[Tests/Models] Add mobinet_v2 model files generated by openvino toolkit
authorWook Song <wook16.song@samsung.com>
Mon, 3 Feb 2020 05:38:54 +0000 (14:38 +0900)
committerMyungJoo Ham <myungjoo.ham@samsung.com>
Wed, 5 Feb 2020 09:35:56 +0000 (01:35 -0800)
This patch adds MobileNet V2 model files generated by model_optimizer
provided by the openvino toolkit.

Signed-off-by: Wook Song <wook16.song@samsung.com>
tests/test_models/models/openvino_mobilenetv2-int8-tf-0001.bin [new file with mode: 0644]
tests/test_models/models/openvino_mobilenetv2-int8-tf-0001.xml [new file with mode: 0644]

diff --git a/tests/test_models/models/openvino_mobilenetv2-int8-tf-0001.bin b/tests/test_models/models/openvino_mobilenetv2-int8-tf-0001.bin
new file mode 100644 (file)
index 0000000..62933ab
Binary files /dev/null and b/tests/test_models/models/openvino_mobilenetv2-int8-tf-0001.bin differ
diff --git a/tests/test_models/models/openvino_mobilenetv2-int8-tf-0001.xml b/tests/test_models/models/openvino_mobilenetv2-int8-tf-0001.xml
new file mode 100644 (file)
index 0000000..0441b06
--- /dev/null
@@ -0,0 +1,2758 @@
+<?xml version="1.0" ?>
+<net batch="1" name="mobilenetv2-int8-tf-0001" version="6">
+       <layers>
+               <layer id="0" name="input/placeholder_port_0" precision="FP32" type="Input">
+                       <output>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>3</dim>
+                                       <dim>224</dim>
+                                       <dim>224</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="1" name="Mul_/Fused_Mul_/FusedScaleShift_" precision="FP32" type="ScaleShift">
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>3</dim>
+                                       <dim>224</dim>
+                                       <dim>224</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>3</dim>
+                                       <dim>224</dim>
+                                       <dim>224</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="0" size="12"/>
+                               <biases offset="12" size="12"/>
+                       </blobs>
+               </layer>
+               <layer id="2" name="MobilenetV2/Conv/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="3,3" output="32" pads_begin="0,0" pads_end="1,1" strides="2,2"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>3</dim>
+                                       <dim>224</dim>
+                                       <dim>224</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>32</dim>
+                                       <dim>112</dim>
+                                       <dim>112</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="24" size="3456"/>
+                               <biases offset="3480" size="128"/>
+                       </blobs>
+               </layer>
+               <layer id="3" name="MobilenetV2/Conv/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>32</dim>
+                                       <dim>112</dim>
+                                       <dim>112</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="1">
+                                       <dim>1</dim>
+                                       <dim>32</dim>
+                                       <dim>112</dim>
+                                       <dim>112</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="4" name="MobilenetV2/expanded_conv/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="32" kernel="3,3" output="32" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>32</dim>
+                                       <dim>112</dim>
+                                       <dim>112</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>32</dim>
+                                       <dim>112</dim>
+                                       <dim>112</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="3608" size="1152"/>
+                               <biases offset="4760" size="128"/>
+                       </blobs>
+               </layer>
+               <layer id="5" name="MobilenetV2/expanded_conv/depthwise/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>32</dim>
+                                       <dim>112</dim>
+                                       <dim>112</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="1">
+                                       <dim>1</dim>
+                                       <dim>32</dim>
+                                       <dim>112</dim>
+                                       <dim>112</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="6" name="MobilenetV2/expanded_conv/project/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="16" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>32</dim>
+                                       <dim>112</dim>
+                                       <dim>112</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>16</dim>
+                                       <dim>112</dim>
+                                       <dim>112</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="4888" size="2048"/>
+                               <biases offset="6936" size="64"/>
+                       </blobs>
+               </layer>
+               <layer id="7" name="MobilenetV2/expanded_conv_1/expand/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="96" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>16</dim>
+                                       <dim>112</dim>
+                                       <dim>112</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>96</dim>
+                                       <dim>112</dim>
+                                       <dim>112</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="7000" size="6144"/>
+                               <biases offset="13144" size="384"/>
+                       </blobs>
+               </layer>
+               <layer id="8" name="MobilenetV2/expanded_conv_1/expand/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>96</dim>
+                                       <dim>112</dim>
+                                       <dim>112</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="1">
+                                       <dim>1</dim>
+                                       <dim>96</dim>
+                                       <dim>112</dim>
+                                       <dim>112</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="9" name="MobilenetV2/expanded_conv_1/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="96" kernel="3,3" output="96" pads_begin="0,0" pads_end="1,1" strides="2,2"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>96</dim>
+                                       <dim>112</dim>
