file_path stringlengths 3 280 | file_language stringclasses 66
values | content stringlengths 1 1.04M | repo_name stringlengths 5 92 | repo_stars int64 0 154k | repo_description stringlengths 0 402 | repo_primary_language stringclasses 108
values | developer_username stringlengths 1 25 | developer_name stringlengths 0 30 | developer_company stringlengths 0 82 |
|---|---|---|---|---|---|---|---|---|---|
src/lib/utils/tracker.py | Python | import numpy as np
from sklearn.utils.linear_assignment_ import linear_assignment
from numba import jit
import copy
class Tracker(object):
def __init__(self, opt):
self.opt = opt
self.reset()
def init_track(self, results):
for item in results:
if item['score'] > self.opt.new_thresh:
self... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/lib/utils/utils.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/main.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import _init_paths
import os
import torch
import torch.utils.data
from opts import opts
from model.model import create_model, load_model, save_model
from model.data_parallel import DataParallel
from logger imp... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/test.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import _init_paths
import os
import json
import cv2
import numpy as np
import time
from progress.bar import Bar
import torch
import copy
from opts import opts
from logger import Logger
from utils.utils import ... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/_init_paths.py | Python | import os.path as osp
import sys
def add_path(path):
if path not in sys.path:
sys.path.insert(0, path)
this_dir = osp.dirname(__file__)
# Add lib to PYTHONPATH
lib_path = osp.join(this_dir, '../lib')
add_path(lib_path)
| xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/annot_bbox.py | Python | import os
import sys
import json
import cv2
import argparse
import numpy as np
image_ext = ['jpg', 'jpeg', 'png', 'webp']
parser = argparse.ArgumentParser()
parser.add_argument('--image_path', default='')
parser.add_argument('--save_path', default='')
MAX_CACHE = 20
CAT_NAMES = ['cat']
def _sort_expt(pts):
t, l, b,... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/convert_crowdhuman_to_coco.py | Python | import os
import numpy as np
import json
import cv2
DATA_PATH = '../../data/crowdhuman/'
OUT_PATH = DATA_PATH + 'annotations/'
SPLITS = ['val', 'train']
DEBUG = False
def load_func(fpath):
print('fpath', fpath)
assert os.path.exists(fpath)
with open(fpath,'r') as fid:
lines = fid.readlines()
r... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/convert_kittitrack_to_coco.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pickle
import json
import numpy as np
import os
import cv2
DATA_PATH = '../../data/kitti_tracking/'
SPLITS = ['train_half', 'val_half', 'train', 'test']
VIDEO_SETS = {'train': range(21), 'test': range(29... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/convert_mot_det_to_results.py | Python | import json
import numpy as np
import os
from collections import defaultdict
split = 'val_half'
DET_PATH = '../../data/mot17/'
ANN_PATH = '../../data/mot17/annotations/{}.json'.format(split)
OUT_DIR = '../../data/mot17/results/'
OUT_PATH = OUT_DIR + '{}_det.json'.format(split)
if __name__ == '__main__':
if not os.p... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/convert_mot_to_coco.py | Python | import os
import numpy as np
import json
import cv2
# Use the same script for MOT16
# DATA_PATH = '../../data/mot16/'
DATA_PATH = '../../data/mot17/'
OUT_PATH = DATA_PATH + 'annotations/'
SPLITS = ['train_half', 'val_half', 'train', 'test']
HALF_VIDEO = True
CREATE_SPLITTED_ANN = True
CREATE_SPLITTED_DET = True
if __... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/convert_nuScenes.py | Python | # Copyright (c) Xingyi Zhou. All Rights Reserved
'''
nuScenes pre-processing script.
This file convert the nuScenes annotation into COCO format.
'''
import json
import numpy as np
import cv2
import copy
import matplotlib.pyplot as plt
from nuscenes.nuscenes import NuScenes
from nuscenes.utils.geometry_utils import BoxV... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/eval_kitti_track/evaluate_tracking.py | Python | #!/usr/bin/env python
# encoding: utf-8
"""
function that does the evaluation
input:
- result_sha (sha key where the results are located
- mail (messenger object for output messages sent via email and to cout)
output:
- True if at least one of the sub-benchmarks could be processed ... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/eval_kitti_track/mailpy.py | Python | class Mail:
""" Dummy class to print messages without sending e-mails"""
def __init__(self,mailaddress):
pass
def msg(self,msg):
print(msg)
def finalize(self,success,benchmark,sha_key,mailaddress=None):
if success:
print("Results for %s (benchmark: %s) sucessfully cre... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/eval_kitti_track/munkres.py | Python | #!/usr/bin/env python
# -*- coding: iso-8859-1 -*-
# Documentation is intended to be processed by Epydoc.
"""
Introduction
============
The Munkres module provides an implementation of the Munkres algorithm
(also called the Hungarian algorithm or the Kuhn-Munkres algorithm),
useful for solving the Assignment Problem... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/eval_motchallenge.py | Python | """py-motmetrics - metrics for multiple object tracker (MOT) benchmarking.
