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[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
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import torch
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import torch.nn as nn
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# Part of the implementation is borrowed and modified from SegLink,
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# publicly available at https://github.com/bgshih/seglink
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import tensorflow as tf
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from . import ops, resnet18_v1, resnet_utils
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""" Contains various versions of ConvNext Networks.
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ConvNext Networks (ConvNext) were proposed in:
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Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell and Saining Xie
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A ConvNet for the 2020s. CVPR 2022.
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Compared to https://github.com/facebookresearch/ConvNeXt,
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we obtain different ConvNext variants by changing the network depth, width,
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feature number, and downsample ratio.
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"""
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# Part of the implementation is borrowed and modified from ConvNext,
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# publicly available at https://github.com/facebookresearch/ConvNeXt
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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'''Referenced from rwightman's pytorch-image-models(timm).
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Github: https://github.com/rwightman/pytorch-image-models
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We use some modules and modify the parameters according to our network.
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'''
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# Part of the implementation is borrowed and modified from timm,
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# publicly available at https://github.com/rwightman/pytorch-image-models
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import collections.abc
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import logging
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import math
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""" Contains various versions of ViTSTR.
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ViTSTR were proposed in:
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Rowel Atienza
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Vision transformer for fast and efficient scene text recognition. ICDAR 2021.
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Compared to https://github.com/roatienza/deep-text-recognition-benchmark,
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we obtain different ViTSTR variants by changing the network patch_size and in_chans.
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"""
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# Part of the implementation is borrowed and modified from ViTSTR,
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# publicly available at https://github.com/roatienza/deep-text-recognition-benchmark
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from __future__ import absolute_import, division, print_function
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import logging
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from copy import deepcopy
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@@ -1,3 +1,5 @@
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# Part of the implementation is borrowed and modified from SegLink,
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# publicly available at https://github.com/bgshih/seglink
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import math
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import os
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import shutil
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@@ -1,3 +1,17 @@
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Contains definitions for the original form of Residual Networks.
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The 'v1' residual networks (ResNets) implemented in this module were proposed
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by:
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Contains building blocks for various versions of Residual Networks.
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Residual networks (ResNets) were proposed in:
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
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@@ -1,3 +1,4 @@
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import cv2
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import numpy as np
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