repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
DRT | DRT-master/caffe/scripts/copy_notebook.py | #!/usr/bin/env python
"""
Takes as arguments:
1. the path to a JSON file (such as an IPython notebook).
2. the path to output file
If 'metadata' dict in the JSON file contains 'include_in_docs': true,
then copies the file to output file, appending the 'metadata' property
as YAML front-matter, adding the field 'categor... | 1,089 | 32.030303 | 87 | py |
DRT | DRT-master/caffe/data/coco/make_trainval.py | #!/usr/bin/env python
# This file is only meant to be run as a script with 0 arguments,
# and depends on steps 1-3 of README.md.
#
# It creates a "trainval" set by combining the COCO 2014 train and val sets.
# The trainval set is intended for use only when training a single final model
# for submission of results on t... | 2,204 | 34 | 78 | py |
DRT | DRT-master/caffe/data/coco/make_test.py | #!/usr/bin/env python
# This file is only meant to be run as a script with 0 arguments,
# and depends on steps 1-3 of README.md.
#
# It creates a test set from the image filenames of the test set.
import json
import os
import re
# get path to directory where this script is
script_dir = os.path.dirname(os.path.realpa... | 1,490 | 38.236842 | 86 | py |
TraceLinkExplanation | TraceLinkExplanation-master/evaluation/eval_glossary.py | import sys
from eval_acronym import read_manual_acrn_eval
from eval_concept import read_concept_answer
from tqdm import tqdm
sys.path.append(".")
from evaluation import utils
import jsonlines
import os
import matplotlib.pyplot as plt
from matplotlib_venn import *
def read_glossary(file_path):
cpt_set = set()
... | 3,495 | 33.613861 | 87 | py |
TraceLinkExplanation | TraceLinkExplanation-master/evaluation/eval_acronym.py | from collections import defaultdict
from typing_extensions import get_args
import jsonlines
import os
from pattmatch import kmp
import sys
from tqdm import tqdm
sys.path.append(".")
sys.path.append("..")
from domain_data_collection.relation_graph import RelationGraph
import utils, argparse
def acronym_as_explain(acr... | 4,850 | 34.933333 | 85 | py |
TraceLinkExplanation | TraceLinkExplanation-master/evaluation/utils.py | import argparse
from jsonlines import jsonlines
import pandas as pd
import os
from collections import defaultdict
import random
from pattmatch import kmp
import pandas as pd
def read_project(dir_path):
sarts = pd.read_csv(os.path.join(dir_path, "source_artifacts.csv"))
tarts = pd.read_csv(os.path.join(dir_pat... | 4,932 | 30.621795 | 74 | py |
TraceLinkExplanation | TraceLinkExplanation-master/evaluation/eval_.py | import jsonlines
import pandas as pd
import os
from collections import defaultdict, Counter
from pattmatch import kmp
from tqdm import tqdm
from domain_data_collection.relation_graph import RelationGraph
import utils
def acronym_as_explain(s_acrns, t_acrns, acrn_index):
res = set()
t_longs = [x.lower() for x... | 7,987 | 36.327103 | 88 | py |
TraceLinkExplanation | TraceLinkExplanation-master/evaluation/eval_concept.py | # how many concept are detected in artifacts
# how many concept have definitions
# how many concept have context
# [manual/random sample] how is the quality of the concept defintion/context
import utils
import os
import json
def evaluate_concept(proj_dir, def_index, ctx_index, eval_dir, def_ans, ctx_ans):
if not... | 5,369 | 36.816901 | 143 | py |
TraceLinkExplanation | TraceLinkExplanation-master/evaluation/eval_relation.py | # how many relations are extracted
# how many links can be explained with the relations? How many are one hop and how many are two hop
# [manual/random sample] how is the quality of the links
import sys
from eval_glossary import find_concpet_in_art
sys.path.append(".")
sys.path.append("..")
from scripts.case_study.cas... | 3,301 | 31.058252 | 99 | py |
TraceLinkExplanation | TraceLinkExplanation-master/concept_detection/EntityDetection.py | import os
import pathlib
import sys
from collections import defaultdict, OrderedDict
import stanza
import pandas as pd
import logging
from stanza.server import CoreNLPClient, StartServer
from tqdm import tqdm
from nltk.stem import WordNetLemmatizer
from concept_detection.DataReader import CM1Reader
logger = logging... | 12,021 | 32.960452 | 197 | py |
TraceLinkExplanation | TraceLinkExplanation-master/concept_detection/DataReader.py | import xml.etree.ElementTree as ET
import pandas as pd
from collections import defaultdict
import json
import os
from pathlib import Path
SART_CSV, TART_CSV, LK_CSV = "source_artifacts.csv", "target_artifacts.csv", "links.csv"
cur_dir = str(Path(__file__).parent.absolute())
class TraceReader:
def __init__(self, ... | 6,353 | 29.257143 | 88 | py |
TraceLinkExplanation | TraceLinkExplanation-master/scripts/annotate_link/trace_link_annotation.py | from typing import Dict, List
from gensim.models import TfidfModel
import argparse
import pandas as pd
import os
import sys
from jsonlines import jsonlines
sys.path.append("../..")
