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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filter-pruning-geometric-median | filter-pruning-geometric-median-master/original_train.py | # https://github.com/pytorch/vision/blob/master/torchvision/models/__init__.py
import argparse
import os, sys
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms... | 11,668 | 36.641935 | 139 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/functions/infer_pruned.py | # https://github.com/pytorch/vision/blob/master/torchvision/models/__init__.py
import argparse
import os
import shutil
import pdb, time
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.o... | 8,795 | 38.981818 | 124 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/resnet_small.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
#from .res_utils import DownsampleA, DownsampleC, DownsampleD
import math,time
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
self.avg = nn.AvgPool2d(kern... | 7,399 | 34.238095 | 132 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/preresnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from .res_utils import DownsampleA, DownsampleC
import math
class ResNetBasicblock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride, downsample, Type):
super(ResNetBasicblock, self).__init__()
... | 4,698 | 29.914474 | 98 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/imagenet_resnet.py | import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/res... | 6,941 | 31.591549 | 78 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/imagenet_resnet_small.py | import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
from torch.autograd import Variable
import torch
import time
__all__ = ['ResNet_small', 'resnet18_small', 'resnet34_small', 'resnet50_small', 'resnet101_small', 'resnet152_small']
model_urls = {
'resnet18': 'https://download.pytorch.org/m... | 12,267 | 37.578616 | 129 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from .res_utils import DownsampleA, DownsampleC, DownsampleD
import math
class ResNetBasicblock(nn.Module):
expansion = 1
"""
RexNet basicblock (https://github.com/facebook/fb.resnet.torch/blob/master/models/resnet.lua)... | 4,484 | 29.931034 | 98 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/vgg.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import math
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
model_urls = {
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
'vgg13': 'https://download.pytorch.or... | 5,756 | 31.162011 | 113 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/densenet.py | import math, torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
def __init__(self, nChannels, growthRate):
super(Bottleneck, self).__init__()
interChannels = 4*growthRate
self.bn1 = nn.BatchNorm2d(nChannels)
self.conv1 = nn.Conv2d(nChannels, interChannels, kernel... | 3,518 | 33.5 | 91 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/vgg_cifar.py | import math
import torch
import torch.nn as nn
from torch.autograd import Variable
__all__ = ['vgg']
defaultcfg = {
11 : [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512],
13 : [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512],
16 : [64, 64, 'M', 128, 128, 'M', 256, 256, 256... | 2,607 | 31.6 | 108 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/resnext.py | import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
import math
class ResNeXtBottleneck(nn.Module):
expansion = 4
"""
RexNeXt bottleneck type C (https://github.com/facebookresearch/ResNeXt/blob/master/models/resnext.lua)
"""
def __init__(self, inplanes, planes, cardinality, base_w... | 4,180 | 31.92126 | 113 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/resnet_feature.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from .res_utils import DownsampleA, DownsampleC, DownsampleD
import math
class ResNetBasicblock(nn.Module):
expansion = 1
"""
RexNet basicblock (https://github.com/facebook/fb.resnet.torch/blob/master/models/resnet.lua)... | 4,484 | 29.931034 | 98 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/caffe_cifar.py | from __future__ import division
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
import math
## http://torch.ch/blog/2015/07/30/cifar.html
class CifarCaffeNet(nn.Module):
def __init__(self, num_classes):
super(CifarCaffeNet, self).__init__()
self.num_classes = nu... | 1,750 | 28.183333 | 64 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/resnet_small_V3.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
#from .res_utils import DownsampleA, DownsampleC, DownsampleD
import math
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
self.avg = nn.AvgPool2d(kernel_si... | 5,825 | 31.915254 | 133 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/resnet_mod.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from .res_utils import DownsampleA, DownsampleC, DownsampleD
import math
class ResNetBasicblock(nn.Module):
expansion = 1
"""
RexNet basicblock (https://github.com/facebook/fb.resnet.torch/blob/master/models/resnet.lua)... | 5,027 | 28.928571 | 98 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/__init__.py | """The models subpackage contains definitions for the following model
architectures:
- `ResNeXt` for CIFAR10 CIFAR100
You can construct a model with random weights by calling its constructor:
.. code:: python
import models
resnext29_16_64 = models.ResNeXt29_16_64(num_classes)
resnext29_8_64 = models.ResNeX... | 1,486 | 38.131579 | 117 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/res_utils.py | import torch
