update upstream copyright and citation

examples/seg/deeplabv3/deeplabv3.py

@@ -1,4 +1,7 @@
-"""DeeplabV3, DeeplabV3+ implement with replaceable backbones"""
+"""
+DeeplabV3, DeeplabV3+ implement with replaceable backbones
+Adapted from https://github.com/pytorch/vision/blob/main/torchvision/models/segmentation/deeplabv3.py
+"""
 
 from typing import List
 

examples/seg/deeplabv3/preprocess/build_seg_data.py

@@ -1,3 +1,5 @@
+# Copy from https://github.com/mindspore-ai/models/blob/master/official/cv/DeepLabv3/src/data/build_seg_data.py
+
 import argparse
 import os
 

examples/seg/deeplabv3/preprocess/get_data_list.py

@@ -1,3 +1,5 @@
+# Copy from https://github.com/mindspore-ai/models/blob/master/official/cv/DeepLabv3/src/data/get_dataset_lst.py
+
 import argparse
 import os
 

mindcv/data/auto_augment.py

@@ -1,8 +1,14 @@
 """
-AutoAugment and RandAugment for mindspore.
 
+Copyright 2019, Ross Wightman
+
+Modifications made by MindSpore team in 2023 to support the MindSpore framework.
 Adapted from:
-    https://github.com/rwightman/pytorch-image-models/blob/main/timm/data/auto_augment.py
+    https://github.com/rwightman/pytorch-image-models/blob/main/timm/data/auto_augment.py.
+
+
+
+AutoAugment and RandAugment for mindspore.
 
 Papers:
     AutoAugment: Learning Augmentation Policies from Data - https://arxiv.org/abs/1805.09501

mindcv/data/dataset_factory.py

@@ -1,5 +1,5 @@
 """
-Create dataset by name
+Create dataset by name on MindSpore
 """
 
 import logging

mindcv/data/distributed_sampler.py

@@ -1,4 +1,5 @@
-""" distributed sampler """
+""" MindSpore Distributed Sampler """
+
 import logging
 import math
 

mindcv/data/loader.py

@@ -1,5 +1,5 @@
 """
-Create dataloader
+Create dataloader on MindSpore
 """
 
 import inspect

mindcv/data/mixup.py

@@ -9,6 +9,10 @@
 CutMix: https://github.com/clovaai/CutMix-PyTorch
 
 Hacked together by / Copyright 2020 Ross Wightman
+
+Modifications made to support the MindSpore framework.
+Adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/data/mixup.py
+
 """
 import numpy as np
 

mindcv/data/transforms_factory.py

@@ -1,5 +1,10 @@
 """
 Transform operation list
+
+Hacked together by / Copyright 2019, Ross Wightman
+Modifications made to support the MindSpore framework.
+Adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/data/transforms_factory.py
+
 """
 
 import math

mindcv/models/bit.py

@@ -1,6 +1,7 @@
 """
 MindSpore implementation of `BiT_ResNet`.
 Refer to Big Transfer (BiT): General Visual Representation Learning.
+Adapted from https://github.com/google-research/big_transfer.
 """
 
 from typing import List, Optional, Type, Union

mindcv/models/cait.py

@@ -1,7 +1,21 @@
+""" Class-Attention in Image Transformers (CaiT)
+
+Paper: 'Going deeper with Image Transformers' - https://arxiv.org/abs/2103.17239
+
+Original code and weights from https://github.com/facebookresearch/deit, copyright below
+
+Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
+
+------------------------------------------------------------------------
+MindSpore adaptation:
+Modified for use with the MindSpore framework.
+This file is part of a derivative work and remains under the original license.
+Adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/cait.py
 """
-MindSpore implementation of `CaiT`.
-Refer to Going deeper with Image Transformers.
-"""
+
+# Copyright (c) 2015-present, Facebook, Inc.
+# All rights reserved.
+
 
 from functools import partial
 

mindcv/models/cmt.py

@@ -1,3 +1,7 @@
+"""
+MindSpore implementation of `CMT`.
+Adapted from https://github.com/junjie18/CMT
+"""
 import numpy as np
 
