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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apicarver | apicarver-main/testCarver/pythonCode/toolExecutionStats.py | import glob
import os
from constants import APPS
from plotCoverage import getAppData
from runCarver import getExistingCarverRun
from utilsRun import importJson, writeCSV, writeCSV_Dict
from urllib.parse import urlparse
def findResultResponses():
allOutputs = {}
for appName in APPS:
carverOutput = None... | 7,591 | 40.714286 | 220 | py |
apicarver | apicarver-main/testCarver/pythonCode/coverageStats.py | import datetime
import glob
import os
import xml.etree.ElementTree as ETree
from bs4 import BeautifulSoup
import constants
# import utilsRun
from subprocess import check_call, CalledProcessError
# combine schemathesis(vanilla) coverage with carver and prober coverage
import utilsRun
from runGeneratedTests import get... | 26,599 | 52.846154 | 170 | py |
apicarver | apicarver-main/testCarver/pythonCode/runCarver.py | import os
import shutil
from datetime import datetime
# from globalNames import FILTER, THRESHOLD_SETS, DB_SETS, APPS, isDockerized, DOCKER_LOCATION, isNd3App, getHostNames, \
# ALGOS, getDockerName, getDockerList, getURLList
import glob
from constants import APPS, RUN_CARVER_COMMAND, STATUS_SUCCESSFUL, STATUS_SKIPPED... | 4,439 | 29.62069 | 121 | py |
apicarver | apicarver-main/testCarver/pythonCode/plotCoverage.py | import json
import os
from urllib.parse import urlsplit, parse_qs
import ruamel.yaml
import constants
import runRestats
import matplotlib.pyplot as plt
def parsePostData(postData):
params = {}
if postData is None:
print("Cannot parse None")
return None
if type(postData) is dict and "str... | 6,614 | 29.767442 | 111 | py |
a2dr | a2dr-master/setup.py | from setuptools import setup, find_packages
import codecs
import os.path
# code for single sourcing versions
# reference: https://packaging.python.org/guides/single-sourcing-package-version/
def read(rel_path):
here = os.path.abspath(os.path.dirname(__file__))
with codecs.open(os.path.join(here, rel_path), 'r'... | 1,673 | 37.045455 | 105 | py |
a2dr | a2dr-master/examples/other_examples/simple_logistic.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 3,761 | 35.524272 | 120 | py |
a2dr | a2dr-master/examples/other_examples/stratified_model.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 3,750 | 30 | 101 | py |
a2dr | a2dr-master/examples/paper_examples/coupled_qp.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 4,224 | 39.625 | 133 | py |
a2dr | a2dr-master/examples/paper_examples/single_commodity_flow.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 6,153 | 38.961039 | 133 | py |
a2dr | a2dr-master/examples/paper_examples/l1_trend_filtering.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 3,203 | 34.6 | 133 | py |
a2dr | a2dr-master/examples/paper_examples/nnls_reg.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 2,825 | 32.642857 | 124 | py |
a2dr | a2dr-master/examples/paper_examples/optimal_control.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 5,945 | 41.471429 | 133 | py |
a2dr | a2dr-master/examples/paper_examples/sparse_inv_cov_est.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 3,718 | 38.147368 | 133 | py |
a2dr | a2dr-master/examples/paper_examples/nnls.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 2,824 | 31.471264 | 101 | py |
a2dr | a2dr-master/examples/paper_examples/multitask_reg_logistic.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 4,133 | 39.135922 | 133 | py |
a2dr | a2dr-master/examples/paper_examples/paper_plots.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 20,198 | 43.393407 | 132 | py |
a2dr | a2dr-master/a2dr/acceleration.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 2,012 | 33.706897 | 84 | py |
a2dr | a2dr-master/a2dr/utilities.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 2,883 | 32.929412 | 81 | py |
a2dr | a2dr-master/a2dr/__init__.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 719 | 30.304348 | 68 | py |
