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leslie-fang-intel/error_generated_module.py

Last active April 22, 2024 04:59
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error_generated_module.py
odel_ in compile_fx is: GraphModule(
(L__mod___conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(L__mod___bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(L__mod___relu): ReLU(inplace=True)
(L__mod___maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(getattr_L__mod___layer1___0___conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer1___0___bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___0___relu): ReLU(inplace=True)
(getattr_L__mod___layer1___0___conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer1___0___bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___0___conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer1___0___bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___0___downsample_0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer1___0___downsample_1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___1___conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer1___1___bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___1___relu): ReLU(inplace=True)
(getattr_L__mod___layer1___1___conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer1___1___bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___1___conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer1___1___bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___2___conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer1___2___bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___2___relu): ReLU(inplace=True)
(getattr_L__mod___layer1___2___conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer1___2___bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___2___conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer1___2___bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___0___conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___0___bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___0___relu): ReLU(inplace=True)
(getattr_L__mod___layer2___0___conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(getattr_L__mod___layer2___0___bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___0___conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___0___bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___0___downsample_0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(getattr_L__mod___layer2___0___downsample_1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___1___conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___1___bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___1___relu): ReLU(inplace=True)
(getattr_L__mod___layer2___1___conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer2___1___bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___1___conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___1___bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___2___conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___2___bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___2___relu): ReLU(inplace=True)
(getattr_L__mod___layer2___2___conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer2___2___bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___2___conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___2___bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___3___conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___3___bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___3___relu): ReLU(inplace=True)
(getattr_L__mod___layer2___3___conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer2___3___bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___3___conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___3___bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___0___conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___0___bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___0___relu): ReLU(inplace=True)
(getattr_L__mod___layer3___0___conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(getattr_L__mod___layer3___0___bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___0___conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___0___bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___0___downsample_0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(getattr_L__mod___layer3___0___downsample_1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___1___conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___1___bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___1___relu): ReLU(inplace=True)
(getattr_L__mod___layer3___1___conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer3___1___bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___1___conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___1___bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___2___conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___2___bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___2___relu): ReLU(inplace=True)
(getattr_L__mod___layer3___2___conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer3___2___bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___2___conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___2___bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___3___conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___3___bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___3___relu): ReLU(inplace=True)
(getattr_L__mod___layer3___3___conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer3___3___bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___3___conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___3___bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___4___conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___4___bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___4___relu): ReLU(inplace=True)
(getattr_L__mod___layer3___4___conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer3___4___bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___4___conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___4___bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___5___conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___5___bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___5___relu): ReLU(inplace=True)
(getattr_L__mod___layer3___5___conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer3___5___bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___5___conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___5___bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___0___conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer4___0___bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___0___relu): ReLU(inplace=True)
