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 |
|---|---|---|---|---|---|---|
coocmap | coocmap-main/experiments/test_accvdim.py | import os
import subprocess
from dataclasses import dataclass
import lzma
import wandb
import argparse
import shutil
import pandas as pd
import numpy as np
import data
import match
import evaluation
import embeddings
# from baselines import VecMap
os.environ['WANDB_IGNORE_GLOBS'] = 'lan1/*,lan2/*'
os.environ["OMP_NU... | 9,329 | 34.340909 | 136 | py |
coocmap | coocmap-main/experiments/test_dropclip.py | import os
import subprocess
from dataclasses import dataclass
import lzma
import wandb
import argparse
import shutil
import pandas as pd
import numpy as np
import data
import match
import evaluation
import embeddings
# from baselines import VecMap
os.environ['WANDB_IGNORE_GLOBS'] = 'lan1/*,lan2/*'
os.environ["OMP_NU... | 9,111 | 34.317829 | 136 | py |
coocmap | coocmap-main/experiments/test_accvsize.py | import os
import subprocess
from dataclasses import dataclass
import lzma
import wandb
import argparse
import shutil
import pandas as pd
import numpy as np
import data
import match
import evaluation
import embeddings
# from baselines import VecMap
os.environ['WANDB_IGNORE_GLOBS'] = 'lan1/*,lan2/*'
os.environ["OMP_NU... | 6,921 | 32.439614 | 136 | py |
coocmap | coocmap-main/experiments/test_coocmap.py | import os
from dataclasses import dataclass
import wandb
import shutil
import pandas as pd
import numpy as np
import data
import match
import evaluation
import embeddings
# experimental parameters
defaults = dict(
lan1='./europarl-v7.hu-en.en',
lan2='./europarl-v7.hu-en.hu',
eval='en-hu',
size1=20,
... | 5,771 | 32.172414 | 136 | py |
coocmap | coocmap-main/experiments/test_matching.py | import os
import subprocess
from dataclasses import dataclass
import lzma
import wandb
import argparse
import shutil
import pandas as pd
import numpy as np
import data
import match
import evaluation
import embeddings
# from baselines import VecMap
os.environ['WANDB_IGNORE_GLOBS'] = 'lan1/*,lan2/*'
os.environ["OMP_NU... | 6,598 | 29.836449 | 136 | py |
coocmap | coocmap-main/experiments/test_accvsize_cooc.py | import os
import subprocess
from dataclasses import dataclass
import lzma
import wandb
import argparse
import shutil
import pandas as pd
import numpy as np
import data
import match
import evaluation
import embeddings
# from baselines import VecMap
os.environ['WANDB_IGNORE_GLOBS'] = 'lan1/*,lan2/*'
os.environ["OMP_NU... | 9,439 | 33.578755 | 136 | py |
MateriAppsInstaller | MateriAppsInstaller-master/docs/sphinx/en/source/conf.py | # -*- coding: utf-8 -*-
#
# MateriApps-Installer documentation build configuration file, created by
# sphinx-quickstart on Sun May 1 14:29:22 2020.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated... | 5,684 | 28.455959 | 79 | py |
MateriAppsInstaller | MateriAppsInstaller-master/docs/sphinx/ja/source/conf.py | # -*- coding: utf-8 -*-
#
# MateriApps-Installer documentation build configuration file, created by
# sphinx-quickstart on Sun May 1 14:29:22 2020.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated... | 5,686 | 28.466321 | 79 | py |
MateriAppsInstaller | MateriAppsInstaller-master/misc/make_readme.py | #!/usr/bin/python
# coding: utf-8
import h5py
import glob
import os
import re
dirs_apps = [x for x in glob.glob("./*") if os.path.isdir(x)]
dict_apps = {}
#get app name
for dir_name in dirs_apps:
dict_apps[dir_name[2:]] = dir_name
print(dict_apps)
apps_table = {"komega":"Kω","alps":"ALPS","xtapp":"xTAPP",
... | 1,555 | 34.363636 | 99 | py |
MateriAppsInstaller | MateriAppsInstaller-master/misc/make_rst.py | #!/usr/bin/python
# coding: utf-8
import h5py
import glob
import os
import re
import subprocess
path_to_sphinx = "../docs/sphinx/en/source/appendix/"
dirs_apps = [x for x in glob.glob("./*") if os.path.isdir(x)]
dirs_apps.sort()
print(dirs_apps)
cwd_path = os.getcwd()
path_to_sphinx = os.path.join(cwd_path, path_to_... | 1,536 | 28 | 89 | py |
MateriAppsInstaller | MateriAppsInstaller-master/misc/make_rst_ja.py | #!/usr/bin/python
# coding: utf-8
import h5py
