这是使用vitamin作为backbone的纯分割项目，提供了简单易懂的分割框架（非建造者模式架构，适合个人和小白实验）
提取了vitamin中的patch_embed输出，vitaminblocks的第1,3,6,9,12,14层的输出。 分别作为en1,en2,...,en6 这些进行通道拼接，然后经过解码器输出分割结果 解码器如下所示(采用了：组反卷积，通道重组，批标准化，Relu激活，进行组合得到)
(up): Sequential(
(0): ConvTranspose2d(2304, 1152, kernel_size=(2, 2), stride=(2, 2), groups=6)
(1): ChannelShuffle()
(2): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU()
(4): ConvTranspose2d(1152, 576, kernel_size=(2, 2), stride=(2, 2), groups=6)
(5): ChannelShuffle()
(6): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=6)
(7): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU()
(9): ConvTranspose2d(576, 288, kernel_size=(2, 2), stride=(2, 2), groups=6)
(10): ChannelShuffle()
(11): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(12): ReLU()
(13): ConvTranspose2d(288, 144, kernel_size=(2, 2), stride=(2, 2), groups=6)
(14): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(15): ReLU()
(16): Conv2d(144, 52, kernel_size=(1, 1), stride=(1, 1))
)采用SegVitamin(size=(512, 512), fact=(2, 2), in_chans=1, out_channel=13)测试模型的计算量和参数量为('15.68 GMac', '24.76 M')
下面是模型的详细参数及计算量分布情况
SegVitamin(
24.65 M, 99.553% Params, 15.64 GMac, 99.771% MACs,
(vitamin): VisionTransformer(
21.79 M, 87.995% Params, 5.79 GMac, 36.917% MACs,
(patch_embed): HybridEmbed(
1.08 M, 4.346% Params, 1.89 GMac, 12.073% MACs,
(backbone): MbConvStages(
1.08 M, 4.346% Params, 1.89 GMac, 12.073% MACs,
(stem): Stem(
39.3 k, 0.159% Params, 643.83 MMac, 4.107% MACs,
(conv1): Conv2d(2.37 k, 0.010% Params, 38.8 MMac, 0.247% MACs, 4, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(norm1): LayerNormAct2d(
0, 0.000% Params, 0.0 Mac, 0.000% MACs, (64,), eps=1e-06, elementwise_affine=True
(drop): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(act): GELU(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(conv2): Conv2d(36.93 k, 0.149% Params, 605.03 MMac, 3.859% MACs, 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
(stages): ModuleList(
(0): Sequential(
71.3 k, 0.288% Params, 497.55 MMac, 3.174% MACs,
(0): MbConvLNBlock(
35.65 k, 0.144% Params, 351.54 MMac, 2.242% MACs,
(shortcut): Downsample2d(
0, 0.000% Params, 1.05 MMac, 0.007% MACs,
(pool): AvgPool2d(0, 0.000% Params, 1.05 MMac, 0.007% MACs, kernel_size=3, stride=2, padding=1)
(expand): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(pre_norm): LayerNormAct2d(
0, 0.000% Params, 0.0 Mac, 0.000% MACs, (64,), eps=1e-06, elementwise_affine=True
(drop): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(act): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(down): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(conv1_1x1): Conv2d(16.64 k, 0.067% Params, 272.63 MMac, 1.739% MACs, 64, 256, kernel_size=(1, 1), stride=(1, 1))
(act1): GELU(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(act2): GELU(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(conv2_kxk): Conv2d(2.56 k, 0.010% Params, 10.49 MMac, 0.067% MACs, 256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=256)
(conv3_1x1): Conv2d(16.45 k, 0.066% Params, 67.37 MMac, 0.430% MACs, 256, 64, kernel_size=(1, 1), stride=(1, 1))
(drop_path): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(1): MbConvLNBlock(
35.65 k, 0.144% Params, 146.01 MMac, 0.931% MACs,
(shortcut): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(pre_norm): LayerNormAct2d(
0, 0.000% Params, 0.0 Mac, 0.000% MACs, (64,), eps=1e-06, elementwise_affine=True
(drop): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(act): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(down): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(conv1_1x1): Conv2d(16.64 k, 0.067% Params, 68.16 MMac, 0.435% MACs, 64, 256, kernel_size=(1, 1), stride=(1, 1))
(act1): GELU(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(act2): GELU(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(conv2_kxk): Conv2d(2.56 k, 0.010% Params, 10.49 MMac, 0.067% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=256)
(conv3_1x1): Conv2d(16.45 k, 0.066% Params, 67.37 MMac, 0.430% MACs, 256, 64, kernel_size=(1, 1), stride=(1, 1))
