repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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neural-splines | neural-splines-main/fit.py | import argparse
import numpy as np
import point_cloud_utils as pcu
import torch
from skimage.measure import marching_cubes
from neural_splines import load_point_cloud, fit_model_to_pointcloud, eval_model_on_grid, point_cloud_bounding_box
def main():
argparser = argparse.ArgumentParser()
argparser.add_argume... | 9,506 | 60.733766 | 120 | py |
neural-splines | neural-splines-main/neural_splines/falkon_kernels.py | import functools
from abc import ABC
from typing import Optional
import cupy as cp
import numpy as np
import torch
from falkon.kernels import Kernel, KeopsKernelMixin
from falkon.options import FalkonOptions
from falkon.sparse.sparse_tensor import SparseTensor
from torch.utils.dlpack import to_dlpack
def _extract_fl... | 22,944 | 39.183888 | 112 | py |
neural-splines | neural-splines-main/neural_splines/kmeans.py | import pykeops.torch as keops
import torch
def kmeans(x, k, num_iters=10):
"""
Implements Lloyd's algorithm for the Euclidean metric.
:param x: A tensor representing a set of N points of dimension D (shape [N, D])
:param k: The number of centroids to compute
:param num_iters: The number of K means... | 1,649 | 35.666667 | 93 | py |
neural-splines | neural-splines-main/neural_splines/geometry.py | import torch
import numpy as np
from scipy.interpolate import RegularGridInterpolator
def normalize_pointcloud_transform(x):
"""
Compute an affine transformation that normalizes the point cloud x to lie in [-0.5, 0.5]^2
:param x: A point cloud represented as a tensor of shape [N, 3]
:return: An affine... | 7,727 | 39.673684 | 117 | py |
neural-splines | neural-splines-main/neural_splines/__init__.py | import time
import warnings
import point_cloud_utils as pcu
import falkon
from falkon.utils.tensor_helpers import create_same_stride
from .falkon_kernels import NeuralSplineKernel, LaplaceKernelSphere, LinearAngleKernel
from .geometry import *
from .kmeans import kmeans
_VERBOSITY_LEVEL_DEBUG = 0
_VERBOSITY_LEVEL_IN... | 12,994 | 46.600733 | 118 | py |
HIWL | HIWL-main/scheme/model_vit.py | """
original code from rwightman:
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""
from functools import partial
from collections import OrderedDict
import torch
import torch.nn as nn
def drop_path(x, drop_prob: float = 0., training: bool = False):
if drop_prob... | 13,700 | 39.178886 | 118 | py |
HIWL | HIWL-main/scheme/model_googlenet.py | import torch.nn as nn
import torch
import torch.nn.functional as F
class GoogLeNet(nn.Module):
def __init__(self, num_classes=1000, aux_logits=True, init_weights=False):
super(GoogLeNet, self).__init__()
self.aux_logits = aux_logits
self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2,... | 5,919 | 33.219653 | 92 | py |
HIWL | HIWL-main/scheme/model_vgg.py | import torch.nn as nn
import torch
# official pretrain weights
model_urls = {
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
'vgg19': 'https://downlo... | 2,616 | 32.551282 | 117 | py |
HIWL | HIWL-main/scheme/model_resnet.py | import torch.nn as nn
import torch
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
... | 6,513 | 33.465608 | 112 | py |
HIWL | HIWL-main/scheme/train_resnet26.py | import os
import math
import argparse
import sys
import copy
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
import numpy as np
from model_resnet26 import resnet26 as create_model
from my_datas... | 14,240 | 47.770548 | 138 | py |
HIWL | HIWL-main/scheme/train_resnet.py | import os
import math
import argparse
import sys
import copy
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
import numpy as np
from model_resnet import resnet34, resnet50, resnet101
from my_d... | 13,921 | 45.875421 | 138 | py |
HIWL | HIWL-main/scheme/utils.py | import os
import sys
import json
import pickle
import random
import numpy as np
import torch
from tqdm import tqdm
import copy
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
# 标签平滑嵌入到loss函数
class SMLoss(nn.Module):
''' Cross Entropy Loss with label smoothing '''
def __init... | 9,877 | 34.789855 | 120 | py |
HIWL | HIWL-main/scheme/B1_nol.py | import os
import math
import argparse
import sys
import copy
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
import numpy as np
from model_efficientnet import efficientnet_b1
from my_dataset im... | 14,720 | 46.640777 | 240 | py |
HIWL | HIWL-main/scheme/train_googlenet.py | import os
import math
import argparse
import sys
import copy
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
import numpy as np
from model_googlenet import GoogLeNet as create_model
from my_dat... | 14,050 | 47.619377 | 240 | py |
HIWL | HIWL-main/scheme/train_vgg.py | # coding=UTF-8
import os
import math
import argparse
import sys
import copy
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
import numpy as np
from model_vgg import vgg as create_model
from my_... | 14,055 | 47.468966 | 240 | py |
HIWL | HIWL-main/scheme/model_resnet26.py | import torch.nn as nn
