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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3SD | 3SD-main/u2net_test.py | import os
from skimage import io, transform
import torch
import torchvision
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms#, utils
# import torch.optim as optim
import numpy as np
from PIL imp... | 4,238 | 32.642857 | 129 | py |
3SD | 3SD-main/smoothness/__init__.py | import torch
import torch.nn.functional as F
# from torch.autograd import Variable
# import numpy as np
def laplacian_edge(img):
laplacian_filter = torch.Tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
filter = torch.reshape(laplacian_filter, [1, 1, 3, 3])
filter = filter.cuda()
lap_edge = F.conv2d(im... | 2,014 | 30.484375 | 78 | py |
3SD | 3SD-main/model/u2net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class REBNCONV(nn.Module):
def __init__(self,in_ch=3,out_ch=3,dirate=1):
super(REBNCONV,self).__init__()
self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate)
self.bn_s1 = nn.BatchNorm2d(out_ch)
... | 14,719 | 26.984791 | 118 | py |
3SD | 3SD-main/model/u2net_transformer_pseudo_dino_final.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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.apach... | 33,498 | 32.499 | 155 | py |
3SD | 3SD-main/model/utils.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 law ... | 21,117 | 33.06129 | 115 | py |
3SD | 3SD-main/model/u2net_refactor.py | import torch
import torch.nn as nn
import math
__all__ = ['U2NET_full', 'U2NET_lite']
def _upsample_like(x, size):
return nn.Upsample(size=size, mode='bilinear', align_corners=False)(x)
def _size_map(x, height):
# {height: size} for Upsample
size = list(x.shape[-2:])
sizes = {}
for h in range(... | 6,097 | 35.08284 | 101 | py |
3SD | 3SD-main/model/__init__.py | from .u2net_transformer_pseudo_dino_final import U2NET
from .u2net import U2NETP
| 81 | 26.333333 | 54 | py |
3SD | 3SD-main/model/u2net_transformer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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.apach... | 30,678 | 31.917382 | 124 | py |
STFTgrad | STFTgrad-main/classifier/classifier_adaptive.py | """
Code for the adaptive classifier with the differentiable STFT front-end
This will be trained on our test input signal, alternating sinusoids of 2 frequencies
"""
# Dependencies
import numpy as np
from tqdm import tqdm
import haiku as hk
import jax.numpy as jnp
import jax
import optax
from dstft import diff_stft
imp... | 3,684 | 25.510791 | 120 | py |
STFTgrad | STFTgrad-main/classifier/classifier_ordinary.py | """
Code for a normal classifier (to obtain the loss function as a function of the window length)
This will be trained on our test input signal, alternating sinusoids of 2 frequencies
"""
# Dependencies
import numpy as np
from tqdm import tqdm
import haiku as hk
import jax.numpy as jnp
import jax
import optax
from dst... | 3,916 | 25.828767 | 120 | py |
STFTgrad | STFTgrad-main/classifier/dstft.py | """
Code for the differentiable STFT front-end
As explained in our paper, we use a Gaussian Window STFT, with N = floor(6\sigma)
"""
# Dependencies
import jax.numpy as jnp
import jax
def diff_stft(xinp,s,hf = 0.5):
"""
Inputs
------
xinp: jnp.array
Input audio signal in time domain
s: jnp.... | 1,290 | 26.468085 | 154 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/adaptive_stft.py | import math
from tqdm import trange
import sys
import pathlib
import torch.autograd
import torch
import numpy as np
import torch.optim
import torch.nn as nn
from celluloid import Camera
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
import torch.nn.functional as F
from adaptive_stft_utils.operator... | 11,853 | 39.875862 | 123 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/MNISTExperiment.py | from models import UMNNMAFFlow
import torch
from lib import dataloader as dl
import lib as transform
import lib.utils as utils
import numpy as np
import os
import pickle
from timeit import default_timer as timer
import torchvision
from tensorboardX import SummaryWriter
writer = SummaryWriter()
def train_mnist(datas... | 13,329 | 49.492424 | 127 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/ToyExperiments.py | from models import UMNNMAFFlow
import torch
import lib.toy_data as toy_data
import numpy as np
import matplotlib.pyplot as plt
from timeit import default_timer as timer
import os
import lib.utils as utils
import lib.visualize_flow as vf
green = '#e15647'
black = '#2d5468'
white_bg = '#ececec'
def summary_plots(x, x_te... | 7,250 | 37.775401 | 128 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/MonotonicMLP.py | import torch
import argparse
