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Error converting checkpoints to OpenVino format
See original GitHub issue(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.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!
Issue Analytics
- State:
- Created 3 years ago
- Comments:13 (6 by maintainers)
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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).