repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/datasets/NTU.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import torch
from .base import Datasets
from torchvision import transforms, set_image_backend
import random, os
from PIL import Image
import numpy as np
def SubSetSampling_func(inputs, reduce=2):
print('Total training examples', len(inputs))
sampl... | 2,462 | 35.761194 | 121 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/datasets/UCF101.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import torch
from .base import Datasets
from torchvision import transforms, set_image_backend
import random, os
from PIL import Image
import numpy as np
def SubSetSampling_func(inputs, reduce=2):
print('Total training examples', len(inputs))
sampl... | 2,117 | 34.898305 | 121 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/datasets/build.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import torch
from .distributed_sampler import DistributedSampler
from .IsoGD import IsoGDData
from .NvGesture import NvData
from .THU_READ import THUREAD
from .Jester import JesterData
from .NTU import NTUData
from .UCF101 import UCFData
from .base import ... | 1,992 | 36.603774 | 130 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/model/DSN.py | '''
This file is modified from:
https://github.com/deepmind/kinetics-i3d/i3d.py
'''
import torch
import torch.nn as nn
from einops.layers.torch import Rearrange
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import cv2
import os, math
import sys
from .DTN import DTNNet
from .FRP... | 6,208 | 37.092025 | 127 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/model/FRP.py | '''
This file is modified from:
https://github.com/zhoubenjia/RAAR3DNet/blob/master/Network_Train/lib/model/RAAR3DNet.py
'''
import torch
import torch.nn as nn
from einops.layers.torch import Rearrange
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import transforms
import numpy a... | 3,236 | 36.206897 | 110 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/model/fusion_Net.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
from torch.autograd import Variable
from collections import OrderedDict
import numpy as np
import os
import sys
from collections import OrderedDict
from einops im... | 14,925 | 38.802667 | 162 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/model/DTN_v2.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import torch
from torch.autograd import Variable
from torch import nn, einsum
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, helpers, DropPath
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
i... | 6,286 | 35.982353 | 146 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/model/utils.py | '''
This file is modified from:
https://github.com/deepmind/kinetics-i3d/blob/master/i3d.py
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import os
import sys
class MaxPool3dSamePadding(nn.MaxPool3d):
def compute_pad(self, dim, s):
... | 6,415 | 40.662338 | 121 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/model/DSN_v2.py | '''
This file is modified from:
https://github.com/deepmind/kinetics-i3d/i3d.py
'''
import torch
from torch import nn, einsum
from einops.layers.torch import Rearrange
import torch.nn.functional as F
from torch.autograd import Variable
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
im... | 8,548 | 36.827434 | 137 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/model/trans_module.py | '''
This file is modified from:
https://github.com/rishikksh20/CrossViT-pytorch/blob/master/crossvit.py
'''
import torch
from torch import nn, einsum
import torch.nn.functional as F
import math
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
class Residual(nn.Module):
def __init__... | 3,442 | 30.87963 | 126 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/model/DTN.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import torch
from torch.autograd import Variable
from torch import nn, einsum
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, helpers, DropPath
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
i... | 18,908 | 42.87239 | 159 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/model/DSN_Fusion.py | '''
This file is modified from:
https://github.com/deepmind/kinetics-i3d/i3d.py
'''
import torch
import torch.nn as nn
from einops.layers.torch import Rearrange