(drop_path): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
)
(1): Sequential(
522.88 k, 2.111% Params, 637.93 MMac, 4.069% MACs,
(0): MbConvLNBlock(
112.38 k, 0.454% Params, 217.58 MMac, 1.388% MACs,
(shortcut): Downsample2d(
8.32 k, 0.034% Params, 8.78 MMac, 0.056% MACs,
(pool): AvgPool2d(0, 0.000% Params, 262.14 KMac, 0.002% MACs, kernel_size=3, stride=2, padding=1)
(expand): Conv2d(8.32 k, 0.034% Params, 8.52 MMac, 0.054% MACs, 64, 128, kernel_size=(1, 1), stride=(1, 1))
)
(pre_norm): LayerNormAct2d(
0, 0.000% Params, 0.0 Mac, 0.000% MACs, (64,), eps=1e-06, elementwise_affine=True
(drop): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(act): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(down): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(conv1_1x1): Conv2d(33.28 k, 0.134% Params, 136.31 MMac, 0.870% MACs, 64, 512, kernel_size=(1, 1), stride=(1, 1))
(act1): GELU(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(act2): GELU(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(conv2_kxk): Conv2d(5.12 k, 0.021% Params, 5.24 MMac, 0.033% MACs, 512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=512)
(conv3_1x1): Conv2d(65.66 k, 0.265% Params, 67.24 MMac, 0.429% MACs, 512, 128, kernel_size=(1, 1), stride=(1, 1))
(drop_path): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(1): MbConvLNBlock(
136.83 k, 0.553% Params, 140.12 MMac, 0.894% MACs,
(shortcut): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(pre_norm): LayerNormAct2d(
0, 0.000% Params, 0.0 Mac, 0.000% MACs, (128,), eps=1e-06, elementwise_affine=True
(drop): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(act): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(down): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(conv1_1x1): Conv2d(66.05 k, 0.267% Params, 67.63 MMac, 0.431% MACs, 128, 512, kernel_size=(1, 1), stride=(1, 1))
(act1): GELU(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(act2): GELU(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(conv2_kxk): Conv2d(5.12 k, 0.021% Params, 5.24 MMac, 0.033% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512)
(conv3_1x1): Conv2d(65.66 k, 0.265% Params, 67.24 MMac, 0.429% MACs, 512, 128, kernel_size=(1, 1), stride=(1, 1))
(drop_path): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(2): MbConvLNBlock(
136.83 k, 0.553% Params, 140.12 MMac, 0.894% MACs,
(shortcut): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(pre_norm): LayerNormAct2d(
0, 0.000% Params, 0.0 Mac, 0.000% MACs, (128,), eps=1e-06, elementwise_affine=True
(drop): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(act): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(down): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(conv1_1x1): Conv2d(66.05 k, 0.267% Params, 67.63 MMac, 0.431% MACs, 128, 512, kernel_size=(1, 1), stride=(1, 1))
(act1): GELU(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(act2): GELU(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(conv2_kxk): Conv2d(5.12 k, 0.021% Params, 5.24 MMac, 0.033% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512)
(conv3_1x1): Conv2d(65.66 k, 0.265% Params, 67.24 MMac, 0.429% MACs, 512, 128, kernel_size=(1, 1), stride=(1, 1))
(drop_path): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(3): MbConvLNBlock(
136.83 k, 0.553% Params, 140.12 MMac, 0.894% MACs,
(shortcut): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(pre_norm): LayerNormAct2d(
0, 0.000% Params, 0.0 Mac, 0.000% MACs, (128,), eps=1e-06, elementwise_affine=True
(drop): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(act): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(down): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(conv1_1x1): Conv2d(66.05 k, 0.267% Params, 67.63 MMac, 0.431% MACs, 128, 512, kernel_size=(1, 1), stride=(1, 1))
(act1): GELU(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(act2): GELU(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(conv2_kxk): Conv2d(5.12 k, 0.021% Params, 5.24 MMac, 0.033% MACs, 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=512)
