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
value |
|---|---|---|---|---|---|---|
EmpTransfo | EmpTransfo-master/pytorch_pretrained_bert/optimization.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# 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/LICEN... | 13,028 | 42 | 139 | py |
EmpTransfo | EmpTransfo-master/pytorch_pretrained_bert/optimization_openai.py | # coding=utf-8
# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
#
# 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
#
# ... | 5,517 | 42.109375 | 134 | py |
EmpTransfo | EmpTransfo-master/pytorch_pretrained_bert/__main__.py | # coding: utf8
def main():
import sys
if (len(sys.argv) != 4 and len(sys.argv) != 5) or sys.argv[1] not in [
"convert_tf_checkpoint_to_pytorch",
"convert_openai_checkpoint",
"convert_transfo_xl_checkpoint",
"convert_gpt2_checkpoint",
]:
print(
"Should be used ... | 4,393 | 51.309524 | 145 | py |
EmpTransfo | EmpTransfo-master/pytorch_pretrained_bert/convert_gpt2_checkpoint_to_pytorch.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# 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... | 3,017 | 40.342466 | 111 | py |
EmpTransfo | EmpTransfo-master/pytorch_pretrained_bert/convert_openai_checkpoint_to_pytorch.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# 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... | 3,106 | 41.561644 | 118 | py |
EmpTransfo | EmpTransfo-master/pytorch_pretrained_bert/tokenization.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# 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/LICEN... | 18,169 | 40.770115 | 135 | py |
EmpTransfo | EmpTransfo-master/pytorch_pretrained_bert/modeling.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... | 85,247 | 51.524954 | 187 | py |
EmpTransfo | EmpTransfo-master/pytorch_pretrained_bert/modeling_gpt2.py | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and 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 copy of the License... | 38,587 | 45.944039 | 146 | py |
EmpTransfo | EmpTransfo-master/pytorch_pretrained_bert/modeling_openai.py | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and 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 copy of the License... | 53,002 | 47.626606 | 186 | py |
EmpTransfo | EmpTransfo-master/pytorch_pretrained_bert/convert_transfo_xl_checkpoint_to_pytorch.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# 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... | 5,671 | 47.478632 | 121 | py |
EmpTransfo | EmpTransfo-master/pytorch_pretrained_bert/file_utils.py | """
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
from __future__ import (absolute_import, division, print_function, unicode_literals)
import sys
import json
import logging
import os
impor... | 9,347 | 32.385714 | 98 | py |
EmpTransfo | EmpTransfo-master/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# 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... | 2,593 | 37.716418 | 101 | py |
EmpTransfo | EmpTransfo-master/pytorch_pretrained_bert/modeling_transfo_xl.py | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University 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 Lice... | 59,065 | 41.40201 | 131 | py |
EmpTransfo | EmpTransfo-master/pytorch_pretrained_bert/tokenization_transfo_xl.py | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University 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 Lice... | 22,060 | 36.582624 | 110 | py |
EmpTransfo | EmpTransfo-master/pytorch_pretrained_bert/modeling_transfo_xl_utilities.py | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University 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 Lice... | 16,108 | 38.972705 | 132 | py |
potapov_interpolation | potapov_interpolation-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# Potapov_interpolation documentation build configuration file, created by
# sphinx-quickstart on Mon Apr 25 15:36:39 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerat... | 10,438 | 30.923547 | 85 | py |
gnn_cff | gnn_cff-main/graphconv.py | """Torch modules for graph convolutions(GCN)."""
