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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VIBUS | VIBUS-master/pretrain/models/modules/common.py | import collections
from enum import Enum
import torch.nn as nn
import MinkowskiEngine as ME
class NormType(Enum):
BATCH_NORM = 0
INSTANCE_NORM = 1
INSTANCE_BATCH_NORM = 2
def get_norm(norm_type, n_channels, D, bn_momentum=0.1):
if norm_type == NormType.BATCH_NORM:
return ME.MinkowskiBatchNorm(n_channels... | 6,916 | 30.875576 | 97 | py |
VIBUS | VIBUS-master/pretrain/models/modules/resnet_block.py | import torch.nn as nn
from models.modules.common import ConvType, NormType, get_norm, conv
from MinkowskiEngine import MinkowskiReLU
class BasicBlockBase(nn.Module):
expansion = 1
NORM_TYPE = NormType.BATCH_NORM
def __init__(self,
inplanes,
planes,
stride=1,
... | 3,174 | 23.423077 | 100 | py |
VIBUS | VIBUS-master/pretrain/demo/scannet.py | # Copyright (c) Chris Choy (chrischoy@ai.stanford.edu).
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, ... | 5,641 | 34.2625 | 94 | py |
VIBUS | VIBUS-master/pretrain/demo/stanford.py | # Copyright (c) Chris Choy (chrischoy@ai.stanford.edu).
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, ... | 6,143 | 34.72093 | 98 | py |
VIBUS | VIBUS-master/pretrain/lib/hack.py | import os
import torch
from tqdm import tqdm
import time
from logging import log, ERROR
def check_mem(cuda_device):
devices_info = os.popen('"/usr/bin/nvidia-smi" --query-gpu=memory.total,memory.used --format=csv,nounits,noheader').read().strip().split("\n")
total, used = devices_info[int(cuda_device)].split('... | 864 | 28.827586 | 146 | py |
VIBUS | VIBUS-master/pretrain/lib/test.py | import logging
import os
import shutil
import tempfile
import warnings
import numpy as np
import torch
import torch.nn as nn
from sklearn.metrics import average_precision_score
from sklearn.preprocessing import label_binarize
from lib.utils import Timer, AverageMeter, precision_at_one, fast_hist, per_class_iu, \
... | 6,589 | 33.322917 | 97 | py |
VIBUS | VIBUS-master/pretrain/lib/dataloader.py | import torch
import math
from torch.utils.data.sampler import Sampler
import torch.distributed as dist
class InfSampler(Sampler):
"""Samples elements randomly, without replacement.
Arguments:
data_source (Dataset): dataset to sample from
"""
def __init__(self, data_source, shuffle=Fals... | 2,169 | 27.933333 | 80 | py |
VIBUS | VIBUS-master/pretrain/lib/utils.py | import json
import logging
import os
import errno
import time
import numpy as np
import torch
from lib.pc_utils import colorize_pointcloud, save_point_cloud
from lib.distributed_utils import get_world_size, get_rank
def load_state_with_same_shape(model, weights):
print(weights.keys())
model_state = model.stat... | 13,615 | 34.643979 | 108 | py |
VIBUS | VIBUS-master/pretrain/lib/dataset.py | from abc import ABC
from pathlib import Path
from collections import defaultdict
import random
import numpy as np
from enum import Enum
import open3d as o3d
import torch
import math
from torch.utils.data import Dataset, DataLoader
import MinkowskiEngine as ME
from plyfile import PlyData
import lib.transforms as t
f... | 21,381 | 32.305296 | 136 | py |
VIBUS | VIBUS-master/pretrain/lib/layers.py | import torch
import torch.nn as nn
from MinkowskiEngine import MinkowskiGlobalPooling, MinkowskiBroadcastAddition, MinkowskiBroadcastMultiplication
class MinkowskiLayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-5, D=-1):
super(MinkowskiLayerNorm, self).__init__()
self.num_features = num_feat... | 2,907 | 32.813953 | 112 | py |
VIBUS | VIBUS-master/pretrain/lib/distributed_utils.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import pickle
import socket
import struct
import subprocess
import warnings
import torch
import torch.distributed as dist
def is... | 7,093 | 36.141361 | 107 | py |
VIBUS | VIBUS-master/pretrain/lib/solvers.py | import logging
from torch.optim import SGD, Adam
from torch.optim.lr_scheduler import LambdaLR, StepLR
class LambdaStepLR(LambdaLR):
def __init__(self, optimizer, lr_lambda, last_step=-1):
super(LambdaStepLR, self).__init__(optimizer, lr_lambda, last_step)
@property
def last_step(self):
"""Use last_e... | 2,625 | 32.240506 | 105 | py |
VIBUS | VIBUS-master/pretrain/lib/train.py | from os import write
import numpy as np
import logging
import os.path as osp
import math
import scipy.ndimage
import torch
from torch import nn
from torch.serialization import default_restore_location
from tensorboardX import SummaryWriter
from lib.test import test
from lib.utils import checkpoint, precision_at_one, \... | 14,268 | 41.59403 | 160 | py |
VIBUS | VIBUS-master/pretrain/lib/math_functions.py | from scipy.sparse import csr_matrix
import torch
class SparseMM(torch.autograd.Function):
"""
Sparse x dense matrix multiplication with autograd support.
