repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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eco-dqn | eco-dqn-master/experiments/BA_40spin/test/test_eco.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,506 | 35.942623 | 108 | py |
eco-dqn | eco-dqn-master/experiments/BA_40spin/test/test_s2v.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,485 | 35.770492 | 108 | py |
eco-dqn | eco-dqn-master/experiments/BA_100spin/test/test_eco.py | """
Tests an agent.
"""
import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
Optimisatio... | 4,535 | 34.716535 | 108 | py |
eco-dqn | eco-dqn-master/experiments/BA_100spin/test/test_s2v.py | """
Tests an agent.
"""
import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
Optimisatio... | 4,512 | 34.81746 | 108 | py |
eco-dqn | eco-dqn-master/experiments/ER_40spin/test/test_eco.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,610 | 35.595238 | 108 | py |
eco-dqn | eco-dqn-master/experiments/ER_40spin/test/test_s2v.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,588 | 35.420635 | 108 | py |
eco-dqn | eco-dqn-master/experiments/BA_200spin/test/test_eco.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,509 | 35.967213 | 108 | py |
eco-dqn | eco-dqn-master/experiments/BA_200spin/test/test_s2v.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,486 | 36.082645 | 108 | py |
eco-dqn | eco-dqn-master/experiments/ER_200spin/test/test_eco.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,510 | 35.97541 | 108 | py |
eco-dqn | eco-dqn-master/experiments/ER_200spin/test/test_s2v.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,487 | 36.090909 | 108 | py |
eco-dqn | eco-dqn-master/experiments/ER_100spin/test/test_eco.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,510 | 35.97541 | 108 | py |
eco-dqn | eco-dqn-master/experiments/ER_100spin/test/test_s2v.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,487 | 36.090909 | 108 | py |
eco-dqn | eco-dqn-master/experiments/ER_60spin/test/test_eco.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,507 | 35.95082 | 108 | py |
eco-dqn | eco-dqn-master/experiments/ER_60spin/test/test_s2v.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,485 | 36.07438 | 108 | py |
eco-dqn | eco-dqn-master/experiments/BA_20spin/test/test_eco.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,506 | 35.942623 | 108 | py |
eco-dqn | eco-dqn-master/experiments/BA_20spin/test/test_s2v.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,485 | 35.770492 | 108 | py |
USLN | USLN-master/test.py | from PIL import Image
import os
import numpy as np
import torch
from model import USLN
from SegDataset import read_file_list
from tqdm import trange
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = USLN()
model.load_state_dict(torch.load(r'logs/UFO.pth'))
model.eval()
model = model... | 961 | 21.904762 | 69 | py |
USLN | USLN-master/loss.py | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
import torch.nn as nn
from torchvision import models
class VGG_loss(nn.Module):
def __init__(self, model):
super(VGG_loss, self).__init__()
self.features = nn.Sequential(*list(mo... | 3,620 | 33.485714 | 114 | py |
USLN | USLN-master/color_change.py | import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# torch.cuda.set_device(0)
# device='cpu'
def rgb2hsi(img):
img = torch.clamp(img, 0, 1)
r = img[:, 0, :, :]
g = img[:, 1, :, :]
b = img[:, 2, :, :]
i = (r + g + b) / 3
s = 1 - 3 * img.min(1)[0] / (r + g + b + 1e-... | 7,245 | 26.656489 | 116 | py |
USLN | USLN-master/model.py | import torch.nn as nn
from ptflops import get_model_complexity_info
from color_change import *
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class WB(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(3, 3, kernel_size=1, stride=1)
self.avgpool ... | 3,686 | 31.342105 | 130 | py |
USLN | USLN-master/SegDataset.py | import os
from PIL import Image
import numpy as np
import torch
from torch.utils.data import Dataset
def read_file_list(type='train'):
path_list_images_train = os.listdir(r"datasets/images_train")
path_list_labels_train = os.listdir(r"datasets/labels_train")
path_list_images_val = os.listdir(r"datasets/... | 2,160 | 28.60274 | 94 | py |
USLN | USLN-master/train.py | import numpy as np
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
import sys
from model import USLN
from SegDataset import SegDataset
from loss import Combinedloss
########################################################
num_workers = 0 if sys.platform.startswith('win32') else 8
device... | 3,435 | 31.415094 | 109 | py |
ccdetection | ccdetection-master/configurator.py | '''
Created on Nov 14, 2015
@author: Tommi Unruh
'''
import os
import stat
class Configurator(object):
"""
Writes and loads data from a config file.