(conv3_1x1): Conv2d(65.66 k, 0.265% Params, 67.24 MMac, 0.429% MACs, 512, 128, kernel_size=(1, 1), stride=(1, 1))
(drop_path): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
)
)
(pool): StridedConv(
442.75 k, 1.788% Params, 113.34 MMac, 0.723% MACs,
(proj): Conv2d(442.75 k, 1.788% Params, 113.34 MMac, 0.723% MACs, 128, 384, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(norm): LayerNorm2d(0, 0.000% Params, 0.0 Mac, 0.000% MACs, (128,), eps=1e-06, elementwise_affine=True)
)
)
(proj): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(pos_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(patch_drop): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(norm_pre): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(blocks): Sequential(
20.71 M, 83.644% Params, 3.89 GMac, 24.844% MACs,
(0): Block(
1.48 M, 5.975% Params, 278.2 MMac, 1.775% MACs,
(norm1): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
591.36 k, 2.388% Params, 50.82 MMac, 0.324% MACs,
(qkv): Linear(443.52 k, 1.791% Params, 113.54 MMac, 0.724% MACs, in_features=384, out_features=1152, bias=True)
(q_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(k_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(attn_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(proj): Linear(147.84 k, 0.597% Params, 37.85 MMac, 0.241% MACs, in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
)
(ls1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(norm2): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(mlp): GeGluMlp(
886.66 k, 3.580% Params, 227.18 MMac, 1.449% MACs,
(norm): LayerNorm(0, 0.000% Params, 0.0 Mac, 0.000% MACs, (384,), eps=1e-06, elementwise_affine=True)
(act): GELU(0, 0.000% Params, 196.61 KMac, 0.001% MACs, approximate='none')
(w0): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w1): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w2): Linear(295.3 k, 1.192% Params, 75.6 MMac, 0.482% MACs, in_features=768, out_features=384, bias=True)
)
(ls2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(1): Block(
1.48 M, 5.975% Params, 278.2 MMac, 1.775% MACs,
(norm1): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
591.36 k, 2.388% Params, 50.82 MMac, 0.324% MACs,
(qkv): Linear(443.52 k, 1.791% Params, 113.54 MMac, 0.724% MACs, in_features=384, out_features=1152, bias=True)
(q_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(k_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(attn_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(proj): Linear(147.84 k, 0.597% Params, 37.85 MMac, 0.241% MACs, in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
)
(ls1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(norm2): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(mlp): GeGluMlp(
886.66 k, 3.580% Params, 227.18 MMac, 1.449% MACs,
(norm): LayerNorm(0, 0.000% Params, 0.0 Mac, 0.000% MACs, (384,), eps=1e-06, elementwise_affine=True)
(act): GELU(0, 0.000% Params, 196.61 KMac, 0.001% MACs, approximate='none')
(w0): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w1): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w2): Linear(295.3 k, 1.192% Params, 75.6 MMac, 0.482% MACs, in_features=768, out_features=384, bias=True)
)
(ls2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(2): Block(
1.48 M, 5.975% Params, 278.2 MMac, 1.775% MACs,
(norm1): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
591.36 k, 2.388% Params, 50.82 MMac, 0.324% MACs,
(qkv): Linear(443.52 k, 1.791% Params, 113.54 MMac, 0.724% MACs, in_features=384, out_features=1152, bias=True)
(q_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(k_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(attn_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(proj): Linear(147.84 k, 0.597% Params, 37.85 MMac, 0.241% MACs, in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
)
(ls1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(norm2): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(mlp): GeGluMlp(
886.66 k, 3.580% Params, 227.18 MMac, 1.449% MACs,
