python_code stringlengths 0 4.04M | repo_name stringlengths 8 58 | file_path stringlengths 5 147 |
|---|---|---|
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... | data2vec_vision-main | 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... | data2vec_vision-main | 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=... | data2vec_vision-main | 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... | data2vec_vision-main | 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
import torch
from torch import nn
from tor... | data2vec_vision-main | 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... | data2vec_vision-main | 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... | data2vec_vision-main | 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... | data2vec_vision-main | 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... | data2vec_vision-main | 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 torch.utils.data
logger = logging.getLogger(__name__)
class Seq2seqDatasetForBert(torch.utils.data.Dataset):
def __init__(
self, features, ... | data2vec_vision-main | 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... | data2vec_vision-main | 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... | data2vec_vision-main | s2s-ft/s2s_ft/modeling.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... | data2vec_vision-main | beit/engine_for_finetuning.py |
"""
Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
Copyright Zhun Zhong & Liang Zheng
Hacked together by / Copyright 2020 Ross Wightman
Modified by Hangbo Bao, for generating the masked position for visual image transformer
"""
# ----------------------------------------------... | data2vec_vision-main | beit/masking_generator.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... | data2vec_vision-main | beit/modeling_discrete_vae.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... | data2vec_vision-main | beit/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... | data2vec_vision-main | beit/engine_for_pretraining.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... | data2vec_vision-main | beit/engine_for_cyclical_joint.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... | data2vec_vision-main | beit/modeling_pretrain.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... | data2vec_vision-main | beit/modeling_cyclical.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... | data2vec_vision-main | beit/datasets.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... | data2vec_vision-main | beit/modeling_cyclical_joint.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... | data2vec_vision-main | beit/engine_for_cyclical.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... | data2vec_vision-main | beit/run_cyclical.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... | data2vec_vision-main | beit/run_class_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
# M... | data2vec_vision-main | beit/dataset_folder.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... | data2vec_vision-main | beit/run_beit_pretraining.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... | data2vec_vision-main | beit/utils.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... | data2vec_vision-main | beit/run_cyclical_joint.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... | data2vec_vision-main | beit/modeling_finetune.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... | data2vec_vision-main | beit/optim_factory.py |
import attr
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from functools import partial
from dall_e.utils import Conv2d
@attr.s(eq=False, repr=False)
class DecoderBlock(nn.Module):
n_in: int = attr.ib(validator=lambda i, a, x: x >= ... | data2vec_vision-main | beit/dall_e/decoder.py |
import io, requests
import torch
import torch.nn as nn
from dall_e.encoder import Encoder
from dall_e.decoder import Decoder
from dall_e.utils import map_pixels, unmap_pixels
def load_model(path: str, device: torch.device = None) -> nn.Module:
if path.startswith('http://') or path.startswith('https://'):
... | data2vec_vision-main | beit/dall_e/__init__.py |
import attr
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from functools import partial
from dall_e.utils import Conv2d
@attr.s(eq=False, repr=False)
class EncoderBlock(nn.Module):
n_in: int = attr.ib(validator=lambda i, a, x: x >= ... | data2vec_vision-main | beit/dall_e/encoder.py |
import attr
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
logit_laplace_eps: float = 0.1
@attr.s(eq=False)
class Conv2d(nn.Module):
n_in: int = attr.ib(validator=lambda i, a, x: x >= 1)
n_out: int = attr.ib(validator=lambda i, a, x: x >= 1)
kw: int = attr.ib(validator=lambda i... | data2vec_vision-main | beit/dall_e/utils.py |
import argparse
import os
import mmcv
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from mmcv.utils import DictAction
from mmseg.apis import multi_gpu_test, single_gpu_test
from mmseg.datasets import build_dataloader,... | data2vec_vision-main | beit/semantic_segmentation/tools/test.py |
import argparse
import copy
import os
import os.path as osp
import time
import mmcv
import mmcv_custom
import torch
from mmcv.runner import init_dist
from mmcv.utils import Config, DictAction, get_git_hash
from mmseg import __version__
from mmseg.apis import set_random_seed
from mmcv_custom import train_segmentor
fro... | data2vec_vision-main | beit/semantic_segmentation/tools/train.py |
import json
from mmcv.runner import OPTIMIZER_BUILDERS, DefaultOptimizerConstructor
from mmcv.runner import get_dist_info
def get_num_layer_for_vit(var_name, num_max_layer):
if var_name in ("backbone.cls_token", "backbone.mask_token", "backbone.pos_embed"):
return 0
elif var_name.startswith("backbone.... | data2vec_vision-main | beit/semantic_segmentation/mmcv_custom/layer_decay_optimizer_constructor.py |
import random
import warnings
import numpy as np
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import build_optimizer, build_runner
from mmseg.core import DistEvalHook, EvalHook
from mmseg.datasets import build_dataloader, build_dataset
from mmseg.utils import get_r... | data2vec_vision-main | beit/semantic_segmentation/mmcv_custom/train_api.py |
import mmcv
import numpy as np
from mmseg.datasets.builder import PIPELINES
@PIPELINES.register_module()
class SETR_Resize(object):
"""Resize images & seg.