"""
DEFAULT_HEAP_SIZE = "6G"
KEY_PHP7 = "php7"
KEY_NEO4J = "neo4j"
KEY_HEAPSIZE = "heap_size"
KEY_GRAPHDBS = "graphdbs"
KEY_BASE... | 9,907 | 34.010601 | 98 | py |
poincare_glove | poincare_glove-master/setup.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2014 Radim Rehurek <radimrehurek@seznam.cz>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
"""
Run with:
sudo python ./setup.py install
"""
import os
import sys
import warnings
import ez_setup
from setuptools import setup, fi... | 12,941 | 38.218182 | 455 | py |
poincare_glove | poincare_glove-master/gensim/models/keyedvectors.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Author: Shiva Manne <manneshiva@gmail.com>
# Copyright (C) 2018 RaRe Technologies s.r.o.
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
"""
Word vector storage and similarity look-ups.
Common code independent of the way the vectors are train... | 117,148 | 41.185452 | 150 | py |
poincare_glove | poincare_glove-master/gensim/models/deprecated/keyedvectors.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2016 Radim Rehurek <me@radimrehurek.com>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
"""
Warnings
--------
.. deprecated:: 3.3.0
Use :mod:`gensim.models.keyedvectors` instead.
Word vector storage and similarity look-ups... | 43,913 | 39.850233 | 119 | py |
poincare_glove | poincare_glove-master/gensim/test/test_sklearn_api.py | import unittest
import numpy
import codecs
import pickle
from scipy import sparse
try:
from sklearn.pipeline import Pipeline
from sklearn import linear_model, cluster
from sklearn.exceptions import NotFittedError
except ImportError:
raise unittest.SkipTest("Test requires scikit-learn to be installed, w... | 60,933 | 49.027915 | 120 | py |
poincare_glove | poincare_glove-master/gensim/test/test_keras_integration.py | import unittest
import numpy as np
from gensim.models import word2vec
try:
from sklearn.datasets import fetch_20newsgroups
except ImportError:
raise unittest.SkipTest("Test requires sklearn to be installed, which is not available")
try:
import keras
from keras.engine import Input
from keras.models... | 6,028 | 38.927152 | 114 | py |
poincare_glove | poincare_glove-master/gensim/test/test_ldaseqmodel.py | """
Tests to check DTM math functions and Topic-Word, Doc-Topic proportions.
"""
import unittest
import logging
import numpy as np # for arrays, array broadcasting etc.
from gensim.models import ldaseqmodel
from gensim.corpora import Dictionary
from gensim.test.utils import datapath
class TestLdaSeq(unittest.Test... | 20,187 | 82.078189 | 119 | py |
allosaurus | allosaurus-master/setup.py | from setuptools import setup,find_packages
setup(
name='allosaurus',
version='1.0.2',
description='a multilingual phone recognizer',
author='Xinjian Li',
author_email='xinjianl@cs.cmu.edu',
url="https://github.com/xinjli/allosaurus",
packages=find_packages(),
install_requires=[
'scipy',
... | 412 | 19.65 | 49 | py |
allosaurus | allosaurus-master/allosaurus/am/utils.py | import torch
from collections import OrderedDict
import numpy as np
def torch_load(model, path, device_id, unit_mask=None):
"""Load torch model states.
Args:
path (str): Model path or snapshot file path to be loaded.
model (torch.nn.Module): Torch model.
