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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ParallelWaveGAN | ParallelWaveGAN-master/test/test_layers.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
import logging
import numpy as np
import pytest
import torch
from parallel_wavegan.layers import (
PQMF,
CausalConv1d,
CausalConvTranspose1d,
Conv1d,
Conv1d1x1,
... | 4,085 | 26.059603 | 84 | py |
ParallelWaveGAN | ParallelWaveGAN-master/test/test_mel_loss.py | #!/usr/bin/env python3
# Copyright 2021 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Test code for Mel-spectrogram loss modules."""
import numpy as np
import torch
from parallel_wavegan.bin.preprocess import logmelfilterbank
from parallel_wavegan.losses import MelSpectrogram
def test_me... | 1,117 | 22.787234 | 65 | py |
ParallelWaveGAN | ParallelWaveGAN-master/test/test_hifigan.py | #!/usr/bin/env python3
# Copyright 2021 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Test code for HiFi-GAN modules."""
import logging
import os
import numpy as np
import pytest
import torch
import yaml
from test_parallel_wavegan import make_mutli_reso_stft_loss_args
import parallel_waveg... | 7,403 | 28.854839 | 85 | py |
ParallelWaveGAN | ParallelWaveGAN-master/test/test_melgan.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
import logging
import numpy as np
import pytest
import torch
from test_parallel_wavegan import (
make_discriminator_args,
make_mutli_reso_stft_loss_args,
make_residual_discr... | 9,711 | 31.15894 | 86 | py |
ParallelWaveGAN | ParallelWaveGAN-master/test/test_parallel_wavegan.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
import logging
import numpy as np
import pytest
import torch
from parallel_wavegan.losses import (
DiscriminatorAdversarialLoss,
GeneratorAdversarialLoss,
MultiResolutionST... | 10,809 | 29.111421 | 86 | py |
ParallelWaveGAN | ParallelWaveGAN-master/test/test_style_melgan.py | #!/usr/bin/env python3
# Copyright 2021 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Test code for StyleMelGAN modules."""
import logging
import numpy as np
import pytest
import torch
from test_parallel_wavegan import make_mutli_reso_stft_loss_args
from parallel_wavegan.losses import (
... | 5,057 | 27.576271 | 82 | py |
ParallelWaveGAN | ParallelWaveGAN-master/egs/vctk/vq1/local/decode_from_text.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Decode text with trained VQ-VAE Generator or discrete symbol vocoder."""
import argparse
import logging
import os
import time
import soundfile as sf
import torch
import yaml
from tq... | 5,353 | 29.420455 | 84 | py |
ParallelWaveGAN | ParallelWaveGAN-master/egs/cvss_c/voc1/local/decode_from_text.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Decode text with trained VQ-VAE Generator or discrete symbol vocoder."""
import argparse
import logging
import os
import time
import soundfile as sf
import torch
import yaml
from tq... | 5,294 | 28.416667 | 85 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/functions/vector_quantizer.py | # -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Vector quantization modules.
These codes are modified from https://github.com/ritheshkumar95/pytorch-vqvae.
"""
import torch
from torch.autograd import Function
class VectorQuantization(Function):
... | 3,630 | 30.573913 | 83 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/models/parallel_wavegan.py | # -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Parallel WaveGAN Modules."""
import logging
import math
import numpy as np
import torch
from parallel_wavegan import models
from parallel_wavegan.layers import Conv1d, Conv1d1x1
from parallel_wavegan.lay... | 18,221 | 34.313953 | 88 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/models/vqvae.py | # -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""VQVAE Modules."""
import logging
import torch
import parallel_wavegan.models
from parallel_wavegan.layers import VQCodebook
class VQVAE(torch.nn.Module):
"""VQVAE module."""
def __init__(
... | 6,165 | 34.848837 | 97 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/models/melgan.py | # -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""MelGAN Modules."""
import logging
import numpy as np
import torch
from parallel_wavegan.layers import CausalConv1d, CausalConvTranspose1d, ResidualStack
from parallel_wavegan.utils import read_hdf5
cla... | 18,873 | 34.278505 | 106 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/models/hifigan.py | # -*- coding: utf-8 -*-
"""HiFi-GAN Modules.