# pylint: disable= no-member, arguments-differ, invalid-name
import torch as th
from torch import nn
from torch.nn import init
from .... import function as fn
from ....base import DGLError
from ....utils import expand_as_pair
from ....transform import reverse
from ....c... | 30,598 | 40.072483 | 102 | py |
gnn_cff | gnn_cff-main/models/gcn.py | import numpy as np
from dgl.nn.pytorch import GraphConv
import dgl
import torch
# class GCNGraphNew(torch.nn.Module):
# def __init__(self, in_feats, h_feats):
# super(GCNGraphNew, self).__init__()
# self.conv1 = GraphConv(in_feats, h_feats)
# self.conv2 = GraphConv(h_feats, h_feats)
# ... | 4,777 | 36.622047 | 112 | py |
gnn_cff | gnn_cff-main/models/explainer_models.py | from re import S
import numpy as np
import torch
import math
import tqdm
import sys
import matplotlib.pyplot as plt
import networkx as nx
from utils.common_utils import mutag_dgl_to_networkx, get_mutag_color_dict, ba_shapes_dgl_to_networkx
class GraphExplainerEdge(torch.nn.Module):
def __init__(self, base_model, ... | 27,933 | 40.079412 | 142 | py |
gnn_cff | gnn_cff-main/scripts/exp_node_tree_cycles.py | import os
import numpy as np
import torch
from utils.argument import arg_parse_exp_node_tree_cycles
from models.explainer_models import NodeExplainerEdgeMulti
from models.gcn import GCNNodeTreeCycles
from utils.preprocessing.tree_cycles_preprocessing import TreeCyclesDataset
import sys
if __name__ == "__main__":
... | 1,650 | 35.688889 | 116 | py |
gnn_cff | gnn_cff-main/scripts/exp_node_ba_shapes.py | import os
import numpy as np
import torch
from utils.argument import arg_parse_exp_node_ba_shapes
from models.explainer_models import NodeExplainerEdgeMulti
from models.gcn import GCNNodeBAShapes
from utils.preprocessing.ba_shapes_preprocessing import BAShapesDataset
import sys
if __name__ == "__main__":
torch.ma... | 1,646 | 37.302326 | 116 | py |
gnn_cff | gnn_cff-main/scripts/train_graph_classification.py | import numpy as np
import torch
import os
import time
from pathlib import Path
from models.gcn import GCNGraph
from utils.argument import arg_parse_train_graph_mutag_0
from utils.graph_init import graph_init_real
from torch.utils.data.sampler import SubsetRandomSampler
from dgl.dataloading import GraphDataLoader
def ... | 3,833 | 39.787234 | 117 | py |
gnn_cff | gnn_cff-main/scripts/exp_graph.py | import os
import numpy as np
import torch
from utils.argument import arg_parse_exp_graph_mutag_0
from models.explainer_models import GraphExplainerEdge
from models.gcn import GCNGraph
from utils.preprocessing.mutag_preprocessing_0 import MutagDataset0
import sys
if __name__ == "__main__":
np.set_printoptions(thre... | 1,428 | 32.232558 | 96 | py |
gnn_cff | gnn_cff-main/scripts/train_node_classification.py | import numpy as np
import torch
import os
import time
from pathlib import Path
from models.gcn import GCNNodeBAShapes
from utils.argument import arg_parse_train_node_ba_shapes
from utils.graph_init import graph_init_real
from torch.utils.data.sampler import SubsetRandomSampler
from dgl.dataloading import GraphDataLoade... | 4,554 | 38.95614 | 104 | py |
gnn_cff | gnn_cff-main/utils/preprocessing/ba_shapes_preprocessing.py | """Read the Mutag dataset and create the graphx"""
import numpy as np
import os
import dgl
from dgl.data import DGLDataset
import torch
import networkx as nx
import matplotlib.pyplot as plt
from dgl import save_graphs, load_graphs
from utils.common_utils import read_file
from utils.common_utils import ba_shapes_dgl_to... | 5,366 | 37.335714 | 109 | py |
gnn_cff | gnn_cff-main/utils/preprocessing/mutag_preprocessing_0.py | """Read the Mutag dataset and create the graphx"""
import numpy as np
import os
import dgl
from dgl.data import DGLDataset
import torch
from dgl import save_graphs, load_graphs
from utils.common_utils import read_file
class MutagDataset0(DGLDataset):
def __init__(self, edges=None, graph_indicator=None, node_labe... | 5,400 | 41.865079 | 128 | py |
gnn_cff | gnn_cff-main/utils/preprocessing/tree_cycles_preprocessing.py | """Read the Mutag dataset and create the graphx"""
import numpy as np
import os
import dgl
from dgl.data import DGLDataset
import torch
import networkx as nx
import matplotlib.pyplot as plt
from dgl import save_graphs, load_graphs
from utils.common_utils import read_file
from utils.common_utils import ba_shapes_dgl_to... | 5,903 | 36.367089 | 109 | py |
gnn_cff | gnn_cff-main/utils/preprocessing/citeseer_preprocessing.py | """Read the Mutag dataset and create the graphx"""
import numpy as np
import os
import dgl
from dgl.data import DGLDataset
import torch
import networkx as nx
import matplotlib.pyplot as plt
from dgl import save_graphs, load_graphs
from utils.common_utils import read_file_citeseer
from utils.common_utils import ba_shap... | 5,767 | 34.170732 | 109 | py |
gnn_cff | gnn_cff-main/utils/preprocessing/nci1_preprocessing.py | """Read the Mutag dataset and create the graphx"""
import numpy as np
import os
import dgl
from dgl.data import DGLDataset
import torch
from dgl import save_graphs, load_graphs
from utils.common_utils import read_file
class NCI1Dataset(DGLDataset):
def __init__(self, edges=None, graph_indicator=None, node_labels... | 4,663 | 41.4 | 112 | py |
