python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
# --------------------------------------------------------
# DIT: SELF-SUPERVISED PRE-TRAINING FOR DOCUMENT IMAGE TRANSFORMER
# Based on Beit
# --------------------------------------------------------'
import argparse
import datetime
import numpy as np
import time
import torch
import torch.backends.cudnn as cudnn
impor... | EXA-1-master | exa/models/unilm-master/dit/classification/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... | EXA-1-master | exa/models/unilm-master/dit/classification/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... | EXA-1-master | exa/models/unilm-master/dit/classification/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... | EXA-1-master | exa/models/unilm-master/dit/classification/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... | EXA-1-master | exa/models/unilm-master/dit/classification/optim_factory.py |
#!/usr/bin/env python
# --------------------------------------------------------------------------------
# MPViT: Multi-Path Vision Transformer for Dense Prediction
# Copyright (c) 2022 Electronics and Telecommunications Research Institute (ETRI).
# All Rights Reserved.
# Written by Youngwan Lee
# ---------------------... | EXA-1-master | exa/models/unilm-master/dit/text_detection/train_net.py |
"""
Mostly copy-paste from DINO and timm library:
https://github.com/facebookresearch/dino
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""
import warnings
import math
import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.laye... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/deit.py |
import os
import json
import copy
import itertools
from collections import OrderedDict
import detectron2.utils.comm as comm
from detectron2.evaluation import COCOEvaluator
from .concern.icdar2015_eval.detection.iou import DetectionIoUEvaluator
class FUNSDEvaluator(COCOEvaluator):
def evaluate(self, img_ids=None)... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/funsd_evaluation.py |
""" Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929
The official jax code is released and available at https://github.com/google-research/vision_transformer
... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/beit.py |
from detectron2.config import CfgNode as CN
def add_vit_config(cfg):
"""
Add config for VIT.
"""
_C = cfg
_C.MODEL.VIT = CN()
# CoaT model name.
_C.MODEL.VIT.NAME = ""
# Output features from CoaT backbone.
_C.MODEL.VIT.OUT_FEATURES = ["layer3", "layer5", "layer7", "layer11"]
... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/config.py |
from detectron2.checkpoint import DetectionCheckpointer
from typing import Any
import torch
import torch.nn as nn
#from fvcore.common.checkpoint import _IncompatibleKeys, _strip_prefix_if_present, TORCH_VERSION, quantization, \
# ObserverBase, FakeQuantizeBase
from fvcore.common.checkpoint import _IncompatibleKeys,... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/mycheckpointer.py |
# --------------------------------------------------------------------------------
# VIT: Multi-Path Vision Transformer for Dense Prediction
# Copyright (c) 2022 Electronics and Telecommunications Research Institute (ETRI).
# All Rights Reserved.
# Written by Youngwan Lee
# This source code is licensed(Dual License(GPL... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/backbone.py |
# --------------------------------------------------------------------------------
# MPViT: Multi-Path Vision Transformer for Dense Prediction
# Copyright (c) 2022 Electronics and Telecommunications Research Institute (ETRI).
# All Rights Reserved.
# Written by Youngwan Lee
# This source code is licensed(Dual License(G... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# from https://github.com/facebookresearch/detr/blob/main/d2/detr/dataset_mapper.py
import copy
import logging
import numpy as np
import torch
from detectron2.data import detection_utils as utils
from detectron2.data import transforms as T
__al... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/dataset_mapper.py |
# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
"""
This file contains components with some default boilerplate logic user may need
in training / testing. They will not work for everyone, but many users may find them useful.
