[Feature] Add Autoformer algorithm

configs/_base_/settings/imagenet_bs2048_AdamW.py

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+# dataset settings
+dataset_type = 'mmcls.ImageNet'
+preprocess_cfg = dict(
+    # RGB format normalization parameters
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    # convert image from BGR to RGB
+    to_rgb=True,
+)
+
+bgr_mean = preprocess_cfg['mean'][::-1]
+bgr_std = preprocess_cfg['std'][::-1]
+
+# Refers to `_RAND_INCREASING_TRANSFORMS` in pytorch-image-models
+rand_increasing_policies = [
+    dict(type='mmcls.AutoContrast'),
+    dict(type='mmcls.Equalize'),
+    dict(type='mmcls.Invert'),
+    dict(type='mmcls.Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
+    dict(type='mmcls.Posterize', magnitude_key='bits', magnitude_range=(4, 0)),
+    dict(type='mmcls.Solarize', magnitude_key='thr', magnitude_range=(256, 0)),
+    dict(
+        type='mmcls.SolarizeAdd',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 110)),
+    dict(
+        type='mmcls.ColorTransform',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.9)),
+    dict(
+        type='mmcls.Contrast',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.9)),
+    dict(
+        type='mmcls.Brightness',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.9)),
+    dict(
+        type='mmcls.Sharpness',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.9)),
+    dict(
+        type='mmcls.Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        direction='horizontal'),
+    dict(
+        type='mmcls.Shear',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.3),
+        direction='vertical'),
+    dict(
+        type='mmcls.Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.45),
+        direction='horizontal'),
+    dict(
+        type='mmcls.Translate',
+        magnitude_key='magnitude',
+        magnitude_range=(0, 0.45),
+        direction='vertical')
+]
+
+train_pipeline = [
+    dict(type='mmcls.LoadImageFromFile'),
+    dict(
+        type='mmcls.RandomResizedCrop',
+        scale=224,
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='mmcls.RandomFlip', prob=0.5, direction='horizontal'),
+    dict(
+        type='mmcls.RandAugment',
+        policies=rand_increasing_policies,
+        num_policies=2,
+        total_level=10,
+        magnitude_level=9,
+        magnitude_std=0.5,
+        hparams=dict(
+            pad_val=[round(x) for x in bgr_mean], interpolation='bicubic')),
+    dict(
+        type='mmcls.RandomErasing',
+        erase_prob=0.25,
+        mode='rand',
+        min_area_ratio=0.02,
+        max_area_ratio=1 / 3,
+        fill_color=bgr_mean,
+        fill_std=bgr_std),
+    dict(type='mmcls.PackClsInputs'),
+]
+
+test_pipeline = [
+    dict(type='mmcls.LoadImageFromFile'),
+    dict(
+        type='mmcls.ResizeEdge',
+        scale=248,
+        edge='short',
+        backend='pillow',
+        interpolation='bicubic'),
+    dict(type='mmcls.CenterCrop', crop_size=224),
+    dict(type='mmcls.PackClsInputs')
+]
+
+train_dataloader = dict(
+    batch_size=64,
+    num_workers=6,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/imagenet',
+        ann_file='meta/train.txt',
+        data_prefix='train',
+        pipeline=train_pipeline),
+    sampler=dict(type='mmcls.RepeatAugSampler'),
+    persistent_workers=True,
+)
+
+val_dataloader = dict(
+    batch_size=256,
+    num_workers=6,
+    dataset=dict(
+        type=dataset_type,
+        data_root='data/imagenet',
+        ann_file='meta/val.txt',
+        data_prefix='val',
+        pipeline=test_pipeline),
+    sampler=dict(type='mmcls.DefaultSampler', shuffle=False),
+    persistent_workers=True,
+)
+val_evaluator = dict(type='mmcls.Accuracy', topk=(1, 5))
+
+# If you want standard test, please manually configure the test dataset
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator
+
+# optimizer
+paramwise_cfg = dict(
+    bias_decay_mult=0.0, norm_decay_mult=0.0, dwconv_decay_mult=0.0)
+
+optim_wrapper = dict(
+    optimizer=dict(
+        type='AdamW',
+        lr=0.002,
+        weight_decay=0.05,
+        eps=1e-8,
+        betas=(0.9, 0.999)),
+    # specific to vit pretrain
+    paramwise_cfg=dict(custom_keys={
+        '.cls_token': dict(decay_mult=0.0),
+        '.pos_embed': dict(decay_mult=0.0)
+    }))
+
+# leanring policy
+param_scheduler = [
+    # warm up learning rate scheduler
+    dict(
+        type='LinearLR',
+        start_factor=1e-3,
+        by_epoch=True,
+        begin=0,
+        # about 10000 iterations for ImageNet-1k
+        end=20,
+        # update by iter
+        convert_to_iter_based=True),
+    # main learning rate scheduler
+    dict(
+        type='CosineAnnealingLR',
+        T_max=500,
+        eta_min=1e-5,
+        by_epoch=True,
+        begin=20,
+        end=500,
+        convert_to_iter_based=True),
+]
+
+# train, val, test setting
+train_cfg = dict(by_epoch=True, max_epochs=500)
+val_cfg = dict()
+test_cfg = dict()
+
+auto_scale_lr = dict(base_batch_size=2048)

