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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drizzle | drizzle-master/drizzle/tests/test_drizzle.py | import math
import os
import pytest
import numpy as np
from astropy import wcs
from astropy.io import fits
from drizzle import drizzle, cdrizzle
TEST_DIR = os.path.abspath(os.path.dirname(__file__))
DATA_DIR = os.path.join(TEST_DIR, 'data')
ok = False
def bound_image(image):
"""
Compute region where ima... | 20,424 | 30.520062 | 83 | py |
drizzle | drizzle-master/drizzle/tests/test_pixmap.py | import os.path
import numpy as np
from numpy.testing import assert_equal, assert_almost_equal
from astropy import wcs
from astropy.io import fits
from drizzle import calc_pixmap
TEST_DIR = os.path.abspath(os.path.dirname(__file__))
DATA_DIR = os.path.join(TEST_DIR, 'data')
def test_map_rectangular():
"""
... | 2,061 | 25.435897 | 67 | py |
drizzle | drizzle-master/drizzle/tests/__init__.py | 0 | 0 | 0 | py | |
Ranger-Deep-Learning-Optimizer | Ranger-Deep-Learning-Optimizer-master/setup.py | #!/usr/bin/env python
import os
from setuptools import find_packages, setup
def read(fname):
with open(os.path.join(os.path.dirname(__file__), fname)) as f:
return f.read()
setup(
name='ranger',
version='0.1.dev0',
packages=find_packages(
exclude=['tests', '*.tests', '*.tests.*', 't... | 696 | 24.814815 | 67 | py |
Ranger-Deep-Learning-Optimizer | Ranger-Deep-Learning-Optimizer-master/ranger/rangerqh.py | # RangerQH - @lessw2020 github
# Combines Quasi Hyperbolic momentum with Hinton Lookahead.
# https://arxiv.org/abs/1810.06801v4 (QH paper)
# #Lookahead paper --> MZhang,G Hinton https://arxiv.org/abs/1907.08610
# Some portions = Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed un... | 6,752 | 35.901639 | 107 | py |
Ranger-Deep-Learning-Optimizer | Ranger-Deep-Learning-Optimizer-master/ranger/ranger913A.py | # Ranger deep learning optimizer - RAdam + Lookahead + calibrated adaptive LR combined.
# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
# Ranger has now been used to capture 12 records on the FastAI leaderboard.
#This version = 9.13.19A
#Credits:
#RAdam --> https://github.com/LiyuanLucasLiu/RAdam
#L... | 8,362 | 39.400966 | 133 | py |
Ranger-Deep-Learning-Optimizer | Ranger-Deep-Learning-Optimizer-master/ranger/ranger.py | # Ranger deep learning optimizer - RAdam + Lookahead + Gradient Centralization, combined into one optimizer.
# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
# and/or
# https://github.com/lessw2020/Best-Deep-Learning-Optimizers
# Ranger has now been used to capture 12 records on the FastAI leaderboard.
... | 7,915 | 41.789189 | 169 | py |
Ranger-Deep-Learning-Optimizer | Ranger-Deep-Learning-Optimizer-master/ranger/ranger2020.py | # Ranger deep learning optimizer - RAdam + Lookahead + Gradient Centralization, combined into one optimizer.
# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
# and/or
# https://github.com/lessw2020/Best-Deep-Learning-Optimizers
# Ranger has been used to capture 12 records on the FastAI leaderboard.
# Th... | 9,051 | 42.311005 | 176 | py |
Ranger-Deep-Learning-Optimizer | Ranger-Deep-Learning-Optimizer-master/ranger/__init__.py | from .ranger import Ranger
from .ranger913A import RangerVA
from .rangerqh import RangerQH
| 91 | 22 | 32 | py |
RADio | RADio-main/data_preprocessing.py | import datetime
import dart.Util
import dart.preprocess.downloads
import dart.preprocess.nlp
import dart.preprocess.enrich_articles
import dart.preprocess.identify_stories
import dart.preprocess.recommendations
def main():
config = dart.Util.read_full_config_file()
# downloads external data sources
... | 1,191 | 29.564103 | 86 | py |
RADio | RADio-main/metrics_calculation.py | import datetime
import dart.Util
import dart.metrics.start_calculations
def main():
# step 0: load config file
config = dart.Util.read_full_config_file()
articles, recommendations, behavior_file = dart.Util.read_files()
print(str(datetime.datetime.now()) + "\tMetrics")
dart.metrics.start_calcul... | 445 | 22.473684 | 113 | py |
RADio | RADio-main/setup.py | # TO DO | 7 | 7 | 7 | py |
RADio | RADio-main/top_analysis.py | import dart.Util
import pandas as pd
from random import sample
import itertools
from dart.external.rbo import rbo
pd.set_option('display.float_format', '{:.5f}'.format)
config = dart.Util.read_full_config_file()
articles = dart.Util.read_pickle(config['articles'])
recommendations = dart.Util.read_pickle(config['reco... | 4,305 | 34.295082 | 101 | py |
RADio | RADio-main/dart/Util.py | import json
import csv
import numpy as np
import random
import string
import pandas as pd
import os
from datetime import datetime
import pickle
from pathlib import Path
ROOT_DIR = os.path.dirname(os.path.realpath(__file__))
BASE_DIR = os.path.dirname(ROOT_DIR)
def read_config_file():
with open(os.path.join(BASE_... | 3,424 | 28.025424 | 176 | py |
RADio | RADio-main/dart/__init__.py | 0 | 0 | 0 | py | |
RADio | RADio-main/dart/external/kl_divergence.py | import numpy as np
from scipy.stats import entropy
from numpy.linalg import norm
import math
def opt_merge_max_mappings(dict1, dict2):
""" Merges two dictionaries based on the largest value in a given mapping.