Implementation by Soumith Chintala:
https://discuss.pytorch.org/t/
does-pytorch-support-autograd-on-sparse-matrix/6156/7
"""
def forward(self, matrix... | 2,060 | 28.028169 | 80 | py |
VIBUS | VIBUS-master/pretrain/lib/transforms.py | import random
import logging
import numpy as np
import scipy
import scipy.ndimage
import scipy.interpolate
import torch
import math
import torchvision.transforms as transforms
import MinkowskiEngine as ME
# A sparse tensor consists of coordinates and associated features.
# You must apply augmentation to both.
# In 2... | 18,565 | 38.586354 | 132 | py |
VIBUS | VIBUS-master/SUField/setup.py | from setuptools import setup
setup(
name='sufield',
version='0.0.1',
install_requires=[
'numpy',
'open3d',
'potpourri3d',
'torch',
'ipykernel',
'plyfile',
'scikit-learn',
'plyfile',
'matplotlib',
'scipy',
'pymeshlab',
... | 327 | 16.263158 | 28 | py |
VIBUS | VIBUS-master/SUField/sufield/spec_cluster.py | """
spec_cluster.py
"""
from copy import deepcopy
from typing import Tuple
import numpy as np
import open3d as o3d
import potpourri3d as pp3d
import torch
from IPython import embed
from plyfile import PlyData
from sklearn.cluster import KMeans
from .downsample import downsample
from .utils import Timer, plydata_to_ar... | 6,176 | 40.18 | 105 | py |
VIBUS | VIBUS-master/instance_segmentation/config.py | import argparse
def str2opt(arg):
assert arg in ['SGD', 'Adam']
return arg
def str2scheduler(arg):
assert arg in ['StepLR', 'PolyLR', 'ExpLR', 'SquaredLR']
return arg
def str2bool(v):
return v.lower() in ('true', '1')
def str2list(l):
return [int(i) for i in l.split(',')]
def add_argum... | 11,969 | 57.676471 | 185 | py |
VIBUS | VIBUS-master/instance_segmentation/new.py | import logging
import os
import time
import random
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import torch.distributed as dist
import torch.multiprocessing as mp
from lib.solvers import initialize_optimizer, initialize_scheduler
from lib.utils import save_predictions
from lib... | 18,445 | 37.26971 | 272 | py |
VIBUS | VIBUS-master/instance_segmentation/models/resnet.py | import torch.nn as nn
import MinkowskiEngine as ME
from models.model import Model
from models.modules.common import ConvType, NormType, get_norm, conv, sum_pool
from models.modules.resnet_block import BasicBlock, Bottleneck
class ResNetBase(Model):
BLOCK = None
LAYERS = ()
INIT_DIM = 64
PLANES = (64, 128, 2... | 5,352 | 23.668203 | 94 | py |
VIBUS | VIBUS-master/instance_segmentation/models/conditional_random_fields.py | import torch
import torch.nn as nn
from torch.autograd import Variable
from MinkowskiEngine import SparseTensor, MinkowskiConvolution, MinkowskiConvolutionFunction, convert_to_int_tensor
from MinkowskiEngine import convert_region_type as me_convert_region_type
from models.model import HighDimensionalModel
from models... | 6,094 | 35.065089 | 115 | py |
VIBUS | VIBUS-master/instance_segmentation/models/resunet.py | from models.resnet import ResNetBase, get_norm
from models.modules.common import ConvType, NormType, conv, conv_tr
from models.modules.resnet_block import BasicBlock, BasicBlockINBN, Bottleneck
import torch.nn as nn
import MinkowskiEngine as ME
from MinkowskiEngine import MinkowskiReLU
import MinkowskiEngine.Minkowsk... | 14,938 | 26.767658 | 91 | py |
VIBUS | VIBUS-master/instance_segmentation/models/wrapper.py | import random
from torch.nn import Module
from MinkowskiEngine import SparseTensor
class Wrapper(Module):
"""
Wrapper for the segmentation networks.
