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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ner-rc-russian | ner-rc-russian-master/nn_methods/BERT/run_language_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... | 34,451 | 42.776366 | 165 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/BERT/run_ner.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... | 33,949 | 42.863049 | 150 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/BERT/bert_for_re.py | import torch
import torch.nn as nn
from transformers import BertModel, BertPreTrainedModel, BertConfig
class FCLayer(nn.Module):
def __init__(self, input_dim, output_dim, dropout_rate=0., use_activation=True):
super(FCLayer, self).__init__()
self.use_activation = use_activation
self.dropou... | 3,515 | 36.806452 | 143 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/BERT/utils_re.py | """
From https://github.com/monologg/R-BERT/blob/master/data_loader.py
"""
import os
import csv
import copy
import json
import logging
import torch
from torch.utils.data import TensorDataset
ADDITIONAL_SPECIAL_TOKENS = ["<e1>", "</e1>", "<e2>", "</e2>"]
logger = logging.getLogger(__name__)
class InputExample(obje... | 9,018 | 34.789683 | 135 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/BERT/bert_for_ner.py | import numpy as np
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from torchcrf import CRF
from transformers import (
PreTrainedModel,
BertConfig,
BertModel,
add_start_docstrings
)
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_calla... | 13,001 | 34.818182 | 145 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/config/Tester.py | # coding:utf-8
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import os
import time
import sys
import datetime
import ctypes
import json
import numpy as np
from sklearn.metrics import roc_auc_score
import copy
from tqdm import tqdm
class Tester(object):
def __in... | 5,393 | 34.721854 | 98 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/config/Trainer.py | # coding:utf-8
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import os
import time
import sys
import datetime
import ctypes
import json
import numpy as np
import copy
from tqdm import tqdm
class Trainer(object):
def __init__(self,
model = None,
data_loa... | 3,413 | 24.477612 | 94 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/BaseModule.py | import torch
import torch.nn as nn
import os
import json
import numpy as np
class BaseModule(nn.Module):
def __init__(self):
super(BaseModule, self).__init__()
self.zero_const = nn.Parameter(torch.Tensor([0]))
self.zero_const.requires_grad = False
self.pi_const = nn.Parameter(torch.Tensor([3.1415926535897932... | 1,501 | 26.309091 | 70 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/loss/SigmoidLoss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .Loss import Loss
class SigmoidLoss(Loss):
def __init__(self, adv_temperature = None):
super(SigmoidLoss, self).__init__()
self.criterion = nn.LogSigmoid()
if adv_temperature != None:
self.adv_temperature = nn.Paramet... | 951 | 30.733333 | 125 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/loss/MarginLoss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from .Loss import Loss
class MarginLoss(Loss):
def __init__(self, adv_temperature = None, margin = 6.0):
super(MarginLoss, self).__init__()
self.margin = nn.Parameter(torch.Tensor([margin]))
... | 1,046 | 30.727273 | 117 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/loss/SoftplusLoss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .Loss import Loss
class SoftplusLoss(Loss):
def __init__(self, adv_temperature = None):
super(SoftplusLoss, self).__init__()
self.criterion = nn.Softplus()
if adv_temperature != None:
self.adv_temperature = nn.Paramet... | 954 | 29.806452 | 124 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/model/DistMult.py | import torch
import torch.nn as nn
from .Model import Model
class DistMult(Model):
def __init__(self, ent_tot, rel_tot, dim = 100, margin = None, epsilon = None):
super(DistMult, self).__init__(ent_tot, rel_tot)
self.dim = dim
self.margin = margin
self.epsilon = epsilon
self.ent_embeddings = nn.Embedding(... | 2,140 | 28.328767 | 96 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/model/ComplEx.py | import torch
import torch.nn as nn
from .Model import Model
class ComplEx(Model):
