repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/datasets/multi_mnist.py | from pathlib import Path
import codecs
import gzip
import urllib
import random
import numpy as np
from scipy import ndimage
from PIL import Image
import torch
class MultiMNIST(torch.utils.data.Dataset):
urls = [
'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
'http://yann.lecun.c... | 8,297 | 42.904762 | 105 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/datasets/__init__.py | from .multi_mnist import MultiMNIST
| 36 | 17.5 | 35 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/submission/pretty_tabular.py | # Source code for ICML submission #640 "Efficient Continuous Pareto Exploration in Multi-Task Learning"
class PrettyTabular(object):
def __init__(self, head):
self.head = head
def head_string(self):
line = ''
for key, value in self.head.items():
try:
dummy = ... | 1,325 | 33.894737 | 103 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/submission/min_norm_solver.py | import sys
from itertools import combinations
import numpy as np
import torch
def _min_norm_element_from2(v1v1, v1v2, v2v2):
"""
Analytical solution for min_{c} |cx_1 + (1-c)x_2|_2^2
d is the distance (objective) optimzed
v1v1 = <x1,x1>
v1v2 = <x1,x2>
v2v2 = <x2,x2>
"""
if v1v2 >= v1v... | 4,675 | 29.966887 | 109 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/submission/common.py | # Source code for ICML submission #640 "Efficient Continuous Pareto Exploration in Multi-Task Learning"
import numpy as np
from matplotlib.patches import FancyArrowPatch
from mpl_toolkits.mplot3d import proj3d
def print_error(*message):
print('\033[91m', 'ERROR ', *message, '\033[0m')
raise RuntimeError
def p... | 4,513 | 35.112 | 116 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/submission/zdt2_variant.py | # Source code for ICML submission #640 "Efficient Continuous Pareto Exploration in Multi-Task Learning"
import numpy as np
from common import *
class Zdt2Variant(object):
def __init__(self):
self.n = 3
self.m = 2
self.eval_f_cnt = 0
self.eval_grad_cnt = 0
self.eval_hvp_cnt... | 6,675 | 30.790476 | 103 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/multi_mnist/weighted_sum.py | import random
from pathlib import Path
from termcolor import colored
import numpy as np
import torch
import torch.nn.functional as F
from torch.optim import SGD
from torch.optim.lr_scheduler import CosineAnnealingLR
from torchvision import transforms
from pareto.metrics import topk_accuracy
from pareto.datasets imp... | 5,501 | 26.928934 | 117 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/multi_mnist/cpmtl.py | import random
from pathlib import Path
from termcolor import colored
import numpy as np
import torch
import torch.nn.functional as F
from torch.optim import SGD
from torchvision import transforms
from pareto.metrics import topk_accuracy
from pareto.optim import VisionHVPSolver, MINRESKKTSolver
from pareto.datasets i... | 5,661 | 25.092166 | 117 | py |
DeepAA | DeepAA-master/resnet_imagenet.py | import os
import tensorflow as tf
# ref: https://github.com/gahaalt/resnets-in-tensorflow2/blob/master/Models/Resnets.py
_bn_momentum = 0.9
def regularized_padded_conv(*args, **kwargs):
return tf.keras.layers.Conv2D(*args, **kwargs, padding='same', kernel_regularizer=_regularizer, bias_regularizer=_regularizer,
... | 6,826 | 46.082759 | 151 | py |
DeepAA | DeepAA-master/lr_scheduler.py | import tensorflow as tf
from tensorflow.keras.optimizers.schedules import LearningRateSchedule
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops, control_flow_ops
class GradualWarmup_Cosine_Scheduler(LearningRateSchedule):
def __init__(self, starting_lr, initial_lr, ending_lr, ... | 2,824 | 46.083333 | 133 | py |
DeepAA | DeepAA-master/DeepAA_utils.py | import os
import logging
import numpy as np
import copy
import random
import datetime
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
tf.get_logger().setLevel(logging.ERROR)
from data_generator import DataGenerator, DataAugmentation
from utils import CTLHistory
from lr_scheduler import GradualWarmup... | 13,983 | 42.7 | 161 | py |
DeepAA | DeepAA-master/imagenet_data_utils.py | import numpy as np
import tensorflow as tf
from torchvision.datasets.imagenet import *
from torch import randperm, default_generator
from torch._utils import _accumulate
from torch.utils.data.dataset import Subset
_DATA_TYPE = tf.float32
CMYK_IMAGES = [
'n01739381_1309.JPEG',
'n02077923_14822.JPEG',
'n02... | 7,325 | 40.625 | 129 | py |
DeepAA | DeepAA-master/augmentation.py | # code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
# https://github.com/ildoonet/pytorch-randaugment/blob/master/RandAugment/augmentations.py
import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
from PIL... | 11,099 | 31.840237 | 103 | py |
DeepAA | DeepAA-master/data_generator.py | import os
import copy
import logging
import numpy as np
import math
from PIL import Image
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
tf.get_logger().setLevel(logging.ERROR)
from tensorflow.keras.utils import Sequence
from augmentation import IMAGENET_SIZE, centerCrop_imagenet
CIFAR_MEANS = np.ar... | 8,476 | 41.174129 | 235 | py |
DeepAA | DeepAA-master/resnet.py | import os
import tensorflow as tf
# ref: https://github.com/gahaalt/resnets-in-tensorflow2/blob/master/Models/Resnets.py
_bn_momentum = 0.9
def regularized_padded_conv(*args, **kwargs):