(norm): LayerNorm(0, 0.000% Params, 0.0 Mac, 0.000% MACs, (384,), eps=1e-06, elementwise_affine=True)
(act): GELU(0, 0.000% Params, 196.61 KMac, 0.001% MACs, approximate='none')
(w0): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w1): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w2): Linear(295.3 k, 1.192% Params, 75.6 MMac, 0.482% MACs, in_features=768, out_features=384, bias=True)
)
(ls2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(3): Block(
1.48 M, 5.975% Params, 278.2 MMac, 1.775% MACs,
(norm1): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
591.36 k, 2.388% Params, 50.82 MMac, 0.324% MACs,
(qkv): Linear(443.52 k, 1.791% Params, 113.54 MMac, 0.724% MACs, in_features=384, out_features=1152, bias=True)
(q_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(k_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(attn_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(proj): Linear(147.84 k, 0.597% Params, 37.85 MMac, 0.241% MACs, in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
)
(ls1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(norm2): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(mlp): GeGluMlp(
886.66 k, 3.580% Params, 227.18 MMac, 1.449% MACs,
(norm): LayerNorm(0, 0.000% Params, 0.0 Mac, 0.000% MACs, (384,), eps=1e-06, elementwise_affine=True)
(act): GELU(0, 0.000% Params, 196.61 KMac, 0.001% MACs, approximate='none')
(w0): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w1): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w2): Linear(295.3 k, 1.192% Params, 75.6 MMac, 0.482% MACs, in_features=768, out_features=384, bias=True)
)
(ls2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(4): Block(
1.48 M, 5.975% Params, 278.2 MMac, 1.775% MACs,
(norm1): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
591.36 k, 2.388% Params, 50.82 MMac, 0.324% MACs,
(qkv): Linear(443.52 k, 1.791% Params, 113.54 MMac, 0.724% MACs, in_features=384, out_features=1152, bias=True)
(q_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(k_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(attn_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(proj): Linear(147.84 k, 0.597% Params, 37.85 MMac, 0.241% MACs, in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
)
(ls1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(norm2): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(mlp): GeGluMlp(
886.66 k, 3.580% Params, 227.18 MMac, 1.449% MACs,
(norm): LayerNorm(0, 0.000% Params, 0.0 Mac, 0.000% MACs, (384,), eps=1e-06, elementwise_affine=True)
(act): GELU(0, 0.000% Params, 196.61 KMac, 0.001% MACs, approximate='none')
(w0): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w1): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w2): Linear(295.3 k, 1.192% Params, 75.6 MMac, 0.482% MACs, in_features=768, out_features=384, bias=True)
)
(ls2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(5): Block(
1.48 M, 5.975% Params, 278.2 MMac, 1.775% MACs,
(norm1): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
591.36 k, 2.388% Params, 50.82 MMac, 0.324% MACs,
(qkv): Linear(443.52 k, 1.791% Params, 113.54 MMac, 0.724% MACs, in_features=384, out_features=1152, bias=True)
(q_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(k_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(attn_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(proj): Linear(147.84 k, 0.597% Params, 37.85 MMac, 0.241% MACs, in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
)
(ls1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(norm2): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(mlp): GeGluMlp(
886.66 k, 3.580% Params, 227.18 MMac, 1.449% MACs,
(norm): LayerNorm(0, 0.000% Params, 0.0 Mac, 0.000% MACs, (384,), eps=1e-06, elementwise_affine=True)
(act): GELU(0, 0.000% Params, 196.61 KMac, 0.001% MACs, approximate='none')
(w0): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w1): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w2): Linear(295.3 k, 1.192% Params, 75.6 MMac, 0.482% MACs, in_features=768, out_features=384, bias=True)
)