Parameters
----------
dict1 : Dict[Any, Comparable]
dict2 : Dict[Any, Comparable]
Re... | 2,641 | 29.72093 | 111 | py |
RADio | RADio-main/dart/external/__init__.py | 0 | 0 | 0 | py | |
RADio | RADio-main/dart/external/discount.py | import math
def harmonic_number(n):
"""Returns an approximate value of n-th harmonic number.
http://en.wikipedia.org/wiki/Harmonic_number
"""
# Euler-Mascheroni constant
gamma = 0.57721566490153286060651209008240243104215933593992
return gamma + math.log(n) + 0.5 / n - 1. / (12 * n ** 2) + 1. ... | 337 | 29.727273 | 83 | py |
RADio | RADio-main/dart/external/rbo.py | """Rank-biased overlap, a ragged sorted list similarity measure.
See http://doi.acm.org/10.1145/1852102.1852106 for details. All functions
directly corresponding to concepts from the paper are named so that they can be
clearly cross-identified.
The definition of overlap has been modified to account for ... | 10,648 | 36.628975 | 87 | py |
RADio | RADio-main/dart/metrics/activation.py | import numpy as np
from sklearn.preprocessing import KBinsDiscretizer
from dart.external.kl_divergence import compute_kl_divergence
from dart.external.discount import harmonic_number
import warnings
class Activation:
"""
Class that calculates the average Affect score based on absolute sentiment polarity value... | 2,550 | 44.553571 | 105 | py |
RADio | RADio-main/dart/metrics/start_calculations.py | import dart.metrics.activation
import dart.metrics.calibration
import dart.metrics.fragmentation
import dart.metrics.representation
import dart.metrics.alternative_voices
import dart.metrics.visualize
import dart.Util
import pandas as pd
import numpy as np
import time
from random import sample
from datetime import dat... | 10,941 | 54.826531 | 122 | py |
RADio | RADio-main/dart/metrics/calibration.py | from dart.external.discount import harmonic_number
from dart.external.kl_divergence import compute_kl_divergence
from sklearn.preprocessing import KBinsDiscretizer
import warnings
import numpy as np
import dart.handler.other.textstat
class Calibration:
"""
Class that calibrates recommender Calibration.
Th... | 4,623 | 46.183673 | 141 | py |
RADio | RADio-main/dart/metrics/fragmentation.py | import numpy as np
from dart.external.discount import harmonic_number
from dart.external.kl_divergence import compute_kl_divergence
class Fragmentation:
"""
Class that calculates to what extent users have seen the same news stories.
A "story" is considered a set of articles that are about the same 'event'... | 2,475 | 38.301587 | 119 | py |
RADio | RADio-main/dart/metrics/alternative_voices.py | from collections import Counter
from dart.external.discount import harmonic_number
from dart.external.kl_divergence import compute_kl_divergence
import numpy as np
class AlternativeVoices:
"""
Class that calculates the number of mentions of minority vs majority people. In the current implementation, what
... | 7,977 | 48.552795 | 136 | py |
RADio | RADio-main/dart/metrics/representation.py | import numpy as np
from dart.external.discount import harmonic_number
from dart.external.kl_divergence import compute_kl_divergence
from collections import defaultdict
class Representation:
"""
Calculates Representation of entities linked to different political parties using KL Divergence.
