in models/hrnet125.py [0:0]
def __init__(self):
super().__init__()
self.conv1 = torch.nn.modules.conv.Conv2d(in_channels=3, out_channels=64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=1, bias=False, padding_mode="zeros")
self.bn1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.relu1 = torch.nn.modules.activation.ReLU(False)
self.downsample_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=64, bias=True, padding_mode="zeros")
self.downsample_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.downsample_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.downsample_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=64, out_channels=64, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.downsample_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.downsample_relu1 = torch.nn.modules.activation.ReLU(False)
self.downsample_conv2_0 = torch.nn.modules.conv.Conv2d(in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), padding=(3, 3), dilation=(3, 3), groups=64, bias=True, padding_mode="zeros")
self.downsample_conv2_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.downsample_conv2_2 = torch.nn.modules.activation.ReLU(False)
self.downsample_conv2_3 = torch.nn.modules.conv.Conv2d(in_channels=64, out_channels=64, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.downsample_conv2_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.downsample_downsample_res_conv_0 = torch.nn.modules.conv.Conv2d(in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=64, bias=True, padding_mode="zeros")
self.downsample_downsample_res_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.downsample_downsample_res_conv_2 = torch.nn.modules.activation.ReLU(False)
self.downsample_downsample_res_conv_3 = torch.nn.modules.conv.Conv2d(in_channels=64, out_channels=64, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.downsample_downsample_res_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.downsample_relu2 = torch.nn.modules.activation.ReLU(False)
self.layer1_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=64, bias=True, padding_mode="zeros")
self.layer1_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.layer1_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.layer1_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=64, out_channels=64, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.layer1_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.layer1_1_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=64, bias=True, padding_mode="zeros")
self.layer1_1_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.layer1_1_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.layer1_1_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=64, out_channels=64, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.layer1_1_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.cut1_0 = torch.nn.modules.conv.Conv2d(in_channels=64, out_channels=64, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.cut1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.cut1_2 = torch.nn.modules.activation.ReLU(False)
self.transition1_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=64, bias=True, padding_mode="zeros")
self.transition1_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.transition1_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.transition1_0_0_3 = torch.nn.modules.conv.Conv2d(in_channels=64, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.transition1_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.transition1_0_2 = torch.nn.modules.activation.ReLU(False)
self.transition1_1_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=64, bias=True, padding_mode="zeros")
self.transition1_1_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.transition1_1_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.transition1_1_0_0_3 = torch.nn.modules.conv.Conv2d(in_channels=64, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.transition1_1_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.transition1_1_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage2_0_branches_0_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.stage2_0_branches_0_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage2_0_branches_0_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.stage2_0_branches_0_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage2_0_branches_0_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage2_0_branches_0_1_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage2_0_branches_0_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage2_0_branches_0_1_2 = torch.nn.modules.activation.ReLU(False)
self.stage2_0_branches_1_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, bias=True, padding_mode="zeros")
self.stage2_0_branches_1_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage2_0_branches_1_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.stage2_0_branches_1_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage2_0_branches_1_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage2_0_branches_1_1_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage2_0_branches_1_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage2_0_branches_1_1_2 = torch.nn.modules.activation.ReLU(False)
self.stage2_0_fuse_layers_0_1_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=False, padding_mode="zeros")
self.stage2_0_fuse_layers_0_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage2_0_fuse_layers_0_1_2 = torch.nn.modules.upsampling.Upsample(size=None, scale_factor=2.0, mode="nearest", align_corners=None)
self.stage2_0_relu_cbrs_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage2_0_relu_cbrs_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage2_0_relu_cbrs_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage2_0_fuse_layers_1_0_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.stage2_0_fuse_layers_1_0_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage2_0_fuse_layers_1_0_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage2_0_fuse_layers_1_0_0_0_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage2_0_fuse_layers_1_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage2_0_relu_cbrs_1_0_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage2_0_relu_cbrs_1_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage2_0_relu_cbrs_1_0_2 = torch.nn.modules.activation.ReLU(False)
self.transition2_2_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=32, bias=True, padding_mode="zeros")
