Could not find any implementation for node X.weight

Hello, I used MobilenetXT model for 8-bit quantization, but I reported this error when onnx was converted to TENSORRT. What is the reason? There was no error when using MobilenetV2

I used tensorrt 8.6.1.6

MobilenetXT The structure is as follows：

(start_block): Sequential(
(0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
)
(SandGlass1): Sequential(
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU6(inplace=True)
(8): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)
(9): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass2): Sequential(
(0): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=96, bias=False)
(1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(96, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): Conv2d(32, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU6(inplace=True)
(8): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
(9): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass3): Sequential(
(0): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(3): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU6(inplace=True)
(5): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)
(6): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass4): Sequential(
(0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU6(inplace=True)
(8): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
(9): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass5): Sequential(
(0): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU6(inplace=True)
(8): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
(9): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass6): Sequential(
(0): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): Conv2d(48, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
(3): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU6(inplace=True)
(5): Conv2d(288, 288, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=288, bias=False)
(6): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass7): Sequential(
(0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
(1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(288, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): Conv2d(48, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU6(inplace=True)
(8): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
(9): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass8): Sequential(
(0): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
(1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(288, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): Conv2d(48, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU6(inplace=True)
(8): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
(9): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass9): Sequential(
(0): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(3): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU6(inplace=True)
)
(SandGlass10): Sequential(
(0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU6(inplace=True)
(8): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
(9): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass11): Sequential(
(0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU6(inplace=True)
(8): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
(9): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass12): Sequential(
(0): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU6(inplace=True)
(8): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
(9): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass13): Sequential(
(0): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
(3): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU6(inplace=True)
(5): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)
(6): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass14): Sequential(
(0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU6(inplace=True)
(8): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
(9): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass15): Sequential(
(0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU6(inplace=True)
(8): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
(9): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass16): Sequential(
(0): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU6(inplace=True)
(8): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
(9): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass17): Sequential(
(0): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
(3): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(4): ReLU6(inplace=True)
)
(SandGlass18): Sequential(
(0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU6(inplace=True)
(8): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
(9): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(SandGlass19): Sequential(
(0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(960, 208, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(208, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): Conv2d(208, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): ReLU6(inplace=True)
(8): Conv2d(1280, 1280, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1280, bias=False)
(9): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(classifier): Sequential(
(0): Dropout(p=0.2, inplace=False)
(1): Linear(in_features=1280, out_features=6, bias=True)
)
)

MobileNetV2 The structure is as follows：
(start_block): Sequential(
(0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
)
(InvertedResidual1): Sequential(
(0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
(4): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual2): Sequential(
(0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)
(4): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual3): Sequential(
(0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
(4): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual4): Sequential(
(0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)
(4): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual5): Sequential(
(0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
(4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual6): Sequential(
(0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
(4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual7): Sequential(
(0): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)
(4): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual8): Sequential(
(0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
(4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual9): Sequential(
(0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
(4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual10): Sequential(
(0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
(4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual11): Sequential(
(0): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
(4): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual12): Sequential(
(0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
(4): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual13): Sequential(
(0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
(4): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual14): Sequential(
(0): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)
(4): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual15): Sequential(
(0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
(4): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual16): Sequential(
(0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
(4): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(InvertedResidual17): Sequential(
(0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
(3): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
(4): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU6(inplace=True)
(6): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)
(7): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(last_Conv2dNormActivation): Sequential(
(0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU6(inplace=True)
)
(conv2): Conv2d(1280, 6, kernel_size=(1, 1), stride=(1, 1))
)

