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DistilledVisionTransformer

class mmpretrain.models.backbones.DistilledVisionTransformer(arch='deit-base', *args, **kwargs)[source]

Distilled Vision Transformer.

A PyTorch implement of : Training data-efficient image transformers & distillation through attention

Parameters:

  • arch (str | dict) –

    Vision Transformer architecture. If use string, choose from ‘small’, ‘base’, ‘large’, ‘deit-tiny’, ‘deit-small’ and ‘deit-base’. 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 ‘deit-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": A tuple with the class token and the distillation token. The shapes of both tensor are (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).

    Defaults to "cls_token".

  • 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.