(getattr_L__mod___layer4___0___conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(getattr_L__mod___layer4___0___bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___0___conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer4___0___bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___0___downsample_0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(getattr_L__mod___layer4___0___downsample_1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___1___conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer4___1___bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___1___relu): ReLU(inplace=True)
(getattr_L__mod___layer4___1___conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer4___1___bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___1___conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer4___1___bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___2___conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer4___2___bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___2___relu): ReLU(inplace=True)
(getattr_L__mod___layer4___2___conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer4___2___bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___2___conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer4___2___bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(L__mod___avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(L__mod___fc): Linear(in_features=2048, out_features=1000, bias=True)
)
def forward(self, L_inputs_0_ : torch.Tensor):
l_inputs_0_ = L_inputs_0_
x = self.L__mod___conv1(l_inputs_0_)
x_1 = self.L__mod___bn1(x); x = None
x_2 = self.L__mod___relu(x_1); x_1 = None
x_3 = self.L__mod___maxpool(x_2); x_2 = None
out = self.getattr_L__mod___layer1___0___conv1(x_3)
out_1 = self.getattr_L__mod___layer1___0___bn1(out); out = None
out_2 = self.getattr_L__mod___layer1___0___relu(out_1); out_1 = None
out_3 = self.getattr_L__mod___layer1___0___conv2(out_2); out_2 = None
out_4 = self.getattr_L__mod___layer1___0___bn2(out_3); out_3 = None
out_5 = self.getattr_L__mod___layer1___0___relu(out_4); out_4 = None
out_6 = self.getattr_L__mod___layer1___0___conv3(out_5); out_5 = None
out_7 = self.getattr_L__mod___layer1___0___bn3(out_6); out_6 = None
getattr_l__mod___layer1___0___downsample_0 = self.getattr_L__mod___layer1___0___downsample_0(x_3); x_3 = None
identity = self.getattr_L__mod___layer1___0___downsample_1(getattr_l__mod___layer1___0___downsample_0); getattr_l__mod___layer1___0___downsample_0 = None
out_7 += identity; out_8 = out_7; out_7 = identity = None
out_9 = self.getattr_L__mod___layer1___0___relu(out_8); out_8 = None
out_10 = self.getattr_L__mod___layer1___1___conv1(out_9)
out_11 = self.getattr_L__mod___layer1___1___bn1(out_10); out_10 = None
out_12 = self.getattr_L__mod___layer1___1___relu(out_11); out_11 = None
out_13 = self.getattr_L__mod___layer1___1___conv2(out_12); out_12 = None
out_14 = self.getattr_L__mod___layer1___1___bn2(out_13); out_13 = None
out_15 = self.getattr_L__mod___layer1___1___relu(out_14); out_14 = None
out_16 = self.getattr_L__mod___layer1___1___conv3(out_15); out_15 = None
out_17 = self.getattr_L__mod___layer1___1___bn3(out_16); out_16 = None
out_17 += out_9; out_18 = out_17; out_17 = out_9 = None
out_19 = self.getattr_L__mod___layer1___1___relu(out_18); out_18 = None
out_20 = self.getattr_L__mod___layer1___2___conv1(out_19)
out_21 = self.getattr_L__mod___layer1___2___bn1(out_20); out_20 = None
out_22 = self.getattr_L__mod___layer1___2___relu(out_21); out_21 = None
out_23 = self.getattr_L__mod___layer1___2___conv2(out_22); out_22 = None
out_24 = self.getattr_L__mod___layer1___2___bn2(out_23); out_23 = None
out_25 = self.getattr_L__mod___layer1___2___relu(out_24); out_24 = None
out_26 = self.getattr_L__mod___layer1___2___conv3(out_25); out_25 = None
out_27 = self.getattr_L__mod___layer1___2___bn3(out_26); out_26 = None
out_27 += out_19; out_28 = out_27; out_27 = out_19 = None
out_29 = self.getattr_L__mod___layer1___2___relu(out_28); out_28 = None
out_30 = self.getattr_L__mod___layer2___0___conv1(out_29)
out_31 = self.getattr_L__mod___layer2___0___bn1(out_30); out_30 = None
out_32 = self.getattr_L__mod___layer2___0___relu(out_31); out_31 = None
out_33 = self.getattr_L__mod___layer2___0___conv2(out_32); out_32 = None
out_34 = self.getattr_L__mod___layer2___0___bn2(out_33); out_33 = None
out_35 = self.getattr_L__mod___layer2___0___relu(out_34); out_34 = None
out_36 = self.getattr_L__mod___layer2___0___conv3(out_35); out_35 = None
out_37 = self.getattr_L__mod___layer2___0___bn3(out_36); out_36 = None
getattr_l__mod___layer2___0___downsample_0 = self.getattr_L__mod___layer2___0___downsample_0(out_29); out_29 = None
identity_1 = self.getattr_L__mod___layer2___0___downsample_1(getattr_l__mod___layer2___0___downsample_0); getattr_l__mod___layer2___0___downsample_0 = None
out_37 += identity_1; out_38 = out_37; out_37 = identity_1 = None
out_39 = self.getattr_L__mod___layer2___0___relu(out_38); out_38 = None
out_40 = self.getattr_L__mod___layer2___1___conv1(out_39)
out_41 = self.getattr_L__mod___layer2___1___bn1(out_40); out_40 = None
out_42 = self.getattr_L__mod___layer2___1___relu(out_41); out_41 = None
out_43 = self.getattr_L__mod___layer2___1___conv2(out_42); out_42 = None
out_44 = self.getattr_L__mod___layer2___1___bn2(out_43); out_43 = None
out_45 = self.getattr_L__mod___layer2___1___relu(out_44); out_44 = None
out_46 = self.getattr_L__mod___layer2___1___conv3(out_45); out_45 = None
out_47 = self.getattr_L__mod___layer2___1___bn3(out_46); out_46 = None
out_47 += out_39; out_48 = out_47; out_47 = out_39 = None
out_49 = self.getattr_L__mod___layer2___1___relu(out_48); out_48 = None
out_50 = self.getattr_L__mod___layer2___2___conv1(out_49)
out_51 = self.getattr_L__mod___layer2___2___bn1(out_50); out_50 = None
out_52 = self.getattr_L__mod___layer2___2___relu(out_51); out_51 = None
out_53 = self.getattr_L__mod___layer2___2___conv2(out_52); out_52 = None
out_54 = self.getattr_L__mod___layer2___2___bn2(out_53); out_53 = None
out_55 = self.getattr_L__mod___layer2___2___relu(out_54); out_54 = None
out_56 = self.getattr_L__mod___layer2___2___conv3(out_55); out_55 = None
out_57 = self.getattr_L__mod___layer2___2___bn3(out_56); out_56 = None
out_57 += out_49; out_58 = out_57; out_57 = out_49 = None
out_59 = self.getattr_L__mod___layer2___2___relu(out_58); out_58 = None
out_60 = self.getattr_L__mod___layer2___3___conv1(out_59)
out_61 = self.getattr_L__mod___layer2___3___bn1(out_60); out_60 = None
out_62 = self.getattr_L__mod___layer2___3___relu(out_61); out_61 = None
out_63 = self.getattr_L__mod___layer2___3___conv2(out_62); out_62 = None
out_64 = self.getattr_L__mod___layer2___3___bn2(out_63); out_63 = None
out_65 = self.getattr_L__mod___layer2___3___relu(out_64); out_64 = None
out_66 = self.getattr_L__mod___layer2___3___conv3(out_65); out_65 = None
out_67 = self.getattr_L__mod___layer2___3___bn3(out_66); out_66 = None