device_id (int): gpu id (-1 ind... | 4,126 | 24.475309 | 95 | py |
allosaurus | allosaurus-master/allosaurus/am/dataset.py | from allosaurus.pm.kdict import read_matrix
from pathlib import Path
from torch.utils.data import Dataset
import numpy as np
class AllosaurusDataset(Dataset):
def __init__(self, data_path):
self.data_path = Path(data_path)
required_files = ['feat.scp', 'token', 'feat.ark', 'shape']
for r... | 3,451 | 26.616 | 140 | py |
allosaurus | allosaurus-master/allosaurus/am/factory.py | from allosaurus.am.allosaurus_torch import AllosaurusTorchModel
from allosaurus.am.utils import *
from allosaurus.lm.inventory import Inventory
from allosaurus.lm.unit import write_unit
import json
from argparse import Namespace
from allosaurus.model import get_model_path
def read_am(model_path, inference_config):
... | 2,194 | 29.486111 | 97 | py |
allosaurus | allosaurus-master/allosaurus/am/allosaurus_torch.py | import torch
import torch.nn as nn
class AllosaurusTorchModel(nn.Module):
def __init__(self, config):
super(AllosaurusTorchModel, self).__init__()
self.hidden_size = config.hidden_size
self.layer_size = config.layer_size
self.proj_size = config.proj_size
# decide input fe... | 4,359 | 40.52381 | 159 | py |
allosaurus | allosaurus-master/allosaurus/am/criterion.py | import torch
import torch.nn as nn
def read_criterion(train_config):
assert train_config.criterion == 'ctc', 'only ctc criterion is supported now'
return CTCCriterion(train_config)
class CTCCriterion(nn.Module):
def __init__(self, train_config):
super().__init__()
self.train_config = tr... | 841 | 29.071429 | 91 | py |
allosaurus | allosaurus-master/allosaurus/am/trainer.py | from allosaurus.am.utils import move_to_tensor, torch_save
from allosaurus.am.criterion import read_criterion
from allosaurus.am.optimizer import read_optimizer
from allosaurus.am.reporter import Reporter
import editdistance
import numpy as np
import torch
from itertools import groupby
from allosaurus.model import get_... | 6,149 | 30.538462 | 172 | py |
allosaurus | allosaurus-master/allosaurus/am/optimizer.py | from torch.optim import SGD
def read_optimizer(model, train_config):
assert train_config.optimizer == 'sgd', 'only sgd is supported now, others optimizers would be easier to add though'
return SGD(model.parameters(), lr=train_config.lr) | 247 | 34.428571 | 120 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/train_sed_CRST.py | import argparse
from copy import deepcopy
import numpy as np
import os
import pandas as pd
import random
import torch
import yaml
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLogger
from desed_task.dataio import ... | 11,870 | 34.121302 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/train_sed_SRST.py | import argparse
from copy import deepcopy
import numpy as np
import os
import pandas as pd
import random
import torch
import yaml
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLogger
from desed_task.dataio import ... | 10,734 | 34.429043 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/train_sed.py | import argparse
from copy import deepcopy
import numpy as np
import os
import pandas as pd
import random
import torch
import yaml
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLogger
from desed_task.dataio import ... | 11,519 | 34.015198 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/local/resample_folder.py | import argparse
import glob
import os
from pathlib import Path
import librosa
import torch
import torchaudio
import tqdm
parser = argparse.ArgumentParser("Resample a folder recursively")
parser.add_argument(
"--in_dir",
type=str,
default="/media/sam/bx500/DCASE_DATA/dataset/audio/validation/",
)
parser.ad... | 2,556 | 29.807229 | 102 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/local/sed_trainer_SRST.py | import os
import random
from copy import deepcopy
from pathlib import Path
import local.config as cfg
import pandas as pd
import pytorch_lightning as pl
import torch
from torchaudio.transforms import AmplitudeToDB, MelSpectrogram
from desed_task.data_augm import mixup, add_noise
from desed_task.utils.scaler import To... | 29,014 | 37.077428 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/local/utils.py | import os
from pathlib import Path
import pandas as pd
import scipy
from desed_task.evaluation.evaluation_measures import compute_sed_eval_metrics
