arisha07's picture
Upload 15 files
3291c10
<?xml version="1.0" ?>
<net name="torch_jit" version="11">
<layers>
<layer id="0" name="onnx::Sub_0" type="Parameter" version="opset1">
<data shape="1,3,512,512" element_type="f32"/>
<rt_info>
<attribute name="fused_names" version="0" value="onnx::Sub_0"/>
<attribute name="old_api_map_element_type" version="0" value="f16"/>
</rt_info>
<output>
<port id="0" precision="FP32" names="onnx::Sub_0">
<dim>1</dim>
<dim>3</dim>
<dim>512</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="1" name="norm_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 3, 1, 1" offset="0" size="6"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>3</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="2" name="norm" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="norm"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>3</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="norm">
<dim>1</dim>
<dim>3</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="3" name="Sub_0" type="Subtract" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="Sub_0"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>512</dim>
<dim>512</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" precision="FP32" names="input">
<dim>1</dim>
<dim>3</dim>
<dim>512</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="4" name="block1.convs.0.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="64, 3, 3, 3" offset="6" size="3456"/>
<output>
<port id="0" precision="FP16">
<dim>64</dim>
<dim>3</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="5" name="block1.convs.0.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block1.convs.0.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>64</dim>
<dim>3</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block1.convs.0.weight">
<dim>64</dim>
<dim>3</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="6" name="convs.0/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="convs.0/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>512</dim>
<dim>512</dim>
</port>
<port id="1" precision="FP32">
<dim>64</dim>
<dim>3</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>512</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="7" name="Reshape_59_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 64, 1, 1" offset="3462" size="128"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="8" name="Reshape_59" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="9" name="convs.0/Conv" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="Concat_58, Reshape_59, convs.0/Conv"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>512</dim>
<dim>512</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="convs.0/Conv_output_0">
<dim>1</dim>
<dim>64</dim>
<dim>512</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="10" name="Relu_2" type="ReLU" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="Relu_2"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>512</dim>
<dim>512</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.3">
<dim>1</dim>
<dim>64</dim>
<dim>512</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="11" name="block1.convs.1.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="64, 64, 3, 3" offset="3590" size="73728"/>
<output>
<port id="0" precision="FP16">
<dim>64</dim>
<dim>64</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="12" name="block1.convs.1.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block1.convs.1.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>64</dim>
<dim>64</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block1.convs.1.weight">
<dim>64</dim>
<dim>64</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="13" name="convs.1/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="convs.1/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>512</dim>
<dim>512</dim>
</port>
<port id="1" precision="FP32">
<dim>64</dim>
<dim>64</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>512</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="14" name="Reshape_108_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 64, 1, 1" offset="77318" size="128"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="15" name="Reshape_108" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="16" name="convs.1/Conv" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="Concat_107, Reshape_108, convs.1/Conv"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>512</dim>
<dim>512</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="convs.1/Conv_output_0">
<dim>1</dim>
<dim>64</dim>
<dim>512</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="17" name="Relu_4" type="ReLU" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="Relu_4"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>512</dim>
<dim>512</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.7">
<dim>1</dim>
<dim>64</dim>
<dim>512</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="18" name="MaxPool_6" type="MaxPool" version="opset8">
<data strides="2, 2" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" kernel="2, 2" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0"/>
<rt_info>
<attribute name="fused_names" version="0" value="MaxPool_6"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>512</dim>
<dim>512</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.11">
<dim>1</dim>
<dim>64</dim>
<dim>256</dim>
<dim>256</dim>
</port>
<port id="2" precision="I64">
