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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|---|---|---|---|---|---|---|
Merak | Merak-main/Merak/mpu/cross_entropy.py | # coding=utf-8
# Copyright (c) 2020, 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 at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | 4,921 | 42.946429 | 115 | py |
Merak | Merak-main/Merak/mpu/utils.py | # coding=utf-8
# Copyright (c) 2020, 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 at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | 2,819 | 38.166667 | 107 | py |
Merak | Merak-main/Merak/mpu/layers.py | # coding=utf-8
# Copyright (c) 2020, 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 at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | 27,441 | 40.390649 | 108 | py |
Merak | Merak-main/Merak/mpu/p2p_communication.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: Swli (lucasleesw9@gmail.com)
#
# 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... | 7,279 | 36.916667 | 155 | py |
Merak | Merak-main/Merak/utils/checkpoint.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: TXacs (txacs1993@gmail.com)
#
# 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:... | 15,099 | 38.528796 | 115 | py |
Merak | Merak-main/Merak/utils/timer.py | '''
Copyright 2019 The Microsoft DeepSpeed Team
'''
# https://github.com/microsoft/DeepSpeed/blob/85ce85dd5f4b18c0019a5121b06900e3a2c3933b/deepspeed/utils/timer.py
import time
import torch
from .logging import log_dist
from . import logger
try:
import psutil
PSUTILS_INSTALLED = True
except ImportError:
... | 6,354 | 33.166667 | 111 | py |
Merak | Merak-main/Merak/utils/dataloader.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: TXacs (txacs1993@gmail.com)
#
# 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:... | 11,426 | 40.252708 | 188 | py |
Merak | Merak-main/Merak/utils/logging.py | # https://github.com/microsoft/DeepSpeed/blob/85ce85dd5f4b18c0019a5121b06900e3a2c3933b/deepspeed/utils/logging.py
import logging
import sys
import torch
from .. import print_rank_0
import torch.distributed as dist
log_levels = {
"debug": logging.DEBUG,
"info": logging.INFO,
"warning": logging.WARNING,
... | 3,741 | 24.986111 | 127 | py |
Merak | Merak-main/Merak/utils/fp16_optimizer.py | '''
Copyright 2019 The Microsoft DeepSpeed Team
Copyright NVIDIA/apex
This file is adapted from FP16_Optimizer in NVIDIA/apex
'''
# the code here are adapted from https://github.com/microsoft/DeepSpeed/blob/v0.5.10/deepspeed/runtime/fp16/unfused_optimizer.py
import torch
from torch._utils import _flatten_dense_tenso... | 17,530 | 40.056206 | 128 | py |
Merak | Merak-main/Merak/utils/merak_args.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: Swli (lucasleesw9@gmail.com), TXacs (txacs1993@gmail.com)
#
# 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 o... | 16,997 | 48.412791 | 191 | py |
Merak | Merak-main/Merak/utils/profiler.py | # code here are adapted from https://github.com/microsoft/DeepSpeed/blob/5218177922a4be5c14cf0db893dbfcb139179ba5/deepspeed/profiling/flops_profiler/profiler.py
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from typing import Callable, List, Optional, Tup... | 48,368 | 37.145899 | 650 | py |
Merak | Merak-main/Merak/autoshard/graph_shard.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: Swli (lucasleesw9@gmail.com)
#
# 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... | 16,956 | 43.623684 | 173 | py |
Merak | Merak-main/Merak/autoshard/convert.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: Swli (lucasleesw9@gmail.com)
#
# 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... | 17,468 | 39.531323 | 143 | py |
Merak | Merak-main/examples/torch-models/config.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------'
import os
import yaml
from yacs.config import CfgNode as CN
_C = CN()
... | 7,072 | 31.74537 | 79 | py |
Merak | Merak-main/examples/torch-models/run_torchvision.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: TXacs (txacs1993@gmail.com)
#
# 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:... | 1,971 | 27.171429 | 102 | py |
Merak | Merak-main/examples/torch-models/models/swin_mlp.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import torch
import torch.nn as nn
import torch.nn.functional as F
impor... | 18,508 | 38.464819 | 118 | py |
Merak | Merak-main/examples/torch-models/models/swin_transformer.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import torch
import torch.nn as nn
import torch.utils.checkpoint as chec... | 27,189 | 40.830769 | 119 | py |
Merak | Merak-main/examples/torch-models/data/build.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import os
import torch
import torch.distributed as dist
from torchvision... | 5,018 | 37.022727 | 113 | py |
Merak | Merak-main/examples/bert_pretraining/run_bert.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: TXacs (txacs1993@gmail.com)
#
# 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:... | 7,887 | 44.333333 | 186 | py |
Merak | Merak-main/examples/bert_pretraining/schedulers.py | # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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
... | 5,075 | 36.323529 | 168 | py |
Merak | Merak-main/examples/bert_pretraining/bert_data.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: TXacs (txacs1993@gmail.com)
#
# 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:... | 6,781 | 38.430233 | 129 | py |
Merak | Merak-main/examples/bert_pretraining/lamb.py | import torch
from torch.optim import Optimizer
# code from https://github.com/cybertronai/pytorch-lamb/blob/master/pytorch_lamb/lamb.py
class Lamb(Optimizer):
r"""Implements Lamb algorithm.