import torch.nn as nn
import matplotlib.pyplot as plt
from models.UMNN import MonotonicNN, IntegrandNN
def f(x_1, x_2, x_3):
return .001*(x_1**3 + x_1) + x_2 ** 2 + torch.sin(x_3)
def create_dataset(n_samples):
x = torch.randn(n_samples, 3)
y = f(x[:, 0], x[:, 1], x[:, 2])
... | 3,487 | 35.715789 | 96 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/UCIExperiments.py | from models import UMNNMAFFlow
import torch
import numpy as np
import os
import pickle
import lib.utils as utils
import datasets
from timeit import default_timer as timer
from tensorboardX import SummaryWriter
writer = SummaryWriter()
def batch_iter(X, batch_size, shuffle=False):
"""
X: feature tensor (shape... | 10,219 | 41.941176 | 131 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/TrainVaeFlow.py | # !/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import argparse
import time
import torch
import torch.utils.data
import torch.optim as optim
import numpy as np
import math
import random
import os
import datetime
import lib.utils as utils
from models.vae_lib.models import VAE
fro... | 13,750 | 39.444118 | 124 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/setup.py | from setuptools import setup
setup(
name='UMNN',
version='0.1',
packages=['UMNN'],
url='',
license='MIT License',
author='awehenkel',
author_email='[email protected]',
description=''
)
| 227 | 16.538462 | 46 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/__init__.py | from .UMNN import UMNNMAFFlow, MADE, ParallelNeuralIntegral, NeuralIntegral
| 76 | 37.5 | 75 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/__init__.py | 0 | 0 | 0 | py | |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/models/VAE.py | from __future__ import print_function
import torch
import torch.nn as nn
from ...vae_lib.models import flows
from ...vae_lib.models.layers import GatedConv2d, GatedConvTranspose2d
class VAE(nn.Module):
"""
The base VAE class containing gated convolutional encoder and decoder architecture.
Can be used as ... | 26,921 | 32.949559 | 136 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/models/CNFVAE.py | import torch
import torch.nn as nn
from train_misc import build_model_tabular
from UMNNMAF import lib as layers
import lib as diffeq_layers
from .VAE import VAE
from lib import NONLINEARITIES
from torchdiffeq import odeint_adjoint as odeint
def get_hidden_dims(args):
return tuple(map(int, args.dims.split("-"))) ... | 14,375 | 33.808717 | 116 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/models/layers.py | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import numpy as np
import torch.nn.functional as F
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, x):
return x
class GatedConv2d(nn.Module):
def __init__(self... | 7,128 | 32.947619 | 115 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/models/__init__.py | 0 | 0 | 0 | py | |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/models/flows.py | """
Collection of flow strategies
"""
from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from ...vae_lib.models.layers import MaskedConv2d, MaskedLinear
import sys
sys.path.append("../../")
from models import... | 10,990 | 32.306061 | 118 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/optimization/loss.py | from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
from ...vae_lib.utils.distributions import log_normal_diag, log_normal_standard, log_bernoulli
import torch.nn.functional as F
def binary_loss_function(recon_x, x, z_mu, z_var, z_0, z_k, ldj, beta=1.):
"""
Computes th... | 10,621 | 38.051471 | 116 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/optimization/training.py | from __future__ import print_function
import time
import torch
from ...vae_lib.optimization.loss import calculate_loss
from ...vae_lib.utils.visual_evaluation import plot_reconstructions
from ...vae_lib.utils.log_likelihood import calculate_likelihood
import numpy as np
def train(epoch, train_loader, model, opt, ar... | 5,533 | 30.443182 | 120 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/optimization/__init__.py | 0 | 0 | 0 | py | |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/utils/distributions.py | from __future__ import print_function
import torch
import torch.utils.data
import math
MIN_EPSILON = 1e-5
MAX_EPSILON = 1. - 1e-5
PI = torch.FloatTensor([math.pi])
if torch.cuda.is_available():
PI = PI.cuda()
# N(x | mu, var) = 1/sqrt{2pi var} exp[-1/(2 var) (x-mean)(x-mean)]
# log N(x| mu, var) = -log sqrt(2pi... | 1,768 | 25.80303 | 86 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/utils/plotting.py | from __future__ import division
from __future__ import print_function