self.transition2_2_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.transition2_2_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.transition2_2_0_0_3 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.transition2_2_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.transition2_2_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_0_branches_0_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.stage3_0_branches_0_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_branches_0_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_0_branches_0_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_0_branches_0_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_branches_0_1_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_0_branches_0_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_branches_0_1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_0_branches_1_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, bias=True, padding_mode="zeros")
self.stage3_0_branches_1_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_branches_1_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_0_branches_1_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_0_branches_1_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_branches_1_1_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_0_branches_1_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_branches_1_1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_0_branches_2_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=56, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=56, bias=True, padding_mode="zeros")
self.stage3_0_branches_2_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_branches_2_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_0_branches_2_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_0_branches_2_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_branches_2_1_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_0_branches_2_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_branches_2_1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_0_fuse_layers_0_1_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=False, padding_mode="zeros")
self.stage3_0_fuse_layers_0_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_fuse_layers_0_1_2 = torch.nn.modules.upsampling.Upsample(size=None, scale_factor=2.0, mode="nearest", align_corners=None)
self.stage3_0_fuse_layers_0_2_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=False, padding_mode="zeros")
self.stage3_0_fuse_layers_0_2_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_fuse_layers_0_2_2 = torch.nn.modules.upsampling.Upsample(size=None, scale_factor=4.0, mode="nearest", align_corners=None)
self.stage3_0_relu_cbrs_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_0_relu_cbrs_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_relu_cbrs_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_0_fuse_layers_1_0_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.stage3_0_fuse_layers_1_0_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_fuse_layers_1_0_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_0_fuse_layers_1_0_0_0_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_0_fuse_layers_1_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_fuse_layers_1_2_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=False, padding_mode="zeros")
self.stage3_0_fuse_layers_1_2_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_fuse_layers_1_2_2 = torch.nn.modules.upsampling.Upsample(size=None, scale_factor=2.0, mode="nearest", align_corners=None)
self.stage3_0_relu_cbrs_1_0_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_0_relu_cbrs_1_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_relu_cbrs_1_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_0_fuse_layers_2_0_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.stage3_0_fuse_layers_2_0_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_fuse_layers_2_0_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_0_fuse_layers_2_0_0_0_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_0_fuse_layers_2_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_fuse_layers_2_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_0_fuse_layers_2_0_1_0_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.stage3_0_fuse_layers_2_0_1_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_fuse_layers_2_0_1_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_0_fuse_layers_2_0_1_0_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_0_fuse_layers_2_0_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_fuse_layers_2_1_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=32, bias=True, padding_mode="zeros")
self.stage3_0_fuse_layers_2_1_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_fuse_layers_2_1_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_0_fuse_layers_2_1_0_0_3 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_0_fuse_layers_2_1_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_relu_cbrs_2_0_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_0_relu_cbrs_2_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_0_relu_cbrs_2_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_1_branches_0_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.stage3_1_branches_0_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_branches_0_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_1_branches_0_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_1_branches_0_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_branches_0_1_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_1_branches_0_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_branches_0_1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_1_branches_1_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, bias=True, padding_mode="zeros")
self.stage3_1_branches_1_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_branches_1_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_1_branches_1_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_1_branches_1_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_branches_1_1_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_1_branches_1_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_branches_1_1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_1_branches_2_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=56, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=56, bias=True, padding_mode="zeros")