It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_.
Arguments:
... | 5,287 | 43.066667 | 109 | py |
Merak | Merak-main/examples/image-classification/utils.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: TXacs (txacs1993@gmail.com)
#
# 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:... | 4,223 | 33.341463 | 164 | py |
Merak | Merak-main/examples/language-modeling/utils.py | # coding=utf-8
# Copyright (c) 2022, HPDL group, PDL lab, NUDT. All rights reserved.
#
# Maintainer: TXacs (txacs1993@gmail.com)
#
# 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:... | 12,648 | 38.652038 | 160 | py |
victim_counts | victim_counts-main/inference.py | import numpy as np
import torch
from torch.utils.data import DataLoader
from text2digits import text2digits
from tqdm import tqdm
import sys
sys.path.append("./")
from calibration.post_hoc import softmax
# generate answer
def get_allowed_answer(tokenizer, num_only):
"""
Helper function to only output digit tok... | 6,593 | 39.703704 | 130 | py |
victim_counts | victim_counts-main/run_regression.py | import sys
import os
import argparse
from sklearn.isotonic import IsotonicRegression
from sklearn.metrics import mean_absolute_percentage_error, mean_squared_error, r2_score
sys.path.append(os.path.abspath("."))
os.environ["WANDB_DISABLED"] = "true"
from torch.utils.data import Subset
import pickle
from torch.utils.dat... | 8,794 | 47.861111 | 136 | py |
victim_counts | victim_counts-main/run_classification.py | """
Fine-tune Classification Task
"""
import sys
import os
sys.path.append(os.path.abspath("."))
os.environ["WANDB_DISABLED"] = "true"
import pickle
import argparse
from torch.utils.data import Subset
from transformers import Trainer, T5Tokenizer, set_seed
from utils import do_eval, get_trainingargs, load_config, get_c... | 5,494 | 40.007463 | 133 | py |
victim_counts | victim_counts-main/Dataset.py | import os
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import torch
from torch.utils.data import Dataset, random_split
class RawDataset:
def __init__(self, direc):
self.header = 0
self.direc = direc
self.fnames = []
self.columns = []
... | 9,493 | 37.437247 | 203 | py |
victim_counts | victim_counts-main/run_generation.py | """
Fine-tune Text Generation Task
"""
import sys
import os
from torch.utils.data import Subset
sys.path.append(os.path.abspath("."))
os.environ["WANDB_DISABLED"] = "true"
import argparse
from transformers import Trainer, set_seed
from utils import get_lm_data, get_nt5_model, get_trainingargs, do_train, do_eval, load_c... | 2,480 | 33.943662 | 111 | py |
victim_counts | victim_counts-main/models.py | from transformers import T5PreTrainedModel, T5EncoderModel
from transformers.modeling_outputs import SequenceClassifierOutput
from torch import nn
class T5Classification(T5PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
if self.num_... | 1,833 | 44.85 | 90 | py |
ori | ori-master/ori/train/target.py | from .base import *
import yaml
import datetime
import tensorflow_addons as tfa
from .util import *
tf.get_logger().setLevel('ERROR')
class oi_target(target_base):
def __init__(self,cfg_file) -> None:
print("Init OpenImage Target ...")