+                                       <dim>112</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>96</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="13528" size="3456"/>
+                               <biases offset="16984" size="384"/>
+                       </blobs>
+               </layer>
+               <layer id="10" name="MobilenetV2/expanded_conv_1/depthwise/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>96</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="1">
+                                       <dim>1</dim>
+                                       <dim>96</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="11" name="MobilenetV2/expanded_conv_1/project/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="24" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>96</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>24</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="17368" size="9216"/>
+                               <biases offset="26584" size="96"/>
+                       </blobs>
+               </layer>
+               <layer id="12" name="MobilenetV2/expanded_conv_2/expand/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="144" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>24</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>144</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="26680" size="13824"/>
+                               <biases offset="40504" size="576"/>
+                       </blobs>
+               </layer>
+               <layer id="13" name="MobilenetV2/expanded_conv_2/expand/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>144</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="1">
+                                       <dim>1</dim>
+                                       <dim>144</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="14" name="MobilenetV2/expanded_conv_2/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="144" kernel="3,3" output="144" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>144</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>144</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="41080" size="5184"/>
+                               <biases offset="46264" size="576"/>
+                       </blobs>
+               </layer>
+               <layer id="15" name="MobilenetV2/expanded_conv_2/depthwise/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>144</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="1">
+                                       <dim>1</dim>
+                                       <dim>144</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="16" name="MobilenetV2/expanded_conv_2/project/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="24" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>144</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>24</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="46840" size="13824"/>
+                               <biases offset="60664" size="96"/>
+                       </blobs>
+               </layer>
+               <layer id="17" name="MobilenetV2/expanded_conv_2/add" precision="FP32" type="Eltwise">
+                       <data operation="sum"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>24</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                               <port id="1">
+                                       <dim>1</dim>
+                                       <dim>24</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="2">
+                                       <dim>1</dim>
+                                       <dim>24</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="18" name="MobilenetV2/expanded_conv_3/expand/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="144" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>24</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>144</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="60760" size="13824"/>
+                               <biases offset="74584" size="576"/>
+                       </blobs>
+               </layer>
+               <layer id="19" name="MobilenetV2/expanded_conv_3/expand/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>144</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="1">
+                                       <dim>1</dim>
+                                       <dim>144</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="20" name="MobilenetV2/expanded_conv_3/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="144" kernel="3,3" output="144" pads_begin="0,0" pads_end="1,1" strides="2,2"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>144</dim>
+                                       <dim>56</dim>
+                                       <dim>56</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>144</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="75160" size="5184"/>
+                               <biases offset="80344" size="576"/>
+                       </blobs>
+               </layer>
+               <layer id="21" name="MobilenetV2/expanded_conv_3/depthwise/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>144</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="1">
+                                       <dim>1</dim>
+                                       <dim>144</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="22" name="MobilenetV2/expanded_conv_3/project/Conv2D_Fold" 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>
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+                                       <dim>28</dim>
+                               </port>
+                       </input>
+                       <output>
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+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
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+                               <weights offset="80920" size="18432"/>
+                               <biases offset="99352" size="128"/>
+                       </blobs>
+               </layer>
+               <layer id="23" name="MobilenetV2/expanded_conv_4/expand/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="192" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
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+                                       <dim>28</dim>
+                               </port>
+                       </input>
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+                               <port id="3">
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+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="99480" size="24576"/>
+                               <biases offset="124056" size="768"/>