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import cv2
import os, math
import sys
from .DTN import DTNNet
from .FRP... | 5,925 | 36.27044 | 127 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/model/models.py | """
This file is modified from:
https://github.com/rwightman/pytorch-image-models/blob/main/timm/models/deit.py
"""
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
import torch
import torch.nn as nn
from functools import partial
from einops import rearrange, repeat
import torch.nn.functional as nn... | 19,803 | 37.984252 | 169 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/model/__init__.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
from .build import *
from .model_ema import *
from .fusion_Net import * | 135 | 18.428571 | 54 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/model/model_ema.py | """
This file is modified from:
https://github.com/rwightman/pytorch-image-models/blob/main/timm/utils/model_ema.py
Exponential Moving Average (EMA) of model updates
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from collections import OrderedDict
from copy import deepcopy
import torch
impor... | 6,797 | 42.858065 | 102 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/model/build.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
from .DSN import DSNNet
from .DSN_v2 import DSNNetV2
from .fusion_Net import CrossFusionNet, SFNNet
from .models import *
from timm.models import create_model
import logging
def build_model(args):
num_classes = dict(
IsoGD=249,
... | 1,019 | 25.153846 | 98 | py |
paper-rule-adherence-dev | paper-rule-adherence-dev/adult_syn_00.py | import os
import pandas as pd
import numpy as np
import mostly_engine.core
## SPLIT data
df = pd.read_csv('data/adult_original.csv.gz')
df = df.loc[df['marital-status'] != 'Married-AF-spouse', :]
cols = ['age', 'education', 'education-num', 'marital-status', 'relationship', 'sex', 'income']
df = df[cols]
trn = df.sa... | 2,036 | 28.521739 | 200 | py |
paper-rule-adherence-dev | paper-rule-adherence-dev/adult_syn_01.py | import os
import pandas as pd
import mostly_engine.core
## TRAIN without RULES
mostly_engine.core.train()
| 108 | 12.625 | 26 | py |
paper-rule-adherence-dev | paper-rule-adherence-dev/adult_syn_02.py | import os
import pandas as pd
import mostly_engine.core
## GENERATE without RULES
syn_data_path = mostly_engine.core.generate(generation_size=100_000)
syn = pd.concat([pd.read_parquet(fn) for fn in syn_data_path.glob("*.parquet")])
syn.to_csv('adult_synthetic.csv.gz', index=False)
## GENERATE with RULES
rule1 = p... | 708 | 29.826087 | 104 | py |
paper-rule-adherence-dev | paper-rule-adherence-dev/adult_syn_03.py | import os
import pandas as pd
import mostly_engine.core
## TRAIN with RULES
rule1 = pd.read_csv('rules/rule1.csv')
rule2 = pd.read_csv('rules/rule2.csv')
rule3 = pd.read_csv('rules/rule3.csv')
rule4 = pd.read_csv('rules/rule4.csv')
mostly_engine.core.train(rules=[rule1, rule2, rule3, rule4], rule_weight=5.0)
| 313 | 23.153846 | 77 | py |
paper-rule-adherence-dev | paper-rule-adherence-dev/adult_syn_04.py | import os
import pandas as pd
import mostly_engine.core
## GENERATE without RULES
syn_data_path = mostly_engine.core.generate(generation_size=100_000)
syn = pd.concat([pd.read_parquet(fn) for fn in syn_data_path.glob("*.parquet")])
syn.to_csv('adult_synthetic_trn.csv.gz', index=False)
## GENERATE with RULES
rule1... | 716 | 30.173913 | 104 | py |
tinyxml2 | tinyxml2-master/setversion.py | #!/usr/bin/env python3
# Python program to set the version.
##############################################
import re
import sys
import optparse
def fileProcess( name, lineFunction ):
filestream = open( name, 'r' )
if filestream.closed:
print( "file " + name + " not open." )
return
output = ""
print( "--- Pro... | 3,571 | 24.15493 | 92 | py |
DMH-Net | DMH-Net-main/visualization_from_json.py | import argparse
import json
import os
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
from PIL import Image
from matplotlib.figure import Figure
from tqdm import trange
from e2plabel.e2plabelconvert import generatePerspective, VIEW_NAME, VIEW_ARGS
from postprocess.postprocess2 import _cal_p... | 10,947 | 39.850746 | 130 | py |
DMH-Net | DMH-Net-main/visualization.py | import io
import math
import os
import time
from typing import Dict
import cv2
import numpy as np
try:
import open3d as o3d
except:
pass
import torch
