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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imaging_MLPs | imaging_MLPs-master/compressed_sensing/networks/vision_transformer.py | '''
This code is modified from https://github.com/facebookresearch/convit. To adapt the vit/convit to image reconstruction, variable input sizes, and patch sizes for both spatial dimensions.
'''
import torch
import torch.nn as nn
from functools import partial
import torch.nn.functional as F
from timm.models.helpers im... | 15,082 | 39.007958 | 186 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/networks/recon_net.py | import torch.nn as nn
import torch.nn.functional as F
from math import ceil, floor
from .unet import Unet
from .vision_transformer import VisionTransformer
class ReconNet(nn.Module):
def __init__(self, net):
super().__init__()
self.net = net
def pad(self, x):
_, _, h, w = x.shape
... | 1,932 | 25.847222 | 90 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/networks/unet.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
from torch import nn
from torch.nn import functional as F
class Unet(nn.Module):
"""
PyTorch implementation of a U-Net... | 5,979 | 31.677596 | 88 | py |
imaging_MLPs | imaging_MLPs-master/compressed_sensing/networks/__init__.py | from .recon_net import ReconNet
from .vision_transformer import VisionTransformer
from .img2img_mixer import Img2Img_Mixer
from .unet import Unet
| 146 | 28.4 | 49 | py |
imaging_MLPs | imaging_MLPs-master/untrained/networks/original_mixer.py | import torch
import torch.nn as nn
from torch.nn import init
import torch.nn.init as init
import einops
from einops.layers.torch import Rearrange
from einops import rearrange
class PatchEmbeddings(nn.Module):
def __init__(
self,
patch_size: int,
hidden_dim: int,
channels: int
... | 3,674 | 26.840909 | 93 | py |
imaging_MLPs | imaging_MLPs-master/untrained/networks/img2img_mixer.py | import torch
import torch.nn as nn
from torch.nn import init
import torch.nn.init as init
import einops
from einops.layers.torch import Rearrange
from einops import rearrange
class PatchEmbedding(nn.Module):
def __init__(
self,
patch_size: int,
embed_dim: int,
channels: int
... | 3,618 | 27.054264 | 127 | py |
imaging_MLPs | imaging_MLPs-master/untrained/networks/vit.py | '''
This code is modified from https://github.com/facebookresearch/convit. To adapt the vit/convit to image reconstruction, variable input sizes, and patch sizes for both spatial dimensions.
'''
import torch
import torch.nn as nn
from functools import partial
import torch.nn.functional as F
from timm.models.helpers im... | 15,082 | 39.007958 | 186 | py |
imaging_MLPs | imaging_MLPs-master/untrained/networks/recon_net.py | import torch.nn as nn
import torch.nn.functional as F
from math import ceil, floor
class ReconNet(nn.Module):
def __init__(self, net):
super().__init__()
self.net = net
def pad(self, x):
_, _, h, w = x.shape
hp, wp = self.net.patch_size
f1 = ( (wp - w % wp) % wp ) / 2
... | 810 | 26.033333 | 90 | py |
imaging_MLPs | imaging_MLPs-master/untrained/networks/unet.py | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import torch
from torch import nn
from torch.nn import functional as F
class Unet(nn.Module):
"""
PyTorch implementation of a U-Net ... | 5,981 | 32.79661 | 88 | py |
imaging_MLPs | imaging_MLPs-master/untrained/networks/__init__.py | from .img2img_mixer import *
from .original_mixer import *
from .unet import *
from .recon_net import ReconNet
from .vit import VisionTransformer
| 146 | 23.5 | 34 | py |
subgraph-counts-hoeffding | subgraph-counts-hoeffding-main/estimate_N.py | #Download the data and compute the lower bound for N from Corollary 5 of our paper.
import sys
import math
import argparse
import pandas as pd
import numpy as np
parser=argparse.ArgumentParser()
parser.add_argument("--h", type=int, help="number of vertices in H")
parser.add_argument("--i_T", type=int, help="number of ... | 2,501 | 42.894737 | 188 | py |
conker | conker-main/driver.py | """
Copyright (C) 2021 Gebri Mishtaku
This program 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.
This program is distributed in the hope ... | 3,468 | 28.649573 | 74 | py |
conker | conker-main/calibrate.py | """
Copyright (C) 2021 Gebri Mishtaku
This program 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.
This program is distributed in the hope ... | 10,318 | 30.750769 | 83 | py |
conker | conker-main/src/parser.py | """
Copyright (C) 2021 Gebri Mishtaku
This program 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.
This program is distributed in the hope ... | 7,093 | 46.932432 | 88 | py |
conker | conker-main/src/centerfinder.py | """
Copyright (C) 2021 Gebri Mishtaku
This program 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.
