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You are reading the documentation for MMClassification 0.x, which will soon be deprecated at the end of 2022. We recommend you upgrade to MMClassification 1.0 to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check the installation tutorial, migration tutorial and changelog for more details.

TNT

class mmcls.models.TNT(arch='b', img_size=224, patch_size=16, in_channels=3, ffn_ratio=4, qkv_bias=False, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0, act_cfg={'type': 'GELU'}, norm_cfg={'type': 'LN'}, first_stride=4, num_fcs=2, init_cfg=[{'type': 'TruncNormal', 'layer': 'Linear', 'std': 0.02}, {'type': 'Constant', 'layer': 'LayerNorm', 'val': 1.0, 'bias': 0.0}])[source]

Transformer in Transformer.

A PyTorch implement of: Transformer in Transformer

Inspiration from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/tnt.py

Parameters
  • arch (str | dict) – Vision Transformer architecture Default: ‘b’

  • img_size (int | tuple) – Input image size. Default to 224

  • patch_size (int | tuple) – The patch size. Deault to 16

  • in_channels (int) – Number of input channels. Default to 3

  • ffn_ratio (int) – A ratio to calculate the hidden_dims in ffn layer. Default: 4

  • qkv_bias (bool) – Enable bias for qkv if True. Default False

  • drop_rate (float) – Probability of an element to be zeroed after the feed forward layer. Default 0.

  • attn_drop_rate (float) – The drop out rate for attention layer. Default 0.

  • drop_path_rate (float) – stochastic depth rate. Default 0.

  • act_cfg (dict) – The activation config for FFNs. Defaults to GELU.

  • norm_cfg (dict) – Config dict for normalization layer. Default layer normalization

  • first_stride (int) – The stride of the conv2d layer. We use a conv2d layer and a unfold layer to implement image to pixel embedding.

  • num_fcs (int) – The number of fully-connected layers for FFNs. Default 2

  • init_cfg (dict, optional) – Initialization config dict

forward(x)[source]

Forward computation.

Parameters

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

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