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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intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/quantization/_quantization_state_utils.py | import dataclasses
from typing import Callable, Tuple, Any, List, Optional, Dict
import torch
import torch.nn.functional as F
import torch.nn.quantized.dynamic as nnqd
from intel_extension_for_pytorch.nn.functional import interaction
import intel_extension_for_pytorch._C as core
functions_supported_by_quantization = ... | 20,844 | 36.969035 | 120 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/quantization/_module_swap_utils.py | from typing import Dict, Callable, Any, Optional
import torch
import torch.nn as nn
from torch.ao.quantization import swap_module
import torch.nn.quantized.dynamic as nnqd
from torch.quantization.qconfig import QConfig
# Default map for swapping dynamic modules
DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS: Dict[Callable, A... | 3,210 | 31.434343 | 86 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/quantization/_quantize.py | import copy
import functools
import os
import warnings
import torch
from torch.ao.quantization import PlaceholderObserver, QConfig, QConfigMapping
from torch.ao.quantization.quantization_mappings import (
get_default_dynamic_quant_module_mappings,
)
import torch.fx.experimental.optimization as optimization
from to... | 14,603 | 38.684783 | 124 | py |
intel-extension-for-pytorch | intel-extension-for-pytorch-master/intel_extension_for_pytorch/fx/concat_linear.py | import torch
import torch.nn as nn
import torch.fx as fx
import torch.fx.experimental.optimization as optimization
import _operator
import copy
import warnings
def concat_linear(model: fx.GraphModule, inplace=False) -> fx.GraphModule:
def concat(compatible_layers, modules):
if len(compatible_layers) < 2:
... | 9,423 | 41.071429 | 114 | py |
Seq2Sick | Seq2Sick-master/opts.py | import argparse
from onmt.modules.SRU import CheckSRU
def model_opts(parser):
"""
These options are passed to the construction of the model.
Be careful with these as they will be used during translation.
"""
# Embedding Options
group = parser.add_argument_group('Model-Embeddings')
group.a... | 28,909 | 48.588336 | 88 | py |
Seq2Sick | Seq2Sick-master/attack.py | #!/usr/bin/env python
from __future__ import division, unicode_literals
import os
import argparse
import math
import codecs
import torch
from torch.autograd import Variable
import numpy as np
from itertools import count
import onmt.io
import onmt.translate
import onmt
import onmt.ModelConstructor
import onmt.modules
... | 13,084 | 40.805112 | 212 | py |
Seq2Sick | Seq2Sick-master/translate.py | #!/usr/bin/env python
from __future__ import division, unicode_literals
import os
import argparse
import math
import codecs
import torch
from itertools import count
import onmt.io
import onmt.translate
import onmt
import onmt.ModelConstructor
import onmt.modules
import opts
parser = argparse.ArgumentParser(
des... | 3,842 | 31.846154 | 74 | py |
Seq2Sick | Seq2Sick-master/train.py | #!/usr/bin/env python
from __future__ import division
import argparse
import glob
import os
import sys
import random
import torch
import torch.nn as nn
from torch import cuda
import onmt
import onmt.io
import onmt.Models
import onmt.ModelConstructor
import onmt.modules
from onmt.Utils import use_gpu
import opts
p... | 11,059 | 30.781609 | 78 | py |
Seq2Sick | Seq2Sick-master/preprocess.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import codecs
import os
import glob
import sys
import torch
import onmt.io
import opts
def check_existing_pt_files(opt):
# We will use glob.glob() to find sharded {train|valid}.[0-9]*.pt
# when training, so check to avoid tampering with existing... | 7,222 | 33.070755 | 78 | py |
Seq2Sick | Seq2Sick-master/tools/embeddings_to_torch.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
from __future__ import division
import six
import sys
import numpy as np
import argparse
import torch
parser = argparse.ArgumentParser(description='embeddings_to_torch.py')
parser.add_argument('-emb_file', required=True,
... | 3,313 | 33.884211 | 74 | py |
Seq2Sick | Seq2Sick-master/tools/extract_embeddings.py | from __future__ import division
import torch
import argparse
import opts
import onmt
import onmt.ModelConstructor
import onmt.io
from onmt.Utils import use_gpu
parser = argparse.ArgumentParser(description='translate.py')
parser.add_argument('-model', required=True,
help='Path to model .pt file')
p... | 2,422 | 31.743243 | 73 | py |
Seq2Sick | Seq2Sick-master/onmt/Loss.py | """
This file handles the details of the loss function during training.