import torch.nn as nn
class DownsampleA(nn.Module):
def __init__(self, nIn, nOut, stride):
super(DownsampleA, self).__init__()
self.avg = nn.AvgPool2d(kernel_size=1, stride=stride)
def forward(self, x):
x = self.avg(x)
return torch.cat((x, x.mul(0)), 1)
class Downsample... | 3,941 | 28.41791 | 89 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/models/vgg_cifar10.py | import math
import torch
import torch.nn as nn
from torch.autograd import Variable
__all__ = ['vgg']
defaultcfg = {
11: [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512],
13: [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512],
16: [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M... | 6,395 | 37.53012 | 107 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/VGG_cifar/main_cifar_vgg.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
sys.path.append(os.path.dirname(os.path.dirnam... | 8,019 | 43.804469 | 115 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/VGG_cifar/pruning_cifar_vgg.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import os, sys, shutil, time, random
from scip... | 29,830 | 48.470978 | 120 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/VGG_cifar/main_cifar_vgg_log.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import os, sys, shutil, time, random
sys.path.... | 10,842 | 44.179167 | 121 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/VGG_cifar/PFEC_vggprune.py | import argparse
import numpy as np
import os
import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import datasets, transforms
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from models import *
# Prune settings
parser = argparse.ArgumentParser(descrip... | 6,938 | 40.550898 | 104 | py |
filter-pruning-geometric-median | filter-pruning-geometric-median-master/VGG_cifar/PFEC_finetune.py | from __future__ import print_function
import argparse
import numpy as np
import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
sys.path.append(os.path.dirname(os.path.dirnam... | 8,193 | 44.021978 | 115 | py |
Im2Hands | Im2Hands-main/init_occ_train.py | import warnings
warnings.filterwarnings('ignore',category=FutureWarning)
import os
import sys
import time
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib; matplotlib.use('Agg')
from torch.utils.tensorboard import SummaryWriter
from artihand import co... | 6,151 | 32.98895 | 112 | py |
Im2Hands | Im2Hands-main/ref_occ_train.py | import warnings
warnings.filterwarnings('ignore',category=FutureWarning)
import os
import sys
import time
import argparse
import torch
import torch.optim as optim
import numpy as np
import matplotlib; matplotlib.use('Agg')
from torch.utils.tensorboard import SummaryWriter
from artihand import config, data
from artiha... | 5,904 | 33.735294 | 117 | py |
Im2Hands | Im2Hands-main/kpts_ref_generate.py | import os
import sys
import time
import torch
import shutil
import trimesh
import argparse
import pandas as pd
import numpy as np
import open3d as o3d
from tqdm import tqdm
from collections import defaultdict
from artihand import config, data
from artihand.checkpoints import CheckpointIO
from artihand.nasa.kpts_ref_t... | 4,947 | 37.96063 | 143 | py |
Im2Hands | Im2Hands-main/kpts_ref_train.py | import warnings
warnings.filterwarnings('ignore',category=FutureWarning)
import os
import sys
import time
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib; matplotlib.use('Agg')
from torch.utils.tensorboard import SummaryWriter
from artihand import co... | 6,214 | 33.148352 | 112 | py |
Im2Hands | Im2Hands-main/init_occ_generate.py | import os
import sys
import time
import torch
import shutil
import trimesh
import argparse
import pandas as pd
import numpy as np
import open3d as o3d
from tqdm import tqdm
from collections import defaultdict
from artihand import config, data
from artihand.checkpoints import CheckpointIO
from dependencies.halo.halo_... | 4,640 | 35.543307 | 174 | py |
Im2Hands | Im2Hands-main/ref_occ_generate.py | import os
import sys
import time
import torch
import shutil
import trimesh
import argparse
import pandas as pd
import numpy as np
from tqdm import tqdm
from collections import defaultdict
from artihand import config, data
from artihand.checkpoints import CheckpointIO
from dependencies.halo.halo_adapter.transform_uti... | 5,374 | 39.11194 | 199 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/dataset/interhand.py | import json
import os.path as osp
from tqdm import tqdm
import cv2 as cv
import numpy as np
import torch
import pickle
from glob import glob
import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from models.manolayer import ManoLayer, rodrigues_batch
from dataset.dat... | 13,958 | 43.597444 | 138 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/models/model.py | import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import torch
import torch.nn as nn
import torch.nn.functional as F
import pickle
import numpy as np
from dataset.dataset_utils import IMG_SIZE
from models.encoder import load_encoder
from models.decoder import load... | 1,863 | 29.557377 | 89 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/models/encoder.py | import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import torch
import torch.nn as nn
import torch.nn.functional as F
import pickle
import numpy as np
from dataset.dataset_utils import IMG_SIZE
from utils.utils import projection_batch