with open(cfg_file, 'r') as stream:
self.cfg = yam... | 2,472 | 36.469697 | 96 | py |
ori | ori-master/ori/train/util.py | import sys, os, json
import tensorflow as tf
import tensorflow.keras as K
import numpy as np
from sklearn.svm import SVC
from sklearn.metrics import classification_report
from sklearn import tree
from sklearn.neighbors import KNeighborsClassifier
from PIL import Image
from sklearn.ensemble import RandomForestClassifier... | 11,517 | 37.912162 | 114 | py |
ori | ori-master/ori/train/infer.py | from .base import *
import yaml
from .util import *
import pandas as pd
import numpy as np
import os
from keras import backend as K
import tensorflow as tf
import pickle
class oi_infer(infer_base):
def __init__(self, cfg_file):
with open(cfg_file, 'r') as stream:
self.cfg = yaml.safe_load(strea... | 9,605 | 39.361345 | 141 | py |
disentangled-retriever | disentangled-retriever-main/setup.py | from setuptools import setup, find_packages
setup(
name='disentangled_retriever',
version='0.0.2',
packages=find_packages("src"),
package_dir={'': 'src'},
description='Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval',
url='https://github.com/jingtaozhan/disentangled-... | 846 | 32.88 | 94 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/splade/finetune/contrast_utils.py | import os
import torch
import random
import logging
from tqdm import tqdm
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Dict, Any, Union, Optional
from torch import nn, Tensor
import torch.nn.functional as F
import torch.distributed as dist
from torch.utils.data ... | 7,463 | 45.943396 | 124 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/splade/finetune/distill_utils.py | import torch
import random
import logging
import gzip, pickle
from tqdm import tqdm
from dataclasses import dataclass
from collections import defaultdict
from typing import List, Dict, Any, Union, Optional
from torch import nn, Tensor
import torch.nn.functional as F
import torch.distributed as dist
from torch.utils.da... | 5,217 | 41.770492 | 140 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/splade/finetune/run_distill.py | import os
import sys
import torch
import logging
import transformers
from typing import List
from transformers import (
AutoConfig,
AutoTokenizer,
HfArgumentParser,
TrainingArguments,
set_seed, )
from transformers.trainer_utils import is_main_process
from dataclasses import dataclass, field
from .d... | 6,759 | 36.555556 | 154 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/splade/evaluate/run_eval.py | import os
import math
import json
import torch
import shutil
import logging
import numpy as np
import torch.distributed as dist
from dataclasses import field, dataclass
import transformers
from transformers import (
AutoConfig,
AutoTokenizer,
HfArgumentParser,
TrainingArguments,
set_seed)
from tr... | 5,909 | 35.036585 | 148 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/splade/evaluate/index_utils.py | import re
import os
import sys
import json
import math
import torch
import faiss
import queue
import logging
import threading
import subprocess
import numpy as np
from tqdm import tqdm
import torch.distributed as dist
import faiss.contrib.torch_utils
from torch.utils.data import Dataset
from typing import Dict, List, T... | 7,158 | 35.712821 | 134 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/splade/modeling/bert_splade.py | import torch
from transformers import BertForMaskedLM
class BertSplade(BertForMaskedLM):
def forward(self, input_ids, attention_mask, token_type_ids=None, position_ids=None, return_dict=False):
outputs = super().forward(
input_ids = input_ids,
attention_mask = attention_mask,
... | 4,109 | 46.241379 | 133 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/splade/modeling/__init__.py | import torch
from torch import Tensor
import torch.nn.functional as F
from transformers import AutoConfig
from .bert_splade import BertSplade
class AutoSpladeModel:
@classmethod
def from_pretrained(cls, model_path: str, config = None):
if config is None:
config = AutoConfig.from_pretraine... | 523 | 26.578947 | 73 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/rerank/finetune/train_utils.py | import os
import torch
import random
import logging
import pytrec_eval
import numpy as np
from tqdm import tqdm
import torch.nn.functional as F
import torch.distributed as dist
from dataclasses import dataclass, field
from collections import defaultdict
from typing import List, Dict, Any, Union, Optional, Tuple
from to... | 8,996 | 38.460526 | 139 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/rerank/finetune/__init__.py | import math
import torch
import faiss
import logging
import numpy as np
from tqdm import tqdm
from collections import defaultdict
import faiss.contrib.torch_utils
from torch.utils.data import Dataset
from typing import Dict, List, Tuple, Optional, Any, Union