Currently the i... | 5,481 | 44.683333 | 143 | py |
RADio | RADio-main/dart/metrics/visualize.py | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from pathlib import Path
class Visualize:
@staticmethod
def print_mean(df):
print(df.groupby('type')['mean'].mean())
print(df.groupby('type')['std'].mean())
@staticmethod
def plot(df, title):
plt.figure... | 3,736 | 40.065934 | 115 | py |
RADio | RADio-main/dart/preprocess/identify_stories.py | from datetime import datetime, timedelta
import pandas as pd
import networkx as nx
import community.community_louvain as community_louvain
from collections import defaultdict
from statistics import mode, StatisticsError
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cos... | 8,819 | 43.1 | 188 | py |
RADio | RADio-main/dart/preprocess/nlp.py | import datetime
# if that doesn't work, do pip install -U numpy
# https://discuss.pytorch.org/t/valueerror-and-importerror-occurred-when-import-torch/5818
import nl_core_news_sm
import pandas as pd
import en_core_web_sm
from textblob import TextBlob
import textstat
import json
import os
import dart.Util
config = da... | 2,500 | 28.081395 | 90 | py |
RADio | RADio-main/dart/preprocess/enrich_articles.py | import pandas as pd
import dart.handler.NLP.enrich_entities
import dart.handler.NLP.cluster_entities
import dart.handler.other.wikidata
import dart.Util
class Enricher:
def __init__(self, config):
self.config = config
self.metrics = config['metrics']
self.language = config['language']
... | 1,704 | 32.431373 | 113 | py |
RADio | RADio-main/dart/preprocess/downloads.py | import requests
from recommenders.models.newsrec.newsrec_utils import get_mind_data_set
from recommenders.models.deeprec.deeprec_utils import download_deeprec_resources
import os
import pandas as pd
import numpy as np
from urllib import request, error
from bs4 import BeautifulSoup
import datetime
import time
def dow... | 3,605 | 32.082569 | 161 | py |
RADio | RADio-main/dart/preprocess/recommendations.py | import pandas as pd
import pickle
import os
from pathlib import Path
import json
import datetime
import random
import csv
import dart.Util
import dart.preprocess.downloads
ROOT_DIR = os.path.dirname(os.path.realpath(__file__))
BASE_DIR = os.path.dirname(ROOT_DIR)
config = dart.Util.read_config_file()
mind_type = c... | 2,743 | 35.586667 | 116 | py |
RADio | RADio-main/dart/handler/other/wikidata.py | import requests
import json
import pandas as pd
class WikidataHandler:
""" Class that constructs Wikidata queries, executes them and reads responses """
def __init__(self, language, country):
self.url = 'https://query.wikidata.org/sparql'
if language == 'dutch':
self.language_tag ... | 8,733 | 40.590476 | 151 | py |
RADio | RADio-main/dart/handler/other/textstat.py | import textstat
class TextStatHandler:
def __init__(self, language):
switcher = {
"dutch": textstat.set_lang("nl"),
"english": textstat.set_lang("en"),
"german": textstat.set_lang("de")
}
switcher.get(language, "Invalid language")
@staticmethod
... | 494 | 21.5 | 50 | py |
RADio | RADio-main/dart/handler/NLP/cluster_entities.py | import networkx as nx
import community.community_louvain as community_louvain
import pandas as pd
import itertools
from difflib import SequenceMatcher
from collections import defaultdict
class Clustering:
def __init__(self, threshold, a, b, metric):
self.threshold = threshold
self.a = a
s... | 4,401 | 39.018182 | 125 | py |
RADio | RADio-main/dart/handler/NLP/enrich_entities.py | import dart.Util
import dart.handler.other.wikidata
import string
import pandas as pd
from collections import defaultdict
import os
class EntityEnricher:
def __init__(self, metrics, config):
self.metrics = metrics
self.language = config['language']
self.country = config['country']
... | 6,789 | 41.974684 | 118 | py |
RADio | RADio-main/dart/handler/NLP/cosine_similarity.py | import math
import itertools
import numpy as np
import collections, functools, operator
from stop_words import get_stop_words
from statistics import StatisticsError
from sklearn.feature_extraction.text import TfidfVectorizer
# basically copied from https://www.datasciencecentral.com/profiles/blogs/
# document-simila... | 3,950 | 38.118812 | 122 | py |
RADio | RADio-main/dart/handler/NLP/sentiment.py | from textblob import TextBlob
from textblob_nl import PatternTagger as PatternTagger_nl, PatternAnalyzer as PatternAnalyzer_nl
class Sentiment:
"""
Class that calculates the average Affect score based on absolute sentiment polarity values.