from domain_data_collection.relation_graph import RelationGraph
from gensim.corpora import Dictionary
from concept_detection.EntityDetec... | 9,982 | 33.424138 | 87 | py |
TraceLinkExplanation | TraceLinkExplanation-master/scripts/extract_regular_concept/extract_regular_concepts.py | import argparse, sys, os
from collections import Counter
from pathlib import Path
import logging
from tqdm import tqdm
import pandas as pd
sys.path.append("../..")
from concept_detection.EntityDetection import DomainKG
import heapq
logger = logging.getLogger(__name__)
def select_concepts(in_file, out_file, ratio):
... | 2,627 | 31.04878 | 92 | py |
TraceLinkExplanation | TraceLinkExplanation-master/scripts/glossay_processing/parse_glossary.py | import pandas as pd
import os
import jsonlines
def is_acronym(term):
for c in term:
if c.isalpha() and not c.isupper():
return False
return True
def write_glossary(out_dir, acrn_dict, def_dict):
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
acrn_file = os.path.join(... | 2,401 | 28.292683 | 85 | py |
TraceLinkExplanation | TraceLinkExplanation-master/scripts/preprocess_dataset/preprocess_dataset.py | import os
import pathlib
import sys
import argparse
sys.path.append(".")
sys.path.append("..")
from concept_detection.EntityDetection import DomainKG
from scripts.preprocess_dataset.remove_regular_concepts import remove_regular_concpts
from concept_detection.DataReader import CCHITReader, PTCReader, CM1Reader, Infusi... | 2,253 | 32.147059 | 107 | py |
TraceLinkExplanation | TraceLinkExplanation-master/scripts/preprocess_dataset/remove_regular_concepts.py | import argparse
import pandas as pd
from jsonlines import jsonlines
from tqdm import tqdm
import re
def get_concepts(concept_file):
cpt_df = pd.read_csv(concept_file)
cpts = set()
for idx, row in cpt_df.iterrows():
art_cpts = eval(row["phrase"])
cpts.update(art_cpts)
return cpts
def... | 2,571 | 27.263736 | 88 | py |
TraceLinkExplanation | TraceLinkExplanation-master/sentence_classifier/predict.py | from collections import defaultdict
import os
import sys
sys.path.append(".")
sys.path.append("..")
from torch import nn
from tqdm import tqdm
from transformers.models.auto.tokenization_auto import AutoTokenizer
from transformers import AutoModelForSequenceClassification
from torch.utils.data import DataLoader
import ... | 3,821 | 31.389831 | 87 | py |
TraceLinkExplanation | TraceLinkExplanation-master/sentence_classifier/eval_model.py | import sys
sys.path.append(".")
sys.path.append("..")
from sentence_classifier.predict import run_prediction
from transformers.models.auto.tokenization_auto import AutoTokenizer
from transformers import AutoModelForSequenceClassification
import argparse
import os
import jsonlines
key_dict = {
"acronym": ("short",... | 2,279 | 33.545455 | 84 | py |
TraceLinkExplanation | TraceLinkExplanation-master/sentence_classifier/train.py | from transformers import TrainingArguments, AutoTokenizer
from transformers import AutoModelForSequenceClassification, Trainer
import torch
from datasets import load_metric
import numpy as np
import sys
sys.path.append("..")
sys.path.append(".")
from evaluation import utils
from nltk.tokenize import sent_tokenize
imp... | 3,452 | 28.512821 | 85 | py |
TraceLinkExplanation | TraceLinkExplanation-master/domain_data_collection/corpus_build_bot_up.py | import argparse
import os
import ssl
import sys
sys.path.append(".")
sys.path.append("..")
from domain_data_collection.utils import clean_paragraph
import pathlib
ssl._create_default_https_context = ssl._create_unverified_context
from queue import Queue
from time import sleep, time
import pandas as pd
import request... | 6,198 | 29.995 | 88 | py |
TraceLinkExplanation | TraceLinkExplanation-master/domain_data_collection/utils.py | import re
import pandas as pd
from nltk import sent_tokenize
def read_regular_concepts(regcpt_file):
regcpt = set()
with open(regcpt_file) as fin:
for line in fin:
cpt, cnt = line.split(',')[-2:]
if int(cnt) > 10000:
regcpt.add(cpt.lower())
else:
... | 1,689 | 22.802817 | 60 | py |
TraceLinkExplanation | TraceLinkExplanation-master/domain_data_collection/basic_concept_relation.py | """
Find the concept relations with more basic rules
"""
import argparse
from pathlib import Path
from nltk import WordNetLemmatizer, PorterStemmer
from nltk.corpus import wordnet
import sys
sys.path.append("..")