This code is based on https://github.com/jik876/hifi-gan.
"""
import copy
import logging
import numpy as np
import torch
import torch.nn.functional as F
from parallel_wavegan.layers import CausalConv1d, CausalConvTranspose1d
from parallel_wavegan.layers import HiFiGANR... | 47,878 | 36.115504 | 108 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/models/style_melgan.py | # Copyright 2021 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""StyleMelGAN Modules."""
import copy
import logging
import numpy as np
import torch
import torch.nn.functional as F
from parallel_wavegan.layers import PQMF, TADEResBlock
from parallel_wavegan.models import MelGANDiscriminator as... | 20,714 | 33.353234 | 103 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/models/tf_models.py | # -*- coding: utf-8 -*-
# Copyright 2020 MINH ANH (@dathudeptrai)
# MIT License (https://opensource.org/licenses/MIT)
"""Tensorflow MelGAN modules complatible with pytorch."""
import numpy as np
import tensorflow as tf
from parallel_wavegan.layers.tf_layers import (
TFConvTranspose1d,
TFReflectionPad1d,
... | 4,922 | 34.417266 | 102 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/models/uhifigan.py | # -*- coding: utf-8 -*-
"""Unet-baed HiFi-GAN Modules.
This code is based on https://github.com/jik876/hifi-gan.
"""
import logging
import numpy as np
import torch
from parallel_wavegan.layers import CausalConv1d, CausalConvTranspose1d
from parallel_wavegan.layers import HiFiGANResidualBlock as ResidualBlock
from... | 14,674 | 36.822165 | 98 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/bin/decode.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Decode with trained Parallel WaveGAN Generator."""
import argparse
import logging
import os
import time
import numpy as np
import soundfile as sf
import torch
import yaml
from tqdm ... | 13,111 | 34.342318 | 87 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/bin/train.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Train Parallel WaveGAN."""
import argparse
import logging
import os
import sys
from collections import defaultdict
import matplotlib
import numpy as np
import soundfile as sf
import... | 59,276 | 37.218569 | 110 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/bin/preprocess.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Perform preprocessing and raw feature extraction."""
import argparse
import logging
import os
import librosa
import numpy as np
import soundfile as sf
import torch
import yaml
from... | 16,741 | 30.410882 | 93 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/distributed/launch.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Distributed process launcher.
This code is modified from https://github.com/pytorch/pytorch/blob/v1.3.0/torch/distributed/launch.py.
"""
import os
import subprocess
import sys
from argparse import REMAINDER, ArgumentParser
def parse_args():
"""Parse arguments."... | 5,262 | 28.903409 | 102 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/datasets/audio_mel_dataset.py | # -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Dataset modules."""
import logging
import os
from multiprocessing import Manager
import numpy as np
from torch.utils.data import Dataset
from parallel_wavegan.utils import find_files, read_hdf5
class A... | 27,965 | 35.894459 | 99 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/datasets/scp_dataset.py | # -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Dataset modules based on kaldi-style scp files."""
import logging
from multiprocessing import Manager
import kaldiio
import numpy as np
from torch.utils.data import Dataset
from parallel_wavegan.utils im... | 11,431 | 31.202817 | 101 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/layers/residual_stack.py | # -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Residual stack module in MelGAN."""
import torch
from parallel_wavegan.layers import CausalConv1d
class ResidualStack(torch.nn.Module):
"""Residual stack module introduced in MelGAN."""
def __i... | 3,073 | 34.744186 | 88 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/layers/pqmf.py | # -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Pseudo QMF modules."""