This approach is an initial approximation of the concept, and shou... | 1,143 | 37.133333 | 111 | py |
RADio | RADio-main/viz/pickleToCSV.py | import pandas as pd
import numpy as np
import pickle
from pathlib import Path
import os
## JS
d = pd.read_pickle("../../data/output.pickle")
columns = list(d.columns)[2:9]
for i, column in enumerate(columns):
_, d[column], _ = d[column].str
# root JS
d.iloc[:,2:8] = d.iloc[:,2:8].apply(np.sqrt)
# d.iloc[:... | 1,837 | 22.265823 | 72 | py |
RADio | RADio-main/viz/mind.py | from recommenders.datasets import mind
from recommenders.datasets.download_utils import unzip_file
# hypers
item_sim_measure = "item_feature_vector" #"item_cooccurrence_count"
p = "../../data/"
trainName = "MINDlarge_train.zip"
devName = "MINDlarge_dev.zip"
mind.download_mind(size="large", dest_path=p)
# mind.extr... | 819 | 26.333333 | 101 | py |
RADio | RADio-main/script/popularity-MIND.py | # Databricks notebook source
import datetime
import time
import numpy as np
import pyspark.sql.functions as F
from pyspark.sql.types import *
from pyspark.sql import Row
from pyspark.sql.window import Window
import math
from pyspark.ml.recommendation import ALS
from pyspark.sql import SparkSession
spark = SparkSessio... | 10,267 | 40.403226 | 154 | py |
the-gan-zoo | the-gan-zoo-master/update.py | # -*- coding: utf-8 -*-
""" Update Readme.md and cumulative_gans.jpg """
from __future__ import print_function
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from bs4 import BeautifulSoup
import requests
import csv
def load_data():
""" Load GANs data from the gans.csv file """... | 2,569 | 31.531646 | 78 | py |
ygm | ygm-master/.cmake-format.py | # -----------------------------
# Options effecting formatting.
#
# Requires cmake-format (pip install cmake_format). See
# https://cmake-format.readthedocs.io/en/latest/configuration.html for more
# examples.
# -----------------------------
with section("format"):
# How wide to allow formatted cmake files
lin... | 1,288 | 32.051282 | 81 | py |
bond | bond-main/coverage/search_OA_CR.py | import time
import re
import unicodedata
import requests
import Levenshtein
""" Cleaning title and identifying author name """
def cleaning_title(title, typ):
if typ == "oa":
n = 6
else:
n = 4
stoplist = [line.strip() for line in open("stopwords-it.txt")]
stoplist = set(stoplist)
... | 7,030 | 33.131068 | 116 | py |
bond | bond-main/coverage/coverage.py | import logging
import os
import json
import csv
from search_OA_CR import *
def calculate_coverage(logger, cand_dict):
cov = dict()
cov["total_CV"] = len(cand_dict["pubbs"])
cov["mag"] = 0
cov["oa"] = 0
cov["cr"] = 0
cov["combined"] = 0
for pub in cand_dict["pubbs"]:
if "PId" in pu... | 5,413 | 38.808824 | 116 | py |
bond | bond-main/data_collection/id_search.py | import time
import os
import re
import json
import unicodedata
import requests
import Levenshtein
""" Cleaning title and identifying author name """
def cleaning_title(title, typ):
if typ == "oa":
n = 6
else:
n = 4
stoplist = [line.strip() for line in open("stopwords-it.txt")]
stoplist... | 13,167 | 38.190476 | 116 | py |
bond | bond-main/data_collection/cit_retrieval.py | import time
import os
import json
import re
import unicodedata
import requests
"""Retrieving Cited MAG"""
def search_cited(loggr, idt):
hdr_mag = {'Ocp-Apim-Subscription-Key': 'ac0d6ea6f26845e8b41c0df9f4e45120'}
query = f"expr=Id={idt}&attributes=Id,DOI,AA.AuN,AA.AuId,Ti,Y,J.JN"
url_mag = f"https://api.l... | 12,080 | 34.221574 | 116 | py |
bond | bond-main/data_collection/add_info.py | import csv
def adding_cov(metrics_dd):
print("adding coverage section")
for asn_year, terms in metrics_dd["cand"].items():
for term, roles in terms.items():
for role, fields in roles.items():
for field, candidates in fields.items():
for cand_id, info i... | 3,983 | 45.325581 | 139 | py |
bond | bond-main/data_collection/bib_retrieval.py | """Retrieving Articles from Author Identifiers:
AuthorIDs > Article IDs"""
import time
import os
import json
import requests
def matching_pubbs(list_pubbs, new):
value = 0
n = len(list_pubbs)
idx = 0
while idx < n:
if "PId" in list_pubbs[idx].keys():
if new["PId"] == list_pubbs[id... | 8,320 | 42.11399 | 119 | py |
bond | bond-main/data_collection/bond_execution.py | import logging
from meta_extraction import *
from add_info import *
from id_search import *
from bib_retrieval import *
from cit_retrieval import *
from graph_analysis import *
def BoND(cand_jsons_path, comm_csv_path, outcomes_path, final_path):
logging.basicConfig(filename='data_collection.log', level=logging.E... | 3,576 | 48.680556 | 116 | py |