This transform resizes the input image to some scale. If the input dict
contains the key "scale", then the scale in the input dict is used,
othe... | data2vec_vision-main | beit/semantic_segmentation/mmcv_custom/resize_transform.py |
# Copyright (c) Open-MMLab. All rights reserved.
import io
import os
import os.path as osp
import pkgutil
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from to... | data2vec_vision-main | beit/semantic_segmentation/mmcv_custom/checkpoint.py |
# -*- coding: utf-8 -*-
from .checkpoint import load_checkpoint
from .layer_decay_optimizer_constructor import LayerDecayOptimizerConstructor
from .resize_transform import SETR_Resize
from .apex_runner.optimizer import DistOptimizerHook
from .train_api import train_segmentor
__all__ = ['load_checkpoint', 'LayerDecayO... | data2vec_vision-main | beit/semantic_segmentation/mmcv_custom/__init__.py |
# Copyright (c) Open-MMLab. All rights reserved.
import os.path as osp
import platform
import shutil
import torch
from torch.optim import Optimizer
import mmcv
from mmcv.runner import RUNNERS, IterBasedRunner
from .checkpoint import save_checkpoint
try:
import apex
except:
print('apex is not installed')
@R... | data2vec_vision-main | beit/semantic_segmentation/mmcv_custom/apex_runner/apex_iter_based_runner.py |
# Copyright (c) Open-MMLab. All rights reserved.
import os.path as osp
import time
from tempfile import TemporaryDirectory
import torch
from torch.optim import Optimizer
import mmcv
from mmcv.parallel import is_module_wrapper
from mmcv.runner.checkpoint import weights_to_cpu, get_state_dict
try:
import apex
exce... | data2vec_vision-main | beit/semantic_segmentation/mmcv_custom/apex_runner/checkpoint.py |
# Copyright (c) Open-MMLab. All rights reserved.
from .checkpoint import save_checkpoint
from .apex_iter_based_runner import IterBasedRunnerAmp
__all__ = [
'save_checkpoint', 'IterBasedRunnerAmp',
]
| data2vec_vision-main | beit/semantic_segmentation/mmcv_custom/apex_runner/__init__.py |
from mmcv.runner import OptimizerHook, HOOKS
try:
import apex
except:
print('apex is not installed')
@HOOKS.register_module()
class DistOptimizerHook(OptimizerHook):
"""Optimizer hook for distributed training."""
def __init__(self, update_interval=1, grad_clip=None, coalesce=True, bucket_size_mb=-1, ... | data2vec_vision-main | beit/semantic_segmentation/mmcv_custom/apex_runner/optimizer.py |
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook', by_epoch=False),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
cudnn_benchmark = True... | data2vec_vision-main | beit/semantic_segmentation/configs/_base_/default_runtime.py |
# dataset settings
dataset_type = 'ADE20KDataset'
data_root = 'data/ade/ADEChallengeData2016'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (640, 640)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_labe... | data2vec_vision-main | beit/semantic_segmentation/configs/_base_/datasets/ade20k_640x640.py |
# dataset settings
dataset_type = 'ADE20KDataset'
data_root = 'data/ade/ADEChallengeData2016'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_labe... | data2vec_vision-main | beit/semantic_segmentation/configs/_base_/datasets/ade20k.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... | data2vec_vision-main | beit/semantic_segmentation/configs/_base_/models/upernet_beit.py |
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
runner = dict(type='IterBasedRunner', max_iters=160000)
checkpoint_config = dict(by_epoch=False, int... | data2vec_vision-main | beit/semantic_segmentation/configs/_base_/schedules/schedule_160k.py |
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
# runtime settings
runner = dict(type='IterBasedRunner', max_iters=320000)
checkpoint_config = dict(by_epoch=False, int... | data2vec_vision-main | beit/semantic_segmentation/configs/_base_/schedules/schedule_320k.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... | data2vec_vision-main | beit/semantic_segmentation/configs/beit/upernet/upernet_beit_large_24_640_slide_160k_ade20k.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... | data2vec_vision-main | beit/semantic_segmentation/configs/beit/upernet/upernet_beit_large_24_512_slide_160k_ade20k.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... | data2vec_vision-main | beit/semantic_segmentation/configs/beit/upernet/upernet_beit_base_12_512_slide_160k_ade20k.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... | data2vec_vision-main | beit/semantic_segmentation/configs/beit/upernet/upernet_beit_base_12_512_slide_160k_ade20k_ms.