"""
OUT_PIXEL_DIST = -1
def __init__(self, NetClass, in_nchannel, out_nchannel, config):
super(Wrapper, self).__init__()
self.initialize_filter(NetCl... | 950 | 29.677419 | 80 | py |
VIBUS | VIBUS-master/instance_segmentation/models/modules/senet_block.py | import torch.nn as nn
import MinkowskiEngine as ME
from models.modules.common import ConvType, NormType
from models.modules.resnet_block import BasicBlock, Bottleneck
class SELayer(nn.Module):
def __init__(self, channel, reduction=16, D=-1):
# Global coords does not require coords_key
super(SELayer, self... | 3,081 | 22 | 90 | py |
VIBUS | VIBUS-master/instance_segmentation/models/modules/common.py | import collections
from enum import Enum
import torch.nn as nn
import MinkowskiEngine as ME
class NormType(Enum):
BATCH_NORM = 0
INSTANCE_NORM = 1
INSTANCE_BATCH_NORM = 2
def get_norm(norm_type, n_channels, D, bn_momentum=0.1):
if norm_type == NormType.BATCH_NORM:
return ME.MinkowskiBatchNorm(n_channels... | 6,971 | 30.835616 | 97 | py |
VIBUS | VIBUS-master/instance_segmentation/models/modules/resnet_block.py | import torch.nn as nn
from models.modules.common import ConvType, NormType, get_norm, conv
from MinkowskiEngine import MinkowskiReLU
class BasicBlockBase(nn.Module):
expansion = 1
NORM_TYPE = NormType.BATCH_NORM
def __init__(self,
inplanes,
planes,
stride=1,
... | 3,174 | 23.423077 | 100 | py |
VIBUS | VIBUS-master/instance_segmentation/lib/test.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import shutil
import tempfile
import warnings
import numpy as np
import torch
import torch.nn as nn
from sklearn.m... | 10,346 | 37.040441 | 134 | py |
VIBUS | VIBUS-master/instance_segmentation/lib/dataloader.py | import torch
import math
from torch.utils.data.sampler import Sampler
import torch.distributed as dist
class InfSampler(Sampler):
"""Samples elements randomly, without replacement.
Arguments:
data_source (Dataset): dataset to sample from
"""
def __init__(self, data_source, shuffle=Fals... | 2,233 | 28.012987 | 80 | py |
VIBUS | VIBUS-master/instance_segmentation/lib/utils.py | import json
import logging
import os
import errno
import time
import numpy as np
import torch
from lib.pc_utils import colorize_pointcloud, save_point_cloud
from lib.distributed_utils import get_world_size, get_rank
def load_state_with_same_shape(model, weights):
print(weights.keys())
model_state = model.stat... | 13,615 | 34.643979 | 108 | py |
VIBUS | VIBUS-master/instance_segmentation/lib/dataset.py | from abc import ABC
from pathlib import Path
from collections import defaultdict
import random
import numpy as np
from enum import Enum
import torch
from torch.utils.data import Dataset, DataLoader
import MinkowskiEngine as ME
from plyfile import PlyData
import lib.transforms as t
from lib.dataloader import InfSamp... | 18,442 | 32.111311 | 155 | py |
VIBUS | VIBUS-master/instance_segmentation/lib/layers.py | import torch
import torch.nn as nn
from MinkowskiEngine import MinkowskiGlobalPooling, MinkowskiBroadcastAddition, MinkowskiBroadcastMultiplication
class MinkowskiLayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-5, D=-1):
super(MinkowskiLayerNorm, self).__init__()
self.num_features = num_feat... | 2,907 | 32.813953 | 112 | py |
VIBUS | VIBUS-master/instance_segmentation/lib/distributed_utils.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import pickle
import socket
import struct
import subprocess
import warnings
import torch
import torch.distributed as dist
def is... | 7,103 | 36.193717 | 107 | py |
VIBUS | VIBUS-master/instance_segmentation/lib/solvers.py | import logging
from torch.optim import SGD, Adam
from torch.optim.lr_scheduler import LambdaLR, StepLR
class LambdaStepLR(LambdaLR):
def __init__(self, optimizer, lr_lambda, last_step=-1):
super(LambdaStepLR, self).__init__(optimizer, lr_lambda, last_step)
@property
def last_step(self):
"""Use last_e... | 2,625 | 32.240506 | 105 | py |
VIBUS | VIBUS-master/instance_segmentation/lib/train.py | import numpy as np
import logging
import os.path as osp
import torch
from torch import nn
from torch.serialization import default_restore_location
from torch.utils.tensorboard import SummaryWriter
from lib.test import test
from lib.utils import checkpoint, precision_at_one, \
Timer, AverageMeter, get_prediction, ... | 9,990 | 39.28629 | 158 | py |
VIBUS | VIBUS-master/instance_segmentation/lib/math_functions.py | from scipy.sparse import csr_matrix
import torch
class SparseMM(torch.autograd.Function):
"""
Sparse x dense matrix multiplication with autograd support.
Implementation by Soumith Chintala:
https://discuss.pytorch.org/t/
does-pytorch-support-autograd-on-sparse-matrix/6156/7
"""
