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RomanGirin/yolov3_to_onnx_output

Created January 16, 2020 18:29
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yolov3_to_onnx_output
sudo python yolov3_to_onnx.py
Downloading from https://raw.githubusercontent.com/pjreddie/darknet/f86901f6177dfc6116360a13cc06ab680e0c86b0/cfg/yolov3.cfg, this may take a while...
100% [................................................................................] 8342 / 8342
Downloading from https://pjreddie.com/media/files/yolov3.weights, this may take a while...
100% [......................................................................] 248007048 / 248007048
Layer of type yolo not supported, skipping ONNX node generation.
Layer of type yolo not supported, skipping ONNX node generation.
Layer of type yolo not supported, skipping ONNX node generation.
graph YOLOv3-608 (
%000_net[FLOAT, 64x3x608x608]
) optional inputs with matching initializers (
%001_convolutional_bn_scale[FLOAT, 32]
%001_convolutional_bn_bias[FLOAT, 32]
%001_convolutional_bn_mean[FLOAT, 32]
%001_convolutional_bn_var[FLOAT, 32]
%001_convolutional_conv_weights[FLOAT, 32x3x3x3]
%002_convolutional_bn_scale[FLOAT, 64]
%002_convolutional_bn_bias[FLOAT, 64]
%002_convolutional_bn_mean[FLOAT, 64]
%002_convolutional_bn_var[FLOAT, 64]
%002_convolutional_conv_weights[FLOAT, 64x32x3x3]
%003_convolutional_bn_scale[FLOAT, 32]
%003_convolutional_bn_bias[FLOAT, 32]
%003_convolutional_bn_mean[FLOAT, 32]
%003_convolutional_bn_var[FLOAT, 32]
%003_convolutional_conv_weights[FLOAT, 32x64x1x1]
%004_convolutional_bn_scale[FLOAT, 64]
%004_convolutional_bn_bias[FLOAT, 64]
%004_convolutional_bn_mean[FLOAT, 64]
%004_convolutional_bn_var[FLOAT, 64]
%004_convolutional_conv_weights[FLOAT, 64x32x3x3]
%006_convolutional_bn_scale[FLOAT, 128]
%006_convolutional_bn_bias[FLOAT, 128]
%006_convolutional_bn_mean[FLOAT, 128]
%006_convolutional_bn_var[FLOAT, 128]
%006_convolutional_conv_weights[FLOAT, 128x64x3x3]
%007_convolutional_bn_scale[FLOAT, 64]
%007_convolutional_bn_bias[FLOAT, 64]
%007_convolutional_bn_mean[FLOAT, 64]
%007_convolutional_bn_var[FLOAT, 64]
%007_convolutional_conv_weights[FLOAT, 64x128x1x1]
%008_convolutional_bn_scale[FLOAT, 128]
%008_convolutional_bn_bias[FLOAT, 128]
%008_convolutional_bn_mean[FLOAT, 128]
%008_convolutional_bn_var[FLOAT, 128]
%008_convolutional_conv_weights[FLOAT, 128x64x3x3]
%010_convolutional_bn_scale[FLOAT, 64]
%010_convolutional_bn_bias[FLOAT, 64]
%010_convolutional_bn_mean[FLOAT, 64]
%010_convolutional_bn_var[FLOAT, 64]
%010_convolutional_conv_weights[FLOAT, 64x128x1x1]
%011_convolutional_bn_scale[FLOAT, 128]
%011_convolutional_bn_bias[FLOAT, 128]
%011_convolutional_bn_mean[FLOAT, 128]
%011_convolutional_bn_var[FLOAT, 128]
%011_convolutional_conv_weights[FLOAT, 128x64x3x3]
%013_convolutional_bn_scale[FLOAT, 256]
%013_convolutional_bn_bias[FLOAT, 256]
%013_convolutional_bn_mean[FLOAT, 256]
%013_convolutional_bn_var[FLOAT, 256]
%013_convolutional_conv_weights[FLOAT, 256x128x3x3]
%014_convolutional_bn_scale[FLOAT, 128]
%014_convolutional_bn_bias[FLOAT, 128]
%014_convolutional_bn_mean[FLOAT, 128]
%014_convolutional_bn_var[FLOAT, 128]
%014_convolutional_conv_weights[FLOAT, 128x256x1x1]
%015_convolutional_bn_scale[FLOAT, 256]
%015_convolutional_bn_bias[FLOAT, 256]
%015_convolutional_bn_mean[FLOAT, 256]
%015_convolutional_bn_var[FLOAT, 256]
%015_convolutional_conv_weights[FLOAT, 256x128x3x3]
%017_convolutional_bn_scale[FLOAT, 128]
%017_convolutional_bn_bias[FLOAT, 128]
%017_convolutional_bn_mean[FLOAT, 128]
%017_convolutional_bn_var[FLOAT, 128]
%017_convolutional_conv_weights[FLOAT, 128x256x1x1]
%018_convolutional_bn_scale[FLOAT, 256]
%018_convolutional_bn_bias[FLOAT, 256]
%018_convolutional_bn_mean[FLOAT, 256]
%018_convolutional_bn_var[FLOAT, 256]
%018_convolutional_conv_weights[FLOAT, 256x128x3x3]
%020_convolutional_bn_scale[FLOAT, 128]
%020_convolutional_bn_bias[FLOAT, 128]
%020_convolutional_bn_mean[FLOAT, 128]
%020_convolutional_bn_var[FLOAT, 128]
%020_convolutional_conv_weights[FLOAT, 128x256x1x1]
%021_convolutional_bn_scale[FLOAT, 256]
%021_convolutional_bn_bias[FLOAT, 256]
%021_convolutional_bn_mean[FLOAT, 256]
%021_convolutional_bn_var[FLOAT, 256]
%021_convolutional_conv_weights[FLOAT, 256x128x3x3]
%023_convolutional_bn_scale[FLOAT, 128]
%023_convolutional_bn_bias[FLOAT, 128]
%023_convolutional_bn_mean[FLOAT, 128]
%023_convolutional_bn_var[FLOAT, 128]
%023_convolutional_conv_weights[FLOAT, 128x256x1x1]
%024_convolutional_bn_scale[FLOAT, 256]
%024_convolutional_bn_bias[FLOAT, 256]
%024_convolutional_bn_mean[FLOAT, 256]
%024_convolutional_bn_var[FLOAT, 256]
%024_convolutional_conv_weights[FLOAT, 256x128x3x3]
%026_convolutional_bn_scale[FLOAT, 128]
%026_convolutional_bn_bias[FLOAT, 128]
%026_convolutional_bn_mean[FLOAT, 128]
%026_convolutional_bn_var[FLOAT, 128]
%026_convolutional_conv_weights[FLOAT, 128x256x1x1]
%027_convolutional_bn_scale[FLOAT, 256]
%027_convolutional_bn_bias[FLOAT, 256]
%027_convolutional_bn_mean[FLOAT, 256]
%027_convolutional_bn_var[FLOAT, 256]
%027_convolutional_conv_weights[FLOAT, 256x128x3x3]
%029_convolutional_bn_scale[FLOAT, 128]
%029_convolutional_bn_bias[FLOAT, 128]
%029_convolutional_bn_mean[FLOAT, 128]
%029_convolutional_bn_var[FLOAT, 128]
%029_convolutional_conv_weights[FLOAT, 128x256x1x1]
%030_convolutional_bn_scale[FLOAT, 256]
%030_convolutional_bn_bias[FLOAT, 256]
%030_convolutional_bn_mean[FLOAT, 256]
%030_convolutional_bn_var[FLOAT, 256]
%030_convolutional_conv_weights[FLOAT, 256x128x3x3]
%032_convolutional_bn_scale[FLOAT, 128]
%032_convolutional_bn_bias[FLOAT, 128]
%032_convolutional_bn_mean[FLOAT, 128]
%032_convolutional_bn_var[FLOAT, 128]
%032_convolutional_conv_weights[FLOAT, 128x256x1x1]
%033_convolutional_bn_scale[FLOAT, 256]
%033_convolutional_bn_bias[FLOAT, 256]