def __init__(self, ent_tot, rel_tot, dim = 100):
super(ComplEx, self).__init__(ent_tot, rel_tot)
self.dim = dim
self.ent_re_embeddings = nn.Embedding(self.ent_tot, self.dim)
self.ent_im_embeddings = n... | 2,305 | 36.193548 | 69 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/model/RotatE.py | import torch
import torch.autograd as autograd
import torch.nn as nn
from .Model import Model
class RotatE(Model):
def __init__(self, ent_tot, rel_tot, dim = 100, margin = 6.0, epsilon = 2.0):
super(RotatE, self).__init__(ent_tot, rel_tot)
self.margin = margin
self.epsilon = epsilon
self.dim_e = dim * 2
... | 3,228 | 30.349515 | 98 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/model/SimplE.py | import torch
import torch.nn as nn
from .Model import Model
class SimplE(Model):
def __init__(self, ent_tot, rel_tot, dim = 100):
super(SimplE, self).__init__(ent_tot, rel_tot)
self.dim = dim
self.ent_embeddings = nn.Embedding(self.ent_tot, self.dim)
self.rel_embeddings = nn.Embed... | 1,961 | 34.672727 | 107 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/model/Analogy.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .Model import Model
class Analogy(Model):
def __init__(self, ent_tot, rel_tot, dim = 100):
super(Analogy, self).__init__(ent_tot, rel_tot)
self.dim = dim
self.ent_re_embeddings = nn.Embedding(self.ent_tot, self.dim)
self.ent_im_embeddin... | 2,605 | 33.746667 | 65 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/model/HolE.py | #coding:utf-8
import numpy as np
import tensorflow as tf
from .Model import Model
class HolE(Model):
def __init__(self, ent_tot, rel_tot, dim = 100):
super(HolE, self).__init__(ent_tot, rel_tot)
self.dim = dim
self.ent_embeddings = nn.Embedding(self.ent_tot, self.dim)
self.rel_embeddings = nn.Embedding(self... | 4,093 | 37.622642 | 117 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/model/TransR.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .Model import Model
class TransR(Model):
def __init__(self, ent_tot, rel_tot, dim_e = 100, dim_r = 100, p_norm = 1, norm_flag = True, rand_init = False, margin = None):
super(TransR, self).__init__(ent_tot, rel_tot)
self.dim_e = dim_e
s... | 3,151 | 29.601942 | 128 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/model/RESCAL.py | import torch
import torch.nn as nn
from .Model import Model
class RESCAL(Model):
def __init__(self, ent_tot, rel_tot, dim = 100):
super(RESCAL, self).__init__(ent_tot, rel_tot)
self.dim = dim
self.ent_embeddings = nn.Embedding(self.ent_tot, self.dim)
self.rel_matrices = nn.Embedding(self.rel_tot, self.dim *... | 1,293 | 27.130435 | 76 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/model/TransE.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .Model import Model
class TransE(Model):
def __init__(self, ent_tot, rel_tot, dim = 100, p_norm = 1, norm_flag = True, margin = None, epsilon = None):
super(TransE, self).__init__(ent_tot, rel_tot)
self.dim = dim
self.margin = margin
... | 2,594 | 26.606383 | 110 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/model/Model.py | import torch
import torch.nn as nn
from ..BaseModule import BaseModule
class Model(BaseModule):
def __init__(self, ent_tot, rel_tot):
super(Model, self).__init__()
self.ent_tot = ent_tot
self.rel_tot = rel_tot
def forward(self):
raise NotImplementedError
def predict(self):
raise NotImplementedError | 318 | 17.764706 | 38 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/model/TransH.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .Model import Model
class TransH(Model):
def __init__(self, ent_tot, rel_tot, dim = 100, p_norm = 1, norm_flag = True, margin = None, epsilon = None):
super(TransH, self).__init__(ent_tot, rel_tot)
self.dim = dim
self.margin = margin
... | 3,373 | 28.33913 | 110 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/OpenKE/openke/module/model/TransD.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .Model import Model
class TransD(Model):
def __init__(self, ent_tot, rel_tot, dim_e = 100, dim_r = 100, p_norm = 1, norm_flag = True, margin = None, epsilon = None):
super(TransD, self).__init__(ent_tot, rel_tot)
self.dim_e = dim_e
self... | 4,867 | 30.406452 | 125 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/Vocab/sp_for_bert.py | import os
import sys
import json
import nltk
import random
import sentencepiece as spm
