Merge branch 'sun_22.10' into 'main'

Sun 22.10

See merge request dcutoolkit/deeplearing/dlexamples_new!51

openmmlab_test/mmclassification-0.24.1/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py

+_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py'
+
+model = dict(backbone=dict(arch='B1g4'), head=dict(in_channels=2048))

openmmlab_test/mmclassification-0.24.1/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py

+_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py'
+
+model = dict(backbone=dict(arch='B2'), head=dict(in_channels=2560))

openmmlab_test/mmclassification-0.24.1/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py

+_base_ = './repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py'
+
+model = dict(backbone=dict(arch='B2g4'))

openmmlab_test/mmclassification-0.24.1/configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py

+_base_ = [
+    '../_base_/models/repvgg-B3_lbs-mixup_in1k.py',
+    '../_base_/datasets/imagenet_bs64_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_200e_coslr_warmup.py',
+    '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-0.24.1/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py

+_base_ = './repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py'
+
+model = dict(backbone=dict(arch='B3g4'))

openmmlab_test/mmclassification-0.24.1/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py

+_base_ = './repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py'
+
+model = dict(backbone=dict(arch='D2se'))

openmmlab_test/mmclassification-0.24.1/configs/res2net/README.md

+# Res2Net
+
+> [Res2Net: A New Multi-scale Backbone Architecture](https://arxiv.org/pdf/1904.01169.pdf)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layer-wise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/26739999/142573547-cde68abf-287b-46db-a848-5cffe3068faf.png" width="50%"/>
+</div>
+
+## Results and models
+
+### ImageNet-1k
+
+|        Model         | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) |                               Config                               |                               Download                                |
+| :------------------: | :--------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------: | :-------------------------------------------------------------------: |
+| Res2Net-50-14w-8s\*  |  224x224   |   25.06   |   4.22   |   78.14   |   93.85   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w14-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w14-s8_3rdparty_8xb32_in1k_20210927-bc967bf1.pth) \| [log](<>) |
+| Res2Net-50-26w-8s\*  |  224x224   |   48.40   |   8.39   |   79.20   |   94.36   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w26-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w26-s8_3rdparty_8xb32_in1k_20210927-f547a94b.pth) \| [log](<>) |
+| Res2Net-101-26w-4s\* |  224x224   |   45.21   |   8.12   |   79.19   |   94.44   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net101-w26-s4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net101-w26-s4_3rdparty_8xb32_in1k_20210927-870b6c36.pth) \| [log](<>) |
+
+*Models with * are converted from the [official repo](https://github.com/Res2Net/Res2Net-PretrainedModels). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.*
+
+## Citation
+
+```
+@article{gao2019res2net,
+  title={Res2Net: A New Multi-scale Backbone Architecture},
+  author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip},
+  journal={IEEE TPAMI},
+  year={2021},
+  doi={10.1109/TPAMI.2019.2938758},
+}
+```

