JPONetモデルの内容確認
構成
構成部分だけにして見やすく
(backbone): EfficientNet(・・・)
(pooling): AdaptiveAvgPool2d(output_size=1)
(dropout): Dropout(p=0.0, inplace=False)
(fc): Linear(in_features=1280, out_features=224, bias=True)
(bn): BatchNorm1d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(final): ArcMarginProduct()
- EfficientNetが大部分
- (pooling)以降は下記「全文表示」の282行目にある
- 後ろにくっつけている
- くっつけるときに接着するようにちょっと加工
全文表示
（287行）
JPONet(
(backbone): EfficientNet(
(conv_stem): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(blocks): Sequential(
(0): Sequential(
(0): DepthwiseSeparableConv(
(conv_dw): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(8, 32, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pw): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): Identity()
)
)
(1): Sequential(
(0): InvertedResidual(
(conv_pw): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(conv_dw): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)
(bn2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(96, 4, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(4, 96, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): InvertedResidual(
(conv_pw): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(conv_dw): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
(bn2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(2): Sequential(
(0): InvertedResidual(
(conv_pw): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(conv_dw): Conv2d(144, 144, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=144, bias=False)
(bn2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(144, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): InvertedResidual(
(conv_pw): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(conv_dw): Conv2d(240, 240, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=240, bias=False)
(bn2): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(3): Sequential(
(0): InvertedResidual(
(conv_pw): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(conv_dw): Conv2d(240, 240, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=240, bias=False)
(bn2): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): InvertedResidual(
(conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(conv_dw): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)
(bn2): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): InvertedResidual(
(conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(conv_dw): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)
(bn2): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(4): Sequential(
(0): InvertedResidual(
(conv_pw): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(conv_dw): Conv2d(480, 480, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=480, bias=False)
(bn2): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(480, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): InvertedResidual(
(conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)
(bn2): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): InvertedResidual(
(conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)
(bn2): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(5): Sequential(
(0): InvertedResidual(
(conv_pw): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(conv_dw): Conv2d(672, 672, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=672, bias=False)
(bn2): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): InvertedResidual(
(conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)
(bn2): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(2): InvertedResidual(
(conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)
(bn2): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(3): InvertedResidual(
(conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(conv_dw): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)
(bn2): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(6): Sequential(
(0): InvertedResidual(
(conv_pw): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act1): SiLU(inplace=True)
(conv_dw): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1152, bias=False)
(bn2): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(se): SqueezeExcite(
(conv_reduce): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))
(act1): SiLU(inplace=True)
(conv_expand): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))
(gate): Sigmoid()
)
(conv_pwl): Conv2d(1152, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
)
(conv_head): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn2): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(act2): SiLU(inplace=True)
(global_pool): Identity()
(classifier): Identity()
)
(pooling): AdaptiveAvgPool2d(output_size=1)
(dropout): Dropout(p=0.0, inplace=False)
(fc): Linear(in_features=1280, out_features=224, bias=True)
(bn): BatchNorm1d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(final): ArcMarginProduct()
)