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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fvcore | fvcore-main/fvcore/nn/smooth_l1_loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
def smooth_l1_loss(
input: torch.Tensor, target: torch.Tensor, beta: float, reduction: str = "none"
) -> torch.Tensor:
"""
Smooth L1 loss defined in the Fast R-CNN paper as:
::
| 0.5 * x ** 2 / ... | 3,039 | 39.533333 | 85 | py |
fvcore | fvcore-main/fvcore/nn/weight_init.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch.nn as nn
def c2_xavier_fill(module: nn.Module) -> None:
"""
Initialize `module.weight` using the "XavierFill" implemented in Caffe2.
Also initializes `module.bias` to 0.
Args:
module (torch.nn.Module): modul... | 967 | 29.25 | 79 | py |
fvcore | fvcore-main/fvcore/nn/flop_count.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# pyre-ignore-all-errors[2,33]
from collections import defaultdict
from typing import Any, Counter, DefaultDict, Dict, Optional, Tuple, Union
import torch.nn as nn
from torch import Tensor
from .jit_analysis import JitModelAnalysis
from .jit_han... | 5,443 | 35.293333 | 85 | py |
fvcore | fvcore-main/fvcore/nn/parameter_count.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import typing
from collections import defaultdict
import tabulate
from torch import nn
def parameter_count(model: nn.Module) -> typing.DefaultDict[str, int]:
"""
Count parameters of a model and its submodules.
Args:
model: a... | 4,891 | 39.429752 | 82 | py |
fvcore | fvcore-main/fvcore/nn/squeeze_excitation.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from typing import Optional
import torch
import torch.nn as nn
class SqueezeExcitation(nn.Module):
"""
Generic 2d/3d extension of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv... | 5,721 | 31.511364 | 96 | py |
fvcore | fvcore-main/fvcore/nn/jit_handles.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# pyre-ignore-all-errors[2,3,16,33,6,23]
# NOTE: most Any type in this file should be torch._C.Value - which was not yet annotated.
# pyre also doesn't work well with many Optional in this file
import typing
from collections import Counter, Ordered... | 9,472 | 32.592199 | 94 | py |
fvcore | fvcore-main/fvcore/nn/precise_bn.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# pyre-ignore-all-errors[2,6,16]
import itertools
import logging
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
import torch
import tqdm
from torch import nn
# pyre-fixme[9]: BN_MODULE_TYPES has type `Tuple[Type[Module]]`; ... | 8,003 | 36.754717 | 87 | py |
fvcore | fvcore-main/fvcore/nn/focal_loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch.nn import functional as F
def sigmoid_focal_loss(
inputs: torch.Tensor,
targets: torch.Tensor,
alpha: float = -1,
gamma: float = 2,
reduction: str = "none",
) -> torch.Tensor:
"""
Loss used in ... | 3,475 | 33.76 | 87 | py |
fvcore | fvcore-main/fvcore/nn/distributed.py | from typing import List, Tuple
import torch
import torch.distributed as dist
from torch.autograd.function import Function
# pyre-ignore-all-errors[2,14,16]
class _AllReduce(Function):
@staticmethod
def forward(ctx, input: torch.Tensor) -> torch.Tensor:
input_list = [torch.zeros_like(input) for k in... | 1,865 | 28.15625 | 87 | py |
fvcore | fvcore-main/fvcore/nn/jit_analysis.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# pyre-ignore-all-errors[2,33]
import logging
import typing
import warnings
from collections import Counter
from copy import copy
from dataclasses import dataclass
from numbers import Number
from typing import Any, Dict, Iterator, List, Optional, S... | 24,623 | 36.709035 | 91 | py |
fvcore | fvcore-main/fvcore/nn/print_model_statistics.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import defaultdict
from typing import Any, Dict, Iterable, List, Optional, Set, Tuple
import tabulate
import torch
from torch import nn
from .activation_count import ActivationCountAnalysis
from .flop_count import FlopCountAnaly... | 25,754 | 36.986726 | 95 | py |
fvcore | fvcore-main/fvcore/common/checkpoint.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# pyre-ignore-all-errors[2,3,58]
import logging
import os
from collections import defaultdict
from typing import Any, cast, Dict, IO, Iterable, List, NamedTuple, Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
from iopath.co... | 23,091 | 37.810084 | 94 | py |
fvcore | fvcore-main/fvcore/common/file_io.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import os
import tempfile
from typing import Optional
from iopath.common.file_io import ( # noqa, unused import required by some deps
file_lock,
HTTPURLHandler,
LazyPath,
NativePathHandler,
OneDrivePathHandler,... | 2,140 | 30.955224 | 82 | py |
fvcore | fvcore-main/fvcore/transforms/transform.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import inspect
import pprint
from abc import ABCMeta, abstractmethod
from typing import Any, Callable, List, Optional, TypeVar
import numpy as np
import torch
from .transform_util import to_float_tensor, to_numpy
__all__ = [
"BlendTransfor... | 29,569 | 32.988506 | 94 | py |
fvcore | fvcore-main/fvcore/transforms/transform_util.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import numpy as np
import torch
# pyre-ignore-all-errors
def to_float_tensor(numpy_array: np.ndarray) -> torch.Tensor:
"""
Convert the numpy array to torch float tensor with dimension of NxCxHxW.