hifi-gan | hifi-gan-master/inference.py | from __future__ import absolute_import, division, print_function, unicode_literals
import glob
import os
import argparse
import json
import torch
from scipy.io.wavfile import write
from env import AttrDict
from meldataset import mel_spectrogram, MAX_WAV_VALUE, load_wav
from models import Generator
h = None
device = N... | 2,652 | 26.635417 | 107 | py |
hifi-gan | hifi-gan-master/inference_e2e.py | from __future__ import absolute_import, division, print_function, unicode_literals
import glob
import os
import numpy as np
import argparse
import json
import torch
from scipy.io.wavfile import write
from env import AttrDict
from meldataset import MAX_WAV_VALUE
from models import Generator
h = None
device = None
de... | 2,444 | 25.868132 | 105 | py |
hifi-gan | hifi-gan-master/meldataset.py | import math
import os
import random
import torch
import torch.utils.data
import numpy as np
from librosa.util import normalize
from scipy.io.wavfile import read
from librosa.filters import mel as librosa_mel_fn
MAX_WAV_VALUE = 32768.0
def load_wav(full_path):
sampling_rate, data = read(full_path)
return data... | 6,314 | 36.366864 | 115 | py |
hifi-gan | hifi-gan-master/utils.py | import glob
import os
import matplotlib
import torch
from torch.nn.utils import weight_norm
matplotlib.use("Agg")
import matplotlib.pylab as plt
def plot_spectrogram(spectrogram):
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
interpolatio... | 1,377 | 22.355932 | 63 | py |
hifi-gan | hifi-gan-master/models.py | import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from utils import init_weights, get_padding
LRELU_SLOPE = 0.1
class ResBlock1(torch.nn.Module):
def __init__... | 9,905 | 33.880282 | 107 | py |
hifi-gan | hifi-gan-master/train.py | import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import itertools
import os
import time
import argparse
import json
import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DistributedSampler, DataLoader
import torch.multip... | 12,153 | 43.683824 | 123 | py |
LowFat | LowFat-master/llvm-4.0.0.src/tools/clang/docs/conf.py | # -*- coding: utf-8 -*-
#
# Clang documentation build configuration file, created by
# sphinx-quickstart on Sun Dec 9 20:01:55 2012.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All c... | 9,128 | 32.317518 | 83 | py |
LowFat | LowFat-master/llvm-4.0.0.src/tools/clang/docs/analyzer/conf.py | # -*- coding: utf-8 -*-
#
# Clang Static Analyzer documentation build configuration file, created by
# sphinx-quickstart on Wed Jan 2 15:54:28 2013.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated... | 8,070 | 31.544355 | 80 | py |
DMASTE | DMASTE-main/Span-ASTE/span_model/training/ner_metrics.py | from overrides import overrides
from typing import Optional
import torch
from allennlp.training.metrics.metric import Metric
from span_model.training.f1 import compute_f1
# TODO: Need to use the decoded predictions so that we catch the gold examples longer than
# the span boundary.
class NERMetrics(Metric):
"... | 2,358 | 29.636364 | 95 | py |
DMASTE | DMASTE-main/Span-ASTE/span_model/models/ner.py | import logging
from typing import Any, Dict, List, Optional, Callable
import torch
from torch.nn import functional as F
from overrides import overrides
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.modules import TimeDistributed
from allennlp.nn import util, InitializerApp... | 10,868 | 39.707865 | 121 | py |
DMASTE | DMASTE-main/Span-ASTE/span_model/models/embedder.py | from typing import Optional, Tuple
from overrides import overrides
import torch
from allennlp.modules.token_embedders import PretrainedTransformerEmbedder, TokenEmbedder
from allennlp.nn import util
from allennlp.modules.scalar_mix import ScalarMix
@TokenEmbedder.register("double_mix_ptm")
class DoubleMixPTMEmbedde... | 7,218 | 44.689873 | 131 | py |
DMASTE | DMASTE-main/Span-ASTE/span_model/models/relation_proper.py | import logging
from typing import Any, Dict, List, Optional, Callable
import torch
import torch.nn.functional as F
from overrides import overrides
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.nn import util, RegularizerApplicator
from allennlp.modules import TimeDistribut... | 20,888 | 39.561165 | 128 | py |
DMASTE | DMASTE-main/Span-ASTE/span_model/models/shared.py | """
Short utility functions.
"""
from typing import Optional, Callable
import torch
import torch.nn.functional as F
from allennlp.modules import FeedForward
from allennlp.modules.span_extractors import EndpointSpanExtractor, SpanExtractor
from allennlp.nn.util import batched_span_select
from overrides import override... | 10,701 | 33.634304 | 106 | py |
DMASTE | DMASTE-main/Span-ASTE/span_model/models/span_model.py | import logging
from typing import Dict, List, Optional, Union
import copy
import torch
import torch.nn.functional as F
from overrides import overrides
from allennlp.data import Vocabulary
from allennlp.common.params import Params
from allennlp.models.model import Model
from allennlp.modules import TextFieldEmbedder, ... | 17,657 | 35.941423 | 111 | py |
DMASTE | DMASTE-main/Span-ASTE/span_model/models/entity_beam_pruner.py | """
This is basically a copy of AllenNLP's Pruner module, but with support for entity beams.