<dim>1</dim>
<dim>64</dim>
<dim>256</dim>
<dim>256</dim>
</port>
</output>
</layer>
<layer id="19" name="block2.convs.0.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="128, 64, 3, 3" offset="77446" size="147456"/>
<output>
<port id="0" precision="FP16">
<dim>128</dim>
<dim>64</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="20" name="block2.convs.0.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block2.convs.0.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>128</dim>
<dim>64</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block2.convs.0.weight">
<dim>128</dim>
<dim>64</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="21" name="convs.0_1/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="convs.0_1/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>256</dim>
<dim>256</dim>
</port>
<port id="1" precision="FP32">
<dim>128</dim>
<dim>64</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>128</dim>
<dim>256</dim>
<dim>256</dim>
</port>
</output>
</layer>
<layer id="22" name="Reshape_206_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 128, 1, 1" offset="224902" size="256"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>128</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="23" name="Reshape_206" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>128</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>128</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="24" name="convs.0_1/Conv" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="Concat_205, Reshape_206, convs.0_1/Conv"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>128</dim>
<dim>256</dim>
<dim>256</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>128</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="convs.0_1/Conv_output_0">
<dim>1</dim>
<dim>128</dim>
<dim>256</dim>
<dim>256</dim>
</port>
</output>
</layer>
<layer id="25" name="Relu_8" type="ReLU" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="Relu_8"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>128</dim>
<dim>256</dim>
<dim>256</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.15">
<dim>1</dim>
<dim>128</dim>
<dim>256</dim>
<dim>256</dim>
</port>
</output>
</layer>
<layer id="26" name="block2.convs.1.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="128, 128, 3, 3" offset="225158" size="294912"/>
<output>
<port id="0" precision="FP16">
<dim>128</dim>
<dim>128</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="27" name="block2.convs.1.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block2.convs.1.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>128</dim>
<dim>128</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block2.convs.1.weight">
<dim>128</dim>
<dim>128</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="28" name="convs.1_1/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="convs.1_1/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>128</dim>
<dim>256</dim>
<dim>256</dim>
</port>
<port id="1" precision="FP32">
<dim>128</dim>
<dim>128</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>128</dim>
<dim>256</dim>
<dim>256</dim>
</port>
</output>
</layer>
<layer id="29" name="Reshape_255_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 128, 1, 1" offset="520070" size="256"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>128</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="30" name="Reshape_255" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>128</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>128</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="31" name="convs.1_1/Conv" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="Concat_254, Reshape_255, convs.1_1/Conv"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>128</dim>
<dim>256</dim>
<dim>256</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>128</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="convs.1_1/Conv_output_0">
<dim>1</dim>
<dim>128</dim>
<dim>256</dim>
<dim>256</dim>
</port>
</output>
</layer>
<layer id="32" name="Relu_10" type="ReLU" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="Relu_10"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>128</dim>
<dim>256</dim>
<dim>256</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.19">
<dim>1</dim>
<dim>128</dim>
<dim>256</dim>
<dim>256</dim>
</port>
</output>
</layer>
<layer id="33" name="MaxPool_12" type="MaxPool" version="opset8">
<data strides="2, 2" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" kernel="2, 2" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0"/>
<rt_info>
<attribute name="fused_names" version="0" value="MaxPool_12"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>128</dim>
<dim>256</dim>
<dim>256</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.23">
<dim>1</dim>
<dim>128</dim>
<dim>128</dim>
<dim>128</dim>
</port>
<port id="2" precision="I64">
<dim>1</dim>
<dim>128</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</output>
</layer>
<layer id="34" name="block3.convs.0.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="256, 128, 3, 3" offset="520326" size="589824"/>
<output>
<port id="0" precision="FP16">
<dim>256</dim>
<dim>128</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="35" name="block3.convs.0.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block3.convs.0.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>256</dim>
<dim>128</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block3.convs.0.weight">
