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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GNS-Modeling | GNS-Modeling-master/gns/inference/optimization/optimize_parallel.py | import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from .parse import Parse
def optimize_parselist_mt(parse_list, image, loss_fn, iterations=3000,
optimizer_init=None, stopper_init=None,
clip_grad=None):
"""
Parallel version ... | 2,183 | 30.2 | 74 | py |
GNS-Modeling | GNS-Modeling-master/gns/inference/optimization/soft_constraints.py | import torch
import torch.autograd as autograd
import torch.nn.functional as F
__all__ = ['soft_lb', 'soft_ub', 'soft_ub_lb', 'passthrough_lb',
'passthrough_ub', 'passthrough_ub_lb']
# ---- soft bounds ----
def soft_lb(x, vmin):
"""
Activation is approximately linear everywhere except near lower ... | 2,544 | 29.662651 | 77 | py |
GNS-Modeling | GNS-Modeling-master/gns/inference/optimization/optimizers.py | """
Stochastic gradient descent with noisy gradients (instead of minibatch).
"""
import math
import random
import torch
import torch.optim as optim
class SGD(optim.SGD):
"""
This version uses multiplicative gradient noise.
"""
def __init__(self, params, noise=0.1, **kwargs):
self.noise = nois... | 5,339 | 35.326531 | 86 | py |
GNS-Modeling | GNS-Modeling-master/gns/inference/optimization/objectives.py | import torch
import torch.distributions as dist
from pybpl.util import nested_map
def to_cuda(x):
return x.cuda()
class FullModel:
"""
Full graphical model with the following joint distribution:
P(type, token, image) = P(type) * P(token | type) * P(image | token)
"""
def __init__(self, ... | 2,914 | 35.898734 | 97 | py |
GNS-Modeling | GNS-Modeling-master/gns/inference/optimization/optimize.py | from tqdm import tqdm
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from .parse import Parse, ParseWithToken
from .early_stopping import EarlyStopper
def optimize_parse(parse, image, loss_fn, iterations=3000, optimizer=None,
tune_blur=True, tune_fn=None, stoppe... | 5,518 | 36.290541 | 85 | py |
GNS-Modeling | GNS-Modeling-master/gns/inference/optimization/tune.py | import numpy as np
import torch
def get_param_grid(pblur, peps, nbins_blur, nbins_eps):
blur_grid = np.linspace(-50, pblur, nbins_blur)
eps_grid = np.linspace(-2, 1, nbins_eps-1)
eps_grid = np.append(eps_grid, peps)
param_grid = np.meshgrid(blur_grid, eps_grid)
param_grid = np.stack(param_grid, a... | 1,624 | 46.794118 | 90 | py |
GNS-Modeling | GNS-Modeling-master/gns/inference/optimization/parse.py | import torch
import torch.nn as nn
from pybpl.splines import get_stk_from_bspline
from pybpl.util.affine import apply_warp
from . import soft_constraints as C
def param_state(p):
return p.detach().cpu().clone()
class Parse(nn.Module):
def __init__(self, init_parse, init_blur=16., init_epsilon=0.5, bound_m... | 3,607 | 32.407407 | 87 | py |
GNS-Modeling | GNS-Modeling-master/experiments/classification/get_base_parses.py | import argparse
import os
import time
import torch
from pybpl.util import nested_map
from pybpl.splines import get_stk_from_bspline
from gns.inference.parsing import get_topK_parses
from gns.omniglot.classification import ClassificationDataset
from gns.type import TypeModel
from gns.utils.experiments import mkdir, time... | 2,925 | 34.253012 | 75 | py |
GNS-Modeling | GNS-Modeling-master/experiments/classification/refit_parses_single.py | import argparse
import os
import time
import torch
from gns.rendering import Renderer
from gns.token import TokenModel
from gns.inference import optimization as opt
from gns.omniglot.classification import ClassificationDataset
from gns.utils.experiments import mkdir, time_string
def config_for_refit(parse):
pars... | 5,095 | 35.4 | 95 | py |
GNS-Modeling | GNS-Modeling-master/experiments/classification/optimize_parses.py | import argparse
import os
import time
import torch
from gns.rendering import Renderer
from gns.token import TokenModel
from gns.type import TypeModel
from gns.inference import optimization as opt
from gns.omniglot.classification import ClassificationDataset
from gns.utils.experiments import mkdir, time_string
def lo... | 4,603 | 34.415385 | 88 | py |
GNS-Modeling | GNS-Modeling-master/experiments/generate_concepts/generate.py | from tqdm.notebook import tqdm
import matplotlib.pylab as plt
import torch
from pybpl.splines import get_stk_from_bspline
from gns.type import TypeModel
from gns.utils import render_strokes
from gns.viz import plot_image
MEAN = torch.tensor([50., -50.])