from models.manolayer import M... | 14,103 | 36.610667 | 127 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/models/decoder.py | import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import torch
import torch.nn as nn
import torch.nn.functional as F
import pickle
import numpy as np
from dataset.dataset_utils import IMG_SIZE, BONE_LENGTH
from utils.utils import projection_batch, get_dense_color_... | 8,780 | 40.814286 | 146 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/models/manolayer.py | import pickle
import numpy as np
import torch
from torch.nn import Module
def convert_mano_pkl(loadPath, savePath):
# in original MANO pkl file, 'shapedirs' component is a chumpy object, convert it to a numpy array
manoData = pickle.load(open(loadPath, 'rb'), encoding='latin1')
output = {}
manoData['s... | 13,352 | 38.158358 | 122 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/models/model_zoo/fc.py | import torch.nn as nn
class noop(nn.Module):
def forward(self, x):
return x
def build_activate_layer(actType):
if actType == 'relu':
return nn.ReLU(inplace=True)
elif actType == 'lrelu':
return nn.LeakyReLU(0.1, inplace=True)
elif actType == 'elu':
return nn.ELU(inpla... | 946 | 25.305556 | 85 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/models/model_zoo/hrnet.py | # ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (Bin.Xiao@microsoft.com)
# Modified by Ke Sun (sunk@mail.ustc.edu.cn)
# ------------------------------------------------------------------------------
from ... | 30,039 | 39.430686 | 121 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/models/model_zoo/graph_utils.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# forked from https://github.com/3d-hand-shape/hand-graph-cnn
def sparse_python_to_torch(sp_python):
L = sp_python.tocoo()
indices = np.column_stack((L.row, L.col)).T
indices = indices.astype(np.int64)
indices = tor... | 7,647 | 31.824034 | 108 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/models/model_zoo/__init__.py | import torch.nn as nn
from .fc import build_fc_layer
from .hrnet import get_hrnet, Bottleneck
from .coarsening import build_graph
from .graph_utils import graph_upsample, graph_avg_pool
__all__ = ['build_fc_layer', 'get_hrnet', 'Bottleneck',
'build_graph', 'GCN_vert_convert', 'graph_upsample', 'graph_avg_p... | 3,086 | 30.824742 | 104 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/models/model_zoo/coarsening.py | import numpy as np
import scipy.sparse
from scipy.sparse.linalg import eigsh
import torch
# forked from https://github.com/3d-hand-shape/hand-graph-cnn
def laplacian(W, normalized=True):
"""Return graph Laplacian"""
# Degree matrix.
d = W.sum(axis=0)
# Laplacian matrix.
if not normalized:
... | 12,729 | 28.67366 | 120 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/models/model_attn/DualGraph.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .gcn import GraphLayer
from .img_attn import img_ex
from .inter_attn import inter_attn
def graph_upsample(x, p):
if p > 1:
x = x.permute(0, 2, 1).contiguous() # x = B x F x V
x = nn.Upsample(scale_factor=p)(x) # B x F x (V... | 5,266 | 36.621429 | 87 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/models/model_attn/inter_attn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .self_attn import SelfAttn
def weights_init(layer):
classname = layer.__class__.__name__
# print(classname)
if classname.find('Conv2d') != -1:
nn.init.xavier_uniform_(layer.weight.data)
elif classname.find('Linear') != -1... | 4,522 | 34.896825 | 97 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/models/model_attn/gcn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def weights_init(layer):
classname = layer.__class__.__name__
# print(classname)
if classname.find('Conv2d') != -1:
nn.init.xavier_uniform_(layer.weight.data)
elif classname.find('Linear') != -1:
nn.i... | 4,537 | 31.647482 | 103 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/models/model_attn/img_attn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .self_attn import SelfAttn
def weights_init(layer):
classname = layer.__class__.__name__
# print(classname)
if classname.find('Conv2d') != -1:
nn.init.xavier_uniform_(layer.weight.data)
elif classname.find('Linear') != -1... | 4,292 | 33.071429 | 105 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/models/model_attn/self_attn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def weights_init(layer):
classname = layer.__class__.__name__
# print(classname)
if classname.find('Conv2d') != -1:
nn.init.xavier_uniform_(layer.weight.data)
elif classname.find('Linear') != -1:
nn.init.xavier_uniform_... | 3,346 | 31.813725 | 95 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/utils/utils.py | import numpy as np
import random
import math
import cv2 as cv
import pickle
import torch
import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from utils.config import get_cfg_defaults
from models.model_zoo import build_graph
def projection(scale, trans2d, label3d,... | 8,999 | 33.090909 | 106 | py |
Im2Hands | Im2Hands-main/dependencies/intaghand/utils/vis_utils.py | import pickle
import numpy as np
import torch
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from models.manolayer import ManoLayer
from utils.config import get_cfg_defaults
from utils.utils import projection_batch, get_mano_path, get_dense_color_path
# Data ... | 10,221 | 39.563492 | 116 | py |
Im2Hands | Im2Hands-main/dependencies/halo/checkpoints.py | import os
import urllib
import torch
from torch.utils import model_zoo
class CheckpointIO(object):
''' CheckpointIO class.