import numpy as np
import matplotlib
# noninteractive background
matplotlib.use('Agg')
import matplotlib.pyplot as plt
def plot_training_curve(train_loss, validation_loss, fname='training_curve.pdf', labels=None):
"""
Plots train_loss and ... | 4,021 | 37.304762 | 106 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/utils/log_likelihood.py | from __future__ import print_function
import time
import numpy as np
from scipy.misc import logsumexp
from ...vae_lib.optimization.loss import calculate_loss_array
def calculate_likelihood(X, model, args, logger, S=5000, MB=500):
# set auxiliary variables for number of training and test sets
N_test = X.size(... | 1,595 | 25.163934 | 110 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/utils/load_data.py | from __future__ import print_function
import torch
import torch.utils.data as data_utils
import pickle
from scipy.io import loadmat
import numpy as np
import os
def load_static_mnist(args, **kwargs):
"""
Dataloading function for static mnist. Outputs image data in vectorized form: each image is a vector of... | 7,580 | 35.800971 | 116 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/utils/__init__.py | 0 | 0 | 0 | py | |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/vae_lib/utils/visual_evaluation.py | from __future__ import print_function
import os
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
def plot_reconstructions(data, recon_mean, loss, loss_type, epoch, args):
if args.input_type == 'multinomial':
# data is already between 0 and 1
... | 2,063 | 37.222222 | 119 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/UMNN/spectral_normalization.py | # Code from https://github.com/christiancosgrove/pytorch-spectral-normalization-gan/blob/master/spectral_normalization.py
import torch
from torch import nn
from torch.nn import Parameter
def l2normalize(v, eps=1e-12):
return v / (v.norm() + eps)
def joint_gaussian(n_samp=1000):
x2 = torch.distributions.Norm... | 2,536 | 32.381579 | 121 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/UMNN/made.py | """
Implements Masked AutoEncoder for Density Estimation, by Germain et al. 2015
Re-implementation by Andrej Karpathy based on https://arxiv.org/abs/1502.03509
Modified by Antoine Wehenkel
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
# -------------------------... | 9,945 | 40.26971 | 151 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/UMNN/ParallelNeuralIntegral.py | import torch
import numpy as np
import math
def _flatten(sequence):
flat = [p.contiguous().view(-1) for p in sequence]
return torch.cat(flat) if len(flat) > 0 else torch.tensor([])
def compute_cc_weights(nb_steps):
lam = np.arange(0, nb_steps + 1, 1).reshape(-1, 1)
lam = np.cos((lam @ lam.T) * math.... | 4,099 | 38.423077 | 138 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/UMNN/MonotonicNN.py | import torch
import torch.nn as nn
from .NeuralIntegral import NeuralIntegral
from .ParallelNeuralIntegral import ParallelNeuralIntegral
def _flatten(sequence):
flat = [p.contiguous().view(-1) for p in sequence]
return torch.cat(flat) if len(flat) > 0 else torch.tensor([])
class IntegrandNN(nn.Module):
... | 1,957 | 34.6 | 145 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/UMNN/__init__.py | from .UMNNMAFFlow import UMNNMAFFlow
from .MonotonicNN import MonotonicNN, IntegrandNN
from .UMNNMAF import IntegrandNetwork, UMNNMAF
from .made import MADE
from .NeuralIntegral import NeuralIntegral
from .ParallelNeuralIntegral import ParallelNeuralIntegral | 258 | 42.166667 | 58 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/UMNN/NeuralIntegral.py | import torch
import numpy as np
import math
def _flatten(sequence):
flat = [p.contiguous().view(-1) for p in sequence]
return torch.cat(flat) if len(flat) > 0 else torch.tensor([])
def compute_cc_weights(nb_steps):
lam = np.arange(0, nb_steps + 1, 1).reshape(-1, 1)
lam = np.cos((lam @ lam.T) * math.... | 2,840 | 31.284091 | 119 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/UMNN/UMNNMAF.py | import torch
import torch.nn as nn
from .NeuralIntegral import NeuralIntegral
from .ParallelNeuralIntegral import ParallelNeuralIntegral
import numpy as np
import math
from .made import MADE, ConditionnalMADE
class ELUPlus(nn.Module):
def __init__(self):
super().__init__()
self.elu = nn.ELU()
de... | 10,524 | 38.716981 | 129 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/models/UMNN/UMNNMAFFlow.py | import torch
import torch.nn as nn
from .UMNNMAF import EmbeddingNetwork, UMNNMAF
import numpy as np
import math
class ListModule(object):
def __init__(self, module, prefix, *args):
"""
The ListModule class is a container for multiple nn.Module.