This includes: LossComputeBase and the standard NMTLossCompute, and
sharded loss compute stuff.
"""
from __future__ import division
import torch
import torch.nn as nn
from torch.autograd import Variable
import onmt
import onmt.io
... | 9,843 | 36.716475 | 79 | py |
Seq2Sick | Seq2Sick-master/onmt/Utils.py | import torch
def aeq(*args):
"""
Assert all arguments have the same value
"""
arguments = (arg for arg in args)
first = next(arguments)
assert all(arg == first for arg in arguments), \
"Not all arguments have the same value: " + str(args)
def sequence_mask(lengths, max_len=None):
... | 726 | 24.068966 | 62 | py |
Seq2Sick | Seq2Sick-master/onmt/ModelConstructor.py | """
This file is for models creation, which consults options
and creates each encoder and decoder accordingly.
"""
import torch
import torch.nn as nn
import onmt
import onmt.io
import onmt.Models
import onmt.modules
from onmt.Models import NMTModel, MeanEncoder, RNNEncoder, \
StdRNNDecoder, Inp... | 8,731 | 37.808889 | 77 | py |
Seq2Sick | Seq2Sick-master/onmt/Trainer.py | from __future__ import division
"""
This is the loadable seq2seq trainer library that is
in charge of training details, loss compute, and statistics.
See train.py for a use case of this library.
Note!!! To make this a general library, we implement *only*
mechanism things here(i.e. what to do), and leave the strategy
t... | 8,234 | 32.612245 | 79 | py |
Seq2Sick | Seq2Sick-master/onmt/Optim.py | import torch.optim as optim
from torch.nn.utils import clip_grad_norm
class Optim(object):
"""
Controller class for optimization. Mostly a thin
wrapper for `optim`, but also useful for implementing
rate scheduling beyond what is currently available.
Also implements necessary methods for training R... | 4,202 | 38.280374 | 77 | py |
Seq2Sick | Seq2Sick-master/onmt/Models.py | from __future__ import division
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence as pack
from torch.nn.utils.rnn import pad_packed_sequence as unpack
import onmt
from onmt.Utils import aeq
class EncoderBase(nn.Module):
"""
Base encoder... | 22,339 | 34.801282 | 79 | py |
Seq2Sick | Seq2Sick-master/onmt/modules/ConvMultiStepAttention.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.Utils import aeq
SCALE_WEIGHT = 0.5 ** 0.5
def seq_linear(linear, x):
# linear transform for 3-d tensor
batch, hidden_size, length, _ = x.size()
h = linear(torch.transpose(x, 1, 2).contiguous().view(
batch * length, hid... | 2,611 | 34.780822 | 77 | py |
Seq2Sick | Seq2Sick-master/onmt/modules/Transformer.py | """
Implementation of "Attention is All You Need"
"""
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import onmt
from onmt.Models import EncoderBase
from onmt.Models import DecoderState
from onmt.Utils import aeq
MAX_SIZE = 5000
class PositionwiseFeedForward(nn.Module):
... | 12,494 | 37.684211 | 79 | py |
Seq2Sick | Seq2Sick-master/onmt/modules/Embeddings.py | import torch
import torch.nn as nn
from torch.autograd import Variable
from onmt.modules import BottleLinear, Elementwise
from onmt.Utils import aeq
class PositionalEncoding(nn.Module):
def __init__(self, dropout, dim, max_len=5000):
pe = torch.arange(0, max_len).unsqueeze(1).expand(max_len, dim)
... | 6,674 | 37.142857 | 77 | py |
Seq2Sick | Seq2Sick-master/onmt/modules/CopyGenerator.py | import torch.nn as nn
import torch.nn.functional as F
import torch
import torch.cuda
import onmt
import onmt.io
from onmt.Utils import aeq
class CopyGenerator(nn.Module):
"""
Generator module that additionally considers copying
words directly from the source.
"""
def __init__(self, opt, src_dict,... | 5,166 | 34.881944 | 79 | py |
Seq2Sick | Seq2Sick-master/onmt/modules/StackedRNN.py | import torch
import torch.nn as nn
class StackedLSTM(nn.Module):
"""
Our own implementation of stacked LSTM.
Needed for the decoder, because we do input feeding.