import numpy as np
import torch
import torch.nn.functional as F
from scipy.signal import kaiser
def design_prototype_filter(taps=62, cutoff_ratio=0.142, beta=9.0):
"""Design pr... | 4,907 | 31.72 | 103 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/layers/tf_layers.py | # -*- coding: utf-8 -*-
# Copyright 2020 MINH ANH (@dathudeptrai)
# MIT License (https://opensource.org/licenses/MIT)
"""Tensorflow Layer modules complatible with pytorch."""
import tensorflow as tf
class TFReflectionPad1d(tf.keras.layers.Layer):
"""Tensorflow ReflectionPad1d module."""
def __init__(self... | 3,916 | 26.780142 | 88 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/layers/duration_predictor.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# 2023 Jiatong Shi
# Adapted from ESPnet fastspeech duration predictor
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Duration predictor related modules."""
import torch
from parallel_wavegan.layers.layer_norm i... | 3,820 | 31.65812 | 88 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/layers/tade_res_block.py | # Copyright 2021 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""StyleMelGAN's TADEResBlock Modules."""
from functools import partial
import torch
class TADELayer(torch.nn.Module):
"""TADE Layer module."""
def __init__(
self,
in_channels=64,
aux_channels=80,
... | 4,805 | 28.850932 | 88 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/layers/sine.py | """Sing generator."""
import numpy as np
import torch
class SineGen(torch.nn.Module):
"""Definition of sine generator."""
def __init__(
self,
samp_rate,
harmonic_num=0,
sine_amp=0.1,
noise_std=0.003,
voiced_threshold=0,
flag_for_pulse=False,
):
... | 5,554 | 36.789116 | 103 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/layers/residual_block.py | # -*- coding: utf-8 -*-
"""Residual block modules.
References:
- https://github.com/r9y9/wavenet_vocoder
- https://github.com/jik876/hifi-gan
"""
import math
import torch
import torch.nn.functional as F
from parallel_wavegan.layers.causal_conv import CausalConv1d
class Conv1d(torch.nn.Conv1d):
"""Co... | 8,832 | 33.104247 | 102 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/layers/variance_predictor.py | #!/usr/bin/env python3
# Copyright 2020 Tomoki Hayashi
# 2023 Jiatong Shi
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Variance predictor related modules."""
import torch
from typeguard import check_argument_types
from parallel_wavegan.layers.layer_norm import LayerNorm
class VarianceP... | 2,637 | 28.977273 | 86 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/layers/causal_conv.py | # -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Causal convolusion layer modules."""
import torch
class CausalConv1d(torch.nn.Module):
"""CausalConv1d module with customized initialization."""
def __init__(
self,
in_channels,... | 2,162 | 26.379747 | 85 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/layers/layer_norm.py | """LayerNorm for specific dimensions.
Adapted from ESPnet Transformer LayerNorm.
"""
import torch
class LayerNorm(torch.nn.LayerNorm):
"""Layer normalization module.
Args:
nout (int): Output dim size.
dim (int): Dimension to be normalized.
"""
def __init__(self, nout, dim=-1):
... | 870 | 20.243902 | 56 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/layers/vector_quantize_codebook.py | # -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Vector quantize codebook modules.
This code is modified from https://github.com/ritheshkumar95/pytorch-vqvae.
"""
import torch
from parallel_wavegan.functions import vector_quantize, vector_quantize_str... | 2,132 | 28.219178 | 88 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/layers/upsample.py | # -*- coding: utf-8 -*-
"""Upsampling module.
This code is modified from https://github.com/r9y9/wavenet_vocoder.
"""
import numpy as np
import torch
import torch.nn.functional as F
from parallel_wavegan.layers import Conv1d
class Stretch2d(torch.nn.Module):
"""Stretch2d module."""
def __init__(self, x_... | 6,489 | 32.282051 | 92 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/layers/length_regulator.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# 2023 Jiatong Shi
# Adapated from ESPnet Fastspeech LengthRegulator
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Length regulator related modules."""
import logging
import torch
def pad_list(xs, pad_value)... | 2,984 | 29.151515 | 87 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/optimizers/radam.py | # -*- coding: utf-8 -*-
"""RAdam optimizer.