SCALE = torch.tensor([20., 20.])
def convert_space(x, inverse=... | 2,233 | 28.394737 | 91 | py |
GNS-Modeling | GNS-Modeling-master/experiments/generate_exemplars/generate.py | import os
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import numpy as np
import torch
import torch.distributions as dist
from pybpl.splines import get_stk_from_bspline
from gns.rendering import Renderer
from gns.token import TokenModel
from gns.inference import optimization as opt
from gns.v... | 3,749 | 29.737705 | 87 | py |
GNS-Modeling | GNS-Modeling-master/experiments/generate_exemplars/get_parses.py | import argparse
import os
import time
import numpy as np
from imageio import imread
import torch
from pybpl.util import nested_map
from pybpl.splines import get_stk_from_bspline
from gns.inference.parsing import get_topK_parses
from gns.rendering import Renderer
from gns.token import TokenModel
from gns.type import Typ... | 5,670 | 32.755952 | 86 | py |
Pluralistic-Inpainting | Pluralistic-Inpainting-master/gui/ui_model.py | from gui.ui.ui_window import Ui_MainWindow
from gui.ui_draw import *
from PIL import Image, ImageQt
import random, io, os
import numpy as np
import torch
import torchvision.transforms as transforms
from util import task, util
from dataloader.image_folder import make_dataset
from model import create_model
from util.visu... | 18,116 | 38.643326 | 212 | py |
Pluralistic-Inpainting | Pluralistic-Inpainting-master/options/base_options.py | import argparse
import os
import torch
import model
from util import util
class BaseOptions():
def __init__(self):
self.parser = argparse.ArgumentParser()
self.initialized = False
def initialize(self, parser):
# base define
parser.add_argument('--name', type=str, default='expe... | 4,701 | 44.211538 | 160 | py |
Pluralistic-Inpainting | Pluralistic-Inpainting-master/evaluations/inception_score_torch.py | import torch
from torch import nn
from torch.autograd import Variable
from torch.nn import functional as F
import torch.utils.data
import torchvision.transforms as transforms
from torchvision.models.inception import inception_v3
import numpy as np
import math
from PIL import Image
from dataloader.image_folder import m... | 3,468 | 32.355769 | 165 | py |
Pluralistic-Inpainting | Pluralistic-Inpainting-master/evaluations/fid_score_torch.py | #!/usr/bin/env python3
"""Calculates the Frechet Inception Distance (FID) to evalulate GANs
The FID metric calculates the distance between two distributions of images.
Typically, we have summary statistics (mean & covariance matrix) of one
of these distributions, while the 2nd distribution is given by a GAN.
When run a... | 9,605 | 38.368852 | 79 | py |
Pluralistic-Inpainting | Pluralistic-Inpainting-master/util/task.py | import torch
import torch.nn.functional as F
import torchvision.transforms as transforms
from random import randint
import numpy as np
import cv2
from PIL import Image
import random
###################################################################
# random mask generation
############################################... | 3,531 | 29.713043 | 83 | py |
Pluralistic-Inpainting | Pluralistic-Inpainting-master/util/util.py | import numpy as np
import os
import imageio
import math
import torch
# convert a tensor into a numpy array
def tensor2im(image_tensor, bytes=255.0, imtype=np.uint8):
if image_tensor.dim() == 3:
image_numpy = image_tensor.cpu().float().numpy()
else:
image_numpy = image_tensor[0].cpu().float().n... | 1,147 | 23.956522 | 85 | py |
Pluralistic-Inpainting | Pluralistic-Inpainting-master/model/base_model.py | import os, ntpath
import torch
from collections import OrderedDict
from util import util
from . import base_function
class BaseModel():
def __init__(self, opt):
self.opt = opt
self.gpu_ids = opt.gpu_ids
self.isTrain = opt.isTrain
self.save_dir = os.path.join(opt.checkpoints_dir, op... | 6,411 | 39.582278 | 117 | py |
Pluralistic-Inpainting | Pluralistic-Inpainting-master/model/pluralistic_model.py | import torch
from .base_model import BaseModel
from . import network, base_function, external_function
from util import task
import itertools
class Pluralistic(BaseModel):
"""This class implements the pluralistic image completion, for 256*256 resolution image inpainting"""
def name(self):
return "Plur... | 11,583 | 48.293617 | 136 | py |
Pluralistic-Inpainting | Pluralistic-Inpainting-master/model/network.py | from .base_function import *
from .external_function import SpectralNorm
import torch.nn.functional as F
##############################################################################################################
# Network function
###################################################################################... | 13,524 | 40.360856 | 141 | py |
Pluralistic-Inpainting | Pluralistic-Inpainting-master/model/base_function.py | import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
from .external_function import SpectralNorm
######################################################################################
# base function for network structure
###################################... | 13,230 | 36.481586 | 141 | py |
Pluralistic-Inpainting | Pluralistic-Inpainting-master/model/external_function.py | import torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
import copy
####################################################################################################
# spectral normalization layer to decouple the magnitude of a weight tensor
################################... | 9,261 | 39.269565 | 132 | py |
Pluralistic-Inpainting | Pluralistic-Inpainting-master/dataloader/data_loader.py | from PIL import Image, ImageFile
import torchvision.transforms as transforms
import torch.utils.data as data
from .image_folder import make_dataset
from util import task
import random
class CreateDataset(data.Dataset):
def __init__(self, opt):
self.opt = opt
self.img_paths, self.img_size = make_da... | 3,932 | 35.757009 | 124 | py |
multinav2 | multinav2-main/multinav/algorithms/policy_net.py | """Policies for agents with composite observations: environment + automaton states."""
