repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_xlnet.py | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Lice... | 72,560 | 52.002922 | 169 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_xlm.py | # coding=utf-8
# Copyright 2019-present, Facebook, Inc and the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... | 45,543 | 50.34611 | 163 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_ctrl.py | # coding=utf-8
# Copyright 2018 Salesforce and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# h... | 23,436 | 47.22428 | 134 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/tokenization_transfo_xl.py | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Lice... | 21,824 | 36.62931 | 133 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/modeling_transfo_xl_utilities.py | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Lice... | 13,568 | 39.747748 | 132 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/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... | 25,678 | 53.52017 | 151 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/classifier_pytorch/transformers/tokenization_utils.py | # coding=utf-8
# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# ... | 54,979 | 50.431244 | 372 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/mrc_pytorch/run_mrc.py | import argparse
import collections
import json
import os
import random
import numpy as np
import torch
from torch import nn
from torch.utils.data import TensorDataset, DataLoader
from tqdm import tqdm
from .preprocess.cmrc2018_evaluate import get_eval
from .pytorch_modeling import BertConfig, BertForQuestionAnswering... | 13,652 | 46.40625 | 117 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/mrc_pytorch/pytorch_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... | 56,477 | 45.830846 | 130 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/mrc_pytorch/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 ... | 5,143 | 40.152 | 105 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/mrc_pytorch/tools/pytorch_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... | 8,435 | 41.606061 | 116 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/mrc_pytorch/tools/utils.py | import collections
import os
import re
from glob import glob
import tensorflow as tf
import tensorflow.contrib.slim as slim
import torch
def check_args(args):
args.setting_file = os.path.join(args.checkpoint_dir, args.setting_file)
args.log_file = os.path.join(args.checkpoint_dir, args.log_file)
os.maked... | 4,999 | 33.013605 | 117 | py |
ChineseGLUE | ChineseGLUE-master/baselines/models_pytorch/mrc_pytorch/tools/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 json
import logging
import os
import shutil
import tempfile
from functools import wraps
from hashlib import sha256
from pathlib imp... | 8,020 | 32.560669 | 98 | py |
LAAT | LAAT-master/src/training.py | from torch import optim
from src.util.preprocessing import *
from src.util.util import to_md5
from src.util.util import get_n_training_labels
from src.data_helpers.dataloaders import TextDataset, TextDataLoader
from src.trainer import Trainer
from src.evaluator import Evaluator
from src.args_parser import *
import pi... | 13,783 | 38.723343 | 114 | py |
LAAT | LAAT-master/src/evaluator.py | from src.data_helpers.dataloaders import *
import warnings
from src.data_helpers.vocab import device
from src.util.util import *
from tqdm import tqdm
from collections import OrderedDict
warnings.filterwarnings('ignore') # want to remove some warning from sklearn
class Evaluator:
def __init__(self,
... | 4,034 | 37.066038 | 113 | py |
LAAT | LAAT-master/src/trainer.py | from src.evaluator import *
import numpy as np
import os
from torch.autograd import Variable
from src.models.attentions.attention_layer import *
np.set_printoptions(precision=5)
from tqdm import tqdm
from collections import OrderedDict
import shutil
class Trainer:
def __init__(self, model: nn.Module,
... | 13,423 | 42.443366 | 120 | py |
LAAT | LAAT-master/src/models/cnn.py | # -*- coding: utf-8 -*-
"""
Word-based CNN model for text classification from the paper
Kim, Yoon. "Convolutional neural networks for sentence classification." arXiv preprint arXiv:1408.5882 (2014)
https://arxiv.org/abs/1408.5882
Add the support of char features learned from RNN or CNN to enrich word em... | 3,884 | 32.491379 | 117 | py |