%033_convolutional_bn_mean[FLOAT, 256]
%033_convolutional_bn_var[FLOAT, 256]
%033_convolutional_conv_weights[FLOAT, 256x128x3x3]
%035_convolutional_bn_scale[FLOAT, 128]
%035_convolutional_bn_bias[FLOAT, 128]
%035_convolutional_bn_mean[FLOAT, 128]
%035_convolutional_bn_var[FLOAT, 128]
%035_convolutional_conv_weights[FLOAT, 128x256x1x1]
%036_convolutional_bn_scale[FLOAT, 256]
%036_convolutional_bn_bias[FLOAT, 256]
%036_convolutional_bn_mean[FLOAT, 256]
%036_convolutional_bn_var[FLOAT, 256]
%036_convolutional_conv_weights[FLOAT, 256x128x3x3]
%038_convolutional_bn_scale[FLOAT, 512]
%038_convolutional_bn_bias[FLOAT, 512]
%038_convolutional_bn_mean[FLOAT, 512]
%038_convolutional_bn_var[FLOAT, 512]
%038_convolutional_conv_weights[FLOAT, 512x256x3x3]
%039_convolutional_bn_scale[FLOAT, 256]
%039_convolutional_bn_bias[FLOAT, 256]
%039_convolutional_bn_mean[FLOAT, 256]
%039_convolutional_bn_var[FLOAT, 256]
%039_convolutional_conv_weights[FLOAT, 256x512x1x1]
%040_convolutional_bn_scale[FLOAT, 512]
%040_convolutional_bn_bias[FLOAT, 512]
%040_convolutional_bn_mean[FLOAT, 512]
%040_convolutional_bn_var[FLOAT, 512]
%040_convolutional_conv_weights[FLOAT, 512x256x3x3]
%042_convolutional_bn_scale[FLOAT, 256]
%042_convolutional_bn_bias[FLOAT, 256]
%042_convolutional_bn_mean[FLOAT, 256]
%042_convolutional_bn_var[FLOAT, 256]
%042_convolutional_conv_weights[FLOAT, 256x512x1x1]
%043_convolutional_bn_scale[FLOAT, 512]
%043_convolutional_bn_bias[FLOAT, 512]
%043_convolutional_bn_mean[FLOAT, 512]
%043_convolutional_bn_var[FLOAT, 512]
%043_convolutional_conv_weights[FLOAT, 512x256x3x3]
%045_convolutional_bn_scale[FLOAT, 256]
%045_convolutional_bn_bias[FLOAT, 256]
%045_convolutional_bn_mean[FLOAT, 256]
%045_convolutional_bn_var[FLOAT, 256]
%045_convolutional_conv_weights[FLOAT, 256x512x1x1]
%046_convolutional_bn_scale[FLOAT, 512]
%046_convolutional_bn_bias[FLOAT, 512]
%046_convolutional_bn_mean[FLOAT, 512]
%046_convolutional_bn_var[FLOAT, 512]
%046_convolutional_conv_weights[FLOAT, 512x256x3x3]
%048_convolutional_bn_scale[FLOAT, 256]
%048_convolutional_bn_bias[FLOAT, 256]
%048_convolutional_bn_mean[FLOAT, 256]
%048_convolutional_bn_var[FLOAT, 256]
%048_convolutional_conv_weights[FLOAT, 256x512x1x1]
%049_convolutional_bn_scale[FLOAT, 512]
%049_convolutional_bn_bias[FLOAT, 512]
%049_convolutional_bn_mean[FLOAT, 512]
%049_convolutional_bn_var[FLOAT, 512]
%049_convolutional_conv_weights[FLOAT, 512x256x3x3]
%051_convolutional_bn_scale[FLOAT, 256]
%051_convolutional_bn_bias[FLOAT, 256]
%051_convolutional_bn_mean[FLOAT, 256]
%051_convolutional_bn_var[FLOAT, 256]
%051_convolutional_conv_weights[FLOAT, 256x512x1x1]
%052_convolutional_bn_scale[FLOAT, 512]
%052_convolutional_bn_bias[FLOAT, 512]
%052_convolutional_bn_mean[FLOAT, 512]
%052_convolutional_bn_var[FLOAT, 512]
%052_convolutional_conv_weights[FLOAT, 512x256x3x3]
%054_convolutional_bn_scale[FLOAT, 256]
%054_convolutional_bn_bias[FLOAT, 256]
%054_convolutional_bn_mean[FLOAT, 256]
%054_convolutional_bn_var[FLOAT, 256]
%054_convolutional_conv_weights[FLOAT, 256x512x1x1]
%055_convolutional_bn_scale[FLOAT, 512]
%055_convolutional_bn_bias[FLOAT, 512]
%055_convolutional_bn_mean[FLOAT, 512]
%055_convolutional_bn_var[FLOAT, 512]
%055_convolutional_conv_weights[FLOAT, 512x256x3x3]
%057_convolutional_bn_scale[FLOAT, 256]
%057_convolutional_bn_bias[FLOAT, 256]
%057_convolutional_bn_mean[FLOAT, 256]
%057_convolutional_bn_var[FLOAT, 256]
%057_convolutional_conv_weights[FLOAT, 256x512x1x1]
%058_convolutional_bn_scale[FLOAT, 512]
%058_convolutional_bn_bias[FLOAT, 512]
%058_convolutional_bn_mean[FLOAT, 512]
%058_convolutional_bn_var[FLOAT, 512]
%058_convolutional_conv_weights[FLOAT, 512x256x3x3]
%060_convolutional_bn_scale[FLOAT, 256]
%060_convolutional_bn_bias[FLOAT, 256]
%060_convolutional_bn_mean[FLOAT, 256]
%060_convolutional_bn_var[FLOAT, 256]
%060_convolutional_conv_weights[FLOAT, 256x512x1x1]
%061_convolutional_bn_scale[FLOAT, 512]
%061_convolutional_bn_bias[FLOAT, 512]
%061_convolutional_bn_mean[FLOAT, 512]
%061_convolutional_bn_var[FLOAT, 512]
%061_convolutional_conv_weights[FLOAT, 512x256x3x3]
%063_convolutional_bn_scale[FLOAT, 1024]
%063_convolutional_bn_bias[FLOAT, 1024]
%063_convolutional_bn_mean[FLOAT, 1024]
%063_convolutional_bn_var[FLOAT, 1024]
%063_convolutional_conv_weights[FLOAT, 1024x512x3x3]
%064_convolutional_bn_scale[FLOAT, 512]
%064_convolutional_bn_bias[FLOAT, 512]
%064_convolutional_bn_mean[FLOAT, 512]
%064_convolutional_bn_var[FLOAT, 512]
%064_convolutional_conv_weights[FLOAT, 512x1024x1x1]
%065_convolutional_bn_scale[FLOAT, 1024]
%065_convolutional_bn_bias[FLOAT, 1024]
%065_convolutional_bn_mean[FLOAT, 1024]
%065_convolutional_bn_var[FLOAT, 1024]
%065_convolutional_conv_weights[FLOAT, 1024x512x3x3]
%067_convolutional_bn_scale[FLOAT, 512]
%067_convolutional_bn_bias[FLOAT, 512]
%067_convolutional_bn_mean[FLOAT, 512]
%067_convolutional_bn_var[FLOAT, 512]
%067_convolutional_conv_weights[FLOAT, 512x1024x1x1]
%068_convolutional_bn_scale[FLOAT, 1024]
%068_convolutional_bn_bias[FLOAT, 1024]
%068_convolutional_bn_mean[FLOAT, 1024]
%068_convolutional_bn_var[FLOAT, 1024]
%068_convolutional_conv_weights[FLOAT, 1024x512x3x3]
%070_convolutional_bn_scale[FLOAT, 512]
%070_convolutional_bn_bias[FLOAT, 512]
%070_convolutional_bn_mean[FLOAT, 512]
%070_convolutional_bn_var[FLOAT, 512]
%070_convolutional_conv_weights[FLOAT, 512x1024x1x1]
%071_convolutional_bn_scale[FLOAT, 1024]
%071_convolutional_bn_bias[FLOAT, 1024]
%071_convolutional_bn_mean[FLOAT, 1024]
%071_convolutional_bn_var[FLOAT, 1024]
%071_convolutional_conv_weights[FLOAT, 1024x512x3x3]
%073_convolutional_bn_scale[FLOAT, 512]
%073_convolutional_bn_bias[FLOAT, 512]
%073_convolutional_bn_mean[FLOAT, 512]
%073_convolutional_bn_var[FLOAT, 512]
%073_convolutional_conv_weights[FLOAT, 512x1024x1x1]
%074_convolutional_bn_scale[FLOAT, 1024]