from transformers import BertTokenizer, TrainingArguments, Tr... | 4,538 | 31.654676 | 110 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/rerank/finetune/run_train.py | import os
import sys
import torch
import logging
import transformers
from typing import List
from transformers import (
AutoConfig,
AutoTokenizer,
HfArgumentParser,
TrainingArguments,
AutoAdapterModel,
set_seed, )
from transformers.trainer_utils import is_main_process
from dataclasses import dat... | 9,501 | 40.675439 | 154 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/rerank/evaluate/run_eval.py | from collections import defaultdict
import os
import json
import torch
import logging
import numpy as np
from tqdm import tqdm
from dataclasses import field, dataclass
import transformers
from transformers import (
AutoConfig,
AutoTokenizer,
HfArgumentParser,
TrainingArguments,
AutoAdapterModel,
... | 4,436 | 33.937008 | 137 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/rerank/evaluate/eval_utils.py | import re
import math
import torch
import faiss
import logging
import numpy as np
from tqdm import tqdm
from collections import defaultdict
import faiss.contrib.torch_utils
from torch.utils.data import Dataset
from typing import Dict, List, Tuple, Optional, Any, Union
from transformers import BertTokenizer, TrainingArg... | 4,600 | 32.100719 | 134 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/unicoil/finetune/contrast_utils.py | import os
import torch
import random
import logging
from tqdm import tqdm
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Dict, Any, Union, Optional
from torch import nn, Tensor
import torch.nn.functional as F
import torch.distributed as dist
from torch.utils.data ... | 5,099 | 44.535714 | 116 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/unicoil/finetune/distill_utils.py | import torch
import random
import logging
import gzip, pickle
from tqdm import tqdm
from dataclasses import dataclass
from collections import defaultdict
from typing import List, Dict, Any, Union, Optional
from torch import nn, Tensor
import torch.nn.functional as F
import torch.distributed as dist
from torch.utils.da... | 2,773 | 38.628571 | 140 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/unicoil/finetune/run_distill.py | import os
import sys
import torch
import logging
import transformers
from typing import List
from transformers import (
AutoConfig,
AutoTokenizer,
HfArgumentParser,
TrainingArguments,
set_seed, )
from transformers.trainer_utils import is_main_process
from dataclasses import dataclass, field
from .d... | 6,467 | 35.96 | 154 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/unicoil/evaluate/run_eval.py | import os
import math
import json
import torch
import shutil
import logging
import numpy as np
import torch.distributed as dist
from dataclasses import field, dataclass
import transformers
from transformers import (
AutoConfig,
AutoTokenizer,
HfArgumentParser,
TrainingArguments,
set_seed)
from tr... | 5,913 | 35.060976 | 148 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/unicoil/evaluate/index_utils.py | import re
import os
import sys
import json
import math
import torch
import faiss
import logging
import subprocess
import numpy as np
from tqdm import tqdm
import faiss.contrib.torch_utils
from torch.utils.data import Dataset
from typing import Dict, List, Tuple, Optional, Any, Union
from transformers import TrainingArg... | 1,180 | 24.673913 | 124 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/unicoil/modeling/bert_unicoil.py | import torch
from transformers import BertAdapterModel
class BertUnicoil(BertAdapterModel):
def forward(self, input_ids, attention_mask, token_type_ids=None, position_ids=None, return_dict=False):
outputs = super().forward(
input_ids = input_ids,
attention_mask = attention_mask,
... | 2,860 | 36.644737 | 117 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/unicoil/modeling/__init__.py | import torch
from torch import Tensor
import torch.nn.functional as F
from transformers import AutoConfig
from .bert_unicoil import BertUnicoil
class AutoUnicoilModel:
@classmethod
def from_pretrained(cls, model_path: str, config = None):
if config is None:
config = AutoConfig.from_pretra... | 527 | 26.789474 | 74 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/dense/finetune/validate_utils.py | import math
import torch
import logging
import pytrec_eval
from typing import Dict
from collections import defaultdict
import transformers
from transformers.trainer_utils import is_main_process
from ..evaluate.index_utils import (
load_corpus, load_queries,
encode_dense_corpus, encode_dense_query, batch_dense... | 2,968 | 40.236111 | 209 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/dense/finetune/contrast_utils.py | import os
import torch
import random
import logging
from tqdm import tqdm
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Dict, Any, Union, Optional