from typing import Any, Callable, Dict, Optional, Sequence
import numpy as np
import tensorflow as tf
from gym import spaces
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from tensorflow import keras, summary
from tensorflow... | 8,393 | 30.675472 | 94 | py |
ARC-Solvers | ARC-Solvers-main/arc_solvers/nn/util.py | import torch
from allennlp.common.checks import ConfigurationError
from allennlp.nn.util import replace_masked_values
from allennlp.nn.util import get_text_field_mask
import allennlp
from typing import Union, Dict
import torch
from allennlp.modules import MatrixAttention, Seq2SeqEncoder
def masked_mean(tensor, dim, m... | 7,076 | 44.954545 | 145 | py |
ARC-Solvers | ARC-Solvers-main/arc_solvers/modules/single_time_distributed.py | """
=====================================================================
Decomposable Graph Entailment Model code replicated from SciTail repo
https://github.com/allenai/scitail
=====================================================================
"""
import torch
class SingleTimeDistributed(torch.nn.Module):
"... | 3,268 | 49.292308 | 98 | py |
ARC-Solvers | ARC-Solvers-main/arc_solvers/models/qa/multi_choice/qa_multi_choice_max_att.py | from allennlp.modules.matrix_attention import MatrixAttention
from typing import Dict, Optional, AnyStr, List, Any
import torch
from allennlp.common import Params
from allennlp.common.checks import ConfigurationError
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.modules imp... | 12,504 | 48.039216 | 163 | py |
ARC-Solvers | ARC-Solvers-main/arc_solvers/models/entailment/tree_attention.py | """
=====================================================================
Decomposable Graph Entailment Model code replicated from SciTail repo
https://github.com/allenai/scitail
=====================================================================
"""
from typing import Dict, List, Any, Tuple
import numpy
import tor... | 16,217 | 50.814696 | 99 | py |
bert-japanese | bert-japanese-main/convert_tf2_ckpt_for_all_frameworks.py | # Copyright 2023 Masatoshi Suzuki (@singletongue)
#
# 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 or a... | 12,201 | 41.964789 | 114 | py |
understanding-momentum | understanding-momentum-master/resnet_on_cifar/main.py | from __future__ import print_function
import argparse
import os
import datetime
import sys
import time
import torch
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from tensorboardX import SummaryWriter
from torchvision import datasets, transforms
from shb import SHB
from utils import get_git_di... | 10,043 | 33.754325 | 81 | py |
understanding-momentum | understanding-momentum-master/resnet_on_cifar/shb.py | import torch
from torch.optim import Optimizer
class SHB(Optimizer):
def __init__(self, params, lr, momentum=0, weight_decay=0):
if lr < 0.0:
raise ValueError("Invalid learning rate: {}".format(lr))
if momentum < 0.0:
raise ValueError("Invalid momentum value: {}".format(momentum))
if weight_... | 1,443 | 28.469388 | 77 | py |
understanding-momentum | understanding-momentum-master/resnet_on_cifar/models.py | '''
resnet for cifar in pytorch
Reference:
[1] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016.
[2] K. He, X. Zhang, S. Ren, and J. Sun. Identity mappings in deep residual networks. In ECCV, 2016.
'''
import torch
import torch.nn as nn
import math
import torch.nn.functi... | 9,557 | 28.140244 | 109 | py |
understanding-momentum | understanding-momentum-master/lr_on_mnist/main.py | from __future__ import print_function
import argparse
import os
import datetime
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
from tensorboardX import SummaryWriter
from torchvision import datasets, transforms
from qhm import QHM
from utils import get_git_diff, get_git_hash, Logger
... | 8,417 | 33.929461 | 85 | py |
understanding-momentum | understanding-momentum-master/lr_on_mnist/qhm.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch.optim.optimizer import Optimizer, required
import param_conv
class QHM(Optimizer):
r"""Implements the quasi-hyp... | 11,422 | 40.689781 | 120 | py |
access | access-main/access/preprocess.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.
