python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
import os
import torch
import collections
import logging
from tqdm import tqdm, trange
import json
import bs4
from os import path as osp
from bs4 import BeautifulSoup as bs
# from transformers.models.bert.tokenization_bert import BasicTokenizer, whitespace_tokenize
from torch.utils.data import Dataset
import networkx a... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/websrc/websrc.py |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import BertConfig, BertModel, BertPreTrainedModel, RobertaConfig
# from transformers.modeling_bert import BertLayerNorm, BertOnlyMLMHead
from transformers.models.bert.modeling_bert import BertOnlyMLMHead
BertLayerN... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/websrc/model.py |
tags_dict = {'a': 0, 'abbr': 1, 'acronym': 2, 'address': 3, 'altGlyph': 4, 'altGlyphDef': 5, 'altGlyphItem': 6,
'animate': 7, 'animateColor': 8, 'animateMotion': 9, 'animateTransform': 10, 'applet': 11, 'area': 12,
'article': 13, 'aside': 14, 'audio': 15, 'b': 16, 'base': 17, 'basefont': 18, '... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/websrc/web_tag_utils.py |
import os
import sys
sys.path.append(os.getcwd())
import torch
import torch.nn as nn
import shutil
import logging
import torch.distributed as dist
from transformers import (
BertTokenizer,
RobertaTokenizer
)
from args import args
from model import (
Layoutlmv1ForQuestionAnswering,
Layoutlmv1Config,... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/websrc/run_websrc.py |
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--exp_name", default='your_exp_name', type=str)
parser.add_argument("--seed", default=42, type=int)
parser.add_argument("--output_dir", default='.', type=str)
parser.add_argument("--overwrite_output_dir", default=True)
parser.add_argument("--mode... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/websrc/args.py |
from genericpath import exists
import os
import torch.nn as nn
import torch
import logging
from tqdm import tqdm, trange
import timeit
import collections
import json
import math
from bs4 import BeautifulSoup
from copy import deepcopy
import string
import re
from torch.utils.tensorboard import SummaryWriter
from torch... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/websrc/trainer.py |
import collections
import json
import logging
import os
from typing import Optional, Tuple
import numpy as np
from tqdm.auto import tqdm
logger = logging.getLogger(__name__)
def postprocess_qa_predictions(
examples,
features,
predictions: Tuple[np.ndarray, np.ndarray],
version_2_with_negative: bool... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/squad/utils_qa.py |
import logging
import os
os.environ['DISABLE_MLFLOW_INTEGRATION'] = 'True'
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
from datasets import load_dataset, load_metric
import transformers
from trainer_qa import QuestionAnsweringTrainer
from transformers import (
A... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/squad/run_squad.py |
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
if is_torch_tpu_available():
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class QuestionAnsweringTrainer(Trainer):
def __init__(self, *args, eval_examples=None,... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/squad/trainer_qa.py |
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import Seq2SeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
if is_torch_tpu_available():
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
clas... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/squad/trainer_seq2seq_qa.py |
#!/usr/bin/env python
# coding=utf-8
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
from datasets import ClassLabel, load_dataset, load_metric
import layoutlmft.data.datasets.funsd
import transformers
from layoutlmft.data import DataCollator... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/run_funsd.py |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import BertConfig, BertModel, BertPreTrainedModel, RobertaConfig
# from transformers.modeling_bert import BertLayerNorm, BertOnlyMLMHead
logger = logging.getLogger(__name__)
LAYOUTLMV1_PRETRAINED_MODEL_ARCHIVE_MA... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/model.py |
import os
import re
import numpy as np
from transformers.utils import logging
logger = logging.get_logger(__name__)
PREFIX_CHECKPOINT_DIR = "checkpoint"
_re_checkpoint = re.compile(r"^" + PREFIX_CHECKPOINT_DIR + r"\-(\d+)$")
def get_last_checkpoint(folder):
content = os.listdir(folder)
checkpoints = [
... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/evaluation.py |
from collections import OrderedDict
from transformers import CONFIG_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_NAMES_MAPPING, TOKENIZER_MAPPING
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS, BertConverter, XLMRobertaConverter
from transformers.models.auto.modeling_auto import auto... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/__init__.py |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple
import torch
from transformers.file_utils import ModelOutput
@dataclass
class ReOutput(ModelOutput):
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = No... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/utils.py |
EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/models/__init__.py | |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier f... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/models/model_args.py |
