basecls.models.regnet#
RegNet Series
RegNet X/Y: “Designing Network Design Spaces”
引用
facebookresearch/pycls facebookresearch/pycls
- class basecls.models.regnet.RegBottleneckBlock(w_in, w_out, stride, bot_mul, group_w, se_r, norm_name, act_name, **kwargs)[源代码]#
基类:
Module
Residual bottleneck block for RegNet: x + f(x), f = 1x1, 3x3 [+SE], 1x1.
- class basecls.models.regnet.RegNet(stem_name, stem_w, block_name, depth, w0, wa, wm, group_w, stride=2, bot_mul=1.0, se_r=0.0, drop_path_prob=0.0, zero_init_final_gamma=False, norm_name='BN', act_name='relu', head=None)[源代码]#
基类:
ResNet
RegNet model.
- 参数
stem_w (
int
) – stem width.depth (
int
) – depth.w0 (
int
) – initial width.wa (
float
) – slope.wm (
float
) – quantization.group_w (
int
) – group width for each stage (applies to bottleneck block).stride (
int
) – stride for each stage (applies to the first block of each stage). Default:2
bot_mul (
float
) – bottleneck multiplier for each stage (applies to bottleneck block). Default:1.0
se_r (
float
) – Squeeze-and-Excitation (SE) ratio. Default:0.0
drop_path_prob (
float
) – drop path probability. Default:0.0
zero_init_final_gamma (
bool
) – enable zero-initialize or not. Default:False
norm_name (
str
) – normalization function. Default:"BN"
act_name (
str
) – activation function. Default:"relu"
head (
Optional
[Mapping
[str
,Any
]]) – head args. Default:None