DenseNet¶
- class mmpretrain.models.backbones.DenseNet(arch='121', in_channels=3, bn_size=4, drop_rate=0, compression_factor=0.5, memory_efficient=False, norm_cfg={'type': 'BN'}, act_cfg={'type': 'ReLU'}, out_indices=-1, frozen_stages=0, init_cfg=None)[source]¶
DenseNet.
A PyTorch implementation of : Densely Connected Convolutional Networks
Modified from the official repo and pytorch.
- Parameters:
The model’s architecture. If string, it should be one of architecture in
DenseNet.arch_settings
. And if dict, it should include the following two keys:growth_rate (int): Each layer of DenseBlock produce k feature maps. Here refers k as the growth rate of the network.
depths (list[int]): Number of repeated layers in each DenseBlock.
init_channels (int): The output channels of stem layers.
Defaults to ‘121’.
in_channels (int) – Number of input image channels. Defaults to 3.
bn_size (int) – Refers to channel expansion parameter of 1x1 convolution layer. Defaults to 4.
drop_rate (float) – Drop rate of Dropout Layer. Defaults to 0.
compression_factor (float) – The reduction rate of transition layers. Defaults to 0.5.
memory_efficient (bool) – If True, uses checkpointing. Much more memory efficient, but slower. Defaults to False. See “paper”.
norm_cfg (dict) – The config dict for norm layers. Defaults to
dict(type='BN')
.act_cfg (dict) – The config dict for activation after each convolution. Defaults to
dict(type='ReLU')
.out_indices (Sequence | int) – Output from which stages. Defaults to -1, means the last stage.
frozen_stages (int) – Stages to be frozen (all param fixed). Defaults to 0, which means not freezing any parameters.
init_cfg (dict, optional) – Initialization config dict.