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5673 lines (5673 loc) · 162 KB
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<?xml version="1.0"?>
<net name="resnet_v1-50" version="11">
<layers>
<layer id="0" name="map/TensorArrayStack/TensorArrayGatherV3" type="Parameter" version="opset1">
<data shape="1,224,224,3" element_type="f32" />
<rt_info>
<attribute name="old_api_map_order" version="0" value="0, 2, 3, 1" />
</rt_info>
<output>
<port id="0" precision="FP32" names="map/TensorArrayStack/TensorArrayGatherV3,map/TensorArrayStack/TensorArrayGatherV3:0">
<dim>1</dim>
<dim>224</dim>
<dim>224</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="1" name="map/TensorArrayStack/TensorArrayGatherV3/Transpose([0 3 1 2])/value60879521" type="Const" version="opset1">
<data element_type="i64" shape="4" offset="0" size="32" />
<output>
<port id="0" precision="I64">
<dim>4</dim>
</port>
</output>
</layer>
<layer id="2" name="Subtract_616" type="Transpose" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>224</dim>
<dim>224</dim>
<dim>3</dim>
</port>
<port id="1" precision="I64">
<dim>4</dim>
</port>
</input>
<output>
<port id="2" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>224</dim>
<dim>224</dim>
</port>
</output>
</layer>
<layer id="3" name="Constant_1624" type="Const" version="opset1">
<data element_type="f32" shape="1, 3, 1, 1" offset="32" size="12" />
<output>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="4" name="Subtract_1626" type="Subtract" version="opset1">
<data auto_broadcast="numpy" />
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>224</dim>
<dim>224</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">
<dim>1</dim>
<dim>3</dim>
<dim>224</dim>
<dim>224</dim>
</port>
</output>
</layer>
<layer id="5" name="Constant_1656" type="Const" version="opset1">
<data element_type="f32" shape="64, 3, 7, 7" offset="44" size="37632" />
<rt_info>
<attribute name="preprocessing" version="0" />
</rt_info>
<output>
<port id="0" precision="FP32">
<dim>64</dim>
<dim>3</dim>
<dim>7</dim>
<dim>7</dim>
</port>
</output>
</layer>
<layer id="6" name="resnet_model/conv2d/Conv2D" type="Convolution" version="opset1">
<data strides="2, 2" dilations="1, 1" pads_begin="3, 3" pads_end="3, 3" auto_pad="explicit" />
<rt_info>
<attribute name="preprocessing" version="0" />
</rt_info>
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>3</dim>
<dim>224</dim>
<dim>224</dim>
</port>
<port id="1" precision="FP32">
<dim>64</dim>
<dim>3</dim>
<dim>7</dim>
<dim>7</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="resnet_model/conv2d/Conv2D:0">
<dim>1</dim>
<dim>64</dim>
<dim>112</dim>
<dim>112</dim>
</port>
</output>
</layer>
<layer id="7" name="resnet_model/conv2d/Conv2D_bn_offset" type="Const" version="opset1">
<data element_type="f32" shape="1, 64, 1, 1" offset="37676" size="256" />
<output>
<port id="0" precision="FP32" names="resnet_model/conv2d/Conv2D_bn_offset:0">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="8" name="resnet_model/batch_normalization/FusedBatchNorm/Add" type="Add" version="opset1">
<data auto_broadcast="numpy" />
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>112</dim>
<dim>112</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="resnet_model/batch_normalization/FusedBatchNorm:0">
<dim>1</dim>
<dim>64</dim>
<dim>112</dim>
<dim>112</dim>
</port>
</output>
</layer>
<layer id="9" name="resnet_model/Relu" type="ReLU" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>112</dim>
<dim>112</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="resnet_model/Relu:0">
<dim>1</dim>
<dim>64</dim>
<dim>112</dim>
<dim>112</dim>
</port>
</output>
</layer>
<layer id="10" name="resnet_model/max_pooling2d/MaxPool" type="MaxPool" version="opset8">
<data strides="2, 2" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" kernel="3, 3" rounding_type="floor" auto_pad="same_upper" index_element_type="i64" axis="0" />
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>112</dim>
<dim>112</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="resnet_model/max_pooling2d/MaxPool:0">
<dim>1</dim>
<dim>64</dim>
<dim>56</dim>
<dim>56</dim>
</port>
<port id="2" precision="I64">
<dim>1</dim>
<dim>64</dim>
<dim>56</dim>
<dim>56</dim>
</port>
</output>
</layer>
<layer id="11" name="resnet_model/conv2d_2/kernel" type="Const" version="opset1">
<data element_type="f32" shape="64, 64, 1, 1" offset="37932" size="16384" />
<output>
<port id="0" precision="FP32" names="resnet_model/conv2d_2/kernel:0">