+                       </blobs>
+               </layer>
+               <layer id="24" name="MobilenetV2/expanded_conv_4/expand/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
+                               <port id="0">
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+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </input>
+                       <output>
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+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="25" name="MobilenetV2/expanded_conv_4/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="192" kernel="3,3" output="192" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
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+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </input>
+                       <output>
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+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="124824" size="6912"/>
+                               <biases offset="131736" size="768"/>
+                       </blobs>
+               </layer>
+               <layer id="26" name="MobilenetV2/expanded_conv_4/depthwise/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
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+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="1">
+                                       <dim>1</dim>
+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="27" name="MobilenetV2/expanded_conv_4/project/Conv2D_Fold" 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">
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+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </input>
+                       <output>
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+                                       <dim>1</dim>
+                                       <dim>32</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="132504" size="24576"/>
+                               <biases offset="157080" size="128"/>
+                       </blobs>
+               </layer>
+               <layer id="28" name="MobilenetV2/expanded_conv_4/add" precision="FP32" type="Eltwise">
+                       <data operation="sum"/>
+                       <input>
+                               <port id="0">
+                                       <dim>1</dim>
+                                       <dim>32</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                               <port id="1">
+                                       <dim>1</dim>
+                                       <dim>32</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="2">
+                                       <dim>1</dim>
+                                       <dim>32</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="29" name="MobilenetV2/expanded_conv_5/expand/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="192" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
+                       <input>
+                               <port id="0">
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+                                       <dim>32</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="157208" size="24576"/>
+                               <biases offset="181784" size="768"/>
+                       </blobs>
+               </layer>
+               <layer id="30" name="MobilenetV2/expanded_conv_5/expand/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
+                               <port id="0">
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+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="1">
+                                       <dim>1</dim>
+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="31" name="MobilenetV2/expanded_conv_5/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="192" kernel="3,3" output="192" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
+                       <input>
+                               <port id="0">
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+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="182552" size="6912"/>
+                               <biases offset="189464" size="768"/>
+                       </blobs>
+               </layer>
+               <layer id="32" name="MobilenetV2/expanded_conv_5/depthwise/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
+                               <port id="0">
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+                                       <dim>192</dim>
+                                       <dim>28</dim>
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+                               </port>
+                       </input>
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+                                       <dim>1</dim>
+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="33" name="MobilenetV2/expanded_conv_5/project/Conv2D_Fold" 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">
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+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>32</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
+                       <blobs>
+                               <weights offset="190232" size="24576"/>
+                               <biases offset="214808" size="128"/>
+                       </blobs>
+               </layer>
+               <layer id="34" name="MobilenetV2/expanded_conv_5/add" precision="FP32" type="Eltwise">
+                       <data operation="sum"/>
+                       <input>
+                               <port id="0">
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+                                       <dim>32</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                               <port id="1">
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+                                       <dim>32</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="2">
+                                       <dim>1</dim>
+                                       <dim>32</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="35" name="MobilenetV2/expanded_conv_6/expand/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="192" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
+                       <input>
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+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </input>
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+                               <port id="3">
+                                       <dim>1</dim>
+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
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+                               <weights offset="214936" size="24576"/>