LAAT | LAAT-master/src/models/rnn.py | # -*- coding: utf-8 -*-
"""
Word-based RNN model for text classification
@author: Thanh Vu <thanh.vu@csiro.au>
@date created: 07/03/2019
@date last modified: 19/08/2020
"""
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from src.models.atten... | 4,760 | 36.488189 | 114 | py |
LAAT | LAAT-master/src/models/tcn.py | # -*- coding: utf-8 -*-
"""
TCN model for text classification
@author: Thanh Vu <thanh.vu@csiro.au>
@date created: 07/05/2019
@date last modified: 19/08/2020
"""
import torch.nn as nn
from torch.nn.utils import weight_norm
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super(Ch... | 2,883 | 35.974359 | 115 | py |
LAAT | LAAT-master/src/models/attentions/util.py | from src.models.attentions.attention_layer import *
def init_attention_layer(model):
if model.args.joint_mode == "flat":
if model.attention_mode is not None:
model.attention = AttentionLayer(args=model.args, size=model.output_size,
n_labels=model.vo... | 5,075 | 51.329897 | 118 | py |
LAAT | LAAT-master/src/models/attentions/attention_layer.py | """
This class is to implement the attention layer which supports hard attention, self-structured attention
and self attention
@author: Thanh Vu <thanh.vu@csiro.au>
@date created: 20/03/2019
@date last modified: 19/08/2020
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
... | 5,695 | 40.275362 | 145 | py |
LAAT | LAAT-master/src/models/embeddings/embedding_layer.py | # -*- coding: utf-8 -*-
"""
This is to create the embedding layer with the support of character-based word embeddings
@author: Thanh Vu <thanh.vu@csiro.au>
@date created: 01/04/2019
@date last modified: 19/08/2020
"""
import torch.nn as nn
import torch
import copy
class EmbeddingLayer(nn.Module):
... | 3,374 | 43.407895 | 132 | py |
LAAT | LAAT-master/src/util/util.py | import numpy as np
from sklearn.metrics import *
from sklearn.preprocessing import *
import random
import torch
import hashlib
# This is for shuffling the data and initialise the "UNKNOWN" word embedding
random.seed(0)
np.random.seed(0)
# This is to set the random seed if needed
def set_random_seed(random_seed):
... | 13,048 | 33.612732 | 122 | py |
LAAT | LAAT-master/src/data_helpers/vocab.py | # -*- coding: utf-8 -*-
"""
This is to create the vocabularies which are used to convert the text data into tensor format
Author: Thanh Vu <thanh.vu@csiro.au>
Date created: 01/03/2019
Date last modified: 19/03/2019
"""
import os
import torch
from collections import Counter
import numpy as np
from gensim... | 7,978 | 33.842795 | 103 | py |
LAAT | LAAT-master/src/data_helpers/dataloaders.py | # -*- coding: utf-8 -*-
"""
This provides the functions to load the data for training and testing the model (e.g., batch)
Author: Thanh Vu <thanh.vu@csiro.au>
Date created: 01/03/2019
Date last modified: 19/08/2020
"""
import torch
from torch.utils.data import DataLoader, Dataset
from src.util.preproces... | 7,256 | 34.4 | 102 | py |
TreeBERT | TreeBERT-master/__main__.py | import argparse
import copy
import torch
from torch.utils.data import DataLoader
from dataset import BPE, TokenVocab, TreeBERTDataset
from model import Decoder, Encoder, Seq2Seq
from trainer import BERTTrainer
def train():
parser = argparse.ArgumentParser()
parser.add_argument("-td", "--train_dataset", type... | 4,944 | 51.606383 | 133 | py |
TreeBERT | TreeBERT-master/trainer/pretrain.py | import sys
import torch
import torch.nn as nn
from torch.optim import Adam
from torch.utils.data import DataLoader
from .optim_schedule import ScheduledOptim
sys.path.append("..")
import tqdm
from model import TreeBERT, transformer
class BERTTrainer:
def __init__(self, transformer, alpha, vocab_size: int,
... | 3,882 | 38.222222 | 141 | py |
TreeBERT | TreeBERT-master/dataset/utils.py | import base64
import json
import os
import random
import socket
import sys
import time
from urllib import request
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
# from torchtext.data.metrics import bleu_score
import torch
import torch.nn as nn
import tqdm
def getPretrainData(str):
socket.setd... | 7,236 | 26.003731 | 149 | py |
TreeBERT | TreeBERT-master/dataset/dataset.py | import json
import os
import torch
from torch.utils.data import Dataset
from dataset import BPE
class TreeBERTDataset(Dataset):
def __init__(self, vocab, corpus_path, path_num, node_num, code_len,