Pytorch is not fully supporting uint8... | 3,307 | 37.465116 | 78 | py |
DRNE | DRNE-master/src/utils.py | import numpy as np
import operator
import tensorflow as tf
import scipy
import networkx as nx
import sys, time, os
def load_from_wv_format(filename):
with open(filename) as f:
l = f.readline().split()
total_num, embedding_size = int(l[0]), int(l[1])
ls = list(map(lambda x: x.strip().split()... | 6,233 | 37.720497 | 209 | py |
vehicle-rear | vehicle-rear-master/config_1.py | # -*- coding: utf-8 -*-
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import tensorflow as tf
from keras import backend as K
from keras.optimizers import Adam
from keras.layers import Lambda
import albumentations as albu
from keras_metrics import *
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
sess ... | 2,466 | 24.968421 | 61 | py |
vehicle-rear | vehicle-rear-master/siamese_three_stream.py | import string
import pandas as pd
from keras.optimizers import Adam
from keras.utils import np_utils
import numpy as np
from config import *
import json
from keras import backend as K
from keras.layers import Dense, Dropout
from keras.models import Model, load_model
from sys import argv
from custom_layers import *
from... | 6,214 | 34.924855 | 127 | py |
vehicle-rear | vehicle-rear-master/siamese_shape_stream.py | from keras.optimizers import Adam
from keras.utils import np_utils
import numpy as np
from config import *
import json
from keras import backend as K
from keras.layers import Dense, Dropout
from keras.models import Model, load_model
from sys import argv
from custom_layers import *
from collections import Counter
import... | 4,647 | 33.42963 | 114 | py |
vehicle-rear | vehicle-rear-master/siamese_two_stream.py | from keras.optimizers import Adam
from keras.utils import np_utils
import numpy as np
from config import *
import json
from keras import backend as K
from keras.layers import Dense, Dropout
from keras.models import Model, load_model
from sys import argv
from custom_layers import *
from collections import Counter
import... | 4,247 | 37.618182 | 106 | py |
vehicle-rear | vehicle-rear-master/siamese_plate_stream.py | from keras.optimizers import Adam
from keras.utils import np_utils
import numpy as np
from config import *
import json
from keras import backend as K
from keras.layers import Dense, Dropout
from keras.models import Model, load_model
from sys import argv
from custom_layers import *
from collections import Counter
import... | 3,725 | 36.636364 | 106 | py |
vehicle-rear | vehicle-rear-master/siamese_temporal2.py | from keras.optimizers import Adam
from keras.utils import np_utils
import numpy as np
from config import *
import json
from keras import backend as K
from keras.layers import Dense, Dropout
from keras.models import Model, load_model
from sys import argv
from custom_layers import *
from collections import Counter
import... | 5,988 | 33.618497 | 130 | py |
vehicle-rear | vehicle-rear-master/config.py | # -*- coding: utf-8 -*-
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import tensorflow as tf
from keras import backend as K
from keras.optimizers import Adam
from keras.layers import Lambda
import albumentations as albu
from keras_metrics import *
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
sess ... | 2,484 | 25.157895 | 61 | py |
vehicle-rear | vehicle-rear-master/siamese_temporal3.py | from keras.optimizers import Adam
from keras.utils import np_utils
import numpy as np
from config import *
import json
from keras import backend as K
from keras.layers import Dense, Dropout
from keras.models import Model, load_model
from sys import argv
from custom_layers import *
from collections import Counter
import... | 5,988 | 33.618497 | 130 | py |
vehicle-rear | vehicle-rear-master/keras_metrics.py | def mae(y_true, y_pred):
from keras import backend as K
return K.mean(K.abs(y_pred - y_true), axis=-1)
def mse(y_true, y_pred):
from keras import backend as K
return K.mean(K.square(y_pred - y_true), axis=-1)
def rmae(y_true, y_pred):
from keras import backend as K
return K.sqrt(K.mean(K.abs... | 2,891 | 27.352941 | 79 | py |
vehicle-rear | vehicle-rear-master/custom_layers.py | from keras.models import Model
from keras.applications import resnet50, vgg16
from keras.utils import np_utils
import numpy as np
from keras.preprocessing import image
from sklearn import metrics
from keras.layers import *
from config import batch_size, image_size_h_p, image_size_w_p, image_size_h_c, image_size_w_c, nc... | 23,220 | 35.85873 | 134 | py |
vehicle-rear | vehicle-rear-master/siamese_two_stream_ocr.py | from keras.optimizers import Adam
from keras.utils import np_utils
import numpy as np
from config import *
import json
from keras import backend as K
from keras.layers import Dense, Dropout