from torch import nn, Tensor
import torch.nn.functional as F
import torch.distributed as dist
from torch.utils.data ... | 11,687 | 41.194946 | 138 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/dense/finetune/distill_utils.py | import torch
import random
import logging
import gzip, pickle
from tqdm import tqdm
from dataclasses import dataclass
from collections import defaultdict
from typing import List, Dict, Any, Union, Optional
from torch import nn, Tensor
import torch.nn.functional as F
import torch.distributed as dist
from torch.utils.da... | 9,108 | 39.847534 | 140 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/dense/finetune/run_distill.py | import os
import sys
import torch
import logging
import transformers
from typing import List
from transformers import (
AutoConfig,
AutoTokenizer,
HfArgumentParser,
TrainingArguments,
set_seed, )
from transformers.trainer_utils import is_main_process
from dataclasses import dataclass, field
from .d... | 7,152 | 36.062176 | 154 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/dense/evaluate/run_eval.py | import os
import math
import json
import torch
import logging
import numpy as np
from dataclasses import field, dataclass
import transformers
from transformers import (
AutoConfig,
AutoTokenizer,
HfArgumentParser,
TrainingArguments,
set_seed)
from transformers.trainer_utils import is_main_process... | 7,264 | 41.48538 | 241 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/dense/evaluate/index_utils.py | import re
import math
import torch
import faiss
import logging
import numpy as np
from tqdm import tqdm
import faiss.contrib.torch_utils
from torch.utils.data import Dataset
from typing import Dict, List, Tuple, Optional, Any, Union
from transformers import TrainingArguments, Trainer
logger = logging.getLogger(__name_... | 8,217 | 35.852018 | 134 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/dense/modeling/utils.py | import torch
from torch import Tensor
import torch.nn.functional as F
SIMILARITY_METRIC_IP = "ip"
SIMILARITY_METRIC_COS = "cos"
SIMILARITY_METRICS = [SIMILARITY_METRIC_IP, SIMILARITY_METRIC_COS]
POOLING_AVERAGE = "average"
POOLING_CLS = "cls"
POOLING_METHODS = [POOLING_AVERAGE, POOLING_CLS]
def extract_text_embed(... | 1,034 | 29.441176 | 92 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/dense/modeling/__init__.py | import torch
from torch import Tensor
import torch.nn.functional as F
from transformers import AutoConfig
from .bert_dense import BertDense
from .roberta_dense import RobertaDense
from .distilbert_dense import DistilBertDense
from .utils import SIMILARITY_METRICS, POOLING_METHODS
class AutoDenseModel:
@classmeth... | 908 | 33.961538 | 78 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/colbert/finetune/contrast_utils.py | import os
import torch
import random
import logging
from tqdm import tqdm
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Dict, Any, Union, Optional
from torch import nn, Tensor
import torch.nn.functional as F
import torch.distributed as dist
from torch.utils.data ... | 5,090 | 44.053097 | 116 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/colbert/finetune/distill_utils.py | import torch
import random
import logging
import gzip, pickle
from tqdm import tqdm
from dataclasses import dataclass
from collections import defaultdict
from typing import List, Dict, Any, Union, Optional
from torch import nn, Tensor
import torch.nn.functional as F
import torch.distributed as dist
from torch.utils.da... | 2,962 | 38.506667 | 133 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/colbert/finetune/run_distill.py | import os
import sys
import torch
import logging
import transformers
from typing import List
from transformers import (
AutoConfig,
AutoTokenizer,
HfArgumentParser,
TrainingArguments,
set_seed, )
from transformers.trainer_utils import is_main_process
from dataclasses import dataclass, field
from .d... | 6,581 | 36.186441 | 154 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/colbert/evaluate/model_inference.py | import torch
from tqdm import tqdm
def _split_into_batches(ids, mask, bsize):
batches = []
for offset in range(0, ids.size(0), bsize):
batches.append((ids[offset:offset+bsize], mask[offset:offset+bsize]))
return batches
def _sort_by_length(ids, mask, bsize):
if ids.size(0) <= bsize:
... | 4,440 | 32.390977 | 117 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/colbert/evaluate/index_faiss.py | import os
import sys
import math
import time
import ujson
import queue
import faiss
import torch
import datetime
import itertools
import threading
import numpy as np
import argparse
def load_doclens(directory, flatten=True):
parts, _, _ = get_parts(directory)
doclens_filenames = [os.path.join(directory, 'doc... | 11,813 | 30.504 | 116 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/colbert/evaluate/retrieve.py | import os