It handles saving and loading checkpoints.
Args:
checkpoint_dir (str): path where checkpoints are saved
'''
def __init__(self, checkpoint_dir='./chkpts', initialize_fr... | 4,467 | 33.90625 | 93 | py |
Im2Hands | Im2Hands-main/dependencies/halo/training.py | # from im2mesh import icp
import numpy as np
from collections import defaultdict
from tqdm import tqdm
class BaseTrainer(object):
''' Base trainer class.
'''
def evaluate(self, val_loader):
''' Performs an evaluation.
Args:
val_loader (dataloader): pytorch dataloader
'... | 1,014 | 23.756098 | 65 | py |
Im2Hands | Im2Hands-main/dependencies/halo/config.py | from models.data.input_helpers import random_rotate
import yaml
from torchvision import transforms
from models import naive
from models import data
method_dict = {
'naive': naive
}
# General config
def load_config(path, default_path=None):
''' Loads config file.
Args:
path (str): path to confi... | 5,471 | 25.955665 | 76 | py |
Im2Hands | Im2Hands-main/dependencies/halo/naive/training.py | import os
from tqdm import trange
import torch
from torch.nn import functional as F
from torch import distributions as dist
# from im2mesh.common import (
# compute_iou, make_3d_grid
# )
from models.utils import visualize as vis
from models.training import BaseTrainer
from models.naive.loss.loss import (BoneLength... | 20,600 | 41.476289 | 143 | py |
Im2Hands | Im2Hands-main/dependencies/halo/naive/config.py | import torch
import torch.distributions as dist
from torch import nn
import os
# from im2mesh.encoder import encoder_dict
# from im2mesh.onet import models, training, generation
# from im2mesh import data
# from im2mesh import config
from models import data
from models import config
from models.naive import models, tr... | 8,530 | 29.90942 | 100 | py |
Im2Hands | Im2Hands-main/dependencies/halo/naive/generation.py | import torch
import torch.optim as optim
from torch import autograd
import numpy as np
import os
from tqdm import trange
import trimesh
from trimesh.base import Trimesh
from im2mesh.utils import libmcubes
from im2mesh.common import make_3d_grid
from im2mesh.utils.libsimplify import simplify_mesh
from im2mesh.utils.libm... | 26,592 | 42.24065 | 148 | py |
Im2Hands | Im2Hands-main/dependencies/halo/naive/models/refine.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class RefineNet(nn.Module):
''' RefineNet class.
Takes noisy joints and object latent vector as input and output the refined joints
Args:
out_dim (int): dimension of output code z
c_dim (int): dimension o... | 1,660 | 31.568627 | 86 | py |
Im2Hands | Im2Hands-main/dependencies/halo/naive/models/core.py | import torch
import torch.nn as nn
from torch import distributions as dist
from models.halo_adapter.adapter import HaloAdapter
class HaloVAE(nn.Module):
''' HALO VAE Network class.
Args:
decoder (nn.Module): decoder network
encoder (nn.Module): encoder network
encoder_latent (nn.Modul... | 16,286 | 36.876744 | 118 | py |
Im2Hands | Im2Hands-main/dependencies/halo/naive/models/encoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def maxpool(x, dim=-1, keepdim=False):
out, _ = x.max(dim=dim, keepdim=keepdim)
return out
class SimpleEncoder(torch.nn.Module):
def __init__(self, D_in, H, D_out):
"""
Crate a two-layers networks with ... | 5,962 | 29.269036 | 90 | py |
Im2Hands | Im2Hands-main/dependencies/halo/naive/models/decoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class SimpleDecoder(torch.nn.Module):
def __init__(self, D_in, H, D_out, mano_params_out=False):
"""
Crate a simple feed-forward networks with relu activation.