#
from functools import wraps
import multiprocessing
import random
import re
from joblib import Parallel, delayed
import t... | 5,957 | 34.676647 | 116 | py |
ANNA-PALM | ANNA-PALM-master/AnetLib/options/base_options.py | import argparse
import os
class BaseOptions():
def __init__(self):
self.parser = argparse.ArgumentParser()
self.initialized = False
self.isTrain = False
def initialize(self):
self.parser.add_argument('--workdir', type=str, required=True, help='work directory')
self.pars... | 8,793 | 76.140351 | 228 | py |
ANNA-PALM | ANNA-PALM-master/AnetLib/data/folder_dataset.py | ################################################################################
# Code from
# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py
# Modified the original code so that it also loads images from the current
# directory as well as the subdirectories
###############################... | 6,594 | 36.050562 | 106 | py |
ANNA-PALM | ANNA-PALM-master/AnetLib/data/image_folder.py | ###############################################################################
# Code from
# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py
# Modified the original code so that it also loads images from the current
# directory as well as the subdirectories
################################... | 1,900 | 27.80303 | 79 | py |
An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization | An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization-master/import_task.py | '''
Imports data and models for the five tasks of the paper (cfr. Table 1).
'''
import os
from experiment_utils import import_cifar
from rotation_rate_utils import get_kernel_layer_names
from models import VGG, resnet_v1, VGG_pytorchBlogStyle, WideResNet
def import_task(experiment, mode = ''):
if experiment not i... | 6,060 | 37.852564 | 114 | py |
An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization | An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization-master/rotation_rate_utils.py | '''
Utilities related to layer-wise angle deviation curves
'''
import numpy as np
from scipy.spatial.distance import cosine
import matplotlib
import matplotlib.pyplot as plt
from keras.callbacks import Callback
from keras.engine.training import _make_batches, _slice_arrays
import keras.backend as K
from keras.losses... | 3,473 | 35.1875 | 130 | py |
An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization | An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization-master/models.py | '''
Functions to create the 5 models used in the paper.
'''
import os
import numpy as np
import keras
import keras.backend as K
from keras.models import Model
from keras.layers import Input, Dense, BatchNormalization, Activation, Dropout, Flatten, add
from keras.layers import Conv2D, ZeroPadding2D, AveragePooling2D,... | 17,068 | 41.779449 | 141 | py |
An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization | An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization-master/experiment_utils.py | import warnings
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from keras.datasets import cifar10, cifar100
from keras.callbacks import History
def one_hot_encoding(labels,nb_labels = None):
n = len(labels)
if not nb_labels:
nb_labels = labels.max() + 1
if nb_labels... | 5,083 | 31.589744 | 117 | py |
An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization | An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization-master/layca_optimizers.py | '''
Code for applying Layca on SGD, Adam, RMSprop and Adagrad.
Source: keras' implementation of the original optimization methods.
'''
from keras.optimizers import Optimizer
import keras.backend as K
from keras.legacy import interfaces
import tensorflow as tf
from keras.optimizers import Optimizer
import keras.backe... | 14,697 | 39.827778 | 158 | py |
An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization | An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization-master/get_training_utils.py | '''
Get training parameters such as learning rate schedules and stopping criteria for training of the five tasks
'''
import numpy as np
from experiment_utils import lr_schedule
from layca_optimizers import SGD,Adam,RMSprop,Adagrad
from keras.callbacks import Callback, LearningRateScheduler
class StoppingCriteria(Cal... | 5,278 | 41.572581 | 137 | py |
An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization | An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization-master/Supplementary Results/training_monitoring.py | '''
Keras callbacks for storing signals of interest (Adam's 2nd raw moment estimation, layer rotation rate per epoch,..) during training.