:nn.Module module: A module to add in the li... | 5,656 | 36.217105 | 115 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/datasets/power.py | import numpy as np
import datasets
class POWER:
class Data:
def __init__(self, data):
self.x = data.astype(np.float32)
self.N = self.x.shape[0]
def __init__(self):
trn, val, tst = load_data_normalised()
self.trn = self.Data(trn)
self.val = self.Da... | 1,940 | 24.88 | 108 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/datasets/download_datasets.py | # -*- coding: utf-8 -*-
"""
Created on Wed Apr 19 15:58:53 2017
@author: Chin-Wei
# some code adapted from https://github.com/yburda/iwae/blob/master/download_mnist.py
LSUN
https://github.com/fyu/lsun
"""
import urllib
import pickle
import os
import struct
import numpy as np
import gzip
import time
import urllib.requ... | 9,226 | 31.60424 | 119 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/datasets/hepmass.py | import pandas as pd
import numpy as np
from collections import Counter
from os.path import join
import datasets
class HEPMASS:
"""
The HEPMASS data set.
http://archive.ics.uci.edu/ml/datasets/HEPMASS
"""
class Data:
def __init__(self, data):
self.x = data.astype(np.float32)... | 2,730 | 28.365591 | 112 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/datasets/gas.py | import pandas as pd
import numpy as np
import datasets
class GAS:
class Data:
def __init__(self, data):
self.x = data.astype(np.float32)
self.N = self.x.shape[0]
def __init__(self):
file = datasets.root + 'gas/ethylene_CO.pickle'
trn, val, tst = load_data_... | 1,672 | 21.917808 | 59 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/datasets/bsds300.py | import numpy as np
import h5py
import datasets
class BSDS300:
"""
A dataset of patches from BSDS300.
"""
class Data:
"""
Constructs the dataset.
"""
def __init__(self, data):
self.x = data[:]
self.N = self.x.shape[0]
def __init__(self):
... | 663 | 17.971429 | 66 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/datasets/miniboone.py | import numpy as np
import datasets
class MINIBOONE:
class Data:
def __init__(self, data):
self.x = data.astype(np.float32)
self.N = self.x.shape[0]
def __init__(self):
file = datasets.root + 'miniboone/data.npy'
trn, val, tst = load_data_normalised(file)
... | 1,955 | 26.942857 | 96 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/UMNN/datasets/__init__.py | root = 'datasets/data/'
from .power import POWER
from .gas import GAS
from .hepmass import HEPMASS
from .miniboone import MINIBOONE
from .bsds300 import BSDS300
| 162 | 19.375 | 32 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/adaptive_stft_utils/operators.py | import torch.autograd
import torch
import torch.nn.functional as F
def dithering_int(n):
if n == int(n):
return int(n)
return int(torch.bernoulli((n - int(n))) + int(n))
class SignPassGrad(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
return input.sign()
@stat... | 1,729 | 23.027778 | 87 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/adaptive_stft_utils/losses.py | import torch.autograd
import torch
import torch.nn.functional as F
from .operators import clip_tensor_norm
def kurtosis(rfft_magnitudes_sq):
epsilon = 1e-7
max_norm = 0.1
kur_part = [
torch.sum(torch.pow(a, 2)) /
(torch.pow(torch.sum(a), 2).unsqueeze(-1) + epsilon)
for a in rfft_ma... | 541 | 24.809524 | 93 | py |
STFTgrad | STFTgrad-main/adaptiveSTFT/adaptive_stft_utils/mappings.py | import math
import sys
import pathlib
import torch
import torch.nn as nn
import torch.nn.functional as F
sys.path.insert(0, pathlib.Path(__file__).parent.parent.parent.absolute())
from UMNN.models.UMNN import MonotonicNN
