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Daniil-Osokin / lightweight-human-pose-estimation-3d-demo.pytorch

Real-time 3D multi-person pose estimation demo in PyTorch. OpenVINO backend can be used for fast inference on CPU.
Apache License 2.0
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Error converting checkpoints to OpenVino format #23

Closed morgunl2 closed 4 years ago

morgunl2 commented 4 years ago 
(cv) user@Descartes:~/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch$ python scripts/convert_to_onnx.py --checkpoint-path human-pose-estimation-3d.pth
[WARNING] Not found pre-trained parameters for fake_conv_heatmaps.weight
[WARNING] Not found pre-trained parameters for fake_conv_pafs.weight
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      %Pose3D.prediction.trunk.0.trunk.0.1.weight : Float(128),
      %Pose3D.prediction.trunk.0.trunk.0.1.bias : Float(128),
      %Pose3D.prediction.trunk.0.trunk.0.1.running_mean : Float(128),
      %Pose3D.prediction.trunk.0.trunk.0.1.running_var : Float(128),
      %Pose3D.prediction.trunk.0.trunk.1.0.weight : Float(128, 128, 3, 3),
      %Pose3D.prediction.trunk.0.trunk.1.0.bias : Float(128),
      %Pose3D.prediction.trunk.0.trunk.1.1.weight : Float(128),
      %Pose3D.prediction.trunk.0.trunk.1.1.bias : Float(128),
      %Pose3D.prediction.trunk.0.trunk.1.1.running_mean : Float(128),
      %Pose3D.prediction.trunk.0.trunk.1.1.running_var : Float(128),
      %Pose3D.prediction.trunk.1.initial.0.weight : Float(128, 128, 1, 1),
      %Pose3D.prediction.trunk.1.initial.0.bias : Float(128),
      %Pose3D.prediction.trunk.1.trunk.0.0.weight : Float(128, 128, 3, 3),
      %Pose3D.prediction.trunk.1.trunk.0.0.bias : Float(128),
      %Pose3D.prediction.trunk.1.trunk.0.1.weight : Float(128),
      %Pose3D.prediction.trunk.1.trunk.0.1.bias : Float(128),
      %Pose3D.prediction.trunk.1.trunk.0.1.running_mean : Float(128),
      %Pose3D.prediction.trunk.1.trunk.0.1.running_var : Float(128),
      %Pose3D.prediction.trunk.1.trunk.1.0.weight : Float(128, 128, 3, 3),
      %Pose3D.prediction.trunk.1.trunk.1.0.bias : Float(128),
      %Pose3D.prediction.trunk.1.trunk.1.1.weight : Float(128),
      %Pose3D.prediction.trunk.1.trunk.1.1.bias : Float(128),
      %Pose3D.prediction.trunk.1.trunk.1.1.running_mean : Float(128),
      %Pose3D.prediction.trunk.1.trunk.1.1.running_var : Float(128),
      %Pose3D.prediction.feature_maps.0.0.weight : Float(128, 128, 1, 1),
      %Pose3D.prediction.feature_maps.0.0.bias : Float(128),
      %Pose3D.prediction.feature_maps.1.0.weight : Float(57, 128, 1, 1),
      %Pose3D.prediction.feature_maps.1.0.bias : Float(57),
      %fake_conv_heatmaps.weight : Float(19, 19, 1, 1),
      %fake_conv_pafs.weight : Float(38, 38, 1, 1)):
  %401 : Float(1, 32, 128, 224) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%data, %model.0.0.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %402 : Float(1, 32, 128, 224) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%401, %model.0.1.weight, %model.0.1.bias, %model.0.1.running_mean, %model.0.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %403 : Float(1, 32, 128, 224) = onnx::Relu(%402) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %404 : Float(1, 32, 128, 224) = onnx::Conv[dilations=[1, 1], group=32, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%403, %model.1.0.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %405 : Float(1, 32, 128, 224) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%404, %model.1.1.weight, %model.1.1.bias, %model.1.1.running_mean, %model.1.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %406 : Float(1, 32, 128, 224) = onnx::Relu(%405) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %407 : Float(1, 64, 128, 224) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%406, %model.1.3.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %408 : Float(1, 64, 128, 224) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%407, %model.1.4.weight, %model.1.4.bias, %model.1.4.running_mean, %model.1.4.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %409 : Float(1, 64, 128, 224) = onnx::Relu(%408) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %410 : Float(1, 64, 64, 112) = onnx::Conv[dilations=[1, 1], group=64, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%409, %model.2.0.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %411 : Float(1, 64, 64, 112) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%410, %model.2.1.weight, %model.2.1.bias, %model.2.1.running_mean, %model.2.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %412 : Float(1, 64, 64, 112) = onnx::Relu(%411) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %413 : Float(1, 128, 64, 112) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%412, %model.2.3.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %414 : Float(1, 128, 64, 112) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%413, %model.2.4.weight, %model.2.4.bias, %model.2.4.running_mean, %model.2.4.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %415 : Float(1, 128, 64, 112) = onnx::Relu(%414) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %416 : Float(1, 128, 64, 112) = onnx::Conv[dilations=[1, 1], group=128, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%415, %model.3.0.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %417 : Float(1, 128, 64, 112) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%416, %model.3.1.weight, %model.3.1.bias, %model.3.1.running_mean, %model.3.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %418 : Float(1, 128, 64, 112) = onnx::Relu(%417) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %419 : Float(1, 128, 64, 112) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%418, %model.3.3.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %420 : Float(1, 128, 64, 112) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%419, %model.3.4.weight, %model.3.4.bias, %model.3.4.running_mean, %model.3.4.