"""
super(SimpleDecoder, self).__ini... | 4,897 | 31.437086 | 82 | py |
Im2Hands | Im2Hands-main/dependencies/halo/naive/loss/loss.py | import numpy as np
import torch
import torch.nn as nn
from models.halo_adapter.converter import PoseConverter, transform_to_canonical, angle2, signed_angle
from models.halo_adapter.interface import convert_joints
def kp3D_to_bones(kp_3D, joint_parent, normalize_length=False):
"""
Converts from joints to bone... | 9,610 | 37.138889 | 128 | py |
Im2Hands | Im2Hands-main/dependencies/halo/mano_converter/mano_converter.py | import torch
import torch.nn as nn
import numpy as np
import sys
sys.path.insert(0, "/home/korrawe/halo_vae")
from models.halo_adapter.converter import transform_to_canonical
from models.halo_adapter.interface import convert_joints, change_axes
def rot_mat_to_axis_angle(R):
"""
Taken from
http://www.eucl... | 10,931 | 33.269592 | 111 | py |
Im2Hands | Im2Hands-main/dependencies/halo/halo_adapter/adapter.py | import sys
import torch
import torch.nn as nn
import numpy as np
from torch.nn.modules import loss
from models.halo_adapter.converter import PoseConverter, transform_to_canonical
from models.halo_adapter.interface import (get_halo_model, convert_joints, change_axes,
get_bone_... | 13,414 | 44.941781 | 148 | py |
Im2Hands | Im2Hands-main/dependencies/halo/halo_adapter/trans_mat_model.py | import torch
from torch._C import device
import torch.nn as nn
import numpy as np
class TransformationModel(nn.Module):
def __init__(self, D_in=21 * 3, H=256, D_out=15 * 3, device="cpu"):
"""
Crate a two-layers networks with relu activation.
"""
super(TransformationModel, self).__i... | 1,438 | 29.617021 | 71 | py |
Im2Hands | Im2Hands-main/dependencies/halo/halo_adapter/transform_utils.py | import torch
def xyz_to_xyz1(xyz):
""" Convert xyz vectors from [BS, ..., 3] to [BS, ..., 4] for matrix multiplication
"""
ones = torch.ones([*xyz.shape[:-1], 1], device=xyz.device)
# print("xyz shape", xyz.shape)
# print("one", ones.shape)
return torch.cat([xyz, ones], dim=-1)
def pad34_to_... | 483 | 31.266667 | 108 | py |
Im2Hands | Im2Hands-main/dependencies/halo/halo_adapter/projection.py | import torch
import torch.nn as nn
import numpy as np
class JointProjectionLayer(nn.Module):
def __init__(self, D_in=21 * 3, H=256, D_out=21 * 3, device="cpu", fix_root=True):
"""
Crate a two-layers networks with relu activation.
"""
super(JointProjectionLayer, self).__init__()
... | 1,415 | 30.466667 | 86 | py |
Im2Hands | Im2Hands-main/dependencies/halo/halo_adapter/converter_ref.py | # ------------------------------------------------------------------------------
# Copyright (c) 2019 Adrian Spurr
# Licensed under the GPL License.
# Written by Adrian Spurr
# ------------------------------------------------------------------------------
import numpy as np
import torch
import torch.nn as nn
import ma... | 54,446 | 40.753834 | 130 | py |
Im2Hands | Im2Hands-main/dependencies/halo/halo_adapter/converter.py | # ------------------------------------------------------------------------------
# Copyright (c) 2019 Adrian Spurr
# Licensed under the GPL License.
# Written by Adrian Spurr
# ------------------------------------------------------------------------------
import numpy as np
import torch
import torch.nn as nn
import mat... | 56,863 | 40.9042 | 130 | py |
Im2Hands | Im2Hands-main/dependencies/halo/halo_adapter/interface.py | # For interfacing with the HALO mesh model code
import argparse
import trimesh
import numpy as np
import os
import torch
import sys
sys.path.insert(0, "../../halo_base")
#from artihand import config #, data
from artihand.checkpoints import CheckpointIO
def get_halo_model(config_file):
'''
Args:
confi... | 4,857 | 35.253731 | 127 | py |
Im2Hands | Im2Hands-main/dependencies/halo/utils/visualize.py | import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
# from torchvision.utils import save_image
# import im2mesh.common as common
# def visualize_data(data, data_type, out_file):
# r''' Visualizes the data with rega... | 11,311 | 34.684543 | 106 | py |
Im2Hands | Im2Hands-main/dependencies/halo/data/inference.py | import os
import logging
from torch.utils import data
import numpy as np
import yaml
import pickle
import torch
import trimesh
from models.data.input_helpers import random_rotate, rot_mat_by_angle
logger = logging.getLogger(__name__)
class InferenceDataset(data.Dataset):
''' Dataset class for inference. Only ob... | 7,009 | 38.382022 | 133 | py |
Im2Hands | Im2Hands-main/dependencies/halo/data/utils.py | import os
import numpy as np
from torch.utils import data
def collate_remove_none(batch):
''' Collater that puts each data field into a tensor with outer dimension
batch size.