configs/nas/mmcls/autoformer/README.md

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+# AutoFormer
+
+> [Searching Transformers for Visual Recognition](https://arxiv.org/abs/2107.00651)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Recently, pure transformer-based models have shown
+great potentials for vision tasks such as image classification and detection. However, the design of transformer networks is challenging. It has been observed that the depth,
+embedding dimension, and number of heads can largely affect the performance of vision transformers. Previous models configure these dimensions based upon manual crafting. In this work, we propose a new one-shot architecture
+search framework, namely AutoFormer, dedicated to vision
+transformer search. AutoFormer entangles the weights of
+different blocks in the same layers during supernet training. Benefiting from the strategy, the trained supernet allows thousands of subnets to be very well-trained. Specifically, the performance of these subnets with weights inherited from the supernet is comparable to those retrained
+from scratch. Besides, the searched models, which we refer to AutoFormers, surpass the recent state-of-the-arts such
+as ViT and DeiT. In particular, AutoFormer-tiny/small/base
+achieve 74.7%/81.7%/82.4% top-1 accuracy on ImageNet
+with 5.7M/22.9M/53.7M parameters, respectively. Lastly,
+we verify the transferability of AutoFormer by providing
+the performance on downstream benchmarks and distillation experiments.
+
+![pipeline](/docs/en/imgs/model_zoo/autoformer/pipeline.png)
+
+## Introduction
+
+### Supernet pre-training on ImageNet
+
+```bash
+python ./tools/train.py \
+  configs/nas/mmcls/autoformer/autoformer_supernet_32xb256_in1k.py \
+  --work-dir $WORK_DIR
+```
+
+### Search for subnet on the trained supernet
+
+```bash
+sh tools/train.py \
+  configs/nas/mmcls/autoformer/autoformer_search_8xb128_in1k.py \
+  $STEP1_CKPT \
+  --work-dir $WORK_DIR
+```
+
+## Results and models
+
+| Dataset  | Supernet |                                                                                                                                               Subnet                                                                                                                                                | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) |                     Config                      | Download                                                                                                                                                                    |     Remarks      |
+| :------: | :------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------: | :------: | :-------: | :-------: | :---------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :--------------: |
+| ImageNet |   vit    | [mutable](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/nas/spos/spos_shufflenetv2_subnet_8xb128_in1k/spos_shufflenetv2_subnet_8xb128_in1k_flops_0.33M_acc_73.87_20211222-454627be_mutable_cfg.yaml?versionId=CAEQHxiBgICw5b6I7xciIGY5MjVmNWFhY2U5MjQzN2M4NDViYzI2YWRmYWE1YzQx) |  52.472   |   10.2   |   82.48   |   95.99   | [config](./autoformer_supernet_32xb256_in1k.py) | [model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/x.pth) \| [log](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/nas/spos/x.log.json) | MMRazor searched |
+
+**Note**:
+
+1. There are some small differences in our experiment in order to be consistent with mmrazor repo. For example, we set the max value of embed_channels 624 while the original repo set it 640. However, the original repo only search 528, 576, 624 embed_channels, so set 624 can also get the same result with orifinal paper.
+2. The original paper get 82.4 top-1 acc with 53.7M Params while we get 82.48 top-1 acc with 52.47M Params.
+
+## Citation
+
+```latex
+@article{xu2021autoformer,
+  title={Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting},
+  author={Xu, Jiehui and Wang, Jianmin and Long, Mingsheng and others},
+  journal={Advances in Neural Information Processing Systems},
+  volume={34},
+  year={2021}
+}
+```
+
+Footer

