Segmentation fault when running python3 detect.py --cfg yolov3-tiny.cfg --weights yolov3-tiny.weights in the provided docker container with Alexeys .cfg and weights files.
Namespace(cfg='yolov3-tiny.cfg', conf_thres=0.3, data='data/coco.data', device='', fourcc='mp4v', half=False, img_size=416, nms_thres=0.5, output='output', source='data/samples', view_img=False, weights='yolov3-tiny.weights')
Using CPU
/home/onnx_tflite_yolov3/models.py:260: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
print("_io.shape", _io.shape)
_io.shape torch.Size([1, 507, 85])
_io.shape torch.Size([1, 2028, 85])
/usr/local/lib/python3.6/dist-packages/torch/onnx/symbolic_helper.py:198: UserWarning: You are trying to export the model with onnx:Upsample for ONNX opset version 9. This operator might cause results to not match the expected results by PyTorch.
ONNX's Upsample/Resize operator did not match Pytorch's Interpolation until opset 11. Attributes to determine how to transform the input were added in onnx:Resize in opset 11 to support Pytorch's behavior (like coordinate_transformation_mode and nearest_mode).
We recommend using opset 11 and above for models using this operator.
"" + str(_export_onnx_opset_version) + ". "
graph(%input.1 : Float(1, 3, 416, 416),
%module_list.0.Conv2d.weight : Float(16, 3, 3, 3),
%module_list.0.BatchNorm2d.weight : Float(16),
%module_list.0.BatchNorm2d.bias : Float(16),
%module_list.0.BatchNorm2d.running_mean : Float(16),
%module_list.0.BatchNorm2d.running_var : Float(16),
%module_list.0.BatchNorm2d.num_batches_tracked : Long(),
%module_list.2.Conv2d.weight : Float(32, 16, 3, 3),
%module_list.2.BatchNorm2d.weight : Float(32),
%module_list.2.BatchNorm2d.bias : Float(32),
%module_list.2.BatchNorm2d.running_mean : Float(32),
%module_list.2.BatchNorm2d.running_var : Float(32),
%module_list.2.BatchNorm2d.num_batches_tracked : Long(),
%module_list.4.Conv2d.weight : Float(64, 32, 3, 3),
%module_list.4.BatchNorm2d.weight : Float(64),
%module_list.4.BatchNorm2d.bias : Float(64),
%module_list.4.BatchNorm2d.running_mean : Float(64),
%module_list.4.BatchNorm2d.running_var : Float(64),
%module_list.4.BatchNorm2d.num_batches_tracked : Long(),
%module_list.6.Conv2d.weight : Float(128, 64, 3, 3),
%module_list.6.BatchNorm2d.weight : Float(128),
%module_list.6.BatchNorm2d.bias : Float(128),
%module_list.6.BatchNorm2d.running_mean : Float(128),
%module_list.6.BatchNorm2d.running_var : Float(128),
%module_list.6.BatchNorm2d.num_batches_tracked : Long(),
%module_list.8.Conv2d.weight : Float(256, 128, 3, 3),
%module_list.8.BatchNorm2d.weight : Float(256),
%module_list.8.BatchNorm2d.bias : Float(256),
%module_list.8.BatchNorm2d.running_mean : Float(256),
%module_list.8.BatchNorm2d.running_var : Float(256),
%module_list.8.BatchNorm2d.num_batches_tracked : Long(),
%module_list.10.Conv2d.weight : Float(512, 256, 3, 3),
%module_list.10.BatchNorm2d.weight : Float(512),
%module_list.10.BatchNorm2d.bias : Float(512),
%module_list.10.BatchNorm2d.running_mean : Float(512),
%module_list.10.BatchNorm2d.running_var : Float(512),
%module_list.10.BatchNorm2d.num_batches_tracked : Long(),
%module_list.12.Conv2d.weight : Float(1024, 512, 3, 3),
%module_list.12.BatchNorm2d.weight : Float(1024),
%module_list.12.BatchNorm2d.bias : Float(1024),
%module_list.12.BatchNorm2d.running_mean : Float(1024),
%module_list.12.BatchNorm2d.running_var : Float(1024),
%module_list.12.BatchNorm2d.num_batches_tracked : Long(),
%module_list.13.Conv2d.weight : Float(256, 1024, 1, 1),
%module_list.13.BatchNorm2d.weight : Float(256),
%module_list.13.BatchNorm2d.bias : Float(256),
%module_list.13.BatchNorm2d.running_mean : Float(256),
%module_list.13.BatchNorm2d.running_var : Float(256),
%module_list.13.BatchNorm2d.num_batches_tracked : Long(),
%module_list.14.Conv2d.weight : Float(512, 256, 3, 3),