a2dr | a2dr-master/a2dr/solver.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 17,229 | 40.518072 | 112 | py |
a2dr | a2dr-master/a2dr/precondition.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 4,872 | 37.984 | 115 | py |
a2dr | a2dr-master/a2dr/tests/test_proximal.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 36,504 | 50.127451 | 134 | py |
a2dr | a2dr-master/a2dr/tests/test_basic.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 5,654 | 37.209459 | 114 | py |
a2dr | a2dr-master/a2dr/tests/test_precondition.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 4,577 | 38.465517 | 128 | py |
a2dr | a2dr-master/a2dr/tests/base_test.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 5,778 | 40.876812 | 157 | py |
a2dr | a2dr-master/a2dr/tests/test_projection.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 3,037 | 37.948718 | 90 | py |
a2dr | a2dr-master/a2dr/tests/__init__.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 660 | 33.789474 | 68 | py |
a2dr | a2dr-master/a2dr/proximal/norm.py | import numpy as np
from scipy import sparse
from a2dr.proximal.interface import NUMPY_FUNS, SPARSE_FUNS, apply_to_nonzeros
from a2dr.proximal.projection import proj_l1
from a2dr.proximal.composition import prox_scale
def prox_norm1(v, t = 1, *args, **kwargs):
"""Proximal operator of :math:`tf(ax-b) + c^Tx + d\\|x\... | 5,328 | 42.680328 | 109 | py |
a2dr | a2dr-master/a2dr/proximal/constraint.py | import numpy as np
from scipy import sparse
from a2dr.proximal.interface import NUMPY_FUNS, SPARSE_FUNS
from a2dr.proximal.composition import prox_scale
def prox_box_constr(v, t = 1, v_lo = -np.inf, v_hi = np.inf, *args, **kwargs):
"""Proximal operator of :math:`tf(ax-b) + c^Tx + d\\|x\\|_2^2`, where :math:`f` is the... | 5,589 | 43.015748 | 116 | py |
a2dr | a2dr-master/a2dr/proximal/misc.py | import numpy as np
import warnings
from scipy import sparse
from scipy.special import expit, lambertw
from scipy.optimize import minimize
from a2dr.proximal.projection import proj_simplex
from a2dr.proximal.composition import prox_scale
def prox_kl(v, t = 1, u = None, *args, **kwargs):
"""Proximal operator of :ma... | 3,886 | 40.351064 | 117 | py |
a2dr | a2dr-master/a2dr/proximal/projection.py | import numpy as np
from scipy.optimize import bisect
TOLERANCE = 1e-6
def proj_l1(x, r = 1, method = "bisection"):
if np.isscalar(x):
x = np.array([x])
if not np.isscalar(r) or r < 0:
raise ValueError("r must be a non-negative scalar.")
if np.linalg.norm(x,1) <= r:
return x
els... | 1,730 | 35.829787 | 111 | py |
a2dr | a2dr-master/a2dr/proximal/__init__.py | """
Copyright 2019 Anqi Fu, Junzi Zhang
This file is part of A2DR.
A2DR is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
A2DR is distribute... | 1,329 | 48.259259 | 117 | py |
a2dr | a2dr-master/a2dr/proximal/interface.py | import numpy as np
import scipy as sp
from scipy import sparse
def shape_to_2d(shape):
if np.isscalar(shape) or len(shape) == 0:
return 1,1
elif len(shape) == 1:
return shape[0],1
else:
return shape
def apply_to_nonzeros(fun, v):
if sparse.issparse(v):
v_new = v.copy()
... | 1,157 | 29.473684 | 100 | py |
a2dr | a2dr-master/a2dr/proximal/elementwise.py | import numpy as np
from scipy import sparse
from scipy.special import lambertw
from a2dr.proximal.interface import NUMPY_FUNS, SPARSE_FUNS, apply_to_nonzeros
from a2dr.proximal.composition import prox_scale
def prox_abs(v, t = 1, *args, **kwargs):
"""Proximal operator of :math:`tf(ax-b) + c^Tx + d\\|x\\|_2^2`, whe... | 6,182 | 44.131387 | 116 | py |
a2dr | a2dr-master/a2dr/proximal/quadratic.py | import numpy as np
from cvxpy import *
from scipy import sparse
from a2dr.proximal.composition import prox_scale
def prox_quad_form(v, t = 1, Q = None, method = "lsqr", *args, **kwargs):
"""Proximal operator of :math:`tf(ax-b) + c^Tx + d\\|x\\|_2^2`, where :math:`f(x) = x^TQx` for symmetric
:math:`Q \\succeq 0... | 6,958 | 43.608974 | 121 | py |
a2dr | a2dr-master/a2dr/proximal/matrix.py | import numpy as np
from scipy import sparse
from a2dr.proximal.composition import prox_scale