(ls2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(6): Block(
1.48 M, 5.975% Params, 278.2 MMac, 1.775% MACs,
(norm1): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
591.36 k, 2.388% Params, 50.82 MMac, 0.324% MACs,
(qkv): Linear(443.52 k, 1.791% Params, 113.54 MMac, 0.724% MACs, in_features=384, out_features=1152, bias=True)
(q_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(k_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(attn_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(proj): Linear(147.84 k, 0.597% Params, 37.85 MMac, 0.241% MACs, in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
)
(ls1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(norm2): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(mlp): GeGluMlp(
886.66 k, 3.580% Params, 227.18 MMac, 1.449% MACs,
(norm): LayerNorm(0, 0.000% Params, 0.0 Mac, 0.000% MACs, (384,), eps=1e-06, elementwise_affine=True)
(act): GELU(0, 0.000% Params, 196.61 KMac, 0.001% MACs, approximate='none')
(w0): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w1): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w2): Linear(295.3 k, 1.192% Params, 75.6 MMac, 0.482% MACs, in_features=768, out_features=384, bias=True)
)
(ls2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(7): Block(
1.48 M, 5.975% Params, 278.2 MMac, 1.775% MACs,
(norm1): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
591.36 k, 2.388% Params, 50.82 MMac, 0.324% MACs,
(qkv): Linear(443.52 k, 1.791% Params, 113.54 MMac, 0.724% MACs, in_features=384, out_features=1152, bias=True)
(q_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(k_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(attn_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(proj): Linear(147.84 k, 0.597% Params, 37.85 MMac, 0.241% MACs, in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
)
(ls1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(norm2): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(mlp): GeGluMlp(
886.66 k, 3.580% Params, 227.18 MMac, 1.449% MACs,
(norm): LayerNorm(0, 0.000% Params, 0.0 Mac, 0.000% MACs, (384,), eps=1e-06, elementwise_affine=True)
(act): GELU(0, 0.000% Params, 196.61 KMac, 0.001% MACs, approximate='none')
(w0): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w1): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w2): Linear(295.3 k, 1.192% Params, 75.6 MMac, 0.482% MACs, in_features=768, out_features=384, bias=True)
)
(ls2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(8): Block(
1.48 M, 5.975% Params, 278.2 MMac, 1.775% MACs,
(norm1): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
591.36 k, 2.388% Params, 50.82 MMac, 0.324% MACs,
(qkv): Linear(443.52 k, 1.791% Params, 113.54 MMac, 0.724% MACs, in_features=384, out_features=1152, bias=True)
(q_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(k_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(attn_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(proj): Linear(147.84 k, 0.597% Params, 37.85 MMac, 0.241% MACs, in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
)
(ls1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(norm2): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(mlp): GeGluMlp(
886.66 k, 3.580% Params, 227.18 MMac, 1.449% MACs,
(norm): LayerNorm(0, 0.000% Params, 0.0 Mac, 0.000% MACs, (384,), eps=1e-06, elementwise_affine=True)
(act): GELU(0, 0.000% Params, 196.61 KMac, 0.001% MACs, approximate='none')
(w0): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w1): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w2): Linear(295.3 k, 1.192% Params, 75.6 MMac, 0.482% MACs, in_features=768, out_features=384, bias=True)
)
(ls2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(9): Block(
1.48 M, 5.975% Params, 278.2 MMac, 1.775% MACs,
(norm1): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
591.36 k, 2.388% Params, 50.82 MMac, 0.324% MACs,
(qkv): Linear(443.52 k, 1.791% Params, 113.54 MMac, 0.724% MACs, in_features=384, out_features=1152, bias=True)