Christoph Heindl, 2017
https://github.com/cheind/py-motmetrics
Modified by Xingyi Zhou
"""
import argparse
import glob
import os
import logging
import motmetrics as mm
import pandas as pd
from collections import OrderedDict
from pathlib import ... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/get_mot_17.sh | Shell | mkdir ../../data/mot17
cd ../../data/mot17
wget https://motchallenge.net/data/MOT17.zip
unzip MOT17.zip
rm MOT17.zip
mkdir annotations
cd ../../src/tools/
python convert_mot_to_coco.py
python convert_mot_det_to_results | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/nuScenes_lib/export_kitti.py | Python | # nuScenes dev-kit.
# Code written by Holger Caesar, 2019.
# Licensed under the Creative Commons [see licence.txt]
"""
This script converts nuScenes data to KITTI format and KITTI results to nuScenes.
It is used for compatibility with software that uses KITTI-style annotations.
We do not encourage this, as:
- KITTI ha... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/nuScenes_lib/utils_kitti.py | Python | # nuScenes dev-kit.
# Code written by Alex Lang and Holger Caesar, 2019.
# Licensed under the Creative Commons [see licence.txt]
import os
from os import path as osp
from typing import List, Tuple, Any, Union
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from matplotlib.axes import Axes
fro... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/remove_optimizers.py | Python | import os
import torch
IN_PATH = '../../centertrack_models/'
OUT_PATH = '../../models/'
REMOVE_KEYS = ['base.fc']
if __name__ == '__main__':
models = sorted(os.listdir(IN_PATH))
for model in models:
model_path = IN_PATH + model
print(model)
data = torch.load(model_path)
state_dict = data['state_dic... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/vis_tracking_kitti.py | Python | import numpy as np
import cv2
import os
import glob
import sys
from collections import defaultdict
from pathlib import Path
DATA_PATH = '../../data/kitti_tracking/'
IMG_PATH = DATA_PATH + 'data_tracking_image_2/testing/image_02/'
SAVE_VIDEO = False
IS_GT = False
cats = ['Pedestrian', 'Car', 'Cyclist']
cat_ids = {cat:... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
src/tools/vis_tracking_mot.py | Python | import numpy as np
import cv2
import os
import glob
import sys
from collections import defaultdict
from pathlib import Path
GT_PATH = '../../data/mot17/test/'
IMG_PATH = GT_PATH
SAVE_VIDEO = True
RESIZE = 2
IS_GT = False
def draw_bbox(img, bboxes, c=(255, 0, 255)):
for bbox in bboxes:
cv2.rectangle(img, (int(bb... | xingyizhou/CenterTrack | 2,472 | Simultaneous object detection and tracking using center points. | Python | xingyizhou | Xingyi Zhou | Meta |
config.py | Python | import os
import numpy as np
class Config:
def __init__(self):
self._configs = {}
self._configs["dataset"] = None
self._configs["sampling_function"] = "kp_detection"
# Training Config
self._configs["display"] = 5
self._configs["snapshot"] = 5000
... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
db/base.py | Python | import os
import h5py
import numpy as np
from config import system_configs
class BASE(object):
def __init__(self):
self._split = None
self._db_inds = []
self._image_ids = []
self._data = None
self._image_hdf5 = None
self._image_file = None
... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
db/coco.py | Python | import sys
sys.path.insert(0, "data/coco/PythonAPI/")
import os
import json
import numpy as np
import pickle
from tqdm import tqdm
from db.detection import DETECTION
from config import system_configs
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
class MSCOCO(DETECTION):
def __init__... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
db/coco_extreme.py | Python | import sys
sys.path.insert(0, "data/coco/PythonAPI/")
import os
import json
import numpy as np
import pickle
from tqdm import tqdm
from db.detection import DETECTION
from config import system_configs
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
class MSCOCOExtreme(DETECTION):
def _... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
db/datasets.py | Python | from db.coco import MSCOCO
from db.coco_extreme import MSCOCOExtreme
datasets = {
"MSCOCO": MSCOCO,
"MSCOCOExtreme": MSCOCOExtreme
}
| xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
db/detection.py | Python | import numpy as np
from db.base import BASE
class DETECTION(BASE):
def __init__(self, db_config):
super(DETECTION, self).__init__()
self._configs["categories"] = 80
self._configs["rand_scales"] = [1]
self._configs["rand_scale_min"] = 0.8
self._configs["rand_scale_... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
demo.py | Python | #!/usr/bin/env python
import os
import json
import torch
import pprint
import argparse
import importlib
import numpy as np
import cv2
import matplotlib
matplotlib.use("Agg")
from config import system_configs
from nnet.py_factory import NetworkFactory
from config import system_configs
from utils import crop_image, no... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
dextr.py | Python | import os
import torch
from collections import OrderedDict
from PIL import Image
import numpy as np
from matplotlib import pyplot as plt