%074_convolutional_bn_bias[FLOAT, 1024]
%074_convolutional_bn_mean[FLOAT, 1024]
%074_convolutional_bn_var[FLOAT, 1024]
%074_convolutional_conv_weights[FLOAT, 1024x512x3x3]
%076_convolutional_bn_scale[FLOAT, 512]
%076_convolutional_bn_bias[FLOAT, 512]
%076_convolutional_bn_mean[FLOAT, 512]
%076_convolutional_bn_var[FLOAT, 512]
%076_convolutional_conv_weights[FLOAT, 512x1024x1x1]
%077_convolutional_bn_scale[FLOAT, 1024]
%077_convolutional_bn_bias[FLOAT, 1024]
%077_convolutional_bn_mean[FLOAT, 1024]
%077_convolutional_bn_var[FLOAT, 1024]
%077_convolutional_conv_weights[FLOAT, 1024x512x3x3]
%078_convolutional_bn_scale[FLOAT, 512]
%078_convolutional_bn_bias[FLOAT, 512]
%078_convolutional_bn_mean[FLOAT, 512]
%078_convolutional_bn_var[FLOAT, 512]
%078_convolutional_conv_weights[FLOAT, 512x1024x1x1]
%079_convolutional_bn_scale[FLOAT, 1024]
%079_convolutional_bn_bias[FLOAT, 1024]
%079_convolutional_bn_mean[FLOAT, 1024]
%079_convolutional_bn_var[FLOAT, 1024]
%079_convolutional_conv_weights[FLOAT, 1024x512x3x3]
%080_convolutional_bn_scale[FLOAT, 512]
%080_convolutional_bn_bias[FLOAT, 512]
%080_convolutional_bn_mean[FLOAT, 512]
%080_convolutional_bn_var[FLOAT, 512]
%080_convolutional_conv_weights[FLOAT, 512x1024x1x1]
%081_convolutional_bn_scale[FLOAT, 1024]
%081_convolutional_bn_bias[FLOAT, 1024]
%081_convolutional_bn_mean[FLOAT, 1024]
%081_convolutional_bn_var[FLOAT, 1024]
%081_convolutional_conv_weights[FLOAT, 1024x512x3x3]
%082_convolutional_conv_bias[FLOAT, 255]
%082_convolutional_conv_weights[FLOAT, 255x1024x1x1]
%085_convolutional_bn_scale[FLOAT, 256]
%085_convolutional_bn_bias[FLOAT, 256]
%085_convolutional_bn_mean[FLOAT, 256]
%085_convolutional_bn_var[FLOAT, 256]
%085_convolutional_conv_weights[FLOAT, 256x512x1x1]
%086_upsample_scale[FLOAT, 4]
%088_convolutional_bn_scale[FLOAT, 256]
%088_convolutional_bn_bias[FLOAT, 256]
%088_convolutional_bn_mean[FLOAT, 256]
%088_convolutional_bn_var[FLOAT, 256]
%088_convolutional_conv_weights[FLOAT, 256x768x1x1]
%089_convolutional_bn_scale[FLOAT, 512]
%089_convolutional_bn_bias[FLOAT, 512]
%089_convolutional_bn_mean[FLOAT, 512]
%089_convolutional_bn_var[FLOAT, 512]
%089_convolutional_conv_weights[FLOAT, 512x256x3x3]
%090_convolutional_bn_scale[FLOAT, 256]
%090_convolutional_bn_bias[FLOAT, 256]
%090_convolutional_bn_mean[FLOAT, 256]
%090_convolutional_bn_var[FLOAT, 256]
%090_convolutional_conv_weights[FLOAT, 256x512x1x1]
%091_convolutional_bn_scale[FLOAT, 512]
%091_convolutional_bn_bias[FLOAT, 512]
%091_convolutional_bn_mean[FLOAT, 512]
%091_convolutional_bn_var[FLOAT, 512]
%091_convolutional_conv_weights[FLOAT, 512x256x3x3]
%092_convolutional_bn_scale[FLOAT, 256]
%092_convolutional_bn_bias[FLOAT, 256]
%092_convolutional_bn_mean[FLOAT, 256]
%092_convolutional_bn_var[FLOAT, 256]
%092_convolutional_conv_weights[FLOAT, 256x512x1x1]
%093_convolutional_bn_scale[FLOAT, 512]
%093_convolutional_bn_bias[FLOAT, 512]
%093_convolutional_bn_mean[FLOAT, 512]
%093_convolutional_bn_var[FLOAT, 512]
%093_convolutional_conv_weights[FLOAT, 512x256x3x3]
%094_convolutional_conv_bias[FLOAT, 255]
%094_convolutional_conv_weights[FLOAT, 255x512x1x1]
%097_convolutional_bn_scale[FLOAT, 128]
%097_convolutional_bn_bias[FLOAT, 128]
%097_convolutional_bn_mean[FLOAT, 128]
%097_convolutional_bn_var[FLOAT, 128]
%097_convolutional_conv_weights[FLOAT, 128x256x1x1]
%098_upsample_scale[FLOAT, 4]
%100_convolutional_bn_scale[FLOAT, 128]
%100_convolutional_bn_bias[FLOAT, 128]
%100_convolutional_bn_mean[FLOAT, 128]
%100_convolutional_bn_var[FLOAT, 128]
%100_convolutional_conv_weights[FLOAT, 128x384x1x1]
%101_convolutional_bn_scale[FLOAT, 256]
%101_convolutional_bn_bias[FLOAT, 256]
%101_convolutional_bn_mean[FLOAT, 256]
%101_convolutional_bn_var[FLOAT, 256]
%101_convolutional_conv_weights[FLOAT, 256x128x3x3]
%102_convolutional_bn_scale[FLOAT, 128]
%102_convolutional_bn_bias[FLOAT, 128]
%102_convolutional_bn_mean[FLOAT, 128]
%102_convolutional_bn_var[FLOAT, 128]
%102_convolutional_conv_weights[FLOAT, 128x256x1x1]
%103_convolutional_bn_scale[FLOAT, 256]
%103_convolutional_bn_bias[FLOAT, 256]
%103_convolutional_bn_mean[FLOAT, 256]
%103_convolutional_bn_var[FLOAT, 256]
%103_convolutional_conv_weights[FLOAT, 256x128x3x3]
%104_convolutional_bn_scale[FLOAT, 128]
%104_convolutional_bn_bias[FLOAT, 128]
%104_convolutional_bn_mean[FLOAT, 128]
%104_convolutional_bn_var[FLOAT, 128]
%104_convolutional_conv_weights[FLOAT, 128x256x1x1]
%105_convolutional_bn_scale[FLOAT, 256]
%105_convolutional_bn_bias[FLOAT, 256]
%105_convolutional_bn_mean[FLOAT, 256]
%105_convolutional_bn_var[FLOAT, 256]
%105_convolutional_conv_weights[FLOAT, 256x128x3x3]
%106_convolutional_conv_bias[FLOAT, 255]
%106_convolutional_conv_weights[FLOAT, 255x256x1x1]
) {
%001_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%000_net, %001_convolutional_conv_weights)
%001_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%001_convolutional, %001_convolutional_bn_scale, %001_convolutional_bn_bias, %001_convolutional_bn_mean, %001_convolutional_bn_var)
%001_convolutional_lrelu = LeakyRelu[alpha = 0.1](%001_convolutional_bn)
%002_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%001_convolutional_lrelu, %002_convolutional_conv_weights)
%002_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%002_convolutional, %002_convolutional_bn_scale, %002_convolutional_bn_bias, %002_convolutional_bn_mean, %002_convolutional_bn_var)
%002_convolutional_lrelu = LeakyRelu[alpha = 0.1](%002_convolutional_bn)
%003_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%002_convolutional_lrelu, %003_convolutional_conv_weights)
%003_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%003_convolutional, %003_convolutional_bn_scale, %003_convolutional_bn_bias, %003_convolutional_bn_mean, %003_convolutional_bn_var)
%003_convolutional_lrelu = LeakyRelu[alpha = 0.1](%003_convolutional_bn)