from tensorflow.keras.utils import Progbar
regex_tokenizer = nltk.RegexpTokenizer("\w+")
def normalize_text(text):
# lowercase text
text = str(text).lower()
# remove non-UTF
text = text.encode("utf-8", "ignore").decode()
... | 2,446 | 25.89011 | 82 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/example.py | import torch
from knowledge_bert import BertTokenizer, BertModel, BertForMaskedLM
# OPTIONAL: if you want to have more information on what's happening, activate the logger as follows
import logging
logging.basicConfig(level=logging.INFO)
# Load pre-trained model tokenizer (vocabulary)
tokenizer = BertTokenizer.from_p... | 3,651 | 28.691057 | 100 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/eval_fewrel.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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... | 22,334 | 40.747664 | 139 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/run_fewrel.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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... | 23,787 | 41.861261 | 130 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/eval_typing.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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... | 22,976 | 38.890625 | 129 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/run_ner.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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... | 24,597 | 41.704861 | 130 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/run_tacred.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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... | 24,597 | 41.704861 | 130 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/iterators.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import itertools
import math
impo... | 8,280 | 32.526316 | 93 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/run_pretrain.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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... | 20,008 | 45.641026 | 263 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/jupyter_pretrain.py | """BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import os
import logging
import argparse
import random
from tqdm import tqdm, trange
import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLo... | 15,692 | 41.760218 | 238 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/create_instances.py | import random
import numpy as np
import collections
import torch
import tensorflow as tf
import indexed_dataset
flags = tf.flags
FLAGS = flags.FLAGS
flags.DEFINE_string("input_file_prefix", None,
"Input text/entity file.")
flags.DEFINE_string(
"output_file", None,
"Output TF exampl... | 13,602 | 38.428986 | 140 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/indexed_dataset.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import os
import struct
import nu... | 6,299 | 29.288462 | 84 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/eval_figer.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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... | 23,270 | 38.984536 | 129 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/eval_tacred.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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... | 22,448 | 40.726766 | 139 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/run_typing.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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... | 23,637 | 40.181185 | 130 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/knowledge_bert/optimization.py | # coding=utf-8
# 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
#
# http://www.apache.org/licenses/LICENS... | 6,803 | 40.742331 | 116 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/knowledge_bert/__main__.py | # coding: utf8
def main():
import sys
try:
from .convert_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ModuleNotFoundError:
print("pytorch_pretrained_bert can only be used from the commandline to convert TensorFlow models in PyTorch, "
"In that case, i... | 932 | 39.565217 | 137 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/knowledge_bert/modeling.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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... | 67,057 | 48.525849 | 158 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/knowledge_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.