out_67 += out_59; out_68 = out_67; out_67 = out_59 = None
out_69 = self.getattr_L__mod___layer2___3___relu(out_68); out_68 = None
out_70 = self.getattr_L__mod___layer3___0___conv1(out_69)
out_71 = self.getattr_L__mod___layer3___0___bn1(out_70); out_70 = None
out_72 = self.getattr_L__mod___layer3___0___relu(out_71); out_71 = None
out_73 = self.getattr_L__mod___layer3___0___conv2(out_72); out_72 = None
out_74 = self.getattr_L__mod___layer3___0___bn2(out_73); out_73 = None
out_75 = self.getattr_L__mod___layer3___0___relu(out_74); out_74 = None
out_76 = self.getattr_L__mod___layer3___0___conv3(out_75); out_75 = None
out_77 = self.getattr_L__mod___layer3___0___bn3(out_76); out_76 = None
getattr_l__mod___layer3___0___downsample_0 = self.getattr_L__mod___layer3___0___downsample_0(out_69); out_69 = None
identity_2 = self.getattr_L__mod___layer3___0___downsample_1(getattr_l__mod___layer3___0___downsample_0); getattr_l__mod___layer3___0___downsample_0 = None
out_77 += identity_2; out_78 = out_77; out_77 = identity_2 = None
out_79 = self.getattr_L__mod___layer3___0___relu(out_78); out_78 = None
out_80 = self.getattr_L__mod___layer3___1___conv1(out_79)
out_81 = self.getattr_L__mod___layer3___1___bn1(out_80); out_80 = None
out_82 = self.getattr_L__mod___layer3___1___relu(out_81); out_81 = None
out_83 = self.getattr_L__mod___layer3___1___conv2(out_82); out_82 = None
out_84 = self.getattr_L__mod___layer3___1___bn2(out_83); out_83 = None
out_85 = self.getattr_L__mod___layer3___1___relu(out_84); out_84 = None
out_86 = self.getattr_L__mod___layer3___1___conv3(out_85); out_85 = None
out_87 = self.getattr_L__mod___layer3___1___bn3(out_86); out_86 = None
out_87 += out_79; out_88 = out_87; out_87 = out_79 = None
out_89 = self.getattr_L__mod___layer3___1___relu(out_88); out_88 = None
out_90 = self.getattr_L__mod___layer3___2___conv1(out_89)
out_91 = self.getattr_L__mod___layer3___2___bn1(out_90); out_90 = None
out_92 = self.getattr_L__mod___layer3___2___relu(out_91); out_91 = None
out_93 = self.getattr_L__mod___layer3___2___conv2(out_92); out_92 = None
out_94 = self.getattr_L__mod___layer3___2___bn2(out_93); out_93 = None
out_95 = self.getattr_L__mod___layer3___2___relu(out_94); out_94 = None
out_96 = self.getattr_L__mod___layer3___2___conv3(out_95); out_95 = None
out_97 = self.getattr_L__mod___layer3___2___bn3(out_96); out_96 = None
out_97 += out_89; out_98 = out_97; out_97 = out_89 = None
out_99 = self.getattr_L__mod___layer3___2___relu(out_98); out_98 = None
out_100 = self.getattr_L__mod___layer3___3___conv1(out_99)
out_101 = self.getattr_L__mod___layer3___3___bn1(out_100); out_100 = None
out_102 = self.getattr_L__mod___layer3___3___relu(out_101); out_101 = None
out_103 = self.getattr_L__mod___layer3___3___conv2(out_102); out_102 = None
out_104 = self.getattr_L__mod___layer3___3___bn2(out_103); out_103 = None
out_105 = self.getattr_L__mod___layer3___3___relu(out_104); out_104 = None
out_106 = self.getattr_L__mod___layer3___3___conv3(out_105); out_105 = None
out_107 = self.getattr_L__mod___layer3___3___bn3(out_106); out_106 = None
out_107 += out_99; out_108 = out_107; out_107 = out_99 = None
out_109 = self.getattr_L__mod___layer3___3___relu(out_108); out_108 = None
out_110 = self.getattr_L__mod___layer3___4___conv1(out_109)
out_111 = self.getattr_L__mod___layer3___4___bn1(out_110); out_110 = None
out_112 = self.getattr_L__mod___layer3___4___relu(out_111); out_111 = None
out_113 = self.getattr_L__mod___layer3___4___conv2(out_112); out_112 = None
out_114 = self.getattr_L__mod___layer3___4___bn2(out_113); out_113 = None
out_115 = self.getattr_L__mod___layer3___4___relu(out_114); out_114 = None
out_116 = self.getattr_L__mod___layer3___4___conv3(out_115); out_115 = None
out_117 = self.getattr_L__mod___layer3___4___bn3(out_116); out_116 = None
out_117 += out_109; out_118 = out_117; out_117 = out_109 = None
out_119 = self.getattr_L__mod___layer3___4___relu(out_118); out_118 = None
out_120 = self.getattr_L__mod___layer3___5___conv1(out_119)
out_121 = self.getattr_L__mod___layer3___5___bn1(out_120); out_120 = None
out_122 = self.getattr_L__mod___layer3___5___relu(out_121); out_121 = None
out_123 = self.getattr_L__mod___layer3___5___conv2(out_122); out_122 = None
out_124 = self.getattr_L__mod___layer3___5___bn2(out_123); out_123 = None
out_125 = self.getattr_L__mod___layer3___5___relu(out_124); out_124 = None
out_126 = self.getattr_L__mod___layer3___5___conv3(out_125); out_125 = None
out_127 = self.getattr_L__mod___layer3___5___bn3(out_126); out_126 = None
out_127 += out_119; out_128 = out_127; out_127 = out_119 = None
out_129 = self.getattr_L__mod___layer3___5___relu(out_128); out_128 = None
out_130 = self.getattr_L__mod___layer4___0___conv1(out_129)
out_131 = self.getattr_L__mod___layer4___0___bn1(out_130); out_130 = None
out_132 = self.getattr_L__mod___layer4___0___relu(out_131); out_131 = None
out_133 = self.getattr_L__mod___layer4___0___conv2(out_132); out_132 = None
out_134 = self.getattr_L__mod___layer4___0___bn2(out_133); out_133 = None
out_135 = self.getattr_L__mod___layer4___0___relu(out_134); out_134 = None
out_136 = self.getattr_L__mod___layer4___0___conv3(out_135); out_135 = None
out_137 = self.getattr_L__mod___layer4___0___bn3(out_136); out_136 = None
getattr_l__mod___layer4___0___downsample_0 = self.getattr_L__mod___layer4___0___downsample_0(out_129); out_129 = None
identity_3 = self.getattr_L__mod___layer4___0___downsample_1(getattr_l__mod___layer4___0___downsample_0); getattr_l__mod___layer4___0___downsample_0 = None
out_137 += identity_3; out_138 = out_137; out_137 = identity_3 = None
out_139 = self.getattr_L__mod___layer4___0___relu(out_138); out_138 = None
out_140 = self.getattr_L__mod___layer4___1___conv1(out_139)
out_141 = self.getattr_L__mod___layer4___1___bn1(out_140); out_140 = None
out_142 = self.getattr_L__mod___layer4___1___relu(out_141); out_141 = None
out_143 = self.getattr_L__mod___layer4___1___conv2(out_142); out_142 = None
out_144 = self.getattr_L__mod___layer4___1___bn2(out_143); out_143 = None
out_145 = self.getattr_L__mod___layer4___1___relu(out_144); out_144 = None
out_146 = self.getattr_L__mod___layer4___1___conv3(out_145); out_145 = None
out_147 = self.getattr_L__mod___layer4___1___bn3(out_146); out_146 = None
out_147 += out_139; out_148 = out_147; out_147 = out_139 = None
out_149 = self.getattr_L__mod___layer4___1___relu(out_148); out_148 = None
out_150 = self.getattr_L__mod___layer4___2___conv1(out_149)
out_151 = self.getattr_L__mod___layer4___2___bn1(out_150); out_150 = None
out_152 = self.getattr_L__mod___layer4___2___relu(out_151); out_151 = None
out_153 = self.getattr_L__mod___layer4___2___conv2(out_152); out_152 = None
out_154 = self.getattr_L__mod___layer4___2___bn2(out_153); out_153 = None
out_155 = self.getattr_L__mod___layer4___2___relu(out_154); out_154 = None
out_156 = self.getattr_L__mod___layer4___2___conv3(out_155); out_155 = None