import glob
import os
import re
import subprocess
path_to_sphinx_ja = "../docs/sphinx/ja/source/appendix/"
dirs_apps = [x for x in glob.glob("./*") if os.path.isdir(x)]
dirs_apps.sort()
print(dirs_apps)
cwd_path = os.getcwd()
path_to_sphinx_ja = os.path.join(cwd_path, pa... | 1,546 | 28.188679 | 92 | py |
MateriAppsInstaller | MateriAppsInstaller-master/misc/get_info_ja.py | import requests
from bs4 import BeautifulSoup
import re
import time
import h5py
def get_retry(url, retry_times, errs):
for t in range(retry_times + 1):
r = requests.get(url)
if t < retry_times:
if r.status_code in errs:
time.sleep(2)
continue
retu... | 2,705 | 40 | 127 | py |
MateriAppsInstaller | MateriAppsInstaller-master/misc/make_readme_ja.py | #!/usr/bin/python
# coding: utf-8
import h5py
import glob
import os
import re
dirs_apps = [x for x in glob.glob("./*") if os.path.isdir(x)]
dict_apps = {}
#get app name
for dir_name in dirs_apps:
dict_apps[dir_name[2:]] = dir_name
print(dict_apps)
apps_table = {"alps":"ALPS","xtapp":"xTAPP", "komega":"Kω",
... | 1,591 | 34.377778 | 99 | py |
MateriAppsInstaller | MateriAppsInstaller-master/misc/get_info.py | import requests
from bs4 import BeautifulSoup
import re
import time
import h5py
def get_retry(url, retry_times, errs):
for t in range(retry_times + 1):
r = requests.get(url)
if t < retry_times:
if r.status_code in errs:
time.sleep(2)
continue
retu... | 2,664 | 41.301587 | 127 | py |
harmonic | harmonic-main/setup.py | import sys
import os
import shutil
import setuptools
from setuptools import setup, Extension
from Cython.Distutils import build_ext
from Cython.Build import cythonize
import numpy
# clean previous build
for root, dirs, files in os.walk("./harmonic/", topdown=False):
for name in dirs:
if (name == "build"):... | 3,064 | 28.471154 | 109 | py |
harmonic | harmonic-main/examples/gaussian_diagcov.py | import numpy as np
import sys
sys.path.append(".")
import harmonic as hm
import emcee
import scipy.special as sp
import time
import matplotlib.pyplot as plt
import utils
import gc
def ln_analytic_evidence(ndim, cov):
"""Compute analytic ln_e evidence.
Args:
ndim: Dimensionality of the multivariate... | 10,229 | 34.275862 | 92 | py |
harmonic | harmonic-main/examples/normal_gamma.py | import numpy as np
import sys
import emcee
import scipy.special as sp
import time
import matplotlib.pyplot as plt
from functools import partial
sys.path.append(".")
import harmonic as hm
sys.path.append("examples")
import utils
def ln_likelihood(x_mean, x_std, x_n, mu, tau):
"""Compute log_e of likelihood.
Args:
... | 15,817 | 29.655039 | 79 | py |
harmonic | harmonic-main/examples/plot_realisations.py | import numpy as np
import matplotlib.pyplot as plt
import argparse
import os
import sys
sys.path.append("examples")
import utils
savefigs = True
# Parse arguments.
parser = argparse.ArgumentParser("Create violin plot of inverse evidences" +
"from many realisations")
parser.add_argument('filename_realisations', m... | 2,344 | 38.083333 | 95 | py |
harmonic | harmonic-main/examples/rosenbrock.py | import numpy as np
import sys
import emcee
import time
import matplotlib.pyplot as plt
from functools import partial
sys.path.append(".")
import harmonic as hm
sys.path.append("examples")
import utils
def ln_prior_uniform(x, xmin=-10.0, xmax=10.0, ymin=-5.0, ymax=15.0):
"""Compute log_e of uniform prior.
Ar... | 17,503 | 37.135076 | 87 | py |
harmonic | harmonic-main/examples/utils.py | import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import LightSource
from mpl_toolkits.mplot3d import Axes3D
import corner
from getdist import plots, MCSamples
import getdist
def plot_corner(samples, labels=None):
"""
Plot triangle plot of marginalised distribu... | 9,458 | 29.220447 | 80 | py |
harmonic | harmonic-main/examples/rastrigin.py | import numpy as np
import sys
import emcee
import time
import matplotlib.pyplot as plt
from functools import partial
sys.path.append(".")
import harmonic as hm
sys.path.append("examples")
import utils
def ln_prior_uniform(x, xmin=-6.0, xmax=6.0, ymin=-6.0, ymax=6.0):
"""Compute log_e of uniform prior.