Args:
batch: batch
'''
batch = list(filter(lambda x: x is not None, batch))
return data.dataloader.defaul... | 568 | 24.863636 | 77 | py |
Im2Hands | Im2Hands-main/dependencies/halo/data/obman.py | import os
import logging
from matplotlib.pyplot import axis
from torch.utils import data
import numpy as np
import yaml
import pickle
import torch
from scipy.spatial import distance
from models.data.input_helpers import random_rotate
from models.utils import visualize as vis
from matplotlib import pyplot as plt
from m... | 7,466 | 38.094241 | 135 | py |
Im2Hands | Im2Hands-main/dependencies/airnets/AIRnet.py | '''
AIR-Nets
Author: Simon Giebenhain
Code: https://github.com/SimonGiebenhain/AIR-Nets
'''
import torch
import torch.nn as nn
import torch.nn.functional as functional
import numpy as np
from time import time
import torch.nn.functional as F
import os
import math
import dependencies.airnets.pointnet2_ops_lib.pointnet2_... | 38,269 | 35.692234 | 143 | py |
Im2Hands | Im2Hands-main/dependencies/airnets/pointnet2_ops_lib/setup.py | import glob
import os
import os.path as osp
from setuptools import find_packages, setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
this_dir = osp.dirname(osp.abspath(__file__))
_ext_src_root = osp.join("pointnet2_ops", "_ext-src")
_ext_sources = glob.glob(osp.join(_ext_src_root, "src", "*.cpp... | 1,185 | 28.65 | 78 | py |
Im2Hands | Im2Hands-main/dependencies/airnets/pointnet2_ops_lib/pointnet2_ops/pointnet2_utils.py | import torch
import torch.nn as nn
import warnings
from torch.autograd import Function
from typing import *
try:
import pointnet2_ops._ext as _ext
except ImportError:
from torch.utils.cpp_extension import load
import glob
import os.path as osp
import os
warnings.warn("Unable to load pointnet2_... | 10,396 | 26.360526 | 103 | py |
Im2Hands | Im2Hands-main/dependencies/airnets/pointnet2_ops_lib/pointnet2_ops/pointnet2_modules.py | from typing import List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from pointnet2_ops import pointnet2_utils
def build_shared_mlp(mlp_spec: List[int], bn: bool = True):
layers = []
for i in range(1, len(mlp_spec)):
layers.append(
nn.Conv2d(mlp_spec... | 6,530 | 30.1 | 106 | py |
Im2Hands | Im2Hands-main/dependencies/airnets/pointnet2_ops_lib/build/lib.linux-x86_64-3.8/pointnet2_ops/pointnet2_utils.py | import torch
import torch.nn as nn
import warnings
from torch.autograd import Function
from typing import *
try:
import pointnet2_ops._ext as _ext
except ImportError:
from torch.utils.cpp_extension import load
import glob
import os.path as osp
import os
warnings.warn("Unable to load pointnet2_... | 10,396 | 26.360526 | 103 | py |
Im2Hands | Im2Hands-main/dependencies/airnets/pointnet2_ops_lib/build/lib.linux-x86_64-3.8/pointnet2_ops/pointnet2_modules.py | from typing import List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from pointnet2_ops import pointnet2_utils
def build_shared_mlp(mlp_spec: List[int], bn: bool = True):
layers = []
for i in range(1, len(mlp_spec)):
layers.append(
nn.Conv2d(mlp_spec... | 6,530 | 30.1 | 106 | py |
Im2Hands | Im2Hands-main/artihand/checkpoints.py | import os
import urllib
import torch
from torch.utils import model_zoo
class CheckpointIO(object):
''' CheckpointIO class.
It handles saving and loading checkpoints.
Args:
checkpoint_dir (str): path where checkpoints are saved
'''
def __init__(self, checkpoint_dir='./chkpts', initialize_fr... | 6,735 | 37.056497 | 140 | py |
Im2Hands | Im2Hands-main/artihand/training.py | # from im2mesh import icp
import numpy as np
from collections import defaultdict
from tqdm import tqdm
class BaseTrainer(object):
''' Base trainer class.
'''
def evaluate(self, val_loader, subset=1):
''' Performs an evaluation.
Args:
val_loader (dataloader): pytorch dataloader... | 1,025 | 23.428571 | 65 | py |
Im2Hands | Im2Hands-main/artihand/config.py | import yaml
from torchvision import transforms
from artihand import data
from artihand import nasa
method_dict = {
'nasa': nasa
}
# General config
def load_config(path, default_path=None):
''' Loads config file.