out_157 = self.getattr_L__mod___layer4___2___bn3(out_156); out_156 = None
out_157 += out_149; out_158 = out_157; out_157 = out_149 = None
out_159 = self.getattr_L__mod___layer4___2___relu(out_158); out_158 = None
x_4 = self.L__mod___avgpool(out_159); out_159 = None
x_5 = torch.flatten(x_4, 1); x_4 = None
x_6 = self.L__mod___fc(x_5); x_5 = None
x_7 = self.L__mod___conv1(l_inputs_0_); l_inputs_0_ = None
x_8 = self.L__mod___bn1(x_7); x_7 = None
x_9 = self.L__mod___relu(x_8); x_8 = None
x_10 = self.L__mod___maxpool(x_9); x_9 = None
out_160 = self.getattr_L__mod___layer1___0___conv1(x_10)
out_161 = self.getattr_L__mod___layer1___0___bn1(out_160); out_160 = None
out_162 = self.getattr_L__mod___layer1___0___relu(out_161); out_161 = None
out_163 = self.getattr_L__mod___layer1___0___conv2(out_162); out_162 = None
out_164 = self.getattr_L__mod___layer1___0___bn2(out_163); out_163 = None
out_165 = self.getattr_L__mod___layer1___0___relu(out_164); out_164 = None
out_166 = self.getattr_L__mod___layer1___0___conv3(out_165); out_165 = None
out_167 = self.getattr_L__mod___layer1___0___bn3(out_166); out_166 = None
getattr_l__mod___layer1___0___downsample_2 = self.getattr_L__mod___layer1___0___downsample_0(x_10); x_10 = None
identity_4 = self.getattr_L__mod___layer1___0___downsample_1(getattr_l__mod___layer1___0___downsample_2); getattr_l__mod___layer1___0___downsample_2 = None
out_167 += identity_4; out_168 = out_167; out_167 = identity_4 = None
out_169 = self.getattr_L__mod___layer1___0___relu(out_168); out_168 = None
out_170 = self.getattr_L__mod___layer1___1___conv1(out_169)
out_171 = self.getattr_L__mod___layer1___1___bn1(out_170); out_170 = None
out_172 = self.getattr_L__mod___layer1___1___relu(out_171); out_171 = None
out_173 = self.getattr_L__mod___layer1___1___conv2(out_172); out_172 = None
out_174 = self.getattr_L__mod___layer1___1___bn2(out_173); out_173 = None
out_175 = self.getattr_L__mod___layer1___1___relu(out_174); out_174 = None
out_176 = self.getattr_L__mod___layer1___1___conv3(out_175); out_175 = None
out_177 = self.getattr_L__mod___layer1___1___bn3(out_176); out_176 = None
out_177 += out_169; out_178 = out_177; out_177 = out_169 = None
out_179 = self.getattr_L__mod___layer1___1___relu(out_178); out_178 = None
out_180 = self.getattr_L__mod___layer1___2___conv1(out_179)
out_181 = self.getattr_L__mod___layer1___2___bn1(out_180); out_180 = None
out_182 = self.getattr_L__mod___layer1___2___relu(out_181); out_181 = None
out_183 = self.getattr_L__mod___layer1___2___conv2(out_182); out_182 = None
out_184 = self.getattr_L__mod___layer1___2___bn2(out_183); out_183 = None
out_185 = self.getattr_L__mod___layer1___2___relu(out_184); out_184 = None
out_186 = self.getattr_L__mod___layer1___2___conv3(out_185); out_185 = None
out_187 = self.getattr_L__mod___layer1___2___bn3(out_186); out_186 = None
out_187 += out_179; out_188 = out_187; out_187 = out_179 = None
out_189 = self.getattr_L__mod___layer1___2___relu(out_188); out_188 = None
out_190 = self.getattr_L__mod___layer2___0___conv1(out_189)
out_191 = self.getattr_L__mod___layer2___0___bn1(out_190); out_190 = None
out_192 = self.getattr_L__mod___layer2___0___relu(out_191); out_191 = None
out_193 = self.getattr_L__mod___layer2___0___conv2(out_192); out_192 = None
out_194 = self.getattr_L__mod___layer2___0___bn2(out_193); out_193 = None
out_195 = self.getattr_L__mod___layer2___0___relu(out_194); out_194 = None
out_196 = self.getattr_L__mod___layer2___0___conv3(out_195); out_195 = None
out_197 = self.getattr_L__mod___layer2___0___bn3(out_196); out_196 = None
getattr_l__mod___layer2___0___downsample_2 = self.getattr_L__mod___layer2___0___downsample_0(out_189); out_189 = None
identity_5 = self.getattr_L__mod___layer2___0___downsample_1(getattr_l__mod___layer2___0___downsample_2); getattr_l__mod___layer2___0___downsample_2 = None
out_197 += identity_5; out_198 = out_197; out_197 = identity_5 = None
out_199 = self.getattr_L__mod___layer2___0___relu(out_198); out_198 = None
out_200 = self.getattr_L__mod___layer2___1___conv1(out_199)
out_201 = self.getattr_L__mod___layer2___1___bn1(out_200); out_200 = None
out_202 = self.getattr_L__mod___layer2___1___relu(out_201); out_201 = None
out_203 = self.getattr_L__mod___layer2___1___conv2(out_202); out_202 = None
out_204 = self.getattr_L__mod___layer2___1___bn2(out_203); out_203 = None
out_205 = self.getattr_L__mod___layer2___1___relu(out_204); out_204 = None
out_206 = self.getattr_L__mod___layer2___1___conv3(out_205); out_205 = None
out_207 = self.getattr_L__mod___layer2___1___bn3(out_206); out_206 = None
out_207 += out_199; out_208 = out_207; out_207 = out_199 = None
out_209 = self.getattr_L__mod___layer2___1___relu(out_208); out_208 = None
out_210 = self.getattr_L__mod___layer2___2___conv1(out_209)
out_211 = self.getattr_L__mod___layer2___2___bn1(out_210); out_210 = None
out_212 = self.getattr_L__mod___layer2___2___relu(out_211); out_211 = None
out_213 = self.getattr_L__mod___layer2___2___conv2(out_212); out_212 = None
out_214 = self.getattr_L__mod___layer2___2___bn2(out_213); out_213 = None
out_215 = self.getattr_L__mod___layer2___2___relu(out_214); out_214 = None
out_216 = self.getattr_L__mod___layer2___2___conv3(out_215); out_215 = None
out_217 = self.getattr_L__mod___layer2___2___bn3(out_216); out_216 = None
out_217 += out_209; out_218 = out_217; out_217 = out_209 = None
out_219 = self.getattr_L__mod___layer2___2___relu(out_218); out_218 = None
out_220 = self.getattr_L__mod___layer2___3___conv1(out_219)
out_221 = self.getattr_L__mod___layer2___3___bn1(out_220); out_220 = None
out_222 = self.getattr_L__mod___layer2___3___relu(out_221); out_221 = None
out_223 = self.getattr_L__mod___layer2___3___conv2(out_222); out_222 = None
out_224 = self.getattr_L__mod___layer2___3___bn2(out_223); out_223 = None
out_225 = self.getattr_L__mod___layer2___3___relu(out_224); out_224 = None
out_226 = self.getattr_L__mod___layer2___3___conv3(out_225); out_225 = None
out_227 = self.getattr_L__mod___layer2___3___bn3(out_226); out_226 = None
out_227 += out_219; out_228 = out_227; out_227 = out_219 = None
out_229 = self.getattr_L__mod___layer2___3___relu(out_228); out_228 = None
out_230 = self.getattr_L__mod___layer3___0___conv1(out_229)
out_231 = self.getattr_L__mod___layer3___0___bn1(out_230); out_230 = None
out_232 = self.getattr_L__mod___layer3___0___relu(out_231); out_231 = None
out_233 = self.getattr_L__mod___layer3___0___conv2(out_232); out_232 = None
out_234 = self.getattr_L__mod___layer3___0___bn2(out_233); out_233 = None
out_235 = self.getattr_L__mod___layer3___0___relu(out_234); out_234 = None
out_236 = self.getattr_L__mod___layer3___0___conv3(out_235); out_235 = None
out_237 = self.getattr_L__mod___layer3___0___bn3(out_236); out_236 = None
getattr_l__mod___layer3___0___downsample_2 = self.getattr_L__mod___layer3___0___downsample_0(out_229); out_229 = None