configs/nas/mmcls/autoformer/autoformer_search_8xb128_in1k.py

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+_base_ = ['./autoformer_supernet_32xb256_in1k.py']
+
+custom_hooks = None
+
+train_cfg = dict(
+    _delete_=True,
+    type='mmrazor.EvolutionSearchLoop',
+    dataloader=_base_.val_dataloader,
+    evaluator=_base_.val_evaluator,
+    max_epochs=20,
+    num_candidates=20,
+    top_k=10,
+    num_mutation=5,
+    num_crossover=5,
+    mutate_prob=0.2,
+    constraints_range=dict(params=(0, 55)),
+    score_key='accuracy/top1')

configs/nas/mmcls/autoformer/autoformer_supernet_32xb256_in1k.py

@@ -0,0 +1,79 @@
+_base_ = [
+    'mmrazor::_base_/settings/imagenet_bs2048_AdamW.py',
+    'mmcls::_base_/default_runtime.py',
+]
+
+# data preprocessor
+data_preprocessor = dict(
+    _scope_='mmcls',
+    type='ClsDataPreprocessor',
+    # RGB format normalization parameters
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    # convert image from BGR to RGB
+    to_rgb=True,
+    num_classes=1000,
+    batch_augments=dict(
+        augments=[
+            dict(type='Mixup', alpha=0.2),
+            dict(type='CutMix', alpha=1.0)
+        ],
+        probs=[0.5, 0.5]))
+
+arch_setting = dict(
+    mlp_ratios=[3.0, 3.5, 4.0],
+    num_heads=[8, 9, 10],
+    depth=[14, 15, 16],
+    embed_dims=[528, 576, 624])
+
+supernet = dict(
+    _scope_='mmrazor',
+    type='SearchableImageClassifier',
+    data_preprocessor=data_preprocessor,
+    backbone=dict(
+        _scope_='mmrazor',
+        type='AutoformerBackbone',
+        arch_setting=arch_setting),
+    neck=None,
+    head=dict(
+        type='DynamicLinearClsHead',
+        num_classes=1000,
+        in_channels=624,
+        loss=dict(
+            type='mmcls.LabelSmoothLoss',
+            mode='original',
+            num_classes=1000,
+            label_smooth_val=0.1,
+            loss_weight=1.0),
+        topk=(1, 5)),
+    connect_head=dict(connect_with_backbone='backbone.last_mutable'),
+)
+
+model = dict(
+    type='mmrazor.Autoformer',
+    architecture=supernet,
+    fix_subnet=None,
+    mutators=dict(
+        channel_mutator=dict(
+            type='mmrazor.OneShotChannelMutator',
+            channel_unit_cfg={
+                'type': 'OneShotMutableChannelUnit',
+                'default_args': {
+                    'unit_predefined': True
+                }
+            },
+            parse_cfg={'type': 'Predefined'}),
+        value_mutator=dict(type='mmrazor.DynamicValueMutator')))
+
+# runtime setting
+custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')]
+
+# checkpoint saving
+_base_.default_hooks.checkpoint = dict(
+    type='CheckpointHook',
+    interval=2,
+    by_epoch=True,
+    save_best='accuracy/top1',
+    max_keep_ckpts=3)
+
+find_unused_parameters = True

configs/nas/mmcls/spos/spos_mobilenet_search_8xb128_in1k.py

@@ -13,5 +13,5 @@
     num_mutation=25,
     num_crossover=25,
     mutate_prob=0.1,
-    flops_range=(0., 465.),
+    constraints_range=dict(flops=(0., 465.)),
     score_key='accuracy/top1')

configs/nas/mmcls/spos/spos_shufflenet_search_8xb128_in1k.py

@@ -13,5 +13,5 @@
     num_mutation=25,
     num_crossover=25,
     mutate_prob=0.1,
-    flops_range=(0., 330.),
+    constraints_range=dict(flops=(0, 330)),
     score_key='accuracy/top1')

configs/nas/mmdet/detnas/detnas_frcnn_shufflenet_search_coco_1x.py

@@ -13,5 +13,5 @@
     num_mutation=20,
     num_crossover=20,
     mutate_prob=0.1,
-    flops_range=(0., 300.),
+    constraints_range=dict(flops=(0, 330)),
     score_key='coco/bbox_mAP')