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| author | Eric Dao <eric@erickhangdao.com> | 2025-03-10 17:54:31 -0400 |
|---|---|---|
| committer | Eric Dao <eric@erickhangdao.com> | 2025-03-10 17:54:31 -0400 |
| commit | ab224e2e6ba65f5a369ec392f99cd8845ad06c98 (patch) | |
| tree | a1e757e9341863ed52b8ad4c5a1c45933aab9da4 /python/openvino/demo/models/model_bmp.xml | |
| parent | 40da1752f2c8639186b72f6838aa415e854d0b1d (diff) | |
| download | thesis-master.tar.gz thesis-master.tar.bz2 thesis-master.zip | |
Diffstat (limited to 'python/openvino/demo/models/model_bmp.xml')
| -rw-r--r-- | python/openvino/demo/models/model_bmp.xml | 1782 |
1 files changed, 1782 insertions, 0 deletions
diff --git a/python/openvino/demo/models/model_bmp.xml b/python/openvino/demo/models/model_bmp.xml new file mode 100644 index 0000000..7cd188d --- /dev/null +++ b/python/openvino/demo/models/model_bmp.xml @@ -0,0 +1,1782 @@ +<?xml version="1.0"?> +<net name="main_graph" version="11"> + <layers> + <layer id="0" name="input.1" type="Parameter" version="opset1"> + <data shape="32,3,128,128" element_type="f32" /> + <output> + <port id="0" precision="FP32" names="input.1"> + <dim>32</dim> + <dim>3</dim> + <dim>128</dim> + <dim>128</dim> + </port> + </output> + </layer> + <layer id="1" name="onnx::Conv_174" type="Const" version="opset1"> + <data element_type="f32" shape="16, 3, 3, 3" offset="0" size="1728" /> + <output> + <port id="0" precision="FP32" names="onnx::Conv_174"> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="2" name="/conv2x_0/conv2x_0.0/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>3</dim> + <dim>128</dim> + <dim>128</dim> + </port> + <port id="1" precision="FP32"> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>32</dim> + <dim>16</dim> + <dim>128</dim> + <dim>128</dim> + </port> + </output> + </layer> + <layer id="3" name="Reshape_44" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="1728" size="64" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="4" name="/conv2x_0/conv2x_0.0/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>16</dim> + <dim>128</dim> + <dim>128</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/conv2x_0/conv2x_0.0/Conv_output_0"> + <dim>32</dim> + <dim>16</dim> + <dim>128</dim> + <dim>128</dim> + </port> + </output> + </layer> + <layer id="5" name="/conv2x_0/conv2x_0.2/Relu" type="ReLU" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>16</dim> + <dim>128</dim> + <dim>128</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/conv2x_0/conv2x_0.2/Relu_output_0"> + <dim>32</dim> + <dim>16</dim> + <dim>128</dim> + <dim>128</dim> + </port> + </output> + </layer> + <layer id="6" name="onnx::Conv_177" type="Const" version="opset1"> + <data element_type="f32" shape="16, 16, 3, 3" offset="1792" size="9216" /> + <output> + <port id="0" precision="FP32" names="onnx::Conv_177"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="7" name="/conv2x_0/conv2x_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" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>16</dim> + <dim>128</dim> + <dim>128</dim> + </port> + <port id="1" precision="FP32"> + <dim>16</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>32</dim> + <dim>16</dim> + <dim>128</dim> + <dim>128</dim> + </port> + </output> + </layer> + <layer id="8" name="Reshape_61" type="Const" version="opset1"> + <data element_type="f32" shape="1, 16, 1, 1" offset="11008" size="64" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="9" name="/conv2x_0/conv2x_0.3/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>16</dim> + <dim>128</dim> + <dim>128</dim> + </port> + <port id="1" precision="FP32"> + <dim>1</dim> + <dim>16</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </input> + <output> + <port id="2" precision="FP32" names="/conv2x_0/conv2x_0.3/Conv_output_0"> + <dim>32</dim> + <dim>16</dim> + <dim>128</dim> + <dim>128</dim> + </port> + </output> + </layer> + <layer