 import mindspore

mindcv/models/coat.py

@@ -1,6 +1,16 @@
 """
 CoaT architecture.
+Paper: Co-Scale Conv-Attentional Image Transformers - https://arxiv.org/abs/2104.06399
+
+Official CoaT code at: https://github.com/mlpc-ucsd/CoaT
 Modified from timm/models/vision_transformer.py
+
+------------------------------------------------------------------------
+MindSpore adaptation:
+Modified for use with the MindSpore framework.
+This file is part of a derivative work and remains under the original license.
+Adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/coat.py
+
 """
 from typing import Union
 

mindcv/models/convit.py

@@ -1,7 +1,32 @@
+""" ConViT Model
+
+@article{d2021convit,
+  title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases},
+  author={d'Ascoli, St{\'e}phane and Touvron, Hugo and Leavitt, Matthew and Morcos,
+  Ari and Biroli, Giulio and Sagun, Levent},
+  journal={arXiv preprint arXiv:2103.10697},
+  year={2021}
+}
+
+Paper link: https://arxiv.org/abs/2103.10697
+Original code: https://github.com/facebookresearch/convit, original copyright below
+
+Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
+
+------------------------------------------------------------------------
+MindSpore adaptation:
+Modified for use with the MindSpore framework.
+This file is part of a derivative work and remains under the original license.
+Adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/convit.py
+
 """
-MindSpore implementation of `ConViT`.
-Refer to ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
-"""
+
+# Copyright (c) 2015-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the CC-by-NC license found in the
+# LICENSE file in the root directory of this source tree.
+#
 
 import numpy as np
 

mindcv/models/convnext.py

@@ -1,8 +1,50 @@
+""" ConvNeXt
+
+Papers:
+* `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf
+@Article{liu2022convnet,
+  author  = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
+  title   = {A ConvNet for the 2020s},
+  journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year    = {2022},
+}
+
+* `ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808
+@article{Woo2023ConvNeXtV2,
+  title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders},
+  author={Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon and Saining Xie},
+  year={2023},
+  journal={arXiv preprint arXiv:2301.00808},
+}
+
+Original code and weights from:
+* https://github.com/facebookresearch/ConvNeXt, original copyright below
+* https://github.com/facebookresearch/ConvNeXt-V2, original copyright below
+
+Model defs atto, femto, pico, nano and _ols / _hnf variants are timm originals.
+
+Modifications and additions for timm hacked together by / Copyright 2022, Ross Wightman
+
+------------------------------------------------------------------------
+MindSpore adaptation:
+Modified for use with the MindSpore framework.
+This file is part of a derivative work and remains under the original license.
+Adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/convnext.py
+
 """
-MindSpore implementation of `ConvNeXt` and `ConvNeXt V2`.
-Refer to: A ConvNet for the 2020s
-          ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
-"""
+# ConvNeXt
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+# This source code is licensed under the MIT license
+
+# ConvNeXt-V2
+# Copyright (c) Meta Platforms, Inc. and 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 (Attribution-NonCommercial 4.0 International (CC BY-NC 4.0))
+# No code was used directly from ConvNeXt-V2, however the weights are CC BY-NC 4.0 so beware if using commercially.
+
+
 from typing import List, Tuple
 
 import numpy as np

mindcv/models/crossvit.py

@@ -1,7 +1,31 @@
+""" CrossViT Model
+
+@inproceedings{
+    chen2021crossvit,
+    title={{CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification}},
+    author={Chun-Fu (Richard) Chen and Quanfu Fan and Rameswar Panda},
+    booktitle={International Conference on Computer Vision (ICCV)},
+    year={2021}
+}
+
+Paper link: https://arxiv.org/abs/2103.14899
+Original code: https://github.com/IBM/CrossViT/blob/main/models/crossvit.py
+
+NOTE: model names have been renamed from originals to represent actual input res all *_224 -> *_240 and *_384 -> *_408
+
+Modifications and additions for timm hacked together by / Copyright 2021, Ross Wightman
+Modified from Timm. https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+
+------------------------------------------------------------------------
+MindSpore adaptation:
+Modified for use with the MindSpore framework.
+This file is part of a derivative work and remains under the original license.
+Adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/crossvit.py
+
 """
-MindSpore implementation of `crossvit`.
-Refer to crossvit: Cross-Attention Multi-Scale Vision Transformer for Image Classification
-"""
+
+# Copyright IBM All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
 
 import numpy as np
 

mindcv/models/densenet.py

@@ -1,6 +1,6 @@
 """
 MindSpore implementation of `DenseNet`.
-Refer to: Densely Connected Convolutional Networks
+Adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/densenet.py
 """
 