%004_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%003_convolutional_lrelu, %004_convolutional_conv_weights)
%004_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%004_convolutional, %004_convolutional_bn_scale, %004_convolutional_bn_bias, %004_convolutional_bn_mean, %004_convolutional_bn_var)
%004_convolutional_lrelu = LeakyRelu[alpha = 0.1](%004_convolutional_bn)
%005_shortcut = Add(%004_convolutional_lrelu, %002_convolutional_lrelu)
%006_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%005_shortcut, %006_convolutional_conv_weights)
%006_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%006_convolutional, %006_convolutional_bn_scale, %006_convolutional_bn_bias, %006_convolutional_bn_mean, %006_convolutional_bn_var)
%006_convolutional_lrelu = LeakyRelu[alpha = 0.1](%006_convolutional_bn)
%007_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%006_convolutional_lrelu, %007_convolutional_conv_weights)
%007_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%007_convolutional, %007_convolutional_bn_scale, %007_convolutional_bn_bias, %007_convolutional_bn_mean, %007_convolutional_bn_var)
%007_convolutional_lrelu = LeakyRelu[alpha = 0.1](%007_convolutional_bn)
%008_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%007_convolutional_lrelu, %008_convolutional_conv_weights)
%008_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%008_convolutional, %008_convolutional_bn_scale, %008_convolutional_bn_bias, %008_convolutional_bn_mean, %008_convolutional_bn_var)
%008_convolutional_lrelu = LeakyRelu[alpha = 0.1](%008_convolutional_bn)
%009_shortcut = Add(%008_convolutional_lrelu, %006_convolutional_lrelu)
%010_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%009_shortcut, %010_convolutional_conv_weights)
%010_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%010_convolutional, %010_convolutional_bn_scale, %010_convolutional_bn_bias, %010_convolutional_bn_mean, %010_convolutional_bn_var)
%010_convolutional_lrelu = LeakyRelu[alpha = 0.1](%010_convolutional_bn)
%011_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%010_convolutional_lrelu, %011_convolutional_conv_weights)
%011_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%011_convolutional, %011_convolutional_bn_scale, %011_convolutional_bn_bias, %011_convolutional_bn_mean, %011_convolutional_bn_var)
%011_convolutional_lrelu = LeakyRelu[alpha = 0.1](%011_convolutional_bn)
%012_shortcut = Add(%011_convolutional_lrelu, %009_shortcut)
%013_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%012_shortcut, %013_convolutional_conv_weights)
%013_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%013_convolutional, %013_convolutional_bn_scale, %013_convolutional_bn_bias, %013_convolutional_bn_mean, %013_convolutional_bn_var)
%013_convolutional_lrelu = LeakyRelu[alpha = 0.1](%013_convolutional_bn)
%014_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%013_convolutional_lrelu, %014_convolutional_conv_weights)
%014_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%014_convolutional, %014_convolutional_bn_scale, %014_convolutional_bn_bias, %014_convolutional_bn_mean, %014_convolutional_bn_var)
%014_convolutional_lrelu = LeakyRelu[alpha = 0.1](%014_convolutional_bn)
%015_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%014_convolutional_lrelu, %015_convolutional_conv_weights)
%015_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%015_convolutional, %015_convolutional_bn_scale, %015_convolutional_bn_bias, %015_convolutional_bn_mean, %015_convolutional_bn_var)
%015_convolutional_lrelu = LeakyRelu[alpha = 0.1](%015_convolutional_bn)
%016_shortcut = Add(%015_convolutional_lrelu, %013_convolutional_lrelu)
%017_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%016_shortcut, %017_convolutional_conv_weights)
%017_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%017_convolutional, %017_convolutional_bn_scale, %017_convolutional_bn_bias, %017_convolutional_bn_mean, %017_convolutional_bn_var)
%017_convolutional_lrelu = LeakyRelu[alpha = 0.1](%017_convolutional_bn)
%018_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%017_convolutional_lrelu, %018_convolutional_conv_weights)
%018_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%018_convolutional, %018_convolutional_bn_scale, %018_convolutional_bn_bias, %018_convolutional_bn_mean, %018_convolutional_bn_var)
%018_convolutional_lrelu = LeakyRelu[alpha = 0.1](%018_convolutional_bn)
%019_shortcut = Add(%018_convolutional_lrelu, %016_shortcut)
%020_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%019_shortcut, %020_convolutional_conv_weights)
%020_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%020_convolutional, %020_convolutional_bn_scale, %020_convolutional_bn_bias, %020_convolutional_bn_mean, %020_convolutional_bn_var)
%020_convolutional_lrelu = LeakyRelu[alpha = 0.1](%020_convolutional_bn)
%021_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%020_convolutional_lrelu, %021_convolutional_conv_weights)
%021_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%021_convolutional, %021_convolutional_bn_scale, %021_convolutional_bn_bias, %021_convolutional_bn_mean, %021_convolutional_bn_var)
%021_convolutional_lrelu = LeakyRelu[alpha = 0.1](%021_convolutional_bn)
%022_shortcut = Add(%021_convolutional_lrelu, %019_shortcut)
%023_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%022_shortcut, %023_convolutional_conv_weights)
%023_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%023_convolutional, %023_convolutional_bn_scale, %023_convolutional_bn_bias, %023_convolutional_bn_mean, %023_convolutional_bn_var)
%023_convolutional_lrelu = LeakyRelu[alpha = 0.1](%023_convolutional_bn)
%024_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%023_convolutional_lrelu, %024_convolutional_conv_weights)