from torch import nn
import soundfile
import glob
class JSD(nn.Module):
def __init__(self):
super(JSD, self).__init__()
self.kld = nn.KLDivLoss().cuda... | 6,982 | 35.369792 | 111 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/local/utilities.py | import numpy as np
import scipy.signal as sp
import wave, struct
import torch
import torch.nn as nn
from scipy.io import wavfile, loadmat
from torchaudio.functional import lfilter
from torchaudio.transforms import Spectrogram
class LinearSpectrogram(nn.Module):
def __init__(self, nCh=128, n_fft=2048, hop_length=256,... | 10,213 | 28.865497 | 101 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/local/sed_trainer.py | import os
import random
from copy import deepcopy
from pathlib import Path
import pandas as pd
import pytorch_lightning as pl
import torch
from torchaudio.transforms import AmplitudeToDB, MelSpectrogram
from desed_task.data_augm import mixup
from desed_task.utils.scaler import TorchScaler
import numpy as np
from .ut... | 29,083 | 37.675532 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/recipes/dcase2021_task4_baseline/local/sed_trainer_CRST.py | import os
import random
from copy import deepcopy
from pathlib import Path
import local.config as cfg
import pandas as pd
import pytorch_lightning as pl
import torch
from torchaudio.transforms import AmplitudeToDB, MelSpectrogram
from desed_task.data_augm import mixup, frame_shift, add_noise, temporal_reverse
from de... | 48,990 | 39.757903 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/nnet/CNN.py | import torch.nn as nn
import torch
import math
import torch.nn.functional as F
class GLU(nn.Module):
def __init__(self, input_num):
super(GLU, self).__init__()
self.sigmoid = nn.Sigmoid()
self.linear = nn.Linear(input_num, input_num)
def forward(self, x):
lin = self.linear(x.p... | 11,753 | 35.616822 | 154 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/nnet/CRNN.py | import warnings
import torch.nn as nn
import torch
from .RNN import BidirectionalGRU
from .CNN import CNN, ResidualCNN
class RCRNN(nn.Module):
def __init__(
self,
n_in_channel=1,
nclass=10,
attention=True,
activation="glu",
dropout=0.5,
train_cnn=True,
... | 9,567 | 31.767123 | 89 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/nnet/RNN.py | import warnings
import torch
from torch import nn as nn
class BidirectionalGRU(nn.Module):
def __init__(self, n_in, n_hidden, dropout=0, num_layers=1):
"""
Initialization of BidirectionalGRU instance
Args:
n_in: int, number of input
n_hidden: int, number of hi... | 1,488 | 26.072727 | 68 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/utils/scaler.py | import tqdm
import torch
class TorchScaler(torch.nn.Module):
"""
This torch module implements scaling for input tensors, both instance based
and dataset-wide statistic based.
Args:
statistic: str, (default='dataset'), represent how to compute the statistic for normalisation.
Choic... | 4,606 | 38.042373 | 119 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/utils/schedulers.py | from asteroid.engine.schedulers import *
import numpy as np
class ExponentialWarmup(BaseScheduler):
""" Scheduler to apply ramp-up during training to the learning rate.
Args:
optimizer: torch.optimizer.Optimizer, the optimizer from which to rampup the value from
max_lr: float, the maximum lear... | 1,094 | 32.181818 | 95 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/utils/torch_utils.py | import torch
import numpy as np
def nantensor(*args, **kwargs):
return torch.ones(*args, **kwargs) * np.nan
def nanmean(v, *args, inplace=False, **kwargs):
if not inplace:
v = v.clone()
is_nan = torch.isnan(v)
v[is_nan] = 0
return v.sum(*args, **kwargs) / (~is_nan).float().sum(*args, **k... | 327 | 20.866667 | 74 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/desed_task/data_augm.py | import numpy as np
import torch
import random
def frame_shift(mels, labels, net_pooling=4):
bsz, n_bands, frames = mels.shape
shifted = []
new_labels = []
for bindx in range(bsz):
shift = int(random.gauss(0, 90))
shifted.append(torch.roll(mels[bindx], shift, dims=-1))
shift = ... | 3,931 | 35.073394 | 112 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/desed_task/dataio/sampler.py | from torch.utils.data import Sampler
import numpy as np
class ConcatDatasetBatchSampler(Sampler):
"""This sampler is built to work with a standard Pytorch ConcatDataset.