Args:... | 16,415 | 37.35514 | 87 | py |
harmonic | harmonic-main/examples/radiata_pine.py | import numpy as np
import sys
import emcee
import scipy.special as sp
import time
import matplotlib.pyplot as plt
from functools import partial
sys.path.append(".")
import harmonic as hm
sys.path.append("examples")
import utils
def ln_likelihood(y, x, n, alpha, beta, tau):
"""Compute log_e of Radiata Pine likeli... | 20,255 | 29.690909 | 96 | py |
harmonic | harmonic-main/examples/pima_indian.py | import numpy as np
import sys
import emcee
import scipy.special as sp
import time
import matplotlib.pyplot as plt
from functools import partial
sys.path.append(".")
import harmonic as hm
sys.path.append("examples")
import utils
def ln_likelihood(y, theta, x):
"""Compute log_e of Pima Indian likelihood.
Args... | 13,648 | 33.467172 | 96 | py |
harmonic | harmonic-main/examples/gaussian_nondiagcov.py | import numpy as np
import sys
import emcee
import time
import matplotlib.pyplot as plt
from functools import partial
from matplotlib import cm
sys.path.append(".")
import harmonic as hm
sys.path.append("examples")
import utils
def ln_analytic_evidence(ndim, cov):
"""Compute analytic evidence for nD Gaussian.
... | 14,359 | 37.810811 | 87 | py |
harmonic | harmonic-main/tests/test_evidence.py | import pytest
import numpy as np
from scipy.stats import kurtosis
import harmonic.chains as ch
import harmonic.model as md
import harmonic.evidence as cbe
import harmonic.utils as utils
def test_constructor():
nchains = 100
ndim = 1000
domain = [np.array([1e-1, 1e1])]
sphere = md.HyperSphere(ndim, do... | 12,445 | 31.83905 | 104 | py |
harmonic | harmonic-main/tests/test_logs.py | import harmonic.logs as lg
import pytest
import numpy as np
def test_incorrect_log_yaml_path():
dir_name = "random/incorrect/filepath/"
# Check cannot add samples with different ndim.
with pytest.raises(ValueError):
lg.setup_logging(custom_yaml_path=dir_name)
def test_general_logging():
lg.se... | 485 | 19.25 | 51 | py |
harmonic | harmonic-main/tests/__init__.py | 0 | 0 | 0 | py | |
harmonic | harmonic-main/tests/test_model.py | import pytest
import harmonic.model as md
import numpy as np
def test_hyper_sphere_constructor():
with pytest.raises(ValueError):
sphere = md.HyperSphere(2, [np.array([0.5,1.5])], hyper_parameters=[5])
with pytest.raises(ValueError):
sphere = md.HyperSphere(2, [np.array([0.5,1.5]),np.array([0.... | 26,554 | 32.360553 | 144 | py |
harmonic | harmonic-main/tests/test_chains.py | import harmonic.chains as ch
import pytest
import numpy as np
def test_constructor():
ndim = 0
with pytest.raises(ValueError):
chains = ch.Chains(ndim)
ndim = 3
chains = ch.Chains(ndim)
assert chains.ndim == ndim
assert chains.nchains == 0
assert chains.nsampl... | 21,450 | 34.456198 | 80 | py |
harmonic | harmonic-main/tests/test_utils.py | import numpy as np
import harmonic.utils as utils
import harmonic.chains as ch
import harmonic.model as md
import pytest
def test_split_data():
ndim = 5
nsamples = 100
nchains = 200
training_proportion = 0.5
chains_all = ch.Chains(ndim)
np.random.seed(3)
samples = np.... | 7,985 | 38.339901 | 108 | py |
harmonic | harmonic-main/docs/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 6,728 | 29.586364 | 100 | py |
harmonic | harmonic-main/harmonic/__init__.py | from .chains import Chains
from . import evidence
from .evidence import Evidence, Shifting
from . import model
from . import utils
from . import logs
| 150 | 20.571429 | 40 | py |
harmonic | harmonic-main/harmonic/logs.py | import os
import logging.config
import logging
import yaml
import harmonic
import colorlog
def setup_logging(custom_yaml_path=None, default_level=logging.DEBUG):
"""initialise and configure logging.