from PIL import Image
from matplotlib import pyplot as plt
from matplotlib.figure import Figure
from e2plabel.e2plabelconvert import VIEW_NAME
from perspective... | 22,450 | 43.021569 | 119 | py |
DMH-Net | DMH-Net-main/eval_general.py | import argparse
import json
import numpy as np
from shapely.geometry import Polygon
from tqdm import tqdm
from eval_cuboid import prepare_gtdt_pairs
from misc import post_proc, panostretch
def sort_xy_filter_unique(xs, ys, y_small_first=True):
xs, ys = np.array(xs), np.array(ys)
idx_sort = np.argsort(xs + y... | 7,264 | 34.612745 | 99 | py |
DMH-Net | DMH-Net-main/verify_vote.py | import argparse
import torch
from torch.utils.data import DataLoader
from tqdm import trange
from config import cfg, cfg_from_yaml_file, cfg_from_list
from e2plabel.e2plabelconvert import VIEW_NAME
from perspective_dataset import PerspectiveDataset
from visualization import getMaskByType, visualize
from postprocess.p... | 3,433 | 46.041096 | 120 | py |
DMH-Net | DMH-Net-main/model.py | import math
import types
from typing import Tuple
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from drn import drn_d_22, drn_d_38, drn_d_54
from e2plabel.e2plabelconvert import VIEW_NAME
from layers import FusionHoughStage, PerspectiveE2PP2E, HoughNewUpSampler
ENCODER_RESNET ... | 11,875 | 45.031008 | 120 | py |
DMH-Net | DMH-Net-main/drn.py | import math
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
BatchNorm = nn.BatchNorm2d
# __all__ = ['DRN', 'drn26', 'drn42', 'drn58']
webroot = 'http://dl.yf.io/drn/'
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'drn-c-26': webroot + 'drn_c_26-dd... | 14,207 | 33.236145 | 88 | py |
DMH-Net | DMH-Net-main/layers.py | import math
import torch
import torch.nn as nn
class PerspectiveE2PP2E(nn.Module):
def __init__(self, cfg, input_h, input_w, pers_h, fov, input_feat, output_feat, hough_angles_num=180,
hoguh_clines_tole=1.0):
super(PerspectiveE2PP2E, self).__init__()
self.cfg = cfg
self.h... | 12,016 | 49.491597 | 117 | py |
DMH-Net | DMH-Net-main/config.py | import warnings
from pathlib import Path
import yaml
from easydict import EasyDict
def log_config_to_file(cfg, pre='cfg', logger=None):
for key, val in cfg.items():
if isinstance(cfg[key], EasyDict):
logger.info('\n%s.%s = edict()' % (pre, key))
log_config_to_file(cfg[key], pre=pr... | 3,026 | 32.633333 | 109 | py |
DMH-Net | DMH-Net-main/eval.py | import argparse
import json
import os
# import ipdb
import sys
import time
import warnings
from pathlib import Path
import cv2
import numpy as np
import torch
import torch.nn as nn
import yaml
from torch.utils.data import DataLoader
from tqdm import trange
from config import cfg, cfg_from_yaml_file, cfg_from_list, me... | 10,926 | 45.300847 | 117 | py |
DMH-Net | DMH-Net-main/perspective_dataset.py | import os
import warnings
import numpy as np
import torch
import torch.utils.data as data
from PIL import Image
from easydict import EasyDict
from scipy.spatial.distance import cdist
from shapely.geometry import LineString
from torch.utils.data._utils.collate import default_collate
from torchvision.transforms import t... | 26,199 | 42.812709 | 118 | py |
DMH-Net | DMH-Net-main/train.py | import argparse
import os
# import ipdb
import sys
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torchvision.utils import make_grid
from... | 18,050 | 44.583333 | 146 | py |
DMH-Net | DMH-Net-main/preprocess.py | '''
This script preprocess the given 360 panorama image under euqirectangular projection
and dump them to the given directory for further layout prediction and visualization.
The script will:
- extract and dump the vanishing points
- rotate the equirect image to align with the detected VP
- extract the VP a... | 6,175 | 36.658537 | 85 | py |
DMH-Net | DMH-Net-main/layout_viewer.py | import json
import open3d
from PIL import Image
from scipy.ndimage import map_coordinates
from tqdm import trange
from misc.panostretch import pano_connect_points
from misc.post_proc import np_coor2xy, np_coory2v
def xyz_2_coorxy(xs, ys, zs, H, W):
''' Mapping 3D xyz coordinates to equirect coordinate '''
u... | 15,893 | 37.115108 | 163 | py |
DMH-Net | DMH-Net-main/eval_cuboid.py | import os
import json
import glob
import argparse
import warnings
import numpy as np
from tqdm import tqdm
from scipy.spatial import HalfspaceIntersection
from scipy.spatial import ConvexHull
from misc import post_proc, panostretch