# Monotonically increasing mapping
class IdxToWindow(nn.Module):
def __init__(self, signal_... | 6,507 | 40.452229 | 216 | py |
TV-parameter-learning | TV-parameter-learning-master/main.py | # -*- coding: utf-8 -*-
#
# Copyright (C) 2022 Kristian Bredies ([email protected])
# Enis Chenchene ([email protected])
# Alireza Hosseini ([email protected])
#
# This file is part of the example code repository for the paper:
#
# K. Br... | 2,885 | 38 | 90 | py |
TV-parameter-learning | TV-parameter-learning-master/structures.py | # -*- coding: utf-8 -*-
#
# Copyright (C) 2022 Kristian Bredies ([email protected])
# Enis Chenchene ([email protected])
# Alireza Hosseini ([email protected])
#
# This file is part of the example code repository for the paper:
#
# K. Br... | 3,390 | 23.751825 | 93 | py |
TV-parameter-learning | TV-parameter-learning-master/experiments.py | # -*- coding: utf-8 -*-
#
# Copyright (C) 2022 Kristian Bredies ([email protected])
# Enis Chenchene ([email protected])
# Alireza Hosseini ([email protected])
#
# This file is part of the example code repository for the paper:
#
# K. Br... | 6,124 | 32.108108 | 94 | py |
TV-parameter-learning | TV-parameter-learning-master/train_quadratic_model.py | # -*- coding: utf-8 -*-
#
# Copyright (C) 2022 Kristian Bredies ([email protected])
# Enis Chenchene ([email protected])
# Alireza Hosseini ([email protected])
#
# This file is part of the example code repository for the paper:
#
# K. Br... | 5,435 | 34.298701 | 94 | py |
TV-parameter-learning | TV-parameter-learning-master/single_patch.py | # -*- coding: utf-8 -*-
#
# Copyright (C) 2022 Kristian Bredies ([email protected])
# Enis Chenchene ([email protected])
# Alireza Hosseini ([email protected])
#
# This file is part of the example code repository for the paper:
#
# K. Br... | 3,956 | 32.252101 | 89 | py |
TV-parameter-learning | TV-parameter-learning-master/data.py | # -*- coding: utf-8 -*-
#
# Copyright (C) 2022 Kristian Bredies ([email protected])
# Enis Chenchene ([email protected])
# Alireza Hosseini ([email protected])
#
# This file is part of the example code repository for the paper:
#
# K. Br... | 3,155 | 33.681319 | 86 | py |
TV-parameter-learning | TV-parameter-learning-master/train_constant_model.py | # -*- coding: utf-8 -*-
#
# Copyright (C) 2022 Kristian Bredies ([email protected])
# Enis Chenchene ([email protected])
# Alireza Hosseini ([email protected])
#
# This file is part of the example code repository for the paper:
#
# K. Br... | 4,346 | 32.438462 | 96 | py |
TV-parameter-learning | TV-parameter-learning-master/plots.py | # -*- coding: utf-8 -*-
#
# Copyright (C) 2022 Kristian Bredies ([email protected])
# Enis Chenchene ([email protected])
# Alireza Hosseini ([email protected])
#
# This file is part of the example code repository for the paper:
#
# K. Br... | 5,081 | 32.88 | 94 | py |
TV-parameter-learning | TV-parameter-learning-master/denoising.py | # -*- coding: utf-8 -*-
#
# Copyright (C) 2022 Kristian Bredies ([email protected])
# Enis Chenchene ([email protected])
# Alireza Hosseini ([email protected])
#
# This file is part of the example code repository for the paper:
#
# K. Br... | 4,600 | 33.081481 | 92 | py |
daanet | daanet-master/app.py | import sys
from gpu_env import DEVICE_ID, MODEL_ID, CONFIG_SET
from utils.helper import set_logger, parse_args, get_args_cli
def run():
set_logger(model_id='%s:%s' % (DEVICE_ID, MODEL_ID))
followup_args = get_args_cli(sys.argv[3:]) if len(sys.argv) > 3 else None
args = parse_args(sys.argv[2] if len(sys.a... | 466 | 28.1875 | 102 | py |
daanet | daanet-master/api.py | import logging
from tensorflow.python.framework.errors_impl import NotFoundError, InvalidArgumentError
