class mmpretrain.models.selfsup.BEiTPretrainViT(arch='base', img_size=224, patch_size=16, in_channels=3, out_indices=-1, drop_rate=0, drop_path_rate=0, norm_cfg={'eps': 1e-06, 'type': 'LN'}, final_norm=True, out_type='raw', frozen_stages=-1, use_abs_pos_emb=False, use_rel_pos_bias=False, use_shared_rel_pos_bias=True, layer_scale_init_value=0.1, interpolate_mode='bicubic', patch_cfg={'padding': 0}, layer_cfgs={}, init_cfg=None)[源代码]

Vision Transformer for BEiT pre-training.

  • arch (str | dict) –

    Vision Transformer architecture. If use string, choose from ‘small’, ‘base’ and ‘large’. If use dict, it should have below keys:

    • embed_dims (int): The dimensions of embedding.

    • num_layers (int): The number of transformer encoder layers.

    • num_heads (int): The number of heads in attention modules.

    • feedforward_channels (int): The hidden dimensions in feedforward modules.

    Defaults to ‘base’.

  • img_size (int | tuple) – The expected input image shape. Because we support dynamic input shape, just set the argument to the most common input image shape. Defaults to 224.

  • patch_size (int | tuple) – The patch size in patch embedding. Defaults to 16.

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

  • out_indices (Sequence | int) – Output from which stages. Defaults to -1, means the last stage.

  • drop_rate (float) – Probability of an element to be zeroed. Defaults to 0.

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

  • qkv_bias (bool) – Whether to add bias for qkv in attention modules. Defaults to True.

  • norm_cfg (dict) – Config dict for normalization layer. Defaults to dict(type='LN').

  • final_norm (bool) – Whether to add a additional layer to normalize final feature map. Defaults to True.

  • out_type (str) –

    The type of output features. Please choose from

    • "cls_token": The class token tensor with shape (B, C).

    • "featmap": The feature map tensor from the patch tokens with shape (B, C, H, W).

    • "avg_featmap": The global averaged feature map tensor with shape (B, C).

    • "raw": The raw feature tensor includes patch tokens and class tokens with shape (B, L, C).

    It only works without input mask. Defaults to "avg_featmap".

  • with_cls_token (bool) – Whether concatenating class token into image tokens as transformer input. Defaults to True.

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

  • use_abs_pos_emb (bool) – Whether or not use absolute position embedding. Defaults to False.

  • use_rel_pos_bias (bool) – Whether or not use relative position bias. Defaults to False.

  • use_shared_rel_pos_bias (bool) – Whether or not use shared relative position bias. Defaults to True.

  • layer_scale_init_value (float) – The initialization value for the learnable scaling of attention and FFN. Defaults to 0.1.

  • interpolate_mode (str) – Select the interpolate mode for position embeding vector resize. Defaults to “bicubic”.

  • patch_cfg (dict) – Configs of patch embeding. Defaults to an empty dict.

  • layer_cfgs (Sequence | dict) – Configs of each transformer layer in encoder. Defaults to an empty dict.

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

forward(x, mask)[源代码]

The BEiT style forward function.

The function supports two kind of forward behaviors. If the mask is not None, the forward function will be executed as masked image modeling pre-training; if the mask is None, the forward function will call super().forward(), which extract features from images without mask.

  • x (torch.Tensor) – Input images, which is of shape (B x C x H x W).

  • mask (torch.Tensor, optional) – Mask for input, which is of shape (B x patch_resolution[0] x patch_resolution[1]).


Hidden features.




Initialize position embedding, patch embedding and cls token.


Rescale the initialized weights.

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