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Hi, i have 2 problems:
1. when i try to convert from yolov3 weights to IR i need to specify the input to [1,412,412,3]. but the original YOLOv3 is 612x612 (with better result then 412x412). can i change it?
2. when i run the command:
./object_detection_demo -i ~/splited.mp4 -m ~/intel/openvino_2019.1.144/deployment_tools/model_optimizer/frozen_darknet_yolov3_model.xml
i get the following error msg:
[ ERROR ] Error reading network: in Layer detector/darknet-53/Conv_1/Conv2D: trying to connect an edge to non existing output port: 2.1
i look in the frozen_darknet_yolov3_model.xml file and i think it correpted because at the end of the file there is a list of connection between the layers. and there is a line in there:
<edge from-layer="2" from-port="1" to-layer="3" to-port="0"/>
but THERE IS NO LAYER NAMED "2"!
also
<edge from-layer="4" from-port="1" to-layer="5" to-port="0"/>
but there is no layer named "4"!
and so on.
can somone help me plz?
thx
Shalom
The XML file:
<?xml version="1.0" ?>
<net batch="1" name="frozen_darknet_yolov3_model" version="5">
<layers>
<layer id="0" name="inputs" precision="FP32" type="Input">
<output>
<port id="0">
<dim>1</dim>
<dim>3</dim>
<dim>416</dim>
<dim>416</dim>
</port>
</output>
</layer>
<layer id="1" name="detector/darknet-53/Conv/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="3,3" output="32" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>3</dim>
<dim>416</dim>
<dim>416</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>32</dim>
<dim>416</dim>
<dim>416</dim>
</port>
</output>
<blobs>
<weights offset="0" size="3456"/>
<biases offset="3456" size="128"/>
</blobs>
</layer>
<layer id="3" name="detector/darknet-53/Conv_1/Conv2D" precision="FP32" type="Convolution">
<data dilations="1,1" group="1" kernel="3,3" output="64" pads_begin="1,1" pads_end="1,1" strides="2,2"/>
<input>
<port id="0">
<dim>1</dim>
<dim>32</dim>
<dim>416</dim>
<dim>416</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>64</dim>
<dim>208</dim>
<dim>208</dim>
</port>
</output>
<blobs>
<weights offset="3584" size="73728"/>
<biases offset="77312" size="256"/>
</blobs>
</layer>
<layer id="5" name="detector/darknet-53/Conv_2/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="32" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>64</dim>
<dim>208</dim>
<dim>208</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>32</dim>
<dim>208</dim>
<dim>208</dim>
</port>
</output>
<blobs>
<weights offset="77568" size="8192"/>
<biases offset="85760" size="128"/>
</blobs>
</layer>
<layer id="7" name="detector/darknet-53/Conv_3/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="3,3" output="64" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>32</dim>
<dim>208</dim>
<dim>208</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>64</dim>
<dim>208</dim>
<dim>208</dim>
</port>
</output>
<blobs>
<weights offset="85888" size="73728"/>
<biases offset="159616" size="256"/>
</blobs>
</layer>
<layer id="9" name="detector/darknet-53/add" precision="FP32" type="Eltwise">
<data operation="sum"/>
<input>
<port id="0">
<dim>1</dim>
<dim>64</dim>
<dim>208</dim>
<dim>208</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>64</dim>
<dim>208</dim>
<dim>208</dim>
</port>
</input>
<output>
<port id="2">
<dim>1</dim>
<dim>64</dim>
<dim>208</dim>
<dim>208</dim>
</port>
</output>
</layer>
<layer id="10" name="detector/darknet-53/Conv_4/Conv2D" precision="FP32" type="Convolution">
<data dilations="1,1" group="1" kernel="3,3" output="128" pads_begin="1,1" pads_end="1,1" strides="2,2"/>
<input>
<port id="0">
<dim>1</dim>
<dim>64</dim>
<dim>208</dim>
<dim>208</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>128</dim>
<dim>104</dim>
<dim>104</dim>
</port>
</output>
<blobs>
<weights offset="159872" size="294912"/>
<biases offset="454784" size="512"/>
</blobs>
</layer>
<layer id="12" name="detector/darknet-53/Conv_5/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="64" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>128</dim>
<dim>104</dim>
<dim>104</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>64</dim>
<dim>104</dim>
<dim>104</dim>
</port>
</output>
<blobs>
<weights offset="455296" size="32768"/>
<biases offset="488064" size="256"/>
</blobs>
</layer>
<layer id="14" name="detector/darknet-53/Conv_6/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="3,3" output="128" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>64</dim>
<dim>104</dim>
<dim>104</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>128</dim>
<dim>104</dim>
<dim>104</dim>
</port>
</output>
<blobs>
<weights offset="488320" size="294912"/>
<biases offset="783232" size="512"/>
</blobs>
</layer>
<layer id="16" name="detector/darknet-53/add_1" precision="FP32" type="Eltwise">
<data operation="sum"/>
<input>
<port id="0">
<dim>1</dim>
<dim>128</dim>
<dim>104</dim>
<dim>104</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>128</dim>
<dim>104</dim>
<dim>104</dim>
</port>
</input>
<output>
<port id="2">
<dim>1</dim>
<dim>128</dim>
<dim>104</dim>
<dim>104</dim>
</port>
</output>
</layer>
<layer id="17" name="detector/darknet-53/Conv_7/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="64" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>128</dim>
<dim>104</dim>
<dim>104</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>64</dim>
<dim>104</dim>
<dim>104</dim>
</port>
</output>
<blobs>
<weights offset="783744" size="32768"/>
<biases offset="816512" size="256"/>
</blobs>
</layer>
<layer id="19" name="detector/darknet-53/Conv_8/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="3,3" output="128" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>64</dim>
<dim>104</dim>
<dim>104</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>128</dim>
<dim>104</dim>
<dim>104</dim>
</port>
</output>
<blobs>
<weights offset="816768" size="294912"/>
<biases offset="1111680" size="512"/>
</blobs>
</layer>
<layer id="21" name="detector/darknet-53/add_2" precision="FP32" type="Eltwise">
<data operation="sum"/>
<input>
<port id="0">
<dim>1</dim>
<dim>128</dim>
<dim>104</dim>
<dim>104</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>128</dim>
<dim>104</dim>
<dim>104</dim>
</port>
</input>
<output>
<port id="2">
<dim>1</dim>
<dim>128</dim>
<dim>104</dim>
<dim>104</dim>
</port>
</output>
</layer>
<layer id="22" name="detector/darknet-53/Conv_9/Conv2D" precision="FP32" type="Convolution">
<data dilations="1,1" group="1" kernel="3,3" output="256" pads_begin="1,1" pads_end="1,1" strides="2,2"/>