<dim>256</dim>
<dim>128</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="36" name="convs.0_2/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="convs.0_2/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>128</dim>
<dim>128</dim>
<dim>128</dim>
</port>
<port id="1" precision="FP32">
<dim>256</dim>
<dim>128</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</output>
</layer>
<layer id="37" name="Reshape_353_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 256, 1, 1" offset="1110150" size="512"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="38" name="Reshape_353" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="39" name="convs.0_2/Conv" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="Concat_352, Reshape_353, convs.0_2/Conv"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="convs.0_2/Conv_output_0">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</output>
</layer>
<layer id="40" name="Relu_14" type="ReLU" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="Relu_14"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.27">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</output>
</layer>
<layer id="41" name="block3.convs.1.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="256, 256, 3, 3" offset="1110662" size="1179648"/>
<output>
<port id="0" precision="FP16">
<dim>256</dim>
<dim>256</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="42" name="block3.convs.1.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block3.convs.1.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>256</dim>
<dim>256</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block3.convs.1.weight">
<dim>256</dim>
<dim>256</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="43" name="convs.1_2/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="convs.1_2/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
<port id="1" precision="FP32">
<dim>256</dim>
<dim>256</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</output>
</layer>
<layer id="44" name="Reshape_402_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 256, 1, 1" offset="2290310" size="512"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="45" name="Reshape_402" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="46" name="convs.1_2/Conv" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="Concat_401, Reshape_402, convs.1_2/Conv"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="convs.1_2/Conv_output_0">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</output>
</layer>
<layer id="47" name="Relu_16" type="ReLU" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="Relu_16"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.31">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</output>
</layer>
<layer id="48" name="block3.convs.2.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="256, 256, 3, 3" offset="2290822" size="1179648"/>
<output>
<port id="0" precision="FP16">
<dim>256</dim>
<dim>256</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="49" name="block3.convs.2.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block3.convs.2.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>256</dim>
<dim>256</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block3.convs.2.weight">
<dim>256</dim>
<dim>256</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="50" name="convs.2/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="convs.2/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
<port id="1" precision="FP32">
<dim>256</dim>
<dim>256</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</output>
</layer>
<layer id="51" name="Reshape_451_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 256, 1, 1" offset="3470470" size="512"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="52" name="Reshape_451" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="53" name="convs.2/Conv" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="Concat_450, Reshape_451, convs.2/Conv"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="convs.2/Conv_output_0">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</output>
</layer>
<layer id="54" name="Relu_18" type="ReLU" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="Relu_18"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.35">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</output>
</layer>
<layer id="55" name="MaxPool_20" type="MaxPool" version="opset8">
<data strides="2, 2" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" kernel="2, 2" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0"/>
<rt_info>
<attribute name="fused_names" version="0" value="MaxPool_20"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.39">
<dim>1</dim>
<dim>256</dim>
<dim>64</dim>
<dim>64</dim>
</port>
<port id="2" precision="I64">
<dim>1</dim>
<dim>256</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</output>
</layer>
<layer id="56" name="block4.convs.0.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="512, 256, 3, 3" offset="3470982" size="2359296"/>
<output>
<port id="0" precision="FP16">
<dim>512</dim>
<dim>256</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="57" name="block4.convs.0.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block4.convs.0.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>512</dim>
<dim>256</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block4.convs.0.weight">
<dim>512</dim>