from a2dr.proximal.norm import prox_norm_inf_base
def prox_neg_log_det(B, t = 1, *args, **kwargs):
"""Proximal operator of :math:`tf(aB-b) + cB + d\\|B\\|_F^2`, where :math:`f(B) = -\\log\\det(B)`, where `B` is a
symme... | 3,154 | 46.089552 | 117 | py |
a2dr | a2dr-master/a2dr/proximal/composition.py | import numpy as np
from scipy import sparse
def prox_scale(prox, *args, **kwargs):
"""Given the proximal operator of a function :math:`f`, returns the proximal operator of :math:`g` defined as
.. math::
g(x) = `tf(ax-b) + <c,x> + d\\|x\\|_F^2`,
where :math:`t > 0`, :math:`a \\neq 0` is a scaling te... | 1,980 | 48.525 | 119 | py |
longitudinalCOVID | longitudinalCOVID-master/main.py | import argparse
import os
import random
from collections import defaultdict
from copy import copy
import numpy as np
import torch
import data_loader as module_data_loader
import dataset as module_dataset
import model as module_arch
import model.utils.loss as module_loss
import model.utils.metric as module_metric
impo... | 6,108 | 41.72028 | 123 | py |
longitudinalCOVID | longitudinalCOVID-master/majority_voting.py | import argparse
import os
import nibabel
import numpy as np
import torch
from scipy.ndimage import rotate
from tqdm import tqdm
import data_loader as module_data_loader
import dataset as module_dataset
import model as module_arch
import model.utils.metric as module_metric
from dataset.DatasetStatic import Phase
from ... | 7,995 | 41.084211 | 115 | py |
longitudinalCOVID | longitudinalCOVID-master/parse_config.py | import logging
import os
from datetime import datetime
from functools import reduce
from importlib.machinery import SourceFileLoader
from operator import getitem
from pathlib import Path
from logger import setup_logging
from utils.util import write_config
def parse_cmd_args(args):
# parse default cli options
... | 5,244 | 35.423611 | 142 | py |
longitudinalCOVID | longitudinalCOVID-master/trainer/LongitudinalWithProgressionTrainer.py | import numpy
from logger import Mode
from trainer.Trainer import Trainer
from utils.illustration_util import log_visualizations
import torch.nn.functional as F
import torch
class LongitudinalWithProgressionTrainer(Trainer):
"""
Trainer class for training with original loss + difference map loss + reverse ord... | 5,986 | 51.982301 | 140 | py |
longitudinalCOVID | longitudinalCOVID-master/trainer/StaticTrainer.py | from logger import Mode
from trainer.Trainer import Trainer
from utils.illustration_util import log_visualizations
import torch.nn.functional as F
class StaticTrainer(Trainer):
"""
Trainer class for base training
"""
def __init__(self, model, loss, metric_ftns, optimizer, config, data_loader, fold=N... | 5,253 | 51.54 | 141 | py |
longitudinalCOVID | longitudinalCOVID-master/trainer/LongitudinalTrainer.py |
import numpy
from logger import Mode
from trainer.Trainer import Trainer
from utils.illustration_util import log_visualizations
import torch.nn.functional as F
import torch
class LongitudinalTrainer(Trainer):
"""
Trainer class
"""
def __init__(self, model, loss, metric_ftns, optimizer, config, data... | 6,063 | 47.512 | 140 | py |
longitudinalCOVID | longitudinalCOVID-master/trainer/Trainer.py | from abc import abstractmethod
import numpy as np
import torch
from base import BaseTrainer
from logger import Mode
from utils import MetricTracker
class Trainer(BaseTrainer):
"""
Trainer class
"""
def __init__(self, model, loss, metric_ftns, optimizer, config, data_loader, fold=None,
... | 5,947 | 40.594406 | 132 | py |
longitudinalCOVID | longitudinalCOVID-master/data_loader/Dataloader.py | from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, WeightedRandomSampler
from torch.utils.data.dataloader import default_collate
import numpy as np
class Dataloader(DataLoader):
"""
data loading -- uncomment the commented lines for reverse weight sampling the classes
"""
def __... | 1,242 | 34.514286 | 125 | py |
longitudinalCOVID | longitudinalCOVID-master/dataset/dataset_utils.py | import csv
import os
import sys
from collections import defaultdict, OrderedDict