"""
import os
import logging
import shutil
import tempfile
import json
from urllib.parse import urlparse
from pathlib import Path
from typing ... | 8,021 | 32.425 | 98 | py |
ner-rc-russian | ner-rc-russian-master/nn_methods/ERNIE/code/knowledge_bert/convert_tf_checkpoint_to_pytorch.py | # coding=utf-8
# Copyright 2018 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 4,463 | 38.504425 | 101 | py |
MutipleFeature-for-PRID | MutipleFeature-for-PRID-master/main.py | # -*- coding: utf-8 -*-
"""
Created on 2019/8/4 上午9:53
@author: mick.yi
入口类
"""
import argparse
import os
import re
import cv2
import numpy as np
import torch
from skimage import io
from torch import nn
from torchvision import models
# from MultipleFeatureswithattentionmno import MultipleFeatures
from reid import R... | 6,258 | 30.452261 | 148 | py |
MutipleFeature-for-PRID | MutipleFeature-for-PRID-master/test.py | from __future__ import print_function, division
import torch
import argparse
from torch.autograd import Variable
from torchvision import datasets, models, transforms
import os
import scipy.io
import math
from MultipleFeatureswithoutattention import MultipleFeatures
parser = argparse.ArgumentParser(description='testin... | 5,113 | 36.881481 | 186 | py |
MutipleFeature-for-PRID | MutipleFeature-for-PRID-master/demo_retinanet.py | # -*- coding: utf-8 -*-
"""
@File : demo_retinanet.py
@Time : 2020/5/16 下午9:59
@Author : yizuotian
@Description :
"""
import argparse
import multiprocessing as mp
import os
import cv2
import detectron2.data.transforms as T
import numpy as np
import torch
from detectron2.checkpoint import DetectionCheckp... | 5,695 | 29.623656 | 103 | py |
MutipleFeature-for-PRID | MutipleFeature-for-PRID-master/preparecuhkl.py | import os
from shutil import copyfile
# You only need to change this line to your dataset download path for each dataset
download_path = '........./cuhk03-np/labeled'
if not os.path.isdir(download_path):
print('please change the download_path')
# os.mkdir('model')
save_path = download_path + '/pytorch'
if not os... | 3,732 | 33.247706 | 85 | py |
MutipleFeature-for-PRID | MutipleFeature-for-PRID-master/evaluate.py | import scipy.io
import torch
import numpy as np
#import time
import os
#######################################################################
# Evaluate
def evaluate(qf,ql,qc,gf,gl,gc):
query = qf
score = np.dot(gf,query)
# predict index
index = np.argsort(score) #from small to large
index = inde... | 3,425 | 30.722222 | 118 | py |
MutipleFeature-for-PRID | MutipleFeature-for-PRID-master/evaluate_gpu.py | import scipy.io
import torch
import numpy as np
#import time
import os
#######################################################################
# Evaluate
def evaluate(qf,ql,qc,gf,gl,gc):
query = qf.view(-1,1)
# print(query.shape)
score = torch.mm(gf,query)
score = score.squeeze(1).cpu()
score = sco... | 3,730 | 31.163793 | 118 | py |
MutipleFeature-for-PRID | MutipleFeature-for-PRID-master/triplet_loss.py | # encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
import torch
from torch import nn
def normalize(x, axis=-1):
"""Normalizing to unit length along the specified dimension.
Args:
x: pytorch Variable
Returns:
x: pytorch Variable, same shape as input
"""
x ... | 5,382 | 35.619048 | 95 | py |
MutipleFeature-for-PRID | MutipleFeature-for-PRID-master/demo.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import multiprocessing as mp
import os
import cv2
import detectron2.data.transforms as T
import numpy as np
import torch
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.d... | 6,925 | 27.979079 | 100 | py |
MutipleFeature-for-PRID | MutipleFeature-for-PRID-master/cbam.py | import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet18_cbam', 'resnet34_cbam', 'resnet50_cbam', 'resnet101_cbam',
'resnet152_cbam']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': ... | 8,965 | 31.251799 | 89 | py |
MutipleFeature-for-PRID | MutipleFeature-for-PRID-master/guided_back_propagation.py | # -*- coding: utf-8 -*-
"""
Created on 2019/8/4 上午9:45
@author: mick.yi
"""
import torch
from torch import nn
import numpy as np
class GuidedBackPropagation(object):
def __init__(self, net):
self.net = net
for (name, module) in self.net.named_modules():