# coding=utf-8
from transformers.models.layoutlm.tokenization_layoutlm import LayoutLMTokenizer
from transformers.utils import logging
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"microsoft/layoutlmv2-base-uncased":... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/models/layoutlmv2/tokenization_layoutlmv2.py |
from .configuration_layoutlmv2 import LayoutLMv2Config
from .modeling_layoutlmv2 import LayoutLMv2ForRelationExtraction, LayoutLMv2ForTokenClassification, LayoutLMv2Model
from .tokenization_layoutlmv2 import LayoutLMv2Tokenizer
from .tokenization_layoutlmv2_fast import LayoutLMv2TokenizerFast
| EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/models/layoutlmv2/__init__.py |
# -*- coding: utf-8 -*-
def add_layoutlmv2_config(cfg):
_C = cfg
# -----------------------------------------------------------------------------
# Config definition
# -----------------------------------------------------------------------------
_C.MODEL.MASK_ON = True
# When using pre-trained m... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/models/layoutlmv2/detectron2_config.py |
# coding=utf-8
import math
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
import detectron2
from detectron2.modeling import META_ARCH_REGISTRY
from transformers import PreTrainedModel
from transformers.modeling_outputs import (
... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/models/layoutlmv2/modeling_layoutlmv2.py |
# coding=utf-8
from transformers.models.layoutlm.tokenization_layoutlm_fast import LayoutLMTokenizerFast
from transformers.utils import logging
from .tokenization_layoutlmv2 import LayoutLMv2Tokenizer
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/models/layoutlmv2/tokenization_layoutlmv2_fast.py |
# coding=utf-8
from transformers.models.layoutlm.configuration_layoutlm import LayoutLMConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/models/layoutlmv2/configuration_layoutlmv2.py |
# coding=utf-8
from transformers.utils import logging
from ..layoutlmv2 import LayoutLMv2Config
logger = logging.get_logger(__name__)
LAYOUTXLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"layoutxlm-base": "https://huggingface.co/layoutxlm-base/resolve/main/config.json",
"layoutxlm-large": "https://huggingface.co/lay... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/models/layoutxlm/configuration_layoutxlm.py |
# coding=utf-8
from transformers import XLMRobertaTokenizerFast
from transformers.file_utils import is_sentencepiece_available
from transformers.utils import logging
if is_sentencepiece_available():
from .tokenization_layoutxlm import LayoutXLMTokenizer
else:
LayoutXLMTokenizer = None
logger = logging.get_l... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/models/layoutxlm/tokenization_layoutxlm_fast.py |
# coding=utf-8
from transformers import XLMRobertaTokenizer
from transformers.utils import logging
logger = logging.get_logger(__name__)
SPIECE_UNDERLINE = "▁"
VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"layoutxlm-base": "https://huggin... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/models/layoutxlm/tokenization_layoutxlm.py |
from .configuration_layoutxlm import LayoutXLMConfig
from .modeling_layoutxlm import LayoutXLMForRelationExtraction, LayoutXLMForTokenClassification, LayoutXLMModel
from .tokenization_layoutxlm import LayoutXLMTokenizer
from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast
| EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/models/layoutxlm/__init__.py |
# coding=utf-8
from transformers.utils import logging
from ..layoutlmv2 import LayoutLMv2ForRelationExtraction, LayoutLMv2ForTokenClassification, LayoutLMv2Model
from .configuration_layoutxlm import LayoutXLMConfig
logger = logging.get_logger(__name__)
LAYOUTXLM_PRETRAINED_MODEL_ARCHIVE_LIST = [
"layoutxlm-base... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/models/layoutxlm/modeling_layoutxlm.py |
from transformers.models.layoutlm import *
| EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/models/layoutlm/__init__.py |
import collections
import time
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from packaging import version
from torch import nn
from torch.utils.data import DataLoader, Dataset
from transformers.trainer_utils import EvalPrediction, PredictionOutput, speed_metrics
from transformers.utils impo... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/trainers/xfun_trainer.py |
from .funsd_trainer import FunsdTrainer
from .xfun_trainer import XfunReTrainer, XfunSerTrainer
| EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/trainers/__init__.py |
from typing import Any, Dict, Union
import torch
from transformers import Trainer
class FunsdTrainer(Trainer):
def _prepare_inputs(self, inputs: Dict[str, Union[torch.Tensor, Any]]) -> Dict[str, Union[torch.Tensor, Any]]:
"""
Prepare :obj:`inputs` before feeding them to the model, converting the... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/trainers/funsd_trainer.py |
EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/modules/__init__.py | |
EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/modules/decoders/__init__.py | |
import copy
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
class BiaffineAttention(torch.nn.Module):
"""Implements a biaffine attention operator for binary relation classification.