from enum import Enum
from glob import glob
import gc
import h5py
import numpy as np
import pickle
from skimage.transform import resize
from dataset.dynamic.preprocessing import DatasetPreprocessor
class Modalities(Enum):
SIMPLE = '... | 19,416 | 41.863135 | 177 | py |
longitudinalCOVID | longitudinalCOVID-master/dataset/rigid_and_deformable_registration.py | from pathlib import Path
import SimpleITK as sitk
import numpy as np
import sys
import torch
import nibabel as nib
from skimage.transform import resize
def iteration_callback(filter):
global itr
print("deformable iter:", itr, "loss:", filter.GetMetricValue(), flush=True)
itr += 1
def save(filter, fixed... | 6,550 | 45.792857 | 118 | py |
longitudinalCOVID | longitudinalCOVID-master/dataset/DatasetStatic.py | import os
import sys
import h5py
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from matplotlib import cm
from skimage.transform import resize
from torch.utils.data import Dataset
from pathlib import Path
from skimage import feature
from torchvision.transforms import transforms
... | 4,367 | 43.571429 | 165 | py |
longitudinalCOVID | longitudinalCOVID-master/dataset/DatasetLongitudinal.py | import os
import h5py
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
from skimage import feature
from skimage.transform import resize
from torch.utils.data import Dataset
from torchvision import transforms
from dataset.dataset_utils import Phase, Modalities, Mode, retrieve_data_d... | 4,962 | 46.721154 | 157 | py |
longitudinalCOVID | longitudinalCOVID-master/dataset/dynamic/preprocessing.py | import os
import yaml
from pathlib import Path
import numpy as np
import pandas as pd
from skimage.transform import resize
from tqdm import tqdm
from dataset.rigid_and_deformable_registration import deformable_registration
from .util import (
load_config_yaml,
split_idxs,
rm_tree,
verify_config_hash,
... | 16,929 | 40.192214 | 164 | py |
longitudinalCOVID | longitudinalCOVID-master/dataset/dynamic/util.py | from pathlib import Path
import yaml
import numpy as np
from sklearn.model_selection import train_test_split
import pandas as pd
import hashlib
import torch
def load_config_yaml(path):
"""loads a yaml config from file and returns a dict"""
path = Path(path)
with open(path) as file:
cfg = yaml.full... | 7,244 | 32.082192 | 217 | py |
longitudinalCOVID | longitudinalCOVID-master/logger/visualization.py | import importlib
from datetime import datetime
from enum import Enum
class Mode(Enum):
TRAIN = 'Train'
VAL = 'Val'
class TensorboardWriter():
def __init__(self, log_dir, logger, enabled):
self.writer = None
self.selected_module = ""
if enabled:
log_dir = str(log_dir)... | 3,020 | 36.296296 | 125 | py |
longitudinalCOVID | longitudinalCOVID-master/logger/logger.py | import logging
import logging.config
from pathlib import Path
from utils import read_json
def setup_logging(save_dir, log_config='logger/logger_config.json', default_level=logging.INFO):
"""
Setup logging configuration
"""
log_config = Path(log_config)
if log_config.is_file():
config = re... | 751 | 30.333333 | 96 | py |
longitudinalCOVID | longitudinalCOVID-master/logger/__init__.py | from .logger import *
from .visualization import *
| 51 | 16.333333 | 28 | py |
longitudinalCOVID | longitudinalCOVID-master/configs/Static_Network.py | import os
from polyaxon_client.tracking import get_outputs_path
CONFIG = {
"name": f"{os.path.basename(__file__).split('.')[0]}",
"n_gpu": 1,
"arch": {
"type": "FCDenseNet",
"args": {
"in_channels": 1,
"n_classes": 5
}
},
"dataset": {
"type"... | 1,637 | 22.73913 | 119 | py |
longitudinalCOVID | longitudinalCOVID-master/configs/Longitudinal_Late_Fusion.py | import os
from polyaxon_client.tracking import get_outputs_path
CONFIG = {
"name": f"{os.path.basename(__file__).split('.')[0]}",
"n_gpu": 1,
"arch": {
"type": "LateLongitudinalFCDenseNet",
"args": {
"in_channels": 1,
"n_classes": 5
}
},
"dataset": {... | 1,693 | 23.911765 | 119 | py |
longitudinalCOVID | longitudinalCOVID-master/configs/Longitudinal_Network.py | import os