def forward(self, matrix... | 2,060 | 28.028169 | 80 | py |
VIBUS | VIBUS-master/instance_segmentation/lib/transforms.py | import random
import logging
import numpy as np
import scipy
import scipy.ndimage
import scipy.interpolate
import torch
import math
import MinkowskiEngine as ME
# A sparse tensor consists of coordinates and associated features.
# You must apply augmentation to both.
# In 2D, flip, shear, scale, and rotation of image... | 13,470 | 37.933526 | 132 | py |
VIBUS | VIBUS-master/instance_segmentation/lib/datasets/stanford_test.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import sys
import numpy as np
from collections import defaultdict
from scipy import spatial
import torch
from plyfi... | 8,676 | 31.992395 | 92 | py |
VIBUS | VIBUS-master/instance_segmentation/lib/datasets/stanford.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import sys
import numpy as np
from collections import defaultdict
from scipy import spatial
import torch
from plyfi... | 8,649 | 32.141762 | 92 | py |
VIBUS | VIBUS-master/instance_segmentation/lib/bfs/bfs.py | import os
import torch
import numpy as np
from torch.autograd import Function
import argparse
#from lib.datasets.scannet.datagen.export_ids_per_vertex import read_segmentation, write_triangle_mesh
#from lib.utils.io import read_triangle_mesh, create_color_palette, write_triangle_mesh
#from lib.utils.scannet_benchmark_u... | 5,867 | 36.139241 | 133 | py |
VIBUS | VIBUS-master/instance_segmentation/lib/bfs/ops/setup.py | from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
import os
_ext_src_root = os.path.abspath(os.environ['CONDA_PREFIX'])
setup(
name='PG_OP',
ext_modules=[
CUDAExtension('PG_OP', [
'src/bfs_cluster.cpp',
'src/bfs_cluster_kernel.cu',
... | 594 | 30.315789 | 83 | py |
VIBUS | VIBUS-master/semantic_segmentation/fit.py | from argparse import ArgumentParser
import os
from plyfile import PlyData
import numpy as np
from sufield.fit import mixture_filter, BetaDistribution, GammaDistribution
from sufield.spec_cluster import geodesic_correlation_matrix, angular_correlation_matrix, spectral_cluster
from sufield.utils import plydata_to_array, ... | 3,429 | 47.309859 | 109 | py |
VIBUS | VIBUS-master/semantic_segmentation/config.py | import argparse
def str2opt(arg):
assert arg in ['SGD', 'Adam']
return arg
def str2scheduler(arg):
assert arg in ['StepLR', 'PolyLR', 'ExpLR', 'SquaredLR']
return arg
def str2bool(v):
return v.lower() in ('true', '1')
def str2list(l):
return [int(i) for i in l.split(',')]
def add_argument_group(na... | 12,283 | 43.34657 | 102 | py |
VIBUS | VIBUS-master/semantic_segmentation/new.py | import logging
import os
import time
import random
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
import torch.distributed as dist
import torch.multiprocessing as mp
from lib.solvers import initialize_optimizer, initialize_scheduler
from lib.utils import save_predictions
from lib... | 41,150 | 38.416667 | 199 | py |
VIBUS | VIBUS-master/semantic_segmentation/models/resnet.py | import torch.nn as nn
import MinkowskiEngine as ME
from models.model import Model
from models.modules.common import ConvType, NormType, get_norm, conv, sum_pool
from models.modules.resnet_block import BasicBlock, Bottleneck
class ResNetBase(Model):
BLOCK = None
LAYERS = ()
INIT_DIM = 64
PLANES = (64, 128, 2... | 5,352 | 23.668203 | 94 | py |
VIBUS | VIBUS-master/semantic_segmentation/models/conditional_random_fields.py | import torch
import torch.nn as nn
from torch.autograd import Variable
from MinkowskiEngine import SparseTensor, MinkowskiConvolution, MinkowskiConvolutionFunction, convert_to_int_tensor
from MinkowskiEngine import convert_region_type as me_convert_region_type
from models.model import HighDimensionalModel
from models... | 6,094 | 35.065089 | 115 | py |
VIBUS | VIBUS-master/semantic_segmentation/models/resunet.py | from models.resnet import ResNetBase, get_norm
from models.modules.common import ConvType, NormType, conv, conv_tr
from models.modules.resnet_block import BasicBlock, BasicBlockINBN, Bottleneck
import torch.nn as nn
import MinkowskiEngine as ME
from MinkowskiEngine import MinkowskiReLU
import MinkowskiEngine.Minkowsk... | 14,938 | 26.767658 | 91 | py |
VIBUS | VIBUS-master/semantic_segmentation/models/wrapper.py | import random
from torch.nn import Module
from MinkowskiEngine import SparseTensor
class Wrapper(Module):
"""
Wrapper for the segmentation networks.
"""
OUT_PIXEL_DIST = -1
def __init__(self, NetClass, in_nchannel, out_nchannel, config):
super(Wrapper, self).__init__()
self.initialize_filter(NetCl... | 950 | 29.677419 | 80 | py |
VIBUS | VIBUS-master/semantic_segmentation/models/modules/senet_block.py | import torch.nn as nn
import MinkowskiEngine as ME
from models.modules.common import ConvType, NormType
from models.modules.resnet_block import BasicBlock, Bottleneck
class SELayer(nn.Module):
def __init__(self, channel, reduction=16, D=-1):
# Global coords does not require coords_key
super(SELayer, self... | 3,081 | 22 | 90 | py |
VIBUS | VIBUS-master/semantic_segmentation/models/modules/common.py | import collections
from enum import Enum
import torch.nn as nn
import MinkowskiEngine as ME
class NormType(Enum):
BATCH_NORM = 0
INSTANCE_NORM = 1
INSTANCE_BATCH_NORM = 2
def get_norm(norm_type, n_channels, D, bn_momentum=0.1):
if norm_type == NormType.BATCH_NORM:
return ME.MinkowskiBatchNorm(n_channels... | 6,971 | 30.835616 | 97 | py |
VIBUS | VIBUS-master/semantic_segmentation/models/modules/resnet_block.py | import torch.nn as nn
from models.modules.common import ConvType, NormType, get_norm, conv
from MinkowskiEngine import MinkowskiReLU
class BasicBlockBase(nn.Module):
expansion = 1
NORM_TYPE = NormType.BATCH_NORM
def __init__(self,
inplanes,
planes,
stride=1,
... | 3,174 | 23.423077 | 100 | py |
VIBUS | VIBUS-master/semantic_segmentation/lib/test.py | import logging
import os
import shutil
import tempfile
import warnings
import numpy as np
import torch
import torch.nn as nn
from sklearn.metrics import average_precision_score
from sklearn.preprocessing import label_binarize
from lib.utils import Timer, AverageMeter, precision_at_one, fast_hist, per_class_iu, \
... | 7,561 | 38.385417 | 136 | py |
VIBUS | VIBUS-master/semantic_segmentation/lib/dataloader.py | import torch
import math
from torch.utils.data.sampler import Sampler
import torch.distributed as dist
class InfSampler(Sampler):
"""Samples elements randomly, without replacement.