bond | bond-main/data_collection/meta_extraction.py | import os
import json
import csv
import re
import unicodedata
def cleaning_doi(doi_raw):
doi_clean = doi_raw.strip("DOI: ").strip("HTTPS://DX.DOI.ORG/").strip("#").replace(" ", "")
return doi_clean
def cleaning_title(title_raw):
title_clean = u"".join([c for c in unicodedata.normalize("NFKD", title_ra... | 8,266 | 45.184358 | 131 | py |
bond | bond-main/data_collection/graph_analysis.py | """ Create graph, measure features and collect results """
import os
import json
import networkx as nx
import numpy as np
''' Create graph'''
def create_graph_pid(graph, list_pubbs, group, author, key, end_date):
for pub in list_pubbs:
if pub[key] < end_date:
if "PId" in pub.keys() or "doi" ... | 8,581 | 34.609959 | 121 | py |
bond | bond-main/visualization/visualization.py | import os
import json
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
from xml.dom import minidom
from wand.api import library
import wand.color
import wand.image
def create_graph_pid(graph, list_pubbs, group, author):
for pub in list_pubbs:
if "PId" in pub.keys() or "doi" in pub.... | 8,404 | 46.485876 | 114 | py |
bond | bond-main/ml_experiment/script/plotDecisionTree.py | import time
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, tree
from sklearn.model_selection import cross_validate
from sklearn.metrics import *
from sklearn.utils import resample
from itertools import combinations
inputFile = "../../complete_metrics.csv"... | 3,415 | 32.821782 | 101 | py |
bond | bond-main/ml_experiment/script/ml_combinations.py | import time
import sys
import pandas as pd
import numpy as np
from sklearn import svm, tree
from sklearn.model_selection import cross_validate
from sklearn.metrics import *
from sklearn.utils import resample
from itertools import combinations
import ray
sections = ["AP", "FP"]
fields = ["10-G1", "13-D4"]
coverages ... | 6,548 | 34.4 | 163 | py |
acoustic-images-distillation | acoustic-images-distillation-master/setup.py | from setuptools import setup
setup(
name='codebase',
version='0.0.1',
packages=['codebase'],
install_requires=['librosa', 'numpy', 'tensorflow-gpu==1.4.0', 'torchfile'],
url='https://gitlab.iit.it/aperez/acoustic-images-distillation',
license='',
author='Andres Perez',
author_email='and... | 423 | 29.285714 | 81 | py |
acoustic-images-distillation | acoustic-images-distillation-master/main_s2.py | import tensorflow as tf
from datetime import datetime
from codebase.loggers import Logger
from codebase.models.audition import HearModel
from codebase.models.audition import SoundNet5Model
from codebase.models.audition import DualCamHybridModel
from codebase.trainers import TwoStreamsTrainer
from codebase.data import ... | 5,898 | 48.158333 | 110 | py |
acoustic-images-distillation | acoustic-images-distillation-master/main_s1.py | import tensorflow as tf
from datetime import datetime
from codebase.loggers import Logger
from codebase.models.vision import ResNet50Model
from codebase.models.vision import ResNet50TemporalModel
from codebase.models.audition import HearModel
from codebase.models.audition import DualCamModel
from codebase.models.audit... | 6,668 | 49.522727 | 120 | py |
acoustic-images-distillation | acoustic-images-distillation-master/utils/convert_data.py | import argparse
import cv2
import glob
import numpy as np
import os
import re
import tensorflow as tf
from collections import namedtuple
from datetime import datetime
from scipy import io as spio
from utils import str2dir
Image = namedtuple('Image', 'rows cols depth data')
Audio = namedtuple('Audio', 'mics samples da... | 8,417 | 45 | 173 | py |
acoustic-images-distillation | acoustic-images-distillation-master/utils/compute_stats.py | from __future__ import division
from datetime import datetime
import argparse
import tensorflow as tf
import numpy as np
import os
import sys
from codebase.data import ActionsDataLoader as DataLoader
parser = argparse.ArgumentParser()
parser.add_argument('root_dir', help='Directory holding generated lists and comput... | 4,791 | 40.310345 | 111 | py |
acoustic-images-distillation | acoustic-images-distillation-master/utils/__init__.py | import argparse
import os
import tensorflow as tf
def str2dir(dir_name):
if not os.path.isdir(dir_name):
raise argparse.ArgumentTypeError('{} is not a directory!'.format(dir_name))
elif os.access(dir_name, os.W_OK) is False:
raise argparse.ArgumentTypeError('{} is not a writeable directory!'.f... | 859 | 34.833333 | 116 | py |
acoustic-images-distillation | acoustic-images-distillation-master/utils/generate_lists.py | import argparse
