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- class mmcls.models.DistilledVisionTransformer(arch='deit-base', *args, **kwargs)¶
Distilled Vision Transformer.
A PyTorch implement of : Training data-efficient image transformers & distillation through attention
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’.
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
final_norm (bool) – Whether to add a additional layer to normalize final feature map. Defaults to True.
with_cls_token (bool) – Whether concatenating class token into image tokens as transformer input. Defaults to True.
output_cls_token (bool) – Whether output the cls_token. If set True,
with_cls_tokenmust be True. Defaults to True.
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.
x (tensor | tuple[tensor]) – x could be a Torch.tensor or a tuple of Torch.tensor, containing input data for forward computation.
Initialize the weights.