添加ocr部分代码的copyright信息 Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10342392master
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# Copyright (c) Alibaba, Inc. and its affiliates. | |||||
import torch | import torch | ||||
import torch.nn as nn | 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 | import tensorflow as tf | ||||
from . import ops, resnet18_v1, resnet_utils | from . import ops, resnet18_v1, resnet_utils | ||||
@@ -1,11 +1,5 @@ | |||||
""" 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 | ||||
import torch.nn as nn | import torch.nn as nn | ||||
import torch.nn.functional as F | import torch.nn.functional as F | ||||
@@ -1,7 +1,5 @@ | |||||
'''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 collections.abc | ||||
import logging | import logging | ||||
import math | import math | ||||
@@ -1,10 +1,5 @@ | |||||
""" 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 | from __future__ import absolute_import, division, print_function | ||||
import logging | import logging | ||||
from copy import deepcopy | from copy import deepcopy | ||||
@@ -1,3 +1,5 @@ | |||||
# Part of the implementation is borrowed and modified from SegLink, | |||||
# publicly available at https://github.com/bgshih/seglink | |||||
import math | import math | ||||
import os | import os | ||||
import shutil | import shutil | ||||
@@ -1,3 +1,17 @@ | |||||
# 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. | """Contains definitions for the original form of Residual Networks. | ||||
The 'v1' residual networks (ResNets) implemented in this module were proposed | The 'v1' residual networks (ResNets) implemented in this module were proposed | ||||
by: | by: | ||||
@@ -1,3 +1,17 @@ | |||||
# 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. | """Contains building blocks for various versions of Residual Networks. | ||||
Residual networks (ResNets) were proposed in: | Residual networks (ResNets) were proposed in: | ||||
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun | Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun | ||||
@@ -1,3 +1,4 @@ | |||||
# Copyright (c) Alibaba, Inc. and its affiliates. | |||||
import cv2 | import cv2 | ||||
import numpy as np | import numpy as np | ||||