return tf.keras.layers.Conv2D(*args, **kwargs, padding='same', kernel_regularizer=_regularizer, bias_regularizer=_regularizer,
... | 12,655 | 49.624 | 183 | py |
DeepAA | DeepAA-master/utils.py | import os
import logging
import numpy as np
import matplotlib
# configure backend here
matplotlib.use('Agg')
# matplotlib.use('tkagg')
import matplotlib.pyplot as plt
import matplotlib.patheffects as PathEffects
from mpl_toolkits.axes_grid1 import ImageGrid
import tensorflow as tf
import math
import sys
from data_gener... | 5,620 | 29.548913 | 93 | py |
DeepAA | DeepAA-master/policy.py | import tensorflow as tf
import numpy as np
import math
import json
from tensorflow_probability import distributions as tfd
from resnet import Resnet
CIFAR_MEANS = np.array([0.49139968, 0.48215841, 0.44653091], dtype=np.float32)
CIFAR_STDS = np.array([0.2023, 0.1994, 0.2010], dtype=np.float32)
SVHN_MEANS = np.array(... | 7,142 | 51.138686 | 139 | py |
DeepAA | DeepAA-master/aug_lib.py | import numpy as np
import re
import PIL
from PIL import ImageOps, ImageEnhance, ImageFilter, Image, ImageDraw
import random
from dataclasses import dataclass
from typing import Union
@dataclass
class MinMax:
min: Union[float, int]
max: Union[float, int]
@dataclass
class MinMaxVals:
shear: MinMax = MinMa... | 23,352 | 29.210867 | 129 | py |
DeepAA | DeepAA-master/__init__.py | 0 | 0 | 0 | py | |
DeepAA | DeepAA-master/DeepAA_search.py | _PARALLEL_BATCH_small, _PARALLEL_BATCH_median, _PARALLEL_BATCH_large = 16, 128, 256 # 64
import os
import sys
import numpy as np
import tensorflow as tf
tf.config.threading.set_inter_op_parallelism_threads(0)
gpus = tf.config.list_physical_devices('GPU')
for gpu in gpus:
tf.config.experimental.set_memory_growth(gp... | 31,956 | 53.908935 | 187 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/lr_scheduler.py | import torch
from theconf import Config as C
def adjust_learning_rate_resnet(optimizer):
"""
Sets the learning rate to the initial LR decayed by 10 on every predefined epochs
Ref: AutoAugment
"""
if C.get()['epoch'] == 90:
return torch.optim.lr_scheduler.MultiStepLR(optimizer, [30, 60, 8... | 645 | 31.3 | 85 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/augmentations.py | # code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
import numpy as np
import torch
from DeepAA_evaluate import autoaugment, fast_autoaugment
import aug_lib
class Lighting(object):
"""Lighting noise(AlexNet - style PCA - based noise)"""... | 2,507 | 30.35 | 72 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/deep_autoaugment.py | # code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
import random
import math
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
import os
import json
import hashlib
import requests
import scipy
from torc... | 16,098 | 30.879208 | 133 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/utils.py | import torch
import numpy as np
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import torchvision.transforms.functional as F
plt.rcParams["savefig.bbox"] = 'tight'
def save_images(imgs, dir):
if not isinstance(imgs, list):
imgs = [imgs]
fix, axs = plt.subplots(ncols=len(i... | 590 | 24.695652 | 75 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/data.py | import logging
import os
import random
from collections import Counter
import torchvision
from PIL import Image
from torch.utils.data import SubsetRandomSampler, Sampler
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data.dataset import ConcatDataset, Subset
from torchvision.transforms i... | 19,585 | 44.761682 | 176 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/fast_autoaugment.py | # code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from torchvision.transforms.transforms import Compose
random_mirror = True
def Shear... | 117,783 | 392.926421 | 55,120 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/autoaugment.py | # Copyright 2018 The TensorFlow 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
#
# Unless required by applicab... | 21,409 | 32.34891 | 517 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/common.py | import logging
import warnings
import random
from copy import copy
from typing import Union
from collections import Counter
import numpy as np
import torch
from torch.utils.checkpoint import check_backward_validity, detach_variable, get_device_states, set_device_states
from torchvision.datasets import VisionDataset, C... | 3,469 | 34.408163 | 230 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/metrics.py | import copy
import torch
from collections import defaultdict
from torch import nn
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified values of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
cor... | 2,281 | 24.076923 | 80 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/__init__.py | 0 | 0 | 0 | py | |
DeepAA | DeepAA-master/DeepAA_evaluate/train.py | import itertools
import json, csv
import logging
import math
import os
from collections import OrderedDict
import gc
import tempfile
import pickle
from dataclasses import dataclass
import random
from time import time
import numpy as np
import torch
from torch import nn, optim
import torch.distributed as dist
import to... | 22,694 | 44.209163 | 286 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/imagenet.py | from torchvision.datasets.imagenet import *
class ImageNet(ImageFolder):
"""`ImageNet <http://image-net.org/>`_ 2012 Classification Dataset.