This program is distributed in the hope ... | 18,655 | 32.67509 | 105 | py |
conker | conker-main/src/utils.py | """
Copyright (C) 2020 Gebri Mishtaku
This program 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.
This program is distributed in the hope ... | 4,645 | 34.19697 | 93 | py |
conker | conker-main/src/correlator.py | """
Copyright (C) 2021 Gebri Mishtaku
This program 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.
This program is distributed in the hope ... | 14,519 | 28.156627 | 84 | py |
conker | conker-main/src/plotter.py | import numpy as np
import matplotlib.pyplot as plt
plt.rc('figure', figsize=[9,9])
plt.rc('font', family='serif')
plt.rc('axes', titlesize=18)
plt.rc('axes', labelsize=12)
plt.rc('xtick', top=True)
plt.rc('xtick.minor', visible=True)
plt.rc('ytick', right=True)
plt.rc('ytick.minor', visible=True)
def _plot_slice(cf,... | 3,772 | 31.525862 | 83 | py |
conker | conker-main/src/kernel.py | """
Copyright (C) 2021 Gebri Mishtaku
This program 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.
This program is distributed in the hope ... | 11,022 | 37.010345 | 93 | py |
DeepIR | DeepIR-main/demo.py | #!/usr/bin/env python
import os
import sys
from pprint import pprint
# Pytorch requires blocking launch for proper working
if sys.platform == 'win32':
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
import numpy as np
from scipy import io
import torch
import torch.nn
torch.backends.cudnn.enabled = True
torch.backends... | 2,636 | 31.555556 | 80 | py |
DeepIR | DeepIR-main/modules/losses.py | #!/usr/bin/env python
import torch
class TVNorm():
def __init__(self, mode='l1'):
self.mode = mode
def __call__(self, img):
grad_x = img[..., 1:, 1:] - img[..., 1:, :-1]
grad_y = img[..., 1:, 1:] - img[..., :-1, 1:]
if self.mode == 'isotropic':
#return torc... | 1,586 | 30.117647 | 80 | py |
DeepIR | DeepIR-main/modules/utils.py | #!/usr/bin/env python
'''
Miscellaneous utilities that are extremely helpful but cannot be clubbed
into other modules.
'''
import torch
# Scientific computing
import numpy as np
import scipy.linalg as lin
from scipy import io
# Plotting
import cv2
import matplotlib.pyplot as plt
def nextpow2(x):
'''
... | 6,530 | 21.996479 | 85 | py |
DeepIR | DeepIR-main/modules/dataset.py | #!/usr/bin/env python
import os
import sys
import tqdm
import pdb
import math
import configparser
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from PIL import Image
from torchvision.transforms import Resize, Compose, ToTensor, ... | 7,382 | 27.287356 | 81 | py |
DeepIR | DeepIR-main/modules/thermal.py | #!/usr/bin/env python
'''
Routines for dealing with thermal images
'''
import tqdm
import copy
import cv2
import numpy as np
from skimage.metrics import structural_similarity as ssim_func
import torch
import kornia
import torch.nn.functional as F
import utils
import losses
import motion
import deep_prior
def ... | 21,820 | 37.485009 | 84 | py |
DeepIR | DeepIR-main/modules/motion.py | #!/usr/bin/env python
'''
Subroutines for estimating motion between images
'''
import os
import sys
import tqdm
import pdb
import math
import numpy as np
from scipy import linalg
from scipy import interpolate
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoad... | 23,853 | 33.772595 | 137 | py |
DeepIR | DeepIR-main/modules/deep_prior.py | #!/usr/bin/env
'''
One single file for all things Deep Image Prior
'''
import os
import sys
import tqdm
import pdb
import numpy as np
import torch
from torch import nn
import torchvision
import cv2
from dmodels.skip import skip
from dmodels.texture_nets import get_texture_nets
from dmodels.resnet import ResNet... | 8,713 | 33.995984 | 138 | py |
DeepIR | DeepIR-main/modules/dmodels/skip.py | import torch
import torch.nn as nn
from .common import *
def skip(
num_input_channels=2, num_output_channels=3,
num_channels_down=[16, 32, 64, 128, 128], num_channels_up=[16, 32, 64, 128, 128], num_channels_skip=[4, 4, 4, 4, 4],
filter_size_down=3, filter_size_up=3, filter_skip_size=1,
... | 3,744 | 36.079208 | 144 | py |
DeepIR | DeepIR-main/modules/dmodels/resnet.py | import torch
import torch.nn as nn
from numpy.random import normal
from numpy.linalg import svd
from math import sqrt
import torch.nn.init
from .common import *
class ResidualSequential(nn.Sequential):
def __init__(self, *args):
super(ResidualSequential, self).__init__(*args)
def forward(self, x):
... | 2,943 | 29.350515 | 195 | py |
DeepIR | DeepIR-main/modules/dmodels/downsampler.py | import numpy as np
import torch
import torch.nn as nn
class Downsampler(nn.Module):
'''
http://www.realitypixels.com/turk/computergraphics/ResamplingFilters.pdf
'''
def __init__(self, n_planes, factor, kernel_type, phase=0, kernel_width=None, support=None, sigma=None, preserve_size=False):
... | 5,379 | 30.83432 | 129 | py |
DeepIR | DeepIR-main/modules/dmodels/dcgan.py | import torch
import torch.nn as nn
def dcgan(inp=2,
ndf=32,
num_ups=4, need_sigmoid=True, need_bias=True, pad='zero', upsample_mode='nearest', need_convT = True):