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %421 : Float(1, 128, 64, 112) = onnx::Relu(%420) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %422 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=128, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[2, 2]](%421, %model.4.0.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %423 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%422, %model.4.1.weight, %model.4.1.bias, %model.4.1.running_mean, %model.4.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %424 : Float(1, 128, 32, 56) = onnx::Relu(%423) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %425 : Float(1, 256, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%424, %model.4.3.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %426 : Float(1, 256, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%425, %model.4.4.weight, %model.4.4.bias, %model.4.4.running_mean, %model.4.4.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %427 : Float(1, 256, 32, 56) = onnx::Relu(%426) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %428 : Float(1, 256, 32, 56) = onnx::Conv[dilations=[1, 1], group=256, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%427, %model.5.0.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %429 : Float(1, 256, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%428, %model.5.1.weight, %model.5.1.bias, %model.5.1.running_mean, %model.5.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %430 : Float(1, 256, 32, 56) = onnx::Relu(%429) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %431 : Float(1, 256, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%430, %model.5.3.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %432 : Float(1, 256, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%431, %model.5.4.weight, %model.5.4.bias, %model.5.4.running_mean, %model.5.4.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %433 : Float(1, 256, 32, 56) = onnx::Relu(%432) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %434 : Float(1, 256, 32, 56) = onnx::Conv[dilations=[1, 1], group=256, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%433, %model.6.0.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %435 : Float(1, 256, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%434, %model.6.1.weight, %model.6.1.bias, %model.6.1.running_mean, %model.6.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %436 : Float(1, 256, 32, 56) = onnx::Relu(%435) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %437 : Float(1, 512, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%436, %model.6.3.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %438 : Float(1, 512, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%437, %model.6.4.weight, %model.6.4.bias, %model.6.4.running_mean, %model.6.4.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %439 : Float(1, 512, 32, 56) = onnx::Relu(%438) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %440 : Float(1, 512, 32, 56) = onnx::Conv[dilations=[2, 2], group=512, kernel_shape=[3, 3], pads=[2, 2, 2, 2], strides=[1, 1]](%439, %model.7.0.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %441 : Float(1, 512, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%440, %model.7.1.weight, %model.7.1.bias, %model.7.1.running_mean, %model.7.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %442 : Float(1, 512, 32, 56) = onnx::Relu(%441) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %443 : Float(1, 512, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%442, %model.7.3.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %444 : Float(1, 512, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%443, %model.7.4.weight, %model.7.4.bias, %model.7.4.running_mean, %model.7.4.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %445 : Float(1, 512, 32, 56) = onnx::Relu(%444) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %446 : Float(1, 512, 32, 56) = onnx::Conv[dilations=[1, 1], group=512, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%445, %model.8.0.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %447 : Float(1, 512, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%446, %model.8.1.weight, %model.8.1.bias, %model.8.1.running_mean, %model.8.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %448 : Float(1, 512, 32, 56) = onnx::Relu(%447) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %449 : Float(1, 512, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%448, %model.8.3.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %450 : Float(1, 512, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%449, %model.8.4.weight, %model.8.4.bias, %model.8.4.running_mean, %model.8.4.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %451 : Float(1, 512, 32, 56) = onnx::Relu(%450) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %452 : Float(1, 512, 32, 56) = onnx::Conv[dilations=[1, 1], group=512, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%451, %model.9.0.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %453 : Float(1, 512, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%452, %model.9.1.weight, %model.9.1.bias, %model.9.1.running_mean, %model.9.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %454 : Float(1, 512, 32, 56) = onnx::Relu(%453) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %455 : Float(1, 512, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%454, %model.9.3.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %456 : Float(1, 512, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%455, %model.9.4.weight, %model.9.4.bias, %model.9.4.running_mean, %model.9.4.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %457 : Float(1, 512, 32, 56) = onnx::Relu(%456) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %458 : Float(1, 512, 32, 56) = onnx::Conv[dilations=[1, 1], group=512, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%457, %model.10.0.