Copied from torchvision, besides warning below.
Args:
root (string): Root directory of the ImageNet Dataset.
split (string, optional): The dataset sp... | 3,096 | 42.013889 | 118 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/networks/resnet.py | # Original code: https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
# gamma is initialized ot 0 in the last BN of each residual block
import torch.nn as nn
import math
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, ... | 6,492 | 34.288043 | 135 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/networks/mlp.py | import torch
from torch import nn
def MLP(D_out,in_dims,adaptive_dropouter_creator):
print('adaptive dropouter', adaptive_dropouter_creator)
in_dim = 1
for d in in_dims: in_dim *= d
ada_dropper = adaptive_dropouter_creator(100) if adaptive_dropouter_creator is not None else None
model = nn.Sequent... | 616 | 28.380952 | 101 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/networks/__init__.py | import torch
from torch import nn
from torch.nn import DataParallel
import torch.backends.cudnn as cudnn
# from torchvision import models
from DeepAA_evaluate.networks.resnet import ResNet
from DeepAA_evaluate.networks.shakeshake.shake_resnet import ShakeResNet
from DeepAA_evaluate.networks.wideresnet import WideResN... | 3,545 | 42.243902 | 316 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/networks/convnet.py | import torch
from torch import nn
class SeqConvNet(nn.Module):
def __init__(self,D_out,fixed_dropout=None,in_channels=3,channels=(64,64),h_dims=(200,100),adaptive_dropout_creator=None,batch_norm=False):
super().__init__()
print("Using SeqConvNet")
assert len(channels) == 2 == len(h_dims)
... | 1,546 | 47.34375 | 143 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/networks/wideresnet.py | import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import numpy as np
_bn_momentum = 0.1
CpG = 8
class ExampleWiseBatchNorm2d(nn.BatchNorm2d):
def __init__(self, num_features, eps=1e-5, momentum=0.1,
affine=True, track_running_stats=True):
su... | 8,885 | 39.949309 | 171 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/networks/shakeshake/shake_resnet.py | # -*- coding: utf-8 -*-
import math
import torch.nn as nn
import torch.nn.functional as F
from DeepAA_evaluate.networks.shakeshake.shakeshake import ShakeShake
from DeepAA_evaluate.networks.shakeshake.shakeshake import Shortcut
class ShakeBlock(nn.Module):
def __init__(self, in_ch, out_ch, stride=1):
... | 2,927 | 32.655172 | 89 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/networks/shakeshake/shake_resnext.py | # -*- coding: utf-8 -*-
import math
import torch.nn as nn
import torch.nn.functional as F
from DeepAA_evaluate.networks.shakeshake.shakeshake import ShakeShake
from DeepAA_evaluate.networks.shakeshake.shakeshake import Shortcut
class ShakeBottleNeck(nn.Module):
def __init__(self, in_ch, mid_ch, out_ch, cardin... | 3,094 | 35.411765 | 97 | py |
DeepAA | DeepAA-master/DeepAA_evaluate/networks/shakeshake/__init__.py | 0 | 0 | 0 | py | |
DeepAA | DeepAA-master/DeepAA_evaluate/networks/shakeshake/shakeshake.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class ShakeShake(torch.autograd.Function):
@staticmethod
def forward(ctx, x1, x2, training=True):
if training:
alpha = torch.cuda.FloatTensor(x1.size(0)).uniform... | 1,413 | 27.857143 | 86 | py |
emrQA | emrQA-master/main.py | from subprocess import check_call
import sys
import os
import csv
PYTHON = sys.executable
#################################### set the full file paths ###############################################
i2b2_relations_challenge_directory = "i2b2/relations/"
i2b2_medications_challenge_directory = "i2b2/medication/"
i2b2_... | 4,047 | 42.06383 | 287 | py |
emrQA | emrQA-master/evaluation/template-analysis.py | import json
import csv
import os
import numpy as np
import collections
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--templates_dir', default='/home/anusri/Desktop/emrQA/templates', help='Directory containing template files in the given format')
args = parser.parse_args()
relations = ["reve... | 5,510 | 28.789189 | 278 | py |
emrQA | emrQA-master/evaluation/paraphrase-analysis.py | import csv
import os
import nltk
from nltk.metrics import *
from nltk.translate.bleu_score import sentence_bleu
import argparse
import itertools