identity_6 = self.getattr_L__mod___layer3___0___downsample_1(getattr_l__mod___layer3___0___downsample_2); getattr_l__mod___layer3___0___downsample_2 = None
out_237 += identity_6; out_238 = out_237; out_237 = identity_6 = None
out_239 = self.getattr_L__mod___layer3___0___relu(out_238); out_238 = None
out_240 = self.getattr_L__mod___layer3___1___conv1(out_239)
out_241 = self.getattr_L__mod___layer3___1___bn1(out_240); out_240 = None
out_242 = self.getattr_L__mod___layer3___1___relu(out_241); out_241 = None
out_243 = self.getattr_L__mod___layer3___1___conv2(out_242); out_242 = None
out_244 = self.getattr_L__mod___layer3___1___bn2(out_243); out_243 = None
out_245 = self.getattr_L__mod___layer3___1___relu(out_244); out_244 = None
out_246 = self.getattr_L__mod___layer3___1___conv3(out_245); out_245 = None
out_247 = self.getattr_L__mod___layer3___1___bn3(out_246); out_246 = None
out_247 += out_239; out_248 = out_247; out_247 = out_239 = None
out_249 = self.getattr_L__mod___layer3___1___relu(out_248); out_248 = None
out_250 = self.getattr_L__mod___layer3___2___conv1(out_249)
out_251 = self.getattr_L__mod___layer3___2___bn1(out_250); out_250 = None
out_252 = self.getattr_L__mod___layer3___2___relu(out_251); out_251 = None
out_253 = self.getattr_L__mod___layer3___2___conv2(out_252); out_252 = None
out_254 = self.getattr_L__mod___layer3___2___bn2(out_253); out_253 = None
out_255 = self.getattr_L__mod___layer3___2___relu(out_254); out_254 = None
out_256 = self.getattr_L__mod___layer3___2___conv3(out_255); out_255 = None
out_257 = self.getattr_L__mod___layer3___2___bn3(out_256); out_256 = None
out_257 += out_249; out_258 = out_257; out_257 = out_249 = None
out_259 = self.getattr_L__mod___layer3___2___relu(out_258); out_258 = None
out_260 = self.getattr_L__mod___layer3___3___conv1(out_259)
out_261 = self.getattr_L__mod___layer3___3___bn1(out_260); out_260 = None
out_262 = self.getattr_L__mod___layer3___3___relu(out_261); out_261 = None
out_263 = self.getattr_L__mod___layer3___3___conv2(out_262); out_262 = None
out_264 = self.getattr_L__mod___layer3___3___bn2(out_263); out_263 = None
out_265 = self.getattr_L__mod___layer3___3___relu(out_264); out_264 = None
out_266 = self.getattr_L__mod___layer3___3___conv3(out_265); out_265 = None
out_267 = self.getattr_L__mod___layer3___3___bn3(out_266); out_266 = None
out_267 += out_259; out_268 = out_267; out_267 = out_259 = None
out_269 = self.getattr_L__mod___layer3___3___relu(out_268); out_268 = None
out_270 = self.getattr_L__mod___layer3___4___conv1(out_269)
out_271 = self.getattr_L__mod___layer3___4___bn1(out_270); out_270 = None
out_272 = self.getattr_L__mod___layer3___4___relu(out_271); out_271 = None
out_273 = self.getattr_L__mod___layer3___4___conv2(out_272); out_272 = None
out_274 = self.getattr_L__mod___layer3___4___bn2(out_273); out_273 = None
out_275 = self.getattr_L__mod___layer3___4___relu(out_274); out_274 = None
out_276 = self.getattr_L__mod___layer3___4___conv3(out_275); out_275 = None
out_277 = self.getattr_L__mod___layer3___4___bn3(out_276); out_276 = None
out_277 += out_269; out_278 = out_277; out_277 = out_269 = None
out_279 = self.getattr_L__mod___layer3___4___relu(out_278); out_278 = None
out_280 = self.getattr_L__mod___layer3___5___conv1(out_279)
out_281 = self.getattr_L__mod___layer3___5___bn1(out_280); out_280 = None
out_282 = self.getattr_L__mod___layer3___5___relu(out_281); out_281 = None
out_283 = self.getattr_L__mod___layer3___5___conv2(out_282); out_282 = None
out_284 = self.getattr_L__mod___layer3___5___bn2(out_283); out_283 = None
out_285 = self.getattr_L__mod___layer3___5___relu(out_284); out_284 = None
out_286 = self.getattr_L__mod___layer3___5___conv3(out_285); out_285 = None
out_287 = self.getattr_L__mod___layer3___5___bn3(out_286); out_286 = None
out_287 += out_279; out_288 = out_287; out_287 = out_279 = None
out_289 = self.getattr_L__mod___layer3___5___relu(out_288); out_288 = None
out_290 = self.getattr_L__mod___layer4___0___conv1(out_289)
out_291 = self.getattr_L__mod___layer4___0___bn1(out_290); out_290 = None
out_292 = self.getattr_L__mod___layer4___0___relu(out_291); out_291 = None
out_293 = self.getattr_L__mod___layer4___0___conv2(out_292); out_292 = None
out_294 = self.getattr_L__mod___layer4___0___bn2(out_293); out_293 = None
out_295 = self.getattr_L__mod___layer4___0___relu(out_294); out_294 = None
out_296 = self.getattr_L__mod___layer4___0___conv3(out_295); out_295 = None
out_297 = self.getattr_L__mod___layer4___0___bn3(out_296); out_296 = None
getattr_l__mod___layer4___0___downsample_2 = self.getattr_L__mod___layer4___0___downsample_0(out_289); out_289 = None
identity_7 = self.getattr_L__mod___layer4___0___downsample_1(getattr_l__mod___layer4___0___downsample_2); getattr_l__mod___layer4___0___downsample_2 = None
out_297 += identity_7; out_298 = out_297; out_297 = identity_7 = None
out_299 = self.getattr_L__mod___layer4___0___relu(out_298); out_298 = None
out_300 = self.getattr_L__mod___layer4___1___conv1(out_299)
out_301 = self.getattr_L__mod___layer4___1___bn1(out_300); out_300 = None
out_302 = self.getattr_L__mod___layer4___1___relu(out_301); out_301 = None
out_303 = self.getattr_L__mod___layer4___1___conv2(out_302); out_302 = None
out_304 = self.getattr_L__mod___layer4___1___bn2(out_303); out_303 = None
out_305 = self.getattr_L__mod___layer4___1___relu(out_304); out_304 = None
out_306 = self.getattr_L__mod___layer4___1___conv3(out_305); out_305 = None
out_307 = self.getattr_L__mod___layer4___1___bn3(out_306); out_306 = None
out_307 += out_299; out_308 = out_307; out_307 = out_299 = None
out_309 = self.getattr_L__mod___layer4___1___relu(out_308); out_308 = None
out_310 = self.getattr_L__mod___layer4___2___conv1(out_309)
out_311 = self.getattr_L__mod___layer4___2___bn1(out_310); out_310 = None
out_312 = self.getattr_L__mod___layer4___2___relu(out_311); out_311 = None
out_313 = self.getattr_L__mod___layer4___2___conv2(out_312); out_312 = None
out_314 = self.getattr_L__mod___layer4___2___bn2(out_313); out_313 = None
out_315 = self.getattr_L__mod___layer4___2___relu(out_314); out_314 = None
out_316 = self.getattr_L__mod___layer4___2___conv3(out_315); out_315 = None
out_317 = self.getattr_L__mod___layer4___2___bn3(out_316); out_316 = None
out_317 += out_309; out_318 = out_317; out_317 = out_309 = None
out_319 = self.getattr_L__mod___layer4___2___relu(out_318); out_318 = None
x_11 = self.L__mod___avgpool(out_319); out_319 = None
x_12 = torch.flatten(x_11, 1); x_11 = None
x_13 = self.L__mod___fc(x_12); x_12 = None
return (x_13,)
Raw
success_generated_module.py
model_ in compile_fx is: GraphModule(
(L__mod___conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(L__mod___bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(L__mod___relu): ReLU(inplace=True)