is_fine_tune=False,
corpus_lines=None, max_subtoken_len=3):
... | 4,323 | 37.607143 | 137 | py |
TreeBERT | TreeBERT-master/dataset/vocab.py | #coding=utf-8
import collections
import json
import os
import pickle
import re
from tqdm import tqdm
class BPE(object):
def __init__(self, corpus_path, BPE_path="data/BPEObject.small", except_list=None, num_merges=5):
print("Read in files")
print('==========')
vocab = self.get_vocab(corp... | 13,962 | 37.786111 | 147 | py |
TreeBERT | TreeBERT-master/model/transformer.py | import random
import numpy as np
import torch
import torch.nn as nn
SEED = 1234
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
class PathEncoder(nn.Module):
def __init__(self,
input_dim,
no... | 9,317 | 31.694737 | 140 | py |
TreeBERT | TreeBERT-master/model/treebert.py | import torch.nn as nn
class TreeBERT(nn.Module):
def __init__(self, transformer, vocab_size):
super().__init__()
self.transformer = transformer
self.mask_lm = MaskedLanguageModel(self.transformer.hidden, vocab_size)
self.node_order_prediction = NodeOrderPrediction(self.transformer.h... | 999 | 30.25 | 82 | py |
machamp | machamp-master/predict.py | import argparse
import logging
import sys
import torch
from machamp.predictor.predict import predict_with_paths
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
level=logging.INFO, handlers=[logging.StreamHandler(sys.stdout)])
logger = logging.getLogger(__name__)... | 2,751 | 47.280702 | 177 | py |
machamp | machamp-master/train.py | import argparse
import sys
import torch
from machamp.model import trainer
parser = argparse.ArgumentParser()
parser.add_argument("--dataset_configs", nargs='+',
help="Path(s) to dataset configurations (use --sequential to train on them sequentially, "
"default is joint tr... | 2,653 | 45.561404 | 126 | py |
machamp | machamp-master/scripts/misc/extract_automodel.py | import os
import sys
import torch
from transformers import AutoTokenizer
from machamp.model.machamp import MachampModel
model = torch.load(sys.argv[1])
outPath = sys.argv[2]
if not os.path.isdir(outPath):
os.mkdir(outPath)
model.mlm.save_pretrained(outPath)
tokenizer = AutoTokenizer.from_pretrained(model.mlm.nam... | 431 | 24.411765 | 65 | py |
machamp | machamp-master/machamp/predictor/predict.py | import json
import logging
import os
from typing import List, Any, Dict
import torch
from torch.utils.data import DataLoader
logger = logging.getLogger(__name__)
from machamp.utils.lemma_edit import apply_lemma_rule
from machamp.utils.myutils import prep_batch, report_metrics
from machamp.data.machamp_dataset_collec... | 11,503 | 47.745763 | 178 | py |
machamp | machamp-master/machamp/modules/allennlp/conditional_random_field.py | """
Conditional random field
"""
import logging
from typing import List, Tuple, Union
import torch
logger = logging.getLogger(__name__)
import machamp.modules.allennlp.util as util
VITERBI_DECODING = Tuple[List[int], float] # a list of tags, and a viterbi score
def allowed_transitions(constraint_type: str, label... | 19,163 | 41.776786 | 121 | py |
machamp | machamp-master/machamp/modules/allennlp/slanted_triangular.py | # Borrowed from allennlp, to avoid having to install the whole allennlp library
import logging
from typing import List, Optional
import torch
from torch.optim.lr_scheduler import _LRScheduler
logger = logging.getLogger(__name__)
class SlantedTriangular(_LRScheduler):
"""
Implements the Slanted Triangular L... | 8,889 | 46.037037 | 97 | py |
machamp | machamp-master/machamp/modules/allennlp/util.py | import logging
import math
from typing import List, Optional, Tuple, Set
import torch
logger = logging.getLogger(__name__)
TypedStringSpan = Tuple[str, Tuple[int, int]]
def viterbi_decode(
tag_sequence: torch.Tensor,
transition_matrix: torch.Tensor,
tag_observations: Optional[List[int]] = N... | 12,281 | 41.794425 | 100 | py |
machamp | machamp-master/machamp/modules/allennlp/bilinear_matrix_attention.py | # Borrowed from allennlp, to avoid having to install the whole allennlp library
import torch
from torch.nn.parameter import Parameter
# linear activation
class Identity:
def forward(self, x: torch.Tensor) -> torch.Tensor:
return x
class BilinearMatrixAttention(torch.nn.Module):
"""
Computes atte... | 3,571 | 40.057471 | 99 | py |
machamp | machamp-master/machamp/modules/allennlp/scalar_mix.py | # Borrowed from allennlp, to avoid having to install the whole allennlp library