id="10" name="/conv2x_0/conv2x_0.5/Relu" type="ReLU" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>16</dim> + <dim>128</dim> + <dim>128</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/conv2x_0/conv2x_0.5/Relu_output_0"> + <dim>32</dim> + <dim>16</dim> + <dim>128</dim> + <dim>128</dim> + </port> + </output> + </layer> + <layer id="11" name="/pool/MaxPool" 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" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>16</dim> + <dim>128</dim> + <dim>128</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/pool/MaxPool_output_0"> + <dim>32</dim> + <dim>16</dim> + <dim>64</dim> + <dim>64</dim> + </port> + <port id="2" precision="I64"> + <dim>32</dim> + <dim>16</dim> + <dim>64</dim> + <dim>64</dim> + </port> + </output> + </layer> + <layer id="12" name="onnx::Conv_180" type="Const" version="opset1"> + <data element_type="f32" shape="32, 16, 3, 3" offset="11072" size="18432" /> + <output> + <port id="0" precision="FP32" names="onnx::Conv_180"> + <dim>32</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="13" name="/conv2x_1/conv2x_1.0/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>16</dim> + <dim>64</dim> + <dim>64</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>16</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>64</dim> + <dim>64</dim> + </port> + </output> + </layer> + <layer id="14" name="Reshape_79" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="29504" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="15" name="/conv2x_1/conv2x_1.0/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>64</dim> + <dim>64</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="/conv2x_1/conv2x_1.0/Conv_output_0"> + <dim>32</dim> + <dim>32</dim> + <dim>64</dim> + <dim>64</dim> + </port> + </output> + </layer> + <layer id="16" name="/conv2x_1/conv2x_1.2/Relu" type="ReLU" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>64</dim> + <dim>64</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/conv2x_1/conv2x_1.2/Relu_output_0"> + <dim>32</dim> + <dim>32</dim> + <dim>64</dim> + <dim>64</dim> + </port> + </output> + </layer> + <layer id="17" name="onnx::Conv_183" type="Const" version="opset1"> + <data element_type="f32" shape="32, 32, 3, 3" offset="29632" size="36864" /> + <output> + <port id="0" precision="FP32" names="onnx::Conv_183"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="18" name="/conv2x_1/conv2x_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" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>64</dim> + <dim>64</dim> + </port> + <port id="1" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </input> + <output> + <port id="2" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>64</dim> + <dim>64</dim> + </port> + </output> + </layer> + <layer id="19" name="Reshape_96" type="Const" version="opset1"> + <data element_type="f32" shape="1, 32, 1, 1" offset="66496" size="128" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>32</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="20" name="/conv2x_1/conv2x_1.3/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>64</dim> + <dim>64</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="/conv2x_1/conv2x_1.3/Conv_output_0"> + <dim>32</dim> + <dim>32</dim> + <dim>64</dim> + <dim>64</dim> + </port> + </output> + </layer> + <layer id="21" name="/conv2x_1/conv2x_1.5/Relu" type="ReLU" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>64</dim> + <dim>64</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/conv2x_1/conv2x_1.5/Relu_output_0"> + <dim>32</dim> + <dim>32</dim> + <dim>64</dim> + <dim>64</dim> + </port> + </output> + </layer> + <layer id="22" name="/pool_1/MaxPool" 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" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>64</dim> + <dim>64</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/pool_1/MaxPool_output_0"> + <dim>32</dim> + <dim>32</dim> + <dim>32</dim> + <dim>32</dim> + </port> + <port id="2" precision="I64"> + <dim>32</dim> + <dim>32</dim> + <dim>32</dim> + <dim>32</dim> + </port> + </output> + </layer> + <layer id="23" name="onnx::Conv_186" type="Const" version="opset1"> + <data element_type="f32" shape="64, 32, 3, 