layers= [nn.ConvTranspose2d(inp, ndf, kernel_size=3, stride=1, padding=0, bias=False),
nn.BatchNorm2d(ndf),
... | 1,244 | 35.617647 | 112 | py |
DeepIR | DeepIR-main/modules/dmodels/texture_nets.py | import torch
import torch.nn as nn
from .common import *
normalization = nn.BatchNorm2d
def conv(in_f, out_f, kernel_size, stride=1, bias=True, pad='zero'):
if pad == 'zero':
return nn.Conv2d(in_f, out_f, kernel_size, stride, padding=(kernel_size - 1) / 2, bias=bias)
elif pad == 'reflection':
... | 2,315 | 27.95 | 146 | py |
DeepIR | DeepIR-main/modules/dmodels/common.py | import torch
import torch.nn as nn
import numpy as np
from .downsampler import Downsampler
def add_module(self, module):
self.add_module(str(len(self) + 1), module)
torch.nn.Module.add = add_module
class Concat(nn.Module):
def __init__(self, dim, *args):
super(Concat, self).__init__()
sel... | 3,531 | 27.483871 | 128 | py |
DeepIR | DeepIR-main/modules/dmodels/unet.py | import torch.nn as nn
import torch
import torch.nn as nn
import torch.nn.functional as F
from .common import *
class ListModule(nn.Module):
def __init__(self, *args):
super(ListModule, self).__init__()
idx = 0
for module in args:
self.add_module(str(idx), module)
id... | 7,324 | 36.953368 | 164 | py |
DeepIR | DeepIR-main/modules/dmodels/__init__.py | from .skip import skip
from .texture_nets import get_texture_nets
from .resnet import ResNet
from .unet import UNet
import torch.nn as nn
def get_net(input_depth, NET_TYPE, pad, upsample_mode, n_channels=3, act_fun='LeakyReLU', skip_n33d=128, skip_n33u=128, skip_n11=4, num_scales=5, downsample_mode='stride'):
if ... | 1,639 | 50.25 | 172 | py |
rude-carnie | rude-carnie-master/export.py | import tensorflow as tf
from model import select_model, get_checkpoint
from utils import RESIZE_AOI, RESIZE_FINAL
from tensorflow.python.framework import graph_util
from tensorflow.contrib.learn.python.learn.utils import export
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.pyt... | 5,970 | 43.559701 | 118 | py |
rude-carnie | rude-carnie-master/detect.py | import numpy as np
import cv2
FACE_PAD = 50
class ObjectDetector(object):
def __init__(self):
pass
def run(self, image_file):
pass
# OpenCV's cascade object detector
class ObjectDetectorCascadeOpenCV(ObjectDetector):
def __init__(self, model_name, basename='frontal-face', tgtdir='.', min_... | 2,287 | 39.140351 | 112 | py |
rude-carnie | rude-carnie-master/yolodetect.py | from detect import ObjectDetector
import numpy as np
import tensorflow as tf
import cv2
class YOLOBase(ObjectDetector):
def __init__(self):
pass
def _conv_layer(self, idx, inputs, filters, size, stride):
channels = inputs.get_shape()[3]
weight = tf.Variable(tf.truncated_normal([size, ... | 12,603 | 43.380282 | 117 | py |
rude-carnie | rude-carnie-master/filter_by_face.py | import numpy as np
import tensorflow as tf
import os
import cv2
import time
import sys
from utils import *
import csv
# YOLO tiny
#python fd.py --filename /media/dpressel/xdata/insights/converted/ --face_detection_model weights/YOLO_tiny.ckpt --face_detection_type yolo_tiny --target yolo.csv
# CV2
#python fd.py --fi... | 2,721 | 34.350649 | 177 | py |
rude-carnie | rude-carnie-master/utils.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six.moves
from datetime import datetime
import sys
import math
import time
from data import inputs, standardize_image
import numpy as np
import tensorflow as tf
from detect import *
import re
RESIZE_AOI... | 5,920 | 32.078212 | 90 | py |
rude-carnie | rude-carnie-master/model.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import time
import os
import numpy as np
import tensorflow as tf
from data import distorted_inputs
import re
from tensorflow.contrib.layers import *
from tensorflow.contrib.slim.p... | 8,852 | 44.168367 | 144 | py |
rude-carnie | rude-carnie-master/data.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import os
import numpy as np
import tensorflow as tf
from distutils.version import LooseVersion
VERSION_GTE_0_12_0 = LooseVersion(tf.__version__) >= LooseVersion('0.12.0')
# Nam... | 8,764 | 36.139831 | 92 | py |
rude-carnie | rude-carnie-master/preproc.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import xrange
from datetime import datetime
import os
import random
import sys
import threading
import numpy as np
import tensorflow as tf
import json
RESIZE_HEIGHT = 256
RESIZE_WIDTH = 256
tf.... | 12,580 | 38.071429 | 137 | py |
rude-carnie | rude-carnie-master/dlibdetect.py | from detect import ObjectDetector
import dlib
import cv2
FACE_PAD = 50
class FaceDetectorDlib(ObjectDetector):
def __init__(self, model_name, basename='frontal-face', tgtdir='.'):
self.tgtdir = tgtdir
self.basename = basename
self.detector = dlib.get_frontal_face_detector()
self.pr... | 1,853 | 36.836735 | 112 | py |
rude-carnie | rude-carnie-master/eval.py | """
At each tick, evaluate the latest checkpoint against some validation data.