def tri2halfspace(pa, pb, p):
''' Helper function for evaluating 3DIoU '''
v... | 7,422 | 35.033981 | 102 | py |
DMH-Net | DMH-Net-main/e2pconvert_torch/convertExUtils.py | from functools import reduce
import numpy as np
import torch
from py360convert import rotation_matrix
from e2pconvert_torch import torch360convert
def rotationMatrix(u, v, in_rot):
Rx = rotation_matrix(v, [1, 0, 0])
Ry = rotation_matrix(u, [0, 1, 0])
Ri = rotation_matrix(in_rot, np.array([0, 0, 1.0]).d... | 2,723 | 35.810811 | 120 | py |
DMH-Net | DMH-Net-main/e2pconvert_torch/e2plabelconvert.py | import numpy as np
import torch
from .convertExUtils import coordE2P
def linesPostProcess(lines, img_hw, is_updown_view):
"""
对线进行处理，筛选掉看不见的线、对线的起终点进行规范化处理
:param lines:(k,7)，表示图中的k条线。每条线用七个数表示，前两个是端点在points中的序号，然后是线的类型：0是竖直的墙壁线，1是天花板线，2是地板线，然后是起点的x、y坐标，然后是终点的x、y坐标
:param img_hw:(2)，图片的宽和高
:return... | 6,202 | 41.197279 | 132 | py |
DMH-Net | DMH-Net-main/e2pconvert_torch/torch360convert.py | import numpy as np
import torch
def coor2uv(coorxy, h, w):
coor_x, coor_y = coorxy[:, 0:1], coorxy[:, 1:2]
u = ((coor_x + 0.5) / w - 0.5) * 2 * np.pi
v = -((coor_y + 0.5) / h - 0.5) * np.pi
return torch.cat([u, v], -1)
def uv2unitxyz(uv):
u, v = uv[:, 0:1], uv[:, 1:2]
y = torch.sin(v)
c... | 1,010 | 20.978261 | 51 | py |
DMH-Net | DMH-Net-main/postprocess/LayoutNet_post_proc2.py | import numpy as np
from scipy.ndimage import map_coordinates
from scipy.spatial.distance import pdist, squareform
from sklearn.decomposition import PCA
PI = float(np.pi)
def fuv2img(fuv, coorW=1024, floorW=1024, floorH=512):
'''
Project 1d signal in uv space to 2d floor plane image
'''
floor_plane_x... | 12,474 | 33.652778 | 148 | py |
DMH-Net | DMH-Net-main/postprocess/LayoutNetv2.py | import numpy as np
import scipy.signal
import torch
from scipy.ndimage.filters import maximum_filter
from torch import optim
import postprocess.LayoutNet_post_proc2 as post_proc
from scipy.ndimage import convolve, map_coordinates
from shapely.geometry import Polygon
def LayoutNetv2PostProcessMain(cor_img: np.ndarray,... | 14,463 | 34.364303 | 127 | py |
DMH-Net | DMH-Net-main/postprocess/postprocess2.py | import argparse
import math
import warnings
from typing import List, Optional, Tuple, Dict
import numpy as np
import py360convert
import scipy.signal
import torch
from matplotlib import pyplot as plt
from torch import nn
from torch.utils.data import DataLoader
from tqdm import trange
from config import cfg_from_yaml_... | 44,265 | 42.060311 | 120 | py |
DMH-Net | DMH-Net-main/postprocess/GDSolver.py | import math
import torch
from torch import nn, optim
def solve(module: nn.Module, *inputs, lr=1e-2, tol=1e-4, max_iter=10000, optimizer=None, stop_tol=None, stop_range=None,
return_best=True, **kwargs):
history = None
if stop_range is not None:
assert stop_tol is not None
best_loss = N... | 1,452 | 32.022727 | 120 | py |
DMH-Net | DMH-Net-main/postprocess/noncuboid.py | import traceback
import warnings
from typing import List, Tuple
import numpy as np
import torch
from easydict import EasyDict
from torch import nn
from e2pconvert_torch.e2plabelconvert import generateOnePerspectiveLabel
from e2pconvert_torch.torch360convert import coor2uv, xyz2uv, uv2unitxyz, uv2coor
from e2plabel.e2... | 29,019 | 43.509202 | 147 | py |
DMH-Net | DMH-Net-main/misc/grab_data.py | '''
Test docstring
'''
# pylint: disable=cell-var-from-loop
# pylint --extension-pkg-whitelist=cv2
import glob
import urllib.request
import json
import math
import numpy as np
import cv2
from tqdm import trange
import argparse
import os
def dump_to_txt(m_path, m_list):
'''Dump a list of string to the given m_path... | 8,623 | 42.77665 | 118 | py |
DMH-Net | DMH-Net-main/misc/panostretch.py | import functools
import numpy as np
from misc.post_proc import np_coor2xy, np_coorx2u, np_coory2v
from scipy.ndimage import map_coordinates
def uv_meshgrid(w, h):
uv = np.stack(np.meshgrid(range(w), range(h)), axis=-1)
uv = uv.astype(np.float64)
uv[..., 0] = ((uv[..., 0] + 0.5) / w - 0.5) * 2 * np.pi
... | 6,401 | 32 | 126 | py |
DMH-Net | DMH-Net-main/misc/pano_lsd_align.py | '''
This script is helper function for preprocessing.
Most of the code are converted from LayoutNet official's matlab code.
All functions, naming rule and data flow follow official for easier
converting and comparing.
Code is not optimized for python or numpy yet.