3" offset="66624" size="73728" /> + <output> + <port id="0" precision="FP32" names="onnx::Conv_186"> + <dim>64</dim> + <dim>32</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="24" name="/conv2x_2/conv2x_2.0/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>32</dim> + <dim>32</dim> + <dim>32</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>32</dim> + <dim>64</dim> + <dim>32</dim> + <dim>32</dim> + </port> + </output> + </layer> + <layer id="25" name="Reshape_114" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="140352" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="26" name="/conv2x_2/conv2x_2.0/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>64</dim> + <dim>32</dim> + <dim>32</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="/conv2x_2/conv2x_2.0/Conv_output_0"> + <dim>32</dim> + <dim>64</dim> + <dim>32</dim> + <dim>32</dim> + </port> + </output> + </layer> + <layer id="27" name="/conv2x_2/conv2x_2.2/Relu" type="ReLU" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>64</dim> + <dim>32</dim> + <dim>32</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/conv2x_2/conv2x_2.2/Relu_output_0"> + <dim>32</dim> + <dim>64</dim> + <dim>32</dim> + <dim>32</dim> + </port> + </output> + </layer> + <layer id="28" name="onnx::Conv_189" type="Const" version="opset1"> + <data element_type="f32" shape="64, 64, 3, 3" offset="140608" size="147456" /> + <output> + <port id="0" precision="FP32" names="onnx::Conv_189"> + <dim>64</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="29" name="/conv2x_2/conv2x_2.3/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>64</dim> + <dim>32</dim> + <dim>32</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>32</dim> + <dim>64</dim> + <dim>32</dim> + <dim>32</dim> + </port> + </output> + </layer> + <layer id="30" name="Reshape_131" type="Const" version="opset1"> + <data element_type="f32" shape="1, 64, 1, 1" offset="288064" size="256" /> + <output> + <port id="0" precision="FP32"> + <dim>1</dim> + <dim>64</dim> + <dim>1</dim> + <dim>1</dim> + </port> + </output> + </layer> + <layer id="31" name="/conv2x_2/conv2x_2.3/Conv" type="Add" version="opset1"> + <data auto_broadcast="numpy" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>64</dim> + <dim>32</dim> + <dim>32</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="/conv2x_2/conv2x_2.3/Conv_output_0"> + <dim>32</dim> + <dim>64</dim> + <dim>32</dim> + <dim>32</dim> + </port> + </output> + </layer> + <layer id="32" name="/conv2x_2/conv2x_2.5/Relu" type="ReLU" version="opset1"> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>64</dim> + <dim>32</dim> + <dim>32</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/conv2x_2/conv2x_2.5/Relu_output_0"> + <dim>32</dim> + <dim>64</dim> + <dim>32</dim> + <dim>32</dim> + </port> + </output> + </layer> + <layer id="33" name="/pool_2/MaxPool" 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" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>64</dim> + <dim>32</dim> + <dim>32</dim> + </port> + </input> + <output> + <port id="1" precision="FP32" names="/pool_2/MaxPool_output_0"> + <dim>32</dim> + <dim>64</dim> + <dim>16</dim> + <dim>16</dim> + </port> + <port id="2" precision="I64"> + <dim>32</dim> + <dim>64</dim> + <dim>16</dim> + <dim>16</dim> + </port> + </output> + </layer> + <layer id="34" name="onnx::Conv_192" type="Const" version="opset1"> + <data element_type="f32" shape="128, 64, 3, 3" offset="288320" size="294912" /> + <output> + <port id="0" precision="FP32" names="onnx::Conv_192"> + <dim>128</dim> + <dim>64</dim> + <dim>3</dim> + <dim>3</dim> + </port> + </output> + </layer> + <layer id="35" name="/conv2x_3/conv2x_3.0/Conv/WithoutBiases" type="Convolution" version="opset1"> + <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" /> + <input> + <port id="0" precision="FP32"> + <dim>32</dim> + <dim>64</dim> + <dim>16</dim> + <dim>16</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>32</dim> + <dim>128</dim> + <dim>16</dim> + <dim>16</dim> + </port> + </output> + 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