SandGlass4.5.weight + /SandGlass4/SandGlass4.5/_weight_quantizer/QuantizeLinear + /SandGlass4/SandGlass4.5/Conv + PWN(/SandGlass4/SandGlass4.7/Clip) (CaskConvolution[0x80000009]) profiling completed in 0.336847 seconds. Fastest Tactic: 0x955d593b1135a423 Time: 0.00395457
[01/03/2024-09:58:05] [TRT] [V] --------------- Timing Runner: SandGlass4.5.weight + /SandGlass4/SandGlass4.5/_weight_quantizer/QuantizeLinear + /SandGlass4/SandGlass4.5/Conv + PWN(/SandGlass4/SandGlass4.7/Clip) (CaskFlattenConvolution[0x80000036])
[01/03/2024-09:58:05] [TRT] [V] CaskFlattenConvolution has no valid tactics for this config, skipping
[01/03/2024-09:58:05] [TRT] [V] >>>>>>>>>>>>>>> Chose Runner Type: CaskConvolution Tactic: 0x955d593b1135a423
[01/03/2024-09:58:05] [TRT] [V] =============== Computing costs for SandGlass4.8.weight + /SandGlass4/SandGlass4.8/_weight_quantizer/QuantizeLinear + /SandGlass4/SandGlass4.8/Conv + /Add
[01/03/2024-09:58:05] [TRT] [V] *************** Autotuning format combination: Int8(768,16:4,4,1), Float(3072,16,4,1) -> Float(3072,16,4,1) ***************
[01/03/2024-09:58:05] [TRT] [V] --------------- Timing Runner: SandGlass4.8.weight + /SandGlass4/SandGlass4.8/_weight_quantizer/QuantizeLinear + /SandGlass4/SandGlass4.8/Conv + /Add (CaskConvolution[0x80000009])
[01/03/2024-09:58:05] [TRT] [V] CaskConvolution has no valid tactics for this config, skipping
[01/03/2024-09:58:05] [TRT] [V] --------------- Timing Runner: SandGlass4.8.weight + /SandGlass4/SandGlass4.8/_weight_quantizer/QuantizeLinear + /SandGlass4/SandGlass4.8/Conv + /Add (CaskFlattenConvolution[0x80000036])
[01/03/2024-09:58:05] [TRT] [V] CaskFlattenConvolution has no valid tactics for this config, skipping
[01/03/2024-09:58:05] [TRT] [V] *************** Autotuning format combination: Int8(96,16:32,4,1), Float(3072,16,4,1) -> Float(3072,16,4,1) ***************
[01/03/2024-09:58:05] [TRT] [V] --------------- Timing Runner: SandGlass4.8.weight + /SandGlass4/SandGlass4.8/_weight_quantizer/QuantizeLinear + /SandGlass4/SandGlass4.8/Conv + /Add (CaskConvolution[0x80000009])
[01/03/2024-09:58:05] [TRT] [V] CaskConvolution has no valid tactics for this config, skipping
[01/03/2024-09:58:05] [TRT] [V] --------------- Timing Runner: SandGlass4.8.weight + /SandGlass4/SandGlass4.8/_weight_quantizer/QuantizeLinear + /SandGlass4/SandGlass4.8/Conv + /Add (CaskFlattenConvolution[0x80000036])
[01/03/2024-09:58:05] [TRT] [V] CaskFlattenConvolution has no valid tactics for this config, skipping
[01/03/2024-09:58:05] [TRT] [V] *************** Autotuning format combination: Int8(96,16:32,4,1), Float(96,16:32,4,1) -> Float(96,16:32,4,1) ***************
[01/03/2024-09:58:05] [TRT] [V] --------------- Timing Runner: SandGlass4.8.weight + /SandGlass4/SandGlass4.8/_weight_quantizer/QuantizeLinear + /SandGlass4/SandGlass4.8/Conv + /Add (CaskConvolution[0x80000009])
[01/03/2024-09:58:05] [TRT] [V] CaskConvolution has no valid tactics for this config, skipping
[01/03/2024-09:58:05] [TRT] [V] --------------- Timing Runner: SandGlass4.8.weight + /SandGlass4/SandGlass4.8/_weight_quantizer/QuantizeLinear + /SandGlass4/SandGlass4.8/Conv + /Add (CaskFlattenConvolution[0x80000036])
[01/03/2024-09:58:05] [TRT] [V] CaskFlattenConvolution has no valid tactics for this config, skipping
[01/03/2024-09:58:05] [TRT] [V] Deleting timing cache: 39 entries, served 0 hits since creation.
[01/03/2024-09:58:05] [TRT] [E] 10: Could not find any implementation for node SandGlass4.8.weight + /SandGlass4/SandGlass4.8/_weight_quantizer/QuantizeLinear + /SandGlass4/SandGlass4.8/Conv + /Add.
[01/03/2024-09:58:05] [TRT] [E] 10: [optimizer.cpp::nvinfer1::builder::cgraph::LeafCNode::computeCosts::3869] Error Code 10: Internal Error (Could not find any implementation for node SandGlass4.8.weight + /SandGlass4/SandGlass4.8/_weight_quantizer/QuantizeLinear + /SandGlass4/SandGlass4.8/Conv + /Add.)
Failed to create the engine

What is the reason for this error? Can we just stratify and skip him?