<dim>256</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="58" name="convs.0_3/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="convs.0_3/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>64</dim>
<dim>64</dim>
</port>
<port id="1" precision="FP32">
<dim>512</dim>
<dim>256</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</output>
</layer>
<layer id="59" name="Reshape_549_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 512, 1, 1" offset="5830278" size="1024"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="60" name="Reshape_549" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="61" name="convs.0_3/Conv" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="Concat_548, Reshape_549, convs.0_3/Conv"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="convs.0_3/Conv_output_0">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</output>
</layer>
<layer id="62" name="Relu_22" type="ReLU" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="Relu_22"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.43">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</output>
</layer>
<layer id="63" name="block4.convs.1.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="512, 512, 3, 3" offset="5831302" size="4718592"/>
<output>
<port id="0" precision="FP16">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="64" name="block4.convs.1.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block4.convs.1.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block4.convs.1.weight">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="65" name="convs.1_3/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="convs.1_3/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
<port id="1" precision="FP32">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</output>
</layer>
<layer id="66" name="Reshape_598_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 512, 1, 1" offset="10549894" size="1024"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="67" name="Reshape_598" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="68" name="convs.1_3/Conv" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="Concat_597, Reshape_598, convs.1_3/Conv"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="convs.1_3/Conv_output_0">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</output>
</layer>
<layer id="69" name="Relu_24" type="ReLU" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="Relu_24"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.47">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</output>
</layer>
<layer id="70" name="block4.convs.2.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="512, 512, 3, 3" offset="10550918" size="4718592"/>
<output>
<port id="0" precision="FP16">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="71" name="block4.convs.2.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block4.convs.2.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block4.convs.2.weight">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="72" name="convs.2_1/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="convs.2_1/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
<port id="1" precision="FP32">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</output>
</layer>
<layer id="73" name="Reshape_647_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 512, 1, 1" offset="15269510" size="1024"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="74" name="Reshape_647" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="75" name="convs.2_1/Conv" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="Concat_646, Reshape_647, convs.2_1/Conv"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="convs.2_1/Conv_output_0">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</output>
</layer>
<layer id="76" name="Relu_26" type="ReLU" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="Relu_26"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.51">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</output>
</layer>
<layer id="77" name="MaxPool_28" type="MaxPool" version="opset8">
<data strides="2, 2" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" kernel="2, 2" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0"/>
<rt_info>
<attribute name="fused_names" version="0" value="MaxPool_28"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.55">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
<port id="2" precision="I64">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="78" name="block5.convs.0.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="512, 512, 3, 3" offset="15270534" size="4718592"/>
<output>
<port id="0" precision="FP16">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="79" name="block5.convs.0.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block5.convs.0.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block5.convs.0.weight">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="80" name="convs.0_4/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="convs.0_4/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