from domain_data_collection import utils
import json
stemmer = PorterStemmer()
lemmatizer = WordNetLemmat... | 3,633 | 27.170543 | 87 | py |
TraceLinkExplanation | TraceLinkExplanation-master/domain_data_collection/corpus_build_top_down.py | import gzip, os, json, argparse
import sys
sys.path.append(".")
sys.path.append("..")
from multiprocessing import Pool, Process, Queue
from domain_data_collection.corpus_build_bot_up import get_concepts
from cleantext import clean
from domain_data_collection.utils import clean_paragraph
from collections import default... | 7,596 | 31.32766 | 95 | py |
TraceLinkExplanation | TraceLinkExplanation-master/domain_data_collection/extract_from_corpus.py | import argparse
import os
from collections import defaultdict
import sys
sys.path.append(".")
sys.path.append("..")
from jsonlines import jsonlines
from nltk.corpus import wordnet
from pattmatch import kmp
from tqdm import tqdm
from abbreviations import schwartz_hearst
from concept_detection.EntityDetection import Do... | 11,747 | 32.855908 | 114 | py |
TraceLinkExplanation | TraceLinkExplanation-master/domain_data_collection/relation_graph.py | import os
from asyncio import start_server
import pandas as pd
from graph_tools import Graph
from pathlib import Path
import sys
from multiprocessing import Pool
from graphviz import Source
from jsonlines import jsonlines
from stanza.server import CoreNLPClient
from nltk.stem import WordNetLemmatizer
from pattmatch im... | 7,405 | 33.287037 | 122 | py |
Opportunistic | Opportunistic-master/DataGenerator.py | import pdb
import numpy as np
from DataPoint import DataPoint
class DataGenerator:
def __init__(self, distribution):
self.__distribution = distribution
def get_data_point(self, label_noise=0.00):
ind, features, label, feature_costs, label_cost = next(self.__distribution)
if np.rand... | 470 | 25.166667 | 83 | py |
Opportunistic | Opportunistic-master/DataPoint.py | import numpy as np
class DataPoint():
def __init__(self, ind, features, label, feature_costs, label_cost):
self.__features = features
self.__label = label
self.__feature_costs = feature_costs
self.__label_cost = label_cost
self.__accumulated_cost = 0
self.__known_features = np.ones(len(features))*np.nan... | 1,079 | 18.636364 | 69 | py |
Opportunistic | Opportunistic-master/nhanes.py | import pdb
import glob
import copy
import os
import pickle
import joblib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats
import sklearn.feature_selection
class FeatureColumn:
def __init__(self, category, field, preprocessor, args=None, cost=None):
self.category = ... | 32,417 | 41.937748 | 122 | py |
Opportunistic | Opportunistic-master/src/utils.py | import gc
import numpy as np
import torch
class ExperienceBuffer():
def __init__(self, buffer_size):
self.buffer = []
self.buffer_size = buffer_size
def push(self,experience):
if len(self.buffer) + 1 >= self.buffer_size:
self.buffer[0:(1+len(self.buffer))-self.buffer_s... | 2,645 | 33.363636 | 95 | py |
DROO | DROO-master/main.py | # #################################################################
# Deep Reinforcement Learning for Online Offloading in Wireless Powered Mobile-Edge Computing Networks
#
# This file contains the main code of DROO. It loads the training samples saved in ./data/data_#.mat, splits the samples into two parts (training... | 6,803 | 39.987952 | 284 | py |
DROO | DROO-master/optimization.py | # -*- coding: utf-8 -*-
"""
Created on Tue Jan 9 10:45:26 2018
@author: Administrator
"""
import numpy as np
from scipy import optimize
from scipy.special import lambertw
import scipy.io as sio # import scipy.io for .mat file I/
import time
def plot_gain( gain_his):
import matplotlib.pyplot ... | 6,614 | 26.911392 | 811 | py |
DROO | DROO-master/demo_alternate_weights.py | # #################################################################
# Deep Reinforcement Learning for Online Offloading in Wireless Powered Mobile-Edge Computing Networks
#
# This file contains a demo evaluating the performance of DROO with laternating-weight WDs. It loads the training samples with default WDs' weigh... | 5,962 | 37.973856 | 468 | py |
DROO | DROO-master/memory.py | # #################################################################