Or, you can run once by passing --run_once. OR, you can pass a --requested_step_seq of comma separated checkpoint #s that already exist that it can run in a row.
This program expects a training base directory with the data, and md.json fil... | 8,021 | 40.138462 | 165 | py |
rude-carnie | rude-carnie-master/train.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six.moves import xrange
from datetime import datetime
import time
import os
import numpy as np
import tensorflow as tf
from data import distorted_inputs
from model import select_model
import json
import re... | 7,702 | 38.911917 | 130 | py |
rude-carnie | rude-carnie-master/guess.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import math
import time
from data import inputs
import numpy as np
import tensorflow as tf
from model import select_model, get_checkpoint
from utils import *
import os
import json
... | 8,091 | 37.903846 | 136 | py |
AdaptiveGCL | AdaptiveGCL-main/Params.py | import argparse
def ParseArgs():
parser = argparse.ArgumentParser(description='Model Params')
parser.add_argument('--lr', default=1e-3, type=float, help='learning rate')
parser.add_argument('--batch', default=4096, type=int, help='batch size')
parser.add_argument('--tstBat', default=256, type=int, help='number of ... | 1,788 | 62.892857 | 107 | py |
AdaptiveGCL | AdaptiveGCL-main/DataHandler.py | import pickle
import numpy as np
from scipy.sparse import csr_matrix, coo_matrix, dok_matrix
from Params import args
import scipy.sparse as sp
from Utils.TimeLogger import log
import torch as t
import torch.utils.data as data
import torch.utils.data as dataloader
class DataHandler:
def __init__(self):
if args.data ... | 3,205 | 29.245283 | 103 | py |
AdaptiveGCL | AdaptiveGCL-main/Main.py | import torch
import Utils.TimeLogger as logger
from Utils.TimeLogger import log
from Params import args
from Model import Model, vgae_encoder, vgae_decoder, vgae, DenoisingNet
from DataHandler import DataHandler
import numpy as np
from Utils.Utils import calcRegLoss, pairPredict
import os
from copy import deepcopy
impo... | 6,846 | 29.9819 | 143 | py |
AdaptiveGCL | AdaptiveGCL-main/Model.py | from torch import nn
import torch.nn.functional as F
import torch
from Params import args
from copy import deepcopy
import numpy as np
import math
import scipy.sparse as sp
from Utils.Utils import contrastLoss, calcRegLoss, pairPredict
import time
import torch_sparse
init = nn.init.xavier_uniform_
class Model(nn.Modu... | 11,377 | 29.180371 | 126 | py |
AdaptiveGCL | AdaptiveGCL-main/Utils/TimeLogger.py | import datetime
logmsg = ''
timemark = dict()
saveDefault = False
def log(msg, save=None, oneline=False):
global logmsg
global saveDefault
time = datetime.datetime.now()
tem = '%s: %s' % (time, msg)
if save != None:
if save:
logmsg += tem + '\n'
elif saveDefault:
logmsg += tem + '\n'
if oneline:
print(... | 476 | 16.666667 | 43 | py |
AdaptiveGCL | AdaptiveGCL-main/Utils/Utils.py | import torch as t
import torch.nn.functional as F
def innerProduct(usrEmbeds, itmEmbeds):
return t.sum(usrEmbeds * itmEmbeds, dim=-1)
def pairPredict(ancEmbeds, posEmbeds, negEmbeds):
return innerProduct(ancEmbeds, posEmbeds) - innerProduct(ancEmbeds, negEmbeds)
def calcRegLoss(model):
ret = 0
for W in model.par... | 694 | 29.217391 | 79 | py |
wcep-mds-dataset | wcep-mds-dataset-master/experiments/summarizer.py | import utils
from nltk import word_tokenize, bigrams
from sent_splitter import SentenceSplitter
from data import Sentence, Article
class Summarizer:
def _deduplicate(self, sents):
seen = set()
uniq_sents = []
for s in sents:
if s.text not in seen:
seen.add(s.te... | 2,737 | 30.471264 | 74 | py |
wcep-mds-dataset | wcep-mds-dataset-master/experiments/baselines.py | import utils
import random
import collections
import numpy as np
import networkx as nx