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %459 : Float(1, 512, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%458, %model.10.1.weight, %model.10.1.bias, %model.10.1.running_mean, %model.10.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %460 : Float(1, 512, 32, 56) = onnx::Relu(%459) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %461 : Float(1, 512, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%460, %model.10.3.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %462 : Float(1, 512, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%461, %model.10.4.weight, %model.10.4.bias, %model.10.4.running_mean, %model.10.4.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %463 : Float(1, 512, 32, 56) = onnx::Relu(%462) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %464 : Float(1, 512, 32, 56) = onnx::Conv[dilations=[1, 1], group=512, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%463, %model.11.0.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %465 : Float(1, 512, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%464, %model.11.1.weight, %model.11.1.bias, %model.11.1.running_mean, %model.11.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %466 : Float(1, 512, 32, 56) = onnx::Relu(%465) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %467 : Float(1, 512, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%466, %model.11.3.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %468 : Float(1, 512, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%467, %model.11.4.weight, %model.11.4.bias, %model.11.4.running_mean, %model.11.4.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %469 : Float(1, 512, 32, 56) = onnx::Relu(%468) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %470 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%469, %cpm.align.0.weight, %cpm.align.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %471 : Float(1, 128, 32, 56) = onnx::Relu(%470) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %472 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=128, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%471, %cpm.trunk.0.0.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %473 : Float(1, 128, 32, 56) = onnx::Elu[alpha=1.](%472) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1154:0
  %474 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%473, %cpm.trunk.0.2.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %475 : Float(1, 128, 32, 56) = onnx::Elu[alpha=1.](%474) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1154:0
  %476 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=128, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%475, %cpm.trunk.1.0.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %477 : Float(1, 128, 32, 56) = onnx::Elu[alpha=1.](%476) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1154:0
  %478 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%477, %cpm.trunk.1.2.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %479 : Float(1, 128, 32, 56) = onnx::Elu[alpha=1.](%478) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1154:0
  %480 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=128, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%479, %cpm.trunk.2.0.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %481 : Float(1, 128, 32, 56) = onnx::Elu[alpha=1.](%480) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1154:0
  %482 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%481, %cpm.trunk.2.2.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %483 : Float(1, 128, 32, 56) = onnx::Elu[alpha=1.](%482) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1154:0
  %484 : Float(1, 128, 32, 56) = onnx::Add(%471, %483) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:20:0
  %485 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%484, %cpm.conv.0.weight, %cpm.conv.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %486 : Float(1, 128, 32, 56) = onnx::Relu(%485) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %487 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%486, %initial_stage.trunk.0.0.weight, %initial_stage.trunk.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %488 : Float(1, 128, 32, 56) = onnx::Relu(%487) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %489 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%488, %initial_stage.trunk.1.0.weight, %initial_stage.trunk.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %490 : Float(1, 128, 32, 56) = onnx::Relu(%489) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %491 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%490, %initial_stage.trunk.2.0.weight, %initial_stage.trunk.2.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %492 : Float(1, 128, 32, 56) = onnx::Relu(%491) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %493 : Float(1, 512, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%492, %initial_stage.heatmaps.0.0.weight, %initial_stage.heatmaps.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %494 : Float(1, 512, 32, 56) = onnx::Relu(%493) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %495 : Float(1, 19, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%494, %initial_stage.heatmaps.1.0.weight, %initial_stage.heatmaps.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %496 : Float(1, 512, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%492, %initial_stage.pafs.0.0.weight, %initial_stage.pafs.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %497 : Float(1, 512, 32, 56) = onnx::Relu(%496) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %498 : Float(1, 38, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%497, %initial_stage.pafs.1.0.weight, %initial_stage.pafs.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %499 : Float(1, 185, 32, 56) = onnx::Concat[axis=1](%486, %495, %498) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:186:0