from keras.models import Model, load_model
from sys import argv
from custom_layers import *
from collections import Counter
import... | 5,677 | 33.412121 | 140 | py |
vehicle-rear | vehicle-rear-master/siamese_shape_stream1.py | from keras.optimizers import Adam
from keras.utils import np_utils
import numpy as np
from config_1 import *
import json
from keras import backend as K
from keras.layers import Dense, Dropout
from keras.models import Model, load_model
from sys import argv
from custom_layers import *
from collections import Counter
impo... | 4,649 | 33.444444 | 114 | py |
cnslab_fmri | cnslab_fmri-master/run_training_save_acc.py | import torch
import torchvision
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from net.st_gcn import Model
import random
from scipy import stats
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
###### **model parameters**
W = ... | 6,838 | 42.56051 | 143 | py |
cnslab_fmri | cnslab_fmri-master/run_training_lstm.py | import torch
import torchvision
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from net.fmri_lstm import fMRI_LSTM
import random
from scipy import stats
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
###### **model parameters... | 4,279 | 35.271186 | 108 | py |
cnslab_fmri | cnslab_fmri-master/preprocessing.py | import numpy as np
import keras
from keras.layers import *
from keras.models import *
from keras.optimizers import *
import keras.backend as K
from scipy import stats
from sklearn.model_selection import StratifiedKFold
if __name__ == "__main__":
demo = np.loadtxt('demo.txt');
L = 1000
S = 0
data... | 2,346 | 27.621951 | 111 | py |
cnslab_fmri | cnslab_fmri-master/run_training_edge_imp.py | import torch
import torchvision
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from net.st_gcn import Model
import random
from scipy import stats
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
###### **model parameters**
W ... | 3,193 | 35.712644 | 114 | py |
cnslab_fmri | cnslab_fmri-master/run_training_save_acc_lstm.py | import torch
import torchvision
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from net.fmri_lstm import fMRI_LSTM
import random
from scipy import stats
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
###### **model parameters... | 6,294 | 42.413793 | 126 | py |
cnslab_fmri | cnslab_fmri-master/run_training.py | import torch
import torchvision
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from net.st_gcn_lstm import Model
import random
from scipy import stats
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
###### **model parameters... | 4,503 | 35.617886 | 107 | py |
cnslab_fmri | cnslab_fmri-master/run_training_st_gcn_lstm.py | import torch
import torchvision
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from net.st_gcn_lstm import Model
import random
from scipy import stats
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
###### **model parameters... | 4,537 | 35.596774 | 107 | py |
cnslab_fmri | cnslab_fmri-master/mlp_baseline.py | import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class mlp(nn.Module):
def __init__(self, input_dim):
... | 2,983 | 37.25641 | 99 | py |
cnslab_fmri | cnslab_fmri-master/run_training_save_acc_ts.py | import torch
import torchvision
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from net.st_gcn import Model
import random
from scipy import stats
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
###### **model parameters**
W = ... | 6,873 | 42.506329 | 140 | py |
cnslab_fmri | cnslab_fmri-master/net/fmri_lstm.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
#import matplotlib.pyplot as plt
import csv
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
class fMRI_LSTM(nn.Module):
def __init__(self, hidden_dim, input_dim, target_size, batc... | 5,226 | 45.256637 | 134 | py |
cnslab_fmri | cnslab_fmri-master/net/st_gcn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from net.utils.tgcn import ConvTemporalGraphical
from net.utils.graph import Graph
import numpy as np
import pdb
class Model(nn.Module):
r"""Spatial temporal graph convolutional networks.
Args:
in_... | 8,221 | 35.705357 | 142 | py |
cnslab_fmri | cnslab_fmri-master/net/st_gcn_lstm.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from net.utils.tgcn import ConvTemporalGraphical
from net.fmri_lstm import fMRI_LSTM
from net.utils.graph import Graph
import numpy as np
from scipy import stats
import pdb
class Model(nn.Module):
r"""Spatial ... | 8,253 | 33.827004 | 110 | py |
cnslab_fmri | cnslab_fmri-master/net/utils/tgcn.py | # The based unit of graph convolutional networks.
import torch
import torch.nn as nn
class ConvTemporalGraphical(nn.Module):
r"""The basic module for applying a graph convolution.
