter_val, (imgs, img_metas, val_vox_label, val_grid, val_pt_labs) in enumerate(val_dataset_loader):
imgs = imgs.cuda()
val_grid_float = val_grid.to(torch.float32).cuda()
val_grid_int = val_grid.to(torch.long).cuda()
vox_label = val_vox_label.type(torch.LongTensor).cuda()
val_pt_labs = val_pt_labs.cuda()
predict_labels_vox, predict_labels_pts = my_model(img=imgs, img_metas=img_metas, points=val_grid_float)
if cfg.lovasz_input == 'voxel':
lovasz_input = predict_labels_vox
lovasz_label = vox_label
else:
lovasz_input = predict_labels_pts
lovasz_label = val_pt_labs
if cfg.ce_input == 'voxel':
ce_input = predict_labels_vox
ce_label = vox_label
else:
ce_input = predict_labels_pts.squeeze(-1).squeeze(-1)
ce_label = val_pt_labs.squeeze(-1)
loss = lovasz_softmax(
torch.nn.functional.softmax(lovasz_input, dim=1).detach(),
lovasz_label, ignore=ignore_label
) + loss_func(ce_input.detach(), ce_label)
predict_labels_pts = predict_labels_pts.squeeze(-1).squeeze(-1)
predict_labels_pts = torch.argmax(predict_labels_pts, dim=1) # bs, n
predict_labels_pts = predict_labels_pts.detach().cpu()
val_pt_labs = val_pt_labs.squeeze(-1).cpu()
predict_labels_vox = torch.argmax(predict_labels_vox, dim=1)
predict_labels_vox = predict_labels_vox.detach().cpu()
for count in range(len(val_grid_int)):
CalMeanIou_pts._after_step(predict_labels_pts[count], val_pt_labs[count])
CalMeanIou_vox._after_step(
predict_labels_vox[count, val_grid_int[count][:, 0], val_grid_int[count][:, 1], val_grid_int[count][:, 2]].flatten(),
val_pt_labs[count])
val_loss_list.append(loss.detach().cpu().numpy())
if i_iter_val % print_freq == 0 and dist.get_rank() == 0:
logger.info('[EVAL] Iter %5d: Loss: %.3f (%.3f)'%(
i_iter_val, loss.item(), np.mean(val_loss_list)))
val_miou_pts = CalMeanIou_pts._after_epoch()
val_miou_vox = CalMeanIou_vox._after_epoch()
logger.info('Current val miou pts is %.3f' % (val_miou_pts))
logger.info('Current val miou vox is %.3f' % (val_miou_vox))
logger.info('Current val loss is %.3f' % (np.mean(val_loss_list)))
if __name__ == '__main__':
# Eval settings
parser = argparse.ArgumentParser(description='')
parser.add_argument('--py-config', default='config/tpv_lidarseg.py')
parser.add_argument('--ckpt-path', type=str, default='')
args = parser.parse_args()
ngpus = torch.cuda.device_count()
args.gpus = ngpus
print(args)
torch.multiprocessing.spawn(main, args=(args,), nprocs=args.gpus)