# This file contains memory operation including encoding and decoding operations.
#
# version 1.0 -- January 2018. Written by Liang Huang (lianghuang AT zjut.edu.cn)
# #################################################################
from __future_... | 7,094 | 36.539683 | 123 | py |
DROO | DROO-master/mainPyTorch.py | # #################################################################
# Deep Reinforcement Learning for Online Offloading in Wireless Powered Mobile-Edge Computing Networks
#
# This file contains the main code of DROO. It loads the training samples saved in ./data/data_#.mat, splits the samples into two parts (training... | 6,830 | 39.904192 | 284 | py |
DROO | DROO-master/memoryPyTorch.py | # #################################################################
# This file contains the main DROO operations, including building DNN,
# Storing data sample, Training DNN, and generating quantized binary offloading decisions.
# version 1.0 -- February 2020. Written based on Tensorflow 2 by Weijian Pan and
# ... | 5,082 | 31.583333 | 109 | py |
DROO | DROO-master/demo_on_off.py | # #################################################################
# Deep Reinforcement Learning for Online Offloading in Wireless Powered Mobile-Edge Computing Networks
#
# This file contains a demo evaluating the performance of DROO by randomly turning on/off some WDs. It loads the training samples from ./data/dat... | 7,281 | 39.681564 | 355 | py |
DROO | DROO-master/memoryTF2.py | # #################################################################
# This file contains the main DROO operations, including building DNN,
# Storing data sample, Training DNN, and generating quantized binary offloading decisions.
# version 1.0 -- January 2020. Written based on Tensorflow 2 by Weijian Pan and
# ... | 5,117 | 33.816327 | 129 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/exps/der_womask/cifar100/b0/10steps/main.py | '''
@Author : Yan Shipeng, Xie Jiangwei
@Contact: yanshp@shanghaitech.edu.cn, xiejw@shanghaitech.edu.cn
'''
import sys
import os
import os.path as osp
import copy
import time
import shutil
import cProfile
import logging
from pathlib import Path
import numpy as np
import random
from easydict import EasyDict as edict
fr... | 8,825 | 37.710526 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/exps/der_womask/cifar100/b50/10steps/main.py | '''
@Author : Yan Shipeng, Xie Jiangwei
@Contact: yanshp@shanghaitech.edu.cn, xiejw@shanghaitech.edu.cn
'''
import sys
import os
import os.path as osp
import copy
import time
import shutil
import cProfile
import logging
from pathlib import Path
import numpy as np
import random
from easydict import EasyDict as edict
fr... | 8,825 | 37.710526 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/exps/der_womask/imagenet-100/b0_10s/main.py | '''
@Author : Yan Shipeng, Xie Jiangwei
@Contact: yanshp@shanghaitech.edu.cn, xiejw@shanghaitech.edu.cn
'''
import sys
import os
import os.path as osp
import copy
import time
import shutil
import cProfile
import logging
from pathlib import Path
import numpy as np
import random
from easydict import EasyDict as edict
fr... | 8,777 | 37.840708 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/exps/weight_align/cifar100/b0/10steps/main.py | '''
@Author : Yan Shipeng, Xie Jiangwei
@Contact: yanshp@shanghaitech.edu.cn, xiejw@shanghaitech.edu.cn
'''
import sys
import os
import os.path as osp
import copy
import time
import shutil
import cProfile
import logging
from pathlib import Path
import numpy as np
import random
from easydict import EasyDict as edict
fr... | 8,825 | 37.710526 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/__init__.py | 0 | 0 | 0 | py | |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/learn/pretrain.py | import os.path as osp
import torch
import torch.nn.functional as F
from inclearn.tools import factory, utils
from inclearn.tools.metrics import ClassErrorMeter, AverageValueMeter
# import line_profiler
# import atexit
# profile = line_profiler.LineProfiler()
# atexit.register(profile.print_stats)
def _compute_loss(... | 3,886 | 36.019048 | 118 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/learn/__init__.py | 0 | 0 | 0 | py | |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/memory.py | import numpy as np
from copy import deepcopy
import torch
from torch.nn import functional as F
from inclearn.tools.utils import get_class_loss
from inclearn.convnet.utils import extract_features
class MemorySize:
def __init__(self, mode, inc_dataset, total_memory=None, fixed_memory_per_cls=None):
self.mo... | 6,375 | 41.791946 | 120 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/results_utils.py | import glob
import json
import math
import os
import numpy as np
import matplotlib.pyplot as plt
from copy import deepcopy
from . import utils
def get_template_results(cfg):
return {"config": cfg, "results": []}
def save_results(results, label):
del results["config"]["device"]
folder_path = os.path.jo... | 1,315 | 28.909091 | 82 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/utils.py | import random
from copy import deepcopy
