更换文档检测模型
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# Runtime Directory
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This directory holds the model files.
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Paddle models must be model.pdmodel and model.pdiparams files.
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ONNX models must be model.onnx files.
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backend: "fastdeploy"
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# Input configuration of the model
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input [
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{
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# input name
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name: "image"
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# input type such as TYPE_FP32、TYPE_UINT8、TYPE_INT8、TYPE_INT16、TYPE_INT32、TYPE_INT64、TYPE_FP16、TYPE_STRING
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data_type: TYPE_FP32
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# input shape, The batch dimension is omitted and the actual shape is [batch, c, h, w]
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dims: [ -1, 3, -1, -1 ]
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},
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{
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name: "scale_factor"
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data_type: TYPE_FP32
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dims: [ -1, 2 ]
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},
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{
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name: "im_shape"
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data_type: TYPE_FP32
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dims: [ -1, 2 ]
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}
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]
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# The output of the model is configured in the same format as the input
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output [
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{
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name: "concat_12.tmp_0"
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data_type: TYPE_FP32
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dims: [ -1, 6 ]
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},
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{
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name: "concat_8.tmp_0"
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data_type: TYPE_INT32
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dims: [ -1 ]
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}
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]
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# Number of instances of the model
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instance_group [
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{
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# The number of instances is 1
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count: 1
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# Use GPU, CPU inference option is:KIND_CPU
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kind: KIND_GPU
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# The instance is deployed on the 0th GPU card
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gpus: [0]
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}
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]
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optimization {
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execution_accelerators {
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gpu_execution_accelerator : [ {
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# use Paddle engine
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name: "paddle",
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}
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]
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}}
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@@ -0,0 +1,63 @@
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backend: "fastdeploy"
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# Input configuration of the model
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input [
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{
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# input name
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name: "image"
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# input type such as TYPE_FP32、TYPE_UINT8、TYPE_INT8、TYPE_INT16、TYPE_INT32、TYPE_INT64、TYPE_FP16、TYPE_STRING
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data_type: TYPE_FP32
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# input shape, The batch dimension is omitted and the actual shape is [batch, c, h, w]
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dims: [ -1, 3, -1, -1 ]
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},
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{
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name: "scale_factor"
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data_type: TYPE_FP32
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dims: [ -1, 2 ]
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},
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{
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name: "im_shape"
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data_type: TYPE_FP32
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dims: [ -1, 2 ]
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}
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]
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# The output of the model is configured in the same format as the input
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output [
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{
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name: "concat_9.tmp_0"
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data_type: TYPE_FP32
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dims: [ -1, 6 ]
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},
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{
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name: "concat_5.tmp_0"
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data_type: TYPE_INT32
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dims: [ -1 ]
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},
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{
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name: "tmp_109"
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data_type: TYPE_INT32
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dims: [ -1, -1, -1 ]
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}
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]
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# Number of instances of the model
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instance_group [
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{
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# The number of instances is 1
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count: 1
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# Use GPU, CPU inference option is:KIND_CPU
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kind: KIND_GPU
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# The instance is deployed on the 0th GPU card
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gpus: [0]
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}
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]
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optimization {
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execution_accelerators {
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gpu_execution_accelerator : [ {
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# use Paddle engine
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name: "paddle",
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}
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]
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}}
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@@ -0,0 +1,58 @@
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backend: "fastdeploy"
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# Input configuration of the model
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input [
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{
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# input name
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name: "image"
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# input type such as TYPE_FP32、TYPE_UINT8、TYPE_INT8、TYPE_INT16、TYPE_INT32、TYPE_INT64、TYPE_FP16、TYPE_STRING
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data_type: TYPE_FP32
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# input shape, The batch dimension is omitted and the actual shape is [batch, c, h, w]
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dims: [ -1, 3, -1, -1 ]
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},
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{
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name: "scale_factor"
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data_type: TYPE_FP32
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dims: [ -1, 2 ]
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},
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{
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name: "im_shape"
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data_type: TYPE_FP32
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dims: [ -1, 2 ]
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}
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]
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# The output of the model is configured in the same format as the input
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output [
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{
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name: "matrix_nms_0.tmp_0"
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data_type: TYPE_FP32
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dims: [ -1, 6 ]
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},
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{
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name: "matrix_nms_0.tmp_2"
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data_type: TYPE_INT32
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dims: [ -1 ]
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}
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]
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# Number of instances of the model
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instance_group [
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{
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# The number of instances is 1
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count: 1
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# Use GPU, CPU inference option is:KIND_CPU
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kind: KIND_GPU
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# The instance is deployed on the 0th GPU card
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gpus: [0]
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}
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]
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optimization {
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execution_accelerators {
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gpu_execution_accelerator : [ {
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# use Paddle engine
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name: "paddle",
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}
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]
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}}
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@@ -0,0 +1,55 @@
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# optional, If name is specified it must match the name of the model repository directory containing the model.
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name: "runtime"
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backend: "fastdeploy"
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# Input configuration of the model
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input [
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{
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# input name
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name: "image"
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# input type such as TYPE_FP32、TYPE_UINT8、TYPE_INT8、TYPE_INT16、TYPE_INT32、TYPE_INT64、TYPE_FP16、TYPE_STRING
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data_type: TYPE_FP32
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# input shape, The batch dimension is omitted and the actual shape is [batch, c, h, w]
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dims: [ -1, 3, -1, -1 ]
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},
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{
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name: "scale_factor"
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data_type: TYPE_FP32
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dims: [ -1, 2 ]
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}
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]
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# The output of the model is configured in the same format as the input
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output [
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{
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name: "multiclass_nms3_0.tmp_0"
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data_type: TYPE_FP32
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dims: [ -1, 6 ]
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},
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{
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name: "multiclass_nms3_0.tmp_2"
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data_type: TYPE_INT32
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dims: [ -1 ]
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}
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]
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# Number of instances of the model
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instance_group [
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{
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# The number of instances is 1
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count: 1
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# Use GPU, CPU inference option is:KIND_CPU
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kind: KIND_GPU
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# The instance is deployed on the 0th GPU card
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gpus: [0]
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}
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]
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optimization {
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execution_accelerators {
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gpu_execution_accelerator : [ {
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# use Paddle engine
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name: "paddle",
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}
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]
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}}
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