"""
from typing import Tuple, Union
from overrides import overrides
import torch
from allennlp.nn import util
from allennlp.modules import TimeDistributed
def make_pruner(scorer, entity_beam=False, gold_beam=False):
""... | 18,631 | 43.46778 | 99 | py |
DMASTE | DMASTE-main/Span-ASTE/aste/main.py | import json
import shutil
import time
from os import remove
from pathlib import Path
from typing import List, Tuple, Optional
import _jsonnet # noqa
import pandas as pd
from fire import Fire
from pydantic import BaseModel
from data_utils import (
LabelEnum,
SplitEnum,
Sentence,
SentimentTriple,
D... | 9,999 | 33.129693 | 99 | py |
DMASTE | DMASTE-main/Span-ASTE/aste/wrapper.py | import json
import os
from pathlib import Path
from typing import List
import _jsonnet
from fire import Fire
from pydantic import BaseModel
from tqdm import tqdm
from data_utils import Data, SentimentTriple, SplitEnum
from main import SpanModelData, SpanModelPrediction
from utils import Shell, safe_divide
class Spa... | 5,884 | 33.617647 | 85 | py |
DMASTE | DMASTE-main/BMRC/main.py | # coding: UTF-8
# @Author: Shaowei Chen, Contact: chenshaowei0507@163.com
# @Date: 2021-5-4
import argparse
import Data
import Model
import utils
import torch
from torch.nn import functional as F
from transformers import AdamW, get_linear_schedule_with_warmup, BertTokenizer
import os
from torch.utils.data import... | 34,866 | 52.3951 | 156 | py |
DMASTE | DMASTE-main/BMRC/DANN_main.py | # coding: UTF-8
# @Author: Shaowei Chen, Contact: chenshaowei0507@163.com
# @Date: 2021-5-4
import argparse
import Data
import DANN_Model as Model
import utils
import torch
from torch.nn import functional as F
from transformers import AdamW, get_linear_schedule_with_warmup, BertTokenizer
import os
from torch.uti... | 40,827 | 54.928767 | 229 | py |
DMASTE | DMASTE-main/BMRC/DANN_Model.py | # coding: UTF-8
# @Author: Shaowei Chen, Contact: chenshaowei0507@163.com
# @Date: 2021-5-4
from transformers import BertTokenizer, BertModel, BertConfig
import torch.nn as nn
from functions import ReverseLayerF
class BERTModel(nn.Module):
def __init__(self, args):
hidden_size = args.hidden_size
... | 2,062 | 38.673077 | 112 | py |
DMASTE | DMASTE-main/BMRC/Data.py | # coding: UTF-8
# @Author: Shaowei Chen, Contact: chenshaowei0507@163.com
# @Date: 2021-5-4
from torch.utils.data import Dataset, DataLoader
import numpy as np
class OriginalDataset(Dataset):
def __init__(self, pre_data):
self._forward_asp_query = pre_data['_forward_asp_query']
self._forwar... | 8,838 | 53.561728 | 87 | py |
DMASTE | DMASTE-main/BMRC/functions.py | from torch.autograd import Function
class ReverseLayerF(Function):
@staticmethod
def forward(ctx, x, alpha):
ctx.alpha = alpha
return x.view_as(x)
@staticmethod
def backward(ctx, grad_output):
output = grad_output.neg() * ctx.alpha
return output, None | 305 | 18.125 | 46 | py |
DMASTE | DMASTE-main/BMRC/dataProcess.py | # @Author: Shaowei Chen, Contact: chenshaowei0507@163.com
# @Date: 2021-5-4
import pickle
import torch
import os
class dual_sample(object):
def __init__(self,
original_sample,
text,
forward_querys,
forward_answers,
backward... | 7,431 | 42.209302 | 230 | py |
DMASTE | DMASTE-main/BMRC/utils.py | # coding: UTF-8
# @Author: Shaowei Chen, Contact: chenshaowei0507@163.com
# @Date: 2021-5-4
import torch
from torch.nn import functional as F
import logging
def normalize_size(tensor):
if len(tensor.size()) == 3:
tensor = tensor.contiguous().view(-1, tensor.size(2))
elif len(tensor.size()) == 2... | 3,575 | 35.121212 | 128 | py |
DMASTE | DMASTE-main/BMRC/data_utils.py | from torch.utils.data import Dataset
import random
import torch
class Domain:
Target = 1
Source = 0
class Unlabeled_Dataset(Dataset):
def __init__(self, path, tokenizer, max_len=256):
self.data = []
self.max_len = max_len
with open(path) as f:
for line in f:
... | 2,601 | 45.464286 | 150 | py |
DMASTE | DMASTE-main/BMRC/makeData_dual.py | # @Author: Shaowei Chen, Contact: chenshaowei0507@163.com
# @Date: 2021-5-4
import torch
from torch.utils.data import Dataset
from transformers import BertTokenizer
import numpy as np
_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
class dual_sample(object):
def __init__(self,
... | 24,242 | 50.037895 | 144 | py |
DMASTE | DMASTE-main/BMRC/Model.py | # coding: UTF-8