import sys
from torch.nn.functional import upsample
this_dir = os.path.dirname(__file__)
sys.path.insert(0, 'dextr')
import networks.deeplab_resnet as resnet
from dataloaders import h... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
eval_dextr_mask.py | Python | from dextr.dextr import Dextr
import pycocotools.coco as cocoapi
from pycocotools.cocoeval import COCOeval
from pycocotools import mask as COCOmask
import numpy as np
import sys
import cv2
import json
from progress.bar import Bar
DEBUG = False
ANN_PATH = 'data/coco/annotations/instances_extreme_val2017.json'
IMG_DIR = ... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
external/nms.pyx | Cython | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
# ----------------------------------------------------------
# Soft-NMS... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
external/setup.py | Python | import numpy
from distutils.core import setup
from distutils.extension import Extension
from Cython.Build import cythonize
extensions = [
Extension(
"nms",
["nms.pyx"],
extra_compile_args=["-Wno-cpp", "-Wno-unused-function"]
)
]
setup(
name="coco",
ext_modules=cythonize(extens... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
models/CornerNet.py | Python | import torch
import torch.nn as nn
from .py_utils import kp, AELoss, _neg_loss, convolution, residual
from .py_utils import TopPool, BottomPool, LeftPool, RightPool
class pool(nn.Module):
def __init__(self, dim, pool1, pool2):
super(pool, self).__init__()
self.p1_conv1 = convolution(3, dim, 128)
... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
models/ExtremeNet.py | Python | import torch
import torch.nn as nn
from .py_utils import exkp, CTLoss, _neg_loss, convolution, residual
def make_pool_layer(dim):
return nn.Sequential()
def make_hg_layer(kernel, dim0, dim1, mod, layer=convolution, **kwargs):
layers = [layer(kernel, dim0, dim1, stride=2)]
layers += [layer(kernel, dim1, ... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
models/py_utils/__init__.py | Python | from .kp import kp, AELoss
from .exkp import exkp, CTLoss
from .kp_utils import _neg_loss
from .utils import convolution, fully_connected, residual
# Un-comment this line if your want to run CornerNet
# from ._cpools import TopPool, BottomPool, LeftPool, RightPool
| xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
models/py_utils/_cpools/__init__.py | Python | import torch
from torch import nn
from torch.autograd import Function
import top_pool, bottom_pool, left_pool, right_pool
class TopPoolFunction(Function):
@staticmethod
def forward(ctx, input):
output = top_pool.forward(input)[0]
ctx.save_for_backward(input)
return output
@static... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
models/py_utils/_cpools/setup.py | Python | from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CppExtension
setup(
name="cpools",
ext_modules=[
CppExtension("top_pool", ["src/top_pool.cpp"]),
CppExtension("bottom_pool", ["src/bottom_pool.cpp"]),
CppExtension("left_pool", ["src/left_pool.cpp"]),
... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
models/py_utils/_cpools/src/bottom_pool.cpp | C++ | #include <torch/torch.h>
#include <vector>
std::vector<at::Tensor> pool_forward(
at::Tensor input
) {
// Initialize output
at::Tensor output = at::zeros_like(input);
// Get height
int64_t height = input.size(2);
// Copy the last column
at::Tensor input_temp = input.select(2, 0);
at:... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
models/py_utils/_cpools/src/left_pool.cpp | C++ | #include <torch/torch.h>
#include <vector>
std::vector<at::Tensor> pool_forward(
at::Tensor input
) {
// Initialize output
at::Tensor output = at::zeros_like(input);
// Get width
int64_t width = input.size(3);
// Copy the last column
at::Tensor input_temp = input.select(3, width - 1);
... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
models/py_utils/_cpools/src/right_pool.cpp | C++ | #include <torch/torch.h>
#include <vector>
std::vector<at::Tensor> pool_forward(
at::Tensor input
) {
// Initialize output
at::Tensor output = at::zeros_like(input);
// Get width
int64_t width = input.size(3);
// Copy the last column
at::Tensor input_temp = input.select(3, 0);
at::T... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
models/py_utils/_cpools/src/top_pool.cpp | C++ | #include <torch/torch.h>
#include <vector>
std::vector<at::Tensor> top_pool_forward(
at::Tensor input
) {
// Initialize output
at::Tensor output = at::zeros_like(input);
// Get height
int64_t height = input.size(2);
// Copy the last column
at::Tensor input_temp = input.select(2, height ... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
models/py_utils/data_parallel.py | Python | import torch
from torch.nn.modules import Module
from torch.nn.parallel.scatter_gather import gather
from torch.nn.parallel.replicate import replicate
from torch.nn.parallel.parallel_apply import parallel_apply
from .scatter_gather import scatter_kwargs
class DataParallel(Module):
r"""Implements data parallelism ... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
models/py_utils/exkp.py | Python | import numpy as np
import torch
import torch.nn as nn