This code is drived from https://github.com/LiyuanLucasLiu/RAdam.
"""
import math
import torch
from torch.optim.optimizer import Optimizer
class RAdam(Optimizer):
"""Rectified Adam optimizer."""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, ... | 3,632 | 35.33 | 87 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/optimizers/__init__.py | from torch.optim import * # NOQA
from .radam import * # NOQA
| 64 | 15.25 | 33 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/utils/utils.py | # -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Utility functions."""
import fnmatch
import logging
import os
import re
import sys
import tarfile
from distutils.version import LooseVersion
import h5py
import numpy as np
import torch
import yaml
from fi... | 14,086 | 32.381517 | 102 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/losses/stft_loss.py | # -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""STFT-based Loss modules."""
from distutils.version import LooseVersion
import torch
import torch.nn.functional as F
is_pytorch_17plus = LooseVersion(torch.__version__) >= LooseVersion("1.7")
def stft(x... | 5,471 | 31 | 97 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/losses/duration_prediction_loss.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2019 Tomoki Hayashi
# 2023 Jiatong SHi
# Adapted from espnet/espnet/net/pytorch_backend/duration_predictor.py
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""Duration predictor related modules."""
import torch
class DurationPredict... | 1,534 | 27.962264 | 88 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/losses/adversarial_loss.py | # -*- coding: utf-8 -*-
# Copyright 2021 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Adversarial loss modules."""
import torch
import torch.nn.functional as F
class GeneratorAdversarialLoss(torch.nn.Module):
"""Generator adversarial loss module."""
def __init__(
self,
... | 4,133 | 32.33871 | 84 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/losses/mel_loss.py | # Copyright 2021 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Mel-spectrogram loss modules."""
from distutils.version import LooseVersion
import librosa
import torch
import torch.nn.functional as F
is_pytorch_17plus = LooseVersion(torch.__version__) >= LooseVersion("1.7")
class MelSpectr... | 4,625 | 26.86747 | 81 | py |
ParallelWaveGAN | ParallelWaveGAN-master/parallel_wavegan/losses/feat_match_loss.py | # -*- coding: utf-8 -*-
# Copyright 2021 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Feature matching loss modules."""
import torch
import torch.nn.functional as F
class FeatureMatchLoss(torch.nn.Module):
"""Feature matching loss module."""
def __init__(
self,
av... | 1,746 | 30.763636 | 76 | py |
BanditZoo | BanditZoo-main/docs/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 2,160 | 32.765625 | 79 | py |
AtLoc | AtLoc-master/eval.py | import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3,4,5,6,7"
import torch
import os.path as osp
import numpy as np
import matplotlib
import sys
DISPLAY = 'DISPLAY' in os.environ
if not DISPLAY:
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from tools.opt... | 4,997 | 35.75 | 143 | py |
AtLoc | AtLoc-master/train.py | import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3,4,5,6,7"
import torch
import sys
import time
import os.path as osp
import numpy as np
from tensorboardX import SummaryWriter
from tools.options import Options
from network.atloc import AtLoc, AtLocPlus
from torchvis... | 7,019 | 42.333333 | 187 | py |
AtLoc | AtLoc-master/tools/saliency_map.py | import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3,4,5,6,7"
import torch
import os.path as osp
import numpy as np
import matplotlib
import sys
import cv2
from tools.options import Options
DISPLAY = 'DISPLAY' in os.environ
if not DISPLAY:
matplotlib.use('Agg')
im... | 3,971 | 30.52381 | 143 | py |
AtLoc | AtLoc-master/tools/utils.py | import os
import torch
from torch import nn
import scipy.linalg as slin
import math
import transforms3d.quaternions as txq
import transforms3d.euler as txe
import numpy as np
import sys
from torch.nn import Module
from torch.autograd import Variable
from torch.nn.functional import pad
from torchvision.datasets.folder ... | 6,007 | 31.652174 | 142 | py |
AtLoc | AtLoc-master/tools/options.py | import argparse
import os
from tools import utils
import torch
class Options():
def __init__(self):
self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
def initialize(self):