%module_list.14.BatchNorm2d.weight : Float(512),
%module_list.14.BatchNorm2d.bias : Float(512),
%module_list.14.BatchNorm2d.running_mean : Float(512),
%module_list.14.BatchNorm2d.running_var : Float(512),
%module_list.14.BatchNorm2d.num_batches_tracked : Long(),
%module_list.15.Conv2d.weight : Float(255, 512, 1, 1),
%module_list.15.Conv2d.bias : Float(255),
%module_list.18.Conv2d.weight : Float(128, 256, 1, 1),
%module_list.18.BatchNorm2d.weight : Float(128),
%module_list.18.BatchNorm2d.bias : Float(128),
%module_list.18.BatchNorm2d.running_mean : Float(128),
%module_list.18.BatchNorm2d.running_var : Float(128),
%module_list.18.BatchNorm2d.num_batches_tracked : Long(),
%module_list.21.Conv2d.weight : Float(256, 384, 3, 3),
%module_list.21.BatchNorm2d.weight : Float(256),
%module_list.21.BatchNorm2d.bias : Float(256),
%module_list.21.BatchNorm2d.running_mean : Float(256),
%module_list.21.BatchNorm2d.running_var : Float(256),
%module_list.21.BatchNorm2d.num_batches_tracked : Long(),
%module_list.22.Conv2d.weight : Float(255, 256, 1, 1),
%module_list.22.Conv2d.bias : Float(255)):
%71 : Float(1, 16, 416, 416) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%input.1, %module_list.0.Conv2d.weight), scope: Darknet/Sequential/Conv2d[Conv2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0
%72 : Float(1, 16, 416, 416) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%71, %module_list.0.BatchNorm2d.weight, %module_list.0.BatchNorm2d.bias, %module_list.0.BatchNorm2d.running_mean, %module_list.0.BatchNorm2d.running_var), scope: Darknet/Sequential/BatchNorm2d[BatchNorm2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0
%73 : Float(1, 16, 416, 416) = onnx::LeakyRelu[alpha=0.1](%72), scope: Darknet/Sequential/LeakyReLU[activation] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0
%74 : Float(1, 16, 208, 208) = onnx::MaxPool[kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2]](%73), scope: Darknet/MaxPool2d # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:488:0
%75 : Float(1, 32, 208, 208) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%74, %module_list.2.Conv2d.weight), scope: Darknet/Sequential/Conv2d[Conv2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0
%76 : Float(1, 32, 208, 208) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%75, %module_list.2.BatchNorm2d.weight, %module_list.2.BatchNorm2d.bias, %module_list.2.BatchNorm2d.running_mean, %module_list.2.BatchNorm2d.running_var), scope: Darknet/Sequential/BatchNorm2d[BatchNorm2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0
%77 : Float(1, 32, 208, 208) = onnx::LeakyRelu[alpha=0.1](%76), scope: Darknet/Sequential/LeakyReLU[activation] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0
%78 : Float(1, 32, 104, 104) = onnx::MaxPool[kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2]](%77), scope: Darknet/MaxPool2d # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:488:0
%79 : Float(1, 64, 104, 104) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%78, %module_list.4.Conv2d.weight), scope: Darknet/Sequential/Conv2d[Conv2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0
%80 : Float(1, 64, 104, 104) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%79, %module_list.4.BatchNorm2d.weight, %module_list.4.BatchNorm2d.bias, %module_list.4.BatchNorm2d.running_mean, %module_list.4.BatchNorm2d.running_var), scope: Darknet/Sequential/BatchNorm2d[BatchNorm2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0
%81 : Float(1, 64, 104, 104) = onnx::LeakyRelu[alpha=0.1](%80), scope: Darknet/Sequential/LeakyReLU[activation] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0
%82 : Float(1, 64, 52, 52) = onnx::MaxPool[kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2]](%81), scope: Darknet/MaxPool2d # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:488:0