Args:
path (str): path to config file
default_path (bool): whether to use ... | 7,339 | 27.449612 | 84 | py |
Im2Hands | Im2Hands-main/artihand/checkpoints_legacy.py | import os
import urllib
import torch
from torch.utils import model_zoo
class CheckpointIO(object):
''' CheckpointIO class.
It handles saving and loading checkpoints.
Args:
checkpoint_dir (str): path where checkpoints are saved
'''
def __init__(self, checkpoint_dir='./chkpts', **kwargs):
... | 2,962 | 28.63 | 70 | py |
Im2Hands | Im2Hands-main/artihand/diff_operators.py | import torch
from torch.autograd import grad
def hessian(y, x):
''' hessian of y wrt x
y: shape (meta_batch_size, num_observations, channels)
x: shape (meta_batch_size, num_observations, 2)
'''
meta_batch_size, num_observations = y.shape[:2]
grad_y = torch.ones_like(y[..., 0]).to(y.device)
... | 1,892 | 30.55 | 132 | py |
Im2Hands | Im2Hands-main/artihand/nasa/training.py | import os
from tqdm import trange
import torch
from torch.nn import functional as F
from torch import distributions as dist
from im2mesh.common import (
compute_iou, make_3d_grid
)
from artihand.utils import visualize as vis
from artihand.training import BaseTrainer
from artihand import diff_operators
# For dubugg... | 14,705 | 37.904762 | 121 | py |
Im2Hands | Im2Hands-main/artihand/nasa/config.py | import os
import torch
import torch.distributions as dist
from torch import nn
from artihand import data
from artihand import config
from artihand.nasa import models, training, generation
from artihand.nasa import init_occ_training, ref_occ_training, kpts_ref_training
def get_model(cfg, device=None, dataset=None, **... | 8,082 | 32.127049 | 115 | py |
Im2Hands | Im2Hands-main/artihand/nasa/ref_occ_training.py | import os
import sys
import torch
import torch.nn as nn
import numpy as np
from torch.nn import functional as F
from torch import distributions as dist
from tqdm import trange
from im2mesh.common import (
compute_iou, make_3d_grid
)
from artihand.utils import visualize as vis
from artihand.training import BaseTr... | 5,242 | 32.825806 | 193 | py |
Im2Hands | Im2Hands-main/artihand/nasa/generation.py | import torch
import torch.nn as nn
import torch.optim as optim
from torch import autograd
import numpy as np
from tqdm import trange
import trimesh
from im2mesh.utils import libmcubes
from im2mesh.common import make_3d_grid
from im2mesh.utils.libsimplify import simplify_mesh
from im2mesh.utils.libmise import MISE
impor... | 25,868 | 38.494656 | 203 | py |
Im2Hands | Im2Hands-main/artihand/nasa/kpts_ref_training.py | import os
import sys
import torch
import torch.nn as nn
import numpy as np
from torch.nn import functional as F
from torch import distributions as dist
from tqdm import trange
from im2mesh.common import (
compute_iou, make_3d_grid
)
from artihand.utils import visualize as vis
from artihand.training import BaseTr... | 8,788 | 38.236607 | 143 | py |
Im2Hands | Im2Hands-main/artihand/nasa/init_occ_training.py | import os
import sys
import torch
import torch.nn as nn
import numpy as np
from torch.nn import functional as F
from torch import distributions as dist
from tqdm import trange
from im2mesh.common import (
compute_iou, make_3d_grid
)
from artihand.utils import visualize as vis
from artihand.training import BaseTr... | 5,651 | 31.113636 | 143 | py |
Im2Hands | Im2Hands-main/artihand/nasa/models/core_init_occ.py | import sys
import torch
import torch.nn as nn
from torch import distributions as dist
from dependencies.halo.halo_adapter.converter import PoseConverter, transform_to_canonical
from dependencies.halo.halo_adapter.interface import (get_halo_model, convert_joints, change_axes, scale_halo_trans_mat)
from dependencies.hal... | 7,445 | 41.067797 | 159 | py |
Im2Hands | Im2Hands-main/artihand/nasa/models/core_ref_occ.py | import sys
import torch
import torch.nn as nn
from torch import distributions as dist
from torch.nn.functional import grid_sample
from im2mesh.common import make_3d_grid
from dependencies.halo.halo_adapter.transform_utils import xyz_to_xyz1
from dependencies.intaghand.models.encoder import ResNetSimple
from depende... | 9,465 | 42.824074 | 211 | py |
Im2Hands | Im2Hands-main/artihand/nasa/models/decoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class SimpleDecoder(nn.Module):
def __init__(
self,
latent_size,
dims,
dropout=None,
dropout_prob=0.0,
norm_layers=(),
latent_in=(),
weight_norm=False,
# xy... | 44,495 | 37.259673 | 128 | py |
Im2Hands | Im2Hands-main/artihand/nasa/models/core_kpts_ref.py | import sys
import torch
import torch.nn as nn
from torch import distributions as dist
from dependencies.intaghand.models.encoder import ResNetSimple
from dependencies.intaghand.models.model_attn.img_attn import *
from dependencies.intaghand.models.model_attn.self_attn import *
from dependencies.intaghand.models.m... | 9,607 | 37.432 | 104 | py |
Im2Hands | Im2Hands-main/artihand/utils/visualize.py | import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from torchvision.utils import save_image
import im2mesh.common as common
def visualize_data(data, data_type, out_file):
r''' Visualizes the data with regard to its type.