+                               <biases offset="239512" size="768"/>
+                       </blobs>
+               </layer>
+               <layer id="36" name="MobilenetV2/expanded_conv_6/expand/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
+                               <port id="0">
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+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </input>
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+                                       <dim>192</dim>
+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="37" name="MobilenetV2/expanded_conv_6/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="192" kernel="3,3" output="192" pads_begin="0,0" pads_end="1,1" strides="2,2"/>
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+                                       <dim>28</dim>
+                                       <dim>28</dim>
+                               </port>
+                       </input>
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+                                       <dim>192</dim>
+                                       <dim>14</dim>
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+                               </port>
+                       </output>
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+                               <weights offset="240280" size="6912"/>
+                               <biases offset="247192" size="768"/>
+                       </blobs>
+               </layer>
+               <layer id="38" name="MobilenetV2/expanded_conv_6/depthwise/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
+                               <port id="0">
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+                                       <dim>14</dim>
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+                                       <dim>14</dim>
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+                               </port>
+                       </output>
+               </layer>
+               <layer id="39" name="MobilenetV2/expanded_conv_6/project/Conv2D_Fold" 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"/>
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+                                       <dim>14</dim>
+                                       <dim>14</dim>
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+                       </input>
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+                       </output>
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+                               <weights offset="247960" size="49152"/>
+                               <biases offset="297112" size="256"/>
+                       </blobs>
+               </layer>
+               <layer id="40" name="MobilenetV2/expanded_conv_7/expand/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="384" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
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+                       </output>
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+                               <weights offset="297368" size="98304"/>
+                               <biases offset="395672" size="1536"/>
+                       </blobs>
+               </layer>
+               <layer id="41" name="MobilenetV2/expanded_conv_7/expand/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
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+                                       <dim>14</dim>
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+                       </output>
+               </layer>
+               <layer id="42" name="MobilenetV2/expanded_conv_7/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="384" kernel="3,3" output="384" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
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+                       </output>
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+                               <biases offset="411032" size="1536"/>
+                       </blobs>
+               </layer>
+               <layer id="43" name="MobilenetV2/expanded_conv_7/depthwise/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
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+                       </output>
+               </layer>
+               <layer id="44" name="MobilenetV2/expanded_conv_7/project/Conv2D_Fold" 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"/>
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+                               <biases offset="510872" size="256"/>
+                       </blobs>
+               </layer>
+               <layer id="45" name="MobilenetV2/expanded_conv_7/add" precision="FP32" type="Eltwise">
+                       <data operation="sum"/>
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+                                       <dim>14</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="46" name="MobilenetV2/expanded_conv_8/expand/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="384" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
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+                                       <dim>14</dim>
+                               </port>
+                       </output>
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+                               <weights offset="511128" size="98304"/>
+                               <biases offset="609432" size="1536"/>
+                       </blobs>
+               </layer>
+               <layer id="47" name="MobilenetV2/expanded_conv_8/expand/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
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+                               <port id="0">
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+                               </port>
+                       </output>
+               </layer>
+               <layer id="48" name="MobilenetV2/expanded_conv_8/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="384" kernel="3,3" output="384" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
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+                               <biases offset="624792" size="1536"/>
+                       </blobs>
+               </layer>
+               <layer id="49" name="MobilenetV2/expanded_conv_8/depthwise/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
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+                                       <dim>14</dim>
+                                       <dim>14</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="50" name="MobilenetV2/expanded_conv_8/project/Conv2D_Fold" 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"/>