%83 : Float(1, 128, 52, 52) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%82, %module_list.6.Conv2d.weight), scope: Darknet/Sequential/Conv2d[Conv2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0
%84 : Float(1, 128, 52, 52) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%83, %module_list.6.BatchNorm2d.weight, %module_list.6.BatchNorm2d.bias, %module_list.6.BatchNorm2d.running_mean, %module_list.6.BatchNorm2d.running_var), scope: Darknet/Sequential/BatchNorm2d[BatchNorm2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0
%85 : Float(1, 128, 52, 52) = onnx::LeakyRelu[alpha=0.1](%84), scope: Darknet/Sequential/LeakyReLU[activation] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0
%86 : Float(1, 128, 26, 26) = onnx::MaxPool[kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2]](%85), scope: Darknet/MaxPool2d # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:488:0
%87 : Float(1, 256, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%86, %module_list.8.Conv2d.weight), scope: Darknet/Sequential/Conv2d[Conv2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0
%88 : Float(1, 256, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%87, %module_list.8.BatchNorm2d.weight, %module_list.8.BatchNorm2d.bias, %module_list.8.BatchNorm2d.running_mean, %module_list.8.BatchNorm2d.running_var), scope: Darknet/Sequential/BatchNorm2d[BatchNorm2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0
%89 : Float(1, 256, 26, 26) = onnx::LeakyRelu[alpha=0.1](%88), scope: Darknet/Sequential/LeakyReLU[activation] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0
%90 : Float(1, 256, 13, 13) = onnx::MaxPool[kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[2, 2]](%89), scope: Darknet/MaxPool2d # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:488:0
%91 : Float(1, 512, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%90, %module_list.10.Conv2d.weight), scope: Darknet/Sequential/Conv2d[Conv2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0
%92 : Float(1, 512, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%91, %module_list.10.BatchNorm2d.weight, %module_list.10.BatchNorm2d.bias, %module_list.10.BatchNorm2d.running_mean, %module_list.10.BatchNorm2d.running_var), scope: Darknet/Sequential/BatchNorm2d[BatchNorm2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0
%93 : Float(1, 512, 13, 13) = onnx::LeakyRelu[alpha=0.1](%92), scope: Darknet/Sequential/LeakyReLU[activation] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0
%94 : Float(1, 512, 14, 14) = onnx::Pad[mode="constant", pads=[0, 0, 0, 0, 0, 0, 1, 1], value=0](%93), scope: Darknet/Sequential/ZeroPad2d[ZeroPad2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2848:0
%95 : Float(1, 512, 13, 13) = onnx::MaxPool[kernel_shape=[2, 2], pads=[0, 0, 0, 0], strides=[1, 1]](%94), scope: Darknet/Sequential/MaxPool2d[MaxPool2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:488:0
%96 : Float(1, 1024, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%95, %module_list.12.Conv2d.weight), scope: Darknet/Sequential/Conv2d[Conv2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0
%97 : Float(1, 1024, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%96, %module_list.12.BatchNorm2d.weight, %module_list.12.BatchNorm2d.bias, %module_list.12.BatchNorm2d.running_mean, %module_list.12.BatchNorm2d.running_var), scope: Darknet/Sequential/BatchNorm2d[BatchNorm2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0
%98 : Float(1, 1024, 13, 13) = onnx::LeakyRelu[alpha=0.1](%97), scope: Darknet/Sequential/LeakyReLU[activation] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0
%99 : Float(1, 256, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%98, %module_list.13.Conv2d.weight), scope: Darknet/Sequential/Conv2d[Conv2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0