import random
import numpy as np
parser = argparse.ArgumentParser()
parser.add_argument('--templates_dir', default='/home/anusri/Desktop/emrQA/templates', help='Directory con... | 2,611 | 33.368421 | 150 | py |
emrQA | emrQA-master/evaluation/basic-stats.py | import json
from nltk.tokenize.stanford import StanfordTokenizer
import os
import numpy as np
import matplotlib.pyplot as plt
import nltk
from random import *
from nltk import sent_tokenize
from nltk import word_tokenize
import random
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--output_dir... | 6,503 | 34.156757 | 127 | py |
emrQA | emrQA-master/generation/i2b2_relations/problem_classfiers.py | from nltk.stem import WordNetLemmatizer
import nltk
from nltk.corpus import stopwords
## Open common names to use in is_common_noun function ##
file = open("generation/i2b2_relations/common_names.txt") ## you can use any set of common nouns to filter, here we call the top 500 high frequency words occuring in our templ... | 6,016 | 23.863636 | 199 | py |
emrQA | emrQA-master/generation/i2b2_relations/relations-answers.py | import csv
from os import listdir
from os.path import isfile, join
import nltk
from nltk.stem import WordNetLemmatizer
from nltk.corpus import wordnet as wn
from problem_classfiers import concept_is_CommonNoun, concept_is_PastTense
import json
import sys
reload(sys)
sys.setdefaultencoding("ISO-8859-1")
import random
im... | 56,886 | 42.227204 | 247 | py |
emrQA | emrQA-master/generation/i2b2_heart_disease_risk/risk-answers.py | from os import listdir
import xmltodict
import csv
import sys
import json
import random
import argparse
import os
reload(sys)
sys.setdefaultencoding("ISO-8859-1")
parser = argparse.ArgumentParser()
parser.add_argument('--i2b2_dir', default='', help='Directory containing i2b2 heart disease risk challange files')
parser... | 105,939 | 46.18931 | 185 | py |
emrQA | emrQA-master/generation/i2b2_medications/medication-answers.py | import csv
import os
from os import listdir
from os.path import isfile, join
import json
import random
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--i2b2_dir', default='', help='Directory containing i2b2 medications challange files')
parser.add_argument('--templates_dir', default='', help='... | 27,595 | 43.509677 | 238 | py |
emrQA | emrQA-master/generation/combine_data/combine_answers.py | import json
import csv
import random
import argparse
import os
parser = argparse.ArgumentParser()
parser.add_argument('--output_dir', default='/home/anusri/Desktop/emrQA/output/', help='Directory of output files')
args = parser.parse_args()
###################################################### SET FILE PATHS ######... | 7,952 | 34.346667 | 174 | py |
emrQA | emrQA-master/generation/i2b2_smoking/smoking-answers.py | import xmltodict
import csv
import json
import argparse
import os
parser = argparse.ArgumentParser()
parser.add_argument('--i2b2_dir', default='', help='Directory containing i2b2 smoking challange files')
parser.add_argument('--templates_dir', default='', help='Directory containing template files in the given format')
... | 4,073 | 29.631579 | 136 | py |
emrQA | emrQA-master/generation/i2b2_obesity/obesity-answers.py | import xmltodict
import csv
import json
import argparse
import os
parser = argparse.ArgumentParser()
parser.add_argument('--i2b2_dir', default='', help='Directory containing i2b2 obesity challange files')
parser.add_argument('--templates_dir', default='', help='Directory containing template files in the given format')... | 11,449 | 36.540984 | 141 | py |
3DTrans | 3DTrans-master/setup.py | import os
import subprocess
from setuptools import find_packages, setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
def get_git_commit_number():
if not os.path.exists('.git'):
return '0000000'
cmd_out = subprocess.run(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE)
... | 3,945 | 31.344262 | 83 | py |
3DTrans | 3DTrans-master/tools/train_active_CLUE.py | import _init_path
import os
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, log_config_to_file, cfg_from_yaml_file, cfg_from_list
from pcdet.utils import common_utils
from pcdet.datasets import build_dataloader, build_dataloader_ada
from pcdet.models import build_... | 10,579 | 42.00813 | 169 | py |
3DTrans | 3DTrans-master/tools/train_multi_db.py | import _init_path