Args:
in_channels (int): Number of channels in the input sequence data
out_channels (int): Number of channels pr... | 2,401 | 34.850746 | 89 | py |
LOTUS | LOTUS-main/setup.py | from setuptools import setup, find_packages
from setuptools.command.install import install as _install
import os, sys, re
import codecs
NAME = "lotus_nlte"
PACKAGES = find_packages(where='src')
META_PATH = os.path.join("src", NAME, "__init__.py")
EXTRA_REQUIRE = {
"advanced-interp": ["rbf", "torch", "gpytorch"],
... | 2,384 | 29.189873 | 79 | py |
LOTUS | LOTUS-main/src/lotus_nlte/optimize.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 18 20:37:47 2020
@author: yangyangli
"""
import numpy as np
import pandas as pd
from numdifftools import Jacobian, Hessian
from scipy.optimize import differential_evolution, shgo
#from gcog import GCOG, MultiGCOG
from sympy import Array
from sympy.... | 26,610 | 40.841195 | 176 | py |
LOTUS | LOTUS-main/src/lotus_nlte/gcogs/multigcogs.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 9 16:27:46 2021
@author: yangyangli
"""
import os, glob
import numpy as np
import pandas as pd
import h5py
import joblib
import tarfile
from astropy.stats.info_theory import bayesian_info_criterion_lsq
from .gcog import SingleGCOG
from .utils im... | 27,598 | 42.73851 | 191 | py |
LOTUS | LOTUS-main/src/lotus_nlte/interpolation/gp_interp.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Aug 13 21:53:54 2020
@author: yangyangli
"""
import numpy as np
import torch
import gpytorch
from ..utils import generate_ranges
#import time
class GPRegressionModel(gpytorch.models.ExactGP):
def __init__(self, train_x, train_y, likelihood, ... | 4,342 | 33.468254 | 120 | py |
LOTUS | LOTUS-main/src/lotus_nlte/theano_op/predict.py | # -*- coding: utf-8 -*-
import theano
import theano.tensor as tt
import numpy as np
class GenerateMets(theano.Op):
itypes=[tt.dvector]
otypes=[tt.dscalar]
def __init__(self, mgcog):
self.mgcog = mgcog
def perform(self, node, inputs, outputs):
ews = self.mgcog.obs_ew
... | 2,116 | 40.509804 | 118 | py |
LOTUS | LOTUS-main/doc/conf.py | from pkg_resources import DistributionNotFound, get_distribution
try:
__version__ = get_distribution("lotus-nlte").version
except DistributionNotFound:
__version__ = "unknown version"
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. ... | 2,961 | 30.849462 | 79 | py |
multimodal-meta-learn | multimodal-meta-learn-main/src/data_loaders.py | import json
import os
import pickle
import random
from pathlib import Path
import clip
import numpy as np
import torch
from PIL import Image
from torch.utils.data import Dataset
from torchvision.transforms import ColorJitter
from parse_coco import get_coco_categories_to_img_caps
from utils import set_device
PATH = s... | 19,768 | 45.625 | 125 | py |
multimodal-meta-learn | multimodal-meta-learn-main/src/meta_trainer.py | import os
from copy import deepcopy
import numpy as np
import torch
from torch import nn
from torch import optim
from torch.nn import functional as F
from meta_learner import MetaLearner
from utils import *
PATH = str(Path.cwd().parent)
MODELS_PATH = PATH + "/models/"
class MetaTrainer(nn.Module):
"""
Adap... | 10,671 | 46.856502 | 121 | py |
multimodal-meta-learn | multimodal-meta-learn-main/src/utils.py | from os import path
from pathlib import Path
import torch
PROJECT_ROOT = str(Path.cwd().parent) # project path
def write_data_to_txt(file_path, data):
if path.exists(file_path):
with open(file_path, 'a', newline='') as file:
file.write(data)
else: # Create the file
with open(fi... | 479 | 19.869565 | 54 | py |
multimodal-meta-learn | multimodal-meta-learn-main/src/main_inference.py | import argparse
import datetime
from torch.utils.data import DataLoader
from transformers import GPT2Tokenizer
import utils
from data_loaders import *
from meta_trainer import MetaTrainer
PATH = str(Path.cwd().parent.parent.parent) # root directory
LOG_PATH = PROJECT_ROOT + "/logs/"
MODELS_PATH = PROJECT_ROOT + "/m... | 5,406 | 57.771739 | 122 | py |
multimodal-meta-learn | multimodal-meta-learn-main/src/meta_learner.py | import math
from typing import Optional
import clip
import torch
from torch import nn
from torch.nn import functional as F
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from utils import set_device
class MetaLearner(nn.Module):
def __init__(self, prefix_length, seq_len, clip_model_type, new_words=Fals... | 9,920 | 41.948052 | 111 | py |
multimodal-meta-learn | multimodal-meta-learn-main/src/main_train.py | import argparse
import datetime
from torch.utils.data import DataLoader
from transformers import GPT2Tokenizer
from coco_trainer import CocoTrainer
from data_loaders import *
from meta_trainer import MetaTrainer
from utils import *
PATH = str(Path.cwd().parent.parent.parent) # root of all projects
LOG_PATH = PROJEC... | 10,007 | 60.777778 | 134 | py |
multimodal-meta-learn | multimodal-meta-learn-main/src/parse_coco.py | import argparse
import json
import os
import pickle
import clip
import torch
from PIL import Image
from tqdm import tqdm
from utils import *
PATH = str(Path.cwd().parent.parent.parent) + '/Datasets/coco' # Local machine Path
device = set_device()
if torch.cuda.is_available():
print('Training on GPU!')