Arguments:
data_source (Dataset): dataset to sample from
"""
def __init__(self, data_source, shuffle=Fals... | 2,233 | 28.012987 | 80 | py |
VIBUS | VIBUS-master/semantic_segmentation/lib/utils.py | import json
import logging
import os
import errno
import time
import numpy as np
import torch
from lib.pc_utils import colorize_pointcloud, save_point_cloud
from lib.distributed_utils import get_world_size, get_rank
def load_state_with_same_shape(model, weights):
print(weights.keys())
model_state = model.stat... | 13,615 | 34.643979 | 108 | py |
VIBUS | VIBUS-master/semantic_segmentation/lib/dataset.py | from abc import ABC
from pathlib import Path
from collections import defaultdict
import random
import numpy as np
from enum import Enum
import torch
from torch.utils.data import Dataset, DataLoader
import MinkowskiEngine as ME
from plyfile import PlyData
import lib.transforms as t
from lib.dataloader import InfSamp... | 16,526 | 31.791667 | 155 | py |
VIBUS | VIBUS-master/semantic_segmentation/lib/layers.py | import torch
import torch.nn as nn
from MinkowskiEngine import MinkowskiGlobalPooling, MinkowskiBroadcastAddition, MinkowskiBroadcastMultiplication
class MinkowskiLayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-5, D=-1):
super(MinkowskiLayerNorm, self).__init__()
self.num_features = num_feat... | 2,907 | 32.813953 | 112 | py |
VIBUS | VIBUS-master/semantic_segmentation/lib/distributed_utils.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import pickle
import socket
import struct
import subprocess
import warnings
import torch
import torch.distributed as dist
def is... | 7,103 | 36.193717 | 107 | py |
VIBUS | VIBUS-master/semantic_segmentation/lib/solvers.py | import logging
from torch.optim import SGD, Adam
from torch.optim.lr_scheduler import LambdaLR, StepLR
class LambdaStepLR(LambdaLR):
def __init__(self, optimizer, lr_lambda, last_step=-1):
super(LambdaStepLR, self).__init__(optimizer, lr_lambda, last_step)
@property
def last_step(self):
"""Use last_e... | 2,625 | 32.240506 | 105 | py |
VIBUS | VIBUS-master/semantic_segmentation/lib/train.py | import numpy as np
import logging
import os.path as osp
import torch
from torch import nn
from torch.serialization import default_restore_location
from torch.utils.tensorboard import SummaryWriter
from lib.test import test
from lib.utils import checkpoint, precision_at_one, \
Timer, AverageMeter, get_prediction, ... | 9,928 | 39.198381 | 158 | py |
VIBUS | VIBUS-master/semantic_segmentation/lib/math_functions.py | from scipy.sparse import csr_matrix
import torch
class SparseMM(torch.autograd.Function):
"""
Sparse x dense matrix multiplication with autograd support.
Implementation by Soumith Chintala:
https://discuss.pytorch.org/t/
does-pytorch-support-autograd-on-sparse-matrix/6156/7
"""