if isinstance(module, nn.ReLU... | 1,111 | 21.693878 | 65 | py |
MutipleFeature-for-PRID | MutipleFeature-for-PRID-master/samplers.py | from __future__ import absolute_import
from __future__ import division
from collections import defaultdict
import numpy as np
import copy
import random
import torch
from torch.utils.data.sampler import Sampler
class RandomIdentitySampler(Sampler):
"""
Randomly sample N identities, then for each identity,
... | 2,435 | 33.309859 | 84 | py |
MutipleFeature-for-PRID | MutipleFeature-for-PRID-master/evaluate_rerank.py | import scipy.io
import torch
import numpy as np
import time
from re_ranking import re_ranking
#######################################################################
# Evaluate
def evaluate(score,ql,qc,gl,gc):
index = np.argsort(score) #from small to large
#index = index[::-1]
# good index
query_index... | 2,735 | 31.188235 | 98 | py |
MutipleFeature-for-PRID | MutipleFeature-for-PRID-master/CBAM.py | import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet18_cbam', 'resnet34_cbam', 'resnet50_cbam', 'resnet101_cbam',
'resnet152_cbam']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': ... | 8,998 | 31.139286 | 89 | py |
MutipleFeature-for-PRID | MutipleFeature-for-PRID-master/train.py | from __future__ import print_function, division
import torch
import torch.optim as optim
from torchvision import datasets, transforms
import matplotlib
matplotlib.use('agg')
import time
import os
import argparse
#you can change here
from MultipleFeatureswithoutattention import MultipleFeatures
import matplot... | 15,240 | 46.777429 | 250 | py |
MutipleFeature-for-PRID | MutipleFeature-for-PRID-master/MultipleFeatureswithoutattention.py | import torch
import torch.nn as nn
from torch.nn import init
from torchvision import models
#####################################################################
def weights_init_kaiming(m):
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
init.kaiming_normal_(m.weight.data, a=0, mod... | 8,797 | 41.298077 | 85 | py |
alda | alda-master/benchmarks/rules/PA/download.py | """ download repos used in PA experiments
"""
import os,sys
def main():
def download(repo):
print('downloading ',repo)
os.system(f'git clone --depth=1 --branch {repo_tag[repo]} {repo_url[repo]}')
#os.system(f'git clone {repo_map[repo]}')
#os.system(f'cd {repo} && git checkout {repo... | 1,269 | 26.608696 | 84 | py |
alda | alda-master/benchmarks/rules/PA/io_time.py | # measure time to read facts from .P and write them to pickle files
import os, time, pickle, collections, re, statistics
def main():
datasets="blender django matplotlib numpy pandas pytorch scikit-learn scipy sympy".split(' ')
cputime0 = {}
cputime1 = {}
cputime2 = {}
cputime3 = {}
classdefsz... | 2,151 | 34.278689 | 252 | py |
alda | alda-master/benchmarks/rules/PA/data_prep/download.py | # download github repos used in PA experiments
import os,sys
def main():
def download(repo):
print('downloading ',repo)
os.system(f'git clone --depth=1 --branch {repo_tag[repo]} {repo_url[repo]}')
#os.system(f'git clone {repo_map[repo]}')
#os.system(f'cd {repo} && git checkout {rep... | 1,270 | 27.244444 | 84 | py |
alda | alda-master/benchmarks/rules/PA/data_prep/pyast_views.py | """ generate pickled facts for the input module: 'folder or file,
and a user readable version by file.
"""
import ast,os,sys,shutil,pickle
from collections.abc import Collection
from pprint import pprint
class Generator:
def __init__(self):
self.reset()
self.pickleFolder = '_state'
self.t... | 9,998 | 40.83682 | 209 | py |
LatentOps | LatentOps-main/code/train_ddpm_latent.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... | 37,591 | 47.884265 | 147 | py |
LatentOps | LatentOps-main/code/my_transformers.py | import copy
from abc import ABC
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
# from sentence_transformers import SentenceTransformer
from transformers.models.bert.modeling_bert import BertModel, BertPooler, BertLayer
from transformers.models.deberta.modeling_deberta import DebertaModel, Cont... | 39,200 | 43.90378 | 126 | py |
LatentOps | LatentOps-main/code/test_SBM_sde.py | import torch
import argparse
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import functools
import os
from my_transformers import *
from transformers import AutoTokenizer, AdamW
import copy