import logging
from typing import List
import torch
from torch.nn import Parameter
logger = logging.getLogger(__name__)
class ScalarMix(torch.nn.Module):
"""
Computes a parameterised scalar mixture of N tensors, `mixture = gamma... | 3,789 | 38.072165 | 100 | py |
machamp | machamp-master/machamp/metrics/las.py | import torch
class LAS:
def __init__(self):
self.cor = 0
self.total = 0
self.str = 'las'
self.metric_scores = {}
def score(self, pred_heads, pred_rels, gold_heads, gold_rels):
pred_rels = pred_rels.flatten()
gold_rels = gold_rels.flatten()
cor_rels = go... | 1,006 | 24.175 | 66 | py |
machamp | machamp-master/machamp/metrics/pearson.py | import torch
# This could better be converted to be fuly in torch, or fully in native python
def get_std_scores(x):
standard_score_x = []
mean_x = sum(x)/len(x)
standard_deviation_x = torch.std(torch.tensor(x))
for observation in x:
standard_score_x.append((observation - mean_x)/standard_deviat... | 1,282 | 28.159091 | 91 | py |
machamp | machamp-master/machamp/metrics/f1.py | import logging
import torch
logger = logging.getLogger(__name__)
class F1:
def __init__(self, type_f1):
self.tps = []
self.fps = []
self.fns = []
self.type_f1 = type_f1
self.str = 'f1_' + type_f1
self.vocabulary = []
self.metric_scores = {}
def score(... | 4,573 | 35.592 | 98 | py |
machamp | machamp-master/machamp/metrics/avg_dist.py | import torch
class AvgDist:
def __init__(self):
self.dists = []
self.str = 'avg_dist'
self.metric_scores = {}
def score(self, preds, golds, vocabulary):
self.dists.extend(torch.abs(preds.flatten() - golds.flatten()).tolist())
def reset(self):
self.dists = []
... | 645 | 22.925926 | 80 | py |
machamp | machamp-master/machamp/metrics/accuracy.py | import torch
class Accuracy:
def __init__(self):
self.cor = 0
self.total = 0
self.str = 'accuracy'
self.metric_scores = {}
def score(self, preds, golds, vocabulary):
preds = torch.flatten(preds)
golds = torch.flatten(golds)
contents = torch.nonzero(gol... | 870 | 21.921053 | 64 | py |
machamp | machamp-master/machamp/metrics/uas.py | import torch
class UAS:
def __init__(self):
self.cor = 0
self.total = 0
self.str = 'uas'
self.metric_scores = {}
def score(self, pred_heads, pred_rels, gold_heads, gold_rels):
pred_heads = pred_heads.flatten()
gold_heads = gold_heads.flatten()
cor_heads... | 891 | 23.777778 | 66 | py |
machamp | machamp-master/machamp/metrics/multi_accuracy.py | import torch
class MultiAccuracy:
def __init__(self):
self.cor = 0
self.total = 0
self.str = 'multi_acc'
self.metric_scores = {}
def score(self, preds, golds, mask=None, vocabulary=None):
# Maybe this be done more efficient by using torch functions?
if len(pred... | 1,262 | 29.804878 | 93 | py |
machamp | machamp-master/machamp/readers/read_raw.py | import logging
from typing import List
import torch
from transformers import AutoTokenizer
from machamp.data.machamp_instance import MachampInstance
from machamp.data.machamp_vocabulary import MachampVocabulary
logger = logging.getLogger(__name__)
def read_raw(
dataset: str,
config: dict,
t... | 3,312 | 31.80198 | 140 | py |
machamp | machamp-master/machamp/readers/read_sequence.py | import logging
from typing import List
import torch
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BasicTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.xlm_roberta.tokenization_xlm_roberta import XLMRobertaTokenizer
... | 20,799 | 47.484848 | 128 | py |
machamp | machamp-master/machamp/readers/read_mlm.py | import logging
from typing import List
import torch
from transformers import AutoTokenizer
from transformers import DataCollatorForLanguageModeling # or DataCollatorForWholeWordMask
from machamp.data.machamp_instance import MachampInstance
from machamp.data.machamp_vocabulary import MachampVocabulary
logger = loggi... | 5,107 | 37.992366 | 131 | py |
machamp | machamp-master/machamp/readers/read_classification.py | import copy
import logging
from typing import List
import torch
from transformers import AutoTokenizer
from machamp.data.machamp_instance import MachampInstance
from machamp.data.machamp_vocabulary import MachampVocabulary
logger = logging.getLogger(__name__)
def lines2data(input_file: str, skip_first_line: bool =... | 8,144 | 39.123153 | 156 | py |
machamp | machamp-master/machamp/utils/myutils.py | import copy
import json
import logging
import re