Author: Cheng Sun
Email : chengsun@gapp.nthu.edu.tw
''... | 31,860 | 32.257829 | 103 | py |
DMH-Net | DMH-Net-main/misc/utils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
def group_weight(module):
# Group module parameters into two group
# One need weight_decay and the other doesn't
group_decay = []
group_no_decay = []
for m in module.modules():
if isinsta... | 4,291 | 32.271318 | 105 | py |
DMH-Net | DMH-Net-main/misc/post_proc.py | import numpy as np
from scipy.ndimage import map_coordinates
from scipy.spatial.distance import pdist, squareform
from sklearn.decomposition import PCA
from scipy.ndimage.filters import maximum_filter
from scipy.spatial import distance
PI = float(np.pi)
def interp_point_by_u(xy0, xy1, u0, u1, u):
c0 = np.linalg.... | 38,034 | 36.436024 | 109 | py |
DMH-Net | DMH-Net-main/misc/__init__.py | 0 | 0 | 0 | py | |
DMH-Net | DMH-Net-main/e2plabel/main.py | import os
import pickle
from typing import Optional, List
import numpy as np
import py360convert
from matplotlib import pyplot as plt
from .e2plabelconvert import generateOnePerspectiveLabel, VIEW_ARGS, VIEW_NAME, VIEW_SIZE
data_dir = "../PanoHough/data/layoutnet_dataset/train"
COLORS = {
0: "red", # 竖直墙壁线红色
... | 3,973 | 30.539683 | 97 | py |
DMH-Net | DMH-Net-main/e2plabel/convertExUtils.py | from functools import reduce
import numpy as np
import py360convert
from py360convert import rotation_matrix
def rotationMatrix(u, v, in_rot):
Rx = rotation_matrix(v, [1, 0, 0])
Ry = rotation_matrix(u, [0, 1, 0])
Ri = rotation_matrix(in_rot, np.array([0, 0, 1.0]).dot(Rx).dot(Ry))
return Rx.dot(Ry).d... | 2,512 | 35.42029 | 117 | py |
DMH-Net | DMH-Net-main/e2plabel/e2plabelconvert.py | from typing import List, Dict
import numpy as np
import py360convert
from .convertExUtils import coordE2P
# 视图顺序： F R B L U D
# -----
# | 4 |
# -----
# | 3 | 0 | 1 | 2 |
# -----
# | 5 |
# -----
VIEW_ARGS = [
[(90, 90), 0, 0],
[(90, 90), 90, 0],
[(90, 90), 180, 0],
[(90, 90), -... | 7,147 | 36.036269 | 132 | py |
lld-public | lld-public-master/rectify_euroc.py | import cv2
import os
import sys
def prepare_rectifier(conf_path):
fs = cv2.FileStorage(conf_path, cv2.FILE_STORAGE_READ)
Kl = fs.getNode("LEFT.K").mat()
Dl = fs.getNode("LEFT.D").mat()
Rl = fs.getNode("LEFT.R").mat()
Pl = fs.getNode("LEFT.P").mat()
wl = 752
hl = 480
Kr = fs.getNode("RI... | 2,476 | 35.970149 | 191 | py |
lld-public | lld-public-master/infer.py | import numpy as np
import cv2
import torch
import torch.nn.functional as F
import pylbd
import matplotlib.pyplot as plt
import torch.nn as nn
class FeatureEncoder(nn.Module):
def initialize_l2(self, g=0):
if g==0:
g = 4
if self.depth == 2:
g = 2
if self.... | 11,351 | 37.744027 | 113 | py |
lld-public | lld-public-master/prepare_kitti_euroc_combined.py | import os
import shutil
def combine_datasets(kitti_path, euroc_rect_path, target_path):
ds_def = [(0,0),
(0,1),
(0,2),
(0,3),
(0,4),
(1,0),
(1,1),
(1,3),
(1,5),
(1,7),
(0,7),... | 1,267 | 22.924528 | 63 | py |
lld-public | lld-public-master/train.py | import torch
import data.batched as ba
import cnn.net_multibatch as nmb
import torch.optim as optim
import os
import train.multibatch_trainer as mbt
import tqdm
from torch.autograd import Variable
import numpy as np
dir_path = '../traindata/'
ptnum = 5
is_noisy = False
def get_net():
return nmb.FeatureEncoder(is_... | 1,661 | 29.777778 | 128 | py |
lld-public | lld-public-master/cnn/__init__.py | 0 | 0 | 0 | py | |
lld-public | lld-public-master/cnn/net_multibatch.py | import torch.nn as nn
import torch
import torch.nn.functional as F
import tqdm
from torch.autograd import Variable
import torch.optim as optim
import time
import sklearn.metrics as metrics
import numpy as np
def compute_distances(x, pos_inds, neg_mask):
#x: b x C x N
#pos_mask: b x n_p, pos_inds: b x n_p, neg_... | 19,314 | 38.418367 | 113 | py |
lld-public | lld-public-master/train/multibatch_trainer.py | import torch
import os
import time
import numpy as np
import data.batched as ba
import torch.optim as optim
import cnn.net_multibatch as nmb
def compose_batch(batch):
batch = batch[0]
n = len(batch[0])
ims = np.asarray(batch[0]).astype(float)
ims = torch.from_numpy(ims).float()
lines = batch[1]
... | 4,564 | 41.268519 | 129 | py |
lld-public | lld-public-master/train/__init__.py | 0 | 0 | 0 | py | |
lld-public | lld-public-master/data/line_sampler.py | import numpy as np
import torch
import time
def prepare_line_grid(lines_pair, margins, s, map_size, is_plain, pt_per_line):
line_grid = prepare_grid_plain(lines_pair, margins, s, is_plain, pt_per_line)
for i in range(0, 2):