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+                               <biases offset="724632" size="256"/>
+                       </blobs>
+               </layer>
+               <layer id="51" name="MobilenetV2/expanded_conv_8/add" precision="FP32" type="Eltwise">
+                       <data operation="sum"/>
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+                               <port id="0">
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+                                       <dim>14</dim>
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+                                       <dim>14</dim>
+                                       <dim>14</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="52" name="MobilenetV2/expanded_conv_9/expand/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="384" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
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+                       </output>
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+                               <biases offset="823192" size="1536"/>
+                       </blobs>
+               </layer>
+               <layer id="53" name="MobilenetV2/expanded_conv_9/expand/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
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+                       </output>
+               </layer>
+               <layer id="54" name="MobilenetV2/expanded_conv_9/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="384" kernel="3,3" output="384" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
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+                       </output>
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+                               <weights offset="824728" size="13824"/>
+                               <biases offset="838552" size="1536"/>
+                       </blobs>
+               </layer>
+               <layer id="55" name="MobilenetV2/expanded_conv_9/depthwise/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
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+                       </output>
+               </layer>
+               <layer id="56" name="MobilenetV2/expanded_conv_9/project/Conv2D_Fold" 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"/>
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+                               <biases offset="938392" size="256"/>
+                       </blobs>
+               </layer>
+               <layer id="57" name="MobilenetV2/expanded_conv_9/add" precision="FP32" type="Eltwise">
+                       <data operation="sum"/>
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+                               <port id="0">
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+                       </output>
+               </layer>
+               <layer id="58" name="MobilenetV2/expanded_conv_10/expand/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="384" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
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+                               <biases offset="1036952" size="1536"/>
+                       </blobs>
+               </layer>
+               <layer id="59" name="MobilenetV2/expanded_conv_10/expand/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
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+                       </output>
+               </layer>
+               <layer id="60" name="MobilenetV2/expanded_conv_10/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="384" kernel="3,3" output="384" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
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+                               <biases offset="1052312" size="1536"/>
+                       </blobs>
+               </layer>
+               <layer id="61" name="MobilenetV2/expanded_conv_10/depthwise/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
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+                       </output>
+               </layer>
+               <layer id="62" name="MobilenetV2/expanded_conv_10/project/Conv2D_Fold" precision="FP32" type="Convolution">
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+                               <biases offset="1201304" size="384"/>
+                       </blobs>
+               </layer>
+               <layer id="63" name="MobilenetV2/expanded_conv_11/expand/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="576" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
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+               <layer id="64" name="MobilenetV2/expanded_conv_11/expand/Relu6" precision="FP32" type="Clamp">
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+                       </output>
+               </layer>
+               <layer id="65" name="MobilenetV2/expanded_conv_11/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
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+                       </output>
+               </layer>
+               <layer id="67" name="MobilenetV2/expanded_conv_11/project/Conv2D_Fold" precision="FP32" type="Convolution">
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+               </layer>
+               <layer id="68" name="MobilenetV2/expanded_conv_11/add" precision="FP32" type="Eltwise">
+                       <data operation="sum"/>
+                       <input>
+                               <port id="0">
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+                                       <dim>14</dim>
+                                       <dim>14</dim>
+                               </port>
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+                                       <dim>96</dim>
+                                       <dim>14</dim>
+                                       <dim>14</dim>
+                               </port>
+                       </input>
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+                                       <dim>96</dim>
+                                       <dim>14</dim>
+                                       <dim>14</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="69" name="MobilenetV2/expanded_conv_12/expand/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="576" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
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+                               </port>
+                       </input>
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+                                       <dim>576</dim>
+                                       <dim>14</dim>