%024_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%024_convolutional, %024_convolutional_bn_scale, %024_convolutional_bn_bias, %024_convolutional_bn_mean, %024_convolutional_bn_var)
%024_convolutional_lrelu = LeakyRelu[alpha = 0.1](%024_convolutional_bn)
%025_shortcut = Add(%024_convolutional_lrelu, %022_shortcut)
%026_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%025_shortcut, %026_convolutional_conv_weights)
%026_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%026_convolutional, %026_convolutional_bn_scale, %026_convolutional_bn_bias, %026_convolutional_bn_mean, %026_convolutional_bn_var)
%026_convolutional_lrelu = LeakyRelu[alpha = 0.1](%026_convolutional_bn)
%027_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%026_convolutional_lrelu, %027_convolutional_conv_weights)
%027_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%027_convolutional, %027_convolutional_bn_scale, %027_convolutional_bn_bias, %027_convolutional_bn_mean, %027_convolutional_bn_var)
%027_convolutional_lrelu = LeakyRelu[alpha = 0.1](%027_convolutional_bn)
%028_shortcut = Add(%027_convolutional_lrelu, %025_shortcut)
%029_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%028_shortcut, %029_convolutional_conv_weights)
%029_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%029_convolutional, %029_convolutional_bn_scale, %029_convolutional_bn_bias, %029_convolutional_bn_mean, %029_convolutional_bn_var)
%029_convolutional_lrelu = LeakyRelu[alpha = 0.1](%029_convolutional_bn)
%030_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%029_convolutional_lrelu, %030_convolutional_conv_weights)
%030_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%030_convolutional, %030_convolutional_bn_scale, %030_convolutional_bn_bias, %030_convolutional_bn_mean, %030_convolutional_bn_var)
%030_convolutional_lrelu = LeakyRelu[alpha = 0.1](%030_convolutional_bn)
%031_shortcut = Add(%030_convolutional_lrelu, %028_shortcut)
%032_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%031_shortcut, %032_convolutional_conv_weights)
%032_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%032_convolutional, %032_convolutional_bn_scale, %032_convolutional_bn_bias, %032_convolutional_bn_mean, %032_convolutional_bn_var)
%032_convolutional_lrelu = LeakyRelu[alpha = 0.1](%032_convolutional_bn)
%033_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%032_convolutional_lrelu, %033_convolutional_conv_weights)
%033_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%033_convolutional, %033_convolutional_bn_scale, %033_convolutional_bn_bias, %033_convolutional_bn_mean, %033_convolutional_bn_var)
%033_convolutional_lrelu = LeakyRelu[alpha = 0.1](%033_convolutional_bn)
%034_shortcut = Add(%033_convolutional_lrelu, %031_shortcut)
%035_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%034_shortcut, %035_convolutional_conv_weights)
%035_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%035_convolutional, %035_convolutional_bn_scale, %035_convolutional_bn_bias, %035_convolutional_bn_mean, %035_convolutional_bn_var)
%035_convolutional_lrelu = LeakyRelu[alpha = 0.1](%035_convolutional_bn)
%036_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%035_convolutional_lrelu, %036_convolutional_conv_weights)
%036_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%036_convolutional, %036_convolutional_bn_scale, %036_convolutional_bn_bias, %036_convolutional_bn_mean, %036_convolutional_bn_var)
%036_convolutional_lrelu = LeakyRelu[alpha = 0.1](%036_convolutional_bn)
%037_shortcut = Add(%036_convolutional_lrelu, %034_shortcut)
%038_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%037_shortcut, %038_convolutional_conv_weights)
%038_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%038_convolutional, %038_convolutional_bn_scale, %038_convolutional_bn_bias, %038_convolutional_bn_mean, %038_convolutional_bn_var)
%038_convolutional_lrelu = LeakyRelu[alpha = 0.1](%038_convolutional_bn)
%039_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%038_convolutional_lrelu, %039_convolutional_conv_weights)
%039_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%039_convolutional, %039_convolutional_bn_scale, %039_convolutional_bn_bias, %039_convolutional_bn_mean, %039_convolutional_bn_var)
%039_convolutional_lrelu = LeakyRelu[alpha = 0.1](%039_convolutional_bn)
%040_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%039_convolutional_lrelu, %040_convolutional_conv_weights)
%040_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%040_convolutional, %040_convolutional_bn_scale, %040_convolutional_bn_bias, %040_convolutional_bn_mean, %040_convolutional_bn_var)
%040_convolutional_lrelu = LeakyRelu[alpha = 0.1](%040_convolutional_bn)
%041_shortcut = Add(%040_convolutional_lrelu, %038_convolutional_lrelu)
%042_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%041_shortcut, %042_convolutional_conv_weights)
%042_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%042_convolutional, %042_convolutional_bn_scale, %042_convolutional_bn_bias, %042_convolutional_bn_mean, %042_convolutional_bn_var)
%042_convolutional_lrelu = LeakyRelu[alpha = 0.1](%042_convolutional_bn)
%043_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%042_convolutional_lrelu, %043_convolutional_conv_weights)
%043_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%043_convolutional, %043_convolutional_bn_scale, %043_convolutional_bn_bias, %043_convolutional_bn_mean, %043_convolutional_bn_var)
%043_convolutional_lrelu = LeakyRelu[alpha = 0.1](%043_convolutional_bn)
%044_shortcut = Add(%043_convolutional_lrelu, %041_shortcut)
%045_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%044_shortcut, %045_convolutional_conv_weights)
%045_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%045_convolutional, %045_convolutional_bn_scale, %045_convolutional_bn_bias, %045_convolutional_bn_mean, %045_convolutional_bn_var)