line_grid[:, :, :, i] -= 0.5 * map_size[i]
line_grid[:, :, :, i] /= 0.5 ... | 4,132 | 35.901786 | 127 | py |
lld-public | lld-public-master/data/batched.py | import os
import cv2
from torch.utils.data import Dataset
import numpy as np
import sys
import random
import torch
import line_sampler
def add_noise(im):
return im.astype(float) + 30*np.random.randn(im.shape[0], im.shape[1])
def get_image_id(call_id, f1):
mtd = 5
main_id = 5 * call_id
pair_id = -1
... | 28,142 | 36.22619 | 177 | py |
lld-public | lld-public-master/data/__init__.py | 0 | 0 | 0 | py | |
DocBank | DocBank-master/scripts/pdf_process.py | import multiprocessing
import argparse
import pdfplumber
import os
from tqdm import tqdm
from pdfminer.layout import LTChar, LTLine
import re
from collections import Counter
import pdf2image
import numpy as np
from PIL import Image
def within_bbox(bbox_bound, bbox_in):
assert bbox_bound[0] <= bbox_bound[2]
as... | 7,516 | 36.029557 | 115 | py |
DocBank | DocBank-master/scripts/coco_format_scripts/DocbankToCOCO.py | import os
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
import pandas as pd
import json
from pprint import PrettyPrinter as pprint
from IPython.display import JSON
import re
from tqdm import tqdm
import traceback
class COCOData:
"""
COCOData class allows DocBank dataset to be ... | 7,813 | 42.653631 | 193 | py |
xcos | xcos-master/src/main.py | import os
import argparse
# import warnings
import torch
from utils.logging_config import logger
from pipeline import TrainingPipeline, TestingPipeline, EvaluationPipeline
def main(args):
# load config file from checkpoint, this will include the training information (epoch, optimizer parameters)
if args.resu... | 2,920 | 35.5125 | 112 | py |
xcos | xcos-master/src/GradCam.py | from PIL import Image
import cv2
import numpy as np
import torch.nn.functional as F
from model.xcos_modules import l2normalize
class GradientExtractor:
""" Extracting activations and
registering gradients from targetted intermediate layers
"""
def __init__(self, model):
self.model = model
... | 2,847 | 27.48 | 92 | py |
xcos | xcos-master/src/pipeline/base_pipeline.py | import os
import json
import datetime
import logging
from abc import ABC, abstractmethod
import torch
import pandas as pd
from utils.util import get_instance
from utils.visualization import WriterTensorboard
from utils.logging_config import logger
from utils.global_config import global_config
import data_loader.data_... | 11,688 | 43.109434 | 119 | py |
xcos | xcos-master/src/pipeline/testing_pipeline.py | import os
import numpy as np
from .base_pipeline import BasePipeline
from worker.tester import Tester
from utils.global_config import global_config
from utils.util import ensure_dir
from utils.logging_config import logger
class TestingPipeline(BasePipeline):
def __init__(self, args):
"""
# You ... | 2,805 | 34.518987 | 110 | py |
xcos | xcos-master/src/pipeline/evaluation_pipeline.py | from .base_pipeline import BasePipeline
from worker.evaluator import Evaluator
from utils.global_config import global_config
class EvaluationPipeline(BasePipeline):
def __init__(self, args):
global_config.setup(args.template_config, args.specified_configs, args.resumed_checkpoint)
self._print_conf... | 1,511 | 35 | 102 | py |
xcos | xcos-master/src/pipeline/__init__.py | from .testing_pipeline import TestingPipeline # NOQA
from .training_pipeline import TrainingPipeline # NOQA
from .evaluation_pipeline import EvaluationPipeline # NOQA
| 170 | 41.75 | 59 | py |
xcos | xcos-master/src/pipeline/training_pipeline.py | import math
import os
import torch
from .base_pipeline import BasePipeline
from worker.trainer import Trainer
from worker.validator import Validator
import model.loss as module_loss
from utils.global_config import global_config
from utils.logging_config import logger
from utils.util import ensure_dir
class Training... | 7,092 | 41.728916 | 120 | py |
xcos | xcos-master/src/worker/tester.py | import os
import time
import torch
from torchvision.utils import save_image
from .worker_template import WorkerTemplate
from data_loader.base_data_loader import BaseDataLoader
from pipeline.base_pipeline import BasePipeline
from utils.global_config import global_config
from utils.logging_config import logger
from utils... | 4,707 | 42.192661 | 118 | py |
xcos | xcos-master/src/worker/training_worker.py | import time
from .worker_template import WorkerTemplate
class TrainingWorker(WorkerTemplate):
"""
The middle class between WorkerTemplate and Trainer/Validator for
trainer/validator's common processing of epoch output.
