add part code

configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py

+_base_ = [
+    '../_base_/models/resnet50.py',
+    '../_base_/datasets/imagenet_bs256_rsb_a12.py',
+    '../_base_/schedules/imagenet_bs2048_rsb.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        drop_path_rate=0.05,
+    ),
+    head=dict(
+        loss=dict(
+            type='LabelSmoothLoss',
+            label_smooth_val=0.1,
+            mode='original',
+            use_sigmoid=True,
+        )),
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.2),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)
+
+# dataset settings
+train_dataloader = dict(sampler=dict(type='RepeatAugSampler', shuffle=True))
+
+# schedule settings
+optim_wrapper = dict(
+    optimizer=dict(weight_decay=0.01),
+    paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.),
+)
+
+param_scheduler = [
+    # warm up learning rate scheduler
+    dict(
+        type='LinearLR',
+        start_factor=0.0001,
+        by_epoch=True,
+        begin=0,
+        end=5,
+        # update by iter
+        convert_to_iter_based=True),
+    # main learning rate scheduler
+    dict(
+        type='CosineAnnealingLR',
+        T_max=595,
+        eta_min=1.0e-6,
+        by_epoch=True,
+        begin=5,
+        end=600)
+]
+
+train_cfg = dict(by_epoch=True, max_epochs=600)

configs/resnet/resnet50_8xb256-rsb-a2-300e_in1k.py

+_base_ = [
+    '../_base_/models/resnet50.py',
+    '../_base_/datasets/imagenet_bs256_rsb_a12.py',
+    '../_base_/schedules/imagenet_bs2048_rsb.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        drop_path_rate=0.05,
+    ),
+    head=dict(loss=dict(use_sigmoid=True)),
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.1),
+        dict(type='CutMix', alpha=1.0)
+    ]))
+
+# dataset settings
+train_dataloader = dict(sampler=dict(type='RepeatAugSampler', shuffle=True))
+
+# schedule settings
+optim_wrapper = dict(
+    paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.))
+
+param_scheduler = [
+    # warm up learning rate scheduler
+    dict(
+        type='LinearLR',
+        start_factor=0.0001,
+        by_epoch=True,
+        begin=0,
+        end=5,
+        # update by iter
+        convert_to_iter_based=True),
+    # main learning rate scheduler
+    dict(
+        type='CosineAnnealingLR',
+        T_max=295,
+        eta_min=1.0e-6,
+        by_epoch=True,
+        begin=5,
+        end=300)
+]
+train_cfg = dict(by_epoch=True, max_epochs=300)

configs/resnet/resnet50_8xb256-rsb-a3-100e_in1k.py

+_base_ = [
+    '../_base_/models/resnet50.py',
+    '../_base_/datasets/imagenet_bs256_rsb_a3.py',
+    '../_base_/schedules/imagenet_bs2048_rsb.py',
+    '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    backbone=dict(norm_cfg=dict(type='SyncBN', requires_grad=True)),
+    head=dict(loss=dict(use_sigmoid=True)),
+    train_cfg=dict(augments=[
+        dict(type='Mixup', alpha=0.1),
+        dict(type='CutMix', alpha=1.0)
+    ]),
+)
+
+# schedule settings
+optim_wrapper = dict(
+    optimizer=dict(lr=0.008),
+    paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.),
+)

configs/resnet/resnet50_8xb32-coslr-preciseBN_in1k.py

+_base_ = 'resnet50_8xb32-coslr_in1k.py'
+
+# Precise BN hook will update the bn stats, so this hook should be executed
+# before CheckpointHook(priority of 'VERY_LOW') and
+# EMAHook(priority of 'NORMAL') So set the priority of PreciseBNHook to
+# 'ABOVENORMAL' here.
+custom_hooks = [
+    dict(
+        type='PreciseBNHook',
+        num_samples=8192,
+        interval=1,
+        priority='ABOVE_NORMAL')
+]

configs/resnet/resnet50_8xb32-coslr_in1k.py

+_base_ = [
+    '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256_coslr.py',
+    '../_base_/default_runtime.py'
+]

configs/resnet/resnet50_8xb32-cutmix_in1k.py

+_base_ = [
+    '../_base_/models/resnet50_cutmix.py',
+    '../_base_/datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/resnet/resnet50_8xb32-fp16-dynamic_in1k.py

+_base_ = ['./resnet50_8xb32_in1k.py']
+
+# schedule settings
+optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')

configs/resnet/resnet50_8xb32-fp16_in1k.py

+_base_ = ['./resnet50_8xb32_in1k.py']
+
+# schedule settings
+optim_wrapper = dict(type='AmpOptimWrapper', loss_scale=512.)

configs/resnet/resnet50_8xb32-lbs_in1k.py

+_base_ = [
+    '../_base_/models/resnet50_label_smooth.py',
+    '../_base_/datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/resnet/resnet50_8xb32-mixup_in1k.py

+_base_ = [
+    '../_base_/models/resnet50_mixup.py',
+    '../_base_/datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/resnet/resnet50_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs32.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/resnet/resnet50_8xb8_cub.py