from typing import List, Dict, Tuple, Optional, Any, Union, Iterator
import _jsonnet
import torch
logger = logging.getLogger(__name__)
from transformers import tokenization_utils
from transformers import AutoTokenizer
from machamp.data.machamp_instance import Machamp... | 19,623 | 39.213115 | 137 | py |
machamp | machamp-master/machamp/utils/tok_utils.py | import logging
import os
from typing import List
import torch
from transformers import AutoTokenizer
from transformers import tokenization_utils
from transformers.models.bert.tokenization_bert import BasicTokenizer
from machamp.utils.lemma_edit import min_edit_script
logger = logging.getLogger(__name__)
class Scrip... | 22,378 | 37.584483 | 177 | py |
machamp | machamp-master/machamp/data/machamp_sampler.py | import logging
import math
import random
from typing import Iterator, List, Tuple
from torch.utils.data import Sampler
from machamp.data.machamp_dataset import MachampDataset
logger = logging.getLogger(__name__)
class MachampBatchSampler(Sampler):
def __init__(self,
data_source: MachampDatas... | 9,016 | 39.986364 | 130 | py |
machamp | machamp-master/machamp/data/machamp_dataset_collection.py | import datetime
import logging
from typing import Dict, Tuple, List
from torch.utils.data import Dataset
from transformers import AutoTokenizer
from machamp.data.machamp_instance import MachampInstance
from machamp.data.machamp_vocabulary import MachampVocabulary
from machamp.data.machamp_dataset import MachampDatase... | 6,231 | 38.694268 | 187 | py |
machamp | machamp-master/machamp/data/machamp_dataset.py | import datetime
import logging
from typing import Dict, Tuple, List
from torch.utils.data import Dataset
from transformers import AutoTokenizer
from machamp.data.machamp_instance import MachampInstance
from machamp.data.machamp_vocabulary import MachampVocabulary
from machamp.readers.read_classification import read_c... | 7,364 | 40.846591 | 120 | py |
machamp | machamp-master/machamp/data/machamp_instance.py | from typing import List, Dict, Any
import torch
class MachampInstance:
def __init__(self,
full_data: List[str],
token_ids: torch.tensor,
seg_ids: torch.tensor,
golds: Dict[Any, torch.tensor],
dataset: str,
offse... | 2,518 | 34.985714 | 89 | py |
machamp | machamp-master/machamp/model/machamp.py | import logging
from typing import List, Dict
import torch
logger = logging.getLogger(__name__)
from transformers import AutoModel, AutoTokenizer, AutoModelForMaskedLM
from transformers import logging as tf_logging
tf_logging.set_verbosity_error()
from machamp.metrics.avg_dist import AvgDist
from machamp.metrics.pe... | 26,289 | 45.862745 | 135 | py |
machamp | machamp-master/machamp/model/seq_label_decoder.py | import torch
import torch.nn.functional as F
from machamp.model.machamp_decoder import MachampDecoder
class MachampSeqDecoder(MachampDecoder, torch.nn.Module):
def __init__(
self,
task: str,
vocabulary,
input_dim: int,
device: str,
loss_weig... | 3,004 | 42.550725 | 120 | py |
machamp | machamp-master/machamp/model/classification_decoder.py | import torch
import torch.nn.functional as F
from machamp.model.machamp_decoder import MachampDecoder
class MachampClassificationDecoder(MachampDecoder, torch.nn.Module):
def __init__(self, task, vocabulary, input_dim, device, loss_weight: float = 1.0, topn: int = 1,
metric: str = 'accuracy', **... | 2,172 | 47.288889 | 111 | py |
machamp | machamp-master/machamp/model/dependency_decoder.py | ## This dependency head is mainly based on the Allennlp implementation:
## https://github.com/allenai/allennlp-models/blob/main/allennlp_models/structured_prediction/models/biaffine_dependency_parser.py
import copy
import logging
from typing import Dict, Tuple
import numpy
import torch
import torch.nn.functional as ... | 32,085 | 48.439137 | 130 | py |
machamp | machamp-master/machamp/model/mlm_decoder.py | import logging
import torch
from machamp.model.machamp_decoder import MachampDecoder
logger = logging.getLogger(__name__)
class MachampLMDecoder(MachampDecoder, torch.nn.Module):
def __init__(
self,
task: str,
vocabulary,
input_dim: int,
device: str,
... | 1,508 | 34.093023 | 107 | py |
machamp | machamp-master/machamp/model/encoder.py | import inspect
import logging
import math
import torch
logger = logging.getLogger(__name__)
from transformers import AutoModel
class MachampEncoder:
def __init__(self,
