为了加快处理速度,我将openpose model从这个tf-openpose库转换为tensorrt。
转换成功,如UFF文件所示。输入是image
,输出是Openpose/concat_stage7
,如下所示。然后换成发动机。在
NOTE: UFF has been tested with TensorFlow 1.12.0. Other versions are not guaranteed to work
UFF Version 0.6.3
=== Automatically deduced input nodes ===
[name: "image"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
attr {
key: "shape"
value {
shape {
dim {
size: -1
}
dim {
size: -1
}
dim {
size: -1
}
dim {
size: 3
}
}
}
}
]
=========================================
=== Automatically deduced output nodes ===
[name: "Openpose/concat_stage7"
op: "ConcatV2"
input: "Mconv7_stage6_L2/BiasAdd"
input: "Mconv7_stage6_L1/BiasAdd"
input: "Openpose/concat_stage7/axis"
attr {
key: "N"
value {
i: 2
}
}
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "Tidx"
value {
type: DT_INT32
}
}
]
==========================================
Using output node Openpose/concat_stage7
Converting to UFF graph
No. nodes: 463
UFF Output written to cmu/cmu_openpose.uff
作为反序列化引擎
^{pr2}$我需要得到heatMat and pafMat from output
作为原始的tf_姿势处理。
tf姿势处理有
self.tensor_image = self.graph.get_tensor_by_name('TfPoseEstimator/image:0')
self.tensor_output = self.graph.get_tensor_by_name('TfPoseEstimator/Openpose/concat_stage7:0')
self.tensor_heatMat = self.tensor_output[:, :, :, :19]
self.tensor_pafMat = self.tensor_output[:, :, :, 19:]
如何从tensorrt处理输出中获取heatMat和pafMat?在
目前没有回答
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