RepMLPNet¶

class mmpretrain.models.backbones.RepMLPNet(arch, img_size=224, in_channels=3, patch_size=4, out_indices=(3,), reparam_conv_kernels=(3,), globalperceptron_ratio=4, conv_cfg=None, norm_cfg={'requires_grad': True, 'type': 'BN'}, patch_cfg={}, final_norm=True, deploy=False, init_cfg=None)[source]¶

RepMLPNet backbone.

A PyTorch impl of : RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

Parameters:

arch (str | dict) –
RepMLP architecture. If use string, choose from ‘base’ and ‘b’. If use dict, it should have below keys:
- channels (List[int]): Number of blocks in each stage.
- depths (List[int]): The number of blocks in each branch.
- sharesets_nums (List[int]): RepVGG Block that declares the need to apply group convolution.
img_size (int | tuple) – The size of input image. Defaults: 224.
in_channels (int) – Number of input image channels. Default: 3.
patch_size (int | tuple) – The patch size in patch embedding. Defaults to 4.
out_indices (Sequence[int]) – Output from which stages. Default: (3, ).
reparam_conv_kernels (Squeue(int) | None) – The conv kernels in the GlobalPerceptron. Default: None.
globalperceptron_ratio (int) – The reducation ratio in the GlobalPerceptron. Default: 4.
num_sharesets (int) – The number of sharesets in the PartitionPerceptron. Default 1.
conv_cfg (dict | None) – The config dict for conv layers. Default: None.
norm_cfg (dict) – The config dict for norm layers. Default: dict(type=’BN’, requires_grad=True).
patch_cfg (dict) – Extra config dict for patch embedding. Defaults to an empty dict.
final_norm (bool) – Whether to add a additional layer to normalize final feature map. Defaults to True.
act_cfg (dict) – Config dict for activation layer. Default: dict(type=’ReLU’).
deploy (bool) – Whether to switch the model structure to deployment mode. Default: False.
init_cfg (dict or list[dict], optional) – Initialization config dict.