"""
def __init__(self, num_layers, input_size, rnn_size, dropout):
super(StackedLSTM, self).__init__()
self.dropout = nn... | 1,755 | 28.266667 | 66 | py |
Seq2Sick | Seq2Sick-master/onmt/modules/MultiHeadedAttn.py | import math
import torch
import torch.nn as nn
from torch.autograd import Variable
from onmt.Utils import aeq
from onmt.modules.UtilClass import BottleLinear, BottleSoftmax
class MultiHeadedAttention(nn.Module):
''' Multi-Head Attention module from
"Attention is All You Need".
'''
def __init__(se... | 3,828 | 34.453704 | 74 | py |
Seq2Sick | Seq2Sick-master/onmt/modules/Gate.py | """
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and produces a
gate.
The gate can be used to select the input from the target side context
(decoder state), from the source context (attention state) or both.
"""
import torch
import ... | 3,600 | 38.571429 | 78 | py |
Seq2Sick | Seq2Sick-master/onmt/modules/UtilClass.py | import torch
import torch.nn as nn
class Bottle(nn.Module):
def forward(self, input):
if len(input.size()) <= 2:
return super(Bottle, self).forward(input)
size = input.size()[:2]
out = super(Bottle, self).forward(input.view(size[0]*size[1], -1))
... | 2,769 | 30.123596 | 78 | py |
Seq2Sick | Seq2Sick-master/onmt/modules/StructuredAttention.py | import torch.nn as nn
import torch
import torch.cuda
from torch.autograd import Variable
class MatrixTree(nn.Module):
"""Implementation of the matrix-tree theorem for computing marginals
of non-projective dependency parsing. This attention layer is used
in the paper "Learning Structured Text Representatio... | 1,597 | 32.291667 | 77 | py |
Seq2Sick | Seq2Sick-master/onmt/modules/Conv2Conv.py | """
Implementation of "Convolutional Sequence to Sequence Learning"
"""
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.autograd import Variable
import onmt.modules
from onmt.modules.WeightNorm import WeightNormConv2d
from onmt.Models import EncoderBase
from o... | 8,735 | 36.016949 | 79 | py |
Seq2Sick | Seq2Sick-master/onmt/modules/GlobalAttention.py | import torch
import torch.nn as nn
from onmt.modules.UtilClass import BottleLinear
from onmt.Utils import aeq, sequence_mask
class GlobalAttention(nn.Module):
"""
Global attention takes a matrix and a query vector. It
then computes a parameterized convex combination of the matrix
based on the input q... | 7,024 | 31.224771 | 79 | py |
Seq2Sick | Seq2Sick-master/onmt/modules/SRU.py | """
Implementation of "Training RNNs as Fast as CNNs".
TODO: turn to pytorch's implementation when it is available.
This implementation is adpoted from the author of the paper:
https://github.com/taolei87/sru/blob/master/cuda_functional.py.
"""
# flake8: noqa
import subprocess
import platform
import os
import re
impo... | 23,334 | 36.57649 | 79 | py |
Seq2Sick | Seq2Sick-master/onmt/modules/WeightNorm.py | """
Implementation of "Weight Normalization: A Simple Reparameterization
to Accelerate Training of Deep Neural Networks"
As a reparameterization method, weight normalization is same
as BatchNormalization, but it doesn't depend on minibatch.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from tor... | 9,574 | 39.231092 | 78 | py |
Seq2Sick | Seq2Sick-master/onmt/modules/AudioEncoder.py | import math
import torch.nn as nn
import torch.nn.functional as F
class AudioEncoder(nn.Module):
"""
Encoder recurrent neural network for Images.
"""
def __init__(self, num_layers, bidirectional, rnn_size, dropout,
sample_rate, window_size):
"""
Args:
num_l... | 2,327 | 33.746269 | 73 | py |
Seq2Sick | Seq2Sick-master/onmt/modules/ImageEncoder.py | import torch.nn as nn
import torch.nn.functional as F
import torch
from torch.autograd import Variable
class ImageEncoder(nn.Module):
"""
Encoder recurrent neural network for Images.