from .utils import convolution, residual
from .utils import make_layer, make_layer_revr
from .kp_utils import _tranpose_and_gather_feat, _exct_decode
from .kp_utils import _sigmoid, _regr_loss, _neg_loss
from .kp_utils import make_kp_layer
from .kp_utils import ma... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
models/py_utils/kp.py | Python | import numpy as np
import torch
import torch.nn as nn
from .utils import convolution, residual
from .utils import make_layer, make_layer_revr
from .kp_utils import _tranpose_and_gather_feat, _decode
from .kp_utils import _sigmoid, _ae_loss, _regr_loss, _neg_loss
from .kp_utils import make_tl_layer, make_br_layer, mak... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
models/py_utils/kp_utils.py | Python | import torch
import torch.nn as nn
from .utils import convolution, residual
class MergeUp(nn.Module):
def forward(self, up1, up2):
return up1 + up2
def make_merge_layer(dim):
return MergeUp()
def make_tl_layer(dim):
return None
def make_br_layer(dim):
return None
def make_pool_layer(dim):
... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
models/py_utils/scatter_gather.py | Python | import torch
from torch.autograd import Variable
from torch.nn.parallel._functions import Scatter, Gather
def scatter(inputs, target_gpus, dim=0, chunk_sizes=None):
r"""
Slices variables into approximately equal chunks and
distributes them across given GPUs. Duplicates
references to objects that are n... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
models/py_utils/utils.py | Python | import torch
import torch.nn as nn
class convolution(nn.Module):
def __init__(self, k, inp_dim, out_dim, stride=1, with_bn=True):
super(convolution, self).__init__()
pad = (k - 1) // 2
self.conv = nn.Conv2d(inp_dim, out_dim, (k, k), padding=(pad, pad), stride=(stride, stride), bias=not wit... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
nnet/py_factory.py | Python | import os
import torch
import importlib
import torch.nn as nn
from config import system_configs
from models.py_utils.data_parallel import DataParallel
torch.manual_seed(317)
class Network(nn.Module):
def __init__(self, model, loss):
super(Network, self).__init__()
self.model = model
self... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
sample/coco.py | Python | import cv2
import math
import numpy as np
import torch
import random
import string
from config import system_configs
from utils import crop_image, normalize_, color_jittering_, lighting_
from .utils import random_crop, draw_gaussian, gaussian_radius
def _full_image_crop(image, detections):
detections = detecti... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
sample/coco_extreme.py | Python | import cv2
import math
import numpy as np
import torch
import random
import string
from config import system_configs
from utils import crop_image, normalize_, color_jittering_, lighting_
from .utils import random_crop_pts, draw_gaussian, gaussian_radius
from utils.debugger import Debugger
def _resize_image_pts(image,... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
sample/utils.py | Python | import cv2
import numpy as np
def gaussian2D(shape, sigma=1):
m, n = [(ss - 1.) / 2. for ss in shape]
y, x = np.ogrid[-m:m+1,-n:n+1]
h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
h[h < np.finfo(h.dtype).eps * h.max()] = 0
return h
def draw_gaussian(heatmap, center, radius, k=1):
diameter... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
test.py | Python | #!/usr/bin/env python
import os
import json
import torch
import pprint
import argparse
import importlib
import numpy as np
import matplotlib
matplotlib.use("Agg")
from config import system_configs
from nnet.py_factory import NetworkFactory
from db.datasets import datasets
torch.backends.cudnn.benchmark = False
def ... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
test/coco.py | Python | import os
import cv2
import json
import numpy as np
import torch
import matplotlib.pyplot as plt
from tqdm import tqdm
from config import system_configs
from utils import crop_image, normalize_
from external.nms import soft_nms, soft_nms_merge
def _rescale_dets(detections, ratios, borders, sizes):
xs, ys = detect... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
test/coco_extreme.py | Python | import os
import cv2
import json
import numpy as np
import torch
import matplotlib.pyplot as plt
from tqdm import tqdm
from config import system_configs
from utils import crop_image, normalize_
from external.nms import soft_nms_with_points as soft_nms
def _rescale_dets(detections, ratios, borders, sizes):
xs, ys ... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
tools/gen_coco_extreme_points.py | Python | import pycocotools.coco as cocoapi
import sys
import cv2
import numpy as np
import pickle
import json
SPLITS = ['val', 'train']
ANN_PATH = '../data/coco/annotations/instances_{}2017.json'
OUT_PATH = '../data/coco/annotations/instances_extreme_{}2017.json'
IMG_DIR = '../data/coco/{}2017/'
DEBUG = False
from scipy.spatia... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
tools/suppress_ghost.py | Python | import pycocotools.coco as coco
from pycocotools.cocoeval import COCOeval
import sys
import cv2
import numpy as np