from gpu_env import ModeKeys, APP_NAME
from utils.helper import build_model
logger = logging.getLogger(APP_NAME)
def train(args):
# check run_mode
if 'run_mode' in args:
args.set_hparam('run_mode'... | 1,758 | 32.826923 | 92 | py |
daanet | daanet-master/grid_search.py | import itertools
import os
import sys
from ruamel.yaml import YAML
from utils.helper import set_logger, fill_gpu_jobs, get_tmp_yaml
def run():
logger = set_logger()
with open('grid.yaml') as fp:
settings = YAML().load(fp)
test_set = sys.argv[1:] if len(sys.argv) > 1 else settings['common'][... | 1,612 | 36.511628 | 108 | py |
daanet | daanet-master/gpu_env.py | import os
from datetime import datetime
from enum import Enum
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
IGNORE_PATTERNS = ('data', '*.pyc', 'CVS', '.git', 'tmp', '.svn', '__pycache__', '.gitignore', '.*.yaml')
MODEL_ID = datetime.now().strftime("%m%d-%H%M%S") + (
os.e... | 1,139 | 23.255319 | 105 | py |
daanet | daanet-master/nlp/match_blocks.py | import tensorflow as tf
from nlp.nn import linear_logit, layer_norm
from nlp.seq2seq.common import dropout, softmax_mask
def Attention_match(context, query, context_mask, query_mask,
num_units=None,
scope='attention_match_block', reuse=None, **kwargs):
with tf.variable_sco... | 7,338 | 41.421965 | 108 | py |
daanet | daanet-master/nlp/nn.py | import tensorflow as tf
initializer = tf.contrib.layers.variance_scaling_initializer(factor=1.0,
mode='FAN_AVG',
uniform=True,
dtype=tf.... | 14,303 | 42.345455 | 119 | py |
daanet | daanet-master/nlp/__init__.py | 0 | 0 | 0 | py | |
daanet | daanet-master/nlp/encode_blocks.py | import tensorflow as tf
from nlp.nn import initializer, regularizer, spatial_dropout, get_lstm_init_state, layer_norm
def LSTM_encode(seqs, scope='lstm_encode_block', reuse=None, **kwargs):
with tf.variable_scope(scope, reuse=reuse):
batch_size = tf.shape(seqs)[0]
_seqs = tf.transpose(seqs, [1, 0... | 3,388 | 39.831325 | 110 | py |
daanet | daanet-master/nlp/seq2seq/pointer_generator.py | import tensorflow as tf
from tensorflow.contrib.seq2seq.python.ops.attention_wrapper import _compute_attention
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
# from tensorflow.contrib.rnn.python.ops.core_rnn_cell import _linear
from tensorflow.python.ops.rnn_cell_impl import _zer... | 22,126 | 49.061086 | 158 | py |
daanet | daanet-master/nlp/seq2seq/common.py | # coding=utf-8
import math
import time
import tensorflow as tf
from tensorflow.python.ops.image_ops_impl import ResizeMethod
from tensorflow.python.ops.rnn_cell_impl import LSTMStateTuple
INF = 1e30
def initializer(): return tf.contrib.layers.variance_scaling_initializer(factor=1.0,
... | 9,076 | 36.508264 | 110 | py |
daanet | daanet-master/nlp/seq2seq/__init__.py | 0 | 0 | 0 | py | |
daanet | daanet-master/nlp/seq2seq/rnn.py | # coding=utf-8
import tensorflow as tf
import tensorflow.contrib as tc
import gpu_env
from .common import dropout, dense, get_var
def single_rnn_cell(cell_name, num_units, is_train=None, keep_prob=0.75):
"""
Get a single rnn cell
"""
cell_name = cell_name.upper()
if cell_name == "GRU":
c... | 10,472 | 40.7251 | 120 | py |
daanet | daanet-master/daanet/base.py | import json
import os
import tensorflow as tf
from base import base_model
from gpu_env import ModeKeys
from model_utils.helper import LossCounter, get_filename
from utils.eval_4.eval import compute_bleu_rouge
from utils.helper import build_model
# model controller
class RCBase(base_model.BaseModel):
def __init_... | 7,115 | 42.656442 | 117 | py |
daanet | daanet-master/daanet/basic.py | """Sequence-to-Sequence with attention model.