'''
import sys
sys.path.insert(0, "../")
import numpy as np
from scipy.spatial.distance import cosine
import matplotlib
import matplotlib.pyplot as plt
from keras.callbacks impor... | 9,381 | 41.645455 | 136 | py |
An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization | An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization-master/Supplementary Results/LARS.py | from keras.optimizers import Optimizer
import keras.backend as K
from keras.legacy import interfaces
import numpy as np
import tensorflow as tf
class LARS(Optimizer):
"""
LARS optimizer (ref: https://arxiv.org/abs/1708.03888)
coded by modifying Keras' SGD optimizer code
"""
def ... | 3,482 | 37.274725 | 153 | py |
An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization | An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization-master/MNIST toy example/training_memory.py | '''
Keras callbacks for storing input and feedbacks of layers during training
'''
import numpy as np
from numpy import random
from keras.callbacks import Callback
import keras.backend as K
from keras.models import Model
def create_gradient_function(model, tensors, loss, add_batchaxis = False):
'''
create gra... | 8,340 | 39.100962 | 183 | py |
An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization | An-experimental-study-of-layer-level-training-speed-and-its-impact-on-generalization-master/MNIST toy example/mnist_experiment_utils.py | import os
import numpy as np
import keras
from keras.datasets import mnist
from keras.layers import Input, Dense
from keras.models import Model
def get_split(nb,x,y):
'''
divides data in two sets, one with nb samples per class, the other contains the rest
'''
train_selection = set()
test_selection... | 3,220 | 35.191011 | 111 | py |
id_prediction_intervals | id_prediction_intervals-main/important_directions/imp_dirs.py | import numpy as np
import scipy
import scipy.stats
import torch
from tqdm.auto import tqdm
from .datasets.regression_datamodule import safe_numpy
def flatten_grad(g):
return torch.cat([p.view(-1) for p in g])
def numel(m: torch.nn.Module, only_trainable: bool = True):
"""
returns the total number of p... | 8,192 | 36.240909 | 120 | py |
id_prediction_intervals | id_prediction_intervals-main/important_directions/method_ens.py | import logging
import os
import numpy as np
import scipy
import scipy.stats
import torch.cuda
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning import loggers as pl_loggers
from important_directions.datasets.regression_datamodule import RegressionDataModule,... | 4,285 | 36.596491 | 115 | py |
id_prediction_intervals | id_prediction_intervals-main/important_directions/method_dropout.py | import logging
import os
import numpy as np
import pytorch_lightning as pl
import scipy
import torch
from pytorch_lightning import loggers as pl_loggers
from scipy.special import logsumexp
from tqdm.auto import tqdm
from .datasets.regression_datamodule import RegressionDataModule, safe_numpy
from .network import Regr... | 6,222 | 37.652174 | 113 | py |
id_prediction_intervals | id_prediction_intervals-main/important_directions/experiment.py | import argparse
import os
import numpy as np
import pandas as pd
import torch.multiprocessing as mp
import yaml
from scipy.stats.stats import pearsonr
from sklearn.metrics import mean_squared_error, r2_score
from important_directions.datasets.regression_datamodule import RegressionDataModule, safe_numpy
from importan... | 4,617 | 38.135593 | 103 | py |
id_prediction_intervals | id_prediction_intervals-main/important_directions/network.py | import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from tqdm.auto import tqdm
import pytorch_lightning as pl
from torchmetrics.regression import ExplainedVariance, MeanSquaredError
from .datasets.regression_datamodule import safe_numpy
class RegressionNet(pl.LightningModule):
... | 3,955 | 30.903226 | 102 | py |
id_prediction_intervals | id_prediction_intervals-main/important_directions/method_id.py | import os
import pytorch_lightning as pl
from pytorch_lightning import loggers as pl_loggers
from pytorch_lightning.callbacks import EarlyStopping
from important_directions.datasets.regression_datamodule import RegressionDataModule, safe_numpy
from important_directions.imp_dirs import ImportantDirectionsPytorch
from ... | 3,620 | 47.28 | 111 | py |
id_prediction_intervals | id_prediction_intervals-main/important_directions/method_id_exact.py | import os
import numpy as np
import scipy
import scipy.stats
from tqdm.auto import tqdm
import pytorch_lightning as pl
import torch
from pytorch_lightning import loggers as pl_loggers
from pytorch_lightning.callbacks import EarlyStopping
from important_directions.datasets.regression_datamodule import RegressionData... | 3,811 | 35.653846 | 107 | py |
id_prediction_intervals | id_prediction_intervals-main/important_directions/datasets/dataloader.py | import numpy as np
import torch
from torch.utils.data import DataLoader
class BatchXYDataLoader:
def __init__(self, X, y, batch_size=128, shuffle=False):
self.X = X
self.y = y
self.batch_size = batch_size
self.shuffle = shuffle
self.n = self.X.shape[0]
self.len =... | 856 | 23.485714 | 60 | py |
id_prediction_intervals | id_prediction_intervals-main/important_directions/datasets/regression_datamodule.py | import numbers
import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader
from .dataloader import BatchXYDataLoader
def max_k_n_ratio(k, n, ratio):
return max(k, int(n * ratio))
def get_index_range(a, idx, start, stop):
return a[idx[start:stop]]
def scale_inplace(t, mean, std... | 5,073 | 30.515528 | 120 | py |
DOCRED-FE | DOCRED-FE-main/code/REBEL/src/test.py | import omegaconf
import hydra
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pl_data_modules import BasePLDataModule
from pl_modules import BasePLModule
from transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer
from pytorch_lightning.loggers... | 6,098 | 50.252101 | 1,961 | py |
DOCRED-FE | DOCRED-FE-main/code/REBEL/src/model_saving.py | from pl_modules import BasePLModule
from transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer
import torch
import omegaconf
config = AutoConfig.from_pretrained(
'facebook/bart-large',
decoder_start_token_id = 0,
early_stopping = False,
no_repeat_ngram_size = 0,
)
tokenizer = AutoTokeniz... | 1,014 | 34 | 172 | py |
DOCRED-FE | DOCRED-FE-main/code/REBEL/src/pl_modules.py | from typing import Any
import nltk
import json
import pytorch_lightning as pl
import torch
import numpy as np
import pandas as pd
from score import score, re_score
from transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer
from transformers.optimization import (
Adafactor,
AdamW,
get_constant... | 37,880 | 65.225524 | 3,629 | py |
DOCRED-FE | DOCRED-FE-main/code/REBEL/src/utils.py |
import math
from typing import Callable, Dict, Iterable, List, Tuple, Union
import numpy as np
import torch
import torch.distributed as dist
from torch import nn
from torch import Tensor
from torch.nn.parameter import Parameter
from transformers.models.bart.modeling_bart import shift_tokens_right
def label_smoothed... | 11,000 | 40.513208 | 165 | py |
DOCRED-FE | DOCRED-FE-main/code/REBEL/src/generate_samples.py | from typing import Sequence
import torch
from pytorch_lightning import Callback, LightningModule, Trainer
from torch import nn
import numpy as np
import pandas as pd
from torch.nn.utils.rnn import pad_sequence
import wandb
class GenerateTextSamplesCallback(Callback): # pragma: no cover
"""
PL Callback to gene... | 3,357 | 42.051282 | 130 | py |
DOCRED-FE | DOCRED-FE-main/code/REBEL/src/scheduler.py |
from torch.optim.lr_scheduler import LambdaLR
def get_inverse_square_root_schedule_with_warmup(
optimizer, num_warmup_steps, warmup_init_lr=-1, last_epoch=-1
):
"""
Create a schedule with a learning rate that decreases as a polynomial decay from the initial lr set in the
optimizer to end lr defined by... | 1,873 | 35.038462 | 115 | py |
DOCRED-FE | DOCRED-FE-main/code/REBEL/src/modeling_bart.py | # coding=utf-8
# Copyright 2021 The Fairseq Authors and 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/... | 85,205 | 43.916183 | 239 | py |
DOCRED-FE | DOCRED-FE-main/code/REBEL/src/pl_data_modules.py | from pyexpat import model
from typing import Any, Union, List, Optional
from omegaconf import DictConfig
import torch
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from datasets import load_dataset, set_caching_enabled
from transformers import (
AutoConfig,
AutoModelForSeq2SeqLM,
... | 9,058 | 45.219388 | 185 | py |
DOCRED-FE | DOCRED-FE-main/code/REBEL/src/train.py | import omegaconf
import hydra
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pl_data_modules import BasePLDataModule
from pl_modules import BasePLModule
from transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer
from pytorch_lightning.loggers... | 7,637 | 47.037736 | 1,961 | py |
DOCRED-FE | DOCRED-FE-main/code/LSTM/config/Config.py | # coding: utf-8
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import numpy as np
import os
import time
import datetime
import json
import sys
import sklearn.metrics
from tqdm import tqdm
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import r... | 25,660 | 31.07625 | 271 | py |
DOCRED-FE | DOCRED-FE-main/code/LSTM/config/EviConfig.py | # coding: utf-8
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import numpy as np
import os
import time
import datetime
import json
import sys
import sklearn.metrics
from tqdm import tqdm
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import r... | 18,017 | 29.435811 | 147 | py |
DOCRED-FE | DOCRED-FE-main/code/LSTM/models/BiLSTM.py | import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch import nn
import numpy as np
import math
from torch.nn import init
from torch.nn.utils import rnn
class BiLSTM(nn.Module):
def __init__(self,... | 11,205 | 36.604027 | 132 | py |
DOCRED-FE | DOCRED-FE-main/code/LSTM/models/CNN3.py | import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
class CNN3(nn.Module):
def __init__(self, config):
super(CNN3, self).__init__()
self.config = config
self.word_emb = nn.Embedding(config.data_word_... | 3,178 | 37.768293 | 143 | py |
DOCRED-FE | DOCRED-FE-main/code/LSTM/models/LSTM.py | import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch import nn
import numpy as np
import math
from torch.nn import init
from torch.nn.utils import rnn
class LSTM(nn.Module):
def __init__(self, c... | 8,656 | 34.048583 | 132 | py |
DOCRED-FE | DOCRED-FE-main/code/GAIN/code/test.py | import sklearn.metrics
import torch
from config import *
from data import DGLREDataset, DGLREDataloader, BERTDGLREDataset
from models.GAIN import GAIN_GloVe, GAIN_BERT
from utils import get_cuda, logging, print_params