import pickle
import json
ANN_PATH = '../data/coco/annotations/instances_val2017.json'
DEBUG = True
def _coco_box_to_bbox(box):
bbox = np.array([box[0], box[1], box[0] + box[2], box[1] + box[3]],
... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
train.py | Python | #!/usr/bin/env python
import os
import json
import torch
import numpy as np
import queue
import pprint
import random
import argparse
import importlib
import threading
import traceback
from tqdm import tqdm
from utils import stdout_to_tqdm
from config import system_configs
from nnet.py_factory import NetworkFactory
fr... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
utils/__init__.py | Python | from .tqdm import stdout_to_tqdm
from .image import crop_image
from .image import color_jittering_, lighting_, normalize_
| xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
utils/color_map.py | Python | # Copyright (c) 2017-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
utils/debugger.py | Python | import numpy as np
import cv2
import matplotlib.pyplot as plt
color_list = np.array(
[
0.000, 0.447, 0.741,
0.850, 0.325, 0.098,
0.929, 0.694, 0.125,
0.494, 0.184, 0.556,
0.466, 0.674, 0.188,
0.301, 0.745, 0.933,
0.635, 0.078, ... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
utils/image.py | Python | import cv2
import numpy as np
import random
def grayscale(image):
return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
def normalize_(image, mean, std):
image -= mean
image /= std
def lighting_(data_rng, image, alphastd, eigval, eigvec):
alpha = data_rng.normal(scale=alphastd, size=(3, ))
image += np.d... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
utils/tqdm.py | Python | import sys
import numpy as np
import contextlib
from tqdm import tqdm
class TqdmFile(object):
dummy_file = None
def __init__(self, dummy_file):
self.dummy_file = dummy_file
def write(self, x):
if len(x.rstrip()) > 0:
tqdm.write(x, file=self.dummy_file)
@contextlib.contextmana... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
utils/visualize.py | Python | import cv2
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
import pycocotools.mask as mask_util
_GRAY = (218, 227, 218)
_GREEN = (18, 127, 15)
_WHITE = (255, 255, 255)
def vis_mask(img, mask, col, alpha=0.4, show_border=True, border_thick=2):
"""Visualizes a single binary... | xingyizhou/ExtremeNet | 1,034 | Bottom-up Object Detection by Grouping Extreme and Center Points | Python | xingyizhou | Xingyi Zhou | Meta |
archs/__init__.py | Python | import importlib
from os import path as osp
from basicsr.utils import scandir
# automatically scan and import arch modules for registry
# scan all the files that end with '_arch.py' under the archs folder
arch_folder = osp.dirname(osp.abspath(__file__))
arch_filenames = [osp.splitext(osp.basename(v))[0] for v in scan... | xinntao/BasicSR-examples | 255 | BasicSR-Examples illustrates how to easily use BasicSR in your own project | Python | xinntao | Xintao | Tencent |
archs/example_arch.py | Python | from torch import nn as nn
from torch.nn import functional as F
from basicsr.archs.arch_util import default_init_weights
from basicsr.utils.registry import ARCH_REGISTRY
@ARCH_REGISTRY.register()
class ExampleArch(nn.Module):
"""Example architecture.
Args:
num_in_ch (int): Channel number of inputs. ... | xinntao/BasicSR-examples | 255 | BasicSR-Examples illustrates how to easily use BasicSR in your own project | Python | xinntao | Xintao | Tencent |
data/__init__.py | Python | import importlib
from os import path as osp
from basicsr.utils import scandir
# automatically scan and import dataset modules for registry
# scan all the files that end with '_dataset.py' under the data folder
data_folder = osp.dirname(osp.abspath(__file__))
dataset_filenames = [osp.splitext(osp.basename(v))[0] for v... | xinntao/BasicSR-examples | 255 | BasicSR-Examples illustrates how to easily use BasicSR in your own project | Python | xinntao | Xintao | Tencent |
data/example_dataset.py | Python | import cv2
import os
import torch
from torch.utils import data as data
from torchvision.transforms.functional import normalize
from basicsr.data.degradations import add_jpg_compression
from basicsr.data.transforms import augment, mod_crop, paired_random_crop
from basicsr.utils import FileClient, imfrombytes, img2tenso... | xinntao/BasicSR-examples | 255 | BasicSR-Examples illustrates how to easily use BasicSR in your own project | Python | xinntao | Xintao | Tencent |
losses/__init__.py | Python | import importlib
from os import path as osp
from basicsr.utils import scandir
# automatically scan and import loss modules for registry
# scan all the files that end with '_loss.py' under the loss folder
loss_folder = osp.dirname(osp.abspath(__file__))
loss_filenames = [osp.splitext(osp.basename(v))[0] for v in scand... | xinntao/BasicSR-examples | 255 | BasicSR-Examples illustrates how to easily use BasicSR in your own project | Python | xinntao | Xintao | Tencent |
losses/example_loss.py | Python | from torch import nn as nn
from torch.nn import functional as F
from basicsr.utils.registry import LOSS_REGISTRY
@LOSS_REGISTRY.register()
class ExampleLoss(nn.Module):
"""Example Loss.