%045_convolutional_lrelu = LeakyRelu[alpha = 0.1](%045_convolutional_bn)
%046_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%045_convolutional_lrelu, %046_convolutional_conv_weights)
%046_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%046_convolutional, %046_convolutional_bn_scale, %046_convolutional_bn_bias, %046_convolutional_bn_mean, %046_convolutional_bn_var)
%046_convolutional_lrelu = LeakyRelu[alpha = 0.1](%046_convolutional_bn)
%047_shortcut = Add(%046_convolutional_lrelu, %044_shortcut)
%048_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%047_shortcut, %048_convolutional_conv_weights)
%048_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%048_convolutional, %048_convolutional_bn_scale, %048_convolutional_bn_bias, %048_convolutional_bn_mean, %048_convolutional_bn_var)
%048_convolutional_lrelu = LeakyRelu[alpha = 0.1](%048_convolutional_bn)
%049_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%048_convolutional_lrelu, %049_convolutional_conv_weights)
%049_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%049_convolutional, %049_convolutional_bn_scale, %049_convolutional_bn_bias, %049_convolutional_bn_mean, %049_convolutional_bn_var)
%049_convolutional_lrelu = LeakyRelu[alpha = 0.1](%049_convolutional_bn)
%050_shortcut = Add(%049_convolutional_lrelu, %047_shortcut)
%051_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%050_shortcut, %051_convolutional_conv_weights)
%051_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%051_convolutional, %051_convolutional_bn_scale, %051_convolutional_bn_bias, %051_convolutional_bn_mean, %051_convolutional_bn_var)
%051_convolutional_lrelu = LeakyRelu[alpha = 0.1](%051_convolutional_bn)
%052_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%051_convolutional_lrelu, %052_convolutional_conv_weights)
%052_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%052_convolutional, %052_convolutional_bn_scale, %052_convolutional_bn_bias, %052_convolutional_bn_mean, %052_convolutional_bn_var)
%052_convolutional_lrelu = LeakyRelu[alpha = 0.1](%052_convolutional_bn)
%053_shortcut = Add(%052_convolutional_lrelu, %050_shortcut)
%054_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%053_shortcut, %054_convolutional_conv_weights)
%054_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%054_convolutional, %054_convolutional_bn_scale, %054_convolutional_bn_bias, %054_convolutional_bn_mean, %054_convolutional_bn_var)
%054_convolutional_lrelu = LeakyRelu[alpha = 0.1](%054_convolutional_bn)
%055_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%054_convolutional_lrelu, %055_convolutional_conv_weights)
%055_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%055_convolutional, %055_convolutional_bn_scale, %055_convolutional_bn_bias, %055_convolutional_bn_mean, %055_convolutional_bn_var)
%055_convolutional_lrelu = LeakyRelu[alpha = 0.1](%055_convolutional_bn)
%056_shortcut = Add(%055_convolutional_lrelu, %053_shortcut)
%057_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%056_shortcut, %057_convolutional_conv_weights)
%057_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%057_convolutional, %057_convolutional_bn_scale, %057_convolutional_bn_bias, %057_convolutional_bn_mean, %057_convolutional_bn_var)
%057_convolutional_lrelu = LeakyRelu[alpha = 0.1](%057_convolutional_bn)
%058_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%057_convolutional_lrelu, %058_convolutional_conv_weights)
%058_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%058_convolutional, %058_convolutional_bn_scale, %058_convolutional_bn_bias, %058_convolutional_bn_mean, %058_convolutional_bn_var)
%058_convolutional_lrelu = LeakyRelu[alpha = 0.1](%058_convolutional_bn)
%059_shortcut = Add(%058_convolutional_lrelu, %056_shortcut)
%060_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%059_shortcut, %060_convolutional_conv_weights)
%060_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%060_convolutional, %060_convolutional_bn_scale, %060_convolutional_bn_bias, %060_convolutional_bn_mean, %060_convolutional_bn_var)
%060_convolutional_lrelu = LeakyRelu[alpha = 0.1](%060_convolutional_bn)
%061_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%060_convolutional_lrelu, %061_convolutional_conv_weights)
%061_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%061_convolutional, %061_convolutional_bn_scale, %061_convolutional_bn_bias, %061_convolutional_bn_mean, %061_convolutional_bn_var)
%061_convolutional_lrelu = LeakyRelu[alpha = 0.1](%061_convolutional_bn)
%062_shortcut = Add(%061_convolutional_lrelu, %059_shortcut)
%063_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [2, 2]](%062_shortcut, %063_convolutional_conv_weights)
%063_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%063_convolutional, %063_convolutional_bn_scale, %063_convolutional_bn_bias, %063_convolutional_bn_mean, %063_convolutional_bn_var)
%063_convolutional_lrelu = LeakyRelu[alpha = 0.1](%063_convolutional_bn)
%064_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%063_convolutional_lrelu, %064_convolutional_conv_weights)
%064_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%064_convolutional, %064_convolutional_bn_scale, %064_convolutional_bn_bias, %064_convolutional_bn_mean, %064_convolutional_bn_var)
%064_convolutional_lrelu = LeakyRelu[alpha = 0.1](%064_convolutional_bn)
%065_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%064_convolutional_lrelu, %065_convolutional_conv_weights)
%065_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%065_convolutional, %065_convolutional_bn_scale, %065_convolutional_bn_bias, %065_convolutional_bn_mean, %065_convolutional_bn_var)
%065_convolutional_lrelu = LeakyRelu[alpha = 0.1](%065_convolutional_bn)