Should be called at the beginning of code to initialise and configure the
desired logging level. Loggi... | 3,175 | 29.538462 | 83 | py |
DeepGAR | DeepGAR-main/test.py | from common import utils
from collections import defaultdict
from datetime import datetime
from sklearn.metrics import roc_auc_score, confusion_matrix
from sklearn.metrics import precision_recall_curve, average_precision_score
import torch
USE_ORCA_FEATS = False # whether to use orca motif counts along with embeddings... | 8,369 | 46.828571 | 115 | py |
DeepGAR | DeepGAR-main/config.py | import argparse
from common import utils
def parse_encoder(parser, arg_str=None):
enc_parser = parser.add_argument_group()
#utils.parse_optimizer(parser)
enc_parser.add_argument('--conv_type', type=str,
help='type of convolution')
enc_parser.add_argument('--method_type', type=s... | 3,166 | 42.986111 | 73 | py |
DeepGAR | DeepGAR-main/hyp_search.py | def parse_encoder(parser):
parser.opt_list('--conv_type', type=str, tunable=True,
options=['GIN', 'SAGE'],#, 'GCN'],#, 'GAT'],
help='type of model')
parser.opt_list('--skip', type=str, tunable=True,
options=['all', 'last'],#, 'GCN'],#, 'GAT'],
help='type of mode... | 3,927 | 46.325301 | 82 | py |
DeepGAR | DeepGAR-main/deepgar.py | HYPERPARAM_SEARCH = False
HYPERPARAM_SEARCH_N_TRIALS = None # how many grid search trials to run
# (set to None for exhaustive search)
import argparse
from itertools import permutations
import pickle
from queue import PriorityQueue
import os
import random
import time
import ne... | 31,768 | 39.31599 | 205 | py |
DeepGAR | DeepGAR-main/common/utils.py | from collections import defaultdict, Counter
from deepsnap.graph import Graph as DSGraph
from deepsnap.batch import Batch
from deepsnap.dataset import GraphDataset
import torch
import torch.optim as optim
import torch_geometric.utils as pyg_utils
from torch_geometric.data import DataLoader
import networkx as nx
import... | 11,535 | 39.477193 | 120 | py |
DeepGAR | DeepGAR-main/common/data.py | import os
import pickle
import random
from deepsnap.graph import Graph as DSGraph
from deepsnap.batch import Batch
from deepsnap.dataset import GraphDataset, Generator
import networkx as nx
import numpy as np
from sklearn.manifold import TSNE
import torch
import torch.multiprocessing as mp
import torch.nn.functional a... | 24,005 | 44.20904 | 159 | py |
DeepGAR | DeepGAR-main/common/models.py | """Defines all graph embedding models"""
from functools import reduce
import random
import networkx as nx
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_geometric.nn as pyg_nn
import torch_geometric.utils as pyg_utils
from common import utils
from common import feat... | 16,350 | 41.250646 | 191 | py |
nepali-ner | nepali-ner-master/main.py | #!/usr/bin/env python3
'''
Main file
Author: Oyesh Mann Singh
How to run:
python main.py -k 1 -d cpu
'''
import os
import argparse
import shutil
import warnings
from utils.dataloader import Dataloader
import utils.utilities as utilities
import utils.splitter as splitter
from tqdm import tqdm
... | 7,120 | 32.909524 | 120 | py |
nepali-ner | nepali-ner-master/app.py | """
Needs code structuring
Date - 08/14/2020
"""
import torch
import logging
import sys
from flask import Flask, render_template, request
from utils.dataloader2 import Dataloader
from models.models import LSTMTagger
from config.config import Configuration
app = Flask(__name__)
def get_logger():
logger =... | 2,497 | 25.294737 | 98 | py |
nepali-ner | nepali-ner-master/wsgi.py | from app.main import app
if __name__ == "__main__":
app.run()
| 70 | 13.2 | 27 | py |
nepali-ner | nepali-ner-master/train.py | #!/usr/bin/env python3
'''
Trainer
Author: Oyesh Mann Singh
'''
import os
from utils.eval import Evaluator
from tqdm import tqdm, tqdm_notebook, tnrange
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.metrics import accuracy_score
torch.manual_seed(163)
tqdm.pandas(desc='Progress'... | 8,092 | 34.034632 | 146 | py |
nepali-ner | nepali-ner-master/config/config.py | '''
Configuration Parser
Author: Oyesh Mann Singh
Date 10/15/2019
'''
import os
import logging
from configparser import ConfigParser
class Configuration(ConfigParser):
def __init__(self, config_file, logger):
super().__init__()
config = ConfigParser(allow_no_value=True)
config... | 5,095 | 25.821053 | 75 | py |
nepali-ner | nepali-ner-master/models/models.py | '''
Models
Author: Oyesh Mann Singh
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
from uniseg.graphemecluster import grapheme_clusters
tqdm.pandas(desc='Progress')
class LSTMTagger(nn.Module):
def __init__(self, config, dataloader):
... | 9,650 | 37.146245 | 104 | py |
nepali-ner | nepali-ner-master/utils/conll_preprocessor.py | #!/usr/bin/env python3
'''
Removes CHUNK column from CoNLL file
Author: Oyesh Mann Singh
Date: 10/16/2019
'''
import os
import argparse
import pandas as pd
import numpy as np
import csv
import shutil
def preprocess(input_file, output_file):
