basecls.models.resnet#
ResNet Series
ResNet: “Deep Residual Learning for Image Recognition”
ResNet-D: “Bag of Tricks for Image Classification with Convolutional Neural Networks”
ResNeXt: “Aggregated Residual Transformations for Deep Neural Networks”
Se-ResNet: “Squeeze-and-Excitation Networks”
Wide ResNet: “Wide Residual Networks”
引用
facebookresearch/pycls facebookresearch/pycls
- class basecls.models.resnet.ResBasicBlock(w_in, w_out, stride, bot_mul, se_r, avg_down, drop_path_prob, norm_name, act_name, **kwargs)[源代码]#
基类:
Module
Residual basic block: x + f(x), f = [3x3 conv, BN, Act] x2.
- class basecls.models.resnet.ResBottleneckBlock(w_in, w_out, stride, bot_mul, group_w, se_r, avg_down, drop_path_prob, norm_name, act_name, **kwargs)[源代码]#
基类:
Module
Residual bottleneck block: x + f(x), f = 1x1, 3x3, 1x1 [+SE].
- class basecls.models.resnet.ResDeepStem(w_in, w_out, norm_name, act_name, **kwargs)[源代码]#
基类:
Module
ResNet-D stem: [3x3, BN, Act] x3, MaxPool.
- class basecls.models.resnet.ResStem(w_in, w_out, norm_name, act_name, **kwargs)[源代码]#
基类:
Module
ResNet stem: 7x7, BN, Act, MaxPool.
- class basecls.models.resnet.SimpleStem(w_in, w_out, norm_name, act_name, **kwargs)[源代码]#
基类:
Module
Simple stem: 3x3, BN, Act.
- class basecls.models.resnet.AnyStage(w_in, w_out, stride, depth, block_func, drop_path_prob, **kwargs)[源代码]#
基类:
Module
AnyNet stage (sequence of blocks w/ the same output shape).
- class basecls.models.resnet.ResNet(stem_name, stem_w, block_name, depths, widths, strides, bot_muls=1.0, group_ws=None, se_r=0.0, avg_down=False, drop_path_prob=0.0, zero_init_final_gamma=False, norm_name='BN', act_name='relu', head=None)[源代码]#
基类:
Module
ResNet model.
- 参数
stem_w (
int
) – stem width.depths (
Sequence
[int
]) – depth for each stage (number of blocks in the stage).widths (
Sequence
[int
]) – width for each stage (width of each block in the stage).strides (
Sequence
[int
]) – strides for each stage (applies to the first block of each stage).bot_muls (
Union
[float
,Sequence
[float
]]) – bottleneck multipliers for each stage (applies to bottleneck block). Default:1.0
group_ws (
Optional
[Sequence
[int
]]) – group widths for each stage (applies to bottleneck block). Default:None
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