 import math

mindcv/models/dpn.py

@@ -1,6 +1,16 @@
 """
-MindSpore implementation of `DPN`.
-Refer to: Dual Path Networks
+Based on original MXNet implementation https://github.com/cypw/DPNs with
+many ideas from another PyTorch implementation https://github.com/oyam/pytorch-DPNs.
+
+This implementation is compatible with the pretrained weights from cypw's MXNet implementation.
+
+Hacked together by / Copyright 2020 Ross Wightman
+
+------------------------------------------------------------------------
+MindSpore adaptation:
+Modified for use with the MindSpore framework.
+This file is part of a derivative work and remains under the original license.
+Adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/dpn.py
 """
 
 import math

mindcv/models/edgenext.py

@@ -1,6 +1,17 @@
 """
-MindSpore implementation of `edgenext`.
-Refer to EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications.
+Paper: `EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications`
+ - https://arxiv.org/abs/2206.10589
+
+Original code and weights from https://github.com/mmaaz60/EdgeNeXt
+
+Modifications and additions for timm by / Copyright 2022, Ross Wightman
+
+------------------------------------------------------------------------
+MindSpore adaptation:
+Modified for use with the MindSpore framework.
+This file is part of a derivative work and remains under the original license.
+Adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/edgenext.py
+
 """
 
 import math

mindcv/models/efficientnet.py

@@ -1,4 +1,13 @@
-"""EfficientNet Architecture."""
+"""EfficientNet Architecture.
+Hacked together by / Copyright 2019, Ross Wightman
+
+------------------------------------------------------------------------
+MindSpore adaptation:
+Modified for use with the MindSpore framework.
+This file is part of a derivative work and remains under the original license.
+Adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/efficientnet.py
+
+"""
 
 import copy
 import math

mindcv/models/ghostnet.py

@@ -1,5 +1,5 @@
 """MindSpore implementation of `GhostNet`.
-Refer to GhostNet: More Features from Cheap Operations.
+Adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/ghostnet.py
 """
 
 import math

mindcv/models/halonet.py

@@ -1,6 +1,7 @@
 """
 MindSpore implementation of `HaloNet`.
 Refer to Scaling Local Self-Attention for Parameter Effificient Visual Backbones.
+Adapted from https://github.com/maurbe/halo-net
 """
 import mindspore as ms
 import mindspore.common.initializer as init

mindcv/models/hrnet.py

@@ -1,6 +1,15 @@
 """
+Copied from https://github.com/HRNet/HRNet-Image-Classification
+
+Original header:
+  Copyright (c) Microsoft
+  Licensed under the MIT License.
+  Written by Bin Xiao (Bin.Xiao@microsoft.com)
+  Modified by Ke Sun (sunk@mail.ustc.edu.cn)
+
+------------------------------------------------------------------------
 MindSpore implementation of `HRNet`.
-Refer to Deep High-Resolution Representation Learning for Visual Recognition
+Adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/hrnet.py
 """
 from typing import Any, Dict, List, Optional, Tuple, Type, Union
 

mindcv/models/inceptionv3.py

@@ -1,6 +1,10 @@
 """
+Originally from torchvision Inception3 model
+Licensed BSD-Clause 3 https://github.com/pytorch/vision/blob/master/LICENSE
+
 MindSpore implementation of `InceptionV3`.
 Refer to Rethinking the Inception Architecture for Computer Vision.
+Adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/inception_v3.py
 """
 
 from typing import Tuple, Union

mindcv/models/inceptionv4.py

@@ -1,6 +1,11 @@
 """
+Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is
+based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License)
+
+
 MindSpore implementation of `InceptionV4`.
 Refer to Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning.
+Adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/inception_v4.py
 """
 
 from typing import Tuple, Union

mindcv/models/layers/compatibility.py

@@ -1,3 +1,5 @@
+# Adapted from https://github.com/huggingface/pytorch-image-models/tree/main/timm/layers
+
 import inspect
 
 import mindspore as ms

mindcv/models/layers/conv_norm_act.py

@@ -1,4 +1,6 @@
-""" Conv2d + BN + Act"""
+""" Conv2d + BN + Act
+Adapted from https://github.com/huggingface/pytorch-image-models/tree/main/timm/layers
+"""
 from typing import Optional
 
 from mindspore import nn