<port id="1" precision="FP32">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="81" name="Reshape_745_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 512, 1, 1" offset="19989126" size="1024"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="82" name="Reshape_745" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="83" name="convs.0_4/Conv" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="Concat_744, Reshape_745, convs.0_4/Conv"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="convs.0_4/Conv_output_0">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="84" name="Relu_30" type="ReLU" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="Relu_30"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.59">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="85" name="block5.convs.1.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="512, 512, 3, 3" offset="19990150" size="4718592"/>
<output>
<port id="0" precision="FP16">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="86" name="block5.convs.1.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block5.convs.1.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block5.convs.1.weight">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="87" name="convs.1_4/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="convs.1_4/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
<port id="1" precision="FP32">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="88" name="Reshape_794_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 512, 1, 1" offset="24708742" size="1024"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="89" name="Reshape_794" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="90" name="convs.1_4/Conv" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="Concat_793, Reshape_794, convs.1_4/Conv"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="convs.1_4/Conv_output_0">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="91" name="Relu_32" type="ReLU" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="Relu_32"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.63">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="92" name="block5.convs.2.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="512, 512, 3, 3" offset="24709766" size="4718592"/>
<output>
<port id="0" precision="FP16">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="93" name="block5.convs.2.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block5.convs.2.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block5.convs.2.weight">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="94" name="convs.2_2/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="convs.2_2/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
<port id="1" precision="FP32">
<dim>512</dim>
<dim>512</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="95" name="Reshape_843_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 512, 1, 1" offset="29428358" size="1024"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="96" name="Reshape_843" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="97" name="convs.2_2/Conv" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="Concat_842, Reshape_843, convs.2_2/Conv"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="convs.2_2/Conv_output_0">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="98" name="Relu_34" type="ReLU" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="Relu_34"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="input.67">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="99" name="block5.projection.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 512, 1, 1" offset="29429382" size="1024"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="100" name="block5.projection.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block5.projection.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block5.projection.weight">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="101" name="projection_4/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="projection_4/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>32</dim>
<dim>32</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="102" name="Reshape_892_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 1, 1, 1" offset="29430406" size="2"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="103" name="Reshape_892" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="104" name="73" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="73, Concat_891, Reshape_892"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>32</dim>
<dim>32</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" names="73">
<dim>1</dim>
<dim>1</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</output>
</layer>
<layer id="106" name="block4.projection.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 512, 1, 1" offset="29430408" size="1024"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="107" name="block4.projection.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block4.projection.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block4.projection.weight">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="108" name="projection_3/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="projection_3/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>64</dim>
<dim>64</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>512</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</output>
</layer>
<layer id="109" name="Reshape_696_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 1, 1, 1" offset="29431432" size="2"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="110" name="Reshape_696" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="111" name="65" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="65, Concat_695, Reshape_696"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>64</dim>