from examples.big_ae.modules import VAE,sample_sequence_conditional, DDPM, LinearModel
import logging
f... | 16,316 | 38.129496 | 121 | py |
LatentOps | LatentOps-main/code/examples/big_ae/run_latent_generation.py | #!/usr/bin/env python3
# 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 c... | 35,004 | 46.17655 | 194 | py |
LatentOps | LatentOps-main/code/examples/big_ae/ddpm_conditional.py | '''
This script does conditional image generation on MNIST, using a diffusion model
This code is modified from,
https://github.com/cloneofsimo/minDiffusion
Diffusion model is based on DDPM,
https://arxiv.org/abs/2006.11239
The conditioning idea is taken from 'Classifier-Free Diffusion Guidance',
https://arxiv.org/ab... | 15,248 | 36.83871 | 176 | py |
LatentOps | LatentOps-main/code/examples/big_ae/ppvae_training.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... | 57,651 | 50.109929 | 165 | py |
LatentOps | LatentOps-main/code/examples/big_ae/lace_sampling_word.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... | 27,709 | 47.024263 | 153 | py |
LatentOps | LatentOps-main/code/examples/big_ae/lace_tst_my.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... | 31,929 | 49.362776 | 152 | py |
LatentOps | LatentOps-main/code/examples/big_ae/conditional_generation.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... | 24,466 | 46.787109 | 151 | py |
LatentOps | LatentOps-main/code/examples/big_ae/run_lm_vae_training.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... | 59,057 | 52.349593 | 191 | py |
LatentOps | LatentOps-main/code/examples/big_ae/lace_tst_content.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... | 32,654 | 50.915739 | 153 | py |
LatentOps | LatentOps-main/code/examples/big_ae/utils.py | import json
import os
import pickle
import random
import subprocess
import numpy as np
import torch
import torch.nn.init as init
from torch import nn
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import DataLoader, Dataset, Sampler
from torch.utils.data.distributed import DistributedSampler
from tq... | 61,018 | 39.544186 | 155 | py |
LatentOps | LatentOps-main/code/examples/big_ae/fgim_tst.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... | 34,465 | 49.985207 | 153 | py |
LatentOps | LatentOps-main/code/examples/big_ae/lace_sampling_wor.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... | 27,645 | 46.747841 | 153 | py |
LatentOps | LatentOps-main/code/examples/big_ae/train_simcse.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... | 47,878 | 50.372318 | 165 | py |
LatentOps | LatentOps-main/code/examples/big_ae/train_ddpm_latent.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... | 34,122 | 47.886819 | 147 | py |
LatentOps | LatentOps-main/code/examples/big_ae/minddpm.py | """
Extremely Minimalistic Implementation of DDPM
https://arxiv.org/abs/2006.11239
Everything is self contained. (Except for pytorch and torchvision... of course)
run it with `python superminddpm.py`
"""
from typing import Dict, Tuple
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.utils.data i... | 6,612 | 29.901869 | 102 | py |
LatentOps | LatentOps-main/code/examples/big_ae/my_transformers.py | import copy
from abc import ABC
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
# from sentence_transformers import SentenceTransformer
from transformers.models.bert.modeling_bert import BertModel, BertPooler, BertLayer
from transformers.models.deberta.modeling_deberta import DebertaModel, Cont... | 39,200 | 43.90378 | 126 | py |
LatentOps | LatentOps-main/code/examples/big_ae/lace_sampling_transfer.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... | 30,852 | 47.511006 | 167 | py |
LatentOps | LatentOps-main/code/examples/big_ae/test_SBM_sde.py | import torch
import argparse
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import functools
import os
from my_transformers import *
from transformers import AutoTokenizer, AdamW
import copy
from examples.big_ae.modules import VAE,sample_sequence_conditional, DDPM, LinearModel
import logging
f... | 16,359 | 38.045346 | 121 | py |
LatentOps | LatentOps-main/code/examples/big_ae/run_lm_daae_training.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... | 46,830 | 52.952765 | 283 | py |
LatentOps | LatentOps-main/code/examples/big_ae/run_lm_vae_training_fixdec.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... | 67,117 | 51.069822 | 200 | py |