Args:
loss_weight (float): Loss weight for Example loss. Default: 1.0.
"""
def __init__(self, loss_weight... | xinntao/BasicSR-examples | 255 | BasicSR-Examples illustrates how to easily use BasicSR in your own project | Python | xinntao | Xintao | Tencent |
models/__init__.py | Python | import importlib
from os import path as osp
from basicsr.utils import scandir
# automatically scan and import model modules for registry
# scan all the files that end with '_model.py' under the model folder
model_folder = osp.dirname(osp.abspath(__file__))
model_filenames = [osp.splitext(osp.basename(v))[0] for v in ... | xinntao/BasicSR-examples | 255 | BasicSR-Examples illustrates how to easily use BasicSR in your own project | Python | xinntao | Xintao | Tencent |
models/example_model.py | Python | from collections import OrderedDict
from basicsr.archs import build_network
from basicsr.losses import build_loss
from basicsr.models.sr_model import SRModel
from basicsr.utils import get_root_logger
from basicsr.utils.registry import MODEL_REGISTRY
@MODEL_REGISTRY.register()
class ExampleModel(SRModel):
"""Exam... | xinntao/BasicSR-examples | 255 | BasicSR-Examples illustrates how to easily use BasicSR in your own project | Python | xinntao | Xintao | Tencent |
scripts/prepare_example_data.py | Python | import os
import requests
def main(url, dataset):
# download
print(f'Download {url} ...')
response = requests.get(url)
with open(f'datasets/example/{dataset}.zip', 'wb') as f:
f.write(response.content)
# unzip
import zipfile
with zipfile.ZipFile(f'datasets/example/{dataset}.zip', ... | xinntao/BasicSR-examples | 255 | BasicSR-Examples illustrates how to easily use BasicSR in your own project | Python | xinntao | Xintao | Tencent |
train.py | Python | # flake8: noqa
import os.path as osp
import archs
import data
import losses
import models
from basicsr.train import train_pipeline
if __name__ == '__main__':
root_path = osp.abspath(osp.join(__file__, osp.pardir))
train_pipeline(root_path)
| xinntao/BasicSR-examples | 255 | BasicSR-Examples illustrates how to easily use BasicSR in your own project | Python | xinntao | Xintao | Tencent |
handycrawler/crawler_util.py | Python | import imghdr
import requests
def sizeof_fmt(size, suffix='B'):
"""Get human readable file size.
Args:
size (int): File size.
suffix (str): Suffix. Default: 'B'.
Return:
str: Formated file siz.
"""
for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']:
if abs(size) < ... | xinntao/HandyCrawler | 9 | Python | xinntao | Xintao | Tencent | |
setup.py | Python | #!/usr/bin/env python
from setuptools import find_packages, setup
import os
import subprocess
import time
version_file = 'handycrawler/version.py'
def readme():
with open('README.md', encoding='utf-8') as f:
content = f.read()
return content
def get_git_hash():
def _minimal_ext_cmd(cmd):
... | xinntao/HandyCrawler | 9 | Python | xinntao | Xintao | Tencent | |
tools/baidu_keywords/baidu_crawler.py | Python | import json
import time
from datetime import datetime
from urllib.parse import urlsplit
from handycrawler.crawler_util import (baidu_decode_url, setup_session,
sizeof_fmt)
try:
import pymongo
except Exception:
raise ImportError('Please install pymongo')
def main():
... | xinntao/HandyCrawler | 9 | Python | xinntao | Xintao | Tencent | |
tools/baike_stars/crawl_image_list.py | Python | import json
import pymongo
import time
from handycrawler.crawler_util import get_content, setup_session, sizeof_fmt
def main():
"""Parse baidu image search engine results to mongodb.
img_url: image url in Baidu cdn
person_id:
person_name:
album_id:
width: image width
height: image heigh... | xinntao/HandyCrawler | 9 | Python | xinntao | Xintao | Tencent | |
tools/baike_stars/crawl_imgs.py | Python | import hashlib
import os
import pymongo
import time
from handycrawler.crawler_util import get_img_content, setup_session
def main():
"""Download the image and save it to the corresponding path.
do not handle images with the same md5, because they may contain different
person.
