查看使用预训练的efficientnetb0:
from efficientnet_pytorch import EfficientNet
model =EfficientNet.from_pretrained('efficientnet-b0',num_classes=2)
print(model)
输出结果：
Loaded pretrained weights for efficientnet-b0
EfficientNet(
(_conv_stem): Conv2dStaticSamePadding(
3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False
(static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)
)
(_bn0): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_blocks): ModuleList(
(0): MBConvBlock(
(_depthwise_conv): Conv2dStaticSamePadding(
32, 32, kernel_size=(3, 3), stride=[1, 1], groups=32, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
32, 8, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
8, 32, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(16, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(1): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
96, 96, kernel_size=(3, 3), stride=[2, 2], groups=96, bias=False
(static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)
)
(_bn1): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
96, 4, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
4, 96, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(2): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
144, 144, kernel_size=(3, 3), stride=(1, 1), groups=144, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
144, 6, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
6, 144, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(3): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
144, 144, kernel_size=(5, 5), stride=[2, 2], groups=144, bias=False
(static_padding): ZeroPad2d(padding=(1, 2, 1, 2), value=0.0)
)
(_bn1): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
144, 6, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
6, 144, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
144, 40, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(4): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
240, 240, kernel_size=(5, 5), stride=(1, 1), groups=240, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
240, 10, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
10, 240, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(5): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
240, 240, kernel_size=(3, 3), stride=[2, 2], groups=240, bias=False
(static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)
)
(_bn1): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
240, 10, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
10, 240, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(6): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
480, 480, kernel_size=(3, 3), stride=(1, 1), groups=480, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
480, 20, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
20, 480, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(7): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
480, 480, kernel_size=(3, 3), stride=(1, 1), groups=480, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
480, 20, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
20, 480, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(8): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
480, 480, kernel_size=(5, 5), stride=[1, 1], groups=480, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
480, 20, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
20, 480, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
480, 112, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(9): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
672, 672, kernel_size=(5, 5), stride=(1, 1), groups=672, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
672, 28, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
28, 672, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(10): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
672, 672, kernel_size=(5, 5), stride=(1, 1), groups=672, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
672, 28, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
28, 672, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(11): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
672, 672, kernel_size=(5, 5), stride=[2, 2], groups=672, bias=False
(static_padding): ZeroPad2d(padding=(1, 2, 1, 2), value=0.0)
)
(_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
672, 28, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
28, 672, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(12): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1152, 48, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
48, 1152, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(13): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1152, 48, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
48, 1152, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(14): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1152, 48, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
48, 1152, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(15): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1152, 1152, kernel_size=(3, 3), stride=[1, 1], groups=1152, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1152, 48, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
48, 1152, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1152, 320, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(320, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
)
(_conv_head): Conv2dStaticSamePadding(
320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn1): BatchNorm2d(1280, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_avg_pooling): AdaptiveAvgPool2d(output_size=1)
(_dropout): Dropout(p=0.2, inplace=False)
(_fc): Linear(in_features=1280, out_features=2, bias=True)
(_swish): MemoryEfficientSwish()
)
b1:
Loaded pretrained weights for efficientnet-b1
EfficientNet(
(_conv_stem): Conv2dStaticSamePadding(
3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False
(static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)
)
(_bn0): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_blocks): ModuleList(
(0): MBConvBlock(
(_depthwise_conv): Conv2dStaticSamePadding(
32, 32, kernel_size=(3, 3), stride=[1, 1], groups=32, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
32, 8, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
8, 32, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(16, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(1): MBConvBlock(
(_depthwise_conv): Conv2dStaticSamePadding(
16, 16, kernel_size=(3, 3), stride=(1, 1), groups=16, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(16, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
16, 4, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
4, 16, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
16, 16, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(16, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(2): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
96, 96, kernel_size=(3, 3), stride=[2, 2], groups=96, bias=False
(static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)
)
(_bn1): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
96, 4, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
4, 96, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(3): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
144, 144, kernel_size=(3, 3), stride=(1, 1), groups=144, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
144, 6, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
6, 144, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(4): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
144, 144, kernel_size=(3, 3), stride=(1, 1), groups=144, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
144, 6, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
6, 144, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(5): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
144, 144, kernel_size=(5, 5), stride=[2, 2], groups=144, bias=False
(static_padding): ZeroPad2d(padding=(1, 2, 1, 2), value=0.0)
)
(_bn1): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
144, 6, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
6, 144, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
144, 40, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(6): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
240, 240, kernel_size=(5, 5), stride=(1, 1), groups=240, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
240, 10, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
10, 240, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(7): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
240, 240, kernel_size=(5, 5), stride=(1, 1), groups=240, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
240, 10, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
10, 240, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(8): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
240, 240, kernel_size=(3, 3), stride=[2, 2], groups=240, bias=False
(static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)
)
(_bn1): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
240, 10, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
10, 240, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(9): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
480, 480, kernel_size=(3, 3), stride=(1, 1), groups=480, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
480, 20, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
20, 480, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(10): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
480, 480, kernel_size=(3, 3), stride=(1, 1), groups=480, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
480, 20, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
20, 480, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(11): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
480, 480, kernel_size=(3, 3), stride=(1, 1), groups=480, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
480, 20, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
20, 480, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(12): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
480, 480, kernel_size=(5, 5), stride=[1, 1], groups=480, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
480, 20, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
20, 480, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
480, 112, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(13): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
672, 672, kernel_size=(5, 5), stride=(1, 1), groups=672, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
672, 28, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
28, 672, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(14): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
672, 672, kernel_size=(5, 5), stride=(1, 1), groups=672, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
672, 28, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
28, 672, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(15): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
672, 672, kernel_size=(5, 5), stride=(1, 1), groups=672, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
672, 28, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
28, 672, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(16): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
672, 672, kernel_size=(5, 5), stride=[2, 2], groups=672, bias=False
(static_padding): ZeroPad2d(padding=(1, 2, 1, 2), value=0.0)
)
(_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
672, 28, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
28, 672, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(17): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1152, 48, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
48, 1152, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(18): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1152, 48, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
48, 1152, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(19): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1152, 48, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
48, 1152, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(20): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1152, 48, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
48, 1152, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(21): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1152, 1152, kernel_size=(3, 3), stride=[1, 1], groups=1152, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1152, 48, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
48, 1152, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1152, 320, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(320, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(22): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
320, 1920, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1920, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1920, 1920, kernel_size=(3, 3), stride=(1, 1), groups=1920, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(1920, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1920, 80, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
80, 1920, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1920, 320, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(320, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
)
(_conv_head): Conv2dStaticSamePadding(
320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn1): BatchNorm2d(1280, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_avg_pooling): AdaptiveAvgPool2d(output_size=1)
(_dropout): Dropout(p=0.2, inplace=False)
(_fc): Linear(in_features=1280, out_features=2, bias=True)
(_swish): MemoryEfficientSwish()
)