with open(input_file,'r', encoding='utf-8') as in_file, open(... | 1,280 | 28.113636 | 119 | py |
nepali-ner | nepali-ner-master/utils/conlleval_perl.py | #!/usr/bin/python
#### Original Perl Script
# conlleval: evaluate result of processing CoNLL-2000 shared task
# usage: conlleval [-l] [-r] [-d delimiterTag] [-o oTag] < file
# README: http://cnts.uia.ac.be/conll2000/chunking/output.html
# options: l: generate LaTeX output for tables like in
# ... | 12,636 | 41.123333 | 168 | py |
nepali-ner | nepali-ner-master/utils/column_extractor.py | #!/usr/bin/env python3
'''
Converts 3 columns into 2 columns
Author: Oyesh Mann Singh
Date: 10/16/2019
'''
import os
import argparse
import pandas as pd
import numpy as np
import csv
import shutil
def preprocess(input_file, output_file):
with open(input_file,'r', encoding='utf-8') as in_file, open(out... | 1,293 | 28.409091 | 134 | py |
nepali-ner | nepali-ner-master/utils/stemmer.py | #!/usr/bin/env python3
'''
Stems the postpositions in
given dataset brute-force approach
Author: Oyesh Mann Singh
Date: 12/08/2019
'''
import os
import argparse
import pandas as pd
import numpy as np
import csv
import shutil
def stem(pp_file, input_file, output_file):
stemmers = open(pp_file... | 2,168 | 29.985714 | 135 | py |
nepali-ner | nepali-ner-master/utils/data_checker.py | #!/usr/bin/env python3
# Simple program to check data statistics of NER file
# Input file should be in standard Stanford format
# Outputs number of PER, LOC, ORG tags
import csv
import argparse
import os
from collections import Counter
def main():
parser = argparse.ArgumentParser(description='Input file name')
p... | 1,316 | 25.34 | 64 | py |
nepali-ner | nepali-ner-master/utils/dataloader2.py | #!/usr/bin/env python3
'''
NER Dataloader
Author: Oyesh Mann Singh
Date: 10/14/2019
Data format:
<WORD> <NER-tag>
'''
import os
import pickle
from torchtext import data, vocab
from torchtext.datasets import SequenceTaggingDataset
class Dataloader():
def __init__(self, config, k):
... | 2,467 | 27.367816 | 99 | py |
nepali-ner | nepali-ner-master/utils/bio_converter.py | #!/usr/bin/python
import os
import sys
def main():
label_list = ['PER', 'ORG', 'LOC']
with open(sys.argv[1], 'r', encoding='utf-8') as in_file, open(sys.argv[2], 'w', encoding='utf-8') as out_file:
prev_label = ' '
for i1, row in enumerate(in_file):
row = row.strip().split()
... | 821 | 29.444444 | 115 | py |
nepali-ner | nepali-ner-master/utils/dataloader.py | #!/usr/bin/env python3
'''
NER Dataloader
Author: Oyesh Mann Singh
Date: 10/14/2019
Data format:
<WORD> <NER-tag>
'''
import os
import numpy as np
import pickle
import torch
from torchtext import data
from torchtext import vocab
from torchtext.datasets import SequenceTaggingDataset
from un... | 6,000 | 34.720238 | 116 | py |
nepali-ner | nepali-ner-master/utils/splitter.py | #!/usr/bin/env python3
'''
Splits dataset into train/test/val
Author: Oyesh Mann Singh
Date: 10/16/2019
'''
import os
import argparse
import pandas as pd
import numpy as np
import csv
import shutil
try:
import utilities as utilities
except ImportError:
import utils.utilities as utilities
MAX_SEQ_... | 10,065 | 36.007353 | 201 | py |
nepali-ner | nepali-ner-master/utils/utilities.py | # -*- coding: UTF-8 -*-
import datetime
import io
import logging
import os
import sys
import time
def get_logger(filepath):
"""
Gets a logger instance to write the program info and errors to.
@params:
filepath (string): File path to the log output.
@returns:
Instanc... | 1,451 | 29.25 | 97 | py |
nepali-ner | nepali-ner-master/utils/conll_eval.py | #!/usr/bin/python
"""
This script applies to IOB2 or IOBES tagging scheme.
If you are using a different scheme, please convert to IOB2 or IOBES.
IOB2:
- B = begin,
- I = inside but not the first,
- O = outside
e.g.
John lives in New York City .
B-PER O O B-LOC I-LOC I-LOC O
IOBES:
- B = begin,
- E = ... | 7,590 | 31.861472 | 163 | py |
nepali-ner | nepali-ner-master/utils/eval.py | '''
Writes result into the file
Author: Oyesh Mann Singh
'''
import os
import torch
from tqdm import tqdm
import utils.conlleval_perl as e
tqdm.pandas(desc='Progress')
class Evaluator:
def __init__(self, config, logger, model, dataloader, model_name):
self.config = config
self.logger = l... | 5,892 | 34.5 | 112 | py |
nepali-ner | nepali-ner-master/utils/converter.py | #!/usr/bin/env python3
'''
File converter into CoNLL format
Author: Oyesh Mann Singh
Date: 10/14/2019
'''
import os
import io
import argparse
import re
parser = argparse.ArgumentParser("POS Tagger Argument Parser")
parser.add_argument("-i", "--input_folder", default="../data/", metavar="PATH", help="Data ... | 1,444 | 32.604651 | 111 | py |
nepali-ner | nepali-ner-master/utils/NNCCorpus.py | # Natural Language Toolkit: Plaintext Corpus Reader
#
# Copyright (C) 2001-2018 NLTK Project
# Author: Edward Loper <edloper@gmail.com>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
"""Corpus reader for the XML version of the British National Corpus."""
