如何找到TensorRT不支持的操作

Question

当我将我的张量流模型（保存为.pb文件）转换为uff文件时，错误日志如下：

Using output node final/lanenet_loss/instance_seg
Using output node final/lanenet_loss/binary_seg
Converting to UFF graph
Warning: No conversion function registered for layer: Slice yet.
Converting as custom op Slice final/lanenet_loss/Slice
name: "final/lanenet_loss/Slice"
op: "Slice"
input: "final/lanenet_loss/Shape_1"
input: "final/lanenet_loss/Slice/begin"
input: "final/lanenet_loss/Slice/size"
attr {
  key: "Index"
  value {
    type: DT_INT32
  }
}
attr {
  key: "T"
  value {
    type: DT_INT32
  }
}

Traceback (most recent call last):
  File "tfpb_to_uff.py", line 16, in <module>
    uff_model = uff.from_tensorflow(graphdef=output_graph_def, output_filename=output_path, output_nodes=["final/lanenet_loss/instance_seg", "final/lanenet_loss/binary_seg"], text=True)
  File "/home/dream/.local/lib/python3.5/site-packages/uff/converters/tensorflow/conversion_helpers.py", line 75, in from_tensorflow
    name="main")
  File "/home/dream/.local/lib/python3.5/site-packages/uff/converters/tensorflow/converter.py", line 64, in convert_tf2uff_graph
    uff_graph, input_replacements)
  File "/home/dream/.local/lib/python3.5/site-packages/uff/converters/tensorflow/converter.py", line 51, in convert_tf2uff_node
    op, name, tf_node, inputs, uff_graph, tf_nodes=tf_nodes)
  File "/home/dream/.local/lib/python3.5/site-packages/uff/converters/tensorflow/converter.py", line 28, in convert_layer
    fields = cls.parse_tf_attrs(tf_node.attr)
  File "/home/dream/.local/lib/python3.5/site-packages/uff/converters/tensorflow/converter.py", line 177, in parse_tf_attrs
    for key, val in attrs.items()}
  File "/home/dream/.local/lib/python3.5/site-packages/uff/converters/tensorflow/converter.py", line 177, in <dictcomp>
    for key, val in attrs.items()}
  File "/home/dream/.local/lib/python3.5/site-packages/uff/converters/tensorflow/converter.py", line 172, in parse_tf_attr_value
    return cls.convert_tf2uff_field(code, val)
  File "/home/dream/.local/lib/python3.5/site-packages/uff/converters/tensorflow/converter.py", line 146, in convert_tf2uff_field
    return TensorFlowToUFFConverter.convert_tf2numpy_dtype(val)
  File "/home/dream/.local/lib/python3.5/site-packages/uff/converters/tensorflow/converter.py", line 74, in convert_tf2numpy_dtype
    return np.dtype(dt[dtype])
TypeError: list indices must be integers or slices, not AttrValue

这意味着TensorRT当前不支持'Slice'层。 因此，我计划在代码中修改此层。 但是，即使我通过函数sess.graph.get_operation_by_name获取有关“ Slice”的信息，我也无法在代码中找到“ Slice”层：

graph list name: "final/lanenet_loss/Slice"
op: "Slice"
input: "final/lanenet_loss/Shape_1"
input: "final/lanenet_loss/Slice/begin"
input: "final/lanenet_loss/Slice/size"
attr {
  key: "Index"
  value {
    type: DT_INT32
  }
}
attr {
  key: "T"
  value {
    type: DT_INT32
  }
}

如何在代码行中找到“ Slice”层，以便可以通过TensorRT自定义层对其进行修改？

Answer 1

由于您是从Tensorflow进行解析的，因此最好查看TensorRT 支持的层。从TensorRT 4开始，支持以下几层：

• 占位符
• const
• 添加，Sub，Mul，Div，最小值和最大值
• BiasAdd
• 负值，Abs，Sqrt，Rsqrt，Pow，Exp和Log
• FusedBatchNorm
• ReLU，TanH，乙状结肠
• SoftMax
• 平均值
• ConcatV2
• 重塑
• 转置
• Conv2D
• DepthwiseConv2dNative
• ConvTranspose2D
• MaxPool
• AvgPool
如果紧随以下TensorFlow层之一，则支持
• Pad： Conv2D，DepthwiseConv2dNative，MaxPool和AvgPool

从我在您的日志中看到的，您正在尝试部署LaneNet，是this paper的LaneNet吗？

如果是这种情况，那么它似乎是H-Net的一种变体，尚未阅读过，但根据该论文，其架构如下：

所以我看到了Convs，Relus，Maxpool和Linear，它们都受支持，不知道该BN，如果不在受支持的网络列表中，请检查一下以了解其引用的是哪一层您将不得不从头开始实施它。 祝你好运！