  %500 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%499, %refinement_stages.0.trunk.0.initial.0.weight, %refinement_stages.0.trunk.0.initial.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %501 : Float(1, 128, 32, 56) = onnx::Relu(%500) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %502 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%501, %refinement_stages.0.trunk.0.trunk.0.0.weight, %refinement_stages.0.trunk.0.trunk.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %503 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%502, %refinement_stages.0.trunk.0.trunk.0.1.weight, %refinement_stages.0.trunk.0.trunk.0.1.bias, %refinement_stages.0.trunk.0.trunk.0.1.running_mean, %refinement_stages.0.trunk.0.trunk.0.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %504 : Float(1, 128, 32, 56) = onnx::Relu(%503) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %505 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[2, 2], group=1, kernel_shape=[3, 3], pads=[2, 2, 2, 2], strides=[1, 1]](%504, %refinement_stages.0.trunk.0.trunk.1.0.weight, %refinement_stages.0.trunk.0.trunk.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %506 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%505, %refinement_stages.0.trunk.0.trunk.1.1.weight, %refinement_stages.0.trunk.0.trunk.1.1.bias, %refinement_stages.0.trunk.0.trunk.1.1.running_mean, %refinement_stages.0.trunk.0.trunk.1.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %507 : Float(1, 128, 32, 56) = onnx::Relu(%506) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %508 : Float(1, 128, 32, 56) = onnx::Add(%501, %507) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:60:0
  %509 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%508, %refinement_stages.0.trunk.1.initial.0.weight, %refinement_stages.0.trunk.1.initial.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %510 : Float(1, 128, 32, 56) = onnx::Relu(%509) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %511 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%510, %refinement_stages.0.trunk.1.trunk.0.0.weight, %refinement_stages.0.trunk.1.trunk.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %512 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%511, %refinement_stages.0.trunk.1.trunk.0.1.weight, %refinement_stages.0.trunk.1.trunk.0.1.bias, %refinement_stages.0.trunk.1.trunk.0.1.running_mean, %refinement_stages.0.trunk.1.trunk.0.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %513 : Float(1, 128, 32, 56) = onnx::Relu(%512) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %514 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[2, 2], group=1, kernel_shape=[3, 3], pads=[2, 2, 2, 2], strides=[1, 1]](%513, %refinement_stages.0.trunk.1.trunk.1.0.weight, %refinement_stages.0.trunk.1.trunk.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %515 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%514, %refinement_stages.0.trunk.1.trunk.1.1.weight, %refinement_stages.0.trunk.1.trunk.1.1.bias, %refinement_stages.0.trunk.1.trunk.1.1.running_mean, %refinement_stages.0.trunk.1.trunk.1.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %516 : Float(1, 128, 32, 56) = onnx::Relu(%515) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %517 : Float(1, 128, 32, 56) = onnx::Add(%510, %516) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:60:0
  %518 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%517, %refinement_stages.0.trunk.2.initial.0.weight, %refinement_stages.0.trunk.2.initial.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %519 : Float(1, 128, 32, 56) = onnx::Relu(%518) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %520 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%519, %refinement_stages.0.trunk.2.trunk.0.0.weight, %refinement_stages.0.trunk.2.trunk.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %521 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%520, %refinement_stages.0.trunk.2.trunk.0.1.weight, %refinement_stages.0.trunk.2.trunk.0.1.bias, %refinement_stages.0.trunk.2.trunk.0.1.running_mean, %refinement_stages.0.trunk.2.trunk.0.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %522 : Float(1, 128, 32, 56) = onnx::Relu(%521) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %523 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[2, 2], group=1, kernel_shape=[3, 3], pads=[2, 2, 2, 2], strides=[1, 1]](%522, %refinement_stages.0.trunk.2.trunk.1.0.weight, %refinement_stages.0.trunk.2.trunk.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %524 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%523, %refinement_stages.0.trunk.2.trunk.1.1.weight, %refinement_stages.0.trunk.2.trunk.1.1.bias, %refinement_stages.0.trunk.2.trunk.1.1.running_mean, %refinement_stages.0.trunk.2.trunk.1.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %525 : Float(1, 128, 32, 56) = onnx::Relu(%524) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %526 : Float(1, 128, 32, 56) = onnx::Add(%519, %525) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:60:0
  %527 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%526, %refinement_stages.0.trunk.3.initial.0.weight, %refinement_stages.0.trunk.3.initial.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %528 : Float(1, 128, 32, 56) = onnx::Relu(%527) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %529 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%528, %refinement_stages.0.trunk.3.trunk.0.0.weight, %refinement_stages.0.trunk.3.trunk.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %530 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%529, %refinement_stages.0.trunk.3.trunk.0.1.weight, %refinement_stages.0.trunk.3.trunk.0.1.bias, %refinement_stages.0.trunk.3.trunk.0.1.running_mean, %refinement_stages.0.trunk.3.trunk.0.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %531 : Float(1, 128, 32, 56) = onnx::Relu(%530) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %532 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[2, 2], group=1, kernel_shape=[3, 3], pads=[2, 2, 2, 2], strides=[1, 1]](%531, %refinement_stages.0.trunk.3.trunk.1.0.weight, %refinement_stages.0.trunk.3.trunk.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %533 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%532, %refinement_stages.0.trunk.3.trunk.1.1.weight, %refinement_stages.0.trunk.3.trunk.1.1.bias, %refinement_stages.0.trunk.3.trunk.1.1.running_mean, %refinement_stages.0.trunk.3.trunk.1.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %534 : Float(1, 128, 32, 56) = onnx::Relu(%533) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %535 : Float(1, 128, 32, 56) = onnx::Add(%528, %534) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:60:0