else:
... | 4,842 | 31.072848 | 115 | py |
multimodal-meta-learn | multimodal-meta-learn-main/src/coco_trainer.py | import logging
import os
import time
import torch
from torch import optim
from torch.nn import functional as F
from tqdm import tqdm
from transformers import get_constant_schedule_with_warmup
from meta_learner import MetaLearner
from utils import *
PROJECT_ROOT = str(Path.cwd().parent) # project path
LOG_PATH = PRO... | 6,859 | 42.144654 | 115 | py |
pase | pase-master/precompute_aco_data.py | from pase.dataset import WavDataset, DictCollater, uttwav_collater
from torchvision.transforms import Compose
from torch.utils.data import DataLoader
from pase.transforms import *
import argparse
from pase.utils import pase_parser
import tqdm
import os
def make_transforms(opts, minions_cfg):
trans = [ToTensor()]
... | 3,583 | 37.956522 | 82 | py |
pase | pase-master/make_trainset_statistics.py | import torch
from torch.utils.data import DataLoader
from pase.dataset import PairWavDataset, DictCollater, MetaWavConcatDataset
from torchvision.transforms import Compose
from pase.transforms import *
import argparse
import pickle
from train import make_transforms
import pase
from pase.utils import *
def build_datase... | 7,778 | 45.861446 | 209 | py |
pase | pase-master/unsupervised_data_cfg_librispeech.py | import json
#import librosa
import argparse
import random
from random import shuffle
import numpy as np
import torchaudio
import os
def get_file_dur(fname):
try:
x, rate = torchaudio.load(fname)
except RuntimeError:
print(f"Error processing {fname}")
return (0)
return x.shape[1]
... | 5,540 | 40.977273 | 84 | py |
pase | pase-master/train.py | # from pase.models.core import Waveminionet
import warnings
# Pawel: this one is for nightly build of pytorch, as it
# spits out massive number of warnings
warnings.filterwarnings('ignore')
import librosa
from pase.models.modules import VQEMA
from pase.dataset import PairWavDataset, DictCollater, MetaWavConcatDataset... | 21,702 | 45.572961 | 233 | py |
pase | pase-master/emorec/neural_networks.py | ##########################################################