from nltk.corpus.reader.util import conca... | 9,564 | 33.781818 | 101 | py |
frozen-in-time | frozen-in-time-main/test.py | import argparse
import pandas as pd
import torch
import transformers
from sacred import Experiment
from tqdm import tqdm
import glob
import data_loader.data_loader as module_data
import model.metric as module_metric
import model.model as module_arch
from model.model import compute_similarity
from parse_config import C... | 11,571 | 39.603509 | 160 | py |
frozen-in-time | frozen-in-time-main/parse_config.py | import inspect
import logging
import os
import time
from datetime import datetime
from functools import reduce
from operator import getitem
from pathlib import Path
from logger import setup_logging
from utils import read_json, write_json
class ConfigParser:
def __init__(self, args, options='', timestamp=True, te... | 5,382 | 35.869863 | 118 | py |
frozen-in-time | frozen-in-time-main/train.py | import argparse
import collections
import os
import transformers
from sacred import Experiment
import data_loader.data_loader as module_data
import model.loss as module_loss
import model.metric as module_metric
import model.model as module_arch
import utils.visualizer as module_vis
from parse_config import ConfigPars... | 5,446 | 41.554688 | 114 | py |
frozen-in-time | frozen-in-time-main/trainer/__init__.py | from .trainer import *
| 23 | 11 | 22 | py |
frozen-in-time | frozen-in-time-main/trainer/trainer.py | import numpy as np
import torch
from torch import nn
from tqdm.auto import tqdm
from base import BaseTrainer
from model.model import sim_matrix
from utils import inf_loop
class Trainer(BaseTrainer):
"""
Trainer class
Note:
Inherited from BaseTrainer.
"""
def __init__(self, model, loss, ... | 10,116 | 43.179039 | 119 | py |
frozen-in-time | frozen-in-time-main/data_loader/MSRVTT_dataset.py | import json
import os
import random
import numpy as np
import pandas as pd
from base.base_dataset import TextVideoDataset
class MSRVTT(TextVideoDataset):
def _load_metadata(self):
json_fp = os.path.join(self.metadata_dir, 'annotation', 'MSR_VTT.json')
with open(json_fp, 'r') as fid:
... | 3,173 | 40.763158 | 103 | py |
frozen-in-time | frozen-in-time-main/data_loader/data_loader.py | from base import BaseDataLoaderExplicitSplit, BaseMultiDataLoader
from data_loader.ConceptualCaptions_dataset import ConceptualCaptions3M
from data_loader.LSMDC_dataset import LSMDC
from data_loader.MSRVTT_dataset import MSRVTT
from data_loader.WebVid_dataset import WebVid
from data_loader.VideoDirectory_dataset import... | 4,625 | 32.280576 | 115 | py |
frozen-in-time | frozen-in-time-main/data_loader/LSMDC_dataset.py | import os
import numpy as np
import pandas as pd
from base.base_dataset import TextVideoDataset
class LSMDC(TextVideoDataset):
def _load_metadata(self):
split_paths = {key: os.path.join(self.metadata_dir, 'structured-symlinks', f'{key}_list.txt') for key in
['train', 'val', 'test'... | 2,542 | 45.236364 | 113 | py |
frozen-in-time | frozen-in-time-main/data_loader/VideoDirectory_dataset.py | import json
import os
import random
import numpy as np
import pandas as pd
import glob
from base.base_dataset import TextVideoDataset
class VideoDirectory(TextVideoDataset):
def _load_metadata(self):
if self.split != 'test':
raise NotImplementedError("Assumes inference, no text, hence cant b... | 2,295 | 33.268657 | 123 | py |
frozen-in-time | frozen-in-time-main/data_loader/ImageDirectory_dataset.py | import json
import os
import random
import numpy as np
import pandas as pd
import glob
from base.base_dataset import TextImageDataset
class ImageDirectory(TextImageDataset):
def _load_metadata(self):
if self.split != 'test':
raise NotImplementedError("Assumes inference, no text, hence cant b... | 1,355 | 27.851064 | 103 | py |
frozen-in-time | frozen-in-time-main/data_loader/WebVid_dataset.py | import os
import pandas as pd
from base.base_dataset import TextVideoDataset
class WebVid(TextVideoDataset):
"""
WebVid Dataset.
Assumes webvid data is structured as follows.