(L__mod___maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(getattr_L__mod___layer1___0___conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer1___0___bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___0___relu): ReLU(inplace=True)
(getattr_L__mod___layer1___0___conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer1___0___bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___0___conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer1___0___bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___0___downsample_0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer1___0___downsample_1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___1___conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer1___1___bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___1___relu): ReLU(inplace=True)
(getattr_L__mod___layer1___1___conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer1___1___bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___1___conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer1___1___bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___2___conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer1___2___bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___2___relu): ReLU(inplace=True)
(getattr_L__mod___layer1___2___conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer1___2___bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer1___2___conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer1___2___bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___0___conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___0___bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___0___relu): ReLU(inplace=True)
(getattr_L__mod___layer2___0___conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(getattr_L__mod___layer2___0___bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___0___conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___0___bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___0___downsample_0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(getattr_L__mod___layer2___0___downsample_1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___1___conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___1___bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___1___relu): ReLU(inplace=True)
(getattr_L__mod___layer2___1___conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer2___1___bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___1___conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___1___bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___2___conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___2___bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___2___relu): ReLU(inplace=True)
(getattr_L__mod___layer2___2___conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer2___2___bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___2___conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___2___bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___3___conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___3___bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___3___relu): ReLU(inplace=True)
(getattr_L__mod___layer2___3___conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer2___3___bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer2___3___conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer2___3___bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___0___conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___0___bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___0___relu): ReLU(inplace=True)
(getattr_L__mod___layer3___0___conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(getattr_L__mod___layer3___0___bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___0___conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___0___bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___0___downsample_0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(getattr_L__mod___layer3___0___downsample_1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___1___conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___1___bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___1___relu): ReLU(inplace=True)
(getattr_L__mod___layer3___1___conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer3___1___bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___1___conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___1___bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___2___conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___2___bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___2___relu): ReLU(inplace=True)
(getattr_L__mod___layer3___2___conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer3___2___bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___2___conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___2___bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___3___conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___3___bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___3___relu): ReLU(inplace=True)
(getattr_L__mod___layer3___3___conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer3___3___bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___3___conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___3___bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___4___conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___4___bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___4___relu): ReLU(inplace=True)
(getattr_L__mod___layer3___4___conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer3___4___bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___4___conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___4___bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___5___conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___5___bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___5___relu): ReLU(inplace=True)
(getattr_L__mod___layer3___5___conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer3___5___bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer3___5___conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer3___5___bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___0___conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer4___0___bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___0___relu): ReLU(inplace=True)
(getattr_L__mod___layer4___0___conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(getattr_L__mod___layer4___0___bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___0___conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer4___0___bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___0___downsample_0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
(getattr_L__mod___layer4___0___downsample_1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___1___conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer4___1___bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___1___relu): ReLU(inplace=True)
