Closed hesamsheikh closed 3 years ago
Clamp
bugs have been fixed and released. f03d58074bca4da333fa5b812dac71ba683c74ad However, the last DetectionOutput
cannot be converted to TensorFlow, so it is necessary to delete the layer and implement post-processing programmatically.
https://github.com/PINTO0309/openvino2tensorflow/releases/tag/v1.15.4
Clamp
bugs have been fixed and released. f03d580 However, the lastDetectionOutput
cannot be converted to TensorFlow, so it is necessary to delete the layer and implement post-processing programmatically. https://github.com/PINTO0309/openvino2tensorflow/releases/tag/v1.15.4
How can I exactly delete that layer and implement it later? @PINTO0309
XML Edit.
Delete.
<layer id="421" name="DetectionOutput_" type="DetectionOutput" version="opset1">
<data background_label_id="0" clip_after_nms="false" clip_before_nms="false" code_type="caffe.PriorBoxParameter.CENTER_SIZE" confidence_threshold="0.0099999997764825821" decrease_label_id="false" input_height="1" input_width="1" keep_top_k="200" nms_threshold="0.44999998807907104" normalized="true" num_classes="3" objectness_score="0" share_location="true" top_k="400" variance_encoded_in_target="false"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>4504</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>3378</dim>
</port>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>2</dim>
<dim>4504</dim>
</port>
</input>
<output>
<port id="3" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>200</dim>
<dim>7</dim>
</port>
</output>
</layer>
Edit.
<edge from-layer="345" from-port="0" to-layer="346" to-port="1"/>
<edge from-layer="346" from-port="2" to-layer="421" to-port="0"/>
<edge from-layer="347" from-port="0" to-layer="348" to-port="1"/>
Edit.
<edge from-layer="418" from-port="0" to-layer="419" to-port="1"/>
<edge from-layer="419" from-port="2" to-layer="421" to-port="1"/>
<edge from-layer="420" from-port="0" to-layer="421" to-port="2"/>
<edge from-layer="421" from-port="3" to-layer="422" to-port="0"/>
Thank you very much for the thorough answer, But I'm new to the program and still have difficulty. Could you maybe provide the saved_model format? If not, how can I implement those deleted layers later and also, to what should I edit those edges? @PINTO0309
Edit it like this. Do a Diff yourself to see what has changed before and after the modification.
<?xml version="1.0" ?>
<net name="vehicle-license-plate-detection-barrier-0106" version="10">
<layers>
<layer id="0" name="Placeholder" type="Parameter" version="opset1">
<data element_type="f32" shape="1, 3, 300, 300"/>
<output>
<port id="0" names="Placeholder:0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>300</dim>
<dim>300</dim>
</port>
</output>
</layer>
<layer id="1" name="data_mul_1352825592" type="Const" version="opset1">
<data element_type="f32" offset="0" shape="1, 1, 1, 1" size="4"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="2" name="Placeholder/scale/Fused_Mul_" type="Multiply" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>300</dim>
<dim>300</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>300</dim>
<dim>300</dim>
</port>
</output>
</layer>
<layer id="3" name="data_add_13530" type="Const" version="opset1">
<data element_type="f32" offset="4" shape="1, 3, 1, 1" size="12"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="4" name="Placeholder/mean/Fused_Add_" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>300</dim>
<dim>300</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" names="MobilenetV2/input:0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>300</dim>
<dim>300</dim>
</port>
</output>
</layer>
<layer id="5" name="MobilenetV2/Conv/BatchNorm/FusedBatchNorm/mean/Fused_Mul__copy" type="Const" version="opset1">
<data element_type="f32" offset="16" shape="16, 3, 3, 3" size="1728"/>
<output>
<port id="0" names="MobilenetV2/Conv/weights/read:0" precision="FP32">
<dim>16</dim>
<dim>3</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="6" name="MobilenetV2/Conv/Conv2D" type="Convolution" version="opset1">