PyTorch implementation of the biaffine attention operator from "End-to-end neural relation
extract... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/modules/decoders/re.py |
# flake8: noqa
from .data_collator import DataCollatorForKeyValueExtraction
from .datasets import *
| EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/data/__init__.py |
import torch
from detectron2.data.detection_utils import read_image
from detectron2.data.transforms import ResizeTransform, TransformList
def normalize_bbox(bbox, size):
return [
int(1000 * bbox[0] / size[0]),
int(1000 * bbox[1] / size[1]),
int(1000 * bbox[2] / size[0]),
int(1000 ... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/data/utils.py |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
task_name: Optional[str] = field(default="ner", metadata={"help": "The name of the task (ner, p... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/data/data_args.py |
from dataclasses import dataclass
from typing import Optional, Union
import torch
from detectron2.structures import ImageList
from transformers import PreTrainedTokenizerBase
from transformers.file_utils import PaddingStrategy
@dataclass
class DataCollatorForKeyValueExtraction:
"""
Data collator that will d... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/data/data_collator.py |
EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/data/datasets/__init__.py | |
# Lint as: python3
import json
import logging
import os
import datasets
from layoutlmft.data.utils import load_image, merge_bbox, normalize_bbox, simplify_bbox
from transformers import AutoTokenizer
_URL = "https://github.com/doc-analysis/XFUN/releases/download/v1.0/"
_LANG = ["zh", "de", "es", "fr", "en", "it", "... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/data/datasets/xfun.py |
# coding=utf-8
import json
import os
import datasets
from layoutlmft.data.utils import load_image, normalize_bbox
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@article{Jaume2019FUNSDAD,
title={FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents},
author={Guillaume Jaume and ... | EXA-1-master | exa/models/unilm-master/xdoc/fine_tuning/funsd/layoutlmft/data/datasets/funsd.py |
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import logging
import math
import os
import pickle
import random
from time import sleep
import numpy as np
import torch
from nltk.translate.bleu_score import sent... | EXA-1-master | exa/models/unilm-master/layoutreader/decode_seq2seq.py |
from io import open
from setuptools import find_packages, setup
extras = {
'serving': ['pydantic', 'uvicorn', 'fastapi'],
'serving-tf': ['pydantic', 'uvicorn', 'fastapi'],
'serving-torch': ['pydantic', 'uvicorn', 'fastapi', 'torch']
}
extras['all'] = [package for package in extras.values()]
setup(
na... | EXA-1-master | exa/models/unilm-master/layoutreader/setup.py |
from __future__ import absolute_import, division, print_function
import argparse
import json
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import (DataLoader, SequentialSampler)
from torch.utils.data.distributed import DistributedSampler
try:
from torch.utils.tensor... | EXA-1-master | exa/models/unilm-master/layoutreader/run_seq2seq.py |
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
from transformers import BertConfig, RobertaConfig
from s2s_ft.configuration_unilm import UnilmConfig
# from s2s_ft.modeling import LayoutlmConfig
logger = logging.getLogger(__name__)
class BertForSeq2SeqConfig(BertCon... | EXA-1-master | exa/models/unilm-master/layoutreader/s2s_ft/config.py |
# coding=utf-8
# The MIT License (MIT)
# Copyright (c) Microsoft Corporation
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# t... | EXA-1-master | exa/models/unilm-master/layoutreader/s2s_ft/configuration_minilm.py |
# coding=utf-8
"""PyTorch BERT model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import json
import logging
import math
import os
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn.modules... | EXA-1-master | exa/models/unilm-master/layoutreader/s2s_ft/modeling_decoding.py |
import numpy as np
from random import randint, shuffle, choice
from random import random as rand
import math
import logging
import torch