<input>
<port id="0">
<dim>1</dim>
<dim>128</dim>
<dim>104</dim>
<dim>104</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
<blobs>
<weights offset="1112192" size="1179648"/>
<biases offset="2291840" size="1024"/>
</blobs>
</layer>
<layer id="24" name="detector/darknet-53/Conv_10/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="128" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>128</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
<blobs>
<weights offset="2292864" size="131072"/>
<biases offset="2423936" size="512"/>
</blobs>
</layer>
<layer id="26" name="detector/darknet-53/Conv_11/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="3,3" output="256" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>128</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
<blobs>
<weights offset="2424448" size="1179648"/>
<biases offset="3604096" size="1024"/>
</blobs>
</layer>
<layer id="28" name="detector/darknet-53/add_3" precision="FP32" type="Eltwise">
<data operation="sum"/>
<input>
<port id="0">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="2">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
</layer>
<layer id="29" name="detector/darknet-53/Conv_12/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="128" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>128</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
<blobs>
<weights offset="3605120" size="131072"/>
<biases offset="3736192" size="512"/>
</blobs>
</layer>
<layer id="31" name="detector/darknet-53/Conv_13/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="3,3" output="256" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>128</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
<blobs>
<weights offset="3736704" size="1179648"/>
<biases offset="4916352" size="1024"/>
</blobs>
</layer>
<layer id="33" name="detector/darknet-53/add_4" precision="FP32" type="Eltwise">
<data operation="sum"/>
<input>
<port id="0">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="2">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
</layer>
<layer id="34" name="detector/darknet-53/Conv_14/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="128" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>128</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
<blobs>
<weights offset="4917376" size="131072"/>
<biases offset="5048448" size="512"/>
</blobs>
</layer>
<layer id="36" name="detector/darknet-53/Conv_15/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="3,3" output="256" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>128</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
<blobs>
<weights offset="5048960" size="1179648"/>
<biases offset="6228608" size="1024"/>
</blobs>
</layer>
<layer id="38" name="detector/darknet-53/add_5" precision="FP32" type="Eltwise">
<data operation="sum"/>
<input>
<port id="0">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="2">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
</layer>
<layer id="39" name="detector/darknet-53/Conv_16/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="128" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>128</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
<blobs>
<weights offset="6229632" size="131072"/>
<biases offset="6360704" size="512"/>
</blobs>
</layer>
<layer id="41" name="detector/darknet-53/Conv_17/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="3,3" output="256" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>128</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
<blobs>
<weights offset="6361216" size="1179648"/>
<biases offset="7540864" size="1024"/>
</blobs>
</layer>
<layer id="43" name="detector/darknet-53/add_6" precision="FP32" type="Eltwise">
<data operation="sum"/>
<input>
<port id="0">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="2">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
</layer>
<layer id="44" name="detector/darknet-53/Conv_18/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="128" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>128</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
<blobs>
<weights offset="7541888" size="131072"/>
<biases offset="7672960" size="512"/>
</blobs>
</layer>
<layer id="46" name="detector/darknet-53/Conv_19/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="3,3" output="256" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>128</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
<blobs>
<weights offset="7673472" size="1179648"/>
<biases offset="8853120" size="1024"/>
</blobs>
</layer>
<layer id="48" name="detector/darknet-53/add_7" precision="FP32" type="Eltwise">
<data operation="sum"/>
<input>
<port id="0">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="2">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
</layer>
<layer id="49" name="detector/darknet-53/Conv_20/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="128" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>128</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
<blobs>
<weights offset="8854144" size="131072"/>
<biases offset="8985216" size="512"/>
</blobs>
</layer>
<layer id="51" name="detector/darknet-53/Conv_21/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="3,3" output="256" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>128</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="3">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
<blobs>
<weights offset="8985728" size="1179648"/>
<biases offset="10165376" size="1024"/>
</blobs>
</layer>
<layer id="53" name="detector/darknet-53/add_8" precision="FP32" type="Eltwise">
<data operation="sum"/>
<input>
<port id="0">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
<port id="1">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</input>
<output>
<port id="2">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
</port>
</output>
</layer>
<layer id="54" name="detector/darknet-53/Conv_22/Conv2D" precision="FP32" type="Convolution">
<data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="128" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
<input>
<port id="0">
<dim>1</dim>
<dim>256</dim>
<dim>52</dim>
<dim>52</dim>
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</net>
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Dear Dimant, Shalom,
Thanks for your patience. I have already addressed your question in your other post
Sincerely,
Shubha
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