%100 : Float(1, 256, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%99, %module_list.13.BatchNorm2d.weight, %module_list.13.BatchNorm2d.bias, %module_list.13.BatchNorm2d.running_mean, %module_list.13.BatchNorm2d.running_var), scope: Darknet/Sequential/BatchNorm2d[BatchNorm2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0
%101 : Float(1, 256, 13, 13) = onnx::LeakyRelu[alpha=0.1](%100), scope: Darknet/Sequential/LeakyReLU[activation] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0
%102 : Float(1, 512, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%101, %module_list.14.Conv2d.weight), scope: Darknet/Sequential/Conv2d[Conv2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0
%103 : Float(1, 512, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%102, %module_list.14.BatchNorm2d.weight, %module_list.14.BatchNorm2d.bias, %module_list.14.BatchNorm2d.running_mean, %module_list.14.BatchNorm2d.running_var), scope: Darknet/Sequential/BatchNorm2d[BatchNorm2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0
%104 : Float(1, 512, 13, 13) = onnx::LeakyRelu[alpha=0.1](%103), scope: Darknet/Sequential/LeakyReLU[activation] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0
%105 : Float(1, 255, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%104, %module_list.15.Conv2d.weight, %module_list.15.Conv2d.bias), scope: Darknet/Sequential/Conv2d[Conv2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0
%106 : Tensor = onnx::Constant[value= 1 3 85 169 [ Variable[CPULongType]{4} ]](), scope: Darknet/YOLOLayer
%107 : Float(1, 3, 85, 169) = onnx::Reshape(%105, %106), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:170:0
%108 : Float(1, 3, 169, 85) = onnx::Transpose[perm=[0, 1, 3, 2]](%107), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:170:0
%109 : Tensor = onnx::Constant[value= 1 3 169 85 [ Variable[CPULongType]{4} ]](), scope: Darknet/YOLOLayer
%110 : Float(1, 3, 169, 85) = onnx::Reshape(%108, %109), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:176:0
%111 : Float(1, 3, 169, 2) = onnx::Slice[axes=[3], ends=[2], starts=[0]](%110), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:177:0
%112 : Float(1, 3, 169, 2) = onnx::Sigmoid(%111), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:177:0
%113 : Float(1, 1, 169, 2) = onnx::Constant[value=<Tensor>]()
%114 : Float(1, 3, 169, 2) = onnx::Add(%112, %113), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:177:0
%115 : Float() = onnx::Constant[value={32}]()
%116 : Float(1, 3, 169, 2) = onnx::Mul(%114, %115)
%117 : Float(1, 3, 169, 2) = onnx::Slice[axes=[3], ends=[4], starts=[2]](%110), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:178:0
%118 : Float(1, 3, 169, 2) = onnx::Exp(%117), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:178:0
%119 : Float(1, 3, 1, 2) = onnx::Constant[value=(1,1,.,.) = 2.5312 2.5625 (1,2,.,.) = 4.2188 5.2812 (1,3,.,.) = 10.7500 9.9688 [ Variable[CPUFloatType]{1,3,1,2} ]]()
%120 : Float(1, 3, 169, 2) = onnx::Mul(%118, %119), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:178:0
%121 : Float() = onnx::Constant[value={32}]()
%122 : Float(1, 3, 169, 2) = onnx::Mul(%120, %121)
%123 : Float(1, 3, 169, 1) = onnx::Slice[axes=[3], ends=[5], starts=[4]](%110), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:182:0
%124 : Float(1, 3, 169, 1) = onnx::Sigmoid(%123), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:182:0
%125 : Float(1, 3, 169, 80) = onnx::Slice[axes=[3], ends=[9223372036854775807], starts=[5]](%110), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:183:0
%126 : Float(1, 3, 169, 80) = onnx::Sigmoid(%125), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:183:0
%127 : Float(1, 3, 169, 85) = onnx::Concat[axis=-1](%116, %122, %124, %126), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:208:0