import time
import ujson
import torch
import faiss
import random
import logging
import itertools
from functools import partial
from itertools import accumulate
from multiprocessing import Pool
from contextlib import contextmanager
from dataclasses import field, dataclass
import transformers
from transformers... | 21,340 | 35.858377 | 131 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/colbert/evaluate/index.py | import os
import time
import torch
import ujson
import numpy as np
import logging
import itertools
import threading
import queue
from dataclasses import field, dataclass
import transformers
from transformers import (
TrainingArguments,
AutoConfig,
AutoModel,
AutoTokenizer,
HfArgumentParser
)
from tr... | 8,776 | 32.757692 | 135 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/colbert/modeling/__init__.py | import torch
from torch import Tensor
import torch.nn.functional as F
from transformers import AutoConfig
from .bert_colbert import ColBERT
class AutoColBERTModel:
@classmethod
def from_pretrained(cls, model_path: str, config = None):
if config is None:
config = AutoConfig.from_pretrained... | 519 | 26.368421 | 70 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/colbert/modeling/bert_colbert.py | import torch
from transformers import BertAdapterModel
class ColBERT(BertAdapterModel):
def forward(self, input_ids, attention_mask, token_type_ids=None, position_ids=None, return_dict=False):
outputs = super().forward(
input_ids = input_ids,
attention_mask = attention_mask,
... | 2,828 | 35.74026 | 117 | py |
disentangled-retriever | disentangled-retriever-main/src/disentangled_retriever/adapt/run_adapt_with_mlm.py | import os
import sys
import gzip
import random
import logging
import numpy as np
from tqdm import tqdm
from torch.utils.data import Dataset
from typing import Optional, List, Dict
from dataclasses import dataclass, field
import transformers
from transformers import (
AutoConfig,
AutoTokenizer,
AutoModelFor... | 6,047 | 34.576471 | 147 | py |
ParallelNeLLoC | ParallelNeLLoC-master/models/train-shearloc.py | import torch
from torch import optim
from shearloc_model import *
import numpy as np
import torch.nn.functional as F
from torch.optim import lr_scheduler
from utils import *
def train_model(opt):
k_h=opt['k_h']
k_w=opt['k_w']
offset=opt['offset']
name='shearloc_res'+str(opt['res_num'])+'_mixnum'+str(o... | 2,753 | 31.4 | 141 | py |
ParallelNeLLoC | ParallelNeLLoC-master/models/train-nelloc.py | import torch
from torch import optim
from nelloc_model import *
import numpy as np
from torch.optim import lr_scheduler
from utils import *
def train_model(opt):
name='nelloc_res'+str(opt['res_num'])+'_mixnum'+str(opt['mix_num'])+'_rf'+str(opt['rf'])
train_data_loader,test_data_loader,_=LoadData(opt)
net ... | 2,147 | 30.588235 | 125 | py |
ParallelNeLLoC | ParallelNeLLoC-master/models/utils.py | import torchvision
from torchvision import transforms
import torch
import numpy as np
from torch.utils import data
rescaling = lambda x : (x - .5) * 2.
rescaling_inv = lambda x : .5 * x + .5
def shear(x,offset=2):
bs=x.size(0)
D=x.size(2)
L=D+(D-1)*offset
sheared_img=torch.zeros(bs,3,D,L)
for... | 3,802 | 38.614583 | 140 | py |
ParallelNeLLoC | ParallelNeLLoC-master/models/shearloc_model.py | import torch.nn as nn
import torch
def discretized_mix_logistic_uniform(x, l,sheared_mask, alpha=0.0001):
xs=list(x.size())
x=x.unsqueeze(2)
mix_num = int(l.size(1)/10)
pi = torch.softmax(l[:, :mix_num,:,:],1).unsqueeze(1).repeat(1,3,1,1,1)
l=l[:, mix_num:,:,:].view(xs[:2]+[-1]+xs[2:])
means... | 3,172 | 37.228916 | 117 | py |
ParallelNeLLoC | ParallelNeLLoC-master/models/nelloc_model.py | import torch.nn as nn
import torch
import torch.nn.functional as F
def discretized_mix_logistic_uniform(x, l, alpha=0.0001):
xs=list(x.size())
x=x.unsqueeze(2)
mix_num = int(l.size(1)/10)
pi = torch.softmax(l[:, :mix_num,:,:],1).unsqueeze(1).repeat(1,3,1,1,1)
l=l[:, mix_num:,:,:].view(xs[:2]+[-1]+... | 3,481 | 39.964706 | 117 | py |
ParallelNeLLoC | ParallelNeLLoC-master/coders/pnelloc_ans.py | import torch.nn.functional as F
from coders.coder_utils import *
def p_ans_compression(model,img,time_index,h,w,rf,p_prec=16):
c_list=[]
p_list=[]
p2d = (rf, rf, rf, 0)
img = F.pad(img, p2d, "constant", 0)
with torch.no_grad():
for t,par_index_list in enumerate(time_index):
pat... | 3,568 | 45.350649 | 135 | py |
ParallelNeLLoC | ParallelNeLLoC-master/coders/shearloc_ans.py | import torch.nn.functional as F
from coders.coder_utils import *
from models.utils import *
def ans_compression(model,img,Q,K,p_prec=16):
model.eval()
c_list=[]
p_list=[]
D,O,T,up_batch,down_batch,bs_batch=Q
sheared_o_img=shear(img,O).to(torch.int32)
kh,kw=K
p2d=[kw,0,kh-1,0]
pa... | 3,834 | 42.579545 | 135 | py |
ParallelNeLLoC | ParallelNeLLoC-master/coders/coder_utils.py | import torch
import numpy as np
rescaling = lambda x : (x - .5) * 2.