def forward(self, matrix... | 2,060 | 28.028169 | 80 | py |
VIBUS | VIBUS-master/semantic_segmentation/lib/transforms.py | import random
import logging
import numpy as np
import scipy
import scipy.ndimage
import scipy.interpolate
import torch
import math
import MinkowskiEngine as ME
# A sparse tensor consists of coordinates and associated features.
# You must apply augmentation to both.
# In 2D, flip, shear, scale, and rotation of image... | 13,353 | 37.707246 | 132 | py |
VIBUS | VIBUS-master/semantic_segmentation/lib/datasets/stanford_test.py | import logging
import os
import sys
import numpy as np
from collections import defaultdict
from scipy import spatial
from plyfile import PlyData
from lib.utils import read_txt, fast_hist, per_class_iu
from lib.dataset import VoxelizationDataset, DatasetPhase, str2datasetphase_type, cache
import lib.transforms as t
c... | 7,164 | 32.481308 | 100 | py |
VIBUS | VIBUS-master/semantic_segmentation/lib/datasets/stanford.py | import logging
import os
import sys
import numpy as np
from collections import defaultdict
from scipy import spatial
from plyfile import PlyData
from lib.utils import read_txt, fast_hist, per_class_iu
from lib.dataset import VoxelizationDataset, DatasetPhase, str2datasetphase_type, cache
import lib.transforms as t
c... | 7,151 | 32.420561 | 100 | py |
DGCN | DGCN-master/load_semigcn_data.py | import sys
import networkx as nx
import scipy.sparse as sp
import pickle as pkl
import numpy as np
def load_data_gcn(dataset_str):
names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']
objects = []
for i in range(len(names)):
with open("data/gcn/ind.{}.{}".format(dataset_str, names[i]), 'rb') as ... | 2,830 | 32.702381 | 81 | py |
DGCN | DGCN-master/run_exps_diff_layers.py | import torch
import torch.nn as nn
import torch.optim as optim
from utils import load_data, process_graph_data
from utils import package_mxl, adj_rw_norm
from utils import sparse_mx_to_torch_sparse_tensor
from utils import ResultRecorder
from model import GCN, GCNBias, SGC, ResGCN, GCNII, APPNP
from layers import Gra... | 16,007 | 41.802139 | 138 | py |
DGCN | DGCN-master/data_loader.py | import numpy as np
import scipy.sparse as sp
from utils import sparse_mx_to_torch_sparse_tensor
import multiprocessing as mp
import time
import os
import torch
class DataLoader(object):
def __init__(self, adj_mat, train_nodes, valid_nodes, test_nodes, device):
self.adj_mat = adj_mat
self.train_nod... | 2,827 | 37.739726 | 81 | py |
DGCN | DGCN-master/utils.py | import numpy as np
import json
import copy
import scipy.sparse as sp
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import f1_score
import torch
import torch.nn as nn
import torch.nn.functional as F
"""
Load data
"""
def load_data(prefix, normalize=True):
adj_full = sp.load_npz('./data/{}/a... | 3,645 | 33.074766 | 94 | py |
DGCN | DGCN-master/model.py | import torch
import torch.nn as nn
import math
#########################################################
#########################################################
#########################################################
class GCN(nn.Module):
def __init__(self, n_feat, n_hid, n_classes, n_layers, dropout, criteri... | 10,225 | 33.200669 | 79 | py |
DGCN | DGCN-master/layers.py | import torch
import torch.nn as nn
from initialization_utils import uniform, zeros
class GraphConv(nn.Module):
def __init__(self, n_in, n_out, bias=False):
super(GraphConv, self).__init__()
self.n_in = n_in
self.n_out = n_out
self.linear = nn.Linear(n_in, n_out, bias=bias)
... | 1,887 | 29.451613 | 74 | py |
DGCN | DGCN-master/initialization_utils.py | import math
import torch
def uniform(size, tensor):
if tensor is not None:
bound = 1.0 / math.sqrt(size)
tensor.data.uniform_(-bound, bound)
def kaiming_uniform(tensor, fan, a):
if tensor is not None:
bound = math.sqrt(6 / ((1 + a**2) * fan))
tensor.data.uniform_(-bound, bou... | 855 | 21.526316 | 69 | py |
restarted-hb | restarted-hb-main/problem/classification_mnist.py | from typing import Sequence
import functools
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from sklearn.model_selection import train_test_split
from torchvision.datasets import MNIST
jax.config.update("jax_enable_x64", True)
N_TARGETS = 10
SCALE_IMAGE = 255
TRAIN_MAX = 60000
# load ... | 4,005 | 33.534483 | 117 | py |
restarted-hb | restarted-hb-main/problem/rosenbrock.py | import jax
import jax.numpy as jnp
import functools
class Problem:
def __init__(self, a, b, d, x0):
self.a = a
self.b = b
self.d = d
self.x0 = jnp.ones(d) * x0
def inner_func(self, x):
return jnp.concatenate((self.a - x[:-1], jnp.sqrt(self.b) * (x[1:] - x[:-1] ** 2)))
... | 517 | 22.545455 | 91 | py |
restarted-hb | restarted-hb-main/problem/ae_mnist.py | from typing import Sequence
import functools
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from sklearn.model_selection import train_test_split
from torchvision.datasets import MNIST
jax.config.update("jax_enable_x64", True)
SCALE_IMAGE = 255
# load data
# from https://jax.readthedo... | 3,056 | 32.228261 | 114 | py |
restarted-hb | restarted-hb-main/problem/mf_movielens.py | import functools
import jax