Note:
Inherited from WorkerTemplate.
"""
def _init_output(self):
... | 2,310 | 37.516667 | 96 | py |
xcos | xcos-master/src/worker/evaluator.py | import time
from .worker_template import WorkerTemplate
from pipeline.base_pipeline import BasePipeline
from data_loader.base_data_loader import BaseDataLoader
from utils.logging_config import logger
from utils.global_config import global_config
class Evaluator(WorkerTemplate):
"""
Evaluator class
Note:... | 3,040 | 33.556818 | 100 | py |
xcos | xcos-master/src/worker/worker_template.py | import time
from abc import ABC, abstractmethod
import torch
from torchvision.utils import make_grid
from data_loader.base_data_loader import BaseDataLoader
from pipeline.base_pipeline import BasePipeline
from utils.global_config import global_config
from utils.util import batch_visualize_xcos
class WorkerTemplate(... | 6,516 | 38.981595 | 101 | py |
xcos | xcos-master/src/worker/validator.py | import torch
from .training_worker import TrainingWorker
from pipeline.base_pipeline import BasePipeline
class Validator(TrainingWorker):
"""
Validator class
Note:
Inherited from WorkerTemplate.
"""
def __init__(self, pipeline: BasePipeline, *args):
super().__init__(pipeline, *arg... | 1,686 | 39.166667 | 113 | py |
xcos | xcos-master/src/worker/trainer.py | import time
import numpy as np
from .training_worker import TrainingWorker
from utils.logging_config import logger
from utils.util import get_lr
from utils.global_config import global_config
from pipeline.base_pipeline import BasePipeline
class Trainer(TrainingWorker):
"""
Trainer class
Note:
I... | 3,137 | 36.357143 | 111 | py |
xcos | xcos-master/src/data_loader/data_loaders.py | import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) # noqa
from torchvision import transforms
from .base_data_loader import BaseDataLoader
from .mnist import MnistDataset
from .mnist_result import MnistResultDataset
from .face_datasets import SiameseImageFolder, InsightF... | 5,081 | 40.655738 | 98 | py |
xcos | xcos-master/src/data_loader/base_data_loader.py | import numpy as np
from torch.utils.data import DataLoader
from torch.utils.data.dataloader import default_collate
from torch.utils.data.sampler import SubsetRandomSampler
# Add this to initialize workers of dataloader to avoid fixed numpy random
# seeds for each training epoch. For a clearer explanation please refer... | 2,242 | 32.477612 | 112 | py |
xcos | xcos-master/src/data_loader/face_datasets.py | import cv2
import os
import os.path as op
import warnings
from glob import glob
import numpy as np
import pandas as pd
from PIL import Image
import bcolz
import torch
import torch.nn as nn
from torchvision import transforms, datasets
from torch.utils.data import Dataset
from torch.utils.data.sampler import BatchSampler... | 49,157 | 36.212718 | 107 | py |
xcos | xcos-master/src/data_loader/mnist_result.py | import numpy as np
from torch.utils.data import Dataset
class MnistResultDataset(Dataset):
"""
Customized MNIST result dataset demo
"""
def __init__(self, result_filename, key='model_output'):
self.key = key
self.results = self._load_data(result_filename, key)
def _load_data(self,... | 661 | 24.461538 | 60 | py |
xcos | xcos-master/src/data_loader/__init__.py | 0 | 0 | 0 | py | |
xcos | xcos-master/src/data_loader/mnist.py | from torchvision import datasets
class MnistDataset(datasets.MNIST):
"""
Customized MNIST dataset demo
"""
def __init__(self, data_dir, train, download, transform):
super().__init__(data_dir, train=train, download=download, transform=transform)
def __getitem__(self, index):
""" Ov... | 532 | 27.052632 | 87 | py |
xcos | xcos-master/src/scripts/generate_masked_training_dataset.py | import os
import errno
import argparse
import numpy as np
from glob import glob
from PIL import Image
from tqdm import tqdm
from joblib import Parallel, delayed
def apply_single_mask(image_file, random_state):
random_state = np.random.RandomState(random_state)
mask_file = random_state.choice(mask_dir)
ima... | 3,083 | 30.793814 | 106 | py |
xcos | xcos-master/src/scripts/make_dataset_list.py | '''
Make a list of files for a large dataset.
Example:
python scripts/make_dataset_list.py -p '../datasets/mnist/*/*' -o ../datasets/mnist_list.txt
'''
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) # NOQA
from glob import glob
import argparse
from utils.loggin... | 1,155 | 24.688889 | 96 | py |
xcos | xcos-master/src/scripts/clean_saved.py | import shutil
import os
from os.path import basename, dirname, abspath
from glob import glob
import argparse
def num_checkpoints(path):
files = glob(os.path.join(path, '*.pth'))
return len(files)
def collect_satisfied(args):
collected = []
if args.exact_path is not None:
exp_paths = sorted(... | 4,275 | 30.441176 | 117 | py |
xcos | xcos-master/src/utils/global_config.py | '''
global_config.py
Global configuration module with a global var "global_config" for other modules to access
all configuration.