%128 : Tensor = onnx::Constant[value= 1 -1 85 [ Variable[CPULongType]{3} ]](), scope: Darknet/YOLOLayer
%129 : Float(1, 507, 85) = onnx::Reshape(%127, %128), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:209:0
%130 : Tensor = onnx::Constant[value= 1 3 13 13 85 [ Variable[CPULongType]{5} ]](), scope: Darknet/YOLOLayer
%131 : Float(1, 3, 13, 13, 85) = onnx::Reshape(%110, %130), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:209:0
%132 : Float(1, 128, 13, 13) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%101, %module_list.18.Conv2d.weight), scope: Darknet/Sequential/Conv2d[Conv2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0
%133 : Float(1, 128, 13, 13) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%132, %module_list.18.BatchNorm2d.weight, %module_list.18.BatchNorm2d.bias, %module_list.18.BatchNorm2d.running_mean, %module_list.18.BatchNorm2d.running_var), scope: Darknet/Sequential/BatchNorm2d[BatchNorm2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0
%134 : Float(1, 128, 13, 13) = onnx::LeakyRelu[alpha=0.1](%133), scope: Darknet/Sequential/LeakyReLU[activation] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0
%135 : Long() = onnx::Constant[value={2}](), scope: Darknet/Upsample
%136 : Tensor = onnx::Shape(%134), scope: Darknet/Upsample
%137 : Long() = onnx::Gather[axis=0](%136, %135), scope: Darknet/Upsample # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2481:0
%138 : Float() = onnx::Cast[to=1](%137), scope: Darknet/Upsample # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2481:0
%139 : Float() = onnx::Constant[value={2}]()
%140 : Float() = onnx::Mul(%138, %139)
%141 : Float() = onnx::Cast[to=1](%140), scope: Darknet/Upsample # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2481:0
%142 : Float() = onnx::Floor(%141), scope: Darknet/Upsample # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2481:0
%143 : Long() = onnx::Constant[value={3}](), scope: Darknet/Upsample
%144 : Tensor = onnx::Shape(%134), scope: Darknet/Upsample
%145 : Long() = onnx::Gather[axis=0](%144, %143), scope: Darknet/Upsample # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2481:0
%146 : Float() = onnx::Cast[to=1](%145), scope: Darknet/Upsample # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2481:0
%147 : Float() = onnx::Constant[value={2}]()
%148 : Float() = onnx::Mul(%146, %147)
%149 : Float() = onnx::Cast[to=1](%148), scope: Darknet/Upsample # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2481:0
%150 : Float() = onnx::Floor(%149), scope: Darknet/Upsample # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2481:0
%151 : Tensor = onnx::Unsqueeze[axes=[0]](%142)
%152 : Tensor = onnx::Unsqueeze[axes=[0]](%150)
%153 : Tensor = onnx::Concat[axis=0](%151, %152)
%154 : Tensor = onnx::Constant[value= 1 1 [ Variable[CPUFloatType]{2} ]](), scope: Darknet/Upsample
%155 : Tensor = onnx::Cast[to=1](%153), scope: Darknet/Upsample
%156 : Tensor = onnx::Shape(%134), scope: Darknet/Upsample
%157 : Tensor = onnx::Slice[axes=[0], ends=[4], starts=[2]](%156), scope: Darknet/Upsample
%158 : Tensor = onnx::Cast[to=1](%157), scope: Darknet/Upsample
%159 : Tensor = onnx::Div(%155, %158), scope: Darknet/Upsample
%160 : Tensor = onnx::Concat[axis=0](%154, %159), scope: Darknet/Upsample
%161 : Float(1, 128, 26, 26) = onnx::Upsample[mode="nearest"](%134, %160), scope: Darknet/Upsample # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2500:0
%162 : Float(1, 384, 26, 26) = onnx::Concat[axis=1](%161, %89), scope: Darknet # /home/onnx_tflite_yolov3/models.py:241:0
%163 : Float(1, 256, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%162, %module_list.21.Conv2d.weight), scope: Darknet/Sequential/Conv2d[Conv2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0