LatentOps | LatentOps-main/code/examples/big_ae/lace_sampling.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... | 29,056 | 48.926117 | 153 | py |
LatentOps | LatentOps-main/code/examples/big_ae/run_lm_vae_training_fix.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... | 59,551 | 50.604853 | 283 | py |
LatentOps | LatentOps-main/code/examples/big_ae/train_cls_gan.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... | 58,836 | 51.160461 | 165 | py |
LatentOps | LatentOps-main/code/examples/big_ae/train_cls_latent.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... | 61,603 | 49.577997 | 165 | py |
LatentOps | LatentOps-main/code/examples/big_ae/ddpm.py | '''
This script does conditional image generation on MNIST, using a diffusion model
This code is modified from,
https://github.com/cloneofsimo/minDiffusion
Diffusion model is based on DDPM,
https://arxiv.org/abs/2006.11239
The conditioning idea is taken from 'Classifier-Free Diffusion Guidance',
https://arxiv.org/ab... | 15,249 | 36.841191 | 176 | py |
LatentOps | LatentOps-main/code/examples/big_ae/lace_sampling_my.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... | 28,235 | 45.06199 | 151 | py |
LatentOps | LatentOps-main/code/examples/big_ae/lace_tst.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... | 32,824 | 50.611635 | 153 | py |
LatentOps | LatentOps-main/code/examples/big_ae/run_lm_lace_training.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... | 56,930 | 51.909851 | 165 | py |
LatentOps | LatentOps-main/code/examples/big_ae/utils_eval.py | import numpy as np
import os, sys
import torch
from torch import nn, optim
import subprocess
from tqdm import tqdm, trange
from torch.utils.data import DataLoader, Dataset, Sampler, SequentialSampler, RandomSampler
from torch.nn.utils.rnn import pad_sequence
import json
import pdb
import torch.nn.init as init
import... | 61,443 | 39.583884 | 164 | py |
LatentOps | LatentOps-main/code/examples/big_ae/run_lm_content_training.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... | 66,089 | 50.552262 | 165 | py |
LatentOps | LatentOps-main/code/examples/big_ae/lace_sampling_find_weight.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... | 21,208 | 54.231771 | 151 | py |
LatentOps | LatentOps-main/code/examples/big_ae/utils_simcse.py | import numpy as np
import os, sys
import torch
from torch import nn, optim
import subprocess
from tqdm import tqdm, trange
from torch.utils.data import DataLoader, Dataset, Sampler, SequentialSampler, RandomSampler
from torch.nn.utils.rnn import pad_sequence
import json
import pdb
import torch.nn.init as init
import... | 60,732 | 39.354153 | 145 | py |
LatentOps | LatentOps-main/code/examples/big_ae/modules/ctrl_gen.py | import math
import torch
import torch.nn as nn
from .utils import log_sum_exp
import pdb
import sys
sys.path.append('../../')
from pytorch_transformers.modeling_bert import BertEmbeddings
import torch.nn.functional as F
class Ctrl_Gen(nn.Module):
def __init__(self, encoder, decoder, tokenizer_encoder, tokenizer_d... | 19,226 | 50.685484 | 159 | py |
LatentOps | LatentOps-main/code/examples/big_ae/modules/cara.py | import math
import torch
import torch.nn as nn
from .utils import log_sum_exp
import pdb
import sys
sys.path.append('../../')
from pytorch_transformers.modeling_bert import BertEmbeddings
import torch.nn.functional as F
class CARA(nn.Module):
def __init__(self, encoder, decoder, tokenizer_encoder, tokenizer_decod... | 19,558 | 51.157333 | 159 | py |
LatentOps | LatentOps-main/code/examples/big_ae/modules/utils.py | import torch
def safe_log(z):
return torch.log(z + 1e-7)
def log_sum_exp(value, dim=None, keepdim=False):
"""Numerically stable implementation of the operation
value.exp().sum(dim, keepdim).log()
"""
if dim is not None:
m, _ = torch.max(value, dim=dim, keepdim=True)
value0 = value ... | 1,162 | 28.075 | 84 | py |
LatentOps | LatentOps-main/code/examples/big_ae/modules/vae.py | import math
import torch
import torch.nn as nn
from .utils import log_sum_exp
import pdb
import numpy as np
import logging
from typing import Dict, Tuple
logger = logging.getLogger(__name__)
def reparameterize(mu, logvar, nsamples=1):
"""sample from posterior Gaussian family
Args:
mu: Tensor
... | 61,553 | 37.208566 | 159 | py |
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