coneslayer.xml

<?xml version="1.0" ?>
<net name="coneslayer" version="11">
	<layers>
		<layer id="0" name="images" type="Parameter" version="opset1">
			<data shape="1,3,416,416" element_type="f32"/>
			<rt_info>
				<attribute name="fused_names" version="0" value="images"/>
				<attribute name="old_api_map_element_type" version="0" value="f16"/>
			</rt_info>
			<output>
				<port id="0" precision="FP32" names="images">
					<dim>1</dim>
					<dim>3</dim>
					<dim>416</dim>
					<dim>416</dim>
				</port>
			</output>
		</layer>
		<layer id="1" name="images/scale_copy_compressed" type="Const" version="opset1">
			<data element_type="f16" shape="32, 3, 3, 3" offset="0" size="1728"/>
			<output>
				<port id="0" precision="FP16">
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="2" name="images/scale_copy" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" version="0" value="images/scale_copy"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32" names="model.0.conv.weight">
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="3" name="Conv_0/WithoutBiases" type="Convolution" version="opset1">
			<data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>
			<rt_info>
				<attribute name="fused_names" version="0" value="Conv_0/WithoutBiases"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>3</dim>
					<dim>416</dim>
					<dim>416</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>208</dim>
					<dim>208</dim>
				</port>
			</output>
		</layer>
		<layer id="4" name="Conv_0/Dims2394_compressed" type="Const" version="opset1">
			<data element_type="f16" shape="1, 32, 1, 1" offset="1728" size="64"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="5" name="Conv_0/Dims2394" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" version="0" value="Conv_0/Dims2394"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="6" name="Conv_0" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" version="0" value="Conv_0"/>
			</rt_info>
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				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>208</dim>
					<dim>208</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32" names="125">
					<dim>1</dim>
					<dim>32</dim>
					<dim>208</dim>
					<dim>208</dim>
				</port>
			</output>
		</layer>
		<layer id="7" name="LeakyRelu_1/weights656211443_compressed" type="Const" version="opset1">
			<data element_type="f16" shape="1" offset="1792" size="2"/>
			<output>
				<port id="0" precision="FP16">
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="8" name="LeakyRelu_1/weights656211443" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" version="0" value="LeakyRelu_1/weights656211443"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32">
					<dim>1</dim>
				</port>
			</output>
		</layer>
		<layer id="9" name="LeakyRelu_1" type="PReLU" version="opset1">
			<rt_info>
				<attribute name="fused_names" version="0" value="LeakyRelu_1"/>
			</rt_info>
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				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>208</dim>
					<dim>208</dim>
				</port>
				<port id="1" precision="FP32">
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				</port>
			</input>
			<output>
				<port id="2" precision="FP32" names="126">
					<dim>1</dim>
					<dim>32</dim>
					<dim>208</dim>
					<dim>208</dim>
				</port>
			</output>
		</layer>
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			<data element_type="f16" shape="64, 32, 3, 3" offset="1794" size="36864"/>
			<output>
				<port id="0" precision="FP16">
					<dim>64</dim>
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="11" name="model.1.conv.weight" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" version="0" value="model.1.conv.weight"/>
			</rt_info>
			<input>
				<port id="0" precision="FP16">
					<dim>64</dim>
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32" names="model.1.conv.weight">
					<dim>64</dim>
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
		<layer id="12" name="Conv_2/WithoutBiases" type="Convolution" version="opset1">
			<data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>
			<rt_info>
				<attribute name="fused_names" version="0" value="Conv_2/WithoutBiases"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>208</dim>
					<dim>208</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>64</dim>
					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
					<dim>104</dim>
					<dim>104</dim>
				</port>
			</output>
		</layer>
		<layer id="13" name="Conv_2/Dims2580_compressed" type="Const" version="opset1">
			<data element_type="f16" shape="1, 64, 1, 1" offset="38658" 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="14" name="Conv_2/Dims2580" type="Convert" version="opset1">
			<data destination_type="f32"/>
			<rt_info>
				<attribute name="decompression" version="0"/>
				<attribute name="fused_names" version="0" value="Conv_2/Dims2580"/>
			</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="15" name="Conv_2" type="Add" version="opset1">
			<data auto_broadcast="numpy"/>
			<rt_info>
				<attribute name="fused_names" version="0" value="Conv_2"/>
			</rt_info>
			<input>
				<port id="0" precision="FP32">
					<dim>1</dim>
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					<dim>104</dim>
					<dim>104</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>1</dim>
					<dim>64</dim>
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			</input>
			<output>
				<port id="2" precision="FP32" names="127">
					<dim>1</dim>
					<dim>64</dim>
					<dim>104</dim>
					<dim>104</dim>
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		</layer>
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			<output>
				<port id="0" precision="FP16">
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			<data destination_type="f32"/>
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			<output>
				<port id="1" precision="FP32">
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			<output>
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		</layer>
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			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit"/>
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					<dim>104</dim>
				</port>
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		</layer>
		<layer id="22" name="Conv_6/Dims2592_compressed" type="Const" version="opset1">
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			<output>
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			<data auto_broadcast="numpy"/>
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