(q_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(k_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(attn_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(proj): Linear(147.84 k, 0.597% Params, 37.85 MMac, 0.241% MACs, in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
)
(ls1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(norm2): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(mlp): GeGluMlp(
886.66 k, 3.580% Params, 227.18 MMac, 1.449% MACs,
(norm): LayerNorm(0, 0.000% Params, 0.0 Mac, 0.000% MACs, (384,), eps=1e-06, elementwise_affine=True)
(act): GELU(0, 0.000% Params, 196.61 KMac, 0.001% MACs, approximate='none')
(w0): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w1): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w2): Linear(295.3 k, 1.192% Params, 75.6 MMac, 0.482% MACs, in_features=768, out_features=384, bias=True)
)
(ls2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(10): Block(
1.48 M, 5.975% Params, 278.2 MMac, 1.775% MACs,
(norm1): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
591.36 k, 2.388% Params, 50.82 MMac, 0.324% MACs,
(qkv): Linear(443.52 k, 1.791% Params, 113.54 MMac, 0.724% MACs, in_features=384, out_features=1152, bias=True)
(q_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(k_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(attn_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(proj): Linear(147.84 k, 0.597% Params, 37.85 MMac, 0.241% MACs, in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
)
(ls1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(norm2): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(mlp): GeGluMlp(
886.66 k, 3.580% Params, 227.18 MMac, 1.449% MACs,
(norm): LayerNorm(0, 0.000% Params, 0.0 Mac, 0.000% MACs, (384,), eps=1e-06, elementwise_affine=True)
(act): GELU(0, 0.000% Params, 196.61 KMac, 0.001% MACs, approximate='none')
(w0): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w1): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w2): Linear(295.3 k, 1.192% Params, 75.6 MMac, 0.482% MACs, in_features=768, out_features=384, bias=True)
)
(ls2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(11): Block(
1.48 M, 5.975% Params, 278.2 MMac, 1.775% MACs,
(norm1): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
591.36 k, 2.388% Params, 50.82 MMac, 0.324% MACs,
(qkv): Linear(443.52 k, 1.791% Params, 113.54 MMac, 0.724% MACs, in_features=384, out_features=1152, bias=True)
(q_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(k_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(attn_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(proj): Linear(147.84 k, 0.597% Params, 37.85 MMac, 0.241% MACs, in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
)
(ls1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(norm2): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(mlp): GeGluMlp(
886.66 k, 3.580% Params, 227.18 MMac, 1.449% MACs,
(norm): LayerNorm(0, 0.000% Params, 0.0 Mac, 0.000% MACs, (384,), eps=1e-06, elementwise_affine=True)
(act): GELU(0, 0.000% Params, 196.61 KMac, 0.001% MACs, approximate='none')
(w0): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w1): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w2): Linear(295.3 k, 1.192% Params, 75.6 MMac, 0.482% MACs, in_features=768, out_features=384, bias=True)
)
(ls2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(12): Block(
1.48 M, 5.975% Params, 278.2 MMac, 1.775% MACs,
(norm1): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
591.36 k, 2.388% Params, 50.82 MMac, 0.324% MACs,
(qkv): Linear(443.52 k, 1.791% Params, 113.54 MMac, 0.724% MACs, in_features=384, out_features=1152, bias=True)
(q_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(k_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(attn_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(proj): Linear(147.84 k, 0.597% Params, 37.85 MMac, 0.241% MACs, in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
)
(ls1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(norm2): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(mlp): GeGluMlp(