import glob
import numpy as np
import os
import re
from utils import str2dir
TRAIN_SET_SIZE = 0.8
VALID_SET_SIZE = 0.1
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('root_dir', help='Dataset root directory', type=str2dir)
parser.add_argument('--location... | 3,928 | 39.927083 | 143 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/__init__.py | 0 | 0 | 0 | py | |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/models/dualcamnet.py | import tensorflow as tf
import tensorflow.contrib.slim as slim
def dualcamnet_v2(inputs,
keep_prob=0.5,
is_training=True,
num_classes=None,
num_frames=12,
num_channels=12,
spatial_squeeze=False, scope='DualCamN... | 2,579 | 48.615385 | 110 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/models/multimodal.py | from collections import OrderedDict
from tensorflow.contrib.slim.nets import resnet_v1
import dualcamnet
import shared
import tensorflow as tf
import tensorflow.contrib.slim as slim
class AVModel(object):
def __init__(self, num_classes=None):
self.scope = 'AVNet'
self.num_classes = num_classes... | 5,061 | 40.491803 | 114 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/models/resnet_temporal.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from codebase.models import resnet_utils
resnet_arg_scope = resnet_utils.resnet_arg_scope
slim = tf.contrib.slim
@slim.add_arg_scope
def bottleneck_normal(inputs,
... | 6,387 | 40.212903 | 91 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/models/soundnet.py | import tensorflow as tf
import tensorflow.contrib.slim as slim
import torchfile
def soundnet_arg_scope(is_training=True,
weight_decay=0.0001):
"""Defines the SoundNet arg scope.
Args:
is_training: Boolean flag indicating whether we are in training or not.
weight_decay: The ... | 15,910 | 51.167213 | 113 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/models/shared.py | import tensorflow as tf
import tensorflow.contrib.slim as slim
def shared_net(inputs, num_classes=None, is_training=True, keep_prob=0.5, spatial_squeeze=True, scope='shared_net'):
"""
Builds a three-layer fully-connected modality agnostic network.
"""
with tf.variable_scope(scope, [inputs]) as sc:
... | 1,434 | 48.482759 | 116 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/models/audition.py | import dualcamnet
import hearnet
import shared
import soundnet
import tensorflow as tf
import tensorflow.contrib.slim as slim
from collections import OrderedDict
flags = tf.app.flags
FLAGS = flags.FLAGS
class DualCamModel(object):
def __init__(self, mode='train', input_shape=None, num_classes=14, num_frames=12... | 12,249 | 40.385135 | 118 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/models/__init__.py | 0 | 0 | 0 | py | |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/models/vision.py | from tensorflow.contrib.slim.nets import resnet_v1
import tensorflow as tf
import tensorflow.contrib.slim as slim
import resnet_temporal as resnet_v2
class ResNet50Model(object):
def __init__(self, input_shape=None, num_classes=14):
self.scope = 'resnet_v1_50'
self.num_classes = num_classes
... | 4,934 | 37.554688 | 120 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/models/hearnet.py | import tensorflow as tf
import tensorflow.contrib.slim as slim
def build_arg_scope(weight_decay=0.0005):
with slim.arg_scope([slim.layers.conv2d, slim.layers.fully_connected],
activation_fn=slim.nn_ops.relu,
weights_regularizer=slim.regularizers.l2_regularizer(weigh... | 3,364 | 41.594937 | 91 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/models/resnet_utils.py | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 14,617 | 43.567073 | 166 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/loggers/logger.py | import tempfile
import tensorflow as tf
class Logger(object):
def __init__(self, log_dir, exp_name):
self.__log_dir = tempfile.mkdtemp() if log_dir is None or not tf.gfile.Exists(log_dir) else log_dir
self.__log_dir = '{}/{}'.format(log_dir, exp_name)
self.summary_op = None
self.... | 1,351 | 34.578947 | 107 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/loggers/__init__.py | from logger import Logger | 25 | 25 | 25 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/data/actions_data.py | from __future__ import division
from tensorflow.contrib.slim.nets import vgg
from preprocessing import vgg_preprocessing
import librosa
import tensorflow as tf
import numpy as np
import math
flags = tf.app.flags
FLAGS = flags.FLAGS