The behavior of functions/classes in this file is subject to chang... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/mytrainer.py |
import importlib
from collections import OrderedDict
import anyconfig
import munch
class Config(object):
def __init__(self):
pass
def load(self, conf):
conf = anyconfig.load(conf)
return munch.munchify(conf)
def compile(self, conf, return_packages=False):
packages = conf... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/config.py |
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/average_meter.py |
import os
import logging
import functools
import json
import time
from datetime import datetime
# from tensorboardX import SummaryWriter
import yaml
import cv2
import numpy as np
from .config import Configurable, State
class Logger(Configurable):
SUMMARY_DIR_NAME = 'summaries'
VISUALIZE_NAME = 'visualize'
... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/log.py |
from PIL import Image
import cv2
import base64
import io
import numpy as np
def convert(data):
if isinstance(data, dict):
ndata = {}
for key, value in data.items():
nkey = key.decode()
if nkey == 'img':
img = Image.open(io.BytesIO(value))
img... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/convert.py |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# File : __init__.py
# Author : Zhaoyi Wan <wanzhaoyi@megvii.com>
# Date : 21.11.2018
# Last Modified Date: 08.01.2019
# Last Modified By : Zhaoyi Wan <wanzhaoyi@megvii.com>
from .log import Logger
from .average_meter import AverageMe... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/__init__.py |
import os
class SignalMonitor(object):
def __init__(self, file_path):
self.file_path = file_path
def get_signal(self):
if self.file_path is None:
return None
if os.path.exists(self.file_path):
with open(self.file_path) as f:
data = self.file.rea... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/signal_monitor.py |
import cv2
import numpy as np
from scipy import interpolate
def intersection(x, p1, p2):
x1, y1 = p1
x2, y2 = p2
if x2 == x1:
return 0
k = (x - x1) / (x2 - x1)
return k * (y2 - y1) + y1
def midpoint(p1, p2, typed=float):
return [typed((p1[0] + p2[0]) / 2), typed((p1[1] + p2[1]) / 2)]
... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/box2seg.py |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# File : visualizer.py
# Author : Zhaoyi Wan <wanzhaoyi@megvii.com>
# Date : 08.01.2019
# Last Modified Date: 02.12.2019
# Last Modified By : Minghui Liao
import torch
import numpy as np
import cv2
class Visualize:
@classmethod
... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/visualizer.py |
#!/usr/bin/env mdl
import os
BASE_DIR = os.path.dirname(os.path.realpath(__file__))
import time
import json
import select
import traceback
import socket
from multiprocessing import Process, Pipe
import gevent
from gevent.pywsgi import WSGIServer
from geventwebsocket.handler import WebSocketHandler
from flask import Fl... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/webcv2/server.py |
#!/usr/bin/env mdl
class WebCV2:
def __init__(self):
import cv2
self._cv2 = cv2
from .manager import global_manager as gm
self._gm = gm
def __getattr__(self, name):
if hasattr(self._gm, name):
return getattr(self._gm, name)
elif hasattr(self._cv2, nam... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/webcv2/__init__.py |
#!/usr/bin/env mdl
import socket
import base64
import cv2
import numpy as np
from collections import OrderedDict
from .server import get_server
def jpeg_encode(img):
return cv2.imencode('.png', img)[1]
def get_free_port(rng, low=2000, high=10000):
in_use = True
while in_use:
port = rng.randint(... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/webcv2/manager.py |
EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/icdar2015_eval/__init__.py | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
from collections import namedtuple
import numpy as np
from shapely.geometry import Polygon
class DetectionMTWI2018Evaluator(object):
def __init__(
self,
area_recall_constraint=0.7, area_precision_constraint=0.7,
ev_param_ind_center_... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/icdar2015_eval/detection/mtwi2018.py |
EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/icdar2015_eval/detection/__init__.py | |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
from collections import namedtuple
import numpy as np
from shapely.geometry import Polygon
class DetectionICDAR2013Evaluator(object):
def __init__(
self,
area_recall_constraint=0.8, area_precision_constraint=0.4,
ev_param_ind_center... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/icdar2015_eval/detection/icdar2013.py |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from collections import namedtuple
import numpy as np
from shapely.geometry import Polygon
class DetectionIoUEvaluator(object):
def __init__(self, iou_constraint=0.5, area_precision_constraint=0.5):
self.iou_constraint = iou_constraint
self.area_precis... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/icdar2015_eval/detection/iou.py |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
from collections import namedtuple
import numpy as np
from shapely.geometry import Polygon
class DetectionDetEvalEvaluator(object):
def __init__(
self,
area_recall_constraint=0.8, area_precision_constraint=0.4,
ev_param_ind_center_d... | EXA-1-master | exa/models/unilm-master/dit/text_detection/ditod/concern/icdar2015_eval/detection/deteval.py |
import os
from PIL import Image
import xml.etree.ElementTree as ET
import numpy as np
import json
from PIL import Image
from shutil import copyfile
def convert(ROOT, TRACK, SPLIT):
coco_data = {
"images": [],
"annotations": [],
"categories": [{"id": 1, "name": "table"}, ],
}
DATA_D... | EXA-1-master | exa/models/unilm-master/dit/object_detection/convert_to_coco_format.py |
import argparse
import cv2
from ditod import add_vit_config
import torch
from detectron2.config import get_cfg
from detectron2.utils.visualizer import ColorMode, Visualizer
from detectron2.data import MetadataCatalog
from detectron2.engine import DefaultPredictor
def main():
parser = argparse.ArgumentParser(d... | EXA-1-master | exa/models/unilm-master/dit/object_detection/inference.py |