%066_shortcut = Add(%065_convolutional_lrelu, %063_convolutional_lrelu)
%067_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%066_shortcut, %067_convolutional_conv_weights)
%067_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%067_convolutional, %067_convolutional_bn_scale, %067_convolutional_bn_bias, %067_convolutional_bn_mean, %067_convolutional_bn_var)
%067_convolutional_lrelu = LeakyRelu[alpha = 0.1](%067_convolutional_bn)
%068_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%067_convolutional_lrelu, %068_convolutional_conv_weights)
%068_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%068_convolutional, %068_convolutional_bn_scale, %068_convolutional_bn_bias, %068_convolutional_bn_mean, %068_convolutional_bn_var)
%068_convolutional_lrelu = LeakyRelu[alpha = 0.1](%068_convolutional_bn)
%069_shortcut = Add(%068_convolutional_lrelu, %066_shortcut)
%070_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%069_shortcut, %070_convolutional_conv_weights)
%070_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%070_convolutional, %070_convolutional_bn_scale, %070_convolutional_bn_bias, %070_convolutional_bn_mean, %070_convolutional_bn_var)
%070_convolutional_lrelu = LeakyRelu[alpha = 0.1](%070_convolutional_bn)
%071_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%070_convolutional_lrelu, %071_convolutional_conv_weights)
%071_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%071_convolutional, %071_convolutional_bn_scale, %071_convolutional_bn_bias, %071_convolutional_bn_mean, %071_convolutional_bn_var)
%071_convolutional_lrelu = LeakyRelu[alpha = 0.1](%071_convolutional_bn)
%072_shortcut = Add(%071_convolutional_lrelu, %069_shortcut)
%073_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%072_shortcut, %073_convolutional_conv_weights)
%073_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%073_convolutional, %073_convolutional_bn_scale, %073_convolutional_bn_bias, %073_convolutional_bn_mean, %073_convolutional_bn_var)
%073_convolutional_lrelu = LeakyRelu[alpha = 0.1](%073_convolutional_bn)
%074_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%073_convolutional_lrelu, %074_convolutional_conv_weights)
%074_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%074_convolutional, %074_convolutional_bn_scale, %074_convolutional_bn_bias, %074_convolutional_bn_mean, %074_convolutional_bn_var)
%074_convolutional_lrelu = LeakyRelu[alpha = 0.1](%074_convolutional_bn)
%075_shortcut = Add(%074_convolutional_lrelu, %072_shortcut)
%076_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%075_shortcut, %076_convolutional_conv_weights)
%076_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%076_convolutional, %076_convolutional_bn_scale, %076_convolutional_bn_bias, %076_convolutional_bn_mean, %076_convolutional_bn_var)
%076_convolutional_lrelu = LeakyRelu[alpha = 0.1](%076_convolutional_bn)
%077_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%076_convolutional_lrelu, %077_convolutional_conv_weights)
%077_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%077_convolutional, %077_convolutional_bn_scale, %077_convolutional_bn_bias, %077_convolutional_bn_mean, %077_convolutional_bn_var)
%077_convolutional_lrelu = LeakyRelu[alpha = 0.1](%077_convolutional_bn)
%078_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%077_convolutional_lrelu, %078_convolutional_conv_weights)
%078_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%078_convolutional, %078_convolutional_bn_scale, %078_convolutional_bn_bias, %078_convolutional_bn_mean, %078_convolutional_bn_var)
%078_convolutional_lrelu = LeakyRelu[alpha = 0.1](%078_convolutional_bn)
%079_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%078_convolutional_lrelu, %079_convolutional_conv_weights)
%079_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%079_convolutional, %079_convolutional_bn_scale, %079_convolutional_bn_bias, %079_convolutional_bn_mean, %079_convolutional_bn_var)
%079_convolutional_lrelu = LeakyRelu[alpha = 0.1](%079_convolutional_bn)
%080_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%079_convolutional_lrelu, %080_convolutional_conv_weights)
%080_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%080_convolutional, %080_convolutional_bn_scale, %080_convolutional_bn_bias, %080_convolutional_bn_mean, %080_convolutional_bn_var)
%080_convolutional_lrelu = LeakyRelu[alpha = 0.1](%080_convolutional_bn)
%081_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%080_convolutional_lrelu, %081_convolutional_conv_weights)
%081_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%081_convolutional, %081_convolutional_bn_scale, %081_convolutional_bn_bias, %081_convolutional_bn_mean, %081_convolutional_bn_var)
%081_convolutional_lrelu = LeakyRelu[alpha = 0.1](%081_convolutional_bn)
%082_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%081_convolutional_lrelu, %082_convolutional_conv_weights, %082_convolutional_conv_bias)
%085_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%080_convolutional_lrelu, %085_convolutional_conv_weights)
%085_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%085_convolutional, %085_convolutional_bn_scale, %085_convolutional_bn_bias, %085_convolutional_bn_mean, %085_convolutional_bn_var)
%085_convolutional_lrelu = LeakyRelu[alpha = 0.1](%085_convolutional_bn)
%086_upsample = Resize[mode = u'nearest'](%085_convolutional_lrelu, %086_upsample_scale)
%087_route = Concat[axis = 1](%086_upsample, %062_shortcut)
%088_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%087_route, %088_convolutional_conv_weights)
%088_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%088_convolutional, %088_convolutional_bn_scale, %088_convolutional_bn_bias, %088_convolutional_bn_mean, %088_convolutional_bn_var)
%088_convolutional_lrelu = LeakyRelu[alpha = 0.1](%088_convolutional_bn)