From SpeechBrain dataio see https://github.com/speechbrain/
It is used to retrieve elements from the different concatenated datasets pl... | 3,147 | 33.217391 | 107 | py |
CRSTmodel | CRSTmodel-main/DCASE2021_baseline_platform/desed_task/dataio/datasets.py | from torch.utils.data import Dataset
import pandas as pd
import os
import numpy as np
import torchaudio
import torch
import glob
def to_mono(mixture, random_ch=False):
if mixture.ndim > 1: # multi channel
if not random_ch:
mixture = torch.mean(mixture, 0)
else: # randomly select on... | 6,460 | 27.337719 | 83 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/main_MT_model.py | # -*- coding: utf-8 -*-
import argparse
import datetime
import inspect
import os
import time
from pprint import pprint
import pandas as pd
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch import nn
from data_utils.Desed import DESED
from data_utils.DataLoad import DataLoadDf, Concat... | 22,648 | 47.189362 | 120 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/main_ICT_model.py | # -*- coding: utf-8 -*-
import argparse
import datetime
import inspect
import os
import time
from pprint import pprint
import pandas as pd
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch import nn
from data_utils.Desed import DESED
from data_utils.DataLoad import DataLoadDf, Concat... | 24,772 | 47.57451 | 124 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/TestModel.py | # -*- coding: utf-8 -*-
import argparse
import os.path as osp
import torch
from torch.utils.data import DataLoader
import numpy as np
import pandas as pd
from data_utils.DataLoad import DataLoadDf
from data_utils.Desed import DESED
from evaluation_measures import psds_score, get_predictions_v2, \
compute_psds_fro... | 8,195 | 40.604061 | 115 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/TestModel_ss_late_integration.py | # -*- coding: utf-8 -*-
import argparse
import os
import os.path as osp
import scipy
import torch
from dcase_util.data import ProbabilityEncoder
import pandas as pd
import numpy as np
from data_utils.DataLoad import DataLoadDf
from data_utils.Desed import DESED
from TestModel import _load_scaler, _load_crnn
from eval... | 12,022 | 48.887967 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/evaluation_measures.py | # -*- coding: utf-8 -*-
import os
from os import path as osp
import psds_eval
import scipy
from dcase_util.data import ProbabilityEncoder
import sed_eval
import numpy as np
import pandas as pd
import torch
from psds_eval import plot_psd_roc, PSDSEval
import config as cfg
from utilities.Logger import create_logger
fro... | 22,296 | 42.044402 | 119 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/TestModel_dual.py | # -*- coding: utf-8 -*-
import argparse
import os.path as osp
import torch
from torch.utils.data import DataLoader
import numpy as np
import pandas as pd
from data_utils.DataLoad import DataLoadDf
from data_utils.Desed import DESED
from evaluation_measures import psds_score, get_predictions_v2, \
compute_psds_fro... | 8,284 | 40.633166 | 115 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/main_CRST_model_v2.py | # -*- coding: utf-8 -*-
import argparse
import datetime
import inspect
import os
import time
from pprint import pprint
import pandas as pd
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch import nn
from data_utils.Desed import DESED
from data_utils.DataLoad import DataLoadDf, Concat... | 28,622 | 48.35 | 158 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/main_CRST_model.py | # -*- coding: utf-8 -*-
import argparse
import datetime
import inspect
import os
import time
from pprint import pprint
import pandas as pd
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch import nn
from data_utils.Desed import DESED
from data_utils.DataLoad import DataLoadDf, Concat... | 28,492 | 48.295848 | 158 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/main_SRST_model.py | # -*- coding: utf-8 -*-
import argparse
import datetime
import inspect
import os
import time
from pprint import pprint
import pandas as pd
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch import nn
from data_utils.Desed import DESED
from data_utils.DataLoad import DataLoadDf, Concat... | 25,288 | 46.535714 | 120 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/models/CNN.py | import torch.nn as nn
import torch
class GLU(nn.Module):
def __init__(self, input_num):