mlm: AutoModel,
max_input_length: int,
end_token_id: int,
start_token_id: i... | 18,489 | 52.285303 | 140 | py |
machamp | machamp-master/machamp/model/multiclas_decoder.py | import logging
import torch
from machamp.model.machamp_decoder import MachampDecoder
logger = logging.getLogger(__name__)
class MachampMulticlasDecoder(MachampDecoder, torch.nn.Module):
def __init__(self, task, vocabulary, input_dim, device, loss_weight: float = 1.0, topn: int = 1,
metric: str... | 2,358 | 42.685185 | 117 | py |
machamp | machamp-master/machamp/model/machamp_decoder.py | import logging
import torch
logger = logging.getLogger(__name__)
from machamp.metrics.metric import Metric
class MachampDecoder(torch.nn.Module):
def __init__(self, task, vocabulary, loss_weight: float = 1.0, metric: str = 'avg_dist', device: str = 'cpu', **kwargs):
super().__init__()
self.task... | 1,712 | 37.066667 | 137 | py |
machamp | machamp-master/machamp/model/multiseq_decoder.py | import logging
import torch
from machamp.model.machamp_decoder import MachampDecoder
logger = logging.getLogger(__name__)
class MachampMultiseqDecoder(MachampDecoder, torch.nn.Module):
def __init__(
self,
task: str,
vocabulary,
input_dim: int,
device: ... | 3,567 | 42.512195 | 124 | py |
machamp | machamp-master/machamp/model/callback.py | import datetime
import json
import logging
import os
import torch
logger = logging.getLogger(__name__)
from machamp.model.machamp import MachampModel
class Callback:
def __init__(self, serialization_dir, num_epochs, keep_best_n: int = 1):
"""
Class that keeps track of performance of models over... | 14,576 | 45.721154 | 127 | py |
machamp | machamp-master/machamp/model/regression_decoder.py | import torch
from machamp.model.machamp_decoder import MachampDecoder
class MachampRegressionDecoder(MachampDecoder, torch.nn.Module):
def __init__(self, task, vocabulary, input_dim, device, loss_weight: float = 1.0, topn: int = 1,
metric: str = 'avg_dist', **kwargs):
super().__init__(ta... | 1,416 | 40.676471 | 118 | py |
machamp | machamp-master/machamp/model/crf_label_decoder.py | import logging
from typing import cast, List
import torch
import torch.nn.functional as F
logger = logging.getLogger(__name__)
from machamp.model.machamp_decoder import MachampDecoder
from machamp.modules.allennlp.conditional_random_field import ConditionalRandomField, allowed_transitions
class MachampCRFDecoder(M... | 4,673 | 41.490909 | 120 | py |
machamp | machamp-master/machamp/model/trainer.py | import datetime
import json
import logging
import os
import random
import sys
from typing import List
import torch
import transformers
from torch.utils.data import DataLoader
from tqdm import tqdm
from machamp.utils import myutils
from machamp.model.machamp import MachampModel
from machamp.model.callback import Callb... | 14,703 | 49.356164 | 158 | py |
osyris | osyris-main/docs/conf.py | # SPDX-License-Identifier: BSD-3-Clause
# Copyright (c) 2022 Osyris contributors (https://github.com/osyris-project/osyris)
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# http://www.sphinx-doc.org... | 2,855 | 33 | 85 | py |
bla | bla-main/experiment.py | # This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but... | 14,086 | 35.306701 | 112 | py |
Bella | Bella-master/bella/word_vectors.py | '''
Contains classes that train and/or load semantic vectors. All classes are sub
classes of WordVectors
Classes:
1. WordVectors - Base class of all classes within this module. Ensures
consistent API for all word vectors classes.
2. GensimVectors - Creates `Word2Vec <https://arxiv.org/pdf/1301.3781.pdf>`_
and `FastTe... | 42,796 | 44.432059 | 101 | py |
Bella | Bella-master/bella/models/base.py | '''
Module contains all of the main base classes for the machine learning models
these are grouped into 3 categories; 1. Mixin, 2. Abstract, and 3. Concrete.
Mixin classes - This is a function based class that contains functions that
do not rely on the type of model and are useful for all:
1. :py:class:`bella.models... | 56,010 | 38.837127 | 89 | py |
Bella | Bella-master/bella/models/tdlstm.py | '''
Module contains all of the classes that represent Machine Learning models
that are within `Tang et al. 2016 paper \
<https://aclanthology.info/papers/C16-1311/c16-1311>`_:
1. :py:class:`bella.models.tdlstm.LSTM` -- LSTM model.
2. :py:class:`bella.models.tdlstm.TDLSTM` -- TDLSTM model.