from polyaxon_client.tracking import get_outputs_path
CONFIG = {
"name": f"{os.path.basename(__file__).split('.')[0]}",
"n_gpu": 1,
"arch": {
"type": "LongitudinalFCDenseNet",
"args": {
"in_channels": 1,
"siamese": False,
"n_classes": 5
... | 1,767 | 24.257143 | 119 | py |
longitudinalCOVID | longitudinalCOVID-master/configs/Longitudinal_Network_with_Progression_Learning.py | import os
from polyaxon_client.tracking import get_outputs_path
CONFIG = {
"name": f"{os.path.basename(__file__).split('.')[0]}",
"n_gpu": 1,
"arch": {
"type": "LongitudinalFCDenseNet",
"args": {
"in_channels": 1,
"siamese": False,
"n_classes": 5
... | 1,782 | 24.471429 | 119 | py |
longitudinalCOVID | longitudinalCOVID-master/base/base_model.py | from abc import abstractmethod
import numpy as np
import torch.nn as nn
class BaseModel(nn.Module):
"""
Base class for all models
"""
@abstractmethod
def forward(self, *inputs):
"""
Forward pass logic
:return: Model output
"""
raise NotImplementedError
... | 650 | 22.25 | 79 | py |
longitudinalCOVID | longitudinalCOVID-master/base/base_trainer.py | from abc import abstractmethod
import torch
from numpy import inf
from logger import TensorboardWriter
class BaseTrainer:
"""
Base class for all trainers
"""
def __init__(self, model, loss, metric_ftns, optimizer, config, fold=None):
self.config = config
self.logger = config.get_log... | 7,505 | 39.354839 | 133 | py |
longitudinalCOVID | longitudinalCOVID-master/base/base_data_loader.py | import numpy as np
from torch.utils.data import DataLoader
from torch.utils.data.dataloader import default_collate
from torch.utils.data.sampler import SubsetRandomSampler
class BaseDataLoader(DataLoader):
"""
Base class for all data loaders
"""
def __init__(self, dataset, batch_size, shuffle, valida... | 1,971 | 30.301587 | 112 | py |
longitudinalCOVID | longitudinalCOVID-master/base/__init__.py | from .base_data_loader import *
from .base_model import *
from .base_trainer import *
| 86 | 20.75 | 31 | py |
longitudinalCOVID | longitudinalCOVID-master/utils/util.py | import json
import pprint
from collections import OrderedDict
from itertools import repeat
from pathlib import Path
import pandas as pd
def write_config(content, fname):
with fname.open('wt') as handle:
handle.write("CONFIG = " + pprint.pformat(content))
handle.close()
def read_json(fname):
... | 3,525 | 34.979592 | 119 | py |
longitudinalCOVID | longitudinalCOVID-master/utils/__init__.py | from .util import *
| 20 | 9.5 | 19 | py |
longitudinalCOVID | longitudinalCOVID-master/utils/illustration_util.py | import cv2
import numpy as np
import torch
from torchvision.transforms import transforms
from torchvision.utils import make_grid
from PIL import Image, ImageDraw
def warp_flow(img, flow):
h, w = flow.shape[:2]
flow = -flow
flow[:, :, 0] += np.arange(w)
flow[:, :, 1] += np.arange(h)[:, np.newaxis]
... | 7,893 | 40.547368 | 158 | py |
longitudinalCOVID | longitudinalCOVID-master/model/FCDenseNet.py | from base import BaseModel
from model.utils.layers import *
class FCDenseNetEncoder(BaseModel):
def __init__(self, in_channels=1, down_blocks=(4, 4, 4, 4, 4), bottleneck_layers=4, growth_rate=12, out_chans_first_conv=48):
super().__init__()
self.down_blocks = down_blocks
self.skip_connecti... | 4,679 | 45.8 | 156 | py |
longitudinalCOVID | longitudinalCOVID-master/model/LongitudinalFCDenseNet.py | from base import BaseModel
from model.FCDenseNet import FCDenseNetEncoder, FCDenseNetDecoder
from model.utils.layers import *
class LongitudinalFCDenseNet(BaseModel):
def __init__(self,
in_channels=1, down_blocks=(4, 4, 4, 4, 4),
up_blocks=(4, 4, 4, 4, 4), bottleneck_layers=4,
... | 1,858 | 45.475 | 128 | py |
longitudinalCOVID | longitudinalCOVID-master/model/LateLongitudinalFCDenseNet.py | from base import BaseModel
from model.FCDenseNet import FCDenseNetEncoder, FCDenseNetDecoder
from model.utils.layers import *
class LateLongitudinalFCDenseNet(BaseModel):
def __init__(self,
in_channels=1, down_blocks=(4, 4, 4, 4, 4),
up_blocks=(4, 4, 4, 4, 4), bottleneck_layers=4... | 1,423 | 38.555556 | 128 | py |