import jax.numpy as jnp
import numpy as np
from scipy.sparse import coo_matrix
from scipy.sparse.linalg import svds
import pandas as pd
jax.config.update("jax_enable_x64", True)
def df_to_sparse_matrix(df: pd.DataFrame):
for cid in ("user", "item"):
vs = df[cid].unique()
... | 4,140 | 35.324561 | 116 | py |
restarted-hb | restarted-hb-main/optimizer/internal.py | import jax
class Oracle:
def __init__(self, instance):
self.__func = instance.func
self.count = dict.fromkeys(["eval", "grad"], 0)
def reset_count(self):
self.count = dict.fromkeys(self.count.keys(), 0)
def func(self, x, counted=True):
if counted:
self.count["... | 645 | 23.846154 | 56 | py |
restarted-hb | restarted-hb-main/optimizer/method.py | import jax.numpy as jnp
import jax
from . import internal
class Base:
def __init__(self):
self.iter = 0
@property
def recorded_params(self):
return {}
@property
def solutions(self):
return {}
def update(self, oracle: internal.Oracle):
pass
class GradientDes... | 13,712 | 28.364026 | 88 | py |
restarted-hb | restarted-hb-main/optimizer/__init__.py | import jax
from .main import SmoothNonconvexMin
jax.config.update("jax_enable_x64", True)
jax.config.update("jax_debug_nans", True)
| 134 | 18.285714 | 41 | py |
jlonevae | jlonevae-main/disentanglement_lib/disentanglement_lib/evaluation/abstract_reasoning/relational_layers.py | # coding=utf-8
# Copyright 2018 The DisentanglementLib Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... | 7,576 | 38.259067 | 80 | py |
jlonevae | jlonevae-main/disentanglement_lib/disentanglement_lib/evaluation/abstract_reasoning/models.py | # coding=utf-8
# Copyright 2018 The DisentanglementLib Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... | 11,089 | 35.60066 | 80 | py |
jlonevae | jlonevae-main/disentanglement_lib/disentanglement_lib/evaluation/abstract_reasoning/relational_layers_test.py | # coding=utf-8
# Copyright 2018 The DisentanglementLib Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... | 4,868 | 39.575 | 80 | py |
jlonevae | jlonevae-main/exampleScripts/createLatentJacobianImages_naturalImages.py | #!/usr/bin/env python3
import numpy as np
import scipy.io as sio
from jlonevae_lib.architecture.load_model import load_model
import jlonevae_lib.architecture.vae_jacobian as vj
import torch
import PIL.Image
from pathlib import Path
import os.path
import glob
import argparse
parser = argparse.ArgumentParser(description... | 7,179 | 43.04908 | 125 | py |
jlonevae | jlonevae-main/exampleScripts/smallEvaluation.py | # coding=utf-8
# Copyright 2018 The DisentanglementLib Authors. All rights reserved.
# Copyright 2021 Travers Rhodes. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
... | 6,930 | 37.72067 | 104 | py |
jlonevae | jlonevae-main/jlonevae_lib/baseline_lib/evaluate/evaluation.py | # coding=utf-8
# Copyright 2018 The DisentanglementLib Authors. All rights reserved.
# Copyright 2021 Travers Rhodes. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
... | 7,226 | 38.708791 | 104 | py |
jlonevae | jlonevae-main/jlonevae_lib/architecture/load_model.py | import pickle
import torch
from jlonevae_lib.architecture.vae import ConvVAE
def load_model(model_folder_path, device="cpu"):
with open(model_folder_path + "/model_type.txt", "r") as f:
model_type = f.readline().strip()
with open(model_folder_path + "/model_args.p", "rb") as f:
kwargs = pickle.lo... | 634 | 34.277778 | 118 | py |
jlonevae | jlonevae-main/jlonevae_lib/architecture/vae.py | import torch
from torch import nn, optim
from torch.nn import functional as F
import math
class VAE(nn.Module):
def __init__(self, beta=1.0):
super(VAE, self).__init__()
def reparameterize(self, mu, logvar):
std = torch.exp(0.5*logvar)
eps = torch.randn((mu.shape[0], mu.shape[1]), ... | 8,103 | 45.045455 | 202 | py |
jlonevae | jlonevae-main/jlonevae_lib/architecture/vae_jacobian.py | import numpy as np
import torch
from torch import nn, optim
from torch.nn import functional as F
import math
import os
from opt_einsum import contract
def compute_generator_jacobian_image_optimized(model, embedding, epsilon_scale = 0.001, device="cpu"):
raw_jacobian = compute_generator_jacobian_optimized(model, e... | 6,064 | 44.94697 | 102 | py |
jlonevae | jlonevae-main/jlonevae_lib/architecture/save_model.py | import pickle
import os
import torch
# Save the model in an custom-code-readable way
def save_conv_vae(convvae, model_folder_path):
kwargs = {"latent_dim": convvae.latent_dim,
"im_side_len": convvae.im_side_len,
"im_channels": convvae.im_channels,
"emb_conv_layers_channels... | 1,355 | 49.222222 | 83 | py |
jlonevae | jlonevae-main/jlonevae_lib/train/train_jlonevae_models.py | __doc__ = """
This code was taken and modified
from https://github.com/AIcrowd/neurips2019_disentanglement_challenge_starter_kit
"""
# Note: _we_ don't use tensorflow, but we call data-loading code that does
# trying so hard to mute tensorflow warnings...
# https://stackoverflow.com/questions/57539273/disable-tensorfl... | 7,721 | 40.074468 | 124 | py |
jlonevae | jlonevae-main/jlonevae_lib/train/train_jlonevae_without_disentanglement_lib.py | # If you want to train a jlonevae model directly