"""
def __init__(self, num_layers, bidirectional, rnn_size, dropout):
"""
Args:
num_layers ... | 4,023 | 36.962264 | 76 | py |
Seq2Sick | Seq2Sick-master/onmt/io/IO.py | # -*- coding: utf-8 -*-
import os
import codecs
from collections import Counter, defaultdict
from itertools import chain, count
import torch
import torchtext.data
import torchtext.vocab
from onmt.Utils import aeq
PAD_WORD = '<blank>'
UNK = 0
BOS_WORD = '<s>'
EOS_WORD = '</s>'
def _getstate(self):
return dict... | 21,995 | 34.477419 | 78 | py |
Seq2Sick | Seq2Sick-master/onmt/io/TextDataset.py | # -*- coding: utf-8 -*-
from collections import Counter
import io
import sys
import torch
import torchtext
from onmt.Utils import aeq
from onmt.io.IO import ONMTDatasetBase, _join_dicts, _peek,\
_construct_example_fromlist, extract_features
class TextDataset(ONMTDatasetBase):
""" Dataset... | 8,181 | 36.87963 | 78 | py |
Seq2Sick | Seq2Sick-master/onmt/translate/Beam.py | from __future__ import division
import torch
"""
Class for managing the internals of the beam search process.
Takes care of beams, back pointers, and scores.
"""
class Beam(object):
def __init__(self, size, pad, bos, eos,
n_best=1, cuda=False,
global_scorer=None):
se... | 5,514 | 32.834356 | 79 | py |
Seq2Sick | Seq2Sick-master/onmt/translate/Translator.py | import torch
from torch.autograd import Variable
import onmt.translate.Beam
import onmt.io
class Translator(object):
def __init__(self, model, fields,
beam_size, n_best,
max_length,
global_scorer, copy_attn, cuda,
beam_trace):
self.model... | 11,549 | 38.02027 | 92 | py |
Seq2Sick | Seq2Sick-master/onmt/translate/Translation.py | from __future__ import division, unicode_literals
import torch
import onmt.io
class TranslationBuilder(object):
def __init__(self, data, fields, n_best, replace_unk, has_tgt):
self.data = data
self.fields = fields
self.n_best = n_best
self.replace_unk = replace_unk
self.ha... | 7,063 | 35.225641 | 106 | py |
DGP | DGP-master/docs/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
import os
import sys
sys.path.insert(0, os.path.abspath("../.."))
# -- Project information --------------------------... | 1,227 | 32.189189 | 87 | py |
SeConvNet | SeConvNet-main/model.py | import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import Input
from tensorflow.keras.layers import Conv2D, Activation,BatchNormalization, Add
import numpy as np
#define SeConv block:
class SeConv_block(keras.layers.Layer):
def __init__(self, kernel_size, input_channels, **kwargs):
... | 3,615 | 38.304348 | 175 | py |
SeConvNet | SeConvNet-main/train.py | import argparse
import numpy as np
from tensorflow import keras
from tensorflow.math import reduce_sum, square
import os
from model import SeConvNet
from SPN import SPN
from data_generator import data_gen
parser = argparse.ArgumentParser()
parser.add_argument('--noise_density', default=0.95, type=float, help='noise d... | 2,919 | 39 | 185 | py |
icd-coding-benchmark | icd-coding-benchmark-main/app.py | #!/usr/bin/env python
"""
The interactive demo of ICD coding benchmark (prototype)
"""
import argparse
import copy
import csv
import numpy as np
import pandas as pd
import seaborn as sns
import streamlit as st
import torch
from captum.attr import LayerIntegratedGradients
from anemic.modules.preprocessors import C... | 13,915 | 33.877193 | 80 | py |
icd-coding-benchmark | icd-coding-benchmark-main/run.py | # Imports
import argparse
import os
import pandas
import torch
from torchsummaryX import summary
from anemic.utils.configuration import Config
from anemic.utils.import_related_ops import pandas_related_ops
from anemic.utils.mapper import ConfigMapper
from anemic.utils.misc import seed
pandas_related_ops()
# Comman... | 2,156 | 23.793103 | 80 | py |
icd-coding-benchmark | icd-coding-benchmark-main/anemic/modules/losses.py | """All criterion functions."""