  %536 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%535, %refinement_stages.0.trunk.4.initial.0.weight, %refinement_stages.0.trunk.4.initial.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %537 : Float(1, 128, 32, 56) = onnx::Relu(%536) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %538 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%537, %refinement_stages.0.trunk.4.trunk.0.0.weight, %refinement_stages.0.trunk.4.trunk.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %539 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%538, %refinement_stages.0.trunk.4.trunk.0.1.weight, %refinement_stages.0.trunk.4.trunk.0.1.bias, %refinement_stages.0.trunk.4.trunk.0.1.running_mean, %refinement_stages.0.trunk.4.trunk.0.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %540 : Float(1, 128, 32, 56) = onnx::Relu(%539) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %541 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[2, 2], group=1, kernel_shape=[3, 3], pads=[2, 2, 2, 2], strides=[1, 1]](%540, %refinement_stages.0.trunk.4.trunk.1.0.weight, %refinement_stages.0.trunk.4.trunk.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %542 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%541, %refinement_stages.0.trunk.4.trunk.1.1.weight, %refinement_stages.0.trunk.4.trunk.1.1.bias, %refinement_stages.0.trunk.4.trunk.1.1.running_mean, %refinement_stages.0.trunk.4.trunk.1.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %543 : Float(1, 128, 32, 56) = onnx::Relu(%542) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %544 : Float(1, 128, 32, 56) = onnx::Add(%537, %543) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:60:0
  %545 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%544, %refinement_stages.0.heatmaps.0.0.weight, %refinement_stages.0.heatmaps.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %546 : Float(1, 128, 32, 56) = onnx::Relu(%545) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %547 : Float(1, 19, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%546, %refinement_stages.0.heatmaps.1.0.weight, %refinement_stages.0.heatmaps.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %548 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%544, %refinement_stages.0.pafs.0.0.weight, %refinement_stages.0.pafs.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %549 : Float(1, 128, 32, 56) = onnx::Relu(%548) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %550 : Float(1, 38, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%549, %refinement_stages.0.pafs.1.0.weight, %refinement_stages.0.pafs.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %551 : Float(1, 19, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%547, %fake_conv_heatmaps.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %heatmaps : Float(1, 19, 32, 56) = onnx::Add(%547, %551) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:190:0
  %553 : Float(1, 38, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%550, %fake_conv_pafs.weight) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %pafs : Float(1, 38, 32, 56) = onnx::Add(%550, %553) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:191:0
  %555 : Float(1, 57, 32, 56) = onnx::Concat[axis=1](%547, %550) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:192:0
  %556 : Float(1, 185, 32, 56) = onnx::Concat[axis=1](%486, %555) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:140:0
  %557 : Float(1, 92, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%556, %Pose3D.stem.0.bottleneck.0.0.weight, %Pose3D.stem.0.bottleneck.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %558 : Float(1, 92, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%557, %Pose3D.stem.0.bottleneck.0.1.weight, %Pose3D.stem.0.bottleneck.0.1.bias, %Pose3D.stem.0.bottleneck.0.1.running_mean, %Pose3D.stem.0.bottleneck.0.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %559 : Float(1, 92, 32, 56) = onnx::Relu(%558) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %560 : Float(1, 92, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%559, %Pose3D.stem.0.bottleneck.1.0.weight, %Pose3D.stem.0.bottleneck.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %561 : Float(1, 92, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%560, %Pose3D.stem.0.bottleneck.1.1.weight, %Pose3D.stem.0.bottleneck.1.1.bias, %Pose3D.stem.0.bottleneck.1.1.running_mean, %Pose3D.stem.0.bottleneck.1.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %562 : Float(1, 92, 32, 56) = onnx::Relu(%561) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %563 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%562, %Pose3D.stem.0.bottleneck.2.0.weight, %Pose3D.stem.0.bottleneck.2.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %564 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%563, %Pose3D.stem.0.bottleneck.2.1.weight, %Pose3D.stem.0.bottleneck.2.1.bias, %Pose3D.stem.0.bottleneck.2.1.running_mean, %Pose3D.stem.0.bottleneck.2.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %565 : Float(1, 128, 32, 56) = onnx::Relu(%564) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %566 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%556, %Pose3D.stem.0.align.0.weight, %Pose3D.stem.0.align.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %567 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%566, %Pose3D.stem.0.align.1.weight, %Pose3D.stem.0.align.1.bias, %Pose3D.stem.0.align.1.running_mean, %Pose3D.stem.0.align.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %568 : Float(1, 128, 32, 56) = onnx::Relu(%567) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %569 : Float(1, 128, 32, 56) = onnx::Add(%568, %565) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:123:0