# pytorch-kaldi v.0.1
# Mirco Ravanelli, Titouan Parcollet
# Mila, University of Montreal
# October 2018
##########################################################
import torch
import torch.nn.functional as F
import torch... | 61,209 | 34.463499 | 304 | py |
pase | pase-master/emorec/train.py | import torch
import torch.nn as nn
import glob
import os
import tqdm
import numpy as np
import argparse
import json
import random
import timeit
from tensorboardX import SummaryWriter
import pase
from random import shuffle
from pase.dataset import *
from pase.models.frontend import wf_builder
import pase.models.classifi... | 13,109 | 39.588235 | 84 | py |
pase | pase-master/emorec/run_IEMOCAP_fast.py | # Mirco Ravanelli
# Mila, June 2019
# This script runs a simple emotion recognition experiment on the top of PASE features.
# The results are reported in terms of Frame Error Rate/ Sentence Error Rate over four emotions of the IEMOCAP dataset
# This system is not designed for an extensive evaluation of PASE features... | 9,314 | 27.39939 | 230 | py |
pase | pase-master/util_scripts/encode_codec2.py | import glob
import os
import multiprocessing as mp
from pase.transforms import *
import tqdm
import argparse
def process_codec(args):
c2 = Codec2Buffer()
infile, outdir = args
bname = os.path.basename(infile)
outpath = os.path.join(outdir, bname)
x, rate = sf.read(infile)
y = c2({'chunk':torch.... | 1,091 | 26.3 | 64 | py |
pase | pase-master/util_scripts/clusterize_frontend.py | from sklearn.cluster import KMeans
from pase.models.frontend import wf_builder
from pase.dataset import PairWavDataset, DictCollater
from torchvision.transforms import Compose
from pase.transforms import *
from torch.utils.data import DataLoader
import numpy as np
import argparse
import timeit
import pickle
import os
i... | 4,097 | 39.574257 | 78 | py |
pase | pase-master/util_scripts/project_features.py | import numpy as np
from tensorboardX import SummaryWriter
import json
import random
random.seed(1)
from random import shuffle
import torch
import tqdm
import os
import glob
SAVE_PATH= 'vctk_projection_paseQRNN_age'
#SAVE_PATH= 'vctk_projection_paseQRNN_id'
if not os.path.exists(SAVE_PATH):
os.makedirs(SAVE_PATH)
... | 1,780 | 25.191176 | 75 | py |
pase | pase-master/util_scripts/prosodic_eval.py | from pase.models.core import Waveminionet
from pase.dataset import PairWavDataset, DictCollater
from torchvision.transforms import Compose
from pase.transforms import *
from pase.losses import *
from pase.utils import pase_parser
from torch.utils.data import DataLoader
import torch
import pickle
import timeit
import to... | 7,133 | 35.397959 | 77 | py |
pase | pase-master/util_scripts/make_contaminated_trainset.py | from pase.dataset import *
from torchvision.transforms import Compose
import json
from pase.transforms import *
import tqdm
from torch.utils.data import DataLoader
import soundfile as sf
from train import config_distortions
import random
import numpy as np
import os
import torch
random.seed(1)
np.random.seed(1)
torch.... | 1,973 | 33.034483 | 74 | py |
pase | pase-master/util_scripts/forward_chunk.py | from pase.models.core import Waveminionet
from pase.models.frontend import wf_builder
from pase.dataset import PairWavDataset, DictCollater
from torchvision.transforms import Compose
from pase.transforms import *
from pase.losses import *
from pase.utils import pase_parser
from torch.utils.data import DataLoader
import... | 4,715 | 36.428571 | 79 | py |
pase | pase-master/util_scripts/eval_ckpts.py | from pase.models.core import Waveminionet
from pase.dataset import PairWavDataset, DictCollater
from torchvision.transforms import Compose
from pase.transforms import *
from pase.losses import *
from pase.utils import pase_parser
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
import torc... | 5,591 | 40.731343 | 79 | py |
pase | pase-master/spk_id/run_minivox_fast.py | # Mirco Ravanelli
# Mila, June 2019
# This script runs a simple speaker recognition experiment on the top of PASE features.
# The results are reported in terms of Frame Error Rate /Sentence Error Rates.
# This system is not designed for an extensive evaluation of PASE features, but mainly for quickly monitoring the p... | 9,013 | 27.257053 | 234 | py |
pase | pase-master/spk_id/neural_networks.py | ##########################################################
# pytorch-kaldi v.0.1
# Mirco Ravanelli, Titouan Parcollet
# Mila, University of Montreal
# October 2018
##########################################################
import torch
import torch.nn.functional as F
import torch... | 61,209 | 34.463499 | 304 | py |
pase | pase-master/spk_id/nnet.py | import numpy as np
from torch.utils.data import Dataset, DataLoader
import pickle
import json
import glob
from tensorboardX import SummaryWriter
import random
import timeit
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from ahoproc_tools.io import read_aco_file
from pase... | 24,652 | 39.816225 | 87 | py |
pase | pase-master/spk_id/mfcc_baseline.py | import numpy as np
from torch.utils.data import Dataset, DataLoader
import pickle
import json
import glob