import json
import os
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from anemic.utils.file_loaders import load_json
from anemic.utils.mapper import ConfigMapper
ConfigMapper.map("losses... | 2,635 | 30.759036 | 80 | py |
icd-coding-benchmark | icd-coding-benchmark-main/anemic/modules/schedulers.py | from torch.optim.lr_scheduler import (
CosineAnnealingLR,
CosineAnnealingWarmRestarts,
CyclicLR,
LambdaLR,
ReduceLROnPlateau,
StepLR,
)
from transformers import get_linear_schedule_with_warmup
from anemic.utils.mapper import ConfigMapper
ConfigMapper.map("schedulers", "step")(StepLR)
ConfigMap... | 674 | 28.347826 | 66 | py |
icd-coding-benchmark | icd-coding-benchmark-main/anemic/modules/optimizers.py | """Method containing activation functions"""
from torch.optim import SGD, Adam, AdamW
from anemic.utils.mapper import ConfigMapper
ConfigMapper.map("optimizers", "adam")(Adam)
ConfigMapper.map("optimizers", "adam_w")(AdamW)
ConfigMapper.map("optimizers", "sgd")(SGD)
| 269 | 29 | 47 | py |
icd-coding-benchmark | icd-coding-benchmark-main/anemic/modules/activations.py | import torch.nn as nn
from anemic.utils.mapper import ConfigMapper
ConfigMapper.map("activations", "relu")(nn.ReLU)
ConfigMapper.map("activations", "logsoftmax")(nn.LogSoftmax)
ConfigMapper.map("activations", "softmax")(nn.Softmax)
| 234 | 28.375 | 60 | py |
icd-coding-benchmark | icd-coding-benchmark-main/anemic/models/multirescnn.py | """
ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural
Network, 2020
https://github.com/foxlf823/Multi-Filter-Residual-Convolutional-Neural-Network
"""
from math import floor
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_ as xav... | 8,926 | 30.10453 | 79 | py |
icd-coding-benchmark | icd-coding-benchmark-main/anemic/models/dcan.py | import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_
from torch.nn.utils import weight_norm as weight_norm_
from anemic.utils.mapper import ConfigMapper
from anemic.utils.text_loggers import get_logger
logger = get_logger(__name__)
@ConfigMapper.m... | 20,705 | 39.759843 | 115 | py |
icd-coding-benchmark | icd-coding-benchmark-main/anemic/models/transicd.py | import math
import torch
import torch.nn as nn
from torch.autograd import Variable
from anemic.utils.mapper import ConfigMapper
from anemic.utils.text_loggers import get_logger
logger = get_logger(__name__)
@ConfigMapper.map("models", "transicd")
class TransICD(nn.Module):
def __init__(self, config):
s... | 10,178 | 37.411321 | 115 | py |
icd-coding-benchmark | icd-coding-benchmark-main/anemic/models/caml.py | """
CAML model (Mullenbach et al. 2018)
https://github.com/jamesmullenbach/caml-mimic
"""
from math import floor
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.init import xavier_uniform
from anemic.utils.mapper import C... | 9,333 | 33.828358 | 80 | py |
icd-coding-benchmark | icd-coding-benchmark-main/anemic/models/fusion.py | """
Fusion model (Luo et al. 2021)
https://github.com/machinelearning4health/Fusion-Towards-Automated-ICD-Coding
"""
from math import floor
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_ as xavier_uniform
from anemic.utils.mappe... | 19,129 | 35.507634 | 81 | py |
icd-coding-benchmark | icd-coding-benchmark-main/anemic/datasets/base_dataset.py | import os
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
from anemic.utils.file_loaders import load_csv_as_df, load_json
from anemic.utils.mapper import ConfigMapper
from anemic.utils.text_loggers import get_logger
logger = get_logger(__name__)
@ConfigMapper.map("datasets"... | 3,753 | 32.81982 | 80 | py |
icd-coding-benchmark | icd-coding-benchmark-main/anemic/utils/checkpoint_savers.py | """
Checkpoint Saver
"""
import json
import os
import torch
from anemic.modules.metrics import load_metric
from anemic.utils.file_loaders import load_json, save_json
from anemic.utils.mapper import ConfigMapper
from anemic.utils.text_loggers import get_logger
logger = get_logger(__name__)
@ConfigMapper.map("ch... | 7,043 | 34.044776 | 78 | py |
icd-coding-benchmark | icd-coding-benchmark-main/anemic/utils/misc.py | """Miscellaneous utility functions."""
import copy
import itertools
import random
import numpy as np
import torch
def seed(value=42):
"""Set random seed for everything.