import numpy as np
import datetime
import torch
from inclearn.tools.metrics import ClassErrorMeter
def get_date():
return datetime.datetime.now().strftime("%Y%m%d")
def to_onehot(targets, n_classes):
if not hasattr(targets, "device"):
targets = torch.from_nu... | 6,969 | 33.85 | 97 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/data_utils.py | import numpy as np
def construct_balanced_subset(x, y):
xdata, ydata = [], []
minsize = np.inf
for cls_ in np.unique(y):
xdata.append(x[y == cls_])
ydata.append(y[y == cls_])
if ydata[-1].shape[0] < minsize:
minsize = ydata[-1].shape[0]
for i in range(len(xdata)):
... | 643 | 29.666667 | 61 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/scheduler.py | import math
from torch.optim.lr_scheduler import _LRScheduler
from torch.optim.lr_scheduler import ReduceLROnPlateau
class ConstantTaskLR:
def __init__(self, lr):
self._lr = lr
def get_lr(self, task_i):
return self._lr
class CosineAnnealTaskLR:
def __init__(self, lr_max, lr_min, task_ma... | 3,749 | 41.134831 | 152 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/factory.py | import torch
from torch import nn
from torch import optim
from inclearn import models
from inclearn.convnet import resnet, cifar_resnet, modified_resnet_cifar, preact_resnet
from inclearn.datasets import data
def get_optimizer(params, optimizer, lr, weight_decay=0.0):
if optimizer == "adam":
return optim... | 2,205 | 30.971014 | 87 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/metrics.py | import numpy as np
import torch
import numbers
import math
class IncConfusionMeter:
"""Maintains a confusion matrix for a given calssification problem.
The ConfusionMeter constructs a confusion matrix for a multi-class
classification problems. It does not support multi-label, multi-class problems:
for... | 7,415 | 37.625 | 107 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/tools/__init__.py | 0 | 0 | 0 | py | |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/resnet.py | """Taken & slightly modified from:
* https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
model_urls = {
... | 8,130 | 32.460905 | 109 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/network.py | import copy
import pdb
import torch
from torch import nn
import torch.nn.functional as F
from inclearn.tools import factory
from inclearn.convnet.imbalance import BiC, WA
from inclearn.convnet.classifier import CosineClassifier
class BasicNet(nn.Module):
def __init__(
self,
convnet_type,
... | 6,100 | 35.532934 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/imbalance.py | import torch
import torch.nn.functional as F
from torch import nn
import numpy as np
from torch.optim.lr_scheduler import CosineAnnealingLR
class BiC(nn.Module):
def __init__(self, lr, scheduling, lr_decay_factor, weight_decay, batch_size, epochs):
super(BiC, self).__init__()
self.beta = torch.nn.... | 5,074 | 40.260163 | 117 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/utils.py | import numpy as np
import torch
from torch import nn
from torch.optim import SGD
import torch.nn.functional as F
from inclearn.tools.metrics import ClassErrorMeter, AverageValueMeter
def finetune_last_layer(
logger,
network,
loader,
n_class,
nepoch=30,
lr=0.1,
scheduling=[15, 35],
lr_d... | 4,496 | 35.266129 | 118 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/classifier.py | import math
import torch
from torch.nn.parameter import Parameter
from torch.nn import functional as F
from torch.nn import Module
class CosineClassifier(Module):
def __init__(self, in_features, n_classes, sigma=True):
super(CosineClassifier, self).__init__()
self.in_features = in_features
... | 1,035 | 31.375 | 92 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/preact_resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class PreActBlock(nn.Module):
'''Pre-activation version of the BasicBlock.'''
expansion = 1
def __init__(self, in_planes, planes, stride=1, remove_last_relu=False):
super(PreActBlock, self).__init__()
self.bn1 = nn.BatchNo... | 5,859 | 37.552632 | 113 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/cifar_resnet.py | ''' Incremental-Classifier Learning
Authors : Khurram Javed, Muhammad Talha Paracha
Maintainer : Khurram Javed
Lab : TUKL-SEECS R&D Lab
Email : 14besekjaved@seecs.edu.pk '''
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
class DownsampleA(nn.Module):
... | 5,944 | 29.331633 | 102 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/modified_resnet_cifar.py | """Taken & slightly modified from:
* https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
"""
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152']
model_urls = {
... | 4,519 | 32.731343 | 109 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/convnet/__init__.py | 0 | 0 | 0 | py | |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/models/base.py | import abc
import logging
import torch
import torch.nn.functional as F
import numpy as np
from inclearn.tools.metrics import ClassErrorMeter
LOGGER = logging.Logger("IncLearn", level="INFO")
class IncrementalLearner(abc.ABC):
"""Base incremental learner.