super(GLU, self).__init__()
self.sigmoid = nn.Sigmoid()
self.linear = nn.Linear(input_num, input_num)
def forward(self, x):
lin = self.linear(x.permute(0, 2, 3, 1))
lin = lin.permut... | 4,002 | 37.12381 | 105 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/models/CRNN.py | import warnings
import torch.nn as nn
import torch
from models.RNN import BidirectionalGRU
from models.CNN import CNN
class CRNN(nn.Module):
def __init__(self, n_in_channel, nclass, attention=False, activation="Relu", dropout=0,
train_cnn=True, rnn_type='BGRU', n_RNN_cell=64, n_layers_RNN=1, d... | 4,037 | 38.588235 | 115 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/models/RNN.py | import warnings
import torch
from torch import nn as nn
class BidirectionalGRU(nn.Module):
def __init__(self, n_in, n_hidden, dropout=0, num_layers=1):
super(BidirectionalGRU, self).__init__()
self.rnn = nn.GRU(n_in, n_hidden, bidirectional=True, dropout=dropout, batch_first=True, num_layers=nu... | 1,498 | 31.586957 | 119 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/utilities/utils.py | from __future__ import print_function
import glob
import warnings
import numpy as np
import pandas as pd
import soundfile
import os
import os.path as osp
import librosa
import torch
from desed.utils import create_folder
from torch import nn
import config as cfg
def median_smoothing(input_tensor, win_length):
n... | 11,860 | 33.988201 | 119 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/utilities/Scaler.py | import time
import warnings
import numpy as np
import torch
import json
from utilities.Logger import create_logger
logger = create_logger(__name__)
class Scaler:
"""
operates on one or multiple existing datasets and applies operations
"""
def __init__(self):
self.mean_ = None
self.... | 6,478 | 31.888325 | 118 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/utilities/Transforms.py | import warnings
import librosa
import random
import numpy as np
import torch
class Transform:
def transform_data(self, data):
# Mandatory to be defined by subclasses
raise NotImplementedError("Abstract object")
def transform_label(self, label):
# Do nothing, to be changed in subclass... | 13,603 | 30.710956 | 155 | py |
CRSTmodel | CRSTmodel-main/DCASE2020_baseline_platform/data_utils/DataLoad.py | import bisect
import numpy as np
import pandas as pd
import torch
import random
import warnings
from torch.utils.data import Dataset
from torch.utils.data.sampler import Sampler
from utilities.Logger import create_logger
import config as cfg
from utilities.Transforms import Compose
torch.manual_seed(0)
random.seed(0)... | 10,066 | 35.607273 | 120 | py |
GalaxyDataset | GalaxyDataset-master/GalaxyDataset.py | # -*- coding: utf-8 -*-
import torch
import torch.utils.data as Data
import numpy as np
import argparse
import os
import random
import yaml
import downloadData
import fdata
import preprocess
import mnist_bias
# 1. download dataset 2. split dataset
def make_dataset():
parser = argparse.ArgumentParser('parameters')... | 16,303 | 42.946092 | 239 | py |
GalaxyDataset | GalaxyDataset-master/downloadData.py | # -*- coding: utf-8 -*-
import argparse
import torch
from torchvision import datasets, transforms
# CIFAR-10,
# mean, [0.5, 0.5, 0.5]
# std, [0.5, 0.5, 0.5]
# CIFAR-100,
# mean, [0.5071, 0.4865, 0.4409]
# std, [0.2673, 0.2564, 0.2762]
def load_data(args):
args.batch_size = 1
train_loader = []
test_load... | 3,725 | 30.310924 | 98 | py |
GalaxyDataset | GalaxyDataset-master/fdata.py | from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, transforms
import torch as t
import numpy as np
import random
from PIL import ImageFilter
from PIL import Image
class GaussianBlur(object):
def __init__(self, sigma=[.1, 2.]):
self.sigma = sigma
def __call__(self, x):
... | 3,878 | 33.945946 | 154 | py |
GalaxyDataset | GalaxyDataset-master/autoencoder.py | # Numpy
import numpy as np
# Torch
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
# Torchvision
import torchvision
import torchvision.transforms as transforms
# Matplotlib
# %matplotlib inline
import matplotlib.pyplot as plt
# OS
im... | 6,675 | 31.565854 | 87 | py |
GalaxyDataset | GalaxyDataset-master/NEI.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.utils.data as Data
from preprocess import load_npy
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
# Torchvision
import torchvision
import torchvision.transforms as transforms