Webvid/
videos/
000001_000050/ ($page_dir)
1.mp4 (videoid.mp4)
... | 1,439 | 31.727273 | 107 | py |
frozen-in-time | frozen-in-time-main/data_loader/ConceptualCaptions_dataset.py | import os
import zlib
import pandas as pd
from base.base_dataset import TextImageDataset
class ConceptualCaptions3M(TextImageDataset):
"""
Conceptual Captions dataset. Split files are specific to my download regime.
"""
def _load_metadata(self):
# download specific
split_files = {
... | 1,441 | 33.333333 | 108 | py |
frozen-in-time | frozen-in-time-main/data_loader/__init__.py | 0 | 0 | 0 | py | |
frozen-in-time | frozen-in-time-main/data_loader/transforms.py | from torchvision import transforms
def init_transform_dict(input_res=224,
center_crop=256,
randcrop_scale=(0.5, 1.0),
color_jitter=(0, 0, 0),
norm_mean=(0.485, 0.456, 0.406),
norm_std=(0.229, 0.224,... | 1,160 | 35.28125 | 112 | py |
frozen-in-time | frozen-in-time-main/logger/visualization.py | import importlib
from utils import Timer
class TensorboardWriter:
def __init__(self, log_dir, logger, enabled):
self.writer = None
self.selected_module = ""
if enabled:
log_dir = str(log_dir)
# Retrieve visualization writer.
for module in ["torch.util... | 2,909 | 35.375 | 120 | py |
frozen-in-time | frozen-in-time-main/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 |
frozen-in-time | frozen-in-time-main/logger/__init__.py | from .logger import *
from .visualization import * | 50 | 24.5 | 28 | py |
frozen-in-time | frozen-in-time-main/scripts/create_faiss_index.py | import argparse
import faiss
from pathlib import Path
import numpy as np
import os
def create_index(
index: str,
embed_dim: int,
nlist: int,
):
index = faiss.index_factory(embed_dim, f"{index}{nlist},Flat", faiss.METRIC_INNER_PRODUCT)
return index
def load_feats(
feat_fp: Path... | 2,903 | 31.629213 | 120 | py |
frozen-in-time | frozen-in-time-main/scripts/agg_ids_embeds.py | import numpy as np
import pandas as pd
import glob
import os
from ast import literal_eval
import argparse
# dir = '/scratch/shared/beegfs/maxbain/datasets/CondensedMoviesShots/features/CC-WebVid2M-4f-pt1f/0522_143949'
# dir = '/scratch/shared/beegfs/maxbain/datasets/CondensedMovies/features/CLIP4CLIP/cmd_batch_size_t... | 2,438 | 40.338983 | 138 | py |
frozen-in-time | frozen-in-time-main/base/base_model.py | import torch.nn as nn
import numpy as np
from abc import abstractmethod
class BaseModel(nn.Module):
"""
Base class for all models
"""
@abstractmethod
def forward(self, *inputs):
"""
Forward pass logic
:return: Model output
"""
raise NotImplementedError
... | 646 | 23.884615 | 79 | py |
frozen-in-time | frozen-in-time-main/base/base_trainer.py | from abc import abstractmethod
import torch
from numpy import inf
class BaseTrainer:
"""
Base class for all trainers
"""
def __init__(self, model, loss, metrics, optimizer, config, writer=None, init_val=False):
self.config = config
self.logger = config.get_logger('trainer', config['tr... | 9,470 | 38.962025 | 117 | py |
frozen-in-time | frozen-in-time-main/base/base_dataset.py | import os
import random
from abc import abstractmethod
import av
import cv2
import decord
import numpy as np
import torch
from PIL import Image
from torch.utils.data import Dataset, get_worker_info
from torchvision import transforms
class TextVideoDataset(Dataset):
def __init__(self,
dataset_nam... | 8,634 | 35.434599 | 116 | py |
frozen-in-time | frozen-in-time-main/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, validat... | 3,447 | 30.633028 | 130 | py |
frozen-in-time | frozen-in-time-main/base/__init__.py | from .base_data_loader import *
from .base_dataset import *
from .base_model import *
from .base_trainer import *
| 114 | 22 | 31 | py |
frozen-in-time | frozen-in-time-main/utils/custom_transforms.py | import numbers
from typing import List, Tuple
import torch
from torch import Tensor
from torchvision.transforms import functional_pil as F_pil, functional_tensor as F_t
from torchvision.transforms.functional import center_crop, crop
def _get_image_size(img: Tensor) -> List[int]:
"""Returns image size as [w, h]
... | 4,569 | 33.360902 | 109 | py |
frozen-in-time | frozen-in-time-main/utils/video.py | import random
import cv2
import numpy as np
import torch
def load_frames_from_video_path(path, num_frames, sample='rand'):
cap = cv2.VideoCapture(path)
assert (cap.isOpened())
vlen = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
acc_samples = min(num_frames, vlen)
intervals = np.linspace(start=0, stop=v... | 1,264 | 29.853659 | 80 | py |
frozen-in-time | frozen-in-time-main/utils/html.py | import os
import dominate
from dominate.tags import a, attr, br, h3, img, meta, p, source, span, table, td, tr, video
class HTML:
"""This HTML class allows us to save images and write texts into a single HTML file.