rescaling_inv = lambda x : .5 * x + .5
def discretized_mix_logistic_cdftable(means, log_scales,pi, alpha=0.0001):
bs=means.size(0)
nr_mix=pi.size(-1)
pi=pi.unsqueeze(1)
x=rescaling(torch.arange(0,256)/255.).view(1,256,1).rep... | 3,462 | 34.336735 | 115 | py |
ParallelNeLLoC | ParallelNeLLoC-master/coders/nelloc_ans.py | import torch.nn.functional as F
from coders.coder_utils import *
def ans_compression(model,img,h,w,rf,p_prec=16):
c_list=[]
p_list=[]
p2d = (rf, rf, rf, 0)
img = F.pad(img, p2d, "constant", 0)
with torch.no_grad():
for i in range(0,h):
for j in range(0,w):
patch... | 2,881 | 47.847458 | 139 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/methods/ADB/pretrain.py | import torch
import torch.nn.functional as F
import numpy as np
import os
import copy
import logging
from torch import nn
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score
from tqdm import trange, tqdm
from losses import loss_map
from utils.functions import save_model, restore_model, centroids_cal
... | 8,067 | 35.506787 | 154 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/methods/ADB/boundary.py | import torch
from torch import nn
import torch.nn.functional as F
class BoundaryLoss(nn.Module):
"""
Deep Open Intent Classification with Adaptive Decision Boundary.
https://arxiv.org/pdf/2012.10209.pdf
"""
def __init__(self, num_labels=10, feat_dim=2, device = None):
super(BoundaryLoss, s... | 1,074 | 30.617647 | 69 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/methods/ADB/manager.py | import torch
import torch.nn.functional as F
import numpy as np
import os
import logging
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score
from tqdm import trange, tqdm
from .boundary import BoundaryLoss
from losses import loss_map
from utils.functions import save_model, euclidean_metric
from utils... | 7,992 | 39.573604 | 133 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/methods/MSP/manager.py | from importlib import import_module
import torch
import torch.nn.functional as F
import copy
import logging
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score
from tqdm import trange, tqdm
from losses import loss_map
from utils.functions import restore_model, save_model
from utils.metrics import F_m... | 5,673 | 33.809816 | 133 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/methods/KNNCL/KNNCL_utils.py | import torch
import random
from typing import Any, Dict, Union
def create_negative_dataset(train_dataloader):
list = []
for step, inputs in enumerate(train_dataloader):
input_ids, input_masks, segment_ids, label_ids = inputs
input_ids = input_ids.tolist()
input_masks = input_masks.tol... | 2,169 | 30.449275 | 112 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/methods/KNNCL/manager.py | import torch
import copy
import pandas as pd
import logging
from tqdm import trange, tqdm
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score
from sklearn.neighbors import LocalOutlierFactor
from utils.functions import restore_model, save_model
from utils.metrics import F_measure
from .KNNCL_utils i... | 6,629 | 38.464286 | 133 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/methods/DOC/manager.py | from importlib import import_module
import torch
import numpy as np
import os
import copy
import logging
from torch import nn
from datetime import datetime
from sklearn.metrics import confusion_matrix, accuracy_score
from tqdm import trange, tqdm
from scipy.stats import norm as dist_model
from losses import loss_map
fr... | 7,495 | 34.028037 | 133 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/methods/K_1_way/manager.py | from importlib import import_module
import torch
import torch.nn.functional as F
import copy
import logging
from sklearn.metrics import confusion_matrix, accuracy_score
from tqdm import trange, tqdm
from losses import loss_map
from utils.functions import restore_model, save_model
from utils.metrics import F_measure
c... | 5,586 | 35.045161 | 133 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/methods/MDF/pretrain.py | import torch
import torch.nn.functional as F
import os
import copy
import logging
import torch.nn as nn
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score
from tqdm import trange, tqdm
from losses import loss_map
from torch.utils.data import RandomSampler, DataLoader