import argparse
import datetime
import glob
import os
from pathlib import Path
from test import repeat_eval_ckpt
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
from pcdet.datasets import bu... | 11,953 | 44.800766 | 142 | py |
3DTrans | 3DTrans-master/tools/train_pointcontrast.py | print('program started',)
import _init_path
import argparse
import datetime
import glob
import os
from pathlib import Path
from test import repeat_eval_ckpt
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
fro... | 9,328 | 44.286408 | 169 | py |
3DTrans | 3DTrans-master/tools/test.py | import _init_path
import argparse
import datetime
import glob
import os
import re
import time
from pathlib import Path
import numpy as np
import torch
from tensorboardX import SummaryWriter
from eval_utils import eval_utils
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
from pcdet... | 8,740 | 40.42654 | 120 | py |
3DTrans | 3DTrans-master/tools/train_ada.py | import os
import math
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, log_config_to_file, cfg_from_yaml_file, cfg_from_list
from pcdet.utils import common_utils
from pcdet.datasets import build_dataloader, build_dataloader_ada
from pcdet.models import build_networ... | 13,489 | 42.376206 | 169 | py |
3DTrans | 3DTrans-master/tools/_init_path.py | import sys
sys.path.insert(0, '../') | 36 | 17.5 | 25 | py |
3DTrans | 3DTrans-master/tools/train_uda.py | import _init_path
import argparse
import datetime
import glob
import os
from pathlib import Path
from test import repeat_eval_ckpt
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
from pcdet.datasets import bu... | 10,156 | 42.592275 | 125 | py |
3DTrans | 3DTrans-master/tools/train_semi.py | import _init_path
import argparse
import datetime
import glob
import os
from pathlib import Path
import copy
import torch
import torch.distributed as dist
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
from pcdet.dataset... | 17,324 | 46.465753 | 148 | py |
3DTrans | 3DTrans-master/tools/train_multi_db_merge_loss.py | import _init_path
import argparse
import datetime
import glob
import os
from pathlib import Path
from test import repeat_eval_ckpt
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
from pcdet.datasets import bu... | 11,823 | 43.119403 | 125 | py |
3DTrans | 3DTrans-master/tools/demo.py | import argparse
import glob
from pathlib import Path
try:
import open3d
from visual_utils import open3d_vis_utils as V
OPEN3D_FLAG = True
except:
import mayavi.mlab as mlab
from visual_utils import visualize_utils as V
OPEN3D_FLAG = False
import numpy as np
import torch
from pcdet.config impo... | 3,748 | 32.176991 | 118 | py |
3DTrans | 3DTrans-master/tools/test_multi_db_sim.py | import _init_path
import argparse
import datetime
import glob
import os
import re
import time
from pathlib import Path
import numpy as np
import torch
from tensorboardX import SummaryWriter
from eval_utils import eval_utils
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
from pcdet... | 10,010 | 43.691964 | 142 | py |
3DTrans | 3DTrans-master/tools/pseudo_label.py | import _init_path
import argparse
import datetime
import glob
import os
from pathlib import Path
from test import repeat_eval_ckpt
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
from pcdet.datasets import bu... | 9,821 | 42.460177 | 149 | py |
3DTrans | 3DTrans-master/tools/train_multi_db_3db.py | import _init_path
import argparse
import datetime
import glob
import os
from pathlib import Path
from test import repeat_eval_ckpt
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
from pcdet.datasets import bu... | 12,450 | 44.441606 | 144 | py |
3DTrans | 3DTrans-master/tools/train_active_dual_target.py | import os
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, log_config_to_file, cfg_from_yaml_file, cfg_from_list
from pcdet.utils import common_utils
from pcdet.datasets import build_dataloader, build_dataloader_ada
from pcdet.models import build_network, model_fn_... | 12,316 | 42.83274 | 169 | py |
3DTrans | 3DTrans-master/tools/train_active_source.py | import _init_path
import os
import math
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, log_config_to_file, cfg_from_yaml_file, cfg_from_list
from pcdet.utils import common_utils
from pcdet.datasets import build_dataloader_ada