<dim>64</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="12" name="resnet_model/conv2d_2/Conv2D" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="same_upper" />
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>56</dim>
<dim>56</dim>
</port>
<port id="1" precision="FP32">
<dim>64</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="resnet_model/conv2d_2/Conv2D:0">
<dim>1</dim>
<dim>64</dim>
<dim>56</dim>
<dim>56</dim>
</port>
</output>
</layer>
<layer id="13" name="resnet_model/conv2d_2/Conv2D_bn_offset" type="Const" version="opset1">
<data element_type="f32" shape="1, 64, 1, 1" offset="54316" size="256" />
<output>
<port id="0" precision="FP32" names="resnet_model/conv2d_2/Conv2D_bn_offset:0">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="14" name="resnet_model/batch_normalization_2/FusedBatchNorm/Add" type="Add" version="opset1">
<data auto_broadcast="numpy" />
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>56</dim>
<dim>56</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="resnet_model/batch_normalization_2/FusedBatchNorm:0">
<dim>1</dim>
<dim>64</dim>
<dim>56</dim>
<dim>56</dim>
</port>
</output>
</layer>
<layer id="15" name="resnet_model/Relu_1" type="ReLU" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>56</dim>
<dim>56</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="resnet_model/Relu_1:0">
<dim>1</dim>
<dim>64</dim>
<dim>56</dim>
<dim>56</dim>
</port>
</output>
</layer>
<layer id="16" name="resnet_model/conv2d_3/kernel" type="Const" version="opset1">
<data element_type="f32" shape="64, 64, 3, 3" offset="54572" size="147456" />
<output>
<port id="0" precision="FP32" names="resnet_model/conv2d_3/kernel:0">
<dim>64</dim>
<dim>64</dim>
<dim>3</dim>
<dim>3</dim>
</port>
</output>
</layer>
<layer id="17" name="resnet_model/conv2d_3/Conv2D" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="same_upper" />
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>56</dim>
<dim>56</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" names="resnet_model/conv2d_3/Conv2D:0">
<dim>1</dim>
<dim>64</dim>
<dim>56</dim>
<dim>56</dim>
</port>
</output>
</layer>
<layer id="18" name="resnet_model/conv2d_3/Conv2D_bn_offset" type="Const" version="opset1">
<data element_type="f32" shape="1, 64, 1, 1" offset="202028" size="256" />
<output>
<port id="0" precision="FP32" names="resnet_model/conv2d_3/Conv2D_bn_offset:0">
<dim>1</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="19" name="resnet_model/batch_normalization_3/FusedBatchNorm/Add" type="Add" version="opset1">
<data auto_broadcast="numpy" />
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>56</dim>
<dim>56</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="resnet_model/batch_normalization_3/FusedBatchNorm:0">
<dim>1</dim>
<dim>64</dim>
<dim>56</dim>
<dim>56</dim>
</port>
</output>
</layer>
<layer id="20" name="resnet_model/Relu_2" type="ReLU" version="opset1">
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>56</dim>
<dim>56</dim>
</port>
</input>
<output>
<port id="1" precision="FP32" names="resnet_model/Relu_2:0">
<dim>1</dim>
<dim>64</dim>
<dim>56</dim>
<dim>56</dim>
</port>
</output>
</layer>
<layer id="21" name="resnet_model/conv2d_4/kernel" type="Const" version="opset1">
<data element_type="f32" shape="256, 64, 1, 1" offset="202284" size="65536" />
<output>
<port id="0" precision="FP32" names="resnet_model/conv2d_4/kernel:0">
<dim>256</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
<layer id="22" name="resnet_model/conv2d_4/Conv2D" type="Convolution" version="opset1">
<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="same_upper" />
<input>
<port id="0" precision="FP32">
<dim>1</dim>
<dim>64</dim>
<dim>56</dim>
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</port>
<port id="1" precision="FP32">
<dim>256</dim>
<dim>64</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</input>
<output>
<port id="2" precision="FP32" names="resnet_model/conv2d_4/Conv2D:0">
<dim>1</dim>
<dim>256</dim>
<dim>56</dim>
<dim>56</dim>
</port>
</output>
</layer>
<layer id="23" name="resnet_model/conv2d_4/Conv2D_bn_offset" type="Const" version="opset1">
<data element_type="f32" shape="1, 256, 1, 1" offset="267820" size="1024" />
<output>
<port id="0" precision="FP32" names="resnet_model/conv2d_4/Conv2D_bn_offset:0">
<dim>1</dim>
<dim>256</dim>
<dim>1</dim>
<dim>1</dim>
</port>
</output>
</layer>
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