(getattr_L__mod___layer4___1___conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer4___1___bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___1___conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer4___1___bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___2___conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer4___2___bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___2___relu): ReLU(inplace=True)
(getattr_L__mod___layer4___2___conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(getattr_L__mod___layer4___2___bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(getattr_L__mod___layer4___2___conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(getattr_L__mod___layer4___2___bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(L__mod___avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(L__mod___fc): Linear(in_features=2048, out_features=1000, bias=True)
)
def forward(self, L_inputs_0_ : torch.Tensor):
l_inputs_0_ = L_inputs_0_
x = self.L__mod___conv1(l_inputs_0_); l_inputs_0_ = None
x_1 = self.L__mod___bn1(x); x = None
x_2 = self.L__mod___relu(x_1); x_1 = None
x_3 = self.L__mod___maxpool(x_2); x_2 = None
out = self.getattr_L__mod___layer1___0___conv1(x_3)
out_1 = self.getattr_L__mod___layer1___0___bn1(out); out = None
out_2 = self.getattr_L__mod___layer1___0___relu(out_1); out_1 = None
out_3 = self.getattr_L__mod___layer1___0___conv2(out_2); out_2 = None
out_4 = self.getattr_L__mod___layer1___0___bn2(out_3); out_3 = None
out_5 = self.getattr_L__mod___layer1___0___relu(out_4); out_4 = None
out_6 = self.getattr_L__mod___layer1___0___conv3(out_5); out_5 = None
out_7 = self.getattr_L__mod___layer1___0___bn3(out_6); out_6 = None
getattr_l__mod___layer1___0___downsample_0 = self.getattr_L__mod___layer1___0___downsample_0(x_3); x_3 = None
identity = self.getattr_L__mod___layer1___0___downsample_1(getattr_l__mod___layer1___0___downsample_0); getattr_l__mod___layer1___0___downsample_0 = None
out_7 += identity; out_8 = out_7; out_7 = identity = None
out_9 = self.getattr_L__mod___layer1___0___relu(out_8); out_8 = None
out_10 = self.getattr_L__mod___layer1___1___conv1(out_9)
out_11 = self.getattr_L__mod___layer1___1___bn1(out_10); out_10 = None
out_12 = self.getattr_L__mod___layer1___1___relu(out_11); out_11 = None
out_13 = self.getattr_L__mod___layer1___1___conv2(out_12); out_12 = None
out_14 = self.getattr_L__mod___layer1___1___bn2(out_13); out_13 = None
out_15 = self.getattr_L__mod___layer1___1___relu(out_14); out_14 = None
out_16 = self.getattr_L__mod___layer1___1___conv3(out_15); out_15 = None
out_17 = self.getattr_L__mod___layer1___1___bn3(out_16); out_16 = None
out_17 += out_9; out_18 = out_17; out_17 = out_9 = None
out_19 = self.getattr_L__mod___layer1___1___relu(out_18); out_18 = None
out_20 = self.getattr_L__mod___layer1___2___conv1(out_19)
out_21 = self.getattr_L__mod___layer1___2___bn1(out_20); out_20 = None
out_22 = self.getattr_L__mod___layer1___2___relu(out_21); out_21 = None
out_23 = self.getattr_L__mod___layer1___2___conv2(out_22); out_22 = None
out_24 = self.getattr_L__mod___layer1___2___bn2(out_23); out_23 = None
out_25 = self.getattr_L__mod___layer1___2___relu(out_24); out_24 = None
out_26 = self.getattr_L__mod___layer1___2___conv3(out_25); out_25 = None
out_27 = self.getattr_L__mod___layer1___2___bn3(out_26); out_26 = None
out_27 += out_19; out_28 = out_27; out_27 = out_19 = None
out_29 = self.getattr_L__mod___layer1___2___relu(out_28); out_28 = None
out_30 = self.getattr_L__mod___layer2___0___conv1(out_29)
out_31 = self.getattr_L__mod___layer2___0___bn1(out_30); out_30 = None
out_32 = self.getattr_L__mod___layer2___0___relu(out_31); out_31 = None
out_33 = self.getattr_L__mod___layer2___0___conv2(out_32); out_32 = None
out_34 = self.getattr_L__mod___layer2___0___bn2(out_33); out_33 = None
out_35 = self.getattr_L__mod___layer2___0___relu(out_34); out_34 = None
out_36 = self.getattr_L__mod___layer2___0___conv3(out_35); out_35 = None
out_37 = self.getattr_L__mod___layer2___0___bn3(out_36); out_36 = None
getattr_l__mod___layer2___0___downsample_0 = self.getattr_L__mod___layer2___0___downsample_0(out_29); out_29 = None
identity_1 = self.getattr_L__mod___layer2___0___downsample_1(getattr_l__mod___layer2___0___downsample_0); getattr_l__mod___layer2___0___downsample_0 = None
out_37 += identity_1; out_38 = out_37; out_37 = identity_1 = None
out_39 = self.getattr_L__mod___layer2___0___relu(out_38); out_38 = None
out_40 = self.getattr_L__mod___layer2___1___conv1(out_39)
out_41 = self.getattr_L__mod___layer2___1___bn1(out_40); out_40 = None
out_42 = self.getattr_L__mod___layer2___1___relu(out_41); out_41 = None
out_43 = self.getattr_L__mod___layer2___1___conv2(out_42); out_42 = None
out_44 = self.getattr_L__mod___layer2___1___bn2(out_43); out_43 = None
out_45 = self.getattr_L__mod___layer2___1___relu(out_44); out_44 = None
out_46 = self.getattr_L__mod___layer2___1___conv3(out_45); out_45 = None
out_47 = self.getattr_L__mod___layer2___1___bn3(out_46); out_46 = None
out_47 += out_39; out_48 = out_47; out_47 = out_39 = None
out_49 = self.getattr_L__mod___layer2___1___relu(out_48); out_48 = None
out_50 = self.getattr_L__mod___layer2___2___conv1(out_49)
out_51 = self.getattr_L__mod___layer2___2___bn1(out_50); out_50 = None
out_52 = self.getattr_L__mod___layer2___2___relu(out_51); out_51 = None
out_53 = self.getattr_L__mod___layer2___2___conv2(out_52); out_52 = None
out_54 = self.getattr_L__mod___layer2___2___bn2(out_53); out_53 = None
out_55 = self.getattr_L__mod___layer2___2___relu(out_54); out_54 = None
out_56 = self.getattr_L__mod___layer2___2___conv3(out_55); out_55 = None
out_57 = self.getattr_L__mod___layer2___2___bn3(out_56); out_56 = None
out_57 += out_49; out_58 = out_57; out_57 = out_49 = None
out_59 = self.getattr_L__mod___layer2___2___relu(out_58); out_58 = None
out_60 = self.getattr_L__mod___layer2___3___conv1(out_59)
out_61 = self.getattr_L__mod___layer2___3___bn1(out_60); out_60 = None
out_62 = self.getattr_L__mod___layer2___3___relu(out_61); out_61 = None
out_63 = self.getattr_L__mod___layer2___3___conv2(out_62); out_62 = None
out_64 = self.getattr_L__mod___layer2___3___bn2(out_63); out_63 = None
out_65 = self.getattr_L__mod___layer2___3___relu(out_64); out_64 = None
out_66 = self.getattr_L__mod___layer2___3___conv3(out_65); out_65 = None
out_67 = self.getattr_L__mod___layer2___3___bn3(out_66); out_66 = None
out_67 += out_59; out_68 = out_67; out_67 = out_59 = None
out_69 = self.getattr_L__mod___layer2___3___relu(out_68); out_68 = None
out_70 = self.getattr_L__mod___layer3___0___conv1(out_69)
out_71 = self.getattr_L__mod___layer3___0___bn1(out_70); out_70 = None
out_72 = self.getattr_L__mod___layer3___0___relu(out_71); out_71 = None
out_73 = self.getattr_L__mod___layer3___0___conv2(out_72); out_72 = None