# base options
self.parser.add_argument('--data_dir', type=str, defaul... | 3,916 | 49.217949 | 126 | py |
AtLoc | AtLoc-master/network/att.py | import torch
from torch import nn
from torch.nn import functional as F
class AttentionBlock(nn.Module):
def __init__(self, in_channels):
super(AttentionBlock, self).__init__()
self.g = nn.Linear(in_channels, in_channels // 8)
self.theta = nn.Linear(in_channels, in_channels // 8)
se... | 996 | 31.16129 | 70 | py |
AtLoc | AtLoc-master/network/atloc.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init
from network.att import AttentionBlock
class FourDirectionalLSTM(nn.Module):
def __init__(self, seq_size, origin_feat_size, hidden_size):
super(FourDirectionalLSTM, self).__init__()
self.feat_size = origin_feat_... | 3,584 | 37.138298 | 109 | py |
AtLoc | AtLoc-master/data/dataset_mean.py | import os.path as osp
import numpy as np
from data.dataloaders import RobotCar, SevenScenes
from torchvision import transforms
from torch.utils.data import DataLoader
from tools.options import Options
opt = Options().parse()
data_transform = transforms.Compose([
transforms.Resize(opt.cropsize),
transforms.Ra... | 1,592 | 29.634615 | 104 | py |
AtLoc | AtLoc-master/data/dataloaders.py | import os
import torch
import numpy as np
import pickle
import os.path as osp
from data.robotcar_sdk.interpolate_poses import interpolate_vo_poses, interpolate_ins_poses
from data.robotcar_sdk.camera_model import CameraModel
from data.robotcar_sdk.image import load_image as robotcar_loader
from tools.utils import proc... | 12,124 | 39.416667 | 160 | py |
AtLoc | AtLoc-master/data/process_robotcar.py | import os.path as osp
import numpy as np
from PIL import Image
from data.dataloaders import RobotCar
from torch.utils.data import DataLoader
from torchvision import transforms
from tools.options import Options
opt = Options().parse()
if opt.val:
print('processing VAL data using {:d} cores'.format(opt.nThreads))
... | 1,998 | 35.345455 | 112 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_ae/batch_entropy.py | import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
import scipy
import scipy.stats
import random
def batch_entropy(x):
""" Estimate the differential entropy by assuming a gaussian distribution of
values for different samples of a mini-batch.
"""
if(x.shape[0] <= 1... | 1,965 | 31.766667 | 113 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_ae/dataloader.py | # coding: utf-8
import numpy as np
import torch
import torch.utils.data
import torchvision
import torchvision.models
from torchvision import transforms
from torchvision import datasets
def get_loader(dataset, batch_size, num_workers):
if dataset == "mnist":
return get_mnist_loader(batch_size, num_worke... | 5,910 | 31.300546 | 95 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_ae/utils.py | import enum
import os
import logging
import io
from random import random
import warnings
from matplotlib.colors import ListedColormap
import numpy as np
import torch
import torch.nn as nn
from torchvision.utils import make_grid
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import matplotlib.p... | 6,717 | 29.675799 | 108 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_ae/create_loss_surface.py | from __future__ import print_function
import argparse
from email.mime import base
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from torchmetrics.functional import ssim as ... | 8,623 | 32.952756 | 136 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_ae/autoencoder.py | # coding: utf-8
from typing import Dict, List, NewType, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
#####################
###### MODULES ######
#####################
class Encoder(nn.Module):
def __init__(
self,
width: int = 256,
... | 10,963 | 26.138614 | 193 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_ae/train.py | #!/usr/bin/env python
# coding: utf-8
from configparser import ParsingError
from enum import auto
from json import encoder
import os
import time
import importlib
import json
from collections import OrderedDict
import logging
import argparse
import numpy as np
import random
import wandb
import torch
import torch.nn as... | 12,920 | 30.36165 | 136 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_resnet/batch_entropy.py | import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
import scipy
import scipy.stats
import random
def batch_entropy(x):
""" Estimate the differential entropy by assuming a gaussian distribution of
values for different samples of a mini-batch.