# The real number of tracks is 128 corresponding to the number of microphones but eve... | 17,906 | 46.248021 | 134 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/data/__init__.py | from actions_data import ActionsDataLoader
| 43 | 21 | 42 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/trainers/one_stream.py | from datetime import datetime
from utils import build_accuracy
import tempfile
import tensorflow as tf
import tensorflow.contrib.slim as slim
flags = tf.app.flags
FLAGS = flags.FLAGS
class OneStreamTrainer(object):
def __init__(self, model, logger=None, display_freq=1,
learning_rate=0.0001, nu... | 15,796 | 44.656069 | 131 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/trainers/two_streams.py | from datetime import datetime
from utils import build_accuracy
import tensorflow as tf
flags = tf.app.flags
slim = tf.contrib.slim
FLAGS = flags.FLAGS
class TwoStreamsTrainer(object):
def __init__(self, teacher_model, student_model, logger=None,
lambda_value=0.5, temperature_value=1.0,
... | 15,751 | 46.161677 | 135 | py |
acoustic-images-distillation | acoustic-images-distillation-master/codebase/trainers/__init__.py | from one_stream import OneStreamTrainer
from two_streams import TwoStreamsTrainer
| 82 | 26.666667 | 41 | py |
synorim-merged | synorim-merged/jittor/evaluate.py | import jittor
import bdb, traceback, pdb
import importlib
import argparse
from pathlib import Path
from utils import exp
from tqdm import tqdm
def test_epoch():
net_model.eval()
net_model.hparams.is_training = False
pbar = tqdm(test_loader, desc='Test')
meter = exp.AverageMeter()
for batch_idx, d... | 1,235 | 25.297872 | 77 | py |
synorim-merged | synorim-merged/jittor/dataset.py | import json
import jittor
from pathlib import Path
import numpy as np
class DatasetSpec:
PC = 200
FULL_FLOW = 300
FULL_MASK = 400
class FlowDataset(jittor.dataset.Dataset):
def __init__(self, batch_size, shuffle, num_workers,
base_folder: str, spec: list, sub_frames: list, split: st... | 2,470 | 35.338235 | 89 | py |
synorim-merged | synorim-merged/jittor/train.py | import argparse
import bdb
import importlib
import pdb
import shutil
import traceback
from pathlib import Path
from omegaconf import OmegaConf
from tqdm import tqdm
import jittor
from utils import exp
jittor.flags.use_cuda = True
def train_epoch():
global global_step
net_model.train()
net_model.hparam... | 2,990 | 28.91 | 94 | py |
synorim-merged | synorim-merged/jittor/metric.py | import jittor
class PairwiseFlowMetric:
def __init__(self, batch_mean: bool = False, compute_epe3d: bool = True, compute_acc3d_outlier: bool = False):
self.batch_mean = batch_mean
self.compute_epe3d = compute_epe3d
self.compute_acc3d_outlier = compute_acc3d_outlier
def evaluate(self, ... | 2,182 | 40.980769 | 114 | py |
synorim-merged | synorim-merged/jittor/models/base_model.py | import functools
import importlib
from pathlib import Path
import jittor
from jittor import nn
from jittor.optim import LambdaLR
from utils.exp import AverageMeter, parse_config_yaml
def lambda_lr_wrapper(it, lr_config, batch_size):
return max(
lr_config['decay_mult'] ** (int(it * batch_size / lr_config... | 1,680 | 34.020833 | 109 | py |
synorim-merged | synorim-merged/jittor/models/desc_net.py | import jittor
from dataset import DatasetSpec as DS, FlowDataset
# Following won't work in jittor.
# from backbones.pointconv import PointConv as Backbone
from backbones.pointnet2 import PN2BackboneLarge as Backbone
from models.base_model import BaseModel
import numpy as np
from utils.misc import cdist
class Model(... | 2,844 | 37.972603 | 91 | py |
synorim-merged | synorim-merged/jittor/models/full_sync.py | from collections import defaultdict
import jittor
from dataset import DatasetSpec as DS, FlowDataset
from metric import PairwiseFlowMetric
from models.base_model import BaseModel
import numpy as np
class Model(BaseModel):
"""
This model runs the full test of our model, taking multiple point clouds as input... | 7,787 | 43.25 | 113 | py |
synorim-merged | synorim-merged/jittor/models/basis_net.py | import jittor
import random
from dataset import DatasetSpec as DS, FlowDataset
from backbones.pointnet2 import PN2BackboneLarge as Backbone
from backbones.pointnet2 import PN2BackboneSmall as BackboneSmall
from models.base_model import BaseModel
import numpy as np
from utils.misc import cdist, cdist_single
from colle... | 11,005 | 42.848606 | 114 | py |
synorim-merged | synorim-merged/jittor/utils/exp.py | import pickle
from collections import OrderedDict
import sys
from pathlib import Path
from omegaconf import OmegaConf
def parse_config_yaml(yaml_path: Path, args: OmegaConf = None, override: bool = True) -> OmegaConf:
"""
Load yaml file, and optionally merge it with existing ones.
