Hi, I have been training an image model on EfficientNet and I would like to take the GradCAM visualization of it. I have been having a lot of trouble because there is a step (which works on other architectures, such as ResNet50 but doesn’t work on EfficientNet at all).
Here is the code I’ve been trying to work out:
learner2.save(f'/home/jfang/fastai/data/covid-19/saved-models/model_2', return_path = True)
m = learner2.model.eval()
idx=0
x,y = newImgDataBunch.valid_ds[idx]
xb,_ = newImgDataBunch.one_item(x)
xb_im = Image(newImgDataBunch.denorm(xb)[0])
xb = xb.cuda()
from fastai.callbacks.hooks import *
def hooked_backward(cat=y):
with hook_output(m[0]) as hook_a:
with hook_output(m[0], grad=True) as hook_g:
preds = m(xb)
preds[0,int(cat)].backward()
return hook_a,hook_g
hook_a,hook_g = hooked_backward()
The last line, hooked_backward() gives me an error.
TypeError Traceback (most recent call last)
<ipython-input-95-cf769ea46d4d> in <module>
----> 1 hook_a,hook_g = hooked_backward()
<ipython-input-94-af7778d53910> in hooked_backward(cat)
1 def hooked_backward(cat=y):
----> 2 with hook_output(m[0]) as hook_a:
3 with hook_output(m[0], grad=True) as hook_g:
4 preds = m(xb)
5 preds[0,int(cat)].backward()
TypeError: 'EfficientNet' object does not support indexing
Here is the result when I type m into jupyter notebook:
EfficientNet(
(_conv_stem): Conv2dStaticSamePadding(
3, 48, kernel_size=(3, 3), stride=(2, 2), bias=False
(static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)
)
(_bn0): BatchNorm2d(48, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_blocks): ModuleList(
(0): MBConvBlock(
(_depthwise_conv): Conv2dStaticSamePadding(
48, 48, kernel_size=(3, 3), stride=[1, 1], groups=48, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(48, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
48, 12, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
12, 48, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
48, 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)
)
(1): MBConvBlock(
(_depthwise_conv): Conv2dStaticSamePadding(
24, 24, kernel_size=(3, 3), stride=(1, 1), groups=24, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
24, 6, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
6, 24, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
24, 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)
)
(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=[2, 2], groups=144, bias=False
(static_padding): ZeroPad2d(padding=(0, 1, 0, 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, 32, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(3): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
192, 192, kernel_size=(3, 3), stride=(1, 1), groups=192, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
192, 8, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
8, 192, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(4): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
192, 192, kernel_size=(3, 3), stride=(1, 1), groups=192, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
192, 8, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
8, 192, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(5): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
192, 192, kernel_size=(3, 3), stride=(1, 1), groups=192, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
192, 8, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
8, 192, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(6): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
192, 192, kernel_size=(5, 5), stride=[2, 2], groups=192, bias=False
(static_padding): ZeroPad2d(padding=(1, 2, 1, 2), value=0.0)
)
(_bn1): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
192, 8, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
8, 192, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
192, 56, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(56, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(7): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
56, 336, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
336, 336, kernel_size=(5, 5), stride=(1, 1), groups=336, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
336, 14, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
14, 336, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
336, 56, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(56, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(8): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
56, 336, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
336, 336, kernel_size=(5, 5), stride=(1, 1), groups=336, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
336, 14, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
14, 336, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
336, 56, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(56, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(9): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
56, 336, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
336, 336, kernel_size=(5, 5), stride=(1, 1), groups=336, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
336, 14, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
14, 336, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
336, 56, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(56, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(10): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
56, 336, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
336, 336, kernel_size=(3, 3), stride=[2, 2], groups=336, bias=False
(static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)
)
(_bn1): BatchNorm2d(336, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
336, 14, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
14, 336, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
336, 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)
)
(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=(3, 3), stride=(1, 1), groups=672, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), 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)
)
(12): 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=(3, 3), stride=(1, 1), groups=672, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), 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)
)
(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=(3, 3), stride=(1, 1), groups=672, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), 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)
)
(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=(3, 3), stride=(1, 1), groups=672, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), 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)
)
(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=(3, 3), stride=(1, 1), groups=672, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), 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)
)
(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=[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, 160, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(160, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(17): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
960, 960, kernel_size=(5, 5), stride=(1, 1), groups=960, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
960, 40, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
40, 960, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(160, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(18): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
960, 960, kernel_size=(5, 5), stride=(1, 1), groups=960, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
960, 40, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
40, 960, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(160, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(19): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
960, 960, kernel_size=(5, 5), stride=(1, 1), groups=960, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
960, 40, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
40, 960, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(160, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(20): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
960, 960, kernel_size=(5, 5), stride=(1, 1), groups=960, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
960, 40, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
40, 960, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(160, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(21): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
960, 960, kernel_size=(5, 5), stride=(1, 1), groups=960, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
960, 40, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
40, 960, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(160, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(22): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
960, 960, kernel_size=(5, 5), stride=[2, 2], groups=960, bias=False
(static_padding): ZeroPad2d(padding=(1, 2, 1, 2), value=0.0)
)
(_bn1): BatchNorm2d(960, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
960, 40, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
40, 960, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
960, 272, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(23): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1632, 1632, kernel_size=(5, 5), stride=(1, 1), groups=1632, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1632, 68, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
68, 1632, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(24): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1632, 1632, kernel_size=(5, 5), stride=(1, 1), groups=1632, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1632, 68, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
68, 1632, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(25): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1632, 1632, kernel_size=(5, 5), stride=(1, 1), groups=1632, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1632, 68, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
68, 1632, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(26): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1632, 1632, kernel_size=(5, 5), stride=(1, 1), groups=1632, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1632, 68, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
68, 1632, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(27): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1632, 1632, kernel_size=(5, 5), stride=(1, 1), groups=1632, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1632, 68, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
68, 1632, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(28): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1632, 1632, kernel_size=(5, 5), stride=(1, 1), groups=1632, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1632, 68, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
68, 1632, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(29): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1632, 1632, kernel_size=(5, 5), stride=(1, 1), groups=1632, bias=False
(static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
)
(_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1632, 68, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
68, 1632, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1632, 272, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(272, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(30): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
272, 1632, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
1632, 1632, kernel_size=(3, 3), stride=[1, 1], groups=1632, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(1632, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
1632, 68, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
68, 1632, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
1632, 448, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(448, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
(31): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(
448, 2688, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn0): BatchNorm2d(2688, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
2688, 2688, kernel_size=(3, 3), stride=(1, 1), groups=2688, bias=False
(static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
)
(_bn1): BatchNorm2d(2688, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(
2688, 112, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_se_expand): Conv2dStaticSamePadding(
112, 2688, kernel_size=(1, 1), stride=(1, 1)
(static_padding): Identity()
)
(_project_conv): Conv2dStaticSamePadding(
2688, 448, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn2): BatchNorm2d(448, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
)
)
(_conv_head): Conv2dStaticSamePadding(
448, 1792, kernel_size=(1, 1), stride=(1, 1), bias=False
(static_padding): Identity()
)
(_bn1): BatchNorm2d(1792, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_fc): Linear(in_features=1792, out_features=3, bias=True)
)
Can anyone show me what I should do in order to resolve this problem?
Thank you.