[to #42322933]add copyright info

添加ocr部分代码的copyright信息
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10342392
This commit is contained in:
xixing.tj
2022-10-11 22:37:57 +08:00
committed by yingda.chen
parent 0d97f8959d
commit da5d5cd10b
9 changed files with 40 additions and 19 deletions

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# Copyright (c) Alibaba, Inc. and its affiliates.
import torch
import torch.nn as nn

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# Part of the implementation is borrowed and modified from SegLink,
# publicly available at https://github.com/bgshih/seglink
import tensorflow as tf
from . import ops, resnet18_v1, resnet_utils

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""" Contains various versions of ConvNext Networks.
ConvNext Networks (ConvNext) were proposed in:
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell and Saining Xie
A ConvNet for the 2020s. CVPR 2022.
Compared to https://github.com/facebookresearch/ConvNeXt,
we obtain different ConvNext variants by changing the network depth, width,
feature number, and downsample ratio.
"""
# Part of the implementation is borrowed and modified from ConvNext,
# publicly available at https://github.com/facebookresearch/ConvNeXt
import torch
import torch.nn as nn
import torch.nn.functional as F

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'''Referenced from rwightman's pytorch-image-models(timm).
Github: https://github.com/rwightman/pytorch-image-models
We use some modules and modify the parameters according to our network.
'''
# Part of the implementation is borrowed and modified from timm,
# publicly available at https://github.com/rwightman/pytorch-image-models
import collections.abc
import logging
import math

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""" Contains various versions of ViTSTR.
ViTSTR were proposed in:
Rowel Atienza
Vision transformer for fast and efficient scene text recognition. ICDAR 2021.
Compared to https://github.com/roatienza/deep-text-recognition-benchmark,
we obtain different ViTSTR variants by changing the network patch_size and in_chans.
"""
# Part of the implementation is borrowed and modified from ViTSTR,
# publicly available at https://github.com/roatienza/deep-text-recognition-benchmark
from __future__ import absolute_import, division, print_function
import logging
from copy import deepcopy

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# Part of the implementation is borrowed and modified from SegLink,
# publicly available at https://github.com/bgshih/seglink
import math
import os
import shutil

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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Contains definitions for the original form of Residual Networks.
The 'v1' residual networks (ResNets) implemented in this module were proposed
by:

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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Contains building blocks for various versions of Residual Networks.
Residual networks (ResNets) were proposed in:
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun

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# Copyright (c) Alibaba, Inc. and its affiliates.
import cv2
import numpy as np