<dim>64</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" names="65">
<dim>1</dim>
<dim>1</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</output>
</layer>
<layer id="113" name="block3.projection.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 256, 1, 1" offset="29431434" size="512"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="114" name="block3.projection.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block3.projection.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block3.projection.weight">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="115" name="projection_2/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="projection_2/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>128</dim>
<dim>128</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</output>
</layer>
<layer id="116" name="Reshape_500_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 1, 1, 1" offset="29431946" size="2"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="117" name="Reshape_500" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="118" name="57" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="57, Concat_499, Reshape_500"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>128</dim>
<dim>128</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" names="57">
<dim>1</dim>
<dim>1</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</output>
</layer>
<layer id="120" name="block2.projection.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 128, 1, 1" offset="29431948" size="256"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>128</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="121" name="block2.projection.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block2.projection.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>128</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block2.projection.weight">
<dim>1</dim>
<dim>128</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="122" name="projection_1/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="projection_1/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>128</dim>
<dim>256</dim>
<dim>256</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>128</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>256</dim>
<dim>256</dim>
</port>
</output>
</layer>
<layer id="123" name="Reshape_304_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 1, 1, 1" offset="29432204" size="2"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="124" name="Reshape_304" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="125" name="49" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="49, Concat_303, Reshape_304"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>256</dim>
<dim>256</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" names="49">
<dim>1</dim>
<dim>1</dim>
<dim>256</dim>
<dim>256</dim>
</port>
</output>
</layer>
<layer id="127" name="block1.projection.weight_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 64, 1, 1" offset="29432206" size="128"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="128" name="block1.projection.weight" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
<attribute name="fused_names" version="0" value="block1.projection.weight"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="block1.projection.weight">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="129" name="projection/Conv/WithoutBiases" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit"/>
<rt_info>
<attribute name="fused_names" version="0" value="projection/Conv/WithoutBiases"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>512</dim>
<dim>512</dim>
</port>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>512</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="130" name="Reshape_157_compressed" type="Const" version="opset1">
<data element_type="f16" shape="1, 1, 1, 1" offset="29432334" size="2"/>
<output>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="131" name="Reshape_157" type="Convert" version="opset1">
<data destination_type="f32"/>
<rt_info>
<attribute name="decompression" version="0"/>
</rt_info>
<input>
<port id="0" precision="FP16">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="1" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="132" name="43" type="Add" version="opset1">
<data auto_broadcast="numpy"/>
<rt_info>
<attribute name="fused_names" version="0" value="43, Concat_156, Reshape_157"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>512</dim>
<dim>512</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" names="43">
<dim>1</dim>
<dim>1</dim>
<dim>512</dim>
<dim>512</dim>
</port>
</output>
</layer>
<layer id="133" name="43/sink_port_0" type="Result" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="43/sink_port_0"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>512</dim>
<dim>512</dim>
</port>
</input>
</layer>
<layer id="126" name="49/sink_port_0" type="Result" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="49/sink_port_0"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>256</dim>
<dim>256</dim>
</port>
</input>
</layer>
<layer id="119" name="57/sink_port_0" type="Result" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="57/sink_port_0"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>128</dim>
<dim>128</dim>
</port>
</input>
</layer>
