VAN¶

class mmpretrain.models.backbones.VAN(arch='tiny', patch_sizes=[7, 3, 3, 3], in_channels=3, drop_rate=0.0, drop_path_rate=0.0, out_indices=(3,), frozen_stages=-1, norm_eval=False, norm_cfg={'type': 'LN'}, block_cfgs={}, init_cfg=None)[source]¶

Visual Attention Network.

A PyTorch implement of : Visual Attention Network

Inspiration from https://github.com/Visual-Attention-Network/VAN-Classification

Parameters:

arch (str | dict) –
Visual Attention Network architecture. If use string, choose from ‘tiny’, ‘small’, ‘base’ and ‘large’. If use dict, it should have below keys:
- embed_dims (List[int]): The dimensions of embedding.
- depths (List[int]): The number of blocks in each stage.
- ffn_ratios (List[int]): The number of expansion ratio of feedforward network hidden layer channels.
Defaults to ‘tiny’.
patch_sizes (List[int | tuple]) – The patch size in patch embeddings. Defaults to [7, 3, 3, 3].
in_channels (int) – The num of input channels. Defaults to 3.
drop_rate (float) – Dropout rate after embedding. Defaults to 0.
drop_path_rate (float) – Stochastic depth rate. Defaults to 0.1.
out_indices (Sequence[int]) – Output from which stages. Default: (3, ).
frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Defaults to -1.
norm_eval (bool) – Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Defaults to False.
norm_cfg (dict) – Config dict for normalization layer for all output features. Defaults to dict(type='LN')
block_cfgs (Sequence[dict] | dict) – The extra config of each block. Defaults to empty dicts.
init_cfg (dict, optional) – The Config for initialization. Defaults to None.

Examples

>>> from mmpretrain.models import VAN
>>> import torch
>>> cfg = dict(arch='tiny')
>>> model = VAN(**cfg)
>>> inputs = torch.rand(1, 3, 224, 224)
>>> outputs = model(inputs)
>>> for out in outputs:
>>>     print(out.size())
(1, 256, 7, 7)