This supports a light-w... | 4,390 | 33.03876 | 110 | py |
synorim-merged | synorim-merged/jittor/utils/misc.py | import jittor
def cdist(src, dst):
"""
Calculate Euclid distance between each two points.
src^T * dst = xn * xm + yn * ym + zn * zm；
sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn;
sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm;
dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2
= sum(src**2,dim=... | 1,000 | 29.333333 | 60 | py |
synorim-merged | synorim-merged/jittor/backbones/pointnet2.py | """
The code here is from https://github.com/Jittor/PointCloudLib.git
"""
from typing import List, Optional, Tuple
import math
import numpy as np
import jittor as jt
from jittor import nn
from jittor.contrib import concat
jt.flags.use_cuda = 1
def index_points(points, idx):
"""
Input:
points: inpu... | 34,807 | 33.947791 | 156 | py |
synorim-merged | synorim-merged/jittor/backbones/pointconv.py | """
The code here is from https://github.com/Jittor/PointCloudLib.git
"""
import numpy as np
import jittor as jt
from jittor import nn
from jittor.contrib import concat
def topk(input, k, dim=None, largest=True, sorted=True):
if dim is None:
dim = -1
if dim < 0:
dim += input.ndim
transpo... | 16,355 | 34.25 | 120 | py |
synorim-merged | synorim-merged/pytorch/evaluate.py | import torch
import bdb, traceback, pdb
import importlib
import argparse
from pathlib import Path
from utils import exp
import numpy as np
from tqdm import tqdm
from dataset.base import DatasetSpec
def visualize(test_result, data):
try:
import open3d as o3d
except ImportError:
print("Please im... | 4,097 | 37.299065 | 120 | py |
synorim-merged | synorim-merged/pytorch/train.py | import argparse
import bdb
import importlib
import pdb
import shutil
import traceback
from pathlib import Path
import torch
from omegaconf import OmegaConf
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from utils import exp
def train_epoch():
global global_step
net_model.train()
... | 3,917 | 31.114754 | 112 | py |
synorim-merged | synorim-merged/pytorch/metric.py | import torch
class PairwiseFlowMetric:
def __init__(self, batch_mean: bool = False, compute_epe3d: bool = True, compute_acc3d_outlier: bool = False,
scene_level: bool = False):
"""
:param batch_mean: Whether to return an array with size (B, ) or a single scalar (mean)
:par... | 3,005 | 45.246154 | 113 | py |
synorim-merged | synorim-merged/pytorch/dataset/base.py | import collections
import multiprocessing
import torch
from numpy.random import RandomState
from torch.utils.data import Dataset
import zlib, json
from enum import Enum
class DatasetSpec(Enum):
FILENAME = 100
PC = 200
# Flow and masks are dictionary with key (view_i, view_j).
FULL_FLOW = 300
FULL... | 3,050 | 32.527473 | 109 | py |
synorim-merged | synorim-merged/pytorch/dataset/flow_dataset.py | import json
from pathlib import Path
import numpy as np
from dataset.base import RandomSafeDataset, DatasetSpec
import MinkowskiEngine as ME
from pyquaternion.quaternion import Quaternion
class DataAugmentor:
"""
Will apply data augmentation to pairwise point clouds, by applying random transformations
... | 8,649 | 41.610837 | 114 | py |
synorim-merged | synorim-merged/pytorch/models/base_model.py | import functools
import importlib
import tempfile
from pathlib import Path
from typing import Mapping, Any, Optional, Callable, Union
import numpy as np
import torch
from torch import nn
from omegaconf import OmegaConf
from torch.optim.lr_scheduler import LambdaLR
from utils.exp import AverageMeter, parse_config_yaml
... | 3,644 | 37.776596 | 109 | py |
synorim-merged | synorim-merged/pytorch/models/basis_net_self.py | from collections import defaultdict
import torch
from torch.utils.data import DataLoader
from dataset.base import DatasetSpec as DS, list_collate
from dataset.flow_dataset import FlowDataset
from models.basis_net import Model as BaseModel
from models.desc_net_self import Model as DescModel
class Model(BaseModel):
... | 3,726 | 50.763889 | 108 | py |
synorim-merged | synorim-merged/pytorch/models/desc_net.py | import torch
import MinkowskiEngine as ME
from torch.nn import Parameter
from torch.utils.data import DataLoader
from dataset.base import DatasetSpec as DS, list_collate
from dataset.flow_dataset import FlowDataset, DataAugmentor
from metric import PairwiseFlowMetric
from models.spconv import ResUNet
from models.base... | 6,939 | 51.180451 | 113 | py |
synorim-merged | synorim-merged/pytorch/models/full_sync.py | from collections import defaultdict
import torch.linalg
from torch.utils.data import DataLoader
from dataset.base import DatasetSpec as DS, list_collate
from dataset.flow_dataset import FlowDataset
from metric import PairwiseFlowMetric
from models.base_model import BaseModel
import numpy as np
from utils.point impor... | 10,385 | 41.740741 | 119 | py |
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