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class mmcls.models.EfficientFormer(arch='l1', in_channels=3, pool_size=3, mlp_ratios=4, reshape_last_feat=False, out_indices=- 1, frozen_stages=- 1, act_cfg={'type': 'GELU'}, drop_rate=0.0, drop_path_rate=0.0, use_layer_scale=True, init_cfg=None)[source]


A PyTorch implementation of EfficientFormer introduced by: EfficientFormer: Vision Transformers at MobileNet Speed

Modified from the official repo <>.

  • arch (str | dict) –

    The model’s architecture. If string, it should be one of architecture in EfficientFormer.arch_settings. And if dict,

    it should include the following 4 keys:

    • layers (list[int]): Number of blocks at each stage.

    • embed_dims (list[int]): The number of channels at each stage.

    • downsamples (list[int]): Has downsample or not in the four stages.

    • vit_num (int): The num of vit blocks in the last stage.

    Defaults to ‘l1’.

  • in_channels (int) – The num of input channels. Defaults to 3.

  • pool_size (int) – The pooling size of Meta4D blocks. Defaults to 3.

  • mlp_ratios (int) – The dimension ratio of multi-head attention mechanism in Meta4D blocks. Defaults to 3.

  • reshape_last_feat (bool) –

    Whether to reshape the feature map from (B, N, C) to (B, C, H, W) in the last stage, when the vit-num

    in arch is not 0. Defaults to False. Usually set to True in downstream tasks.

  • out_indices (Sequence[int]) – Output from which stages. Defaults to -1.

  • frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Defaults to -1.

  • act_cfg (dict) – The config dict for activation between pointwise convolution. Defaults to dict(type='GELU').

  • drop_rate (float) – Dropout rate. Defaults to 0.

  • drop_path_rate (float) – Stochastic depth rate. Defaults to 0.

  • use_layer_scale (bool) – Whether to use use_layer_scale in MetaFormer block. Defaults to True.

  • init_cfg (dict, optional) – Initialization config dict. Defaults to None.

  • Example

    >>> from mmcls.models import EfficientFormer
    >>> import torch
    >>> inputs = torch.rand((1, 3, 224, 224))
    >>> # build EfficientFormer backbone for classification task
    >>> model = EfficientFormer(arch="l1")
    >>> model.eval()
    >>> level_outputs = model(inputs)
    >>> for level_out in level_outputs:
    ...     print(tuple(level_out.shape))
    (1, 448, 49)
    >>> # build EfficientFormer backbone for downstream task
    >>> model = EfficientFormer(
    >>>    arch="l3",
    >>>    out_indices=(0, 1, 2, 3),
    >>>    reshape_last_feat=True)
    >>> model.eval()
    >>> level_outputs = model(inputs)
    >>> for level_out in level_outputs:
    ...     print(tuple(level_out.shape))
    (1, 64, 56, 56)
    (1, 128, 28, 28)
    (1, 320, 14, 14)
    (1, 512, 7, 7)


Forward computation.


x (tensor | tuple[tensor]) – x could be a Torch.tensor or a tuple of Torch.tensor, containing input data for forward computation.


Set module status before forward computation.


mode (bool) – Whether it is train_mode or test_mode

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