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... | data2vec_vision-main | beit/semantic_segmentation/configs/beit/upernet/upernet_beit_large_24_512_slide_160k_ade20k_ms.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... | data2vec_vision-main | beit/semantic_segmentation/configs/beit/upernet/upernet_beit_base_12_640_slide_160k_ade20k_ms.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... | data2vec_vision-main | beit/semantic_segmentation/configs/beit/upernet/upernet_beit_large_24_640_slide_160k_ade20k_ms.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... | data2vec_vision-main | beit/semantic_segmentation/configs/beit/upernet/upernet_beit_base_12_640_slide_160k_ade20k.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... | data2vec_vision-main | beit/semantic_segmentation/backbone/beit.py |
#!/usr/bin/env python3
import torch
from setuptools import find_packages, setup
torch_ver = [int(x) for x in torch.__version__.split(".")[:2]]
assert torch_ver >= [1, 4], "Requires PyTorch >= 1.4"
setup(
name="layoutlm",
version="0.0",
author="Yiheng Xu",
url="https://github.com/microsoft/unilm/tree/m... | data2vec_vision-main | layoutlm/deprecated/setup.py |
# coding=utf-8
from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from ... | data2vec_vision-main | layoutlm/deprecated/examples/classification/run_classification.py |
import argparse
import json
import os
from PIL import Image
from transformers import AutoTokenizer
def bbox_string(box, width, length):
return (
str(int(1000 * (box[0] / width)))
+ " "
+ str(int(1000 * (box[1] / length)))
+ " "
+ str(int(1000 * (box[2] / width)))
+... | data2vec_vision-main | layoutlm/deprecated/examples/seq_labeling/preprocess.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... | data2vec_vision-main | layoutlm/deprecated/examples/seq_labeling/run_seq_labeling.py |
# flake8: noqa
from .data.funsd import FunsdDataset
from .modeling.layoutlm import (
LayoutlmConfig,
LayoutlmForSequenceClassification,
LayoutlmForTokenClassification,
)
| data2vec_vision-main | layoutlm/deprecated/layoutlm/__init__.py |
data2vec_vision-main | layoutlm/deprecated/layoutlm/modeling/__init__.py | |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import BertConfig, BertModel, BertPreTrainedModel
from transformers.modeling_bert import BertLayerNorm
logger = logging.getLogger(__name__)
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_MAP = {}
LAYOUTLM_PRETRAINED... | data2vec_vision-main | layoutlm/deprecated/layoutlm/modeling/layoutlm.py |
# coding=utf-8
import copy
import json
import logging
import os
import re
from multiprocessing import Pool
import torch
from lxml import html
from torch.utils.data import TensorDataset
from tqdm import tqdm
from transformers import DataProcessor
logger = logging.getLogger(__name__)
def get_text(node):
textnodes... | data2vec_vision-main | layoutlm/deprecated/layoutlm/data/rvl_cdip.py |
# flake8: noqa
from .funsd import FunsdDataset
| data2vec_vision-main | layoutlm/deprecated/layoutlm/data/__init__.py |
import logging
import os
import torch
from torch.utils.data import Dataset
logger = logging.getLogger(__name__)
class FunsdDataset(Dataset):
def __init__(self, args, tokenizer, labels, pad_token_label_id, mode):
if args.local_rank not in [-1, 0] and mode == "train":
torch.distributed.barrier... | data2vec_vision-main | layoutlm/deprecated/layoutlm/data/funsd.py |
from setuptools import setup, find_packages
setup(
name = "adalm",
version = "0.0",
author = "Microsoft",
author_email = "",
description = "domain adaptation toolkit",
keywords = "domain adaptation with extended vocab",
license='Apache',
url = "https://github.com/littlefive5/AdaLM",
... | data2vec_vision-main | adalm/setup.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... | data2vec_vision-main | adalm/finetune/run_ner.py |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | data2vec_vision-main | adalm/finetune/utils_ner.py |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | data2vec_vision-main | adalm/finetune/run_pico.py |
data2vec_vision-main | adalm/finetune/__init__.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... | data2vec_vision-main | adalm/finetune/run_classifier.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... | data2vec_vision-main | adalm/finetune/utils_for_glue.py |
from __future__ import absolute_import
from __future__ import division
from numpy.core.fromnumeric import argsort
from text_encoder import SubwordTextEncoder
import tokenizer
import tempfile
import argparse
from transformers import BertTokenizer
import random
import math
import numpy as np
def merge_output_file_with_b... | data2vec_vision-main | adalm/incr_bpe/vocab_extend.py |