3. :py:class:`bella.models.td... | 39,413 | 45.699052 | 81 | py |
Bella | Bella-master/docs/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Bella documentation build configuration file, created by
# sphinx-quickstart on Thu Jun 7 12:24:24 2018.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# auto... | 5,732 | 29.657754 | 83 | py |
Large-Scale-OT | Large-Scale-OT-master/simple.py | from typing import Union
import numpy as np
import torch
import torch.nn as nn
from torch.distributions import MultivariateNormal
from torch.nn import Parameter
from torch.optim import Adam, SGD
def l2_distance(x: torch.Tensor, y: torch.Tensor) \
-> torch.Tensor:
"""Compute the Gram matrix holding all ||... | 9,747 | 32.156463 | 77 | py |
Large-Scale-OT | Large-Scale-OT-master/StochasticOTClasses/StochasticOTSemiDiscrete.py | import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as func
from StochasticOT import PyTorchStochasticOT
class PyTorchStochasticSemiDiscreteOT(PyTorchStochasticOT):
def __init__(self, xt=None, wt=None, source_dual_variable_NN=None, reg_type='entropy', reg_val=0.1, device... | 7,375 | 36.065327 | 178 | py |
Large-Scale-OT | Large-Scale-OT-master/StochasticOTClasses/StochasticOTDiscrete.py | import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as func
from StochasticOT import PyTorchStochasticOT
class PyTorchStochasticDiscreteOT(PyTorchStochasticOT):
def __init__(self, xs=None, ws=None, xt=None, wt=None, reg_type='entropy', reg_val=0.1, device_type='cpu', dev... | 6,363 | 34.752809 | 157 | py |
Large-Scale-OT | Large-Scale-OT-master/StochasticOTClasses/StochasticOT.py | import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as func
class PyTorchStochasticOT:
def __init__(self, reg_type='entropy', reg_val=0.1, device_type='cpu', device_index=0):
self.reg_type = reg_type
self.reg_val = reg_val
self.dtype = torch.floa... | 2,721 | 38.449275 | 181 | py |
Large-Scale-OT | Large-Scale-OT-master/toyXps/pytorch_semi_discrete_test.py | #%% 2-dimensional test script for the computation of regularized OT between a Gaussian and a discrete data set (semi-discrete OT)
import numpy as np
import matplotlib.pylab as pl
import torch
from StochasticOTClasses.StochasticOTSemiDiscrete import PyTorchStochasticSemiDiscreteOT
reg_type = 'l2'
reg_val = 0.005
devic... | 2,190 | 36.135593 | 182 | py |
NTIRE2021-IQA-MACS-Pytorch | NTIRE2021-IQA-MACS-Pytorch-main/main.py | # PyTorch
import torch
from torch.utils.data import Dataset, DataLoader
from torch.autograd import Variable
import torchvision.transforms as tr
import torch.nn.functional as F
from torch.nn import Sequential
# Models
from unet import Unet
from siamunet_conc import SiamUnet_conc
from SiamUnet_conc import SiamUnet_conc
f... | 19,546 | 34.734918 | 173 | py |
NTIRE2021-IQA-MACS-Pytorch | NTIRE2021-IQA-MACS-Pytorch-main/siamunet_conc.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.padding import ReplicationPad2d
class SiamUnet_conc(nn.Module):
"""SiamUnet_conc segmentation network."""
def __init__(self, input_nbr, label_nbr):
super(SiamUnet_conc, self).__init__()
self.input_nbr = ... | 7,752 | 40.459893 | 105 | py |
NTIRE2021-IQA-MACS-Pytorch | NTIRE2021-IQA-MACS-Pytorch-main/submission.py | import glob
import os
import numpy as np
from skimage import io
import random
from tqdm import tqdm
from torch.autograd import Variable
from siamunet_diff import SiamUnet_diff
from siamunet_conc import SiamUnet_conc
from checkpoint.cinavad_sever.siamunet_conc_extrahead import SiamUnet_conc
import torch
from img_ensambl... | 3,037 | 36.04878 | 179 | py |
NTIRE2021-IQA-MACS-Pytorch | NTIRE2021-IQA-MACS-Pytorch-main/siamunet_conc_extrahead.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.padding import ReplicationPad2d
class SiamUnet_conc(nn.Module):
"""SiamUnet_conc segmentation network."""
def __init__(self, input_nbr, label_nbr):
super(SiamUnet_conc, self).__init__()
self.input_nbr = ... | 8,057 | 40.751295 | 105 | py |
NTIRE2021-IQA-MACS-Pytorch | NTIRE2021-IQA-MACS-Pytorch-main/img_ensamble.py | import numpy as np
import torch
device = "cuda"
def reshape_for_torch(I):
"""Transpose image for PyTorch coordinates."""
# out = np.swapaxes(I,1,2)
# out = np.swapaxes(out,0,1)
# out = out[np.newaxis,:]
out = I.transpose((2, 0, 1))
out = np.expand_dims(out, axis=0)
return torc... | 1,822 | 26.621212 | 84 | py |
NTIRE2021-IQA-MACS-Pytorch | NTIRE2021-IQA-MACS-Pytorch-main/siamunet_diff.py | # Rodrigo Caye Daudt
# https://rcdaudt.github.io/
# Daudt, R. C., Le Saux, B., & Boulch, A. "Fully convolutional siamese networks for change detection". In 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 4063-4067). IEEE.