# @Author: Shaowei Chen, Contact: chenshaowei0507@163.com
# @Date: 2021-5-4
from transformers import BertTokenizer, BertModel, BertConfig
import torch.nn as nn
class BERTModel(nn.Module):
def __init__(self, args):
hidden_size = args.hidden_size
super(BERTModel, self).__init... | 1,477 | 35.04878 | 104 | py |
DMASTE | DMASTE-main/BMRC/makeData_standard.py | # @Author: Shaowei Chen, Contact: chenshaowei0507@163.com
# @Date: 2021-5-4
import torch
import pickle
from dataProcess import get_text
def make_standard(home_path, dataset_name, dataset_type):
# read triple
f = open(home_path + dataset_name + "/" + dataset_type + ".txt", "r", encoding="utf-8")
te... | 2,219 | 35.393443 | 113 | py |
DMASTE | DMASTE-main/Generative-ABSA/main.py | import argparse
import os
import logging
import time
import pickle
from tqdm import tqdm
import torch
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning import seed_everything
from transformers import AdamW, T5ForConditionalGeneration, T5Tokenizer
from transformers import ge... | 16,407 | 42.638298 | 158 | py |
DMASTE | DMASTE-main/Generative-ABSA/data_utils.py | # This file contains all data loading and transformation functions
import time
from torch.utils.data import Dataset
senttag2word = {'POS': 'positive', 'NEG': 'negative', 'NEU': 'neutral'}
def read_line_examples_from_file(data_path):
"""
Read data from file, each line is: sent####labels
Return List[List[... | 11,367 | 34.304348 | 105 | py |
DMASTE | DMASTE-main/GTS/code/NNModel/main.py | #coding utf-8
import json, os
import random
import argparse
import numpy
import torch
import torch.nn.functional as F
from tqdm import trange
import numpy as np
from data import load_data_instances, DataIterator
from model import MultiInferRNNModel, MultiInferCNNModel
import utils
def train(args):
# load doub... | 7,166 | 39.954286 | 127 | py |
DMASTE | DMASTE-main/GTS/code/NNModel/attention_module.py | import copy
import math
import torch
import torch.nn.functional as F
def attention(query, key, value, mask=None, dropout=None):
"Compute 'Scaled Dot Product Attention'"
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) \
/ math.sqrt(d_k)
if mask is not None:
... | 4,281 | 38.648148 | 112 | py |
DMASTE | DMASTE-main/GTS/code/NNModel/model.py | import torch
import torch.nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
from attention_module import MultiHeadedAttention, SelfAttention
class MultiInferRNNModel(torch.nn.Module):
def __init__(self, gen_emb, domain_emb, args):
'''double embedd... | 7,886 | 41.632432 | 123 | py |
DMASTE | DMASTE-main/GTS/code/NNModel/data.py | import math
import torch
sentiment2id = {'negative': 3, 'neutral': 4, 'positive': 5}
def get_spans(tags):
'''for BIO tag'''
tags = tags.strip().split()
length = len(tags)
spans = []
start = -1
for i in range(length):
if tags[i].endswith('B'):
if start != -1:
... | 5,123 | 36.955556 | 93 | py |
DMASTE | DMASTE-main/GTS/code/BertModel/main.py | #coding utf-8
import json, os
import random
import argparse
import torch
import torch.nn.functional as F
from tqdm import trange
from data import load_data_instances, DataIterator
from model import MultiInferBert
import utils
def train(args):
# load dataset
train_sentence_packs = json.load(open(args.prefi... | 6,176 | 37.60625 | 124 | py |
DMASTE | DMASTE-main/GTS/code/BertModel/model.py | import torch
import torch.nn
from transformers import BertModel, BertTokenizer
class MultiInferBert(torch.nn.Module):
def __init__(self, args):
super(MultiInferBert, self).__init__()
self.args = args
self.bert = BertModel.from_pretrained(args.bert_model_path)
self.tokenizer = Ber... | 2,451 | 37.3125 | 114 | py |
DMASTE | DMASTE-main/GTS/code/BertModel/data.py | import math
import torch
import numpy as np
sentiment2id = {'negative': 3, 'neutral': 4, 'positive': 5}
from transformers import BertTokenizer
def get_spans(tags):
'''for BIO tag'''
tags = tags.strip().split()
length = len(tags)
spans = []
start = -1
for i in range(length):
if tags[i... | 7,269 | 36.864583 | 100 | py |
DMASTE | DMASTE-main/BARTABSA/peng/train.py | import sys
sys.path.append('../')
import os
if 'p' in os.environ:
os.environ['CUDA_VISIBLE_DEVICES'] = os.environ['p']
# os.environ['CUDA_VISIBLE_DEVICES'] = '7'
import warnings
warnings.filterwarnings('ignore')
from data.pipe import BartBPEABSAPipe