"""
import tensorflow as tf
from tensorflow.python.layers import core as layers_core
from gpu_env import ModeKeys, SummaryType
from model_utils.helper import mblock
from nlp.encode_blocks import LSTM_encode, CNN_encode
from nlp.match_blocks import dot_attention, Transform... | 30,506 | 51.238014 | 127 | py |
daanet | daanet-master/base/base_model.py | import importlib
import json
import logging
import os
from collections import defaultdict
from math import ceil
import numpy as np
import tensorflow as tf
from ruamel.yaml import YAML
from gpu_env import ModeKeys, APP_NAME, SummaryType
from model_utils.helper import mblock, partial_restore, sample_element_from_var
... | 20,931 | 43.918455 | 118 | py |
daanet | daanet-master/base/base_io.py | import logging
from typing import List
from dataio_utils.helper import build_vocab
from gpu_env import APP_NAME, ModeKeys
class BaseDataIO:
def __init__(self, args):
self.args = args
self.logger = logging.getLogger(APP_NAME)
self.vocab = build_vocab(args.word_embedding_files)
self... | 866 | 31.111111 | 71 | py |
daanet | daanet-master/dataio_utils/helper.py | import copy
import json
import random
import re
def _tokenize(x):
tokens = [v for v in re.findall(r"\w+|[^\w]", x, re.UNICODE) if len(v)] # fix last hanging space
token_shifts = []
char_token_map = []
c, j = 0, 0
for v in tokens:
if v.strip():
token_shifts.append(j)
... | 2,653 | 30.975904 | 110 | py |
daanet | daanet-master/dataio_utils/full_load_io.py | import random
from base import base_io
from gpu_env import ModeKeys
class DataIO(base_io.BaseDataIO):
def __init__(self, args):
super().__init__(args)
if args.is_serving:
self.logger.info('model is serving request, ignoring train & dev sets!')
else:
self.datasets =... | 2,018 | 34.421053 | 86 | py |
daanet | daanet-master/dataio_utils/flow_io.py | import json
from typing import List
import tensorflow as tf
from base import base_io
from gpu_env import ModeKeys
# dataio controller
class FlowDataIO(base_io.BaseDataIO):
def __init__(self, args):
super().__init__(args)
if args.is_serving:
self.logger.info('model is serving request,... | 2,238 | 31.449275 | 98 | py |
daanet | daanet-master/dataio_utils/__init__.py | 0 | 0 | 0 | py | |
daanet | daanet-master/utils/predictor.py | import os
import re
import sys
import tensorflow as tf
from gpu_env import MODEL_ID
MODEL_PATH = './ext'
print(sys.path)
class Predictor:
def __init__(self, batch_size=32):
self.batch_size = batch_size
print("Loading model..., please wait!", flush=True)
self.models, self.graphs, self.m... | 3,379 | 29.178571 | 101 | py |
daanet | daanet-master/utils/helper.py | import importlib
import logging
import math
import os
import re
import shutil
import subprocess
import sys
import time
import traceback
from collections import defaultdict
from random import shuffle
import GPUtil
import tensorflow as tf
from ruamel.yaml import YAML
from ruamel.yaml.comments import CommentedMap
from te... | 11,978 | 32 | 113 | py |
daanet | daanet-master/utils/vocab.py | import logging
import pickle
import numpy as np
from gpu_env import APP_NAME
class Vocab:
@staticmethod
def load_from_pickle(fp):
with open(fp, 'rb') as fin:
return pickle.load(fin)
def __init__(self, embedding_files, lower=True):
self.logger = logging.getLogger(APP_NAME)
... | 5,793 | 34.987578 | 117 | py |
daanet | daanet-master/utils/__init__.py | 0 | 0 | 0 | py | |
daanet | daanet-master/utils/eval_4/eval.py | from .bleu_metric.bleu import Bleu
from .exact_f1.exact_f1 import f1_exact_eval
from .meteor.meter import compute_meter_score
from .rouge_metric.rouge import Rouge
def normalize(s):
"""
Normalize strings to space joined chars.
Args:
s: a list of strings.