import torch.utils.data
logger = logging.getLogger(__name__)
def get_random_word(vocab_words):
i = randint(0, len(vocab_words)-1)
return vocab_words[i]
def batch_list_t... | EXA-1-master | exa/models/unilm-master/layoutreader/s2s_ft/s2s_loader.py |
import torch
import logging
from transformers.modeling_utils import cached_path, WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME
logger = logging.getLogger(__name__)
def get_checkpoint_from_transformer_cache(
archive_file, pretrained_model_name_or_path, pretrained_model_archive_map,
cache_dir, force... | EXA-1-master | exa/models/unilm-master/layoutreader/s2s_ft/convert_state_dict.py |
# coding=utf-8
# The MIT License (MIT)
# Copyright (c) Microsoft Corporation
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# t... | EXA-1-master | exa/models/unilm-master/layoutreader/s2s_ft/tokenization_unilm.py |
# coding=utf-8
# The MIT License (MIT)
# Copyright (c) Microsoft Corporation
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# t... | EXA-1-master | exa/models/unilm-master/layoutreader/s2s_ft/configuration_unilm.py |
from __future__ import absolute_import, division, print_function
import logging
import os
import json
import random
import glob
import re
import torch
import tqdm
import torch.utils.data
logger = logging.getLogger(__name__)
class Seq2seqDatasetForBert(torch.utils.data.Dataset):
def __init__(
self, ... | EXA-1-master | exa/models/unilm-master/layoutreader/s2s_ft/utils.py |
# coding=utf-8
# The MIT License (MIT)
# Copyright (c) Microsoft Corporation
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# t... | EXA-1-master | exa/models/unilm-master/layoutreader/s2s_ft/tokenization_minilm.py |
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import math
import os
import torch
from torch import nn
from torch.nn.modules.loss import _Loss
import torch.nn.functional as F
from transformers import BertConfig
from transformers.modeling_bert import \
BertPreTra... | EXA-1-master | exa/models/unilm-master/layoutreader/s2s_ft/modeling.py |
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import json
import glob
import logging
import argparse
import math
from tqdm import tqdm
import numpy as np
import torch
import random
import pickle
from s2s_ft.modelin... | EXA-1-master | exa/models/unilm-master/s2s-ft/decode_seq2seq.py |
from io import open
from setuptools import find_packages, setup
extras = {
'serving': ['pydantic', 'uvicorn', 'fastapi'],
'serving-tf': ['pydantic', 'uvicorn', 'fastapi'],
'serving-torch': ['pydantic', 'uvicorn', 'fastapi', 'torch']
}
extras['all'] = [package for package in extras.values()]
setup(
na... | EXA-1-master | exa/models/unilm-master/s2s-ft/setup.py |
import pickle
import math
import argparse
import glob
import logging
from pathlib import Path
from tqdm import tqdm
import unicodedata
from transformers import BertTokenizer, RobertaTokenizer, XLMRobertaTokenizer
from s2s_ft.tokenization_unilm import UnilmTokenizer
from s2s_ft.tokenization_minilm import MinilmTokenize... | EXA-1-master | exa/models/unilm-master/s2s-ft/gen_seq_from_trace.py |
from __future__ import absolute_import, division, print_function
import argparse
import logging
import os
import json
import random
import numpy as np
import torch
from torch.utils.data import (DataLoader, SequentialSampler)
from torch.utils.data.distributed import DistributedSampler
try:
from torch.utils.tensor... | EXA-1-master | exa/models/unilm-master/s2s-ft/run_seq2seq.py |
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import json
import argparse
import math
import string
from multiprocessing import Pool, cpu_count
from tqdm import tqdm, trange
from pathlib i... | EXA-1-master | exa/models/unilm-master/s2s-ft/evaluations/eval_for_xsum.py |
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import json
import argparse
import math
import string
from multiprocessing import Pool, cpu_count
from tqdm import tqdm, trange
from pathlib i... | EXA-1-master | exa/models/unilm-master/s2s-ft/evaluations/eval_for_gigaword.py |
from __future__ import print_function, unicode_literals, division
import os
import re
import codecs
import platform
from subprocess import check_output
from tempfile import mkdtemp