Args:
data (tensor): batch of data
... | 4,668 | 30.33557 | 98 | py |
Im2Hands | Im2Hands-main/artihand/data/ref_occ_sample_hands.py | import os
import sys
import json
import pickle
import logging
import trimesh
import torch
import torchvision.transforms
import numpy as np
import cv2 as cv
import open3d as o3d
from glob import glob
from torch.utils import data
from manopth.manolayer import ManoLayer
from dependencies.halo.halo_adapter.converter impo... | 8,986 | 38.244541 | 151 | py |
Im2Hands | Im2Hands-main/artihand/data/init_occ_sample_hands.py | import os
import sys
import json
import pickle
import logging
import torch
import torchvision.transforms
import numpy as np
import cv2 as cv
import open3d as o3d
from glob import glob
from torch.utils import data
from manopth.manolayer import ManoLayer
from dependencies.halo.halo_adapter.converter import PoseConverte... | 7,874 | 35.971831 | 151 | py |
Im2Hands | Im2Hands-main/artihand/data/utils.py | import os
import numpy as np
from torch.utils import data
def collate_remove_none(batch):
''' Collater that puts each data field into a tensor with outer dimension
batch size.
Args:
batch: batch
'''
batch = list(filter(lambda x: x is not None, batch))
return data.dataloader.defaul... | 568 | 24.863636 | 77 | py |
Im2Hands | Im2Hands-main/artihand/data/kpts_ref_sample_hands.py | import os
import sys
import json
import pickle
import logging
import torch
import torchvision.transforms
import numpy as np
import cv2 as cv
import open3d as o3d
from glob import glob
from torch.utils import data
from manopth.manolayer import ManoLayer
from dependencies.halo.halo_adapter.converter import PoseConvert... | 8,990 | 37.75431 | 162 | py |
Im2Hands | Im2Hands-main/artihand/data/transforms.py | import numpy as np
import torch
# Transforms
class PointcloudNoise(object):
''' Point cloud noise transformation class.
It adds noise to point cloud data.
Args:
stddev (int): standard deviation
'''
def __init__(self, stddev):
self.stddev = stddev
def __call__(self, data):
... | 5,631 | 24.6 | 84 | py |
Im2Hands | Im2Hands-main/im2mesh/checkpoints.py | import os
import urllib
import torch
from torch.utils import model_zoo
class CheckpointIO(object):
''' CheckpointIO class.
It handles saving and loading checkpoints.
Args:
checkpoint_dir (str): path where checkpoints are saved
'''
def __init__(self, checkpoint_dir='./chkpts', **kwargs):
... | 2,963 | 28.346535 | 70 | py |
Im2Hands | Im2Hands-main/im2mesh/training.py | # from im2mesh import icp
import numpy as np
from collections import defaultdict
from tqdm import tqdm
class BaseTrainer(object):
''' Base trainer class.
'''
def evaluate(self, val_loader):
''' Performs an evaluation.
Args:
val_loader (dataloader): pytorch dataloader
'... | 1,014 | 23.756098 | 65 | py |
Im2Hands | Im2Hands-main/im2mesh/layers.py | import torch
import torch.nn as nn
# Resnet Blocks
class ResnetBlockFC(nn.Module):
''' Fully connected ResNet Block class.
Args:
size_in (int): input dimension
size_out (int): output dimension
size_h (int): hidden dimension
'''
def __init__(self, size_in, size_out=None, size_... | 8,471 | 28.213793 | 71 | py |
Im2Hands | Im2Hands-main/im2mesh/config.py | import yaml
from torchvision import transforms
from im2mesh import data
from im2mesh import onet, r2n2, psgn, pix2mesh, dmc
from im2mesh import preprocess
method_dict = {
'onet': onet,
'r2n2': r2n2,
'psgn': psgn,
'pix2mesh': pix2mesh,
'dmc': dmc,
}
# General config
def load_config(path, default_... | 7,343 | 27.355212 | 76 | py |
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