+                                       <dim>14</dim>
+                               </port>
+                       </output>
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+                               <weights offset="1669784" size="221184"/>
+                               <biases offset="1890968" size="2304"/>
+                       </blobs>
+               </layer>
+               <layer id="70" name="MobilenetV2/expanded_conv_12/expand/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
+                               <port id="0">
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+                               </port>
+                       </output>
+               </layer>
+               <layer id="71" name="MobilenetV2/expanded_conv_12/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="576" kernel="3,3" output="576" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
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+                       </input>
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+                                       <dim>14</dim>
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+                               </port>
+                       </output>
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+                               <weights offset="1893272" size="20736"/>
+                               <biases offset="1914008" size="2304"/>
+                       </blobs>
+               </layer>
+               <layer id="72" name="MobilenetV2/expanded_conv_12/depthwise/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
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+                                       <dim>576</dim>
+                                       <dim>14</dim>
+                                       <dim>14</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="73" name="MobilenetV2/expanded_conv_12/project/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="96" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
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+                               </port>
+                       </input>
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+                                       <dim>96</dim>
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+                               </port>
+                       </output>
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+                               <biases offset="2137496" size="384"/>
+                       </blobs>
+               </layer>
+               <layer id="74" name="MobilenetV2/expanded_conv_12/add" precision="FP32" type="Eltwise">
+                       <data operation="sum"/>
+                       <input>
+                               <port id="0">
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+                                       <dim>96</dim>
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+                                       <dim>96</dim>
+                                       <dim>14</dim>
+                                       <dim>14</dim>
+                               </port>
+                       </input>
+                       <output>
+                               <port id="2">
+                                       <dim>1</dim>
+                                       <dim>96</dim>
+                                       <dim>14</dim>
+                                       <dim>14</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="75" name="MobilenetV2/expanded_conv_13/expand/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="576" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
+                       <input>
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+                                       <dim>96</dim>
+                                       <dim>14</dim>
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+                               </port>
+                       </input>
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+                                       <dim>576</dim>
+                                       <dim>14</dim>
+                                       <dim>14</dim>
+                               </port>
+                       </output>
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+                               <weights offset="2137880" size="221184"/>
+                               <biases offset="2359064" size="2304"/>
+                       </blobs>
+               </layer>
+               <layer id="76" name="MobilenetV2/expanded_conv_13/expand/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
+                               <port id="0">
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+                                       <dim>576</dim>
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+                                       <dim>14</dim>
+                               </port>
+                       </output>
+               </layer>
+               <layer id="77" name="MobilenetV2/expanded_conv_13/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="576" kernel="3,3" output="576" pads_begin="0,0" pads_end="1,1" strides="2,2"/>
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+                               </port>
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+                       </output>
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+                               <weights offset="2361368" size="20736"/>
+                               <biases offset="2382104" size="2304"/>
+                       </blobs>
+               </layer>
+               <layer id="78" name="MobilenetV2/expanded_conv_13/depthwise/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
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+                       </output>
+               </layer>
+               <layer id="79" name="MobilenetV2/expanded_conv_13/project/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="160" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
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+                                       <dim>7</dim>
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+                       </output>
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+                               <weights offset="2384408" size="368640"/>
+                               <biases offset="2753048" size="640"/>
+                       </blobs>
+               </layer>
+               <layer id="80" name="MobilenetV2/expanded_conv_14/expand/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="960" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