from utils import *
from tensorboardX import SummaryWriter
import random
import timeit
import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torch.nn.... | 16,853 | 40.925373 | 84 | py |
pase | pase-master/spk_id/utils.py | import numpy as np
import random
from torch.utils.data import Dataset, DataLoader
import torch
import torch.optim.lr_scheduler as lr_scheduler
from ahoproc_tools.io import *
import os
import pickle
import torch.nn as nn
import torch.optim as optim
from random import shuffle
import librosa
def build_valid_list(tr_list... | 5,947 | 32.988571 | 81 | py |
pase | pase-master/pase/losses.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class ContextualizedLoss(object):
""" With a possible composition of r
consecutive frames
"""
def __init__(self, criterion, r=None):
self.criterion = criterion
self.r = r
def contextualize_r(self, tensor):
... | 7,888 | 34.859091 | 78 | py |
pase | pase-master/pase/utils.py | import json
import shlex
import subprocess
import random
import torch
import torch.nn as nn
try:
from .losses import *
except ImportError:
from losses import *
import random
from random import shuffle
from pase.models.discriminator import *
import torch.optim as optim
from torch.autograd import Function
def p... | 13,141 | 36.764368 | 89 | py |
pase | pase-master/pase/dataset.py | import torch
import torch.nn.functional as F
import re
import glob
from torch.utils.data import Dataset, ConcatDataset
import math
import torchaudio
import json
import tqdm
import pickle
import os
try:
from .utils import *
except ImportError:
from utils import *
import random
import numpy as np
from collections... | 32,931 | 40.062344 | 94 | py |
pase | pase-master/pase/log.py | from tensorboardX import SummaryWriter
import numpy as np
import torch
import pickle
import os
class PklWriter(object):
def __init__(self, save_path):
from datetime import datetime
curr_time = datetime.now().strftime('%b%d_%H-%M-%S')
fname = 'losses_{}.pkl'.format(curr_time)
self.... | 2,138 | 35.87931 | 78 | py |
pase | pase-master/pase/transforms.py | import torch
import torch.nn.functional as F
import tqdm
import gammatone
import tempfile
from gammatone.gtgram import gtgram
import numpy as np
import subprocess
import shlex
import random
import pysptk
import os
from python_speech_features import logfbank
import librosa
import struct
import glob
import pickle
import ... | 89,087 | 36.089092 | 122 | py |
pase | pase-master/pase/models/aspp.py | import math
import torch
import torch.nn as nn
from .modules import *
import torch.nn.functional as F
class _ASPPModule(Model):
def __init__(self, inplanes, planes, kernel_size, padding, dilation):
super(_ASPPModule, self).__init__()
self.atrous_conv = nn.Conv1d(inplanes, planes, kernel_size=kerne... | 8,764 | 37.442982 | 145 | py |
pase | pase-master/pase/models/core.py | from .minions import *
from ..losses import *
from ..utils import AuxiliarSuperviser, get_grad_norms
from ..log import *
#from tensorboardX import SummaryWriter
import soundfile as sf
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import numpy as np
import random
import timeit
import os
c... | 37,644 | 47.077905 | 92 | py |
pase | pase-master/pase/models/neural_networks.py | ##########################################################
# pytorch-kaldi v.0.1
# Mirco Ravanelli, Titouan Parcollet
# Mila, University of Montreal
# October 2018
##########################################################
import torch
import torch.nn.functional as F
import torch... | 61,209 | 34.463499 | 304 | py |
pase | pase-master/pase/models/discriminator.py | import torch
import torch.nn as nn
import math
import torch.nn.functional as F
from torch.nn.utils.spectral_norm import spectral_norm
import numpy as np
import json
import os
try:
from modules import *
except ImportError:
from .modules import *
class WaveDiscriminator(nn.Module):
def __init__(self, ninpu... | 2,422 | 32.191781 | 80 | py |
pase | pase-master/pase/models/modules.py | import torch
import torch.nn as nn
import math
import torch.nn.functional as F
from torch.distributions import Binomial
from torch.nn.utils.spectral_norm import spectral_norm
from torch.nn.utils.weight_norm import weight_norm
import numpy as np
import json
import os
try:
from torchqrnn import QRNN
except ImportErro... | 48,879 | 36.030303 | 131 | py |
pase | pase-master/pase/models/tdnn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from .modules import *
except ImportError:
from modules import *
class StatisticalPooling(nn.Module):
def forward(self, x):
# x is 3-D with axis [B, feats, T]
mu = x.mean(dim=2, keepdim=True)
std = x.std(dim=2... | 3,530 | 33.617647 | 73 | py |
pase | pase-master/pase/models/classifiers.py | import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from .modules import *
except ImportError:
from modules import *
class EmoDRNLSTM(Model):
""" Based on https://ieeexplore.ieee.org/document/8682154
(Li et al. 2019), without MHA
"""
def __init__(self, num_inputs, num... | 8,288 | 35.196507 | 77 | py |
pase | pase-master/pase/models/attention_block.py | from .modules import *
from .neural_networks import MLP