Methods are called in this order (& repeated for each... | 6,449 | 40.883117 | 120 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/models/align.py | import numpy as np
import random
import time
import math
import os
from copy import deepcopy
from scipy.spatial.distance import cdist
import torch
from torch.nn import DataParallel
from torch.nn import functional as F
from inclearn.convnet import network
from inclearn.models.base import IncrementalLearner
from inclea... | 14,420 | 42.436747 | 137 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/models/__init__.py | from .incmodel import IncModel
from .align import Weight_Align
from .bic import BiC
| 84 | 20.25 | 31 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/models/incmodel.py | import numpy as np
import random
import time
import math
import os
from copy import deepcopy
from scipy.spatial.distance import cdist
import torch
from torch.nn import DataParallel
from torch.nn import functional as F
from inclearn.convnet import network
from inclearn.models.base import IncrementalLearner
from inclea... | 19,955 | 43.445434 | 137 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/datasets/dataset.py | import os.path as osp
import numpy as np
import glob
import albumentations as A
from albumentations.pytorch import ToTensorV2
from torchvision import datasets, transforms
import torch
def get_datasets(dataset_names):
return [get_dataset(dataset_name) for dataset_name in dataset_names.split("-")]
def get_datas... | 16,245 | 51.918567 | 120 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/datasets/data.py | import random
import cv2
import numpy as np
import os.path as osp
from copy import deepcopy
from PIL import Image
import multiprocessing as mp
from multiprocessing import Pool
import albumentations as A
from albumentations.pytorch import ToTensorV2
import warnings
warnings.filterwarnings("ignore", "Corrupt EXIF data",... | 14,954 | 37.44473 | 119 | py |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/inclearn/datasets/__init__.py | 0 | 0 | 0 | py | |
DER-ClassIL.pytorch | DER-ClassIL.pytorch-main/codes/base/main.py | '''
@Author : Yan Shipeng, Xie Jiangwei
@Contact: yanshp@shanghaitech.edu.cn, xiejw@shanghaitech.edu.cn
'''
import sys
import os
import os.path as osp
import copy
import time
import shutil
import cProfile
import logging
from pathlib import Path
import numpy as np
import random
from easydict import EasyDict as edict
fr... | 8,825 | 37.710526 | 119 | py |
TransportNet | TransportNet-master/Stable Dynamic & Beckman/dual_func_calculator.py | import numpy as np
class PrimalDualCalculator:
def __init__(self, phi_big_oracle, h_oracle, freeflowtimes, capacities, rho = 10.0, mu = 0.25, base_flows = None):
self.links_number = len(freeflowtimes)
self.rho = rho
self.mu = mu
self.freeflowtimes = freeflowtimes #\bar{t}
se... | 2,630 | 42.85 | 118 | py |
TransportNet | TransportNet-master/Stable Dynamic & Beckman/model.py | # model parameters:
import copy
import numpy as np
import transport_graph as tg
import oracles
import dual_func_calculator as dfc
from grad_methods import universal_similar_triangles_method as ustm
from grad_methods import universal_gradient_descent_method as ugd
from grad_methods import subgradient_descent_method as... | 6,187 | 50.566667 | 117 | py |
TransportNet | TransportNet-master/Stable Dynamic & Beckman/oracles.py | #import multiprocessing as mp
from collections import defaultdict
#from scipy.misc import logsumexp
import numpy as np
import time
import numba
from numba import njit
from numba.typed import List, Dict
@njit
def get_tree_order(nodes_number, targets, pred_arr):
#get nodes visiting order for flow calculation
vi... | 11,657 | 34.327273 | 114 | py |
TransportNet | TransportNet-master/Stable Dynamic & Beckman/transport_graph.py | # Attention: as shown on the table above
# nodes indexed from 0 to ...
# edges indexed from 0 to ...
import graph_tool.all as gt
import graph_tool.topology as gtt
import numpy as np
import math
class TransportGraph:
def __init__(self, graph_table, nodes_number, links_number, maxpath_const = 3):
self.n... | 3,201 | 38.04878 | 102 | py |
TransportNet | TransportNet-master/Stable Dynamic & Beckman/data_handler.py | from scanf import scanf
import re
import numpy as np
import pandas as pd
#TODO: DOCUMENTATION!!!