longitudinalCOVID | longitudinalCOVID-master/model/utils/metric_utils.py | import numpy as np
import torch
def asymmetric_loss(beta, output, target):
g = flatten(target)
p = flatten(output)
pg = (p * g).sum(-1)
beta_sq = beta ** 2
a = beta_sq / (1 + beta_sq)
b = 1 / (1 + beta_sq)
g_p = ((1 - p) * g).sum(-1)
p_g = (p * (1 - g)).sum(-1)
loss = (1. + pg) / (... | 1,646 | 32.612245 | 70 | py |
longitudinalCOVID | longitudinalCOVID-master/model/utils/loss.py | import torch
import torch.nn.functional as F
from model.utils import metric_utils
import numpy as np
def inf(*args):
return torch.as_tensor(float("Inf"))
def gradient_loss(s):
dy = torch.abs(s[:, :, 1:, :] - s[:, :, :-1, :]) ** 2
dx = torch.abs(s[:, :, :, 1:] - s[:, :, :, :-1]) ** 2
return (torch.me... | 1,817 | 26.969231 | 108 | py |
longitudinalCOVID | longitudinalCOVID-master/model/utils/layers.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class SpatialTransformer(nn.Module):
def __init__(self, size, mode='bilinear'):
super(SpatialTransformer, self).__init__()
vectors = [torch.arange(0, s) for s in size]
grid = torch.unsqueeze(torch.stack(torch.meshgrid(vect... | 3,420 | 32.871287 | 142 | py |
longitudinalCOVID | longitudinalCOVID-master/model/utils/metric.py | import numpy as np
import torch
from sklearn.metrics import f1_score, precision_score, recall_score, roc_curve
from medpy import metric
from model.utils import metric_utils
def precision(output, target):
with torch.no_grad():
target = metric_utils.flatten(target).cpu().detach().float()
output = me... | 5,769 | 30.703297 | 99 | py |
CONTAIN | CONTAIN-main/contain.py | import networkx as nx
import networkx.algorithms.community as nx_comm
import matplotlib.pyplot as plt
from networkx.generators.random_graphs import gnm_random_graph
import random as rnd
import time
from SparseShield_NIvsHS.Scripts.SparseShieldSolver import SparseShieldSolver
from SparseShield_NIvsHS.Scripts.NetShieldSo... | 2,793 | 41.333333 | 217 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/Simulator.py | import time
import networkx as nx
import random
import numpy as np
import logging
from collections import defaultdict
import warnings
from joblib import Parallel, delayed
class Simulator():
def __init__(self, G, seeds):
self.G = G
self.seeds = seeds
self.blocked = {}
self.log = {}... | 4,867 | 37.634921 | 115 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/main.py | from DomSolver import DomSolver
from networkx.generators.random_graphs import gnm_random_graph
import random as rnd
import time
if __name__ == "__main__":
en_pair = (978, 10217)
G = gnm_random_graph(en_pair[0], en_pair[1], seed=None, directed=True)
E = []
for e in G.edges:
E.append((e[0], e[... | 496 | 28.235294 | 74 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/RandomSolver.py | import networkx as nx
import time
import numpy as np
from Solver import *
class RandomSolver(Solver):
def run(self):
t1 = time.time()
random_blocked_set = np.random.choice([node for node in self.G.nodes() if node not in self.seeds], self.k, replace=False)
t2 = time.time()
self.log... | 424 | 27.333333 | 129 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/SparseShieldSeedlessSolver.py | from Solver import *
import networkx as nx
import numpy as np
import time
import sys
import itertools
from scipy.sparse.linalg import eigsh
import os
from heapq import *
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
class PriorityQueue:
def __init__(self, initlist):
self.counter = itertools.... | 3,440 | 32.086538 | 89 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/run_solver.py | from Simulator import *
from SparseShieldSolver import *
from SparseShieldSeedlessSolver import *
from SparseShieldPlusSolver import *
from NetShieldSolver import *
from NetShapeSolver import *
from RandomSolver import *
from DomSolver import *
from DegreeSolver import *
import os
import sys
import time
import argparse... | 2,623 | 34.945205 | 100 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/NetShieldSolver.py | import networkx as nx
import numpy as np
import time