from peng.model.bart_absa import BartSeq2SeqModel
from fastN... | 7,183 | 37.623656 | 128 | py |
DMASTE | DMASTE-main/BARTABSA/peng/model/losses.py |
from fastNLP import LossBase
import torch.nn.functional as F
from fastNLP import seq_len_to_mask
class Seq2SeqLoss(LossBase):
def __init__(self):
super().__init__()
def get_loss(self, tgt_tokens, tgt_seq_len, pred):
"""
:param tgt_tokens: bsz x max_len, [sos, tokens, eos]
:p... | 671 | 27 | 81 | py |
DMASTE | DMASTE-main/BARTABSA/peng/model/bart_absa.py | import torch
from .modeling_bart import BartEncoder, BartDecoder, BartModel
from transformers import BartTokenizer
from fastNLP import seq_len_to_mask
from fastNLP.modules import Seq2SeqEncoder, Seq2SeqDecoder, State
import torch.nn.functional as F
from fastNLP.models import Seq2SeqModel
from torch import nn
import mat... | 14,844 | 46.428115 | 145 | py |
DMASTE | DMASTE-main/BARTABSA/peng/model/modeling_bart.py | # coding=utf-8
# Copyright 2020 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
#
# 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/LIC... | 58,282 | 41.234058 | 213 | py |
DMASTE | DMASTE-main/BARTABSA/peng/model/generator.py | r"""Modify from fastNLP"""
import torch
from torch import nn
from fastNLP.models.seq2seq_model import Seq2SeqModel
from fastNLP.modules.decoder.seq2seq_decoder import Seq2SeqDecoder, State
import torch.nn.functional as F
from fastNLP.core.utils import _get_model_device
from functools import partial
class SequenceGen... | 23,989 | 44.435606 | 126 | py |
DMASTE | DMASTE-main/mySpanASTE/main.py | import os
import random
import argparse
import torch
from transformers import BertTokenizer, BertModel
from torch.utils.data import DataLoader
from torch.optim import AdamW
from tqdm import tqdm
from transformers.optimization import get_linear_schedule_with_warmup
from torch.utils.tensorboard import SummaryWriter
... | 8,336 | 46.369318 | 190 | py |
DMASTE | DMASTE-main/mySpanASTE/DANN_main.py | import os
import random
import argparse
import torch
from transformers import BertTokenizer, BertModel
from torch.utils.data import DataLoader
from torch.optim import AdamW
from tqdm import tqdm
from transformers.optimization import get_linear_schedule_with_warmup
from torch.utils.tensorboard import SummaryWriter
... | 10,335 | 47.754717 | 232 | py |
DMASTE | DMASTE-main/mySpanASTE/models/relation.py | from os import read
import torch
import math
from utils.data_utils import RelationLabel, SpanLabel
from utils.index_select import batched_index_select
from models.feedForward import FeedForward
def bucket_values(
distances: torch.Tensor, num_identity_buckets: int = 4, num_total_buckets: int = 10
) -> torch.Tens... | 8,024 | 51.796053 | 186 | py |
DMASTE | DMASTE-main/mySpanASTE/models/feedForward.py | import torch
class FeedForward(torch.nn.Module):
def __init__(self, input_dim, hidden_dim, num_layers, activation, dropout):
super(FeedForward, self).__init__()
hidden_dims = [hidden_dim] * num_layers # type: ignore
activations = [activation] * num_layers # type: ignore
dropout =... | 1,421 | 39.628571 | 79 | py |
DMASTE | DMASTE-main/mySpanASTE/models/DANN_span_aste.py | import torch
from torch.nn import functional as F
from utils.index_select import batched_index_select
from models.ner import NERModel
from models.relation import RelationModel
from models.functions import ReverseLayerF
class SpanModel(torch.nn.Module):
def __init__(self, encoder, width_embedding_dim=20, max_width... | 3,605 | 59.1 | 157 | py |
DMASTE | DMASTE-main/mySpanASTE/models/ner.py | import torch
from torch.nn.modules import dropout
import torch.nn.functional as F
from utils.data_utils import SpanLabel
from models.feedForward import FeedForward
class NERModel(torch.nn.Module):
def __init__(self, span_embed_dim, hidden_dim=150, num_layers=2, activation=torch.nn.ReLU(), dropout=0.4, n_labels=3... | 2,651 | 48.111111 | 188 | py |
DMASTE | DMASTE-main/mySpanASTE/models/functions.py | from torch.autograd import Function
class ReverseLayerF(Function):
@staticmethod
def forward(ctx, x, alpha):
ctx.alpha = alpha
return x.view_as(x)
@staticmethod
def backward(ctx, grad_output):
output = grad_output.neg() * ctx.alpha
return output, None | 305 | 18.125 | 46 | py |
DMASTE | DMASTE-main/mySpanASTE/models/span_aste.py | import torch
from utils.index_select import batched_index_select