# coding=utf-8
# Copyright 2018 The Tensor2Tensor Authors.
#
# 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... | data2vec_vision-main | adalm/incr_bpe/text_encoder.py |
# coding=utf-8
# Copyright 2018 The Tensor2Tensor Authors.
#
# 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... | data2vec_vision-main | adalm/incr_bpe/tokenizer.py |
#-*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from text_encoder import SubwordTextEncoder
import tokenizer
import os
import tempfile
import tensorflow as tf
tf.flags.DEFINE_string('output_filename', '/tmp/my.subword_text_encoder',
'where to store... | data2vec_vision-main | adalm/incr_bpe/subword_builder.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import logging
from typing import List, Optional
import numpy as np
from base import BaseModel
from stats import DirichletMultinomial, DirichletPrior, NormalInverseGammaNormal
logging.basicConfig(level=logging.INFO)
logger = logging.getLogg... | clara-main | gibbs.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import math
from collections import defaultdict
from typing import Dict, Generic, List, Optional, TypeVar
import numpy as np
from scipy.special import loggamma
from scipy.stats import norm
logger = logging.getLogger(__name__)
... | clara-main | stats.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import numpy as np
import pandas as pd
logger = logging.getLogger(__name__)
def generate_score(
true_ratings: np.array, score_means: np.array, score_stdvs: np.array
):
num_items = len(true_ratings)
num_ones = np.... | clara-main | simulator.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
from abc import ABC, abstractmethod
class BaseModel(ABC):
def __init__(self, name: str, **kwargs):
self.name = name
@abstractmethod
def fit(self, **kwargs):
pass
@abstractmethod
def predict(self, **kwargs... | clara-main | base.py |
from collections import defaultdict
from typing import Dict, List, NamedTuple
import numpy as np
import pandas as pd
def generate_common_cm(L: int, h: float, gamma: float) -> np.ndarray:
"""
Generates the L x L common confusion matrix using the heterogeneity factor, h and
the lower bound on accuracy, gam... | clara-main | mapping-aware-model/simulator.py |
stan_code="""
data {
int<lower=1> A; // number of annotators
int<lower=2> K; // number of categories
int<lower=1> N; // number of annotations
int<lower=1> I; // number of items
int<lower=1> L; // total number of flat labels (L i... | clara-main | mapping-aware-model/mapping_aware_model.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import json
import logging
import os
from utils import file_tqdm
logging.basicCon... | code-prediction-transformer-main | generate_new_trees.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import json
import logging
import pickle
from collections import Counter
from utils... | code-prediction-transformer-main | generate_vocab.py |
#!/usr/bin/env python3
# Copyright (c) 2019 OpenAI, HugginFace Inc. team. and TaeHwan Jung
# Copyright (c) Facebook, Inc. and its affiliates.
# ----------------------------------------------------------------------------
# MIT LICENSE
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of... | code-prediction-transformer-main | model.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import json
import logging
import os
import pickle
import torch
import utils
logging.basicConfig(... | code-prediction-transformer-main | dataset.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import multiprocessing as mp
from tqdm import tqdm
def line_positions(file_path):
... | code-prediction-transformer-main | utils.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import json
import logging
from functools import partial
from typing import Set
import torch
tor... | code-prediction-transformer-main | train.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import LSTMModel
import Dataset, Vocab
import json
import os
import torch
import argparse
import... | code-prediction-transformer-main | demo.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import json
import logging
import pickle
import re
from collections import Counter
... | code-prediction-transformer-main | code2seq/generate_vocab.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import json
import logging
import torch
import utils
logging.basicConfig(level=logging.INFO)
UN... | code-prediction-transformer-main | code2seq/dataset.py |
import argparse
import json
import os
import pickle
import random
import re
from collections import defaultdict
from itertools import chain, combinations, product
from utils import get_ancestors, get_terminal_nodes, parallelize, tokenize
from tqdm import tqdm
PLACEHOLDER = "<placeholder_token>"
UNK = "<unk_token>"
... | code-prediction-transformer-main | code2seq/generate_data.py |
#!/usr/bin/env python3
# Copyright (c) 2019 Technion
# Copyright (c) Facebook, Inc. and its affiliates.
# ----------------------------------------------------------------------------
# MIT License
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documenta... | code-prediction-transformer-main | code2seq/code2seq_model.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
from dataset.dataset import BaseDataset, BaseSetup, BaseVocab
class Setup(BaseSetup):... | code-prediction-transformer-main | models/path_trans_variation/dataset.py |
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