from pcdet.models import build_networ... | 11,822 | 42.307692 | 169 | py |
3DTrans | 3DTrans-master/tools/test_multi_db.py | import _init_path
import argparse
import datetime
import glob
import os
import re
import time
from pathlib import Path
import numpy as np
import torch
from tensorboardX import SummaryWriter
from eval_utils import eval_utils
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
from pcdet... | 10,103 | 42.74026 | 142 | py |
3DTrans | 3DTrans-master/tools/train_random.py | import _init_path
import os
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, log_config_to_file, cfg_from_yaml_file, cfg_from_list
from pcdet.utils import common_utils, active_learning_utils
from pcdet.datasets import build_dataloader, build_dataloader_ada
from pcd... | 10,606 | 43.195833 | 171 | py |
3DTrans | 3DTrans-master/tools/test_semi.py | import _init_path
import argparse
import datetime
import glob
import os
import re
import time
from pathlib import Path
import copy
import numpy as np
import torch
from tensorboardX import SummaryWriter
from eval_utils import eval_utils
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_fil... | 9,442 | 41.15625 | 120 | py |
3DTrans | 3DTrans-master/tools/train_random_target.py | import _init_path
import os
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, log_config_to_file, cfg_from_yaml_file, cfg_from_list
from pcdet.utils import common_utils, active_learning_utils
from pcdet.datasets import build_dataloader, build_dataloader_ada
from pcd... | 9,802 | 42.568889 | 169 | py |
3DTrans | 3DTrans-master/tools/train.py | print('program started',)
import _init_path
import argparse
import datetime
import glob
import os
from pathlib import Path
from test import repeat_eval_ckpt
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
fro... | 9,054 | 43.605911 | 125 | py |
3DTrans | 3DTrans-master/tools/test_multi_db_3db.py | import _init_path
import argparse
import datetime
import glob
import os
import re
import time
from pathlib import Path
import numpy as np
import torch
from tensorboardX import SummaryWriter
from eval_utils import eval_utils
from pcdet.config import cfg, cfg_from_list, cfg_from_yaml_file, log_config_to_file
from pcdet... | 10,576 | 43.441176 | 144 | py |
3DTrans | 3DTrans-master/tools/train_active_TQS.py | import _init_path
import os
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, log_config_to_file, cfg_from_yaml_file, cfg_from_list
from pcdet.utils import common_utils
from pcdet.datasets import build_dataloader, build_dataloader_ada
from pcdet.models import build_... | 12,020 | 42.554348 | 169 | py |
3DTrans | 3DTrans-master/tools/train_bi3d_st3d.py | import _init_path
import os
import torch
import torch.nn as nn
from tensorboardX import SummaryWriter
from pcdet.config import cfg, log_config_to_file, cfg_from_yaml_file, cfg_from_list
from pcdet.utils import common_utils
from pcdet.datasets import build_dataloader, build_dataloader_ada
from pcdet.models import build_... | 11,970 | 42.530909 | 169 | py |
3DTrans | 3DTrans-master/tools/tools_utils/static_once.py | import pickle
import numpy as np
import pandas as pd
import json
once_car = None
once_truck = None
once_bus = None
once_veh = None
once_cyc = None
once_ped = None
with open('./once_infos_train.pkl', 'rb') as f:
once_train_info = pickle.load(f)
json_str = json.dumps(once_train_info[5])
with open('./example.json', ... | 7,801 | 55.536232 | 202 | py |
3DTrans | 3DTrans-master/tools/tools_utils/merge_labels.py | import pickle
from re import L
from turtle import st
import numpy as np
import argparse
def main(args):
assert args.raw_data_pkl != None, 'raw_data path cannot be None'
with open(args.raw_data_pkl, 'rb') as f:
raw_data_info = pickle.load(f)
class_names = []
if args.vehicle_pkl:
w... | 4,034 | 35.026786 | 123 | py |
3DTrans | 3DTrans-master/tools/tools_utils/vis_openmdf.py | import os
import boto3
import io
import numpy as np
import argparse
import pickle
import os
import pickle
import open3d_vis_utils as V
from dataset import Dataset
def read_s3_pkl(bucket_name, pkl_path):
obj = client.get_object(Bucket=bucket_name, Key=pkl_path)
infos = pickle.load(io.BytesIO(obj['Body'].read(... | 2,499 | 30.25 | 106 | py |
3DTrans | 3DTrans-master/tools/tools_utils/open3d_vis_utils.py | """
Open3d visualization tool box
Written by Jihan YANG
All rights preserved from 2021 - present.