  %570 : Float(1, 64, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%569, %Pose3D.stem.1.bottleneck.0.0.weight, %Pose3D.stem.1.bottleneck.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %571 : Float(1, 64, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%570, %Pose3D.stem.1.bottleneck.0.1.weight, %Pose3D.stem.1.bottleneck.0.1.bias, %Pose3D.stem.1.bottleneck.0.1.running_mean, %Pose3D.stem.1.bottleneck.0.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %572 : Float(1, 64, 32, 56) = onnx::Relu(%571) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %573 : Float(1, 64, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%572, %Pose3D.stem.1.bottleneck.1.0.weight, %Pose3D.stem.1.bottleneck.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %574 : Float(1, 64, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%573, %Pose3D.stem.1.bottleneck.1.1.weight, %Pose3D.stem.1.bottleneck.1.1.bias, %Pose3D.stem.1.bottleneck.1.1.running_mean, %Pose3D.stem.1.bottleneck.1.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %575 : Float(1, 64, 32, 56) = onnx::Relu(%574) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %576 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%575, %Pose3D.stem.1.bottleneck.2.0.weight, %Pose3D.stem.1.bottleneck.2.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %577 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%576, %Pose3D.stem.1.bottleneck.2.1.weight, %Pose3D.stem.1.bottleneck.2.1.bias, %Pose3D.stem.1.bottleneck.2.1.running_mean, %Pose3D.stem.1.bottleneck.2.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %578 : Float(1, 128, 32, 56) = onnx::Relu(%577) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %579 : Float(1, 128, 32, 56) = onnx::Add(%569, %578) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:123:0
  %580 : Float(1, 64, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%579, %Pose3D.stem.2.bottleneck.0.0.weight, %Pose3D.stem.2.bottleneck.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %581 : Float(1, 64, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%580, %Pose3D.stem.2.bottleneck.0.1.weight, %Pose3D.stem.2.bottleneck.0.1.bias, %Pose3D.stem.2.bottleneck.0.1.running_mean, %Pose3D.stem.2.bottleneck.0.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %582 : Float(1, 64, 32, 56) = onnx::Relu(%581) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %583 : Float(1, 64, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%582, %Pose3D.stem.2.bottleneck.1.0.weight, %Pose3D.stem.2.bottleneck.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %584 : Float(1, 64, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%583, %Pose3D.stem.2.bottleneck.1.1.weight, %Pose3D.stem.2.bottleneck.1.1.bias, %Pose3D.stem.2.bottleneck.1.1.running_mean, %Pose3D.stem.2.bottleneck.1.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %585 : Float(1, 64, 32, 56) = onnx::Relu(%584) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %586 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%585, %Pose3D.stem.2.bottleneck.2.0.weight, %Pose3D.stem.2.bottleneck.2.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %587 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%586, %Pose3D.stem.2.bottleneck.2.1.weight, %Pose3D.stem.2.bottleneck.2.1.bias, %Pose3D.stem.2.bottleneck.2.1.running_mean, %Pose3D.stem.2.bottleneck.2.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %588 : Float(1, 128, 32, 56) = onnx::Relu(%587) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %589 : Float(1, 128, 32, 56) = onnx::Add(%579, %588) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:123:0
  %590 : Float(1, 64, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%589, %Pose3D.stem.3.bottleneck.0.0.weight, %Pose3D.stem.3.bottleneck.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %591 : Float(1, 64, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%590, %Pose3D.stem.3.bottleneck.0.1.weight, %Pose3D.stem.3.bottleneck.0.1.bias, %Pose3D.stem.3.bottleneck.0.1.running_mean, %Pose3D.stem.3.bottleneck.0.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %592 : Float(1, 64, 32, 56) = onnx::Relu(%591) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %593 : Float(1, 64, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%592, %Pose3D.stem.3.bottleneck.1.0.weight, %Pose3D.stem.3.bottleneck.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %594 : Float(1, 64, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%593, %Pose3D.stem.3.bottleneck.1.1.weight, %Pose3D.stem.3.bottleneck.1.1.bias, %Pose3D.stem.3.bottleneck.1.1.running_mean, %Pose3D.stem.3.bottleneck.1.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %595 : Float(1, 64, 32, 56) = onnx::Relu(%594) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %596 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%595, %Pose3D.stem.3.bottleneck.2.0.weight, %Pose3D.stem.3.bottleneck.2.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %597 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%596, %Pose3D.stem.3.bottleneck.2.1.weight, %Pose3D.stem.3.bottleneck.2.1.bias, %Pose3D.stem.3.bottleneck.2.1.running_mean, %Pose3D.stem.3.bottleneck.2.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %598 : Float(1, 128, 32, 56) = onnx::Relu(%597) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %599 : Float(1, 128, 32, 56) = onnx::Add(%589, %598) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:123:0