openmmlab_test/mmclassification-0.24.1/configs/res2net/metafile.yml

+Collections:
+  - Name: Res2Net
+    Metadata:
+      Training Data: ImageNet-1k
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Architecture:
+        - Batch Normalization
+        - Convolution
+        - Global Average Pooling
+        - ReLU
+        - Res2Net Block
+    Paper:
+      Title: 'Res2Net: A New Multi-scale Backbone Architecture'
+      URL: https://arxiv.org/pdf/1904.01169.pdf
+    README: configs/res2net/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmclassification/blob/v0.17.0/mmcls/models/backbones/res2net.py
+      Version: v0.17.0
+
+Models:
+  - Name: res2net50-w14-s8_3rdparty_8xb32_in1k
+    Metadata:
+      FLOPs: 4220000000
+      Parameters: 25060000
+    In Collection: Res2Net
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.14
+          Top 5 Accuracy: 93.85
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w14-s8_3rdparty_8xb32_in1k_20210927-bc967bf1.pth
+    Converted From:
+      Weights: https://1drv.ms/u/s!AkxDDnOtroRPdOTqhF8ne_aakDI?e=EVb8Ri
+      Code: https://github.com/Res2Net/Res2Net-PretrainedModels/blob/master/res2net.py#L221
+    Config: configs/res2net/res2net50-w14-s8_8xb32_in1k.py
+  - Name: res2net50-w26-s8_3rdparty_8xb32_in1k
+    Metadata:
+      FLOPs: 8390000000
+      Parameters: 48400000
+    In Collection: Res2Net
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 79.20
+          Top 5 Accuracy: 94.36
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w26-s8_3rdparty_8xb32_in1k_20210927-f547a94b.pth
+    Converted From:
+      Weights: https://1drv.ms/u/s!AkxDDnOtroRPdTrAd_Afzc26Z7Q?e=slYqsR
+      Code: https://github.com/Res2Net/Res2Net-PretrainedModels/blob/master/res2net.py#L201
+    Config: configs/res2net/res2net50-w26-s8_8xb32_in1k.py
+  - Name: res2net101-w26-s4_3rdparty_8xb32_in1k
+    Metadata:
+      FLOPs: 8120000000
+      Parameters: 45210000
+    In Collection: Res2Net
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 79.19
+          Top 5 Accuracy: 94.44
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/res2net/res2net101-w26-s4_3rdparty_8xb32_in1k_20210927-870b6c36.pth
+    Converted From:
+      Weights: https://1drv.ms/u/s!AkxDDnOtroRPcJRgTLkahL0cFYw?e=nwbnic
+      Code: https://github.com/Res2Net/Res2Net-PretrainedModels/blob/master/res2net.py#L181
+    Config: configs/res2net/res2net101-w26-s4_8xb32_in1k.py

openmmlab_test/mmclassification-0.24.1/configs/res2net/res2net101-w26-s4_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/res2net101-w26-s4.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-0.24.1/configs/res2net/res2net50-w14-s8_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/res2net50-w14-s8.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-0.24.1/configs/res2net/res2net50-w26-s8_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/res2net50-w26-s8.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

openmmlab_test/mmclassification-0.24.1/configs/resnest/README.md

+# ResNeSt
+
+> [ResNeSt: Split-Attention Networks](https://arxiv.org/abs/2004.08955)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+It is well known that featuremap attention and multi-path representation are important for visual recognition. In this paper, we present a modularized architecture, which applies the channel-wise attention on different network branches to leverage their success in capturing cross-feature interactions and learning diverse representations. Our design results in a simple and unified computation block, which can be parameterized using only a few variables. Our model, named ResNeSt, outperforms EfficientNet in accuracy and latency trade-off on image classification. In addition, ResNeSt has achieved superior transfer learning results on several public benchmarks serving as the backbone, and has been adopted by the winning entries of COCO-LVIS challenge. The source code for complete system and pretrained models are publicly available.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/26739999/142573827-a8189607-614b-4385-b579-b0db148b3db7.png" width="60%"/>
+</div>
+
+## Citation
+
+```
+@misc{zhang2020resnest,
+      title={ResNeSt: Split-Attention Networks},
+      author={Hang Zhang and Chongruo Wu and Zhongyue Zhang and Yi Zhu and Haibin Lin and Zhi Zhang and Yue Sun and Tong He and Jonas Mueller and R. Manmatha and Mu Li and Alexander Smola},
+      year={2020},
+      eprint={2004.08955},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```