"""
if(x.shape[0] <= 1... | 2,157 | 31.208955 | 113 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_resnet/resnet.py | # coding: utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
def initialize_weights(module):
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight.data, mode='fan_out')
elif isinstance(module, nn.BatchNorm2d):
module.weight.data.fill_(1)
module.... | 8,024 | 29.865385 | 78 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_resnet/dataloader.py | # coding: utf-8
import numpy as np
import torch
import torch.utils.data
import torchvision
import torchvision.models
from torchvision import transforms
from torchvision import datasets
def get_loader(dataset, batch_size, num_workers):
if dataset == "mnist":
return get_mnist_loader(batch_size, num_worke... | 8,138 | 32.9125 | 104 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_resnet/train.py | #!/usr/bin/env python
# coding: utf-8
# From https://github.dev/hysts/pytorch_resnet/blob/master/main.py
from email.policy import default
import os
import time
import importlib
import json
from collections import OrderedDict
import logging
import argparse
import numpy as np
import random
import wandb
import torch
im... | 10,646 | 29.682997 | 136 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_fnn/batch_entropy.py | import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
import scipy
import scipy.stats
import random
def batch_entropy(x):
""" Estimate the differential entropy by assuming a gaussian distribution of
values for different samples of a mini-batch.
"""
if(x.shape[0] <= 1... | 2,157 | 31.208955 | 113 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_fnn/train.py | from __future__ import print_function
import argparse
import torch
import copy
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch_optimizer as optim_special
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from batch_entropy import batc... | 8,759 | 38.638009 | 125 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_neuron_dist/batch_entropy.py | import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
import scipy
import scipy.stats
import random
def batch_entropy(x):
""" Estimate the differential entropy by assuming a gaussian distribution of
values for different samples of a mini-batch.
"""
if(x.shape[0] <= 1... | 2,157 | 31.208955 | 113 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_neuron_dist/train.py | from __future__ import print_function
import argparse
import torch
import copy
import torch.nn as nn
from random import randint
import torch.nn.functional as F
import torch.optim as optim
import torch_optimizer as optim_special
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
fro... | 8,566 | 36.08658 | 121 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_info_flow/batch_entropy.py | import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
import scipy
import scipy.stats
import random
def batch_entropy(x):
""" Estimate the differential entropy by assuming a gaussian distribution of
values for different samples of a mini-batch.
"""
if(x.shape[0] <= 1... | 2,157 | 31.208955 | 113 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_info_flow/train.py | from __future__ import print_function
import argparse
import torch
import copy
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch_optimizer as optim_special
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from batch_entropy import batc... | 7,561 | 38.181347 | 124 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_normalization/batch_entropy.py | import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
import scipy
import scipy.stats
import random
def batch_entropy(x):
""" Estimate the differential entropy by assuming a gaussian distribution of
values for different samples of a mini-batch.
"""
if(x.shape[0] <= 1... | 2,157 | 31.208955 | 113 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_normalization/train.py | from __future__ import print_function
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import argparse
from typing import Dict
import torch
import copy
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.weight_norm import weight_norm
import torch.optim as optim
import torch_optimizer as optim... | 9,979 | 38.760956 | 177 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_deep_vanilla_fnn/batch_entropy.py | import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
import scipy
import scipy.stats
import random
def batch_entropy(x):
""" Estimate the differential entropy by assuming a gaussian distribution of
values for different samples of a mini-batch.