import warnings
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.cluster import MiniBatchKMeans
from summarizer import Summarizer
warnings.filterwarning... | 11,786 | 33.364431 | 80 | py |
wcep-mds-dataset | wcep-mds-dataset-master/experiments/evaluate.py | import argparse
import collections
import numpy as np
import utils
from newsroom.analyze.rouge import ROUGE_L, ROUGE_N
def print_mean(results, rouge_types):
for rouge_type in rouge_types:
precs = results[rouge_type]['p']
recalls = results[rouge_type]['r']
fscores = results[rouge_type]['f']... | 3,425 | 29.589286 | 78 | py |
wcep-mds-dataset | wcep-mds-dataset-master/experiments/utils.py | import json
import gzip
import pickle
def read_lines(path):
with open(path) as f:
for line in f:
yield line
def read_json(path):
with open(path) as f:
object = json.loads(f.read())
return object
def write_json(object, path):
with open(path, 'w') as f:
f.write(js... | 1,835 | 20.6 | 60 | py |
wcep-mds-dataset | wcep-mds-dataset-master/experiments/oracles.py | import argparse
from collections import Counter
from nltk import word_tokenize, ngrams
from summarizer import Summarizer
import utils
def compute_rouge_n(hyp, ref, rouge_n=1, tokenize=True):
hyp_words = word_tokenize(hyp) if tokenize else hyp
ref_words = word_tokenize(ref) if tokenize else ref
if rouge_n... | 8,710 | 33.027344 | 78 | py |
wcep-mds-dataset | wcep-mds-dataset-master/experiments/data.py | import string
from spacy.lang.en import STOP_WORDS
STOP_WORDS |= set(string.punctuation)
class Article:
def __init__(self, title, sents):
self.title = title
self.sents = sents
def words(self):
if self.title is None:
return [w for s in self.sents for w in s.words]
e... | 724 | 25.851852 | 74 | py |
wcep-mds-dataset | wcep-mds-dataset-master/experiments/sent_splitter.py | import re
from nltk import sent_tokenize
class SentenceSplitter:
"""
NLTK sent_tokenize + some fixes for common errors in news articles.
"""
def unglue(self, x):
g = x.group(0)
fixed = '{} {}'.format(g[0], g[1])
return fixed
def fix_glued_sents(self, text):
return ... | 766 | 25.448276 | 71 | py |
wcep-mds-dataset | wcep-mds-dataset-master/dataset_reproduction/extract_cc_articles.py | import argparse
import pathlib
import logging
import json
import subprocess
import multiprocessing
import newspaper
import sys
import time
import utils
from warcio.archiveiterator import ArchiveIterator
def read_warc_gz(path):
with open(path, 'rb') as f:
for record in ArchiveIterator(f):
# rec... | 6,569 | 29.137615 | 78 | py |
wcep-mds-dataset | wcep-mds-dataset-master/dataset_reproduction/combine_and_split.py | import argparse
import json
import pathlib
import shutil
import utils
from collections import defaultdict
def get_article_to_cluster_mappings(clusters):
url_to_cluster_idxs = defaultdict(list)
id_to_cluster_idx = {}
for i, c in enumerate(clusters):
for a in c['wcep_articles']:
url_to_c... | 5,119 | 32.907285 | 89 | py |
wcep-mds-dataset | wcep-mds-dataset-master/dataset_reproduction/utils.py | import json
def read_lines(path):
with open(path) as f:
for line in f:
yield line.strip()
def read_jsonl(path):
with open(path) as f:
for line in f:
yield json.loads(line)
def write_jsonl(items, path, mode='a'):
assert mode in ['w', 'a']
lines = [json.dumps(... | 421 | 18.181818 | 42 | py |
wcep-mds-dataset | wcep-mds-dataset-master/dataset_reproduction/extract_wcep_articles.py | import argparse
import multiprocessing
import time
import pathlib
import random
import newspaper
import json
import numpy as np
import utils
def extract_article(todo_article):
url = todo_article['archive_url']
extracted = newspaper.Article(url)
try:
extracted.download()
extracted.parse()
... | 3,803 | 25.601399 | 74 | py |
wcep-mds-dataset | wcep-mds-dataset-master/dataset_generation/step5_combine_dataset.py | import argparse
from general import utils