<layer id="112" name="65/sink_port_0" type="Result" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="65/sink_port_0"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>64</dim>
<dim>64</dim>
</port>
</input>
</layer>
<layer id="105" name="73/sink_port_0" type="Result" version="opset1">
<rt_info>
<attribute name="fused_names" version="0" value="73/sink_port_0"/>
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>1</dim>
<dim>32</dim>
<dim>32</dim>
</port>
</input>
</layer>
</layers>
<edges>
<edge from-layer="0" from-port="0" to-layer="3" to-port="0"/>
<edge from-layer="1" from-port="0" to-layer="2" to-port="0"/>
<edge from-layer="2" from-port="1" to-layer="3" to-port="1"/>
<edge from-layer="3" from-port="2" to-layer="6" to-port="0"/>
<edge from-layer="4" from-port="0" to-layer="5" to-port="0"/>
<edge from-layer="5" from-port="1" to-layer="6" to-port="1"/>
<edge from-layer="6" from-port="2" to-layer="9" to-port="0"/>
<edge from-layer="7" from-port="0" to-layer="8" to-port="0"/>
<edge from-layer="8" from-port="1" to-layer="9" to-port="1"/>
<edge from-layer="9" from-port="2" to-layer="10" to-port="0"/>
<edge from-layer="10" from-port="1" to-layer="13" to-port="0"/>
<edge from-layer="11" from-port="0" to-layer="12" to-port="0"/>
<edge from-layer="12" from-port="1" to-layer="13" to-port="1"/>
<edge from-layer="13" from-port="2" to-layer="16" to-port="0"/>
<edge from-layer="14" from-port="0" to-layer="15" to-port="0"/>
<edge from-layer="15" from-port="1" to-layer="16" to-port="1"/>
<edge from-layer="16" from-port="2" to-layer="17" to-port="0"/>
<edge from-layer="17" from-port="1" to-layer="18" to-port="0"/>
<edge from-layer="17" from-port="1" to-layer="129" to-port="0"/>
<edge from-layer="18" from-port="1" to-layer="21" to-port="0"/>
<edge from-layer="19" from-port="0" to-layer="20" to-port="0"/>
<edge from-layer="20" from-port="1" to-layer="21" to-port="1"/>
<edge from-layer="21" from-port="2" to-layer="24" to-port="0"/>
<edge from-layer="22" from-port="0" to-layer="23" to-port="0"/>
<edge from-layer="23" from-port="1" to-layer="24" to-port="1"/>
<edge from-layer="24" from-port="2" to-layer="25" to-port="0"/>
<edge from-layer="25" from-port="1" to-layer="28" to-port="0"/>
<edge from-layer="26" from-port="0" to-layer="27" to-port="0"/>
<edge from-layer="27" from-port="1" to-layer="28" to-port="1"/>
<edge from-layer="28" from-port="2" to-layer="31" to-port="0"/>
<edge from-layer="29" from-port="0" to-layer="30" to-port="0"/>
<edge from-layer="30" from-port="1" to-layer="31" to-port="1"/>
<edge from-layer="31" from-port="2" to-layer="32" to-port="0"/>
<edge from-layer="32" from-port="1" to-layer="33" to-port="0"/>
<edge from-layer="32" from-port="1" to-layer="122" to-port="0"/>
<edge from-layer="33" from-port="1" to-layer="36" to-port="0"/>
<edge from-layer="34" from-port="0" to-layer="35" to-port="0"/>
<edge from-layer="35" from-port="1" to-layer="36" to-port="1"/>
<edge from-layer="36" from-port="2" to-layer="39" to-port="0"/>
<edge from-layer="37" from-port="0" to-layer="38" to-port="0"/>
<edge from-layer="38" from-port="1" to-layer="39" to-port="1"/>
<edge from-layer="39" from-port="2" to-layer="40" to-port="0"/>
<edge from-layer="40" from-port="1" to-layer="43" to-port="0"/>
<edge from-layer="41" from-port="0" to-layer="42" to-port="0"/>
<edge from-layer="42" from-port="1" to-layer="43" to-port="1"/>
<edge from-layer="43" from-port="2" to-layer="46" to-port="0"/>
<edge from-layer="44" from-port="0" to-layer="45" to-port="0"/>
<edge from-layer="45" from-port="1" to-layer="46" to-port="1"/>
<edge from-layer="46" from-port="2" to-layer="47" to-port="0"/>
<edge from-layer="47" from-port="1" to-layer="50" to-port="0"/>
<edge from-layer="48" from-port="0" to-layer="49" to-port="0"/>
<edge from-layer="49" from-port="1" to-layer="50" to-port="1"/>
<edge from-layer="50" from-port="2" to-layer="53" to-port="0"/>
<edge from-layer="51" from-port="0" to-layer="52" to-port="0"/>
<edge from-layer="52" from-port="1" to-layer="53" to-port="1"/>
<edge from-layer="53" from-port="2" to-layer="54" to-port="0"/>
<edge from-layer="54" from-port="1" to-layer="55" to-port="0"/>
<edge from-layer="54" from-port="1" to-layer="115" to-port="0"/>
<edge from-layer="55" from-port="1" to-layer="58" to-port="0"/>
<edge from-layer="56" from-port="0" to-layer="57" to-port="0"/>
<edge from-layer="57" from-port="1" to-layer="58" to-port="1"/>
<edge from-layer="58" from-port="2" to-layer="61" to-port="0"/>
<edge from-layer="59" from-port="0" to-layer="60" to-port="0"/>
<edge from-layer="60" from-port="1" to-layer="61" to-port="1"/>
<edge from-layer="61" from-port="2" to-layer="62" to-port="0"/>
<edge from-layer="62" from-port="1" to-layer="65" to-port="0"/>
<edge from-layer="63" from-port="0" to-layer="64" to-port="0"/>
<edge from-layer="64" from-port="1" to-layer="65" to-port="1"/>
<edge from-layer="65" from-port="2" to-layer="68" to-port="0"/>
<edge from-layer="66" from-port="0" to-layer="67" to-port="0"/>
<edge from-layer="67" from-port="1" to-layer="68" to-port="1"/>
<edge from-layer="68" from-port="2" to-layer="69" to-port="0"/>
<edge from-layer="69" from-port="1" to-layer="72" to-port="0"/>
<edge from-layer="70" from-port="0" to-layer="71" to-port="0"/>
<edge from-layer="71" from-port="1" to-layer="72" to-port="1"/>
<edge from-layer="72" from-port="2" to-layer="75" to-port="0"/>
<edge from-layer="73" from-port="0" to-layer="74" to-port="0"/>
<edge from-layer="74" from-port="1" to-layer="75" to-port="1"/>