<data auto_pad="same_upper" dilations="1, 1" pads_begin="0, 0" pads_end="1, 1" strides="2, 2"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>300</dim>
<dim>300</dim>
</port>
<port id="1" precision="FP32">
<dim>16</dim>
<dim>3</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</output>
</layer>
<layer id="7" name="data_add_1353313538" type="Const" version="opset1">
<data element_type="f32" offset="1744" shape="1, 16, 1, 1" size="64"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="8" name="MobilenetV2/Conv/BatchNorm/FusedBatchNorm/variance/Fused_Add_" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" names="MobilenetV2/Conv/BatchNorm/FusedBatchNorm:0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</output>
</layer>
<layer id="9" name="MobilenetV2/Conv/Relu6" type="Clamp" version="opset1">
<data max="6" min="0"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</input>
<output>
<port id="1" names="MobilenetV2/Conv/Relu6:0,MobilenetV2/expanded_conv/input:0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</output>
</layer>
<layer id="10" name="MobilenetV2/expanded_conv/depthwise/BatchNorm/FusedBatchNorm/mean/Fused_Mul__copy" type="Const" version="opset1">
<data element_type="f32" offset="1808" shape="16, 1, 1, 3, 3" size="576"/>
<output>
<port id="0" names="MobilenetV2/expanded_conv/depthwise/depthwise_weights/read:0" precision="FP32">
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="11" name="MobilenetV2/expanded_conv/depthwise/depthwise" type="GroupConvolution" version="opset1">
<data auto_pad="same_upper" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
<port id="1" precision="FP32">
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</output>
</layer>
<layer id="12" name="data_add_1354113546" type="Const" version="opset1">
<data element_type="f32" offset="2384" shape="1, 16, 1, 1" size="64"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="13" name="MobilenetV2/expanded_conv/depthwise/BatchNorm/FusedBatchNorm/variance/Fused_Add_" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" names="MobilenetV2/expanded_conv/depthwise/BatchNorm/FusedBatchNorm:0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</output>
</layer>
<layer id="14" name="MobilenetV2/expanded_conv/depthwise/Relu6" type="Clamp" version="opset1">
<data max="6" min="0"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</input>
<output>
<port id="1" names="MobilenetV2/expanded_conv/depthwise/Relu6:0,MobilenetV2/expanded_conv/depthwise_output:0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</output>
</layer>
<layer id="15" name="MobilenetV2/expanded_conv/project/BatchNorm/FusedBatchNorm/mean/Fused_Mul__copy" type="Const" version="opset1">
<data element_type="f32" offset="2448" shape="16, 16, 1, 1" size="1024"/>
<output>
<port id="0" names="MobilenetV2/expanded_conv/project/weights/read:0" precision="FP32">
<dim>16</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="16" name="MobilenetV2/expanded_conv/project/Conv2D" type="Convolution" version="opset1">
<data auto_pad="same_upper" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
<port id="1" precision="FP32">
<dim>16</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</output>
</layer>
<layer id="17" name="data_add_1354913554" type="Const" version="opset1">
<data element_type="f32" offset="3472" shape="1, 16, 1, 1" size="64"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="18" name="MobilenetV2/expanded_conv/project/BatchNorm/FusedBatchNorm/variance/Fused_Add_" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" names="MobilenetV2/expanded_conv/project/BatchNorm/FusedBatchNorm:0,MobilenetV2/expanded_conv/project/Identity:0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</output>
</layer>
<layer id="19" name="MobilenetV2/expanded_conv/add" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</input>
<output>
<port id="2" names="MobilenetV2/expanded_conv/add:0,MobilenetV2/expanded_conv/output:0,MobilenetV2/expanded_conv_1/input:0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</output>
</layer>
<layer id="20" name="MobilenetV2/expanded_conv_1/expand/BatchNorm/FusedBatchNorm/mean/Fused_Mul__copy" type="Const" version="opset1">