And we will only crop t... | xinntao/HandyCrawler | 9 | Python | xinntao | Xintao | Tencent | |
tools/baike_stars/crawl_star_album_list.py | Python | import pymongo
import re
import time
from bs4 import BeautifulSoup
from urllib.parse import unquote
from handycrawler.crawler_util import get_content, setup_session, sizeof_fmt
def main():
"""Parse baidu image search engine results to mongodb.
"""
# configuration
star_list_path = 'tools/baike_stars/... | xinntao/HandyCrawler | 9 | Python | xinntao | Xintao | Tencent | |
tools/baike_stars/crawl_star_list_from_baidu_starrank.py | Python | import time
from bs4 import BeautifulSoup
from selenium import webdriver
from urllib.parse import unquote
def get_name_relpath_from_html(html):
soup = BeautifulSoup(html, 'html.parser')
results = []
for tr in soup.findAll('tr', {'class': ''}): # each for a celebrity
if tr.find('a') is not None:
... | xinntao/HandyCrawler | 9 | Python | xinntao | Xintao | Tencent | |
tools/baike_stars/url_downloader.py | Python | import hashlib
import imghdr
import os
import time
from handycrawler.crawler_util import setup_session
try:
import pymongo
except Exception:
raise ImportError('Please install pymongo')
def main():
"""Download the image and save it to the corresponding path."""
# configuration
save_root = 'old_ph... | xinntao/HandyCrawler | 9 | Python | xinntao | Xintao | Tencent | |
html/css/flow.css | CSS | body {
background-color: #eee;
font-size: 84%;
text-align: justify;
margin: 0px;
}
a {
color: #1772d0;
text-decoration: none;
}
a:focus,
a:hover {
color: #f09228;
text-decoration: none;
}
.navbar-fixed-top {
position: fixed;
right: 0;
left: 0;
z-index: 999;
}
.navbar {
bor... | xinntao/HandyFigure | 187 | HandyFigure provides the sources file (ususally PPT files) for paper figures | JavaScript | xinntao | Xintao | Tencent |
html/data/data.js | JavaScript | var data = [
{
"title": "Template",
"url_img": "https://raw.githubusercontent.com/xinntao/HandyFigure/master/figures/template.png",
"url_paper": "#",
"url_src": "https://github.com/xinntao/HandyFigure/releases/download/PPT-source/template.pptx",
"url_project": "#",
},
{
"title": "basic-neurons",... | xinntao/HandyFigure | 187 | HandyFigure provides the sources file (ususally PPT files) for paper figures | JavaScript | xinntao | Xintao | Tencent |
html/js/waterfall.js | JavaScript | var waterFall = {
container: document.getElementById("container"),
columnWidth: 400, // the column number is based on this value
columnInitNum: 5, // number of images inited in each column
scrollTop: document.documentElement.scrollTop || document.body.scrollTop,
detectLeft: 0,
sensitivity: 50,... | xinntao/HandyFigure | 187 | HandyFigure provides the sources file (ususally PPT files) for paper figures | JavaScript | xinntao | Xintao | Tencent |
index.html | HTML | <!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>HandyFigure</title>
<link rel="stylesheet" href="html/css/flow.css">
</head>
<body>
<!-- navigation bar -->
<div class="navbar navbar-fixed-top">
<img src="icon_text.png" alt="icon_text" height="30">
<a href="https://xinnt... | xinntao/HandyFigure | 187 | HandyFigure provides the sources file (ususally PPT files) for paper figures | JavaScript | xinntao | Xintao | Tencent |
process_data.py | Python | import yaml
with open('figures/database.yml', mode='r') as f:
data = yaml.load(f, Loader=yaml.FullLoader)['figures']
# generate .js file for html
file_js = open('html/data/data.js', mode='w')
file_js.write('var data = [\n')
for entry in data:
title = entry['title']
url_img = entry['url_img']
url_paper... | xinntao/HandyFigure | 187 | HandyFigure provides the sources file (ususally PPT files) for paper figures | JavaScript | xinntao | Xintao | Tencent |
handyinfer/__init__.py | Python | # flake8: noqa
from .depth_estimation import *
from .face_alignment import *
from .saliency_detection import *
from .utils import *
from .visualization import *
| xinntao/HandyInfer | 7 | Python | xinntao | Xintao | Tencent | |
handyinfer/depth_estimation/DPT_BEiT_L_384_arch.py | Python | import numpy as np
# from timm.models.layers import get_act_layer
import timm
import torch
import torch.nn as nn
import torch.nn.functional as F
import types
from timm.models.beit import gen_relative_position_index
from torch.utils.checkpoint import checkpoint
from typing import Optional
class Interpolate(nn.Module):... | xinntao/HandyInfer | 7 | Python | xinntao | Xintao | Tencent | |