from functools import partial
try:
from configparser import ConfigParser
except ImportError:
from ConfigParser import ConfigPars... | EXA-1-master | exa/models/unilm-master/s2s-ft/evaluations/bs_pyrouge.py |
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import logging
import glob
import json
import argparse
import math
import string
from multiprocessing import Pool, cpu_count
from tqdm import tqdm, trange
from pathlib i... | EXA-1-master | exa/models/unilm-master/s2s-ft/evaluations/eval_for_cnndm.py |
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
from transformers import BertConfig, RobertaConfig
from s2s_ft.configuration_unilm import UnilmConfig
logger = logging.getLogger(__name__)
class BertForSeq2SeqConfig(BertConfig):
def __init__(self, label_smoothing=... | EXA-1-master | exa/models/unilm-master/s2s-ft/s2s_ft/config.py |
# coding=utf-8
# The MIT License (MIT)
# Copyright (c) Microsoft Corporation
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# t... | EXA-1-master | exa/models/unilm-master/s2s-ft/s2s_ft/configuration_minilm.py |
# coding=utf-8
"""PyTorch BERT model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import copy
import json
import math
import logging
import tarfile
import tempfile
import shutil
import numpy as np
from functools import partial
import torc... | EXA-1-master | exa/models/unilm-master/s2s-ft/s2s_ft/modeling_decoding.py |
import numpy as np
from random import randint, shuffle, choice
from random import random as rand
import math
import logging
import torch
import torch.utils.data
logger = logging.getLogger(__name__)
def get_random_word(vocab_words):
i = randint(0, len(vocab_words)-1)
return vocab_words[i]
def batch_list_t... | EXA-1-master | exa/models/unilm-master/s2s-ft/s2s_ft/s2s_loader.py |
import torch
import logging
from transformers.modeling_utils import cached_path, WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME
logger = logging.getLogger(__name__)
def get_checkpoint_from_transformer_cache(
archive_file, pretrained_model_name_or_path, pretrained_model_archive_map,
cache_dir, force... | EXA-1-master | exa/models/unilm-master/s2s-ft/s2s_ft/convert_state_dict.py |
# coding=utf-8
# The MIT License (MIT)
# Copyright (c) Microsoft Corporation
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# t... | EXA-1-master | exa/models/unilm-master/s2s-ft/s2s_ft/tokenization_unilm.py |
# coding=utf-8
# The MIT License (MIT)
# Copyright (c) Microsoft Corporation
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# t... | EXA-1-master | exa/models/unilm-master/s2s-ft/s2s_ft/configuration_unilm.py |
from __future__ import absolute_import, division, print_function
import logging
import os
import json
import random
import glob
import torch
import tqdm
import array
import collections
import torch.utils.data
from transformers.file_utils import WEIGHTS_NAME
try:
import lmdb
except:
pass
OPTIM_NAME = "optimize... | EXA-1-master | exa/models/unilm-master/s2s-ft/s2s_ft/utils.py |
# coding=utf-8
# The MIT License (MIT)
# Copyright (c) Microsoft Corporation
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# t... | EXA-1-master | exa/models/unilm-master/s2s-ft/s2s_ft/tokenization_minilm.py |
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
import math
import os
import torch
from torch import nn
from torch.nn.modules.loss import _Loss
import torch.nn.functional as F
from transformers.modeling_bert import \
BertPreTrainedModel, BertSelfOutput, BertInter... | EXA-1-master | exa/models/unilm-master/s2s-ft/s2s_ft/modeling.py |
import torch.nn as nn
import torch
from fairseq.modules.quant_noise import quant_noise
from fairseq.modules import MultiheadAttention
from fairseq.modules.transformer_layer import TransformerDecoderLayerBase
from fairseq.models.transformer import TransformerDecoderBase, TransformerDecoder
from fairseq.modules.checkpoi... | EXA-1-master | exa/models/unilm-master/trocr/unilm_models.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.vision_transformer import Attention, Block
fr... | EXA-1-master | exa/models/unilm-master/trocr/deit.py |
import os
from fairseq import search
from fairseq import scoring, utils, metrics
from fairseq.data import Dictionary, encoders