# for ablation
# from models.GCNRE_nomention import GAIN_GloVe, GAIN_BERT
def test(model, dataloa... | 8,393 | 34.719149 | 136 | py |
DOCRED-FE | DOCRED-FE-main/code/GAIN/code/evaluate.py | import sklearn.metrics
import torch
from config import *
from data import DGLREDataset, DGLREDataloader, BERTDGLREDataset
from models.GAIN import GAIN_GloVe, GAIN_BERT
from utils import get_cuda, logging, print_params
# for ablation
# from models.GCNRE_nomention import GAIN_GloVe, GAIN_BERT
def eval(model, dataloa... | 8,545 | 36.156522 | 165 | py |
DOCRED-FE | DOCRED-FE-main/code/GAIN/code/utils.py | from datetime import datetime
import numpy as np
import torch
# 返回一个tensor的cuda版本
def get_cuda(tensor):
if torch.cuda.is_available():
return tensor.cuda()
# return tensor # 宁愿报错，也拒绝返回CPU
def logging(s):
print(datetime.now(), s)
class Accuracy(object):
def __init__(self):
self.corre... | 884 | 20.071429 | 109 | py |
DOCRED-FE | DOCRED-FE-main/code/GAIN/code/data.py | import json
import math
import os
import pickle
import random
from collections import defaultdict
import dgl
import numpy as np
import torch
from torch.utils.data import IterableDataset, DataLoader
from tqdm import tqdm
from transformers import *
from models.GAIN import Bert
from utils import get_cuda
IGNORE_INDEX =... | 37,359 | 47.082368 | 186 | py |
DOCRED-FE | DOCRED-FE-main/code/GAIN/code/train.py | import time
import matplotlib
import matplotlib.pyplot as plt
import torch
from torch import nn
from torch import optim
from tqdm import tqdm
from config import *
from data import DGLREDataset, DGLREDataloader, BERTDGLREDataset # 这是从data.py 中引入几个类
from models.GAIN import GAIN_GloVe, GAIN_BERT
from test import test
f... | 9,725 | 37.595238 | 154 | py |
DOCRED-FE | DOCRED-FE-main/code/GAIN/code/models/GAIN.py | import dgl
import dgl.nn.pytorch as dglnn
import numpy as np
import torch
import torch.nn as nn
from transformers import *
from utils import get_cuda
class GAIN_GloVe(nn.Module):
def __init__(self, config):
super(GAIN_GloVe, self).__init__()
self.config = config
word_emb_size = config.... | 41,175 | 47.78673 | 180 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex_train.py | import hydra
from hydra.core.config_store import ConfigStore
from omegaconf import OmegaConf
from torch import seed
import warnings
warnings.filterwarnings("ignore")
from configs import TrainConfig
from jerex import model, util
cs = ConfigStore.instance()
cs.store(name="train", node=TrainConfig)
def seed_everything(... | 1,039 | 25.666667 | 97 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/loss.py | from abc import ABC
import torch
class Loss(ABC):
def compute(self, *args, **kwargs):
pass
class JointLoss(Loss):
def __init__(self, task_weights=None):
self._mention_criterion = torch.nn.BCEWithLogitsLoss(reduction='none')
self._entity_criterion = torch.nn.CrossEntropyLoss(reductio... | 5,942 | 33.754386 | 103 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/model.py | import os
import pickle
import warnings
from multiprocessing import Lock
import pytorch_lightning as pl
import torch
import transformers
from pytorch_lightning import loggers
from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor
from transformers import AdamW, BertConfig, BertTokenizer
from con... | 20,682 | 51.628499 | 119 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/datasets.py | import json
from collections import OrderedDict
import torch
from typing import List
from torch.utils.data import Dataset as TorchDataset
from tqdm import tqdm
from jerex import util
from jerex.entities import Relation, Document, Entity, Token, Sentence, EntityMention
from jerex.task_types import TaskType
from jerex... | 10,283 | 37.373134 | 117 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/util.py | import os
import torch
from hydra.utils import to_absolute_path
from jerex.entities import TokenSpan
def create_directories_file(f):
d = os.path.dirname(f)
if d and not os.path.exists(d):
os.makedirs(d)
return f
def create_directories_dir(d):
if d and not os.path.exists(d):
os.ma... | 3,134 | 24.696721 | 104 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/data_module.py | import json
from collections import OrderedDict
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from transformers import BertTokenizer
from jerex.datasets import DocREDDataset
from jerex.entities import EntityType, RelationType
from jerex.sampling.sampling_common import collate_fn_padding
cla... | 6,985 | 43.782051 | 113 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/evaluation/conversion.py | import torch
def convert_gt_cluster(entity, include_entity_type=False):
t = set([m.orig_span for m in entity.entity_mentions])
if include_entity_type:
t = (t, entity.entity_type)
return t
def convert_gt_relation(relation, include_entity_type=False):
head = convert_gt_cluster(relation.head_... | 3,258 | 31.59 | 110 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/evaluation/joint_evaluator.py | import json
import os
import jinja2
import torch
from typing import List, Tuple, Dict
import jerex.evaluation.scoring
from jerex import util
from jerex.entities import Document
from jerex.evaluation import conversion, scoring
from jerex.evaluation.evaluator import Evaluator
SCRIPT_PATH = os.path.dirname(os.path.real... | 12,679 | 42.129252 | 121 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/models/classification_models.py | import torch