Args:
value (int): Seed
"""
np.random.seed(value)
torch.manual_seed(value)
torch.backends.cudnn.deterministic = Tr... | 6,349 | 28.812207 | 80 | py |
icd-coding-benchmark | icd-coding-benchmark-main/anemic/utils/graph_writers.py | import json
import os
import torch
from torch.utils.tensorboard import SummaryWriter
from anemic.utils.mapper import ConfigMapper
from anemic.utils.text_loggers import get_logger
logger = get_logger(__name__)
class GraphWriterBase:
def __init__(self, config):
self.config = config
def writer_scalar... | 1,279 | 26.234043 | 78 | py |
icd-coding-benchmark | icd-coding-benchmark-main/anemic/trainers/base_trainer.py | import math
import os
import numpy as np
import torch
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import DataLoader
from tqdm import tqdm
from anemic.modules.metrics import load_metric
from anemic.utils.configuration import Config
from anemic.utils.file_loaders import save_json
from a... | 16,526 | 39.211679 | 80 | py |
pykg2vec | pykg2vec-master/pykg2vec/models/Domain.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Domain module for building Knowledge Graphs
"""
from torch.nn import Embedding
class NamedEmbedding(Embedding):
""" Associate embeddings with human-readable names"""
def __init__(self, name, *args, **kwargs):
super(NamedEmbedding, self).__init__(*args,... | 418 | 22.277778 | 61 | py |
pykg2vec | pykg2vec-master/pykg2vec/models/pairwise.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from pykg2vec.models.KGMeta import PairwiseModel
from pykg2vec.models.Domain import NamedEmbedding
from pykg2vec.utils.criterion import Criterion
class TransE(PairwiseModel):
"""
... | 46,920 | 40.050744 | 286 | py |
pykg2vec | pykg2vec-master/pykg2vec/models/projection.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from pykg2vec.models.KGMeta import ProjectionModel
from pykg2vec.models.Domain import NamedEmbedding
from pykg2vec.utils.criterion import Criterion
class ConvE(ProjectionModel):
"""
... | 30,964 | 40.452477 | 479 | py |
pykg2vec | pykg2vec-master/pykg2vec/models/pointwise.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import numpy as np
from numpy.random import RandomState
from pykg2vec.models.KGMeta import PointwiseModel
from pykg2vec.models.Domain import NamedEmbedding
from pykg2vec.utils.criterion import Criterion
class ANALOGY(PointwiseModel):
... | 48,166 | 41.475309 | 178 | py |
pykg2vec | pykg2vec-master/pykg2vec/models/KGMeta.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Knowledge Graph Meta Class
====================================
It provides Abstract class for the Knowledge graph models.
"""
from pykg2vec.common import TrainingStrategy
from abc import ABCMeta
import torch.nn as nn
class Model:
""" Meta Class for knowledge gra... | 2,497 | 29.839506 | 76 | py |
pykg2vec | pykg2vec-master/pykg2vec/test/test_generator.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This module is for testing unit functions of generator
"""
import torch
from pykg2vec.data.generator import Generator
from pykg2vec.common import Importer, KGEArgParser
from pykg2vec.data.kgcontroller import KnowledgeGraph
def test_generator_projection():
"""Functi... | 2,641 | 30.082353 | 77 | py |
pykg2vec | pykg2vec-master/pykg2vec/utils/riemannian_optimizer.py | import torch
from torch.optim.optimizer import Optimizer
class RiemannianOptimizer(Optimizer):
"""Riemannian stochastic gradient descent"""
def __init__(self, params, lr, param_names):
defaults = dict(lr=lr)
super(RiemannianOptimizer, self).__init__(params, defaults)
self.param_names ... | 2,205 | 35.163934 | 93 | py |
pykg2vec | pykg2vec-master/pykg2vec/utils/visualization.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This module is for visualizing the results
"""
import os
import seaborn
import torch
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd
from sklearn.manifold import TSNE
from matplotlib import colors as mcolors
from pykg2vec.ut... | 19,354 | 40.893939 | 202 | py |
pykg2vec | pykg2vec-master/pykg2vec/utils/evaluator.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This module is for evaluating the results
"""
import os
import timeit
import torch
import numpy as np
import pandas as pd
from pykg2vec.utils.logger import Logger
from tqdm import tqdm
class MetricCalculator:
'''
MetricCalculator aims to
1) address... | 13,276 | 38.632836 | 164 | py |
pykg2vec | pykg2vec-master/pykg2vec/utils/criterion.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class Criterion:
"""Utility for calculating KGE losses
Loss Functions in Knowledge Graph Embedding Models
http://ceur-ws.org/Vol-2377/paper_1.pdf
"""
@staticmethod
def pariwise_logistic(pos_preds, neg_preds, neg_rate, a... | 1,926 | 34.036364 | 89 | py |
pykg2vec | pykg2vec-master/pykg2vec/utils/trainer.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import warnings
import torch
import torch.optim as optim
import numpy as np
import pandas as pd
from tqdm import tqdm
from pathlib import Path
from pykg2vec.utils.evaluator import Evaluator
from pykg2vec.utils.visualization import Visualization
from pykg2vec.util... | 19,650 | 39.85447 | 127 | py |
pykg2vec | pykg2vec-master/pykg2vec/data/generator.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This module is for generating the batch data for training and testing.