handyinfer/depth_estimation/__init__.py | Python | import torch
from handyinfer.utils import load_file_from_url
from .DPT_BEiT_L_384_arch import DPTDepthModel
from .midas import MidasCore
from .zoedepth_arch import ZoeDepth
__all__ = ['ZoeDepth']
def init_depth_estimation_model(model_name, device='cuda', model_rootpath=None, img_size=[384, 512]):
if model_name ... | xinntao/HandyInfer | 7 | Python | xinntao | Xintao | Tencent | |
handyinfer/depth_estimation/midas.py | Python | # MIT License
# Copyright (c) 2022 Intelligent Systems Lab Org
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, m... | xinntao/HandyInfer | 7 | Python | xinntao | Xintao | Tencent | |
handyinfer/depth_estimation/zoedepth_arch.py | Python | # MIT License
# Copyright (c) 2022 Intelligent Systems Lab Org
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, m... | xinntao/HandyInfer | 7 | Python | xinntao | Xintao | Tencent | |
handyinfer/face_alignment/__init__.py | Python | import torch
from handyinfer.utils import load_file_from_url
from .awing_arch import FAN
from .convert_98_to_68_landmarks import landmark_98_to_68
__all__ = ['FAN', 'landmark_98_to_68']
def init_face_alignment_model(model_name, half=False, device='cuda', model_rootpath=None):
if model_name == 'awing_fan':
... | xinntao/HandyInfer | 7 | Python | xinntao | Xintao | Tencent | |
handyinfer/face_alignment/awing_arch.py | Python | import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def calculate_points(heatmaps):
# change heatmaps to landmarks
B, N, H, W = heatmaps.shape
HW = H * W
BN_range = np.arange(B * N)
heatline = heatmaps.reshape(B, N, HW)
indexes = np.argmax(heatline... | xinntao/HandyInfer | 7 | Python | xinntao | Xintao | Tencent | |
handyinfer/face_alignment/convert_98_to_68_landmarks.py | Python | import numpy as np
def load_txt_file(file_path):
"""Load data or string from txt file."""
with open(file_path, 'r') as cfile:
content = cfile.readlines()
cfile.close()
content = [x.strip() for x in content]
num_lines = len(content)
return content, num_lines
def anno_parser(anno_path... | xinntao/HandyInfer | 7 | Python | xinntao | Xintao | Tencent | |
handyinfer/saliency_detection/__init__.py | Python | import torch
from handyinfer.utils import load_file_from_url
from .inspyrenet_arch import InSPyReNet_SwinB
__all__ = ['InSPyReNet_SwinB']
def init_saliency_detection_model(model_name, half=False, device='cuda', model_rootpath=None):
if model_name == 'inspyrenet':
model = InSPyReNet_SwinB()
model... | xinntao/HandyInfer | 7 | Python | xinntao | Xintao | Tencent | |
handyinfer/saliency_detection/inspyrenet_arch.py | Python | import cv2
import torch
import torch.nn as nn
import torch.nn.functional as F
from handyinfer.utils import img2tensor
from .inspyrenet_modules import SICA, ImagePyramid, PAA_d, PAA_e, Transition
from .swin_transformer import SwinB
class InSPyReNet(nn.Module):
def __init__(self, backbone, in_channels, depth=64, ... | xinntao/HandyInfer | 7 | Python | xinntao | Xintao | Tencent | |
handyinfer/saliency_detection/inspyrenet_modules.py | Python | import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from typing import List, Optional
# dilation and erosion functions are copied from
# https://github.com/kornia/kornia/blob/master/kornia/morphology/morphology.py
def _neight2chan... | xinntao/HandyInfer | 7 | Python | xinntao | Xintao | Tencent | |
handyinfer/saliency_detection/swin_transformer.py | Python | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu, Yutong Lin, Yixuan Wei
# --------------------------------------------------------
import collections.abc
import math
import numpy a... | xinntao/HandyInfer | 7 | Python | xinntao | Xintao | Tencent | |
handyinfer/utils/__init__.py | Python | from .misc import img2tensor, load_file_from_url, scandir, tensor2img_fast
__all__ = ['load_file_from_url', 'img2tensor', 'scandir', 'tensor2img_fast']
| xinntao/HandyInfer | 7 | Python | xinntao | Xintao | Tencent | |
handyinfer/utils/misc.py | Python | import cv2
import os
import os.path as osp
import torch
from torch.hub import download_url_to_file, get_dir
from urllib.parse import urlparse
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
def imwrite(img, file_path, params=None, auto_mkdir=True):
"""Write image to file.
... | xinntao/HandyInfer | 7 | Python | xinntao | Xintao | Tencent |
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