import sys
import itertools
from scipy.linalg import eigh
import os
from heapq import *
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from Solver import *
class PriorityQueue:
def __init__(self, initlist):
self.counter = itertools.count() ... | 3,193 | 32.270833 | 85 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/SparseShieldPlusSolver.py | from Solver import *
import networkx as nx
import numpy as np
import time
import sys
import itertools
from scipy.sparse.linalg import eigsh
import os
from heapq import *
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
class PriorityQueue:
def __init__(self, initlist):
self.counter = itertools.... | 3,424 | 31.311321 | 78 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/SparseShieldSolver.py | from Solver import *
import networkx as nx
import numpy as np
import time
import sys
import itertools
from scipy.sparse.linalg import eigsh
import os
from heapq import *
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
class PriorityQueue:
def __init__(self, initlist):
self.counter = itertools.... | 3,240 | 31.41 | 78 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/Generator.py | import networkx as nx
from networkx.algorithms import approximation
import random
import sys
import time
import os
import argparse
import numpy as np
import hashlib
from scipy.sparse import csr_matrix
class Generator:
def __init__(self, params):
self.params = params
self.generators = {
... | 5,605 | 43.141732 | 155 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/SetSelector.py | import sys
import os
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import helpers
import time
import logging
from collections import defaultdict, Counter
class SetSelector():
def __init__(self, ranking, is_weighted=True):
self.ranking = ranking
self.is_weighted = is_weighted
... | 4,718 | 38.991525 | 120 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/Solver.py | import time
class Solver:
def __init__(self, G, seeds, k, **params):
if len(G) == 0:
raise Exception("Graph can not be empty")
if len(seeds) == 0:
raise Exception("Seeds can not be empty")
if k > len(G) - len(seeds):
raise Exception("Seeds can not be bloc... | 730 | 27.115385 | 68 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/DomSolver.py | '''
The class implements DAVA - the seed-aware immunization algorithm based on dominator trees.
'''
import networkx as nx
import time
from collections import defaultdict
import sys
import os
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import Solver as slv
from functools import reduce
import math
import... | 4,932 | 43.044643 | 142 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/DegreeSolver.py | import networkx as nx
import time
from Solver import *
class DegreeSolver(Solver):
def run(self):
t1 = time.time()
degrees = [(node, self.G.degree([node])[node]) for node in self.G.nodes() if node not in self.seeds]
blocked = []
degrees.sort(key=lambda t: t[1])
for i in ran... | 512 | 27.5 | 108 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/NetShapeSolver.py | import networkx as nx
import numpy as np
from scipy.linalg import eigh
import time
import sys
import os
import math
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from Solver import *
class NetShapeSolver(Solver):
def clear(self):
np.warnings.filterwarnings('ignore')
self.epsilon = se... | 4,674 | 31.465278 | 105 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/helpers/runners.py | from os import system
from joblib import Parallel, delayed
def run_against_config(results_path, nodes_to_cut, algorithm_name, graph_file, seed_file, just_solve):
output_name = results_path + "result_" + \
str(nodes_to_cut) + "_" + algorithm_name + "_" + \
graph_file.replace('.pkl', ... | 1,322 | 54.125 | 244 | py |
CONTAIN | CONTAIN-main/SparseShield_NIvsHS/Scripts/helpers/graph_builder_helpers.py | import networkx as nx
import pickle
import json
import os
from os import listdir
from os.path import isfile, join
from networkx.utils import open_file
user_list_property = 'UniqueUsers'
user_count_property = 'UniqueUsersCount'