#!/usr/bin/env python
# --------------------------------------------------------------------------------
# MPViT: Multi-Path Vision Transformer for Dense Prediction
# Copyright (c) 2022 Electronics and Telecommunications Research Institute (ETRI).
# All Rights Reserved.
# Written by Youngwan Lee
# ---------------------... | EXA-1-master | exa/models/unilm-master/dit/object_detection/train_net.py |
import argparse
import os
import cv2
import tqdm
def convert(fn):
# given a file name, convert it into binary and store at the same position
img = cv2.imread(fn)
gim = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gim = cv2.adaptiveThreshold(gim, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 45, 11)... | EXA-1-master | exa/models/unilm-master/dit/object_detection/adaptive_binarize.py |
"""
Mostly copy-paste from DINO and timm library:
https://github.com/facebookresearch/dino
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""
import warnings
import math
import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.laye... | EXA-1-master | exa/models/unilm-master/dit/object_detection/ditod/deit.py |
import copy
import itertools
import os
import os.path as osp
import shutil
from collections import OrderedDict
from xml.dom.minidom import Document
import detectron2.utils.comm as comm
import torch
from detectron2.evaluation import COCOEvaluator
from detectron2.utils.file_io import PathManager
from .table_evaluation.... | EXA-1-master | exa/models/unilm-master/dit/object_detection/ditod/icdar_evaluation.py |
""" Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929
The official jax code is released and available at https://github.com/google-research/vision_transformer
... | EXA-1-master | exa/models/unilm-master/dit/object_detection/ditod/beit.py |
from detectron2.config import CfgNode as CN
def add_vit_config(cfg):
"""
Add config for VIT.
"""
_C = cfg
_C.MODEL.VIT = CN()
# CoaT model name.
_C.MODEL.VIT.NAME = ""
# Output features from CoaT backbone.
_C.MODEL.VIT.OUT_FEATURES = ["layer3", "layer5", "layer7", "layer11"]
... | EXA-1-master | exa/models/unilm-master/dit/object_detection/ditod/config.py |
from detectron2.checkpoint import DetectionCheckpointer
from typing import Any
import torch
import torch.nn as nn
from fvcore.common.checkpoint import _IncompatibleKeys, _strip_prefix_if_present, TORCH_VERSION, quantization, \
ObserverBase, FakeQuantizeBase
from torch import distributed as dist
from scipy import i... | EXA-1-master | exa/models/unilm-master/dit/object_detection/ditod/mycheckpointer.py |
# --------------------------------------------------------------------------------
# VIT: Multi-Path Vision Transformer for Dense Prediction
# Copyright (c) 2022 Electronics and Telecommunications Research Institute (ETRI).
# All Rights Reserved.
# Written by Youngwan Lee
# This source code is licensed(Dual License(GPL... | EXA-1-master | exa/models/unilm-master/dit/object_detection/ditod/backbone.py |
# --------------------------------------------------------------------------------
# MPViT: Multi-Path Vision Transformer for Dense Prediction
# Copyright (c) 2022 Electronics and Telecommunications Research Institute (ETRI).
# All Rights Reserved.
# Written by Youngwan Lee
# This source code is licensed(Dual License(G... | EXA-1-master | exa/models/unilm-master/dit/object_detection/ditod/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# from https://github.com/facebookresearch/detr/blob/main/d2/detr/dataset_mapper.py
import copy
import logging
import numpy as np
import torch
from detectron2.data import detection_utils as utils
from detectron2.data import transforms as T
__al... | EXA-1-master | exa/models/unilm-master/dit/object_detection/ditod/dataset_mapper.py |
# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
"""
This file contains components with some default boilerplate logic user may need
in training / testing. They will not work for everyone, but many users may find them useful.