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+                       </output>
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+                               <weights offset="2753688" size="614400"/>
+                               <biases offset="3368088" size="3840"/>
+                       </blobs>
+               </layer>
+               <layer id="81" name="MobilenetV2/expanded_conv_14/expand/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
+                       <input>
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+                       </output>
+               </layer>
+               <layer id="82" name="MobilenetV2/expanded_conv_14/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="960" kernel="3,3" output="960" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
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+                               <weights offset="3371928" size="34560"/>
+                               <biases offset="3406488" size="3840"/>
+                       </blobs>
+               </layer>
+               <layer id="83" name="MobilenetV2/expanded_conv_14/depthwise/Relu6" precision="FP32" type="Clamp">
+                       <data max="6" min="0"/>
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+                       </output>
+               </layer>
+               <layer id="84" name="MobilenetV2/expanded_conv_14/project/Conv2D_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="1" kernel="1,1" output="160" pads_begin="0,0" pads_end="0,0" strides="1,1"/>
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+                               <biases offset="4024728" size="640"/>
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+               </layer>
+               <layer id="85" name="MobilenetV2/expanded_conv_14/add" precision="FP32" type="Eltwise">
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+                       </output>
+               </layer>
+               <layer id="86" name="MobilenetV2/expanded_conv_15/expand/Conv2D_Fold" precision="FP32" type="Convolution">
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+                               <biases offset="4639768" size="3840"/>
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+               </layer>
+               <layer id="87" name="MobilenetV2/expanded_conv_15/expand/Relu6" precision="FP32" type="Clamp">
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+                       </output>
+               </layer>
+               <layer id="88" name="MobilenetV2/expanded_conv_15/depthwise/depthwise_Fold" precision="FP32" type="Convolution">
+                       <data auto_pad="same_upper" dilations="1,1" group="960" kernel="3,3" output="960" pads_begin="1,1" pads_end="1,1" strides="1,1"/>
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+               <layer id="89" name="MobilenetV2/expanded_conv_15/depthwise/Relu6" precision="FP32" type="Clamp">
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+                       </output>
+               </layer>
+               <layer id="90" name="MobilenetV2/expanded_conv_15/project/Conv2D_Fold" precision="FP32" type="Convolution">
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+               </layer>
+               <layer>
+                       <name>MobilenetV2/expanded_conv_15/expand/Relu6</name>
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+               <layer>
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+                       <max>26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414, 26.046374915145414</max>
+               </layer>
+       </statistics>
+       <meta_data>
+               <MO_version value="2019.3.0-227-g3a4f9de20"/>
+               <cli_parameters>
+                       <blobs_as_inputs value="False"/>
+                       <caffe_parser_path value="DIR"/>
+                       <data_type value="FP32"/>
+                       <disable_nhwc_to_nchw value="False"/>
+                       <disable_omitting_optional value="False"/>
+                       <disable_resnet_optimization value="False"/>
+                       <enable_concat_optimization value="False"/>
+                       <enable_flattening_nested_params value="False"/>
+                       <enable_ssd_gluoncv value="False"/>
+                       <extensions value="DIR"/>
+                       <framework value="tf"/>
+                       <freeze_placeholder_with_value value="{}"/>
+                       <generate_experimental_IR_V10 value="False"/>
+                       <input value="input"/>
+                       <input_model value="DIR/mobilenetv2-int8.pb"/>
+                       <input_model_is_text value="False"/>
+                       <input_shape value="[1,224,224,3]"/>
+                       <k value="DIR/CustomLayersMapping.xml"/>
+                       <keep_quantize_ops_in_IR value="False"/>
+                       <keep_shape_ops value="False"/>
+                       <legacy_mxnet_model value="False"/>
+                       <log_level value="ERROR"/>
+                       <mean_scale_values value="{'input': {'scale': array([127.50000414]), 'mean': array([127.5, 127.5, 127.5])}}"/>
+                       <mean_values value="input[127.5,127.5,127.5]"/>
+                       <model_name value="mobilenetv2-int8-tf-0001"/>
+                       <move_to_preprocess value="False"/>
+                       <output value="['net_output/Reshape_1']"/>
+                       <output_dir value="DIR"/>
+                       <placeholder_shapes value="{'input': array([  1, 224, 224,   3])}"/>
+                       <remove_memory value="False"/>
+                       <remove_output_softmax value="False"/>
+                       <reverse_input_channels value="True"/>
+                       <save_params_from_nd value="False"/>
+                       <scale_values value="input[127.50000414375013]"/>
+                       <silent value="False"/>
+                       <steps value="False"/>
+                       <version value="False"/>
+                       <unset unset_cli_parameters="batch, counts, disable_fusing, disable_gfusing, finegrain_fusing, generate_deprecated_IR_V2, input_checkpoint, input_meta_graph, input_proto, input_symbol, mean_file, mean_file_offsets, nd_prefix_name, pretrained_model_name, saved_model_dir, saved_model_tags, scale, tensorboard_logdir, tensorflow_custom_layer_libraries, tensorflow_custom_operations_config_update, tensorflow_object_detection_api_pipeline_config, tensorflow_operation_patterns, tensorflow_subgraph_patterns, tensorflow_use_custom_operations_config"/>
+               </cli_parameters>
+       </meta_data>
+</net>