# without using disentanglement_lib, you can do so by using this file.
# For our paper, this is the implementation we use for the naturalImage results
# for which we do not have ground-truth factors of variation.
import datetime
import torch
import glob
import math
impor... | 6,267 | 43.140845 | 131 | py |
jlonevae | jlonevae-main/jlonevae_lib/train/loss_function.py | import torch
TESTING=False # Set to True to run a bunch of extra asserts
# Reconstruction + KL divergence losses summed over all pixels and batch
def vae_loss_function(recon_x, x, mu, logvar, beta):
# To make the units work properly, this should be equal to
# the log reconstruction probability, which is
#... | 2,464 | 46.403846 | 102 | py |
jlonevae | jlonevae-main/jlonevae_lib/train/jlonevae_trainer.py | # if you want to train without using the disentanglement_lib infrastructure
# you can train using this file.
# This file's train method is very, very similar to
# jlonevae_lib/train/train_jlonevae_models.py's train method
# This is just an object-oriented version of that more script-based version
# this file's train m... | 4,103 | 47.282353 | 114 | py |
jlonevae | jlonevae-main/jlonevae_lib/evaluate/evaluate_helper.py | # coding=utf-8
# Copyright 2018 The DisentanglementLib Authors. All rights reserved.
# Copyright 2021 Travers Rhodes. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
... | 9,585 | 44.866029 | 128 | py |
jlonevae | jlonevae-main/jlonevae_lib/evaluate/evaluation.py | # coding=utf-8
# Copyright 2018 The DisentanglementLib Authors. All rights reserved.
# Copyright 2021 Travers Rhodes. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
... | 7,569 | 37.820513 | 104 | py |
jlonevae | jlonevae-main/jlonevae_lib/utils/utils_pytorch.py | from copy import deepcopy
import os
from collections import namedtuple
import numpy as np
import torch
from torch.jit import trace
# ------ Data Loading ------
from torch.utils.data.dataset import Dataset
from torch.utils.data.dataloader import DataLoader
import os
if 'DISENTANGLEMENT_LIB_DATA' not in os.environ:
... | 10,443 | 34.164983 | 157 | py |
jlonevae | jlonevae-main/jlonevae_lib/utils/pytorch_npz_dataset.py | #### If, instead of using disentanglement_lib's infrastructure
#### you want to just train on a npz file of data directly
#### (eg: no ground-truth factors, like for natural images)
#### you can use this dataloader
import torch
import numpy as np
# maybe I'm just being overly fancy here,
# but this is basically just a... | 1,740 | 43.641026 | 90 | py |
jlonevae | jlonevae-main/experimentScripts/visualizations/createLatentJacobianImages_naturalImages.py | #!/usr/bin/env python3
import numpy as np
import scipy.io as sio
from jlonevae_lib.architecture.load_model import load_model
import jlonevae_lib.architecture.vae_jacobian as vj
import torch
import PIL.Image
from pathlib import Path
import os.path
import glob
import argparse
parser = argparse.ArgumentParser(description... | 7,120 | 42.95679 | 125 | py |
jlonevae | jlonevae-main/experimentScripts/visualizations/analyticNaturalImages/01a-trainAnalyticModel.py | #!/usr/bin/env python3
import numpy as np
import datetime
import glob
import sys
sys.path.append("../../..") # include base dir
from jlonevae_lib.utils.pytorch_npz_dataset import PytorchNpzDataset
from sklearn.decomposition import FastICA, PCA
import torch.utils
from pathlib import Path
import os.path
import argpar... | 3,290 | 42.302632 | 174 | py |
jlonevae | jlonevae-main/experimentScripts/train_linear/trainLinearModels_naturalImages.py | #!/usr/bin/env python3
import numpy as np
import datetime
import glob
from jlonevae_lib.utils.pytorch_npz_dataset import PytorchNpzDataset
from sklearn.decomposition import FastICA, PCA
import torch.utils
from pathlib import Path
import os.path
import argparse
parser = argparse.ArgumentParser(description='Train ICA ... | 3,201 | 42.863014 | 174 | py |
SimpleDG | SimpleDG-main/ddp_training/main.py | import os
import argparse
import math
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.distributed as dist
import torch.utils.data as data
from pprint import pprint
from model import build_model
from dataset import build_dataset
from augment import FMixup
from transform imp... | 10,265 | 30.29878 | 138 | py |
SimpleDG | SimpleDG-main/ddp_training/test.py | import os
import csv
import json
import argparse
from glob import glob
from collections import OrderedDict
import torch
from torch.utils import data
from tqdm.auto import tqdm
from model import build_model
from dataset import NICOTestDataset
from transform import TestTransform
from utils import load_config_from_file
... | 4,230 | 32.054688 | 84 | py |
SimpleDG | SimpleDG-main/ddp_training/transform.py | from random import random, randint
import numpy as np
from PIL import Image, ImageFilter
from torchvision import transforms
import torchvision.transforms.functional as F
from timm.data import RandAugment, rand_augment_ops
def fourier_domain_adaptation(img, target_img, beta):
img = np.squeeze(img)
target_img =... | 5,159 | 31.45283 | 86 | py |
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