"""
import open3d
import torch
import matplotlib
import numpy as np
box_colormap = [
[1, 1, 1],
[0, 1, 0],
[0, 1, 1],
[1, 1, 0],
]
def get_coor_colors(obj_labels):
"""
Args:
obj_labels: 1... | 3,478 | 28.483051 | 145 | py |
3DTrans | 3DTrans-master/tools/tools_utils/static_kitti.py | import pickle
import numpy as np
import pandas as pd
with open('kitti_infos_trainval.pkl', 'rb') as f:
kitti_infos = pickle.load(f)
kitti_car = None
kitti_ped = None
kitti_cyc = None
for i, item in enumerate(kitti_infos):
gt_info = item['annos']
mask_dontcare = gt_info['name'] != 'DontCare'
mask_car ... | 3,878 | 68.267857 | 197 | py |
3DTrans | 3DTrans-master/tools/tools_utils/static_waymo.py | import pickle
import numpy as np
import pandas as pd
# with open('nuscenes_infos_10sweeps_train.pkl', 'rb') as f:
# nusc_info = pickle.load(f)
# nusc_car = None
# for i, item in enumerate(nusc_info):
# gt_boxes = item['gt_boxes']
# gt_names = item['gt_names']
# mask = gt_names == 'car'
# car_info... | 5,325 | 45.719298 | 197 | py |
3DTrans | 3DTrans-master/tools/tools_utils/dataset.py | from ast import arg
# from http.client import _DataType
import os
import matplotlib.pyplot as plt
import boto3
import io
import pickle
import numpy as np
import argparse
import pickle
import os
from collections import defaultdict
import time, copy
import numpy as np
import torch
import open3d as o3d
import open3d
impor... | 9,518 | 43.274419 | 131 | py |
3DTrans | 3DTrans-master/tools/tools_utils/getlist.py |
import os
from os.path import basename
def file_extension(path):
return os.path.splitext(path)[1]
def file_name(path):
return os.path.splitext(path)[0]
root = #PATH_TO_DATASET
path = os.listdir(root) # 6
path.sort()
#vp = 1 #
file = open(root, 'w')
i = 0
print (path)
for line in path:
#subdir = ... | 744 | 18.605263 | 51 | py |
3DTrans | 3DTrans-master/tools/tools_utils/split_kitti_train.py | import os
import torch
import pickle
import json
import copy
import random
nuscenes_info_path_train = ""
with open(nuscenes_info_path_train, 'rb') as f:
infos_train = pickle.load(f)
random.shuffle(infos_train)
total_len = len(infos_train)
# list_01 = infos_train[:int(total_len*0.01)]
list_05 = infos_train[:int... | 1,128 | 24.088889 | 52 | py |
3DTrans | 3DTrans-master/tools/tools_utils/split_nuscenes_location.py | import os
import torch
import pickle
import json
location_info_path = ""
nuscenes_info_path_train = ""
nuscenes_info_path_val = ""
with open(nuscenes_info_path_train, 'rb') as f:
infos_train = pickle.load(f)
with open(nuscenes_info_path_val, 'rb') as f:
infos_val = pickle.load(f)
with open(location_inf... | 3,656 | 33.828571 | 128 | py |
3DTrans | 3DTrans-master/tools/tools_utils/calibration_kitti.py | import numpy as np
def get_calib_from_file(calib_file, oss_flag):
if oss_flag == False:
with open(calib_file) as f:
lines = f.readlines()
obj = lines[2].strip().split(' ')[1:]
P2 = np.array(obj, dtype=np.float32)
obj = lines[3].strip().split(' ')[1:]
P3... | 5,027 | 34.914286 | 116 | py |
3DTrans | 3DTrans-master/tools/tools_utils/split_nusc_train.py | import os
import torch
import pickle
import json
import random
import copy
nuscenes_info_path_train = ""
once_info_path_train = ""
kitti_info = ""
with open(once_info_path_train, 'rb') as f:
infos_train = pickle.load(f)
# random.shuffle(infos_train)
total_len = len(infos_train)
N = 10
infos_train_enlarge = copy... | 801 | 21.914286 | 56 | py |
3DTrans | 3DTrans-master/tools/tools_utils/random_selectlist.py | import os
from os.path import basename
import random
# 1: aeroplane
# 2: bicycle
# 3: bird
# 4: boat
# 5: bottle
# 6: bus
# 7: car
# 8: cat
# 9: chair
# 10: cow
# 11: diningtable
# 12: dog
# 13: horse
# 14: motorbike
# 15: person
# 16: pottedplant
# 17: sheep
# 18: sofa
# 19: train
# 20: tvmonitor
ratio = 0.01
in_fil... | 547 | 13.421053 | 42 | py |
3DTrans | 3DTrans-master/tools/tools_utils/static_nusc.py | import pickle
import numpy as np
import pandas as pd
