<p>since you are parsing from Tensorflow maybe it's better to see which layers TensorRT <em>DOES</em> support. As of TensorRT 4, these following layers are supported:</p>

<ul>
<li>Placeholder</li>
<li>Const</li>
<li>Add, Sub, Mul, Div, Minimum and Maximum</li>
<li>BiasAdd</li>
<li>Negative, Abs, Sqrt, Rsqrt, Pow, Exp and Log</li>
<li>FusedBatchNorm</li>
<li>ReLU, TanH, Sigmoid</li>
<li>SoftMax</li>
<li>Mean</li>
<li>ConcatV2</li>
<li>Reshape</li>
<li>Transpose</li>
<li>Conv2D</li>
<li>DepthwiseConv2dNative</li>
<li>ConvTranspose2D</li>
<li>MaxPool</li>
<li>AvgPool</li>
<li>Pad is supported if followed by one of these TensorFlow layers:
Conv2D, DepthwiseConv2dNative, MaxPool, and AvgPool</li>
</ul>

<p>From what I see in your logs you are trying to deploy LaneNet, is it the LaneNet of <a href="https://arxiv.org/pdf/1802.05591.pdf" rel="nofollow noreferrer">this paper</a>?</p>

<p>If that is the case it seems to be a variant of H-Net, haven't read about it but the architecture is the following, according to the paper:</p>

<p><a href="https://i.stack.imgur.com/wqCIG.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/wqCIG.png" alt="LaneNet Architecture"></a></p>

<p>So I see Convs, Relus, Maxpool and Linear, all of which are supported, don't know about that BN, maybe check that out to see which layer does it refer, if it is not on the list of supported networks you'll have to implement it from scratch.
Best of luck!</p>
How to locate an operation unsupported by TensorRT
<p>When I convert my tensorflow model (saved as .pb file) to uff file, error log like this:</p>

<pre><code>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 &lt;module&gt;
    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 &lt;dictcomp&gt;
    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
</code></pre>

<p>It meaning that the layer: 'Slice' is not supported by TensorRT currently.
So I plan to modify this layer in my code.
However, I can't locate 'Slice' layer in my code, even I get information about 'Slice' by function sess.graph.get_operation_by_name:</p>

<pre><code>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
  }
}
</code></pre>

<p>How can I locate the 'Slice' layer in my code lines so that I can modify it by TensorRT custom layer?</p>