openmmlab_test/mmclassification-0.24.1/configs/resnest/resnest101_32xb64_in1k.py

+_base_ = ['../_base_/models/resnest101.py', '../_base_/default_runtime.py']
+
+# dataset settings
+dataset_type = 'ImageNet'
+img_lighting_cfg = dict(
+    eigval=[55.4625, 4.7940, 1.1475],
+    eigvec=[[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140],
+            [-0.5836, -0.6948, 0.4203]],
+    alphastd=0.1,
+    to_rgb=True)
+policies = [
+    dict(type='AutoContrast', prob=0.5),
+    dict(type='Equalize', prob=0.5),
+    dict(type='Invert', prob=0.5),
+    dict(
+        type='Rotate',
+        magnitude_key='angle',
+        magnitude_range=(0, 30),
+        pad_val=0,
+        prob=0.5,
+        random_negative_prob=0.5),
+    dict(
+        type='Posterize',
+        magnitude_key='bits',
+        magnitude_range=(0, 4),
+        prob=0.5),
+    dict(
+        type='Solarize',
+        magnitude_key='thr',
+        magnitude_range=(0, 256),
+        prob=0.5),
+    dict(
+        type='SolarizeAdd',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 110),
+        thr=128,
+        prob=0.5),
+    dict(
+        type='ColorTransform',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Contrast',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Brightness',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Sharpness',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='horizontal',
+        random_negative_prob=0.5),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='vertical',
+        random_negative_prob=0.5),
+    dict(
+        type='Cutout',
+        magnitude_key='shape',
+        magnitude_range=(1, 41),
+        pad_val=0,
+        prob=0.5),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='horizontal',
+        random_negative_prob=0.5,
+        interpolation='bicubic'),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='vertical',
+        random_negative_prob=0.5,
+        interpolation='bicubic')
+]
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandAugment',
+        policies=policies,
+        num_policies=2,
+        magnitude_level=12),
+    dict(
+        type='RandomResizedCrop',
+        size=256,
+        efficientnet_style=True,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
+    dict(type='Lighting', **img_lighting_cfg),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=False),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='CenterCrop',
+        crop_size=256,
+        efficientnet_style=True,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=64,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='accuracy')
+
+# optimizer
+optimizer = dict(
+    type='SGD',
+    lr=0.8,
+    momentum=0.9,
+    weight_decay=1e-4,
+    paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.))
+optimizer_config = dict(grad_clip=None)
+
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    min_lr=0,
+    warmup='linear',
+    warmup_iters=5,
+    warmup_ratio=1e-6,
+    warmup_by_epoch=True)
+runner = dict(type='EpochBasedRunner', max_epochs=270)

openmmlab_test/mmclassification-0.24.1/configs/resnest/resnest101_b64x32_imagenet.py

+_base_ = 'resnest101_32xb64_in1k.py'
+
+_deprecation_ = dict(
+    expected='resnest101_32xb64_in1k.py',
+    reference='https://github.com/open-mmlab/mmclassification/pull/508',
+)

openmmlab_test/mmclassification-0.24.1/configs/resnest/resnest200_64xb32_in1k.py

+_base_ = ['../_base_/models/resnest200.py', '../_base_/default_runtime.py']
+
+# dataset settings
+dataset_type = 'ImageNet'
+img_lighting_cfg = dict(
+    eigval=[55.4625, 4.7940, 1.1475],
+    eigvec=[[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140],
+            [-0.5836, -0.6948, 0.4203]],
+    alphastd=0.1,
+    to_rgb=True)
+policies = [
+    dict(type='AutoContrast', prob=0.5),
+    dict(type='Equalize', prob=0.5),
+    dict(type='Invert', prob=0.5),
+    dict(
+        type='Rotate',
+        magnitude_key='angle',
+        magnitude_range=(0, 30),
+        pad_val=0,
+        prob=0.5,
+        random_negative_prob=0.5),
+    dict(
+        type='Posterize',
+        magnitude_key='bits',
+        magnitude_range=(0, 4),
+        prob=0.5),
+    dict(
+        type='Solarize',
+        magnitude_key='thr',
+        magnitude_range=(0, 256),
+        prob=0.5),
+    dict(
+        type='SolarizeAdd',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 110),
+        thr=128,
+        prob=0.5),
+    dict(
+        type='ColorTransform',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Contrast',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Brightness',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Sharpness',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='horizontal',
+        random_negative_prob=0.5),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='vertical',
+        random_negative_prob=0.5),
+    dict(
+        type='Cutout',
+        magnitude_key='shape',
+        magnitude_range=(1, 41),
+        pad_val=0,
+        prob=0.5),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='horizontal',
+        random_negative_prob=0.5,
+        interpolation='bicubic'),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='vertical',
+        random_negative_prob=0.5,
+        interpolation='bicubic')
+]
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandAugment',
+        policies=policies,
+        num_policies=2,
+        magnitude_level=12),
+    dict(
+        type='RandomResizedCrop',
+        size=320,
+        efficientnet_style=True,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
+    dict(type='Lighting', **img_lighting_cfg),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=False),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='CenterCrop',
+        crop_size=320,
+        efficientnet_style=True,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=32,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='accuracy')
+
+# optimizer
+optimizer = dict(
+    type='SGD',
+    lr=0.8,
+    momentum=0.9,
+    weight_decay=1e-4,
+    paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.))
+optimizer_config = dict(grad_clip=None)
+
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    min_lr=0,
+    warmup='linear',
+    warmup_iters=5,
+    warmup_ratio=1e-6,
+    warmup_by_epoch=True)
+runner = dict(type='EpochBasedRunner', max_epochs=270)