import torch
#源tf码中全局池化前有bn，不同深度的先对输入进行bn-relu再变成shortcut，同深度shortcut直接对输入下采样（maxpooling k=1*1 strid=s）
class BasicBlock(nn.Module):
def __init__(self, m, k=2, dropoutrate=0.2, istop : bool = False,isbottom : bool = False):
super(BasicBlock, self).__init__()
self.in_channel =... | 4,981 | 37.921875 | 128 | py |
HIWL | HIWL-main/scheme/model_efficientnet.py | import math
import copy
from functools import partial
from collections import OrderedDict
from typing import Optional, Callable
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import functional as F
def _make_divisible(ch, divisor=8, min_ch=None):
if min_ch is None:
min_ch = di... | 12,752 | 37.645455 | 102 | py |
HIWL | HIWL-main/scheme/model_dieleman.py | import torch.nn as nn
import torch
class Dieleman(nn.Module):
def __init__(self, num_classes=1000):
super(Dieleman, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=6, bias=True), # input[3, 224, 224] output[48, 55, 55]
nn.ReLU(inplace=True),
... | 1,519 | 37.974359 | 97 | py |
HIWL | HIWL-main/scheme/train_efficientnet.py | import os
import argparse
import sys
import copy
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
import numpy as np
from model_efficientnet import efficientnet_b0, efficientnet_b1, efficientnet... | 14,958 | 46.640127 | 240 | py |
HIWL | HIWL-main/scheme/train_dieleman.py | import os
import math
import argparse
import sys
import copy
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
import numpy as np
from model_dieleman import Dieleman as create_model
from my_datas... | 13,800 | 47.424561 | 138 | py |
HIWL | HIWL-main/scheme/train_vit.py | import os
import math
import argparse
import sys
import copy
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
import numpy as np
from model_vit import vit_base_patch16_224_in21k as create_model
... | 14,169 | 48.372822 | 240 | py |
HIWL | HIWL-main/scheme/my_dataset.py | from PIL import Image
import torch
from torch.utils.data import Dataset
import numpy as np
import copy
class MyDataSet(Dataset):
"""自定义数据集"""
def __init__(self, images_path: list, images_class: list, transform=None):
self.images_path = images_path
self.images_class = images_class
self.... | 4,059 | 40.010101 | 151 | py |
HIWL | HIWL-main/noscheme/model_vit.py | """
original code from rwightman:
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""
from functools import partial
from collections import OrderedDict
import torch
import torch.nn as nn
def drop_path(x, drop_prob: float = 0., training: bool = False):
if drop_prob... | 13,049 | 38.189189 | 118 | py |
HIWL | HIWL-main/noscheme/model_googlenet.py | import torch.nn as nn
import torch
import torch.nn.functional as F
class GoogLeNet(nn.Module):
def __init__(self, num_classes=1000, aux_logits=True, init_weights=False):
super(GoogLeNet, self).__init__()
self.aux_logits = aux_logits
self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2,... | 5,919 | 33.219653 | 92 | py |
HIWL | HIWL-main/noscheme/model_vgg.py | import torch.nn as nn
import torch
class VGG(nn.Module):
def __init__(self, features, num_classes=1000, init_weights=False):
super(VGG, self).__init__()
self.features = features
self.classifier = nn.Sequential(
nn.Linear(512*7*7, 4096),
nn.ReLU(True),
nn... | 2,287 | 31.685714 | 117 | py |
HIWL | HIWL-main/noscheme/model_resnet.py | import torch.nn as nn
import torch
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
... | 6,446 | 32.931579 | 112 | py |
HIWL | HIWL-main/noscheme/B1_noh.py | import os
import math
import argparse
import sys
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
import numpy as np
from model_efficientnet import efficientnet_b1 as create_model
from my_datas... | 7,347 | 42.47929 | 147 | py |
HIWL | HIWL-main/noscheme/train_resnet26.py | import os
import math
import argparse
import sys
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
from model_resnet26 import resnet26 as create_model
from my_dataset import MyDataSet
from utils... | 7,618 | 44.35119 | 132 | py |
HIWL | HIWL-main/noscheme/train_resnet.py | import os
import math
import argparse
import sys
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
from model_resnet import resnet34, resnet50, resnet101
from my_dataset import MyDataSet
from ut... | 7,886 | 44.327586 | 147 | py |
HIWL | HIWL-main/noscheme/utils.py | import os
import sys
import json
import pickle
import random
import numpy as np
import torch
from tqdm import tqdm
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
# 标签平滑嵌入到loss函数
class SMLoss(nn.Module):
''' Cross Entropy Loss with label smoothing '''
def __init__(self, lab... | 9,482 | 34.384328 | 120 | py |
HIWL | HIWL-main/noscheme/train_googlenet.py | import os
import math
import argparse
import sys
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
from model_googlenet import GoogLeNet as create_model
from my_dataset import MyDataSet
from uti... | 7,500 | 44.460606 | 147 | py |
HIWL | HIWL-main/noscheme/train_vgg.py | import os
import math
import argparse
import sys
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