<data element_type="f32" offset="3536" shape="96, 16, 1, 1" size="6144"/>
<output>
<port id="0" names="MobilenetV2/expanded_conv_1/expand/weights/read:0" precision="FP32">
<dim>96</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="21" name="MobilenetV2/expanded_conv_1/expand/Conv2D" type="Convolution" version="opset1">
<data auto_pad="same_upper" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>150</dim>
<dim>150</dim>
</port>
<port id="1" precision="FP32">
<dim>96</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</output>
</layer>
<layer id="22" name="data_add_1355713562" type="Const" version="opset1">
<data element_type="f32" offset="9680" shape="1, 96, 1, 1" size="384"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="23" name="MobilenetV2/expanded_conv_1/expand/BatchNorm/FusedBatchNorm/variance/Fused_Add_" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>150</dim>
<dim>150</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" names="MobilenetV2/expanded_conv_1/expand/BatchNorm/FusedBatchNorm:0" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</output>
</layer>
<layer id="24" name="MobilenetV2/expanded_conv_1/expand/Relu6" type="Clamp" version="opset1">
<data max="6" min="0"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</input>
<output>
<port id="1" names="MobilenetV2/expanded_conv_1/expand/Relu6:0,MobilenetV2/expanded_conv_1/expansion_output:0" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>150</dim>
<dim>150</dim>
</port>
</output>
</layer>
<layer id="25" name="MobilenetV2/expanded_conv_1/depthwise/BatchNorm/FusedBatchNorm/mean/Fused_Mul__copy" type="Const" version="opset1">
<data element_type="f32" offset="10064" shape="96, 1, 1, 3, 3" size="3456"/>
<output>
<port id="0" names="MobilenetV2/expanded_conv_1/depthwise/depthwise_weights/read:0" precision="FP32">
<dim>96</dim>
<dim>1</dim>
<dim>1</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="26" name="MobilenetV2/expanded_conv_1/depthwise/depthwise" type="GroupConvolution" version="opset1">
<data auto_pad="same_upper" dilations="1, 1" pads_begin="0, 0" pads_end="1, 1" strides="2, 2"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>150</dim>
<dim>150</dim>
</port>
<port id="1" precision="FP32">
<dim>96</dim>
<dim>1</dim>
<dim>1</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>75</dim>
<dim>75</dim>
</port>
</output>
</layer>
<layer id="27" name="data_add_1356513570" type="Const" version="opset1">
<data element_type="f32" offset="13520" shape="1, 96, 1, 1" size="384"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="28" name="MobilenetV2/expanded_conv_1/depthwise/BatchNorm/FusedBatchNorm/variance/Fused_Add_" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>75</dim>
<dim>75</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" names="MobilenetV2/expanded_conv_1/depthwise/BatchNorm/FusedBatchNorm:0" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>75</dim>
<dim>75</dim>
</port>
</output>
</layer>
<layer id="29" name="MobilenetV2/expanded_conv_1/depthwise/Relu6" type="Clamp" version="opset1">
<data max="6" min="0"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>75</dim>
<dim>75</dim>
</port>
</input>
<output>
<port id="1" names="MobilenetV2/expanded_conv_1/depthwise/Relu6:0,MobilenetV2/expanded_conv_1/depthwise_output:0" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>75</dim>
<dim>75</dim>
</port>
</output>
</layer>
<layer id="30" name="MobilenetV2/expanded_conv_1/project/BatchNorm/FusedBatchNorm/mean/Fused_Mul__copy" type="Const" version="opset1">
<data element_type="f32" offset="13904" shape="16, 96, 1, 1" size="6144"/>
<output>
<port id="0" names="MobilenetV2/expanded_conv_1/project/weights/read:0" precision="FP32">
<dim>16</dim>
<dim>96</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="31" name="MobilenetV2/expanded_conv_1/project/Conv2D" type="Convolution" version="opset1">
<data auto_pad="same_upper" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>96</dim>
<dim>75</dim>
<dim>75</dim>
</port>
<port id="1" precision="FP32">
<dim>16</dim>