def load_urls(path):
url_to_arc = {}
arc_to_url = {}
with open(path) as f:
for line in f:
parts = line.split()
if len(parts) == 2:
url, arc_url = parts
url_to_arc[url] = arc_url
arc_to... | 1,505 | 25.421053 | 65 | py |
wcep-mds-dataset | wcep-mds-dataset-master/dataset_generation/step2_process_wcep_html.py | import argparse
import datetime
import calendar
import pathlib
import collections
import arrow
import json
import uuid
from bs4 import BeautifulSoup
def make_month_to_int():
month_to_int = {}
for i, month in enumerate(calendar.month_name):
if i > 0:
month_to_int[month] = i
return month... | 6,015 | 29.231156 | 80 | py |
wcep-mds-dataset | wcep-mds-dataset-master/dataset_generation/step4_scrape_sources.py | import argparse
import multiprocessing
import json
import os
import time
import pathlib
import random
import newspaper
import json
import numpy as np
def scrape_article(url):
a = newspaper.Article(url)
error = None
try:
a.download()
a.parse()
if a.publish_date is None:
... | 3,582 | 23.710345 | 79 | py |
wcep-mds-dataset | wcep-mds-dataset-master/dataset_generation/step1_store_wcep_html.py | import requests
import argparse
import pathlib
from bs4 import BeautifulSoup
ROOT_URL = 'https://en.wikipedia.org/wiki/Portal:Current_events'
def extract_month_urls():
html = requests.get(ROOT_URL).text
soup = BeautifulSoup(html, 'html.parser')
e = soup.find('div', class_='NavContent hlist')
urls = [... | 1,096 | 26.425 | 80 | py |
wcep-mds-dataset | wcep-mds-dataset-master/dataset_generation/step3_snapshot_source_urls.py | import argparse
import savepagenow
import json
import os
import random
import time
from requests.exceptions import ConnectionError
def read_jsonl(path):
with open(path) as f:
for line in f:
yield json.loads(line)
def write_jsonl(items, path, batch_size=100, override=True):
if override:
... | 2,971 | 27.576923 | 79 | py |
SARS-CoV-2_origins | SARS-CoV-2_origins-master/scripts/python/ACE2.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 5 12:49:25 2020
@author: Erwan Sallard erwan.sallard@ens.psl.eu
"""
'''goal: this program compares the ACE2 proteins of various organisms with a
reference ACE2 (one of the sequences in the alignment) and identify their level
of similarity on the... | 2,773 | 33.246914 | 79 | py |
SARS-CoV-2_origins | SARS-CoV-2_origins-master/scripts/python/detection_insertion.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 26 14:41:12 2020
@author: erwan
"""
import sys
alignment=sys.argv[1]
reference=sys.argv[2]
filename=sys.argv[3]
'''identifies the insertions in the sequence "reference" out of a multiple
alignment file in .clw format. Is considered an insertion ev... | 2,581 | 32.102564 | 75 | py |
SARS-CoV-2_origins | SARS-CoV-2_origins-master/scripts/python/mutation_analyser.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon May 25 21:38:57 2020
@author: erwan
"""
import sys
import matplotlib.pyplot as plt
from Bio import pairwise2
from Bio.Seq import Seq
from Bio.SubsMat import MatrixInfo as matlist
''' This program compares two nucleotide sequences, identifies indels,
s... | 4,657 | 38.142857 | 102 | py |
RG | RG-master/Image Classification/main.py | from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
import torchvision
import torchvision.transforms as transforms
import os
import argparse
import random
from re... | 4,105 | 30.343511 | 109 | py |
RG | RG-master/Image Classification/resnet.py | '''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
cla... | 3,941 | 32.982759 | 102 | py |
RG | RG-master/Image Classification/utils.py | import os
import sys
import time
term_width = 5
TOTAL_BAR_LENGTH = 20.
last_time = time.time()
begin_time = last_time
def progress_bar(current, total, msg=None):
global last_time, begin_time
if current == 0:
begin_time = time.time() # Reset for new bar.