class DataHandler:
def GetGraphData(self, file_name, columns):
graph_data = {}
metadata = ''
with open(file_name, 'r') as myfile:
for index, line in enumerate(myfile):
... | 3,029 | 40.506849 | 129 | py |
TransportNet | TransportNet-master/Stable Dynamic & Beckman/history.py | class History():
"""
history handler
"""
def __init__(self, *attributes):
self.dict = {}
self.attributes = list(attributes)
for attribute in self.attributes:
self.dict[attribute] = []
def update(self, *values):
for index, value in enumerate(values... | 461 | 27.875 | 59 | py |
TransportNet | TransportNet-master/Stable Dynamic & Beckman/grad_methods/subgradient_descent_method.py | import numpy as np
from history import History
def subgradient_descent_method(oracle, prox, primal_dual_oracle,
t_start, max_iter = 1000,
eps = 1e-5, eps_abs = None, stop_crit = 'dual_gap_rel',
verbose_step = 100, verbose = Fa... | 2,664 | 38.191176 | 94 | py |
TransportNet | TransportNet-master/Stable Dynamic & Beckman/grad_methods/weighted_dual_averages_method.py | from math import sqrt
import numpy as np
from history import History
def weighted_dual_averages_method(oracle, prox, primal_dual_oracle,
t_start, max_iter = 1000,
eps = 1e-5, eps_abs = None, stop_crit = 'dual_gap_rel',
... | 2,922 | 36.961039 | 94 | py |
TransportNet | TransportNet-master/Stable Dynamic & Beckman/grad_methods/frank_wolfe_method.py | from math import sqrt
import numpy as np
from history import History
def frank_wolfe_method(oracle, primal_dual_oracle,
t_start, max_iter = 1000,
eps = 1e-5, eps_abs = None, stop_crit = 'dual_gap_rel',
verbose_step = 100, verbose = False, save_histor... | 2,500 | 38.078125 | 87 | py |
TransportNet | TransportNet-master/Stable Dynamic & Beckman/grad_methods/universal_similar_triangles_method.py | from math import sqrt
import numpy as np
from history import History
def universal_similar_triangles_method(oracle, prox, primal_dual_oracle,
t_start, L_init = None, max_iter = 1000,
eps = 1e-5, eps_abs = None, stop_crit = 'dual_gap_rel',
... | 4,828 | 37.632 | 98 | py |
TransportNet | TransportNet-master/Stable Dynamic & Beckman/grad_methods/__init__.py | 1 | 0 | 0 | py | |
TransportNet | TransportNet-master/Stable Dynamic & Beckman/grad_methods/universal_gradient_descent_method.py | import numpy as np
from history import History
def universal_gradient_descent_method(oracle, prox, primal_dual_oracle,
t_start, L_init = None, max_iter = 1000,
eps = 1e-5, eps_abs = None, stop_crit = 'dual_gap_rel',
... | 3,435 | 38.045455 | 97 | py |
TransportNet | TransportNet-master/Stochastic Nash-Wardrop equilibrium/dual_func_calculator.py | import numpy as np
#from numba import jit
class PrimalDualCalculator:
def __init__(self, phi_big_oracle, h_oracle, freeflowtimes, capacities, rho = 10.0, mu = 0.25):
self.links_number = len(freeflowtimes)
self.rho = rho
self.mu = mu
self.freeflowtimes = freeflowtimes #\bar{t}
... | 2,133 | 40.038462 | 99 | py |
TransportNet | TransportNet-master/Stochastic Nash-Wardrop equilibrium/model.py | # model parameters:
import copy
import numpy as np
import transport_graph as tg
import oracles
import dual_func_calculator as dfc
import universal_similar_triangles_method as ustm
import universal_gradient_descent_method as ugd
#from numba import jit
import math
class Model:
def __init__(self, graph_data, grap... | 4,564 | 47.56383 | 114 | py |
TransportNet | TransportNet-master/Stochastic Nash-Wardrop equilibrium/oracles.py | # from scipy.special import expit
#import multiprocessing as mp
from collections import defaultdict
#from scipy.misc import logsumexp
from scipy.special import expit
import numpy as np
import time
from transport_graph import JitTransportGraph
from numba.experimental import jitclass
from numba import jit, int32, int64, ... | 16,548 | 37.575758 | 122 | py |
TransportNet | TransportNet-master/Stochastic Nash-Wardrop equilibrium/universal_similar_triangles_method.py | from math import sqrt
import numpy as np
from history import History
def universal_similar_triangles_method(oracle, prox, primal_dual_oracle,
t_start, L_init = None, max_iter = 1000,
eps = 1e-5, eps_abs = None, stop_crit = 'dual_gap_rel',
... | 4,846 | 37.776 | 98 | py |
TransportNet | TransportNet-master/Stochastic Nash-Wardrop equilibrium/transport_graph.py | # Attention: as shown on the table above
# nodes indexed from 1 to ...
# edges indexed from 0 to ...
#import networkx as nx
import numpy as np
import copy
import scipy.sparse as sp
import math
from numba.experimental import jitclass
from numba import int64, float64
spec = [
('_nodes_number', int64),
('_links_n... | 7,589 | 36.95 | 105 | py |
TransportNet | TransportNet-master/Stochastic Nash-Wardrop equilibrium/data_handler.py | from scanf import scanf
import re
import numpy as np
import pandas as pd
#TODO: DOCUMENTATION!!!