out_74 = self.getattr_L__mod___layer3___0___bn2(out_73); out_73 = None
out_75 = self.getattr_L__mod___layer3___0___relu(out_74); out_74 = None
out_76 = self.getattr_L__mod___layer3___0___conv3(out_75); out_75 = None
out_77 = self.getattr_L__mod___layer3___0___bn3(out_76); out_76 = None
getattr_l__mod___layer3___0___downsample_0 = self.getattr_L__mod___layer3___0___downsample_0(out_69); out_69 = None
identity_2 = self.getattr_L__mod___layer3___0___downsample_1(getattr_l__mod___layer3___0___downsample_0); getattr_l__mod___layer3___0___downsample_0 = None
out_77 += identity_2; out_78 = out_77; out_77 = identity_2 = None
out_79 = self.getattr_L__mod___layer3___0___relu(out_78); out_78 = None
out_80 = self.getattr_L__mod___layer3___1___conv1(out_79)
out_81 = self.getattr_L__mod___layer3___1___bn1(out_80); out_80 = None
out_82 = self.getattr_L__mod___layer3___1___relu(out_81); out_81 = None
out_83 = self.getattr_L__mod___layer3___1___conv2(out_82); out_82 = None
out_84 = self.getattr_L__mod___layer3___1___bn2(out_83); out_83 = None
out_85 = self.getattr_L__mod___layer3___1___relu(out_84); out_84 = None
out_86 = self.getattr_L__mod___layer3___1___conv3(out_85); out_85 = None
out_87 = self.getattr_L__mod___layer3___1___bn3(out_86); out_86 = None
out_87 += out_79; out_88 = out_87; out_87 = out_79 = None
out_89 = self.getattr_L__mod___layer3___1___relu(out_88); out_88 = None
out_90 = self.getattr_L__mod___layer3___2___conv1(out_89)
out_91 = self.getattr_L__mod___layer3___2___bn1(out_90); out_90 = None
out_92 = self.getattr_L__mod___layer3___2___relu(out_91); out_91 = None
out_93 = self.getattr_L__mod___layer3___2___conv2(out_92); out_92 = None
out_94 = self.getattr_L__mod___layer3___2___bn2(out_93); out_93 = None
out_95 = self.getattr_L__mod___layer3___2___relu(out_94); out_94 = None
out_96 = self.getattr_L__mod___layer3___2___conv3(out_95); out_95 = None
out_97 = self.getattr_L__mod___layer3___2___bn3(out_96); out_96 = None
out_97 += out_89; out_98 = out_97; out_97 = out_89 = None
out_99 = self.getattr_L__mod___layer3___2___relu(out_98); out_98 = None
out_100 = self.getattr_L__mod___layer3___3___conv1(out_99)
out_101 = self.getattr_L__mod___layer3___3___bn1(out_100); out_100 = None
out_102 = self.getattr_L__mod___layer3___3___relu(out_101); out_101 = None
out_103 = self.getattr_L__mod___layer3___3___conv2(out_102); out_102 = None
out_104 = self.getattr_L__mod___layer3___3___bn2(out_103); out_103 = None
out_105 = self.getattr_L__mod___layer3___3___relu(out_104); out_104 = None
out_106 = self.getattr_L__mod___layer3___3___conv3(out_105); out_105 = None
out_107 = self.getattr_L__mod___layer3___3___bn3(out_106); out_106 = None
out_107 += out_99; out_108 = out_107; out_107 = out_99 = None
out_109 = self.getattr_L__mod___layer3___3___relu(out_108); out_108 = None
out_110 = self.getattr_L__mod___layer3___4___conv1(out_109)
out_111 = self.getattr_L__mod___layer3___4___bn1(out_110); out_110 = None
out_112 = self.getattr_L__mod___layer3___4___relu(out_111); out_111 = None
out_113 = self.getattr_L__mod___layer3___4___conv2(out_112); out_112 = None
out_114 = self.getattr_L__mod___layer3___4___bn2(out_113); out_113 = None
out_115 = self.getattr_L__mod___layer3___4___relu(out_114); out_114 = None
out_116 = self.getattr_L__mod___layer3___4___conv3(out_115); out_115 = None
out_117 = self.getattr_L__mod___layer3___4___bn3(out_116); out_116 = None
out_117 += out_109; out_118 = out_117; out_117 = out_109 = None
out_119 = self.getattr_L__mod___layer3___4___relu(out_118); out_118 = None
out_120 = self.getattr_L__mod___layer3___5___conv1(out_119)
out_121 = self.getattr_L__mod___layer3___5___bn1(out_120); out_120 = None
out_122 = self.getattr_L__mod___layer3___5___relu(out_121); out_121 = None
out_123 = self.getattr_L__mod___layer3___5___conv2(out_122); out_122 = None
out_124 = self.getattr_L__mod___layer3___5___bn2(out_123); out_123 = None
out_125 = self.getattr_L__mod___layer3___5___relu(out_124); out_124 = None
out_126 = self.getattr_L__mod___layer3___5___conv3(out_125); out_125 = None
out_127 = self.getattr_L__mod___layer3___5___bn3(out_126); out_126 = None
out_127 += out_119; out_128 = out_127; out_127 = out_119 = None
out_129 = self.getattr_L__mod___layer3___5___relu(out_128); out_128 = None
out_130 = self.getattr_L__mod___layer4___0___conv1(out_129)
out_131 = self.getattr_L__mod___layer4___0___bn1(out_130); out_130 = None
out_132 = self.getattr_L__mod___layer4___0___relu(out_131); out_131 = None
out_133 = self.getattr_L__mod___layer4___0___conv2(out_132); out_132 = None
out_134 = self.getattr_L__mod___layer4___0___bn2(out_133); out_133 = None
out_135 = self.getattr_L__mod___layer4___0___relu(out_134); out_134 = None
out_136 = self.getattr_L__mod___layer4___0___conv3(out_135); out_135 = None
out_137 = self.getattr_L__mod___layer4___0___bn3(out_136); out_136 = None
getattr_l__mod___layer4___0___downsample_0 = self.getattr_L__mod___layer4___0___downsample_0(out_129); out_129 = None
identity_3 = self.getattr_L__mod___layer4___0___downsample_1(getattr_l__mod___layer4___0___downsample_0); getattr_l__mod___layer4___0___downsample_0 = None
out_137 += identity_3; out_138 = out_137; out_137 = identity_3 = None
out_139 = self.getattr_L__mod___layer4___0___relu(out_138); out_138 = None
out_140 = self.getattr_L__mod___layer4___1___conv1(out_139)
out_141 = self.getattr_L__mod___layer4___1___bn1(out_140); out_140 = None
out_142 = self.getattr_L__mod___layer4___1___relu(out_141); out_141 = None
out_143 = self.getattr_L__mod___layer4___1___conv2(out_142); out_142 = None
out_144 = self.getattr_L__mod___layer4___1___bn2(out_143); out_143 = None
out_145 = self.getattr_L__mod___layer4___1___relu(out_144); out_144 = None
out_146 = self.getattr_L__mod___layer4___1___conv3(out_145); out_145 = None
out_147 = self.getattr_L__mod___layer4___1___bn3(out_146); out_146 = None
out_147 += out_139; out_148 = out_147; out_147 = out_139 = None
out_149 = self.getattr_L__mod___layer4___1___relu(out_148); out_148 = None
out_150 = self.getattr_L__mod___layer4___2___conv1(out_149)
out_151 = self.getattr_L__mod___layer4___2___bn1(out_150); out_150 = None
out_152 = self.getattr_L__mod___layer4___2___relu(out_151); out_151 = None
out_153 = self.getattr_L__mod___layer4___2___conv2(out_152); out_152 = None
out_154 = self.getattr_L__mod___layer4___2___bn2(out_153); out_153 = None
out_155 = self.getattr_L__mod___layer4___2___relu(out_154); out_154 = None
out_156 = self.getattr_L__mod___layer4___2___conv3(out_155); out_155 = None
out_157 = self.getattr_L__mod___layer4___2___bn3(out_156); out_156 = None
out_157 += out_149; out_158 = out_157; out_157 = out_149 = None
out_159 = self.getattr_L__mod___layer4___2___relu(out_158); out_158 = None
x_4 = self.L__mod___avgpool(out_159); out_159 = None
x_5 = torch.flatten(x_4, 1); x_4 = None
x_6 = self.L__mod___fc(x_5); x_5 = None
return (x_6,)
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