It consists of functions such as <add_header> (add a text header to the HTML file),
<ad... | 6,098 | 41.354167 | 97 | py |
frozen-in-time | frozen-in-time-main/utils/visualisation.py | import matplotlib
import numpy as np
import torch
matplotlib.use('Agg')
def visualise_path(pred, target, window):
"""
:param pred: (P, 2) Tensor where P is the number of predictions, and 2 is the (i,j) coordinate
:param target: (T, 2) Tensor where T is the number of targets, and 2 is the (i,j) coordinate... | 1,768 | 28.983051 | 104 | py |
frozen-in-time | frozen-in-time-main/utils/visualizer.py | """A simple HTML visualizer.
It is based on the Cycle-GAN codebase:
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
"""
import os
from pathlib import Path
import numpy as np
from . import html
class RetrievalVis:
"""This class includes several functions that can display/save images.
It uses a Pyth... | 6,109 | 36.030303 | 89 | py |
frozen-in-time | frozen-in-time-main/utils/util.py | import functools
import json
import os
import socket
import time
from collections import OrderedDict
from datetime import datetime
from itertools import repeat
from pathlib import Path
import humanize
import numpy as np
import psutil
def replace_nested_dict_item(obj, key, replace_value):
for k, v in obj.items():... | 4,602 | 29.084967 | 90 | py |
frozen-in-time | frozen-in-time-main/utils/__init__.py | from .util import *
| 20 | 9.5 | 19 | py |
frozen-in-time | frozen-in-time-main/model/loss.py | import torch
import torch.nn.functional as F
from torch import nn
class NormSoftmaxLoss(nn.Module):
def __init__(self, temperature=0.05):
super().__init__()
self.temperature = temperature
def forward(self, x):
"Assumes input x is similarity matrix of N x M \in [-1, 1], computed using... | 2,813 | 27.424242 | 132 | py |
frozen-in-time | frozen-in-time-main/model/model.py | import timm
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoModel
from base import BaseModel
from model.video_transformer import SpaceTimeTransformer
from utils.util import state_dict_data_parallel_fix
class FrozenInTime(BaseModel):
def __init__(self,
... | 7,958 | 43.463687 | 116 | py |
frozen-in-time | frozen-in-time-main/model/__init__.py | 0 | 0 | 0 | py | |
frozen-in-time | frozen-in-time-main/model/video_transformer.py | """
Implementations of Video Transformers in PyTorch
A PyTorch implementation of space-time transformer as described in
'Frozen in Time: A Joint Image and Video Encoder for End-to-End Retrieval' - https://arxiv.org/abs/2104.00650
A PyTorch implementation of timesformer as described in
'Is Space-Time Attention All You... | 14,164 | 40.784661 | 145 | py |
frozen-in-time | frozen-in-time-main/model/metric.py | """Module for computing performance metrics
"""
from pathlib import Path
import numpy as np
import scipy.stats
import torch
def t2v_metrics(sims, query_masks=None):
"""Compute retrieval metrics from a similarity matrix.
Args:
sims (th.Tensor): N x M matrix of similarities between embeddings, where
... | 14,381 | 38.839335 | 93 | py |
local-astar | local-astar-master/src/main.py | """
Code modified from:
https://github.com/ignavierng/golem/blob/main/src/main.py
Each run creates a directory based on current datetime to save:
- log file of training process
- experiment configurations
- observational data and ground truth
- final estimated solution
- visualization of final estimated solution
"""
... | 3,597 | 37.688172 | 100 | py |
local-astar | local-astar-master/src/data_loader/__init__.py | from data_loader.synthetic_dataset import SyntheticDataset
| 59 | 29 | 58 | py |
local-astar | local-astar-master/src/data_loader/synthetic_dataset.py | """
Code modified from:
- https://github.com/ignavier/golem/blob/main/src/data_loader/synthetic_dataset.py
- https://github.com/xunzheng/notears/blob/master/notears/utils.py
"""
import logging
import networkx as nx
import numpy as np
from utils.dag import get_cpdag, get_skeleton
from utils.dag import is_dag
class S... | 5,873 | 31.274725 | 99 | py |
local-astar | local-astar-master/src/search/exact_search.py | """
Some code of DP and astar is modified from:
https://github.com/jmschrei/pomegranate/blob/master/pomegranate/BayesianNetwork.pyx
Several tricks to save memory of parent_graphs and prune edges in order_graph are based on:
https://arxiv.org/abs/1608.02682
Several other tricks of A* (e.g., dynamic k-cycle conflict he... | 18,517 | 35.097466 | 102 | py |
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