from models.ner import NERModel
from models.relation import RelationModel
class SpanModel(torch.nn.Module):
def __init__(self, encoder, width_embedding_dim=20, max_width=512, spans_per_word=0.5):
super(SpanModel, self).__init__()
sel... | 2,370 | 52.886364 | 157 | py |
DMASTE | DMASTE-main/mySpanASTE/utils/data_utils_unlabeled.py | import os
from enum import IntEnum
from torch.utils.data import Dataset
class DomainLabel(IntEnum):
Source = 0
Target = 1
class UnlabeledDataset(Dataset):
def __init__(self, features):
self.features = features
def __getitem__(self, index):
return self.features[index]
... | 3,572 | 33.68932 | 92 | py |
DMASTE | DMASTE-main/mySpanASTE/utils/collate_unlabeled.py | import torch
from utils.data_utils import RelationLabel
from utils.data_utils_unlabeled import DomainLabel
def collate_fn_target(data):
"""批处理，填充同一batch中句子最大的长度"""
def pad_and_tensor(data, pad_value=0):
max_len = max([len(x) for x in data])
new_data = []
mask = []
for x in dat... | 1,535 | 36.463415 | 99 | py |
DMASTE | DMASTE-main/mySpanASTE/utils/data_utils.py | import os
from enum import IntEnum
from pydantic import BaseModel
from typing import List
from torch.utils.data import Dataset
import torch
class SpanLabel(IntEnum):
INVALID = 0
ASPECT = 1
OPINION = 2
class RelationLabel(IntEnum):
INVALID = 0
POS = 1
NEG = 2
NEU = 3
class ABSADataset... | 8,811 | 38.339286 | 155 | py |
DMASTE | DMASTE-main/mySpanASTE/utils/collate.py | import torch
from utils.data_utils import RelationLabel
def collate_fn(data):
"""批处理，填充同一batch中句子最大的长度"""
def pad_and_tensor(data, pad_value=0):
max_len = max([len(x) for x in data])
new_data = []
mask = []
for x in data:
tmp_data = torch.tensor(x)
siz... | 2,502 | 39.370968 | 106 | py |
DMASTE | DMASTE-main/mySpanASTE/utils/index_select.py | import torch
def batched_index_select(target, indices):
"""
target : `torch.Tensor`, required.
A 3 dimensional tensor of shape (batch_size, sequence_length, embedding_size).
This is the tensor to be indexed.
indices : `torch.LongTensor`
A tensor of shape (batch_size, ...), where eac... | 1,622 | 42.864865 | 111 | py |
DMASTE | DMASTE-main/mySpanASTE/utils/metric.py | import torch
from utils.data_utils import convert_pad_tensor_to_list, convert_predictions_to_triples, SpanLabel, RelationLabel
from sklearn.metrics import precision_score, recall_score, f1_score
def convert_relations_to_list(relations, mask):
ret = []
for i in range(relations.shape[0]):
r, m = relatio... | 6,970 | 51.022388 | 159 | py |
Learning-Debiased-Disentangled | Learning-Debiased-Disentangled-master/test.py | import numpy as np
import torch
import random
from learner import Learner
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Learning Debiased Representation via Disentangled Feature Augmentation (NeurIPS 21 Oral)')
# training
parser.add_argument("--batch_size", help=... | 3,784 | 63.152542 | 170 | py |
Learning-Debiased-Disentangled | Learning-Debiased-Disentangled-master/learner.py | from tqdm import tqdm
import wandb
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import os
import torch.optim as optim
from data.util import get_dataset, IdxDataset
from module.loss import GeneralizedCELoss
from module.util import get_model
from util import EMA
class ... | 25,007 | 39.400646 | 161 | py |
Learning-Debiased-Disentangled | Learning-Debiased-Disentangled-master/util.py | '''Modified from https://github.com/alinlab/LfF/blob/master/util.py'''
import io
import torch
import numpy as np
import torch.nn as nn
class EMA:
def __init__(self, label, num_classes=None, alpha=0.9):
self.label = label.cuda()
self.alpha = alpha
self.parameter = torch.zeros(label.size(0))... | 1,178 | 35.84375 | 118 | py |
Learning-Debiased-Disentangled | Learning-Debiased-Disentangled-master/train.py | import numpy as np
import torch
import random
from learner import Learner
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Learning Debiased Representation via Disentangled Feature Augmentation (NeurIPS 21 Oral)')
# training
parser.add_argument("--batch_size", help=... | 4,084 | 59.970149 | 170 | py |
Learning-Debiased-Disentangled | Learning-Debiased-Disentangled-master/module/resnet.py | ''' From https://github.com/alinlab/LfF/blob/master/module/resnet.py '''
"""
Properly implemented ResNet-s for CIFAR10 as described in paper [1].