'''
from copy import deepcopy
import json
from attrdict import AttrDict
from .logging_config import logger
def flatten_nested_dict(nested_dict: dict, root_path: str, flattened_dict: dic... | 7,905 | 40.39267 | 119 | py |
xcos | xcos-master/src/utils/visualization.py | try:
from torch.utils.tensorboard import SummaryWriter
except ImportError:
print("Using tensorboardX instead of built-in tensorboard (need PyTorch 1.2+ with Tensorboard 1.14+)")
from tensorboardX import SummaryWriter
class WriterTensorboard():
def __init__(self, writer_dir, logger, enable):
se... | 1,723 | 37.311111 | 114 | py |
xcos | xcos-master/src/utils/align.py | import cv2
import numpy as np
from skimage import transform as trans
class Alignment:
def __init__(self):
image_size = (112, 112)
self.image_size = image_size
src = np.array([
[30.2946, 51.6963],
[65.5318, 51.5014],
[48.0252, 71.7366],
[33.54... | 1,439 | 32.488372 | 64 | py |
xcos | xcos-master/src/utils/logging_config.py | import logging
import sys
stream_handler = logging.StreamHandler(sys.stdout)
format_ = ('[%(asctime)s] {%(filename)s:%(lineno)d} '
'%(levelname)s - %(message)s')
try:
# use colored logs if installed
import coloredlogs
formatter = coloredlogs.ColoredFormatter(fmt=format_)
stream_handler.set... | 533 | 21.25 | 57 | py |
xcos | xcos-master/src/utils/insight2xcos.py | # from model.face_recog import Backbone_FC2Conv, Backbone
# from model.xcos_modules import XCosAttention
# backbone = Backbone_FC2Conv(50, 0.6, 'ir_se')
# attention = XCosAttention(use_softmax=True, softmax_t=1, chw2hwc=True)
# backbone_target = Backbone(50,
# 0.6,
# ... | 2,258 | 43.294118 | 120 | py |
xcos | xcos-master/src/utils/insight_to_normal_face_model.py | # from model.face_recog import Backbone_FC2Conv, Backbone
# from model.xcos_modules import XCosAttention
# backbone = Backbone_FC2Conv(50, 0.6, 'ir_se')
# attention = XCosAttention(use_softmax=True, softmax_t=1, chw2hwc=True)
# backbone_target = Backbone(50,
# 0.6,
# ... | 1,476 | 33.348837 | 86 | py |
xcos | xcos-master/src/utils/util.py | import os
import os.path as op
from glob import glob
import importlib.util
import torch
import numpy as np
import io
import cv2
import base64
import seaborn as sns
from PIL import Image
from torchvision.transforms import ToTensor
from matplotlib import pyplot as plt
lib_path = op.abspath(op.join(__file__, op.pardir... | 11,115 | 31.127168 | 90 | py |
xcos | xcos-master/src/utils/verification.py | import numpy as np
from sklearn.model_selection import KFold
import matplotlib.pyplot as plt
import io
from PIL import Image
from torchvision import transforms
def calculate_accuracy(threshold, dist, actual_issame, useCos=False):
'''
if useCos = True, then view 'dist' variable as cos
'''
if useCos:
... | 4,936 | 33.284722 | 95 | py |
xcos | xcos-master/src/utils/__init__.py | 0 | 0 | 0 | py | |
xcos | xcos-master/src/utils/util_python.py | def read_lines_into_list(filename):
content = []
with open(filename) as f:
for line in f:
content.append(line.strip())
# # you may also want to remove whitespace characters like `\n` at the end of each line
# content = [x.strip() for x in content]
return content
| 303 | 32.777778 | 91 | py |
xcos | xcos-master/src/model/base_model.py | import torch.nn as nn
import numpy as np
from utils.logging_config import logger
class BaseModel(nn.Module):
"""
Base class for all models
"""
def __init__(self):
super(BaseModel, self).__init__()
def forward(self, *input):
"""
Forward pass logic
:return: Model ... | 672 | 20.709677 | 79 | py |
xcos | xcos-master/src/model/loss.py | import torch
import torch.nn as nn
class BaseLoss(nn.Module):
def __init__(self, output_key, target_key, nickname=None, weight=1):
super().__init__()
self.output_key = output_key
self.target_key = target_key
self.weight = weight
self.nickname = self.__class__.__name__ if ni... | 4,210 | 32.688 | 90 | py |
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