"""
import torch
import numpy as np
from multiprocessing import Process, Queue
from pykg2vec.common import TrainingStrategy
def raw_data_generator(command_queue, raw_queue, config):
"""Function ... | 11,620 | 35.775316 | 143 | py |
BertGen | BertGen-master/external/pytorch_pretrained_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 |
BertGen | BertGen-master/external/pytorch_pretrained_bert/optimization_openai.py | # coding=utf-8
# Copyright 2018 The Open AI 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/LICENSE-2.0
#
# U... | 5,661 | 39.156028 | 116 | py |
BertGen | BertGen-master/external/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 |
BertGen | BertGen-master/external/pytorch_pretrained_bert/convert_gpt2_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 ... | 3,046 | 40.739726 | 111 | py |
BertGen | BertGen-master/external/pytorch_pretrained_bert/convert_openai_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 ... | 3,141 | 42.041096 | 118 | py |
BertGen | BertGen-master/external/pytorch_pretrained_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... | 60,198 | 48.18219 | 139 | py |
BertGen | BertGen-master/external/pytorch_pretrained_bert/modeling_gpt2.py | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and 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 of the License ... | 29,887 | 42.632117 | 146 | py |
BertGen | BertGen-master/external/pytorch_pretrained_bert/modeling_openai.py | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and 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 of the License ... | 37,647 | 45.421702 | 152 | py |
BertGen | BertGen-master/external/pytorch_pretrained_bert/convert_transfo_xl_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 ... | 5,642 | 47.230769 | 121 | py |
BertGen | BertGen-master/external/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 json
import logging
import os
import shutil
im... | 8,280 | 32.124 | 112 | py |
BertGen | BertGen-master/external/pytorch_pretrained_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 ... | 2,538 | 39.301587 | 109 | py |
BertGen | BertGen-master/external/pytorch_pretrained_bert/modeling_transfo_xl.py | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University 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 Licen... | 58,702 | 41.476845 | 131 | py |
BertGen | BertGen-master/external/pytorch_pretrained_bert/tokenization_transfo_xl.py | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University 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 Licen... | 24,851 | 35.927192 | 109 | py |
BertGen | BertGen-master/external/pytorch_pretrained_bert/modeling_transfo_xl_utilities.py | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University 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 Licen... | 16,113 | 38.985112 | 132 | py |
BertGen | BertGen-master/common/lr_scheduler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from bisect import bisect_right
import torch
# FIXME ideally this would be achieved with a CombinedLRScheduler,
# separating MultiStepLR with WarmupLR
# but the current LRScheduler design doesn't allow it
class WarmupMultiStepLR(torch.optim.lr_s... | 1,810 | 33.169811 | 80 | py |
BertGen | BertGen-master/common/fast_rcnn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from common.backbone.resnet.resnet import *
from common.backbone.resnet.resnet import Bottleneck, BasicBlock
from common.backbone.resnet.resnet import model_urls
from common.lib.roi_pooling.roi_pool import ROI... | 10,223 | 49.117647 | 155 | py |
BertGen | BertGen-master/common/visual_linguistic_bert.py | import torch
import torch.nn as nn
from external.pytorch_pretrained_bert.modeling import BertLayerNorm, BertEncoder, BertPooler, ACT2FN, BertOnlyMLMHead
# todo: add this to config
NUM_SPECIAL_WORDS = 1000
class BaseModel(nn.Module):
def __init__(self, config, **kwargs):
self.config = config
super... | 43,500 | 49.700466 | 139 | py |
BertGen | BertGen-master/common/module.py | from collections import namedtuple
from typing import Dict
import torch
import torch.nn as nn
import torch.nn.functional as F
class Module(nn.Module):
def __init__(self, config):
super(Module, self).__init__()
self.config = config
def init_weight(self):
raise NotImplementedError()
... | 1,786 | 26.921875 | 65 | py |
BertGen | BertGen-master/common/trainer.py | import os
import time
from collections import namedtuple
import torch
try:
from apex import amp
from apex.amp import _amp_state
except ImportError:
pass
#raise ImportError("Please install apex from https://www.github.com/nvidia/apex if you want to use fp16.")