  %600 : Float(1, 64, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%599, %Pose3D.stem.4.bottleneck.0.0.weight, %Pose3D.stem.4.bottleneck.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %601 : Float(1, 64, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%600, %Pose3D.stem.4.bottleneck.0.1.weight, %Pose3D.stem.4.bottleneck.0.1.bias, %Pose3D.stem.4.bottleneck.0.1.running_mean, %Pose3D.stem.4.bottleneck.0.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %602 : Float(1, 64, 32, 56) = onnx::Relu(%601) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %603 : Float(1, 64, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%602, %Pose3D.stem.4.bottleneck.1.0.weight, %Pose3D.stem.4.bottleneck.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %604 : Float(1, 64, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%603, %Pose3D.stem.4.bottleneck.1.1.weight, %Pose3D.stem.4.bottleneck.1.1.bias, %Pose3D.stem.4.bottleneck.1.1.running_mean, %Pose3D.stem.4.bottleneck.1.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %605 : Float(1, 64, 32, 56) = onnx::Relu(%604) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %606 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%605, %Pose3D.stem.4.bottleneck.2.0.weight, %Pose3D.stem.4.bottleneck.2.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %607 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%606, %Pose3D.stem.4.bottleneck.2.1.weight, %Pose3D.stem.4.bottleneck.2.1.bias, %Pose3D.stem.4.bottleneck.2.1.running_mean, %Pose3D.stem.4.bottleneck.2.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %608 : Float(1, 128, 32, 56) = onnx::Relu(%607) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %609 : Float(1, 128, 32, 56) = onnx::Add(%599, %608) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:123:0
  %610 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%609, %Pose3D.prediction.trunk.0.initial.0.weight, %Pose3D.prediction.trunk.0.initial.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %611 : Float(1, 128, 32, 56) = onnx::Relu(%610) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %612 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%611, %Pose3D.prediction.trunk.0.trunk.0.0.weight, %Pose3D.prediction.trunk.0.trunk.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %613 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%612, %Pose3D.prediction.trunk.0.trunk.0.1.weight, %Pose3D.prediction.trunk.0.trunk.0.1.bias, %Pose3D.prediction.trunk.0.trunk.0.1.running_mean, %Pose3D.prediction.trunk.0.trunk.0.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %614 : Float(1, 128, 32, 56) = onnx::Relu(%613) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %615 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[2, 2], group=1, kernel_shape=[3, 3], pads=[2, 2, 2, 2], strides=[1, 1]](%614, %Pose3D.prediction.trunk.0.trunk.1.0.weight, %Pose3D.prediction.trunk.0.trunk.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %616 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%615, %Pose3D.prediction.trunk.0.trunk.1.1.weight, %Pose3D.prediction.trunk.0.trunk.1.1.bias, %Pose3D.prediction.trunk.0.trunk.1.1.running_mean, %Pose3D.prediction.trunk.0.trunk.1.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %617 : Float(1, 128, 32, 56) = onnx::Relu(%616) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %618 : Float(1, 128, 32, 56) = onnx::Add(%611, %617) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:60:0
  %619 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%618, %Pose3D.prediction.trunk.1.initial.0.weight, %Pose3D.prediction.trunk.1.initial.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %620 : Float(1, 128, 32, 56) = onnx::Relu(%619) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %621 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[3, 3], pads=[1, 1, 1, 1], strides=[1, 1]](%620, %Pose3D.prediction.trunk.1.trunk.0.0.weight, %Pose3D.prediction.trunk.1.trunk.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %622 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%621, %Pose3D.prediction.trunk.1.trunk.0.1.weight, %Pose3D.prediction.trunk.1.trunk.0.1.bias, %Pose3D.prediction.trunk.1.trunk.0.1.running_mean, %Pose3D.prediction.trunk.1.trunk.0.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %623 : Float(1, 128, 32, 56) = onnx::Relu(%622) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %624 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[2, 2], group=1, kernel_shape=[3, 3], pads=[2, 2, 2, 2], strides=[1, 1]](%623, %Pose3D.prediction.trunk.1.trunk.1.0.weight, %Pose3D.prediction.trunk.1.trunk.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %625 : Float(1, 128, 32, 56) = onnx::BatchNormalization[epsilon=1.0000000000000001e-05, momentum=0.90000000000000002](%624, %Pose3D.prediction.trunk.1.trunk.1.1.weight, %Pose3D.prediction.trunk.1.trunk.1.1.bias, %Pose3D.prediction.trunk.1.trunk.1.1.running_mean, %Pose3D.prediction.trunk.1.trunk.1.1.running_var) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1923:0
  %626 : Float(1, 128, 32, 56) = onnx::Relu(%625) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %627 : Float(1, 128, 32, 56) = onnx::Add(%620, %626) # /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/models/with_mobilenet.py:60:0
  %628 : Float(1, 128, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%627, %Pose3D.prediction.feature_maps.0.0.weight, %Pose3D.prediction.feature_maps.0.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  %629 : Float(1, 128, 32, 56) = onnx::Relu(%628) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/functional.py:1061:0
  %features : Float(1, 57, 32, 56) = onnx::Conv[dilations=[1, 1], group=1, kernel_shape=[1, 1], pads=[0, 0, 0, 0], strides=[1, 1]](%629, %Pose3D.prediction.feature_maps.1.0.weight, %Pose3D.prediction.feature_maps.1.0.bias) # /home/user/.virtualenvs/cv/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
  return (%features, %heatmaps, %pafs)