<dim>96</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>75</dim>
<dim>75</dim>
</port>
</output>
</layer>
<layer id="32" name="data_add_1357313578" type="Const" version="opset1">
<data element_type="f32" offset="20048" shape="1, 16, 1, 1" size="64"/>
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="33" name="MobilenetV2/expanded_conv_1/project/BatchNorm/FusedBatchNorm/variance/Fused_Add_" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>75</dim>
<dim>75</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" names="MobilenetV2/expanded_conv_1/output:0,MobilenetV2/expanded_conv_1/project/BatchNorm/FusedBatchNorm:0,MobilenetV2/expanded_conv_1/project/Identity:0,MobilenetV2/expanded_conv_2/input:0" precision="FP32">
<dim>1</dim>
<dim>16</dim>
<dim>75</dim>
<dim>75</dim>
</port>
</output>
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<meta_data>
<MO_version value="2021.4.0-3827-c5b65f2cb1d-releases/2021/4"/>
<cli_parameters>
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<data_type value="FP32"/>
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<disable_omitting_optional value="False"/>
<disable_resnet_optimization value="False"/>
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<extensions value="DIR"/>
<framework value="tf"/>
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<generate_deprecated_IR_V7 value="False"/>
<input value="Placeholder"/>
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<input_shape value="[1,300,300,3]"/>
<k value="DIR/CustomLayersMapping.xml"/>
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<model_name value="vehicle-license-plate-detection-barrier-0106"/>
<output value="['SSD/concat_reshape_softmax/mbox_loc_final', 'SSD/concat_reshape_softmax/mbox_conf_final', 'SSD/concat_reshape_softmax/mbox_priorbox']"/>
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Thank you for the effort,
Though now I get this error:
python3 openvino2tensorflow.py --output_saved_model --model_path /home/hessam/Downloads/Compressed/openvino2tensorflow-1.15.4/models/vehicle-license-plate-detection-barrier-0106.xml --model_output_path /home/hessam/Downloads/Compressed/openvino2tensorflow-1.15.4/models/vehicle-license-plate-detection-barrier-0106-CONVERTED TensorFlow/Keras model building process starts ====================================== Segmentation fault (core dumped)
UBUNTU 18 TensorFlow v2.4.1 OpenVINO 2021.4
I want to convert the open model zoo , vehicle-license-plate-detection-barrier-0106 into saved_model format, Using the following command:
python3 openvino2tensorflow.py --model_path /opt/intel/openvino_2021/deployment_tools/open_model_zoo/tools/downloader/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml --model_output_path /opt/intel/openvino_2021/deployment_tools/open_model_zoo/tools/downloader/intel/vehicle-license-plate-detection-barrier-0106/FP16/weights --output_saved_model
But I recieve this log:
===== ERROR: Cannot convert 2147483647.0 to EagerTensor of dtype int64 ERROR: model_path : /opt/intel/openvino_2021/deployment_tools/open_model_zoo/tools/downloader/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml ERROR: weights_path: /opt/intel/openvino_2021/deployment_tools/open_model_zoo/tools/downloader/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.bin ERROR: layer_id : 405 ERROR: input_layer0 layer_id=404: tf.Tensor([3], shape=(1,), dtype=int64) ERROR: The trace log is below. Traceback (most recent call last): File "openvino2tensorflow.py", line 555, in convert clip_value_max=cmax File "/home/hessam/.local/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py", line 201, in wrapper return target(*args, **kwargs) File "/home/hessam/.local/lib/python3.6/site-packages/tensorflow/python/ops/clip_ops.py", line 111, in clip_by_value t_min = math_ops.minimum(values, clip_value_max) File "/home/hessam/.local/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py", line 5929, in minimum _ctx, "Minimum", name, x, y) TypeError: Cannot convert 2147483647.0 to EagerTensor of dtype int64
How can I see the weights of this layer and is there a way to convert this model into saved_model?