openmmlab_test/mmclassification-0.24.1/configs/resnest/resnest200_b32x64_imagenet.py

+_base_ = 'resnest200_64xb32_in1k.py'
+
+_deprecation_ = dict(
+    expected='resnest200_64xb32_in1k.py',
+    reference='https://github.com/open-mmlab/mmclassification/pull/508',
+)

openmmlab_test/mmclassification-0.24.1/configs/resnest/resnest269_64xb32_in1k.py

+_base_ = ['../_base_/models/resnest269.py', '../_base_/default_runtime.py']
+
+# dataset settings
+dataset_type = 'ImageNet'
+img_lighting_cfg = dict(
+    eigval=[55.4625, 4.7940, 1.1475],
+    eigvec=[[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140],
+            [-0.5836, -0.6948, 0.4203]],
+    alphastd=0.1,
+    to_rgb=True)
+policies = [
+    dict(type='AutoContrast', prob=0.5),
+    dict(type='Equalize', prob=0.5),
+    dict(type='Invert', prob=0.5),
+    dict(
+        type='Rotate',
+        magnitude_key='angle',
+        magnitude_range=(0, 30),
+        pad_val=0,
+        prob=0.5,
+        random_negative_prob=0.5),
+    dict(
+        type='Posterize',
+        magnitude_key='bits',
+        magnitude_range=(0, 4),
+        prob=0.5),
+    dict(
+        type='Solarize',
+        magnitude_key='thr',
+        magnitude_range=(0, 256),
+        prob=0.5),
+    dict(
+        type='SolarizeAdd',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 110),
+        thr=128,
+        prob=0.5),
+    dict(
+        type='ColorTransform',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Contrast',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Brightness',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Sharpness',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='horizontal',
+        random_negative_prob=0.5),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='vertical',
+        random_negative_prob=0.5),
+    dict(
+        type='Cutout',
+        magnitude_key='shape',
+        magnitude_range=(1, 41),
+        pad_val=0,
+        prob=0.5),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='horizontal',
+        random_negative_prob=0.5,
+        interpolation='bicubic'),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='vertical',
+        random_negative_prob=0.5,
+        interpolation='bicubic')
+]
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandAugment',
+        policies=policies,
+        num_policies=2,
+        magnitude_level=12),
+    dict(
+        type='RandomResizedCrop',
+        size=416,
+        efficientnet_style=True,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
+    dict(type='Lighting', **img_lighting_cfg),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=False),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='CenterCrop',
+        crop_size=416,
+        efficientnet_style=True,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=32,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='accuracy')
+
+# optimizer
+optimizer = dict(
+    type='SGD',
+    lr=0.8,
+    momentum=0.9,
+    weight_decay=1e-4,
+    paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.))
+optimizer_config = dict(grad_clip=None)
+
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    min_lr=0,
+    warmup='linear',
+    warmup_iters=5,
+    warmup_ratio=1e-6,
+    warmup_by_epoch=True)
+runner = dict(type='EpochBasedRunner', max_epochs=270)

openmmlab_test/mmclassification-0.24.1/configs/resnest/resnest269_b32x64_imagenet.py