+_base_ = [
+    '../_base_/models/resnet50.py',
+    '../_base_/datasets/cub_bs8_448.py',
+    '../_base_/schedules/cub_bs64.py',
+    '../_base_/default_runtime.py',
+]
+
+# model settings
+# use pre-train weight converted from https://github.com/Alibaba-MIIL/ImageNet21K # noqa
+pretrained = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth'  # noqa
+
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        init_cfg=dict(
+            type='Pretrained', checkpoint=pretrained, prefix='backbone')),
+    head=dict(num_classes=200, ))
+
+# runtime settings
+default_hooks = dict(logger=dict(type='LoggerHook', interval=20))

configs/resnet/resnetv1c101_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/resnetv1c50.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
+
+model = dict(backbone=dict(depth=101))

configs/resnet/resnetv1c152_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/resnetv1c50.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
+
+model = dict(backbone=dict(depth=152))

configs/resnet/resnetv1c50_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/resnetv1c50.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/resnet/resnetv1d101_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/resnetv1d101.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/resnet/resnetv1d152_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/resnetv1d152.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/resnet/resnetv1d50_8xb32_in1k.py

+_base_ = [
+    '../_base_/models/resnetv1d50.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]

configs/resnext/README.md

+# ResNeXt
+
+> [Aggregated Residual Transformations for Deep Neural Networks](https://openaccess.thecvf.com/content_cvpr_2017/html/Xie_Aggregated_Residual_Transformations_CVPR_2017_paper.html)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call "cardinality" (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/26739999/142574479-21fb00a2-e63e-4bc6-a9f2-989cd6e15528.png" width="70%"/>
+</div>
+
+## How to use it?
+
+<!-- [TABS-BEGIN] -->
+
+**Predict image**
+
+```python
+from mmpretrain import inference_model
+
+predict = inference_model('resnext50-32x4d_8xb32_in1k', 'demo/bird.JPEG')
+print(predict['pred_class'])
+print(predict['pred_score'])
+```
+
+**Use the model**
+
+```python
+import torch
+from mmpretrain import get_model
+
+model = get_model('resnext50-32x4d_8xb32_in1k', pretrained=True)
+inputs = torch.rand(1, 3, 224, 224)
+out = model(inputs)
+print(type(out))
+# To extract features.
+feats = model.extract_feat(inputs)
+print(type(feats))
+```
+
+**Train/Test Command**
+
+Prepare your dataset according to the [docs](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html#prepare-dataset).
+
+Train:
+
+```shell
+python tools/train.py configs/resnext/resnext50-32x4d_8xb32_in1k.py
+```
+
+Test:
+
+```shell
+python tools/test.py configs/resnext/resnext50-32x4d_8xb32_in1k.py https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.pth
+```
+
+<!-- [TABS-END] -->
+
+## Models and results
+
+### Image Classification on ImageNet-1k
+
+| Model                         |   Pretrain   | Params (M) | Flops (G) | Top-1 (%) | Top-5 (%) |                  Config                  |                                      Download                                      |
+| :---------------------------- | :----------: | :--------: | :-------: | :-------: | :-------: | :--------------------------------------: | :--------------------------------------------------------------------------------: |
+| `resnext50-32x4d_8xb32_in1k`  | From scratch |   25.03    |   4.27    |   77.90   |   93.66   | [config](resnext50-32x4d_8xb32_in1k.py)  | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.json) |
+| `resnext101-32x4d_8xb32_in1k` | From scratch |   44.18    |   8.03    |   78.61   |   94.17   | [config](resnext101-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.json) |
+| `resnext101-32x8d_8xb32_in1k` | From scratch |   88.79    |   16.50   |   79.27   |   94.58   | [config](resnext101-32x8d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.json) |
+| `resnext152-32x4d_8xb32_in1k` | From scratch |   59.95    |   11.80   |   78.88   |   94.33   | [config](resnext152-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.json) |
+
+## Citation
+
+```bibtex
+@inproceedings{xie2017aggregated,
+  title={Aggregated residual transformations for deep neural networks},
+  author={Xie, Saining and Girshick, Ross and Doll{\'a}r, Piotr and Tu, Zhuowen and He, Kaiming},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={1492--1500},
+  year={2017}
+}
+```

configs/resnext/metafile.yml

+Collections:
+  - Name: ResNeXt
+    Metadata:
+      Training Data: ImageNet-1k
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Epochs: 100
+      Batch Size: 256
+      Architecture:
+        - ResNeXt
+    Paper:
+      URL: https://openaccess.thecvf.com/content_cvpr_2017/html/Xie_Aggregated_Residual_Transformations_CVPR_2017_paper.html
+      Title: "Aggregated Residual Transformations for Deep Neural Networks"
+    README: configs/resnext/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmpretrain/blob/v0.15.0/mmcls/models/backbones/resnext.py#L90
+      Version: v0.15.0
+
+Models:
+  - Name: resnext50-32x4d_8xb32_in1k
+    Metadata:
+      FLOPs: 4270000000
+      Parameters: 25030000
+    In Collection: ResNeXt
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 77.90
+          Top 5 Accuracy: 93.66
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.pth
+    Config: configs/resnext/resnext50-32x4d_8xb32_in1k.py
+  - Name: resnext101-32x4d_8xb32_in1k
+    Metadata:
+      FLOPs: 8030000000
+      Parameters: 44180000
+    In Collection: ResNeXt
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.61
+          Top 5 Accuracy: 94.17
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth
+    Config: configs/resnext/resnext101-32x4d_8xb32_in1k.py
+  - Name: resnext101-32x8d_8xb32_in1k
+    Metadata:
+      FLOPs: 16500000000
+      Parameters: 88790000
+    In Collection: ResNeXt
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 79.27
+          Top 5 Accuracy: 94.58
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.pth
+    Config: configs/resnext/resnext101-32x8d_8xb32_in1k.py
+  - Name: resnext152-32x4d_8xb32_in1k
+    Metadata:
+      FLOPs: 11800000000
+      Parameters: 59950000
+    In Collection: ResNeXt
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 78.88
+          Top 5 Accuracy: 94.33
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.pth
+    Config: configs/resnext/resnext152-32x4d_8xb32_in1k.py