from transformers import BertConfig
from transformers import BertModel
from transformers import BertPreTrainedModel
from jerex import util
from jerex.evaluation.classification_evaluator import MentionLocalizationEvaluator, RelClassificationEvaluator, \
EntityClassificationEvaluator, CoreferenceResolut... | 13,412 | 48.677778 | 155 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/models/misc.py | import torch
from sklearn.cluster import AgglomerativeClustering
from jerex import util
from jerex.sampling import sampling_common
def create_coref_mention_pairs(valid_mentions, mention_spans, encodings, tokenizer):
""" Creates pairs of of mentions associated edit distances and sample masks for coreference resol... | 12,736 | 47.064151 | 113 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/models/joint_models.py | from abc import abstractmethod
import torch
from transformers import BertConfig, BertTokenizer
from transformers import BertModel
from transformers import BertPreTrainedModel
from jerex import util
from jerex.evaluation.joint_evaluator import JointEvaluator
from jerex.loss import JointLoss
from jerex.task_types impor... | 19,226 | 59.462264 | 155 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/models/__init__.py | import torch
from torch import nn
from transformers import BertPreTrainedModel, BertConfig, BertTokenizer
from jerex.models.classification_models import RelClassificationMultiInstanceModel, \
EntityClassificationModel, CoreferenceResolutionModel, MentionLocalizationModel, RelClassificationGlobal
from jerex.models.... | 3,443 | 46.833333 | 138 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/models/modules/relation_classification_global.py | from torch import nn as nn
class RelationClassificationGlobal(nn.Module):
def __init__(self, hidden_size, relation_types):
super().__init__()
self.rel_classifier = nn.Linear(hidden_size, relation_types)
def forward(self, entity_pair_reprs):
rel_clf = self.rel_classifier(entity_pair_r... | 350 | 24.071429 | 68 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/models/modules/mention_localization.py | import torch
from torch import nn as nn
class MentionLocalization(nn.Module):
def __init__(self, hidden_size, meta_embedding_size, size_embeddings_count, prop_drop):
super().__init__()
self.linear = nn.Linear(hidden_size + meta_embedding_size, hidden_size)
self.mention_classifier = nn.Lin... | 915 | 38.826087 | 91 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/models/modules/relation_classification_multi_instance.py | import torch
from torch import nn as nn
from jerex import util
import numpy as np
class RelationClassificationMultiInstance(nn.Module):
def __init__(self, hidden_size, entity_types, relation_types, meta_embedding_size,
token_dist_embeddings_count, sentence_dist_embeddings_count, prop_drop, use_f... | 8,504 | 54.227273 | 154 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/models/modules/entity_pair_representation.py | import torch
from torch import nn as nn
from jerex import util
class EntityPairRepresentation(nn.Module):
def __init__(self, hidden_size, entity_types, meta_embedding_size, prop_drop):
super().__init__()
self.entity_pair_linear = nn.Linear(hidden_size * 2 + meta_embedding_size * 2, hidden_size)
... | 1,437 | 34.073171 | 106 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/models/modules/entity_classification.py | import torch
from torch import nn as nn
class EntityClassification(nn.Module):
def __init__(self, hidden_size, entity_types, prop_drop):
super().__init__()
self.linear = nn.Linear(hidden_size, hidden_size)
self.entity_classifier = nn.Linear(hidden_size, entity_types)
self.dropout ... | 542 | 29.166667 | 74 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/models/modules/entity_representation.py | from torch import nn as nn
from jerex import util
class EntityRepresentation(nn.Module):
def __init__(self, prop_drop):
super().__init__()
self.dropout = nn.Dropout(prop_drop)
def forward(self, mention_reprs, entities, entity_masks):
mention_clusters = util.batch_index(mention_reprs... | 652 | 28.681818 | 68 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/models/modules/relation_classification_multi_instance copy.py | import torch
from torch import nn as nn
from jerex import util
import numpy as np
class RelationClassificationMultiInstance(nn.Module):
def __init__(self, hidden_size, entity_types, relation_types, meta_embedding_size,
token_dist_embeddings_count, sentence_dist_embeddings_count, prop_drop):
... | 7,586 | 53.582734 | 150 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/models/modules/mention_representation.py | import torch
from torch import nn as nn
class MentionRepresentation(nn.Module):
def __init__(self):
super().__init__()
def forward(self, h, mention_masks, max_spans=None):
mention_reprs = torch.zeros([mention_masks.shape[0], mention_masks.shape[1],
h.shape... | 1,097 | 33.3125 | 84 | py |
DOCRED-FE | DOCRED-FE-main/code/JEREX/jerex/models/modules/coreference_resolution.py | import torch
from torch import nn as nn
from jerex import util
class CoreferenceResolution(nn.Module):
def __init__(self, hidden_size, meta_embedding_size, ed_embeddings_count, prop_drop):
super().__init__()
self.coref_linear = nn.Linear(hidden_size * 2 + meta_embedding_size, hidden_size)
... | 2,196 | 39.685185 | 97 | py |
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