+_base_ = 'resnest269_64xb32_in1k.py'
+
+_deprecation_ = dict(
+    expected='resnest269_64xb32_in1k.py',
+    reference='https://github.com/open-mmlab/mmclassification/pull/508',
+)

openmmlab_test/mmclassification-0.24.1/configs/resnest/resnest50_32xb64_in1k.py

+_base_ = ['../_base_/models/resnest50.py', '../_base_/default_runtime.py']
+
+# dataset settings
+dataset_type = 'ImageNet'
+img_lighting_cfg = dict(
+    eigval=[55.4625, 4.7940, 1.1475],
+    eigvec=[[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140],
+            [-0.5836, -0.6948, 0.4203]],
+    alphastd=0.1,
+    to_rgb=True)
+policies = [
+    dict(type='AutoContrast', prob=0.5),
+    dict(type='Equalize', prob=0.5),
+    dict(type='Invert', prob=0.5),
+    dict(
+        type='Rotate',
+        magnitude_key='angle',
+        magnitude_range=(0, 30),
+        pad_val=0,
+        prob=0.5,
+        random_negative_prob=0.5),
+    dict(
+        type='Posterize',
+        magnitude_key='bits',
+        magnitude_range=(0, 4),
+        prob=0.5),
+    dict(
+        type='Solarize',
+        magnitude_key='thr',
+        magnitude_range=(0, 256),
+        prob=0.5),
+    dict(
+        type='SolarizeAdd',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 110),
+        thr=128,
+        prob=0.5),
+    dict(
+        type='ColorTransform',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Contrast',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Brightness',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Sharpness',
+        magnitude_key='magnitude',
+        magnitude_range=(-0.9, 0.9),
+        prob=0.5,
+        random_negative_prob=0.),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='horizontal',
+        random_negative_prob=0.5),
+    dict(
+        type='Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='vertical',
+        random_negative_prob=0.5),
+    dict(
+        type='Cutout',
+        magnitude_key='shape',
+        magnitude_range=(1, 41),
+        pad_val=0,
+        prob=0.5),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='horizontal',
+        random_negative_prob=0.5,
+        interpolation='bicubic'),
+    dict(
+        type='Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        pad_val=0,
+        prob=0.5,
+        direction='vertical',
+        random_negative_prob=0.5,
+        interpolation='bicubic')
+]
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='RandAugment',
+        policies=policies,
+        num_policies=2,
+        magnitude_level=12),
+    dict(
+        type='RandomResizedCrop',
+        size=224,
+        efficientnet_style=True,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),
+    dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4),
+    dict(type='Lighting', **img_lighting_cfg),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=False),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='CenterCrop',
+        crop_size=224,
+        efficientnet_style=True,
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=64,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/train',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline),
+    test=dict(
+        # replace `data/val` with `data/test` for standard test
+        type=dataset_type,
+        data_prefix='data/imagenet/val',
+        ann_file='data/imagenet/meta/val.txt',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='accuracy')
+
+# optimizer
+optimizer = dict(
+    type='SGD',
+    lr=0.8,
+    momentum=0.9,
+    weight_decay=1e-4,
+    paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.))
+optimizer_config = dict(grad_clip=None)
+
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    min_lr=0,
+    warmup='linear',
+    warmup_iters=5,
+    warmup_ratio=1e-6,
+    warmup_by_epoch=True)
+runner = dict(type='EpochBasedRunner', max_epochs=270)

openmmlab_test/mmclassification-0.24.1/configs/resnest/resnest50_b64x32_imagenet.py

+_base_ = 'resnest50_32xb64_in1k.py'
+
+_deprecation_ = dict(
+    expected='resnest50_32xb64_in1k.py',
+    reference='https://github.com/open-mmlab/mmclassification/pull/508',
+)