from model_vgg import vgg as create_model
from my_dataset import MyDataSet
from utils import re... | 7,542 | 43.370588 | 147 | py |
HIWL | HIWL-main/noscheme/model_resnet26.py | import torch.nn as nn
import torch
#源tf码中全局池化前有bn，不同深度的先对输入进行bn-relu再变成shortcut，同深度shortcut直接对输入下采样（maxpooling k=1*1 strid=s）
class BasicBlock(nn.Module):
def __init__(self, m, k=2, dropoutrate=0.2, istop : bool = False,isbottom : bool = False):
super(BasicBlock, self).__init__()
self.in_channel =... | 4,994 | 37.72093 | 128 | py |
HIWL | HIWL-main/noscheme/model_efficientnet.py | import math
import copy
from functools import partial
from collections import OrderedDict
from typing import Optional, Callable
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import functional as F
def _make_divisible(ch, divisor=8, min_ch=None):
if min_ch is None:
min_ch = di... | 12,752 | 37.645455 | 102 | py |
HIWL | HIWL-main/noscheme/model_dieleman.py | import torch.nn as nn
import torch
class Dieleman(nn.Module):
def __init__(self, num_classes=1000):
super(Dieleman, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=6, bias=True),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, str... | 1,269 | 31.564103 | 58 | py |
HIWL | HIWL-main/noscheme/train_efficientnet.py | import os
import math
import argparse
import sys
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
from model_efficientnet import efficientnet_b0, efficientnet_b1, efficientnet_b2
from my_datase... | 8,040 | 42.939891 | 147 | py |
HIWL | HIWL-main/noscheme/train_dieleman.py | import os
import math
import argparse
import sys
import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler
from model_dieleman import Dieleman as create_model
from my_dataset import MyDataSet
from utils... | 7,607 | 43.232558 | 147 | py |
HIWL | HIWL-main/noscheme/train_vit.py | import os
import math
import argparse
import sys
import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from my_dataset import MyDataSet
from model_vit import vit_base_patch16_224_in21k as create_mod... | 7,325 | 42.868263 | 147 | py |
HIWL | HIWL-main/noscheme/my_dataset.py | from PIL import Image
import torch
from torch.utils.data import Dataset
class MyDataSet(Dataset):
"""自定义数据集"""
def __init__(self, images_path: list, images_class: list, transform=None):
self.images_path = images_path
self.images_class = images_class
self.transform = transform
def... | 952 | 25.472222 | 88 | py |
HIWL | HIWL-main/noscheme/log/model_efficientnet.py | import math
import copy
from functools import partial
from collections import OrderedDict
from typing import Optional, Callable
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import functional as F
def _make_divisible(ch, divisor=8, min_ch=None):
if min_ch is None:
min_ch = di... | 12,752 | 37.645455 | 102 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/fusion.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import os, random, math
import time
import glob
import numpy as np
import shutil
import torch
import logging
import argparse
import traceback
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn... | 16,382 | 45.279661 | 160 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/train.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import time
import glob
import numpy as np
import shutil
import cv2
import os, random, math
import sys
# sys.path.append(os.path.join('..', os.path.abspath(os.path.join(os.getcwd()))) )
from pathlib import Path
from timm.scheduler import create_scheduler
f... | 33,323 | 44.154472 | 182 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/train_fusion.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import time
import glob
import numpy as np
import shutil
import cv2
import os, random, math
import sys
from pathlib import Path
from timm.scheduler import create_scheduler
from timm.utils import ApexScaler, NativeScaler
from timm.optim import create_optimi... | 30,632 | 45.064662 | 183 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/tools/fusion.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import os, random, math
import time
import glob
import numpy as np
import shutil
import torch
import logging
import argparse
import traceback
import torch.nn as nn
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn... | 16,564 | 40.830808 | 192 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/demo/cluster.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import torch
import torch.nn.functional as F
from scipy.spatial.distance import pdist
import pandas as pd
from sklearn import manifold
import numpy as np
import sys
import sklearn
from sklearn import metrics
import matplotlib
matplotlib.use('agg')
from mat... | 2,693 | 37.485714 | 121 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/demo/plot.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import matplotlib
# matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
from mpl_toolkits import axisartist
import seaborn as sns
import numpy as np
import re
import sys
import os, argparse, random