class Autoencoder(nn.M... | 3,661 | 35.62 | 121 | py |
GalaxyDataset | GalaxyDataset-master/mnist_bias.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
import random, os, time, argparse, pickle
def mnist_image_raw2bias(image_raw, label, background, digit, id_1, id_2):
b = []
d = []
for i in range(8):
i_0 = i//4
... | 2,547 | 33.432432 | 139 | py |
GalaxyDataset | GalaxyDataset-master/preprocess.py | # -*- coding: utf-8 -*-
import torch
import torch.utils.data as Data
import numpy as np
from torchvision import datasets, transforms
import argparse
import os
import random
import yaml
import downloadData
def load_npy(path):
# npy file: [[imgs, label], [imgs, label]...., [imgs, label]]
# when allow_pickle=True... | 1,436 | 29.574468 | 107 | py |
GalaxyDataset | GalaxyDataset-master/digitfive/femnist.py | from torchvision.datasets import MNIST, utils
from PIL import Image
import os.path
import torch
class FEMNIST(MNIST):
"""
This dataset is derived from the Leaf repository
(https://github.com/TalwalkarLab/leaf) pre-processing of the Extended MNIST
dataset, grouping examples by writer. Details about Lea... | 3,128 | 36.25 | 110 | py |
GalaxyDataset | GalaxyDataset-master/digitfive/usps.py | import scipy.io as scio
import numpy as np
from PIL import Image
import os
import os.path
import torch
import torchvision
from torchvision import datasets, transforms
from torchvision.datasets import MNIST, utils
from torch.utils.data import DataLoader, Dataset
# dataFile = 'usps_28x28.mat'
# data = scio.loadmat(dataF... | 5,890 | 30.502674 | 215 | py |
GalaxyDataset | GalaxyDataset-master/digitfive/svhn.py | import scipy.io as scio
import numpy as np
from PIL import Image
import os
import os.path
import torch
import torchvision
from torchvision import datasets, transforms
from torchvision.datasets import MNIST, utils
from torch.utils.data import DataLoader, Dataset
# dataFile = 'svhn_train_32x32.mat'
# data = scio.loadmat... | 5,699 | 29.15873 | 215 | py |
GalaxyDataset | GalaxyDataset-master/digitfive/syn.py | import scipy.io as scio
import numpy as np
from PIL import Image
import os
import os.path
import torch
import torchvision
from torchvision import datasets, transforms
from torchvision.datasets import MNIST, utils
from torch.utils.data import DataLoader, Dataset
# dataFile = 'syn_number.mat'
# data = scio.loadmat(dataF... | 5,471 | 30.630058 | 215 | py |
GalaxyDataset | GalaxyDataset-master/digitfive/mnistm.py | import scipy.io as scio
import numpy as np
from PIL import Image
import os
import os.path
import torch
import torchvision
from torchvision import datasets, transforms
from torchvision.datasets import MNIST, utils
from torch.utils.data import DataLoader, Dataset
# dataFile = 'mnistm_with_label.mat'
# data = scio.loadma... | 5,512 | 29.97191 | 215 | py |
GalaxyDataset | GalaxyDataset-master/digitfive/mnist.py | import scipy.io as scio
import numpy as np
from PIL import Image
import os
import os.path
import torch
import torchvision
from torchvision import datasets, transforms
from torchvision.datasets import MNIST, utils
from torch.utils.data import DataLoader, Dataset
# dataFile = 'mnist_data.mat'
# data = scio.loadmat(data... | 5,753 | 30.966667 | 215 | py |
skimulator | skimulator-master/doc/source/conf.py | # -*- coding: utf-8 -*-
#
# S4 documentation build configuration file, created by
# sphinx-quickstart on Thu Jul 10 16:54:19 2014.
#
# 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 file.
#
# All co... | 10,718 | 30.342105 | 80 | py |
CompositionSpaceNFDI | CompositionSpaceNFDI-main/docs/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/master... | 1,349 | 23.545455 | 83 | py |
fuzzyJoiner | fuzzyJoiner-master/build_model.py | from random import shuffle
import pickle
import numpy as np
# import pandas
import tensorflow as tf
import random as random
import json
from keras import backend as K
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, Input, Flatten, ... | 25,195 | 37.118003 | 249 | py |
fuzzyJoiner | fuzzyJoiner-master/preloaded_runner.py | import pickle
import numpy as np
import tensorflow as tf
import random as random
import json
from keras import backend as K
#from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Input, Lambda, GRU
from keras.layers import Embedding
from kera... | 17,465 | 35.848101 | 139 | py |
fuzzyJoiner | fuzzyJoiner-master/old/TripletLossFacenetLSTM.py | import numpy as np