with open('nuscenes_infos_10sweeps_train.pkl', 'rb') as f:
nusc_info = pickle.load(f)
nusc_car = None
nusc_ped = None
nusc_cyc = None
for i, item in enumerate(nusc_info):
gt_boxes = item['gt_boxes']
gt_names = item['gt_names']
mask_car = gt_names =... | 3,878 | 57.772727 | 192 | py |
3DTrans | 3DTrans-master/tools/unsupervised_utils/pointcontrast_utils.py | import os
import glob
# from plotly import data
from pcdet.models import load_data_to_gpu
import torch
import tqdm
from pcdet.models import load_data_to_gpu
from torch.nn.utils import clip_grad_norm_
from ssl_utils.semi_utils import random_world_flip, random_world_rotation, random_world_scaling
from pcdet.models.detect... | 8,215 | 39.27451 | 117 | py |
3DTrans | 3DTrans-master/tools/eval_utils/dataset_statistic_check.py | import os
import pickle
import io
from pathlib import Path
from petrel_client.client import Client
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from skimage import io as sk_io
client = Client("~/.petreloss.conf")
def list_oss_dir(oss... | 8,936 | 37.356223 | 127 | py |
3DTrans | 3DTrans-master/tools/eval_utils/eval_utils.py | import pickle
import time
import numpy as np
import torch
import tqdm
from pcdet.models import load_data_to_gpu
from pcdet.utils import common_utils
def statistics_info(cfg, ret_dict, metric, disp_dict):
for cur_thresh in cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST:
metric['recall_roi_%s' % str(cur_thr... | 10,327 | 37.251852 | 151 | py |
3DTrans | 3DTrans-master/tools/show_squence_demo/demo.py | import os
import copy
import pickle
from collections import defaultdict
import json
import numpy as np
from pathlib import Path
import argparse
import torch
from utils import Visualizer, LabelLUT
from utils.base_dataset import DataCollect
from pcdet.ops.roiaware_pool3d.roiaware_pool3d_utils import points_in_boxes_gp... | 6,236 | 32.532258 | 124 | py |
3DTrans | 3DTrans-master/tools/show_squence_demo/utils/base_dataset.py | import copy
import numpy as np
from collections import defaultdict
from .components import Object3D
class DataCollect:
def __init__(self, name='Waymo',
color_attr=[],
text_attr=[],
show_text=False):
# super().__init__(name=name)
self.name = nam... | 8,841 | 31.627306 | 96 | py |
3DTrans | 3DTrans-master/tools/show_squence_demo/utils/gui.py | import math
import sys
import numpy as np
import threading
import open3d as o3d
from open3d.visualization import gui
from open3d.visualization import rendering
from collections import deque
from .components import *
import time
import os
class Model:
"""The class that helps build visualization models based on at... | 73,715 | 38.294243 | 129 | py |
3DTrans | 3DTrans-master/tools/show_squence_demo/utils/components.py | import numpy as np
import open3d as o3d
from PIL import Image, ImageDraw
from colorsys import rgb_to_yiq
class LabelLUT:
"""The class to manage look-up table for assigning colors to labels."""
class Label:
def __init__(self, name, value, color):
self.name = name
self.value = v... | 21,218 | 39.649425 | 85 | py |
3DTrans | 3DTrans-master/tools/show_squence_demo/utils/__init__.py | from .gui import *
| 19 | 9 | 18 | py |
3DTrans | 3DTrans-master/tools/ssl_utils/semi_train_utils.py | import glob
import os
import torch
import tqdm
from torch.nn.utils import clip_grad_norm_
from .sess import sess
from .pseudo_label import pseudo_label
from .iou_match_3d import iou_match_3d
from .se_ssd import se_ssd
semi_learning_methods = {
'SESS': sess,
'Pseudo-Label': pseudo_label,
'3DIoUMatch': iou_m... | 9,752 | 42.346667 | 159 | py |
3DTrans | 3DTrans-master/tools/ssl_utils/semi_utils.py | import torch
import numpy as np
from pcdet.models.model_utils import model_nms_utils
try:
import kornia
except:
pass
def load_data_to_gpu(batch_dict):
# for key, val in batch_dict.items():
# if not isinstance(val, np.ndarray):
# continue
# if key in ['frame_id', 'metadata', 'c... | 7,199 | 34.46798 | 91 | py |
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