number_of_nodes = 2789474.0
active_factor = 0.5
active_multiplier = active_factor * (1 / (... | 4,138 | 36.972477 | 192 | py |
RAML | RAML-master/incremental/main.py | from tqdm import tqdm
import network
import utils
import os
import random
import argparse
import numpy as np
import torch.nn.functional as F
from torch.utils import data
from datasets import VOCSegmentation, Cityscapes, cityscapes
from utils import ext_transforms as et
from metrics import StreamSegMetrics
import torc... | 28,621 | 42.170437 | 171 | py |
RAML | RAML-master/incremental/main_metric.py | from tqdm import tqdm
import network
import utils
import os
import random
import argparse
import numpy as np
import torch.nn.functional as F
from torch.utils import data
from datasets import VOCSegmentation, Cityscapes, cityscapes, Cityscapes_Novel
from utils import ext_transforms as et
from metrics import StreamSegMe... | 40,855 | 43.408696 | 152 | py |
RAML | RAML-master/incremental/test_metric.py | from datasets.cityscapes_novel import Cityscapes_Novel
from tqdm import tqdm
import network
import utils
import os
import random
import argparse
import numpy as np
import torch.nn.functional as F
from torch.utils import data
from datasets import VOCSegmentation, Cityscapes, Cityscapes_Novel
from utils import ext_trans... | 31,049 | 46.40458 | 153 | py |
RAML | RAML-master/incremental/metrics/stream_metrics.py | import numpy as np
from sklearn.metrics import confusion_matrix
class _StreamMetrics(object):
def __init__(self):
""" Overridden by subclasses """
raise NotImplementedError()
def update(self, gt, pred):
""" Overridden by subclasses """
raise NotImplementedError()
def get_r... | 3,982 | 30.611111 | 82 | py |
RAML | RAML-master/incremental/metrics/__init__.py | from .stream_metrics import StreamSegMetrics, AverageMeter
| 60 | 19.333333 | 58 | py |
RAML | RAML-master/incremental/datasets/voc.py | import os
import sys
import tarfile
import collections
import torch.utils.data as data
import shutil
import numpy as np
from PIL import Image
from torchvision.datasets.utils import download_url, check_integrity
DATASET_YEAR_DICT = {
'2012': {
'url': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrain... | 6,061 | 36.190184 | 128 | py |
RAML | RAML-master/incremental/datasets/cityscapes.py | import json
import os
from collections import namedtuple
from matplotlib import set_loglevel
import torch
import torch.utils.data as data
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from torchvision import transforms
import cv2
class Cityscapes(data.Dataset):
"""Cityscapes <http://ww... | 11,663 | 51.540541 | 168 | py |
RAML | RAML-master/incremental/datasets/utils.py | import os
import os.path
import hashlib
import errno
from tqdm import tqdm
def gen_bar_updater(pbar):
def bar_update(count, block_size, total_size):
if pbar.total is None and total_size:
pbar.total = total_size
progress_bytes = count * block_size
pbar.update(progress_bytes - pb... | 3,804 | 29.198413 | 93 | py |
RAML | RAML-master/incremental/datasets/cityscapes_novel.py | import json
import os
from collections import namedtuple
from matplotlib import set_loglevel
import torch
import torch.utils.data as data
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from torchvision import transforms
class Cityscapes_Novel(data.Dataset):
"""Cityscapes <http://www.ci... | 8,742 | 48.39548 | 168 | py |
RAML | RAML-master/incremental/datasets/__init__.py | from .voc import VOCSegmentation
from .cityscapes import Cityscapes
from .cityscapes_novel import Cityscapes_Novel | 114 | 37.333333 | 46 | py |
RAML | RAML-master/incremental/datasets/.ipynb_checkpoints/cityscapes-checkpoint.py | import json
import os
from collections import namedtuple
from matplotlib import set_loglevel
import torch
import torch.utils.data as data
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
from torchvision import transforms
import cv2
class Cityscapes(data.Dataset):
"""Cityscapes <http://ww... | 11,663 | 51.540541 | 168 | py |
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