import random as random
# """
# The below is necessary in Python 3.2.3 onwards to
# have reproducible behavior for certain hash-based operations.
# See these references for further details:
# https://docs.python.org/3.4/using/cmdline.html#envvar-PYTHONHASHSEED
# https://github.com/keras-team/keras/is... | 21,712 | 37.227113 | 167 | py |
fuzzyJoiner | fuzzyJoiner-master/old/TripletLossFacenetLSTM-8.20.18.py | import numpy as np
import pandas
import tensorflow as tf
import random as random
import json
from keras import backend as K
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, Input, Flatten, Dropout, Lambda, GRU, Activation
from keras... | 21,235 | 36.061082 | 163 | py |
fuzzyJoiner | fuzzyJoiner-master/old/ANNBasedSampleSelection.py | import Named_Entity_Recognition_Modified
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from embeddings import KazumaCharEmbedding
from annoy import AnnoyIndex
from matcher_functions import connect
import argparse
import numpy as np
from keras.layers import Embeddi... | 6,038 | 32.181319 | 138 | py |
fuzzyJoiner | fuzzyJoiner-master/old/TripletLossFacenetLSTM-angular.py | import numpy as np
import tensorflow as tf
import random as random
# import cntk as C
# """
# The below is necessary in Python 3.2.3 onwards to
# have reproducible behavior for certain hash-based operations.
# See these references for further details:
# https://docs.python.org/3.4/using/cmdline.html#envvar-PYTHONHASHSE... | 21,847 | 37.329825 | 167 | py |
fuzzyJoiner | fuzzyJoiner-master/old/Triplet_Iteration.py | from sys import argv
from keras import backend as K
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, Input, Flatten, Dropout, Lambda
from keras.layers import Conv1D, MaxPooling1D, Embedding
from keras.models import Model, model_fr... | 14,236 | 37.374663 | 199 | py |
fuzzyJoiner | fuzzyJoiner-master/old/ContrastiveLossLSTM-8.20.18.py | import numpy as np
import pandas
import tensorflow as tf
import random as random
import json
from keras import backend as K
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, Input, Flatten, Dropout, Lambda, GRU, Activation
from keras... | 19,575 | 35.86629 | 163 | py |
fuzzyJoiner | fuzzyJoiner-master/old/seq2seq.py | '''Sequence to sequence example in Keras (character-level).
This script demonstrates how to implement a basic character-level
sequence-to-sequence model. We apply it to translating
short English sentences into short French sentences,
character-by-character. Note that it is fairly unusual to
do character-level machine t... | 9,104 | 40.013514 | 79 | py |
fuzzyJoiner | fuzzyJoiner-master/old/Named_Entity_Recognition_Modified.py | """
This code is modified from
https://github.com/fchollet/keras/blob/master/examples/pretrained_word_embeddings.py
and ttps://github.com/fchollet/keras/blob/master/examples/
for our own purposes
"""
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
from matcher_functio... | 23,976 | 36.289269 | 258 | py |
fuzzyJoiner | fuzzyJoiner-master/old/Named_Entity_Recognition.py | """
This code is modified from
https://github.com/fchollet/keras/blob/master/examples/pretrained_word_embeddings.py
and ttps://github.com/fchollet/keras/blob/master/examples/
for our own purposes
"""
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
from matcher_functio... | 19,852 | 31.176661 | 254 | py |
fuzzyJoiner | fuzzyJoiner-master/old/TripletLossFacenetLSTM-modified.py | import numpy as np
import tensorflow as tf
import random as random
# import cntk as C
# """
# The below is necessary in Python 3.2.3 onwards to
# have reproducible behavior for certain hash-based operations.
# See these references for further details:
# https://docs.python.org/3.4/using/cmdline.html#envvar-PYTHONHASHSE... | 21,841 | 37.319298 | 167 | py |
fuzzyJoiner | fuzzyJoiner-master/old/ANNCharacteristics.py | import numpy as np
import pandas
import tensorflow as tf
import random as random
import json
from keras import backend as K
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, Input, Flatten, Dropout, Lambda, GRU, Activation
from keras... | 19,558 | 35.355019 | 134 | py |
fuzzyJoiner | fuzzyJoiner-master/old/TripletLossFacenet.py | from sys import argv
from keras import backend as K
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, Input, Flatten, Dropout, Lambda, LSTM
from keras.layers import Conv1D, MaxPooling1D, Embedding
from keras.models import Model, mo... | 12,095 | 37.893891 | 199 | py |
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