%164 : Float(1, 256, 26, 26) = onnx::BatchNormalization[epsilon=1e-05, momentum=0.9](%163, %module_list.21.BatchNorm2d.weight, %module_list.21.BatchNorm2d.bias, %module_list.21.BatchNorm2d.running_mean, %module_list.21.BatchNorm2d.running_var), scope: Darknet/Sequential/BatchNorm2d[BatchNorm2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1670:0
%165 : Float(1, 256, 26, 26) = onnx::LeakyRelu[alpha=0.1](%164), scope: Darknet/Sequential/LeakyReLU[activation] # /usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1061:0
%166 : Float(1, 255, 26, 26) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%165, %module_list.22.Conv2d.weight, %module_list.22.Conv2d.bias), scope: Darknet/Sequential/Conv2d[Conv2d] # /usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py:342:0
%167 : Tensor = onnx::Constant[value= 1 3 85 676 [ Variable[CPULongType]{4} ]](), scope: Darknet/YOLOLayer
%168 : Float(1, 3, 85, 676) = onnx::Reshape(%166, %167), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:170:0
%169 : Float(1, 3, 676, 85) = onnx::Transpose[perm=[0, 1, 3, 2]](%168), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:170:0
%170 : Tensor = onnx::Constant[value= 1 3 676 85 [ Variable[CPULongType]{4} ]](), scope: Darknet/YOLOLayer
%171 : Float(1, 3, 676, 85) = onnx::Reshape(%169, %170), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:176:0
%172 : Float(1, 3, 676, 2) = onnx::Slice[axes=[3], ends=[2], starts=[0]](%171), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:177:0
%173 : Float(1, 3, 676, 2) = onnx::Sigmoid(%172), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:177:0
%174 : Float(1, 1, 676, 2) = onnx::Constant[value=<Tensor>]()
%175 : Float(1, 3, 676, 2) = onnx::Add(%173, %174), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:177:0
%176 : Float() = onnx::Constant[value={16}]()
%177 : Float(1, 3, 676, 2) = onnx::Mul(%175, %176)
%178 : Float(1, 3, 676, 2) = onnx::Slice[axes=[3], ends=[4], starts=[2]](%171), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:178:0
%179 : Float(1, 3, 676, 2) = onnx::Exp(%178), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:178:0
%180 : Float(1, 3, 1, 2) = onnx::Constant[value=(1,1,.,.) = 0.6250 0.8750 (1,2,.,.) = 1.4375 1.6875 (1,3,.,.) = 2.3125 3.6250 [ Variable[CPUFloatType]{1,3,1,2} ]]()
%181 : Float(1, 3, 676, 2) = onnx::Mul(%179, %180), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:178:0
%182 : Float() = onnx::Constant[value={16}]()
%183 : Float(1, 3, 676, 2) = onnx::Mul(%181, %182)
%184 : Float(1, 3, 676, 1) = onnx::Slice[axes=[3], ends=[5], starts=[4]](%171), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:182:0
%185 : Float(1, 3, 676, 1) = onnx::Sigmoid(%184), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:182:0
%186 : Float(1, 3, 676, 80) = onnx::Slice[axes=[3], ends=[9223372036854775807], starts=[5]](%171), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:183:0
%187 : Float(1, 3, 676, 80) = onnx::Sigmoid(%186), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:183:0
%188 : Float(1, 3, 676, 85) = onnx::Concat[axis=-1](%177, %183, %185, %187), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:208:0
%189 : Tensor = onnx::Constant[value= 1 -1 85 [ Variable[CPULongType]{3} ]](), scope: Darknet/YOLOLayer
%190 : Float(1, 2028, 85) = onnx::Reshape(%188, %189), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:209:0
%191 : Tensor = onnx::Constant[value= 1 3 26 26 85 [ Variable[CPULongType]{5} ]](), scope: Darknet/YOLOLayer
%192 : Float(1, 3, 26, 26, 85) = onnx::Reshape(%171, %191), scope: Darknet/YOLOLayer # /home/onnx_tflite_yolov3/models.py:209:0
%193 : Float(1, 2535, 85) = onnx::Concat[axis=1](%129, %190), scope: Darknet # /home/onnx_tflite_yolov3/models.py:262:0
return (%193, %131, %192)
Segmentation fault (core dumped)