"""
if(x.shape[0] <= 1... | 2,157 | 31.208955 | 113 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_deep_vanilla_fnn/train.py | from __future__ import print_function
import argparse
import torch
import copy
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch_optimizer as optim_special
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from batch_entropy import batc... | 9,103 | 39.283186 | 159 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_transformer/batch_entropy.py | import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
import scipy
import scipy.stats
import random
def batch_entropy(x):
""" Estimate the differential entropy by assuming a gaussian distribution of
values for different samples of a mini-batch.
"""
if(x.shape[0] <= 1... | 2,157 | 31.208955 | 113 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_transformer/run_glue.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team. 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/LI... | 27,224 | 40.063348 | 145 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_transformer/models/bert/modeling_bert.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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 cop... | 80,584 | 41.682733 | 213 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_loss_surface/batch_entropy.py | import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
import scipy
import scipy.stats
import random
def batch_entropy(x):
""" Estimate the differential entropy by assuming a gaussian distribution of
values for different samples of a mini-batch.
"""
if(x.shape[0] <= 1... | 2,157 | 31.208955 | 113 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_loss_surface/utils.py | import torch
import numpy as np
import copy
# Thanks to https://gitlab.com/qbeer/loss-landscape/-/blob/main/loss_landscape/landscape_utils.py
def init_directions(model):
noises = []
n_params = 0
for name, param in model.named_parameters():
delta = torch.normal(.0, 1, size=param.size())
nu... | 1,013 | 23.731707 | 97 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_loss_surface/create_loss_surface.py | from __future__ import print_function
import argparse
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR
from batch_entropy import batch_entropy, LBELoss, CELoss
import time
imp... | 7,982 | 35.122172 | 125 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_deep_vanilla_cnn/batch_entropy.py | import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
import scipy
import scipy.stats
import random
def batch_entropy(x):
""" Estimate the differential entropy by assuming a gaussian distribution of
values for different samples of a mini-batch.
"""
if(x.shape[0] <= 1... | 2,157 | 31.208955 | 113 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_deep_vanilla_cnn/delta_orth.py | import math
import torch
""" The implementation below corresponds to Tensorflow implementation.
Refer https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/init_ops.py for details.
From https://github.com/yl-1993/ConvDeltaOrthogonal-Init/
We tried this version as well as the version ... | 2,997 | 35.120482 | 109 | py |
layerwise-batch-entropy | layerwise-batch-entropy-main/experiment_deep_vanilla_cnn/train.py | from __future__ import print_function
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from delta_orth import init_delta_orthogonal_1, init_delta_orthogonal_2
from torchvision import datasets, transforms... | 11,081 | 38.72043 | 159 | py |
swav | swav-main/eval_linear.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import os
import time
from logging import getLogger
import torch
import torch.nn as nn
import torch.nn.p... | 13,429 | 33.260204 | 104 | py |
swav | swav-main/main_deepclusterv2.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import math
import os
import shutil
import time
from logging import getLogger
import numpy as np
import ... | 17,265 | 39.625882 | 123 | py |
swav | swav-main/hubconf.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
from torchvision.models.resnet import resnet50 as _resnet50
from src.resnet50 import resnet50w2 as _resnet50... | 2,830 | 31.54023 | 96 | py |
swav | swav-main/eval_semisup.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import os
import time
from logging import getLogger
import urllib
import torch
import torch.nn as nn
imp... | 12,149 | 33.615385 | 165 | py |
swav | swav-main/main_swav.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import math
import os
import shutil
import time
from logging import getLogger
import numpy as np
import t... | 14,998 | 38.367454 | 123 | py |
swav | swav-main/src/multicropdataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import random
from logging import getLogger
from PIL import ImageFilter
import numpy as np
import torchvision.datasets as... | 3,029 | 30.894737 | 76 | py |
swav | swav-main/src/utils.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
from logging import getLogger
import pickle
import os
import numpy as np
import torch
from .logger impo... | 5,506 | 26.954315 | 102 | py |
swav | swav-main/src/resnet50.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convo... | 11,025 | 30.146893 | 106 | py |
GPSKet | GPSKet-master/GPSKet/__init__.py | # enable x64 on jax
# must be done at 0 startup.
from jax.config import config
config.update("jax_enable_x64", True)
del config
__all__ = [
"models",
"nn",
"operator",
"optimizer",
"sampler",
"hilbert",
"driver",
"datasets",
"vqs"
]
from . import models
from . import nn
from . imp... | 462 | 15.535714 | 37 | py |
GPSKet | GPSKet-master/GPSKet/nn/initializers.py | import jax
import jax.numpy as jnp
from jax import dtypes
def normal(sigma=0.1, dtype=jnp.float_):
"""
Constructs an initializer for a qGPS model.