<edge from-layer="75" from-port="2" to-layer="76" to-port="0"/>
<edge from-layer="76" from-port="1" to-layer="77" to-port="0"/>
<edge from-layer="76" from-port="1" to-layer="108" to-port="0"/>
<edge from-layer="77" from-port="1" to-layer="80" to-port="0"/>
<edge from-layer="78" from-port="0" to-layer="79" to-port="0"/>
<edge from-layer="79" from-port="1" to-layer="80" to-port="1"/>
<edge from-layer="80" from-port="2" to-layer="83" to-port="0"/>
<edge from-layer="81" from-port="0" to-layer="82" to-port="0"/>
<edge from-layer="82" from-port="1" to-layer="83" to-port="1"/>
<edge from-layer="83" from-port="2" to-layer="84" to-port="0"/>
<edge from-layer="84" from-port="1" to-layer="87" to-port="0"/>
<edge from-layer="85" from-port="0" to-layer="86" to-port="0"/>
<edge from-layer="86" from-port="1" to-layer="87" to-port="1"/>
<edge from-layer="87" from-port="2" to-layer="90" to-port="0"/>
<edge from-layer="88" from-port="0" to-layer="89" to-port="0"/>
<edge from-layer="89" from-port="1" to-layer="90" to-port="1"/>
<edge from-layer="90" from-port="2" to-layer="91" to-port="0"/>
<edge from-layer="91" from-port="1" to-layer="94" to-port="0"/>
<edge from-layer="92" from-port="0" to-layer="93" to-port="0"/>
<edge from-layer="93" from-port="1" to-layer="94" to-port="1"/>
<edge from-layer="94" from-port="2" to-layer="97" to-port="0"/>
<edge from-layer="95" from-port="0" to-layer="96" to-port="0"/>
<edge from-layer="96" from-port="1" to-layer="97" to-port="1"/>
<edge from-layer="97" from-port="2" to-layer="98" to-port="0"/>
<edge from-layer="98" from-port="1" to-layer="101" to-port="0"/>
<edge from-layer="99" from-port="0" to-layer="100" to-port="0"/>
<edge from-layer="100" from-port="1" to-layer="101" to-port="1"/>
<edge from-layer="101" from-port="2" to-layer="104" to-port="0"/>
<edge from-layer="102" from-port="0" to-layer="103" to-port="0"/>
<edge from-layer="103" from-port="1" to-layer="104" to-port="1"/>
<edge from-layer="104" from-port="2" to-layer="105" to-port="0"/>
<edge from-layer="106" from-port="0" to-layer="107" to-port="0"/>
<edge from-layer="107" from-port="1" to-layer="108" to-port="1"/>
<edge from-layer="108" from-port="2" to-layer="111" to-port="0"/>
<edge from-layer="109" from-port="0" to-layer="110" to-port="0"/>
<edge from-layer="110" from-port="1" to-layer="111" to-port="1"/>
<edge from-layer="111" from-port="2" to-layer="112" to-port="0"/>
<edge from-layer="113" from-port="0" to-layer="114" to-port="0"/>
<edge from-layer="114" from-port="1" to-layer="115" to-port="1"/>
<edge from-layer="115" from-port="2" to-layer="118" to-port="0"/>
<edge from-layer="116" from-port="0" to-layer="117" to-port="0"/>
<edge from-layer="117" from-port="1" to-layer="118" to-port="1"/>
<edge from-layer="118" from-port="2" to-layer="119" to-port="0"/>
<edge from-layer="120" from-port="0" to-layer="121" to-port="0"/>
<edge from-layer="121" from-port="1" to-layer="122" to-port="1"/>
<edge from-layer="122" from-port="2" to-layer="125" to-port="0"/>
<edge from-layer="123" from-port="0" to-layer="124" to-port="0"/>
<edge from-layer="124" from-port="1" to-layer="125" to-port="1"/>
<edge from-layer="125" from-port="2" to-layer="126" to-port="0"/>
<edge from-layer="127" from-port="0" to-layer="128" to-port="0"/>
<edge from-layer="128" from-port="1" to-layer="129" to-port="1"/>
<edge from-layer="129" from-port="2" to-layer="132" to-port="0"/>
<edge from-layer="130" from-port="0" to-layer="131" to-port="0"/>
<edge from-layer="131" from-port="1" to-layer="132" to-port="1"/>
<edge from-layer="132" from-port="2" to-layer="133" to-port="0"/>
</edges>
<meta_data>
<MO_version value="2022.2.0-7713-af16ea1d79a-releases/2022/2"/>
<Runtime_version value="2022.2.0-7713-af16ea1d79a-releases/2022/2"/>
<legacy_path value="False"/>
<cli_parameters>
<caffe_parser_path value="DIR"/>
<compress_fp16 value="True"/>
<data_type value="FP32"/>
<disable_nhwc_to_nchw value="False"/>
<disable_omitting_optional value="False"/>
<disable_resnet_optimization value="False"/>
<disable_weights_compression value="False"/>
<enable_concat_optimization value="False"/>
<enable_flattening_nested_params value="False"/>
<enable_ssd_gluoncv value="False"/>
<extensions value="DIR"/>
<framework value="onnx"/>
<freeze_placeholder_with_value value="{}"/>
<input_model value="DIR\hed.onnx"/>
<input_model_is_text value="False"/>
<k value="DIR\CustomLayersMapping.xml"/>
<layout value="()"/>
<layout_values value="{}"/>
<legacy_mxnet_model value="False"/>
<log_level value="ERROR"/>
<mean_scale_values value="{}"/>
<mean_values value="()"/>
<model_name value="hed"/>
<output_dir value="DIR"/>
<placeholder_data_types value="{}"/>
<progress value="False"/>
<remove_memory value="False"/>
<remove_output_softmax value="False"/>
<reverse_input_channels value="False"/>
<save_params_from_nd value="False"/>
<scale_values value="()"/>
<silent value="False"/>
<source_layout value="()"/>
<static_shape value="False"/>
<stream_output value="False"/>
<target_layout value="()"/>
<transform value=""/>
<use_legacy_frontend value="False"/>
<use_new_frontend value="False"/>
<unset unset_cli_parameters="batch, counts, disable_fusing, finegrain_fusing, input, input_checkpoint, input_meta_graph, input_proto, input_shape, input_symbol, mean_file, mean_file_offsets, nd_prefix_name, output, placeholder_shapes, 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_use_custom_operations_config, transformations_config"/>
</cli_parameters>
</meta_data>
</net>