import torch
import torch.nn.functional as F
class attention_block(Model):
def __init__(self, emb_dim, name, options, K, strides, chunksize, avg_factor=0, mode="concat"):
super().__init__(name=name)
self.name = name
self.mode... | 2,683 | 29.850575 | 111 | py |
pase | pase-master/pase/models/decoders.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from .frontend import *
#from .Minions.minions import *
import random
class SpectrumLM(nn.Module):
""" RNN lang model for spectrum frame preds """
def __init__(self, rnn_size, rnn_layers, out_dim,
... | 3,649 | 33.433962 | 74 | py |
pase | pase-master/pase/models/pase.py | try:
from .Minions.minions import *
from .Minions.cls_minions import *
from .attention_block import attention_block
from .frontend import wf_builder
from .WorkerScheduler.encoder import *
except ImportError:
from Minions.minions import *
from Minions.cls_minions import *
from attention_b... | 12,942 | 35.254902 | 154 | py |
pase | pase-master/pase/models/frontend.py | import torch
import torch.nn.functional as F
import torch.nn as nn
import json
from pase.models.WorkerScheduler.encoder import encoder
import torchvision.models as models
try:
from modules import *
from aspp import aspp_resblock
from tdnn import TDNN
except ImportError:
from .modules import *
from .... | 15,045 | 35.342995 | 280 | py |
pase | pase-master/pase/models/encoders.py | import torch
import torch.nn as nn
from .core import LayerNorm
class AhoCNNEncoder(nn.Module):
def __init__(self, input_dim, kwidth=3, dropout=0.5, layer_norm=False):
super().__init__()
pad = (kwidth - 1) // 2
if layer_norm:
norm_layer = LayerNorm
else:
no... | 2,834 | 29.815217 | 75 | py |
pase | pase-master/pase/models/WorkerScheduler/radam.py | import math
import torch
from torch.optim.optimizer import Optimizer, required
class RAdam(Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
self.buffer = [[None, None, None] for in... | 8,025 | 37.586538 | 189 | py |
pase | pase-master/pase/models/WorkerScheduler/encoder.py | import torch.nn as nn
from ..modules import *
from ..aspp import ASPP, aspp_resblock
import torch.nn.functional as F
import json
import random
class encoder(Model):
def __init__(self, frontend, name='encoder'):
super().__init__(name)
self.frontend = frontend
self.emb_dim = self.frontend.em... | 3,054 | 28.095238 | 199 | py |
pase | pase-master/pase/models/WorkerScheduler/worker_scheduler.py | import torch
import random
import numpy as np
import torch.nn.functional as F
from .min_norm_solvers import MinNormSolver, gradient_normalizers
from torch.autograd import Variable
class backprop_scheduler(object):
def __init__(self, model, mode=None):
self.model = model
self.mode = mode
s... | 14,430 | 32.174713 | 164 | py |
pase | pase-master/pase/models/WorkerScheduler/min_norm_solvers.py | import numpy as np
import torch
# https://github.com/intel-isl/MultiObjectiveOptimization/blob/master/multi_task/min_norm_solvers.py
class MinNormSolver:
MAX_ITER = 250
STOP_CRIT = 1e-5
def _min_norm_element_from2(v1v1, v1v2, v2v2):
"""
Analytical solution for min_{c} |cx_1 + (1-c)x_2|_2^2... | 7,539 | 37.080808 | 147 | py |
pase | pase-master/pase/models/WorkerScheduler/trainer.py | from ..Minions.minions import *
from ..Minions.cls_minions import *
from .encoder import encoder
from .lr_scheduler import LR_Scheduler
from ..pase import pase, pase_attention, pase_chunking
from .worker_scheduler import backprop_scheduler
from ...utils import AuxiliarSuperviser, get_grad_norms
from .radam import *
imp... | 18,839 | 40.681416 | 121 | py |
pase | pase-master/pase/models/Minions/cls_minions.py | import torch
import torch.nn as nn
from ..frontend import WaveFe
from ..modules import *
from .minions import *
import torch.nn.functional as F
import json
import random
def cls_worker_maker(cfg, emb_dim):
print("=" * 50)
print("name", cfg["name"])
print("=" * 50)
if cfg["name"] == "mi":
return... | 3,762 | 24.773973 | 125 | py |
pase | pase-master/pase/models/Minions/minions.py | import torch
import torch.nn as nn
from ..frontend import WaveFe
from ..modules import *
import torch.nn.functional as F
import json
import random
from pase.utils import *
import sys
def minion_maker(cfg):
if isinstance(cfg, str):
with open(cfg, "r") as f:
cfg = json.load(f)
print("=" * 50)... | 24,446 | 33.627479 | 158 | py |
pase | pase-master/pase/test/dataset.py | from pase.dataset import LibriSpeechSegTupleWavDataset
from pase.transforms import *
from argparse import ArgumentParser
from torch.utils.data import DataLoader
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--data_root", type=str, required=True)
parser.add_argument("--data_cfg... | 916 | 29.566667 | 100 | py |
pase | pase-master/ASR/run_TIMIT_fast.py | # Mirco Ravanelli
# Mila, June 2019
# This script runs a simple speech recognition experiment on the top of PASE features.
# The results are reported in terms of Frame Error Rate over phonemes (context-independent).
# This system is not designed for an extensive evaluation of PASE features, but mainly for quickly mo... | 10,629 | 28.123288 | 196 | py |
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