cur_len = int(TOTAL_BAR_LENGTH*current/tota... | 2,068 | 23.927711 | 64 | py |
RG | RG-master/pix2pix/pix2pix.py | import argparse
import os
import numpy as np
import math
import itertools
import time
import datetime
import sys
import random
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variab... | 7,224 | 36.827225 | 123 | py |
RG | RG-master/pix2pix/datasets.py | import glob
import random
import os
import numpy as np
from torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transforms
class ImageDataset(Dataset):
def __init__(self, root, transforms_=None, mode='train'):
self.transform = transforms.Compose(transforms_)
sel... | 1,056 | 28.361111 | 85 | py |
RG | RG-master/pix2pix/models.py | import torch.nn as nn
import torch.nn.functional as F
import torch
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm2d') != -1:
torch.nn.init.normal_(m.weight.data, 1.0... | 4,289 | 32.515625 | 81 | py |
RG | RG-master/Semantic Segmentation/model.py | import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch
import numpy as np
affine_par = True
import torch.nn.functional as F
def outS(i):
i = int(i)
i = (i+1)/2
i = int(np.ceil((i+1)/2.0))
i = (i+1)/2
return i
def conv3x3(in_planes, out_planes, stride=1):
"3x3 ... | 11,339 | 36.549669 | 139 | py |
RG | RG-master/Semantic Segmentation/train.py | import datetime
import os
import random
import time
from math import sqrt
import torchvision.transforms as standard_transforms
import torchvision.utils as vutils
# from tensorboard import SummaryWriter
from torch import optim
from torch.autograd import Variable
from torch.backends import cudnn
from torch.optim.lr_sched... | 9,955 | 38.19685 | 219 | py |
metacorps-nn | metacorps-nn-master/different_layers_experiment.py | import sys
import pandas as pd
from modelrun import ModelRun
verbose = True
n_nodes = 500
# Keeping a ModelRun allows us to not have to re-load GoogleNews model.
rows = [] # Used to build data frame and latex table.
w2v_model_loc='GoogleNews-vectors-negative300.bin'
if len(sys.argv) > 1:
run_directory = sys.a... | 1,460 | 28.22 | 105 | py |
metacorps-nn | metacorps-nn-master/test_util.py | from util import get_window
from nose.tools import eq_
def test_get_window():
word = 'attack'
window_size = 5
# Test when we have enough space on both sides for full window.
text = 'he has to go on the attack if he wants to win the debate'
window = get_window(text, word, window_size)
eq_(win... | 1,256 | 38.28125 | 99 | py |
metacorps-nn | metacorps-nn-master/modelrun.py | '''
'''
from uuid import uuid4
# Command-line interface: read it CLIck.
import click
import numpy as np
import os
import tensorflow as tf
# See https://radimrehurek.com/gensim/models/keyedvectors.html
import gensim
from eval import Eval
from model import train_network
from util import MetaphorData
# WORKFLOW
# 1.... | 5,085 | 31.602564 | 81 | py |
metacorps-nn | metacorps-nn-master/model.py | '''
Following Do Dinh, E.-L., & Gurevych, I. (2016) using TensorFlow.
Do Dinh, E.-L., & Gurevych, I. (2016). Token-Level Metaphor
Detection using Neural Networks. Proceedings of the Fourth Workshop on
Metaphor in NLP, (June), 28–33.
Author: Matthew A. Turner
Date: 2017-12-11
'''
import tensorflow as tf
def ... | 5,240 | 35.908451 | 77 | py |
metacorps-nn | metacorps-nn-master/util.py | '''
Utilities for training a neural network for automated identification of
metaphorical violence.
Author: Matthew A. Turner
Date: 2017-11-21
'''
import itertools
import numpy as np
import pandas as pd
import random
import warnings
def get_window(text, focal_token, window_size):
'''
Given some text and a tok... | 11,959 | 34.176471 | 79 | py |
metacorps-nn | metacorps-nn-master/prepare_csv_input.py | '''
Export script to create tabular dataset from the metacorps web app's
MongoDB database. A mongodump of this database is available at
http://metacorps.io/static/data/nov-15-2017-metacorps-dump.zip (594M)
'''
import numpy as np
import pandas as pd
from nltk.tokenize import RegexpTokenizer
from pymongo import MongoCli... | 2,717 | 26.18 | 77 | py |
metacorps-nn | metacorps-nn-master/eval.py | '''
Code to evaluate a particular trained network. Think about formatting results
here well to be tables in the paper.
'''
import sklearn.metrics as skmetrics
from collections import Counter
from util import get_window
class Eval:
'''
Methods to evaluate different models.