886.66 k, 3.580% Params, 227.18 MMac, 1.449% MACs,
(norm): LayerNorm(0, 0.000% Params, 0.0 Mac, 0.000% MACs, (384,), eps=1e-06, elementwise_affine=True)
(act): GELU(0, 0.000% Params, 196.61 KMac, 0.001% MACs, approximate='none')
(w0): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w1): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w2): Linear(295.3 k, 1.192% Params, 75.6 MMac, 0.482% MACs, in_features=768, out_features=384, bias=True)
)
(ls2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
(13): Block(
1.48 M, 5.975% Params, 278.2 MMac, 1.775% MACs,
(norm1): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
591.36 k, 2.388% Params, 50.82 MMac, 0.324% MACs,
(qkv): Linear(443.52 k, 1.791% Params, 113.54 MMac, 0.724% MACs, in_features=384, out_features=1152, bias=True)
(q_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(k_norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(attn_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(proj): Linear(147.84 k, 0.597% Params, 37.85 MMac, 0.241% MACs, in_features=384, out_features=384, bias=True)
(proj_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
)
(ls1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path1): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(norm2): LayerNorm(768, 0.003% Params, 98.3 KMac, 0.001% MACs, (384,), eps=1e-06, elementwise_affine=True)
(mlp): GeGluMlp(
886.66 k, 3.580% Params, 227.18 MMac, 1.449% MACs,
(norm): LayerNorm(0, 0.000% Params, 0.0 Mac, 0.000% MACs, (384,), eps=1e-06, elementwise_affine=True)
(act): GELU(0, 0.000% Params, 196.61 KMac, 0.001% MACs, approximate='none')
(w0): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w1): Linear(295.68 k, 1.194% Params, 75.69 MMac, 0.483% MACs, in_features=384, out_features=768, bias=True)
(w2): Linear(295.3 k, 1.192% Params, 75.6 MMac, 0.482% MACs, in_features=768, out_features=384, bias=True)
)
(ls2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(drop_path2): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
)
)
(norm): Identity(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(fc_norm): LayerNorm(768, 0.003% Params, 384.0 Mac, 0.000% MACs, (384,), eps=1e-06, elementwise_affine=True)
(head_drop): Dropout(0, 0.000% Params, 0.0 Mac, 0.000% MACs, p=0.0, inplace=False)
(head): Linear(385, 0.002% Params, 385.0 Mac, 0.000% MACs, in_features=384, out_features=1, bias=True)
)
(up): Sequential(
2.86 M, 11.558% Params, 9.85 GMac, 62.854% MACs,
(0): ConvTranspose2d(1.77 M, 7.150% Params, 1.81 GMac, 11.566% MACs, 2304, 1152, kernel_size=(2, 2), stride=(2, 2), groups=6)
(1): ChannelShuffle(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(2): BatchNorm2d(2.3 k, 0.009% Params, 2.36 MMac, 0.015% MACs, 1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): ReLU(0, 0.000% Params, 1.18 MMac, 0.008% MACs, )
(4): ConvTranspose2d(442.94 k, 1.789% Params, 1.81 GMac, 11.573% MACs, 1152, 576, kernel_size=(2, 2), stride=(2, 2), groups=6)
(5): ChannelShuffle(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(6): Conv2d(498.24 k, 2.012% Params, 2.04 GMac, 13.018% MACs, 576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=6)
(7): BatchNorm2d(1.15 k, 0.005% Params, 4.72 MMac, 0.030% MACs, 576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU(0, 0.000% Params, 2.36 MMac, 0.015% MACs, )
(9): ConvTranspose2d(110.88 k, 0.448% Params, 1.82 GMac, 11.588% MACs, 576, 288, kernel_size=(2, 2), stride=(2, 2), groups=6)
(10): ChannelShuffle(0, 0.000% Params, 0.0 Mac, 0.000% MACs, )
(11): BatchNorm2d(576, 0.002% Params, 9.44 MMac, 0.060% MACs, 288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(12): ReLU(0, 0.000% Params, 4.72 MMac, 0.030% MACs, )
(13): ConvTranspose2d(27.79 k, 0.112% Params, 1.82 GMac, 11.618% MACs, 288, 144, kernel_size=(2, 2), stride=(2, 2), groups=6)
(14): BatchNorm2d(288, 0.001% Params, 18.87 MMac, 0.120% MACs, 144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(15): ReLU(0, 0.000% Params, 9.44 MMac, 0.060% MACs, )
(16): Conv2d(7.54 k, 0.030% Params, 494.14 MMac, 3.152% MACs, 144, 52, kernel_size=(1, 1), stride=(1, 1))
)
)