The behavior of functions/classes in this file is subject to chang... | EXA-1-master | exa/models/unilm-master/dit/object_detection/ditod/mytrainer.py |
from .evaluate import calc_table_score | EXA-1-master | exa/models/unilm-master/dit/object_detection/ditod/table_evaluation/__init__.py |
"""
Evaluation of -.tar.gz file.
Yu Fang - March 2019
"""
import os
import xml.dom.minidom
# from eval import eval
reg_gt_path = os.path.abspath("data/test")
reg_gt_path_archival = os.path.abspath("data/test")
reg_gt_path_modern = os.path.abspath("data/test")
str_gt_path_1 = os.path.abspath("data/test")
str_gt_path_... | EXA-1-master | exa/models/unilm-master/dit/object_detection/ditod/table_evaluation/evaluate.py |
"""
Data structures used by the evaluation process.
Yu Fang - March 2019
"""
from collections import Iterable
import numpy as np
from shapely.geometry import Polygon
# helper functions
def flatten(lis):
for item in lis:
if isinstance(item, Iterable) and not isinstance(item, str):
for x in fl... | EXA-1-master | exa/models/unilm-master/dit/object_detection/ditod/table_evaluation/data_structure.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/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
"""
# ----------------------------------------------... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/beit/run_linear_eval.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/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... | EXA-1-master | exa/models/unilm-master/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 >= ... | EXA-1-master | exa/models/unilm-master/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://'):
... | EXA-1-master | exa/models/unilm-master/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 >= ... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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,... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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.... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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',
]
| EXA-1-master | exa/models/unilm-master/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, ... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/beit/semantic_segmentation/configs/beit/upernet/upernet_beit_large_24_512_slide_160k_ade20k_pt2ft.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/beit/semantic_segmentation/configs/beit/upernet/upernet_beit_base_12_512_slide_160k_ade20k_pt2ft.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/beit/semantic_segmentation/configs/beit/upernet/upernet_beit_base_12_640_slide_160k_ade20k_pt2ft.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/beit/semantic_segmentation/configs/beit/upernet/upernet_beit_base_12_512_slide_160k_ade20k_pt.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/beit/semantic_segmentation/configs/beit/upernet/upernet_beit_large_24_640_slide_160k_ade20k_pt2ft.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/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/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... | EXA-1-master | exa/models/unilm-master/beit/semantic_segmentation/backbone/beit.py |
"""
Simple check list from AllenNLP repo: https://github.com/allenai/allennlp/blob/master/setup.py
To create the package for pypi.
1. Change the version in __init__.py, setup.py as well as docs/source/conf.py.
2. Commit these changes with the message: "Release: VERSION"
3. Add a tag in git to mark the release: "git... | EXA-1-master | exa/models/unilm-master/xtune/setup.py |
# coding=utf-8
# Copyright 2020 Google and DeepMind.
#
# 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 law o... | EXA-1-master | exa/models/unilm-master/xtune/utils_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... | EXA-1-master | exa/models/unilm-master/xtune/src/run_qa.py |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors,
# The HuggingFace Inc. team, and The XTREME Benchmark Authors.
# 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 th... | EXA-1-master | exa/models/unilm-master/xtune/src/run_tag.py |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors,
# The HuggingFace Inc. team, and The XTREME Benchmark Authors.
# 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 th... | EXA-1-master | exa/models/unilm-master/xtune/src/utils_tag.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... | EXA-1-master | exa/models/unilm-master/xtune/src/run_cls.py |
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--translation_path",
default=None,
type=str,
required=True,
help="",
)
drop_languages = ["en", "zh-CN", "zh", "ja", "ko", "th", "my", "... | EXA-1-master | exa/models/unilm-master/xtune/src/tools/check_many2many_alignment.py |
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