from utils.functions import sav... | 6,375 | 37.409639 | 133 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/methods/MDF/manager.py | import torch
import torch.nn.functional as F
import logging
import os
import torch.nn as nn
import numpy as np
import copy
import json
from sklearn import svm
import sklearn
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score, roc_curve, auc
from tqdm import trange, tqdm
from losses import loss_map... | 8,840 | 37.43913 | 133 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/methods/DeepUnk/manager.py | import torch
import torch.nn.functional as F
import numpy as np
import os
import copy
import pandas as pd
import logging
from sklearn.metrics import confusion_matrix, accuracy_score
from tqdm import trange, tqdm
from sklearn.neighbors import LocalOutlierFactor
from losses import loss_map
from utils.functions import sa... | 6,374 | 35.016949 | 133 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/methods/OpenMax/manager.py | from importlib import import_module
import torch
import torch.nn.functional as F
import numpy as np
import copy
import logging
from losses import loss_map
from torch import nn
from datetime import datetime
from sklearn.metrics import confusion_matrix, accuracy_score
from tqdm import trange, tqdm
from utils.functions im... | 8,615 | 32.65625 | 133 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/methods/SEG/manager.py | import torch
import torch.nn.functional as F
import numpy as np
import os
import copy
import logging
import pandas as pd
from torch import nn
from datetime import datetime
from sklearn.metrics import confusion_matrix, accuracy_score
from tqdm import trange, tqdm
from utils.functions import save_model
from utils.metric... | 8,313 | 37.669767 | 155 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/methods/ARPL/pretrain.py | import torch
import torch.nn.functional as F
import numpy as np
import os
import copy
import logging
from torch import nn
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score
from tqdm import trange, tqdm
from losses import loss_map
from utils.functions import save_model, restore_model
class Pretrai... | 5,122 | 34.331034 | 133 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/methods/ARPL/manager.py | import torch
import torch.nn.functional as F
import numpy as np
import os
import copy
import logging
from torch import nn
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score
from tqdm import trange, tqdm
from losses import loss_map
from utils.metrics import F_measure
from utils.functions import resto... | 6,461 | 37.011765 | 96 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/dataloaders/base.py | import numpy as np
import os
import random
import torch
import logging
from .__init__ import max_seq_lengths, backbone_loader_map, benchmark_labels
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.determini... | 2,025 | 29.238806 | 112 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/dataloaders/bert_loader.py | import numpy as np
import torch
import os
import csv
import sys
import logging
from transformers import BertTokenizer
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset)
class BERT_Loader:
def __init__(self, args, base_attrs, logger_name = 'Detection'):
self.logger =... | 12,120 | 41.381119 | 164 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/dataloaders/__init__.py | from .bert_loader import BERT_Loader
max_seq_lengths = {
'stackoverflow':45,
'banking':55,
'oos':30,
'snips':35
}
backbone_loader_map = {
'bert': BERT_Loader,
... | 6,404 | 72.62069 | 181 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/utils/functions.py | import os
import torch
import numpy as np
import pandas as pd
from tqdm import tqdm
from transformers import WEIGHTS_NAME, CONFIG_NAME
def mask_tokens(inputs, tokenizer, special_tokens_mask=None, mlm_probability=0.15):
"""
Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, ... | 5,183 | 35.765957 | 157 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/losses/Dist.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class Dist(nn.Module):
def __init__(self, num_classes=10, num_centers=1, feat_dim=2, init='random'):
super(Dist, self).__init__()
self.feat_dim = feat_dim
self.num_classes = num_classes
self.num_ce... | 1,612 | 39.325 | 119 | py |
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