self.stage3_1_branches_2_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_branches_2_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_1_branches_2_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_1_branches_2_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_branches_2_1_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_1_branches_2_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_branches_2_1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_1_fuse_layers_0_1_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=False, padding_mode="zeros")
self.stage3_1_fuse_layers_0_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_fuse_layers_0_1_2 = torch.nn.modules.upsampling.Upsample(size=None, scale_factor=2.0, mode="nearest", align_corners=None)
self.stage3_1_fuse_layers_0_2_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=False, padding_mode="zeros")
self.stage3_1_fuse_layers_0_2_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_fuse_layers_0_2_2 = torch.nn.modules.upsampling.Upsample(size=None, scale_factor=4.0, mode="nearest", align_corners=None)
self.stage3_1_relu_cbrs_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_1_relu_cbrs_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_relu_cbrs_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_1_fuse_layers_1_0_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.stage3_1_fuse_layers_1_0_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_fuse_layers_1_0_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_1_fuse_layers_1_0_0_0_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_1_fuse_layers_1_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_fuse_layers_1_2_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=False, padding_mode="zeros")
self.stage3_1_fuse_layers_1_2_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_fuse_layers_1_2_2 = torch.nn.modules.upsampling.Upsample(size=None, scale_factor=2.0, mode="nearest", align_corners=None)
self.stage3_1_relu_cbrs_1_0_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_1_relu_cbrs_1_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_relu_cbrs_1_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_1_fuse_layers_2_0_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.stage3_1_fuse_layers_2_0_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_fuse_layers_2_0_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_1_fuse_layers_2_0_0_0_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_1_fuse_layers_2_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_fuse_layers_2_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_1_fuse_layers_2_0_1_0_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.stage3_1_fuse_layers_2_0_1_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_fuse_layers_2_0_1_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_1_fuse_layers_2_0_1_0_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_1_fuse_layers_2_0_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_fuse_layers_2_1_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=32, bias=True, padding_mode="zeros")
self.stage3_1_fuse_layers_2_1_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_fuse_layers_2_1_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_1_fuse_layers_2_1_0_0_3 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_1_fuse_layers_2_1_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_relu_cbrs_2_0_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_1_relu_cbrs_2_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_1_relu_cbrs_2_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_2_branches_0_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.stage3_2_branches_0_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_branches_0_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_2_branches_0_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_2_branches_0_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_branches_0_1_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_2_branches_0_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_branches_0_1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_2_branches_1_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, bias=True, padding_mode="zeros")
self.stage3_2_branches_1_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_branches_1_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_2_branches_1_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_2_branches_1_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_branches_1_1_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_2_branches_1_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_branches_1_1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_2_branches_2_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=56, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=56, bias=True, padding_mode="zeros")
self.stage3_2_branches_2_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_branches_2_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_2_branches_2_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_2_branches_2_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_branches_2_1_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_2_branches_2_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_branches_2_1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_2_fuse_layers_0_1_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=False, padding_mode="zeros")
self.stage3_2_fuse_layers_0_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_fuse_layers_0_1_2 = torch.nn.modules.upsampling.Upsample(size=None, scale_factor=2.0, mode="nearest", align_corners=None)
self.stage3_2_fuse_layers_0_2_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=False, padding_mode="zeros")
self.stage3_2_fuse_layers_0_2_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_fuse_layers_0_2_2 = torch.nn.modules.upsampling.Upsample(size=None, scale_factor=4.0, mode="nearest", align_corners=None)
self.stage3_2_relu_cbrs_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_2_relu_cbrs_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_relu_cbrs_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_2_fuse_layers_1_0_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.stage3_2_fuse_layers_1_0_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_fuse_layers_1_0_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_2_fuse_layers_1_0_0_0_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_2_fuse_layers_1_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_fuse_layers_1_2_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=False, padding_mode="zeros")