# Parameter to pass to batch_end_callback
... | 7,661 | 37.31 | 122 | py |
BertGen | BertGen-master/common/backbone/resnet/resnet.py | """
Modified from torchvision, but exposes features from different stages
"""
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import torch
import warnings
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pyt... | 17,247 | 40.461538 | 135 | py |
BertGen | BertGen-master/common/callbacks/epoch_end_callbacks/checkpoint.py | import torch
class Checkpoint(object):
def __init__(self, prefix, frequent):
super(Checkpoint, self).__init__()
self.prefix = prefix
self.frequent = frequent
def __call__(self, epoch_num, net, optimizer, writer, validation_monitor=None):
if (epoch_num + 1) % self.frequent == 0... | 1,119 | 42.076923 | 87 | py |
BertGen | BertGen-master/common/nlp/misc.py | import torch
import random
def get_align_matrix(aligned_ids, sparse=False, device=None, dtype=torch.float32):
"""
Get aligned matrix for feature alignment in sentence embedding
:param aligned_ids: list, aligned_ids[k] means original index of k-th token
:param sparse: whether to return sparse matrix
... | 2,726 | 30.344828 | 104 | py |
BertGen | BertGen-master/common/nlp/time_distributed.py | """
A wrapper that unrolls the second (time) dimension of a tensor
into the first (batch) dimension, applies some other ``Module``,
and then rolls the time dimension back up.
"""
import torch
class TimeDistributed(torch.nn.Module):
"""
Given an input shaped like ``(batch_size, time_steps, [rest])`` and a ``M... | 2,245 | 42.192308 | 99 | py |
BertGen | BertGen-master/common/nlp/encoder_base.py | from typing import Tuple, Union, Optional, Callable
import torch
from torch.nn.utils.rnn import pack_padded_sequence, PackedSequence
# We have two types here for the state, because storing the state in something
# which is Iterable (like a tuple, below), is helpful for internal manipulation
# - however, the states are... | 18,404 | 52.502907 | 109 | py |
BertGen | BertGen-master/common/nlp/bert_encoder_wrapper.py | import torch
import torch.nn as nn
from external.pytorch_pretrained_bert.modeling import BertEncoder, BertLayerNorm
class BertEncoderWrapper(nn.Module):
def __init__(self, bert_config, input_size, output_all_encoded_layers=False):
super(BertEncoderWrapper, self).__init__()
self.bert_config = bert_... | 3,207 | 49.125 | 112 | py |
BertGen | BertGen-master/common/nlp/input_variational_dropout.py | import torch
class InputVariationalDropout(torch.nn.Dropout):
"""
Apply the dropout technique in Gal and Ghahramani, "Dropout as a Bayesian Approximation:
Representing Model Uncertainty in Deep Learning" (https://arxiv.org/abs/1506.02142) to a
3D tensor.
This module accepts a 3D tensor of shape ``... | 1,324 | 37.970588 | 98 | py |
BertGen | BertGen-master/common/nlp/roberta/utils.py | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import sys
import os
try:
from functools import lru_cache
except ImportError:
# Just a dummy decorator to get the checks to run on python2
# because honestly I don't want to support a byte-level un... | 40,379 | 45.736111 | 380 | py |
BertGen | BertGen-master/common/nlp/roberta/modeling_roberta.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... | 17,448 | 53.021672 | 134 | py |
BertGen | BertGen-master/common/nlp/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... | 8,633 | 44.925532 | 130 | py |
BertGen | BertGen-master/common/metrics/eval_metric.py | import torch
import torch.distributed as distributed
class EvalMetric(object):
"""Base class for all evaluation metrics.
.. note::
This is a base class that provides common metric interfaces.
One should not use this class directly, but instead create new metric
classes that extend it.
... | 2,371 | 33.376812 | 80 | py |
BertGen | BertGen-master/common/metrics/pretrain_metrics.py | import torch
from .eval_metric import EvalMetric
class LossLogger(EvalMetric):
def __init__(self, output_name, display_name=None,
allreduce=False, num_replicas=1):
self.output_name = output_name
if display_name is None:
display_name = output_name
super(LossLogg... | 6,797 | 40.2 | 131 | py |
BertGen | BertGen-master/common/metrics/composite_eval_metric.py | import numpy as np
from .eval_metric import EvalMetric
import torch
class CompositeEvalMetric(EvalMetric):
"""Manages multiple evaluation metrics.
Args:
metrics (list of EvalMetric): List of child metrics.
name (str): Name of this metric instance for display.
"""
def __init__(self, met... | 2,153 | 29.771429 | 80 | py |
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