The implementation and structure of this file is hugely influenced by [2]
which is implemented for ImageNet and doesn't have option A for identity.
Moreover, most of the imp... | 6,270 | 27.375566 | 78 | py |
Learning-Debiased-Disentangled | Learning-Debiased-Disentangled-master/module/mlp.py | ''' Modified from https://github.com/alinlab/LfF/blob/master/module/mlp.py'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class MLP_DISENTANGLE(nn.Module):
def __init__(self, num_classes = 10):
super(MLP_DISENTANGLE, self).__init__()
self.feature = nn.Sequential(
... | 2,245 | 26.728395 | 77 | py |
Learning-Debiased-Disentangled | Learning-Debiased-Disentangled-master/module/loss.py | '''From https://github.com/alinlab/LfF/blob/master/module/loss.py'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class GeneralizedCELoss(nn.Module):
def __init__(self, q=0.7):
super(GeneralizedCELoss, self).__init__()
self.q = q
... | 813 | 28.071429 | 79 | py |
Learning-Debiased-Disentangled | Learning-Debiased-Disentangled-master/module/util.py | ''' Modified from https://github.com/alinlab/LfF/blob/master/module/util.py '''
import torch.nn as nn
from module.resnet import resnet20
from module.mlp import *
from torchvision.models import resnet18, resnet50
def get_model(model_tag, num_classes):
if model_tag == "ResNet20":
return resnet20(num_classes... | 1,099 | 34.483871 | 79 | py |
Learning-Debiased-Disentangled | Learning-Debiased-Disentangled-master/data/util.py | '''Modified from https://github.com/alinlab/LfF/blob/master/data/util.py'''
import os
import torch
from torch.utils.data.dataset import Dataset, Subset
from torchvision import transforms as T
from glob import glob
from PIL import Image
class IdxDataset(Dataset):
def __init__(self, dataset):
self.dataset =... | 9,788 | 31.73913 | 160 | py |
fast-dpsgd | fast-dpsgd-main/opacusdp.py | '''
Opacus experiments for all the models
'''
import time
import torch
import torch.nn.functional as F
from opacus import PrivacyEngine
from opacus.layers import DPLSTM
from torch import nn, optim
import data
import utils
from pytorch import get_data, model_dict
class LSTMNet(nn.Module):
def __init__(self, voca... | 3,656 | 31.078947 | 99 | py |
fast-dpsgd | fast-dpsgd-main/runtime_experiment.py | import argparse
import pprint
import subprocess
from utils import pr_green, pr_red
def launch(expt, batch_size, epochs):
"""Runs expt at batch_size for all the scripts"""
errors = []
# yapf: disable
cmds = [
('jax', f'CUDA_VISIBLE_DEVICES=0 python jaxdp.py {expt} --no_dpsgd --epochs {epochs} ... | 5,190 | 59.360465 | 163 | py |
fast-dpsgd | fast-dpsgd-main/pytorch.py | '''
Model file and non-differentially private file
'''
import time
import torch
import torch.nn.functional as F
from torch import nn, optim
import data
import utils
class EmbeddingNet(nn.Module):
def __init__(self, vocab_size: int, **_):
super().__init__()
# Embedding dimension: vocab_size + <un... | 5,651 | 30.4 | 92 | py |
fast-dpsgd | fast-dpsgd-main/data.py | import numpy as np
import tensorflow as tf
from keras.preprocessing import sequence
def dataloader(x, y, batch_size):
if batch_size > len(x):
raise ValueError('Batch Size too big.')
num_eg = len(x)
assert num_eg == len(y)
for i in range(0, num_eg, batch_size):
yield x[i:i + batch_size]... | 5,648 | 36.410596 | 96 | py |
fast-dpsgd | fast-dpsgd-main/jaxdp.py | '''
Code for JAX implementations presented in: Enabling Fast
Differentially Private SGD via Just-in-Time Compilation and Vectorization
'''
import itertools
import time
from functools import partial
import haiku as hk
import jax
import jax.numpy as jnp
import numpy as np
from jax import grad, jit, random, vmap
from ja... | 11,056 | 35.734219 | 99 | py |
fast-dpsgd | fast-dpsgd-main/tf2dp.py | import time
from functools import partial
import tensorflow as tf
from tensorflow_privacy.privacy.analysis.gdp_accountant import (compute_eps_poisson,
compute_mu_poisson)
from jax.tree_util import tree_multimap
import data
import utils
def get_logreg_m... | 10,368 | 39.346304 | 100 | py |
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