self.stage3_2_fuse_layers_1_2_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_fuse_layers_1_2_2 = torch.nn.modules.upsampling.Upsample(size=None, scale_factor=2.0, mode="nearest", align_corners=None)
self.stage3_2_relu_cbrs_1_0_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_2_relu_cbrs_1_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_relu_cbrs_1_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_2_fuse_layers_2_0_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.stage3_2_fuse_layers_2_0_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_fuse_layers_2_0_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_2_fuse_layers_2_0_0_0_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_2_fuse_layers_2_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_fuse_layers_2_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_2_fuse_layers_2_0_1_0_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.stage3_2_fuse_layers_2_0_1_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_fuse_layers_2_0_1_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_2_fuse_layers_2_0_1_0_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_2_fuse_layers_2_0_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_fuse_layers_2_1_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=32, bias=True, padding_mode="zeros")
self.stage3_2_fuse_layers_2_1_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_fuse_layers_2_1_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_2_fuse_layers_2_1_0_0_3 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_2_fuse_layers_2_1_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_relu_cbrs_2_0_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_2_relu_cbrs_2_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_2_relu_cbrs_2_0_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_3_branches_0_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.stage3_3_branches_0_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_3_branches_0_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_3_branches_0_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_3_branches_0_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_3_branches_0_1_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_3_branches_0_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_3_branches_0_1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_3_branches_1_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, bias=True, padding_mode="zeros")
self.stage3_3_branches_1_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_3_branches_1_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_3_branches_1_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_3_branches_1_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_3_branches_1_1_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=32, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_3_branches_1_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_3_branches_1_1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_3_branches_2_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=56, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=56, bias=True, padding_mode="zeros")
self.stage3_3_branches_2_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_3_branches_2_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_3_branches_2_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_3_branches_2_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_3_branches_2_1_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=56, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_3_branches_2_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=56, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_3_branches_2_1_2 = torch.nn.modules.activation.ReLU(False)
self.stage3_3_fuse_layers_0_1_0 = torch.nn.modules.conv.Conv2d(in_channels=32, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=False, padding_mode="zeros")
self.stage3_3_fuse_layers_0_1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_3_fuse_layers_0_1_2 = torch.nn.modules.upsampling.Upsample(size=None, scale_factor=2.0, mode="nearest", align_corners=None)
self.stage3_3_fuse_layers_0_2_0 = torch.nn.modules.conv.Conv2d(in_channels=56, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=False, padding_mode="zeros")
self.stage3_3_fuse_layers_0_2_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_3_fuse_layers_0_2_2 = torch.nn.modules.upsampling.Upsample(size=None, scale_factor=4.0, mode="nearest", align_corners=None)
self.stage3_3_relu_cbrs_0_0_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.stage3_3_relu_cbrs_0_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.stage3_3_relu_cbrs_0_0_2 = torch.nn.modules.activation.ReLU(False)
self.final_layers_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=46, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.deconv_layers_0_0_0 = torch.nn.modules.upsampling.Upsample(size=None, scale_factor=2.0, mode="nearest", align_corners=None)
self.deconv_layers_0_0_1 = torch.nn.modules.conv.Conv2d(in_channels=62, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=False, padding_mode="zeros")
self.deconv_layers_0_0_2 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.deconv_layers_0_0_3 = torch.nn.modules.activation.ReLU(False)
self.deconv_layers_0_1_0_conv1_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=16, bias=True, padding_mode="zeros")
self.deconv_layers_0_1_0_conv1_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.deconv_layers_0_1_0_conv1_2 = torch.nn.modules.activation.ReLU(False)
self.deconv_layers_0_1_0_conv1_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.deconv_layers_0_1_0_conv1_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.deconv_layers_0_1_0_relu1 = torch.nn.modules.activation.ReLU(False)
self.deconv_layers_0_1_0_conv2_0 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(3, 3), stride=(1, 1), padding=(3, 3), dilation=(3, 3), groups=16, bias=True, padding_mode="zeros")
self.deconv_layers_0_1_0_conv2_1 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.deconv_layers_0_1_0_conv2_2 = torch.nn.modules.activation.ReLU(False)
self.deconv_layers_0_1_0_conv2_3 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=16, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")
self.deconv_layers_0_1_0_conv2_4 = torch.nn.modules.batchnorm.BatchNorm2d(num_features=16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
self.final_layers_1 = torch.nn.modules.conv.Conv2d(in_channels=16, out_channels=23, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, padding_mode="zeros")