'''
def __init__(self, test... | 3,028 | 32.285714 | 79 | py |
metacorps-nn | metacorps-nn-master/n_layers_experiment.py | import sys
import pandas as pd
from modelrun import ModelRun
verbose = True
n_nodes = 500
# Keeping a ModelRun allows us to not have to re-load GoogleNews model.
rows = [] # Used to build data frame and latex table.
w2v_model_loc='GoogleNews-vectors-negative300.bin'
if len(sys.argv) > 1:
run_directory = sys.a... | 1,344 | 27.020833 | 71 | py |
CD-Flow | CD-Flow-main/main.py | import torch
from trainnet import trainNet
import pandas as pd
import argparse
def parse_config():
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=100)
parser.add_argument("--resume_path", type=str, default=None)
parser.add_argument("--learning_rate", type=float, defa... | 3,377 | 48.676471 | 175 | py |
CD-Flow | CD-Flow-main/test.py | import time
from EMA import EMA
import torch
from torch.utils.data import DataLoader
from model import CDFlow
from DataLoader import CD_128
from coeff_func import *
import os
from loss import createLossAndOptimizer
from torch.autograd import Variable
import torchvision
import torch.autograd as autograd
from function im... | 4,109 | 41.8125 | 117 | py |
CD-Flow | CD-Flow-main/flow.py | import torch
from torch import nn
from torch.nn import functional as F
from math import log, pi, exp
import numpy as np
from scipy import linalg as la
logabs = lambda x: torch.log(torch.abs(x))
class ActNorm(nn.Module):
def __init__(self, in_channel, logdet=True):
super().__init__()
self.loc = nn.... | 10,847 | 28.720548 | 88 | py |
CD-Flow | CD-Flow-main/DataLoader.py | import os
import torch
import random
import numpy as np
from torch.utils.data import Dataset
from PIL import Image
from torchvision import transforms
import torchvision
class CD_128(Dataset):
def __init__(self, jnd_info, root_dir, test=False):
self.ref_name = jnd_info[:, 0]
self.test_name = jnd_inf... | 1,425 | 30 | 81 | py |
CD-Flow | CD-Flow-main/loss.py | import torch
import numpy as np
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
def createLossAndOptimizer(net, learning_rate, scheduler_step, scheduler_gamma):
loss = LossFunc()
# optimizer = optim.Adam([{'params': net.parameters(), 'lr':learning_rate}], lr = learning_rate, w... | 909 | 34 | 119 | py |
CD-Flow | CD-Flow-main/model.py | import math
import time
import torch
import torch.nn as nn
from flow import *
import os
class CDFlow(nn.Module):
def __init__(self):
super(CDFlow, self).__init__()
self.glow = Glow(3, 8, 6, affine=True, conv_lu=True)
def coordinate_transform(self, x_hat, rev=False):
if not rev:
... | 3,702 | 47.090909 | 122 | py |
CD-Flow | CD-Flow-main/EMA.py | class EMA():
def __init__(self, model, decay):
self.model = model
self.decay = decay
self.shadow = {}
self.backup = {}
def register(self):
for name, param in self.model.named_parameters():
if param.requires_grad:
self.shadow[name] = param... | 1,138 | 32.5 | 94 | py |
CD-Flow | CD-Flow-main/function.py | import shutil
import random
import torch
import numpy as np
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
def copy_codes(trainpath1,trainpath2,trainpath3,trainpath4, path1,path2,path3,... | 484 | 25.944444 | 85 | py |
CD-Flow | CD-Flow-main/coeff_func.py | from cgi import print_form
import numpy as np
import pandas as pd
from scipy.stats.stats import pearsonr, spearmanr, kendalltau
from scipy.optimize import fmin
from math import sqrt
from sklearn.metrics import mean_squared_error
def logistic(t, x):
return 0.5 - (1 / (1 + np.exp(t * x)))
def fitfun(t, x):
res... | 1,583 | 23 | 78 | py |
CD-Flow | CD-Flow-main/trainnet.py | import time
from EMA import EMA
import torch
from torch.utils.data import DataLoader
from model import CDFlow
from DataLoader import CD_128
from coeff_func import *
import os
from loss import createLossAndOptimizer
from torch.autograd import Variable
import torch.autograd as autograd
from function import setup_seed, co... | 11,783 | 44.85214 | 156 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/setup.py | from distutils.core import setup
"""
install the packages
"""
setup(name='rl_utils',
version='0.0',
description='rl utils for the rl algorithms',
author='Tianhong Dai',
author_email='xxx@xxx.com',
url='no',
packages=['rl_utils'],
)
| 275 | 17.4 | 51 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/dqn_algos/arguments.py | import argparse
def get_args():
parse = argparse.ArgumentParser()
parse.add_argument('--gamma', type=float, default=0.99, help='the discount factor of RL')
parse.add_argument('--seed', type=int, default=123, help='the random seeds')
parse.add_argument('--env-name', type=str, default='PongNoFrameskip-v4... | 2,247 | 73.933333 | 129 | py |
reinforcement-learning-algorithms | reinforcement-learning-algorithms-master/rl_algorithms/dqn_algos/utils.py | import numpy as np
import random
# linear exploration schedule
class linear_schedule:
def __init__(self, total_timesteps, final_ratio, init_ratio=1.0):
self.total_timesteps = total_timesteps
self.final_ratio = final_ratio
self.init_ratio = init_ratio
def get_value(self, timestep):
... | 1,621 | 28.490909 | 115 | py |
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