import torch
import torch.nn as nn
import torch.nn.functional as F
from t... | 7,814 | 42.176796 | 195 | py |
NTIRE2021-IQA-MACS-Pytorch | NTIRE2021-IQA-MACS-Pytorch-main/accloss.py | import torch
def m_pearsonr(output, target):
x = output
y = target
vx = x - torch.mean(x)
vy = y - torch.mean(y)
pr = torch.sum(vx * vy) / (torch.sqrt(torch.sum(vx ** 2)) * torch.sqrt(torch.sum(vy ** 2)))
return pr
def accloss(output, target):
pr = m_pearsonr(output, target)
retur... | 449 | 17 | 95 | py |
NTIRE2021-IQA-MACS-Pytorch | NTIRE2021-IQA-MACS-Pytorch-main/fresunet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.padding import ReplicationPad2d
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1)
class BasicBlock_ss(nn.Module)... | 6,150 | 30.22335 | 118 | py |
NTIRE2021-IQA-MACS-Pytorch | NTIRE2021-IQA-MACS-Pytorch-main/sodeep_master/sodeep.py | """
****************** COPYRIGHT AND CONFIDENTIALITY INFORMATION ******************
Copyright (c) 2019 [Thomson Licensing]
All Rights Reserved
This program contains proprietary information which is a trade secret/business \
secret of [Thomson Licensing] and is protected, even if unpublished, under \
applicable Copyrigh... | 4,792 | 34.768657 | 131 | py |
NTIRE2021-IQA-MACS-Pytorch | NTIRE2021-IQA-MACS-Pytorch-main/sodeep_master/utils.py | """
****************** COPYRIGHT AND CONFIDENTIALITY INFORMATION ******************
Copyright (c) 2019 [Thomson Licensing]
All Rights Reserved
This program contains proprietary information which is a trade secret/business \
secret of [Thomson Licensing] and is protected, even if unpublished, under \
applicable Copyrigh... | 2,917 | 32.159091 | 101 | py |
NTIRE2021-IQA-MACS-Pytorch | NTIRE2021-IQA-MACS-Pytorch-main/sodeep_master/model.py | """
****************** COPYRIGHT AND CONFIDENTIALITY INFORMATION ******************
Copyright (c) 2019 [Thomson Licensing]
All Rights Reserved
This program contains proprietary information which is a trade secret/business \
secret of [Thomson Licensing] and is protected, even if unpublished, under \
applicable Copyrigh... | 9,461 | 29.621359 | 124 | py |
NTIRE2021-IQA-MACS-Pytorch | NTIRE2021-IQA-MACS-Pytorch-main/sodeep_master/dataset.py | """
****************** COPYRIGHT AND CONFIDENTIALITY INFORMATION ******************
Copyright (c) 2019 [Thomson Licensing]
All Rights Reserved
This program contains proprietary information which is a trade secret/business \
secret of [Thomson Licensing] and is protected, even if unpublished, under \
applicable Copyrigh... | 3,835 | 34.192661 | 103 | py |
NTIRE2021-IQA-MACS-Pytorch | NTIRE2021-IQA-MACS-Pytorch-main/sodeep_master/train.py | """
****************** COPYRIGHT AND CONFIDENTIALITY INFORMATION ******************
Copyright (c) 2019 [Thomson Licensing]
All Rights Reserved
This program contains proprietary information which is a trade secret/business \
secret of [Thomson Licensing] and is protected, even if unpublished, under \
applicable Copyrigh... | 7,607 | 38.015385 | 117 | py |
fgivenx | fgivenx-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 6,325 | 28.699531 | 78 | py |
CuPL | CuPL-main/imagenetdataset.py | import pathlib
import tarfile
import requests
import shutil
from collections import defaultdict
from PIL import Image
from tqdm import tqdm
from torch.utils.data import Dataset, DataLoader
from torchvision.datasets import ImageFolder
import os
from imagenet_classnames.sense_to_idx import sense
from imagenet_classnames... | 1,553 | 25.338983 | 65 | py |
CuPL | CuPL-main/classify_imagenet.py | import numpy as np
import torch
import clip
from pkg_resources import packaging
from imagenet_prompts.standard_image_prompts import imagenet_templates
import pdb
from collections import defaultdict
from imagenetdataset import ImagenetDataset
from PIL import Image
import PIL
import json
from tqdm import tqdm
PATH_TO_IM... | 3,807 | 30.213115 | 126 | py |
CuPL | CuPL-main/imagenet_classnames/sense_to_idx.py | sense = {0: {'id': '01440764-n',
'label': 'tench, Tinca tinca',
'uri': 'http://wordnet-rdf.princeton.edu/wn30/01440764-n'},
1: {'id': '01443537-n',
'label': 'goldfish, Carassius auratus',
'uri': 'http://wordnet-rdf.princeton.edu/wn30/01443537-n'},
2: {'id': '01484850-n',
'label': 'great white... | 134,353 | 43.769743 | 140 | py |
CuPL | CuPL-main/imagenet_classnames/imagenet_classes.py | imagenet_classes = ["tench", "goldfish", "great white shark", "tiger shark", "hammerhead shark", "electric ray", "stingray", "rooster", "hen", "ostrich", "brambling", "goldfinch", "house finch", "junco", "indigo bunting", "American robin", "bulbul", "jay", "magpie", "chickadee", "American dipper", "kite (bird of prey)"... | 14,834 | 7,416.5 | 14,833 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.