from fairseq.tasks import LegacyFairseqTask, register_task
from fairseq.tasks.fairseq_task import FairseqTask
try:
from .data import SROIETextRecognitionDataset, Receipt53KDataset, Synthe... | EXA-1-master | exa/models/unilm-master/trocr/task.py |
from fairseq.models import FairseqEncoder, register_model, FairseqEncoderDecoderModel, register_model_architecture
from fairseq.models.transformer import TransformerDecoder, Embedding, TransformerModel
from fairseq.models.transformer import base_architecture as base_transformer
from fairseq.models.fairseq_encoder impor... | EXA-1-master | exa/models/unilm-master/trocr/trocr_models.py |
from fairseq.scoring import BaseScorer, register_scorer
from nltk.metrics.distance import edit_distance
from fairseq.dataclass import FairseqDataclass
import fastwer
from Levenshtein import distance
import string
@register_scorer("cer", dataclass=FairseqDataclass)
class CERScorer(BaseScorer):
def __init__(self, cf... | EXA-1-master | exa/models/unilm-master/trocr/scoring.py |
import os
from data import SROIETask2
from tqdm import tqdm
import shutil
import zipfile
if __name__ == '__main__':
test_dir = '../SROIE_Task2_Original/test'
output_dir = 'temp'
os.makedirs(output_dir, exist_ok=True)
generate_txt_path = '../generate-test.txt'
output_file = None
output_fp = None... | EXA-1-master | exa/models/unilm-master/trocr/convert_to_SROIE_format.py |
from .task import TextRecognitionTask
from .vit_models import ViTTRModel, ViT_TR_base
from .scoring import AccEDScorer
from .deit import *
from .trocr_models import TrOCRModel
from .bpe import GPT2BPEEnhancedSpace | EXA-1-master | exa/models/unilm-master/trocr/__init__.py |
import torch
import math
from typing import Dict, List, Optional
from fairseq.sequence_generator import SequenceGenerator
from torch import Tensor
class TextRecognitionGenerator(SequenceGenerator):
def _generate(
self,
sample: Dict[str, Dict[str, Tensor]],
prefix_tokens: Optional[Tensor]... | EXA-1-master | exa/models/unilm-master/trocr/generator.py |
import torch.nn as nn
from fairseq.models import FairseqEncoder, register_model, FairseqEncoderDecoderModel, register_model_architecture
from fairseq.models.transformer import TransformerDecoder, Embedding, TransformerModel
from fairseq.models.fairseq_encoder import EncoderOut
from fairseq import utils
# from timm.mod... | EXA-1-master | exa/models/unilm-master/trocr/vit_models.py |
import torchvision.transforms as transforms
# from torchvision.transforms.functional import InterpolationMode
from PIL import Image, ImageFilter
import random
import torch
import numpy as np
import logging
from enum import Enum
from .augmentation.warp import Curve, Distort, Stretch
from .augmentation.geometry import Ro... | EXA-1-master | exa/models/unilm-master/trocr/data_aug.py |
import task
import deit
import trocr_models
import torch
import fairseq
from fairseq import utils
from fairseq_cli import generate
from PIL import Image
import torchvision.transforms as transforms
def init(model_path, beam=5):
model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[mode... | EXA-1-master | exa/models/unilm-master/trocr/pic_inference.py |
import glob
import logging
import os
import random
import torch
from fairseq.data import FairseqDataset, data_utils
from natsort import natsorted
from PIL import Image
from tqdm import tqdm
logger = logging.getLogger(__name__)
def default_collater(target_dict, samples, dataset=None):
if not samples:
ret... | EXA-1-master | exa/models/unilm-master/trocr/data.py |
from tempfile import tempdir
from fairseq.data.encoders.gpt2_bpe import GPT2BPE, GPT2BPEConfig
from fairseq.data.encoders import register_bpe
import logging
logger = logging.getLogger(__name__)
INSERT_OR_REPLACE = 0 # 1 for replace and 0 for insert
@register_bpe("gpt2es", dataclass=GPT2BPEConfig) # as stands for att... | EXA-1-master | exa/models/unilm-master/trocr/bpe.py |
import cv2
import numpy as np
import math
from PIL import Image, ImageOps, ImageDraw
from skimage import color
from scipy import interpolate
from pkg_resources import resource_filename
from io import BytesIO
from .ops import plasma_fractal, clipped_zoom, MotionImage
'''
PIL resize (W,H)
'''
class Fog:
def __in... | EXA-1-master | exa/models/unilm-master/trocr/augmentation/weather.py |
EXA-1-master | exa/models/unilm-master/trocr/augmentation/__init__.py | |
import cv2
import numpy as np
from PIL import Image, ImageOps, ImageDraw
'''
PIL resize (W,H)
Torch resize is (H,W)
'''
class VGrid:
def __init__(self):
pass
def __call__(self, img, copy=True, max_width=4, mag=-1, prob=1.):
if np.random.uniform(0,1) > prob:
return img
... | EXA-1-master | exa/models/unilm-master/trocr/augmentation/pattern.py |
import os
import cv2
from warp import Curve, Distort, Stretch
from geometry import Rotate, Perspective, Shrink, TranslateX, TranslateY
from pattern import VGrid, HGrid, Grid, RectGrid, EllipseGrid
from noise import GaussianNoise, ShotNoise, ImpulseNoise, SpeckleNoise
from blur import GaussianBlur, DefocusBlur, MotionB... | EXA-1-master | exa/models/unilm-master/trocr/augmentation/test.py |
import cv2
import numpy as np
from wand.image import Image as WandImage
from scipy.ndimage import zoom as scizoom
from wand.api import library as wandlibrary
class MotionImage(WandImage):
def motion_blur(self, radius=0.0, sigma=0.0, angle=0.0):
wandlibrary.MagickMotionBlurImage(self.wand, radius, sigma, a... | EXA-1-master | exa/models/unilm-master/trocr/augmentation/ops.py |
import cv2
import numpy as np
from PIL import Image, ImageOps
import torchvision.transforms as transforms
from wand.image import Image as WandImage
from scipy.ndimage import zoom as scizoom
from skimage.filters import gaussian
from wand.api import library as wandlibrary
from io import BytesIO
#from skimage import col... | EXA-1-master | exa/models/unilm-master/trocr/augmentation/blur.py |
import cv2
import numpy as np
import skimage as sk
from PIL import Image, ImageOps
from io import BytesIO
from skimage import color
'''
PIL resize (W,H)
cv2 image is BGR
PIL image is RGB
'''
class Contrast:
def __init__(self):
pass
def __call__(self, img, mag=-1, prob=1.):
if np.r... | EXA-1-master | exa/models/unilm-master/trocr/augmentation/camera.py |
import numpy as np
import skimage as sk
from PIL import Image
'''
PIL resize (W,H)
'''
class GaussianNoise:
def __init__(self):
pass
def __call__(self, img, mag=-1, prob=1.):
if np.random.uniform(0,1) > prob:
return img
W, H = img.size
#c = np.random.uniform(.... | EXA-1-master | exa/models/unilm-master/trocr/augmentation/noise.py |
import cv2
import numpy as np
from PIL import Image, ImageOps
'''
PIL resize (W,H)
Torch resize is (H,W)
'''
class Shrink:
def __init__(self):
self.tps = cv2.createThinPlateSplineShapeTransformer()
self.translateXAbs = TranslateXAbs()
self.translateYAbs = TranslateYAbs()
def _... | EXA-1-master | exa/models/unilm-master/trocr/augmentation/geometry.py |
import cv2
import numpy as np
from PIL import Image, ImageOps
'''
PIL resize (W,H)
Torch resize is (H,W)
'''
class Stretch:
def __init__(self):
self.tps = cv2.createThinPlateSplineShapeTransformer()
def __call__(self, img, mag=-1, prob=1.):
if np.random.uniform(0,1) > prob:
... | EXA-1-master | exa/models/unilm-master/trocr/augmentation/warp.py |
from PIL import Image
import PIL.ImageOps, PIL.ImageEnhance
import numpy as np
class Posterize:
def __init__(self):
pass
def __call__(self, img, mag=-1, prob=1.):
if np.random.uniform(0,1) > prob:
return img
c = [1, 3, 6]
if mag<0 or mag>=len(c):
index... | EXA-1-master | exa/models/unilm-master/trocr/augmentation/process.py |
# --------------------------------------------------------
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# By Hangbo Bao
# B... | EXA-1-master | exa/models/unilm-master/dit/classification/engine_for_finetuning.py |
# --------------------------------------------------------
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# By Hangbo Bao
# B... | EXA-1-master | exa/models/unilm-master/dit/classification/transforms.py |
# --------------------------------------------------------
# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
# Github source: https://github.com/microsoft/unilm/tree/master/beit
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# By Hangbo Bao
# B... | EXA-1-master | exa/models/unilm-master/dit/classification/datasets.py |
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