Real parameters are normally distributed around 1.0, while complex parameters have unit length
and have normally distributed phases around 0.
Args:
... | 1,960 | 34.654545 | 102 | py |
GPSKet | GPSKet-master/GPSKet/nn/causal_conv.py | import numpy as np
import jax.numpy as jnp
from flax import linen as nn
from jax.nn.initializers import lecun_normal, zeros
from netket.utils.types import Callable, DType, Array, NNInitFunc
default_kernel_init = lecun_normal()
# Part of the code was inspired by the tutorial on autoregressive image modelling at
# htt... | 4,957 | 31.618421 | 120 | py |
GPSKet | GPSKet-master/GPSKet/sampler/autoreg.py | import jax
import numpy as np
from jax import numpy as jnp
from functools import partial
from netket.sampler import Sampler, SamplerState
from netket.utils import struct, HashableArray
from netket.utils.types import PRNGKeyT
def batch_choice(key, a, p):
"""
Batched version of `jax.random.choice`.
Attribu... | 5,605 | 31.593023 | 158 | py |
GPSKet | GPSKet-master/GPSKet/sampler/metropolis_fast.py | import jax
import jax.numpy as jnp
from netket.utils import struct
from netket.sampler.metropolis import MetropolisSampler, MetropolisRule
from netket.sampler.rules.exchange import compute_clusters
class MetropolisRuleWithUpdate(MetropolisRule):
pass
@struct.dataclass
class MetropolisFastSampler(MetropolisSampler... | 4,198 | 40.574257 | 169 | py |
GPSKet | GPSKet-master/GPSKet/sampler/rules/exchange_with_update.py | import jax
import jax.numpy as jnp
from flax import struct
from netket.sampler.rules.exchange import ExchangeRule_
@struct.dataclass
class ExchangeRuleWithUpdate(ExchangeRule_):
"""
Exchange Update rule which also returns the list of affected sites which is required for the fast metropolis sampler
"""
... | 1,055 | 33.064516 | 120 | py |
GPSKet | GPSKet-master/GPSKet/sampler/rules/fermionic_hopping.py | import jax
import jax.numpy as jnp
from flax import struct
from netket.sampler.metropolis import MetropolisRule
from typing import Optional
from netket.utils.types import Array
def transition_function(key, sample, hop_probability, transition_probs=None, return_updates=False):
def apply_electron_hop(samp, key):
... | 3,703 | 49.739726 | 177 | py |
GPSKet | GPSKet-master/GPSKet/operator/hamiltonian/ab_initio_sparse.py | import numpy as np
import netket as nk
import jax.numpy as jnp
import jax
from numba import jit
import netket.jax as nkjax
from typing import Optional
from functools import partial
from GPSKet.operator.hamiltonian.ab_initio import AbInitioHamiltonianOnTheFly, get_parity_multiplicator_hop
from netket.utils.types imp... | 15,532 | 54.27758 | 170 | py |
GPSKet | GPSKet-master/GPSKet/operator/hamiltonian/J1J2.py | import jax
import jax.numpy as jnp
import netket as nk
import netket.jax as nkjax
from netket.vqs.mc.mc_state.state import MCState
from GPSKet.models import qGPS
import GPSKet.vqs.mc.mc_state.expect
from typing import Optional
# dummy class used if the local energy should be evaluated on the fly (allowing for fast up... | 4,803 | 47.525253 | 159 | py |
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