Hi experts
please help over the below error that i get
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py in forward(self, input)
90     def forward(self, input):
91         for module in self._modules.values():
—> 92             input = module(input)
93         return input
94
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
491             result = self._slow_forward(*input, **kwargs)
492         else:
–> 493             result = self.forward(*input, **kwargs)
494         for hook in self._forward_hooks.values():
495             hook_result = hook(self, input, result)
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py in forward(self, input)
90     def forward(self, input):
91         for module in self._modules.values():
—> 92             input = module(input)
Model details: it is sequence of two modules


Efficient Net
Sequential(
(0): Sequential(
(0): Conv2dStaticSamePadding(
3, 48, kernel_size=(3, 3), stride=(2, 2), bias=False
(static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)
)
(1): BatchNorm2d(48, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(2): 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()
) … and so on…


Customheadeff(
(head): Sequential(
(0): AdaptiveConcatPool2d(
(ap): AdaptiveAvgPool2d(output_size=1)
(mp): AdaptiveMaxPool2d(output_size=1)
)
(1): Flatten()
(2): BatchNorm1d(4096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): Dropout(p=0.25)
(4): Linear(in_features=4096, out_features=2048, bias=True)
(5): LeakyReLU(negative_slope=0.01, inplace)
(6): BatchNorm1d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): Dropout(p=0.5)
(8): Linear(in_features=2048, out_features=1024, bias=True)
)
(arc_margin): ArcMarginProduct()
)