%089_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%088_convolutional_lrelu, %089_convolutional_conv_weights)
%089_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%089_convolutional, %089_convolutional_bn_scale, %089_convolutional_bn_bias, %089_convolutional_bn_mean, %089_convolutional_bn_var)
%089_convolutional_lrelu = LeakyRelu[alpha = 0.1](%089_convolutional_bn)
%090_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%089_convolutional_lrelu, %090_convolutional_conv_weights)
%090_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%090_convolutional, %090_convolutional_bn_scale, %090_convolutional_bn_bias, %090_convolutional_bn_mean, %090_convolutional_bn_var)
%090_convolutional_lrelu = LeakyRelu[alpha = 0.1](%090_convolutional_bn)
%091_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%090_convolutional_lrelu, %091_convolutional_conv_weights)
%091_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%091_convolutional, %091_convolutional_bn_scale, %091_convolutional_bn_bias, %091_convolutional_bn_mean, %091_convolutional_bn_var)
%091_convolutional_lrelu = LeakyRelu[alpha = 0.1](%091_convolutional_bn)
%092_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%091_convolutional_lrelu, %092_convolutional_conv_weights)
%092_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%092_convolutional, %092_convolutional_bn_scale, %092_convolutional_bn_bias, %092_convolutional_bn_mean, %092_convolutional_bn_var)
%092_convolutional_lrelu = LeakyRelu[alpha = 0.1](%092_convolutional_bn)
%093_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%092_convolutional_lrelu, %093_convolutional_conv_weights)
%093_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%093_convolutional, %093_convolutional_bn_scale, %093_convolutional_bn_bias, %093_convolutional_bn_mean, %093_convolutional_bn_var)
%093_convolutional_lrelu = LeakyRelu[alpha = 0.1](%093_convolutional_bn)
%094_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%093_convolutional_lrelu, %094_convolutional_conv_weights, %094_convolutional_conv_bias)
%097_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%092_convolutional_lrelu, %097_convolutional_conv_weights)
%097_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%097_convolutional, %097_convolutional_bn_scale, %097_convolutional_bn_bias, %097_convolutional_bn_mean, %097_convolutional_bn_var)
%097_convolutional_lrelu = LeakyRelu[alpha = 0.1](%097_convolutional_bn)
%098_upsample = Resize[mode = u'nearest'](%097_convolutional_lrelu, %098_upsample_scale)
%099_route = Concat[axis = 1](%098_upsample, %037_shortcut)
%100_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%099_route, %100_convolutional_conv_weights)
%100_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%100_convolutional, %100_convolutional_bn_scale, %100_convolutional_bn_bias, %100_convolutional_bn_mean, %100_convolutional_bn_var)
%100_convolutional_lrelu = LeakyRelu[alpha = 0.1](%100_convolutional_bn)
%101_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%100_convolutional_lrelu, %101_convolutional_conv_weights)
%101_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%101_convolutional, %101_convolutional_bn_scale, %101_convolutional_bn_bias, %101_convolutional_bn_mean, %101_convolutional_bn_var)
%101_convolutional_lrelu = LeakyRelu[alpha = 0.1](%101_convolutional_bn)
%102_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%101_convolutional_lrelu, %102_convolutional_conv_weights)
%102_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%102_convolutional, %102_convolutional_bn_scale, %102_convolutional_bn_bias, %102_convolutional_bn_mean, %102_convolutional_bn_var)
%102_convolutional_lrelu = LeakyRelu[alpha = 0.1](%102_convolutional_bn)
%103_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%102_convolutional_lrelu, %103_convolutional_conv_weights)
%103_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%103_convolutional, %103_convolutional_bn_scale, %103_convolutional_bn_bias, %103_convolutional_bn_mean, %103_convolutional_bn_var)
%103_convolutional_lrelu = LeakyRelu[alpha = 0.1](%103_convolutional_bn)
%104_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%103_convolutional_lrelu, %104_convolutional_conv_weights)
%104_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%104_convolutional, %104_convolutional_bn_scale, %104_convolutional_bn_bias, %104_convolutional_bn_mean, %104_convolutional_bn_var)
%104_convolutional_lrelu = LeakyRelu[alpha = 0.1](%104_convolutional_bn)
%105_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [3, 3], strides = [1, 1]](%104_convolutional_lrelu, %105_convolutional_conv_weights)
%105_convolutional_bn = BatchNormalization[epsilon = 1e-05, momentum = 0.99](%105_convolutional, %105_convolutional_bn_scale, %105_convolutional_bn_bias, %105_convolutional_bn_mean, %105_convolutional_bn_var)
%105_convolutional_lrelu = LeakyRelu[alpha = 0.1](%105_convolutional_bn)
%106_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]](%105_convolutional_lrelu, %106_convolutional_conv_weights, %106_convolutional_conv_bias)
return %082_convolutional, %094_convolutional, %106_convolutional
}
Traceback (most recent call last):
File "yolov3_to_onnx.py", line 811, in <module>
main()
File "yolov3_to_onnx.py", line 804, in main
onnx.checker.check_model(yolov3_model_def)
File "/usr/local/lib/python2.7/dist-packages/onnx/checker.py", line 91, in check_model
C.check_model(model.SerializeToString())
onnx.onnx_cpp2py_export.checker.ValidationError: Node (086_upsample) has input size 2 not in range [min=3, max=4].
==> Context: Bad node spec: input: "085_convolutional_lrelu" input: "086_upsample_scale" output: "086_upsample" name: "086_upsample" op_type: "Resize" attribute { name: "mode" s: "nearest" type: STRING }
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