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fusion_scannet_swin3d.py
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import os
import torch
import importlib
import imageio
import argparse
from os.path import join, exists
import numpy as np
from glob import glob
from tqdm import tqdm, trange
from fusion_util import extract_lseg_img_feature, PointCloudToImageMapper, save_fused_feature_with_locs
import sys
sys.path.append('..')
sys.path.append('Swin3D_Task/SemanticSeg')
from datasets.scannet_v2 import (
Scannetv2,
Scannetv2_Normal,
Scannetv2_Point,
Scannetv2_Normal_Point,
Scannetv2_Normal_Point_Subsample
)
from util import config
from util.data_util import collate_fn, collate_fn_pts
import MinkowskiEngine as ME
from Swin3D.modules.swin3d_layers import knn_linear_interpolation, get_offset
def get_args():
# command line args
parser = argparse.ArgumentParser(
description='Multi-view feature fusion of LSeg on ScanNet.')
parser.add_argument('--output_dir', type=str, help='Where is the base logging directory')
parser.add_argument('--scan_dir', type=str, default='dataset/ScanNet/scans', help='Where is the ScanNet dataset')
parser.add_argument('--process_id_range', nargs='+', default=None, help='the id range to process')
parser.add_argument('--voxel_size', type=float, default=0.05, help='Voxel size for voxelization')
parser.add_argument('--prefix', type=str, default='swin3d', help='prefix for the output file')
# Hyper parameters
parser.add_argument('--hparams', default=[], nargs="+")
args = parser.parse_args()
return args
def main(args):
seed = 1457
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
scan_dir = 'dataset/ScanNet/scans/'
out_dir = args.output_dir
os.makedirs(out_dir, exist_ok=True)
process_id_range = args.process_id_range
args.n_split_points = 2000000
##############################
##### load the Swin3D model ####
args_model = config.load_cfg_from_cfg_file('Swin3D_Task/SemanticSeg/config/scannetv2/swin3D_RGBN_L.yaml')
model_module = importlib.import_module("model.Swin3D_RGBN")
model = model_module.Swin3D(
depths=args_model.depths,
channels=args_model.channels,
num_heads=args_model.num_heads,
window_sizes=args_model.window_size,
up_k=args_model.up_k,
quant_sizes=args_model.quant_size,
drop_path_rate=args_model.drop_path_rate,
num_classes=args_model.classes,
num_layers=args_model.num_layers,
stem_transformer=args_model.stem_transformer,
upsample=args_model.upsample,
down_stride=args_model.get("down_stride", 2),
knn_down=args_model.get("knn_down", True),
signal=args_model.get("signal", True),
in_channels=args_model.get("fea_dim", 6),
use_offset=args_model.get("use_offset", False),
fp16_mode=args_model.get("fp16_mode", 1),
)
model.backbone.load_pretrained_model('swin3d/ckpt/Swin3D_RGBN_L.pth')
model = model.eval().cuda()
args.evaluator = model
device = torch.device('cpu')
val_data = Scannetv2_Normal_Point_Subsample( # =====================> Remember to change this to Scannetv2_Normal_Point
split='new',
data_root=scan_dir,
voxel_size=args_model.voxel_size,
voxel_max=800000,
transform=None,
)
val_loader = torch.utils.data.DataLoader(
val_data,
batch_size=args_model.batch_size_val,
shuffle=False,
num_workers=args_model.workers,
pin_memory=True,
sampler=None,
collate_fn=collate_fn_pts,
)
id_range = None
if process_id_range is not None:
id_range = [int(process_id_range[0].split(',')[0]), int(process_id_range[0].split(',')[1])]
# obtain scene names from scan_dir
data_paths = sorted(glob(join(scan_dir, "*/*_vh_clean_2.ply")))
scene_names = [data_path.split('/')[-2] for data_path in data_paths]
scene_names = [x for x in scene_names if "scene0044_02" not in x and "scene0586_01" not in x] # =====================> Remember to delete this line
data_3d_paths = sorted(glob('dataset/ScanNet/openscene/scannet_3d/*/*.pth'))
# For val_loader, only iterate id_range[0] to id_range[1]
for i, (coord, feat, target, offset, target_pts, inverse_map) in tqdm(enumerate(val_loader)):
scene_id = scene_names[i]
if id_range is not None and \
(i<id_range[0] or i>id_range[1]):
print('skip ', i, scene_id)
continue
if exists(join(out_dir, args.prefix+'_points', scene_id + '.npy')):
print(scene_id +'.pt' + ' already exists, skip!')
continue
offset_ = offset.clone()
offset_[1:] = offset_[1:] - offset_[:-1]
batch = torch.cat(
[torch.tensor([ii] * o) for ii, o in enumerate(offset_)], 0
).long()
coord, feat, target, offset = (
coord.cuda(non_blocking=True),
feat.cuda(non_blocking=True),
target.cuda(non_blocking=True),
offset.cuda(non_blocking=True),
)
batch = batch.cuda(non_blocking=True)
inverse_map = inverse_map.cuda(non_blocking=True)
target_pts = target_pts.cuda(non_blocking=True)
assert batch.shape[0] == feat.shape[0]
if target.shape[-1] == 1:
target = target[:, 0] # for cls
target_pts = target_pts[:, 0] # for cls
if args_model.concat_xyz:
feat = torch.cat([feat, coord], 1)
with torch.no_grad():
sp_stack, coords_sp_stack = model(feat, coord, batch)
# coords_sp.C (int) [80000, 4] coords_sp.F (float) [80000, 10]
# sp.C (int) [80000, 4] sp.F (float) [80000, 80]
feats = sp_stack[4].F
xyz = coords_sp_stack[4].F[:, 1:4].detach().contiguous()
support_xyz = coords_sp_stack[0].F[:, 1:4].detach().contiguous()
offset = get_offset(sp_stack[4].C[:, 0])
support_offset = get_offset(sp_stack[0].C[:, 0])
feats_4_interpolated = knn_linear_interpolation(xyz, support_xyz, feats, offset, support_offset, K=3) # torch.Size([80020, 640])
feats = sp_stack[2].F
xyz = coords_sp_stack[2].F[:, 1:4].detach().contiguous()
support_xyz = coords_sp_stack[0].F[:, 1:4].detach().contiguous()
offset = get_offset(sp_stack[2].C[:, 0])
support_offset = get_offset(sp_stack[0].C[:, 0])
feats_2_interpolated = knn_linear_interpolation(xyz, support_xyz, feats, offset, support_offset, K=3) # torch.Size([80020, 320])
# concatenate the interpolated features 4, 2, and original 0
feats = torch.cat([feats_4_interpolated, feats_2_interpolated, sp_stack[0].F], dim=1) # torch.Size([80020, 1040])
# use inverse_map to get the original point cloud
n_points = inverse_map.shape[0]
n_points_cur = n_points
feats = feats[inverse_map]
point_ids_all = torch.arange(n_points_cur, device=device)
# load 3D data (point cloud) from data_3d_paths
# first find data_3d_paths that contains scene_id
# then load the data
for data_3d_path in data_3d_paths:
if scene_id in data_3d_path:
locs_in = torch.load(data_3d_path)[0]
break
save_fused_feature_with_locs(feats, point_ids_all, locs_in, n_points, out_dir, scene_id, args)
if __name__ == "__main__":
args = get_args()
print("Arguments:")
print(args)
main(args)