import torch
def p... | 20,739 | 39.826772 | 187 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/utils/evaluate_metric.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
# -------------------
# import modules
# -------------------
import random, os
import numpy as np
import cv2
import heapq
import shutil
from textwrap import wrap
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.image as mpimage
from sk... | 5,573 | 39.100719 | 150 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/utils/utils.py | '''
This file is modified from:
https://github.com/yuhuixu1993/PC-DARTS/blob/master/utils.py
'''
import os
import numpy as np
import torch
import torch.distributed as dist
import shutil
import torchvision.transforms as transforms
from torch.autograd import Variable
from collections import OrderedDict
import random
fro... | 21,656 | 41.216374 | 338 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/utils/mixup.py | """
This file is modified from:
https://github.com/rwightman/pytorch-image-models/blob/main/timm/data/mixup.py
Mixup and Cutmix
Papers:
mixup: Beyond Empirical Risk Minimization (https://arxiv.org/abs/1710.09412)
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (https://arxiv.or... | 17,254 | 44.890957 | 120 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/utils/shufflemix.py | import numpy as np
import torch
import random
def Vmixup(x, lam):
x_flipped = x.flip(0).mul_(1. - lam)
x.mul_(lam).add_(x_flipped)
def ShuffleMix(x, lam):
x_flipped = x.flip(0)
replace_idx = random.sample(range(0, x.size(2)), round((1. - lam)*x.size(2)))
x[:, :, replace_idx, :, :] = x_flipped[:, :... | 5,200 | 35.626761 | 127 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/utils/build.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import torch
import math
import torch.nn.functional as F
# from .utils import cosine_scheduler
import matplotlib.pyplot as plt
import numpy as np
class LabelSmoothingCrossEntropy(torch.nn.Module):
def __init__(self, smoothing: float = 0.1,
... | 5,901 | 37.575163 | 118 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/datasets/base.py | '''
This file is modified from:
https://github.com/zhoubenjia/RAAR3DNet/blob/master/Network_Train/lib/datasets/base.py
'''
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, set_image_backend
import torch.nn.functional as F
from PIL import Image
from PIL import ImageFilt... | 12,341 | 41.412371 | 210 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/datasets/Jester.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
import logging
# import accimage
# set_image_backend('accimage')
np.random.seed(123)
class JesterData(... | 2,115 | 37.472727 | 186 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/datasets/IsoGD.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
# import accimage
# set_image_backend('accimage')
class IsoGDData(Datasets):
def __init__(self, ar... | 1,844 | 40 | 155 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/datasets/THU_READ.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
import logging
import cv2
from einops import rearrange, repeat
from torchvision.utils import save_imag... | 1,708 | 33.18 | 118 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/datasets/transforms_factory.py | """ Transforms Factory
Factory methods for building image transforms for use with TIMM (PyTorch Image Models)
Hacked together by / Copyright 2019, Ross Wightman
"""
import math
import torch
from torchvision import transforms
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PCT... | 8,665 | 34.08502 | 115 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/datasets/distributed_sampler.py | '''
This file is modified from:
https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/samplers/distributed_sampler.py
'''
import math
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
class DistributedSampler(_DistributedSampler):
def __init__(self, dataset, num_r... | 1,240 | 33.472222 | 100 | py |
MotionRGBD-PAMI | MotionRGBD-PAMI-main/lib/datasets/NvGesture.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
import logging
# import accimage
# set_image_backend('accimage')
np.random.seed(123)
class NvData(Data... | 1,963 | 36.769231 | 146 | py |
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/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 |
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/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/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/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/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/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 |
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/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/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/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 |
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/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 |
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