The resulting onnx file seems normal to me. Then, according to your manual:

(cv) user@Descartes:~/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch$ python /opt/intel/openvino/deployment_tools/model_optimizer/mo.py --input_model human-pose-estimation-3d.onnx --input=data --mean_values=data[128.0,128.0,128.0] --scale_values=data[255.0,255.0,255.0] --output=features,heatmaps,pafs

Returns an error:

human-pose-estimation-3d.onnx --input=data --mean_values=data[128.0,128.0,128.0] --scale_values=data[255.0,255.0,255.0] --output=features,heatmaps,pafs
Model Optimizer arguments:
Common parameters:
        - Path to the Input Model:      /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/human-pose-estimation-3d.onnx
        - Path for generated IR:        /home/user/human-pose-estimation/lightweight-human-pose-estimation-3d-demo.pytorch/.
        - IR output name:       human-pose-estimation-3d
        - Log level:    ERROR
        - Batch:        Not specified, inherited from the model
        - Input layers:         data
        - Output layers:        features,heatmaps,pafs
        - Input shapes:         Not specified, inherited from the model
        - Mean values:  data[128.0,128.0,128.0]
        - Scale values:         data[255.0,255.0,255.0]
        - Scale factor:         Not specified
        - Precision of IR:      FP32
        - Enable fusing:        True
        - Enable grouped convolutions fusing:   True
        - Move mean values to preprocess section:       False
        - Reverse input channels:       False
ONNX specific parameters:
Model Optimizer version:        2020.2.0-60-g0bc66e26ff
[ ERROR ]  Exception occurred during running replacer "REPLACEMENT_ID" (<class 'extensions.front.user_data_repack.UserDataRepack'>): No node with name features.
 For more information please refer to Model Optimizer FAQ (https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_Model_Optimizer_FAQ.html), question #51.

What can it be? Following the link provided, question 51 doesn't clear the situation. Thank you in advance!

Daniil-Osokin commented 4 years ago

This possibly an internal error in model optimizer (see #19). You can try to use OpenVINO scripts to download and convert model or try to convert it with previous OpenVINO release.

Daniil-Osokin commented 4 years ago

As @ZlodeiBaal suggested, fix it with roll back to torch 1.4 version (from 1.5).

morgunl2 commented 4 years ago

Thank you for your help. I'll check it out.

Daniil-Osokin commented 4 years ago

Hope, it works.

LightingMc commented 4 years ago

Hey, so I am having the same issue. How do I install rollback to the previous issue 1.4 Pytorch?

Daniil-Osokin commented 4 years ago

Hi, if you are using pip, then execute the following command:

pip install torch==1.4.0 torchvision==0.4.0 -f https://download.pytorch.org/whl/torch_stable.html

If not, select proper way from the official docs.

thang7345 commented 3 years ago

Same problem for me here. I have install 1.4 torch but it's not work

Daniil-Osokin commented 3 years ago

Hi! Use model downloader to download already converted model in OpenVINO format, if this does not work for some chance.

thang7345 commented 3 years ago

Hi! Use model downloader to download already converted model in OpenVINO format, if this does not work for some chance.

i 've done it already to but still error maybe because of my openvino version?

Daniil-Osokin commented 3 years ago

This is likely the case. But the model downloader downloads model already in OpenVINO format, so you cannot have such error (just nothing to convert).

thang7345 commented 3 years ago

This is likely the case. But the model downloader downloads model already in OpenVINO format, so you cannot have such error (just nothing to convert). i've found my error. It's because of my onnx, i create a new one and run it well. Thank you!

StianHanssen commented 3 years ago

This is likely the case. But the model downloader downloads model already in OpenVINO format, so you cannot have such error (just nothing to convert).

I tried this approach, but the downloader only provides the pytorch checkpoint, no OpenVino model is downloaded using these scripts. Furthermore, their conversion script has the same issue as this repo. For anyone reading, just go straight for PyTorch downgrade.

thomas9190 commented 3 years ago

Hi, if you are using pip, then execute the following command:

pip install torch==1.4.0 torchvision==0.4.0 -f https://download.pytorch.org/whl/torch_stable.html

If not, select proper way from the official docs.

thanks! it works!