class DataHandler:
def GetGraphData(self, file_name, columns):
graph_data = {}
metadata = ''
with open(file_name, 'r') as myfile:
for index, line in enumerate(myfile):
... | 3,029 | 40.506849 | 129 | py |
TransportNet | TransportNet-master/Stochastic Nash-Wardrop equilibrium/history.py | class History():
"""
history handler
"""
def __init__(self, *attributes):
self.dict = {}
self.attributes = list(attributes)
for attribute in self.attributes:
self.dict[attribute] = []
def update(self, *values):
for index, value in enumerate(values... | 461 | 27.875 | 59 | py |
TransportNet | TransportNet-master/Stochastic Nash-Wardrop equilibrium/universal_gradient_descent_method.py | import numpy as np
from history import History
def universal_gradient_descent_method(oracle, prox, primal_dual_oracle,
t_start, L_init = None, max_iter = 1000,
eps = 1e-5, eps_abs = None, stop_crit = 'dual_gap_rel',
... | 3,442 | 38.125 | 100 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/setup.py | from setuptools import setup, find_packages
setup(
name='pareto',
version='0.1',
packages=find_packages(),
zip_safe=False,
install_requires=[
'numpy',
'scipy',
'torch',
'torchvision',
'tqdm',
],
)
| 262 | 15.4375 | 43 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/utils.py | from typing import Iterable
from itertools import product
from termcolor import colored
import numpy as np
class TopTrace(object):
def __init__(
self,
num_objs: int,
*,
indent_size: int = 4,
):
self.tops = [[] for _ in range(num_objs)]
sel... | 1,500 | 30.270833 | 129 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/metrics.py | from typing import Iterable
from torch import Tensor
__all__ = ['topk_accuracies', 'topk_accuracy']
def topk_accuracies(
output: Tensor,
label: Tensor,
ks: Iterable[int] = (1,),
):
assert output.dim() == 2
assert label.dim() == 1
assert output.size(0) == label.size(0)
m... | 771 | 19.864865 | 65 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/__init__.py | from . import optim
from . import metrics
from . import networks
from . import datasets
from . import utils
| 108 | 17.166667 | 22 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/networks/multi_lenet.py | from typing import Tuple, List
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
class MultiLeNet(nn.Module):
def __init__(self) -> None:
super(MultiLeNet, self).__init__()
self.conv1 = nn.Conv2d(1, 10, (5, 5))
self.conv2 = nn.Conv2d(10, 20, (5, 5... | 927 | 27.121212 | 81 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/networks/__init__.py | from .multi_lenet import MultiLeNet
| 36 | 17.5 | 35 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/optim/hvp_solver.py | from functools import partial
from typing import Tuple, List, Iterable, Callable
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn.utils import parameters_to_vector
__all__ = ['HVPSolver', 'AutogradHVPSolver', 'VisionHVPSolver']
class HVPSolver(object):
"""
Hessian-Vector product ca... | 7,426 | 26.712687 | 101 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/optim/linalg_solver.py | from contextlib import contextmanager
from functools import partial
from typing import Tuple
import numpy as np
from scipy.sparse.linalg import LinearOperator, minres
import torch
import torch.nn as nn
from torch import Tensor
from .hvp_solver import HVPSolver
__all__ = ['PDError', 'HVPLinearOperator', 'KrylovSol... | 8,325 | 26.66113 | 104 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/optim/min_norm_solver.py | # This code is from
# Multi-Task Learning as Multi-Objective Optimization
# Ozan Sener, Vladlen Koltun
# Neural Information Processing Systems (NeurIPS) 2018
# https://github.com/intel-isl/MultiObjectiveOptimization
from itertools import combinations
import numpy as np
import torch
__all__ = ['find_min_norm_elem... | 4,953 | 29.9625 | 109 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/optim/kkt_solver.py | from typing import Tuple, Mapping
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
from .hvp_solver import HVPSolver
from .min_norm_solver import find_min_norm_element
from .linalg_solver import KrylovSolver, MINRESSolver, CGSolver
__all__ = ['KKTSolver', 'KrylovKKTSolver', 'CGKKTSolv... | 8,382 | 29.483636 | 111 | py |
ContinuousParetoMTL | ContinuousParetoMTL-master/pareto/optim/__init__.py | from .hvp_solver import VisionHVPSolver
from .kkt_solver import CGKKTSolver, MINRESKKTSolver
from .min_norm_solver import find_min_norm_element
| 144 | 35.25 | 52 | py |
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