Returns:
A list of normalized... | 1,497 | 28.96 | 79 | py |
daanet | daanet-master/utils/eval_4/__init__.py | 0 | 0 | 0 | py | |
daanet | daanet-master/utils/eval_4/meteor/__init__.py | __author__ = 'larryjfyan'
| 26 | 12.5 | 25 | py |
daanet | daanet-master/utils/eval_4/meteor/meter.py | import os
def compute_meter_score(pred, ref):
cwd = os.path.dirname(__file__)
test_path = '{}/test'.format(cwd)
ref_path = '{}/reference'.format(cwd)
jar_path = '{}/meteor-1.5.jar'.format(cwd)
save_path = '{}/res.txt'.format(cwd)
with open(test_path, 'w') as f:
f.write('\n'.join(pred))... | 635 | 30.8 | 108 | py |
daanet | daanet-master/utils/eval_4/exact_f1/exact_f1.py | """Official evaluation script for SQuAD version 2.0.
In addition to basic functionality, we also compute additional statistics and
plot precision-recall curves if an additional na_prob.json file is provided.
This file is expected to map question ID's to the model's predicted probability
that a question is unanswerable... | 2,407 | 31.986301 | 82 | py |
daanet | daanet-master/utils/eval_4/exact_f1/__init__.py | 0 | 0 | 0 | py | |
daanet | daanet-master/utils/eval_4/rouge_metric/rouge.py | #!/usr/bin/env python
#
# File Name : rouge.py
#
# Description : Computes ROUGE-L metric as described by Lin and Hovey (2004)
#
# Creation Date : 2015-01-07 06:03
# Author : Ramakrishna Vedantam <[email protected]>
import numpy as np
def my_lcs(string, sub):
"""
Calculates longest common subsequence for a pair... | 3,391 | 35.085106 | 123 | py |
daanet | daanet-master/utils/eval_4/rouge_metric/__init__.py | 0 | 0 | 0 | py | |
daanet | daanet-master/utils/eval_4/bleu_metric/bleu_scorer.py | #!/usr/bin/env python
# bleu_scorer.py
# David Chiang <[email protected]>
# Copyright (c) 2004-2006 University of Maryland. All rights
# reserved. Do not redistribute without permission from the
# author. Not for commercial use.
# Modified by:
# Hao Fang <[email protected]>
# Tsung-Yi Lin <[email protected]>
'''Provides:
... | 8,791 | 31.442804 | 150 | py |
daanet | daanet-master/utils/eval_4/bleu_metric/bleu.py | #!/usr/bin/env python
#
# File Name : bleu.py
#
# Description : Wrapper for BLEU scorer.
#
# Creation Date : 06-01-2015
# Last Modified : Thu 19 Mar 2015 09:13:28 PM PDT
# Authors : Hao Fang <[email protected]> and Tsung-Yi Lin <[email protected]>
from .bleu_scorer import BleuScorer
class Bleu:
def __init__(self, n=... | 1,297 | 27.844444 | 80 | py |
daanet | daanet-master/utils/eval_4/bleu_metric/__init__.py | 0 | 0 | 0 | py | |
daanet | daanet-master/model_utils/helper.py | import json
import logging
import os
import time
import tensorflow as tf
from gpu_env import APP_NAME, ModeKeys
def sample_element_from_var(all_var):
result = {}
for v in all_var:
try:
v_rank = len(v.get_shape())
v_ele1, v_ele2 = v, v
for j in range(v_rank):
... | 4,728 | 35.099237 | 107 | py |
daanet | daanet-master/model_utils/__init__.py | 0 | 0 | 0 | py | |
Tim-TSENet | Tim-TSENet-main/TSENET/test_tasnet.py | import os
import torch
from data_loader.AudioReader import AudioReader, write_wav
import argparse
from model.model import TSENet,TSENet_one_hot
from logger.set_logger import setup_logger
import logging
from config.option import parse
import torchaudio
from utils.util import handle_scp, handle_scp_inf
def read_wav(fnam... | 7,559 | 48.090909 | 176 | py |
Tim-TSENet | Tim-TSENet-main/TSENET/train_Tasnet.py | import sys
sys.path.append('./')
from torch.utils.data import DataLoader as Loader
from data_loader.Dataset import Datasets
from model.model import TSENet,TSENet_one_hot
from logger import set_logger
import logging
from config import option
import argparse
import torch
from trainer import trainer_Tasnet,trainer_Tasnet_... | 7,435 | 48.245033 | 131 | py |
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