Custom SSD_v2 model is not convert to TRT_engine

Dear Team,

Kindly help me to get out of this problem.

[TensorRT] INFO: UFFParser: Applying order forwarding to: Squeeze
[TensorRT] INFO: UFFParser: parsing GridAnchor
[libprotobuf FATAL[b]/home/erisuser/p4sw/sw/gpgpu/MachineLearning/DIT/externals/protobuf/aarch64/10.0/include/google/protobuf/repeated_field.h:1408] CHECK failed: (index) < (current_size_)

Note:
The custom model of SSD_v2 has been trained on tensorflow_v1.12.0. Now I try to convert the frozen_graph.pb to trt engine .bin and getting the error.

If I try to convert the pre-trained model of SSD_v2 then it’s easily converted to trt engine format. Then why the problem occurs when I try to convert the SSD_v2 custom model?

Hi,

This is a known limitation.

A workaround is to re-train the model this this change in multiple_grid_anchor_generator.py.

diff --git a/multiple_grid_anchor_generator.py b/multiple_grid_anchor_generator.py
index 86007c9..12da3bc 100644
--- a/multiple_grid_anchor_generator.py
+++ b/multiple_grid_anchor_generator.py
@@ -95,7 +95,8 @@ class MultipleGridAnchorGenerator(anchor_generator.AnchorGenerator):
       raise ValueError('box_specs_list is expected to be a '
                        'list of lists of pairs')
     if base_anchor_size is None:
-      base_anchor_size = [256, 256]
+      base_anchor_size = [256., 256.]
+    base_anchor_size = tf.constant(base_anchor_size, dtype=tf.float32)
     self._base_anchor_size = base_anchor_size
     self._anchor_strides = anchor_strides
     self._anchor_offsets = anchor_offsets

You can find more information in this topic:
https://devtalk.nvidia.com/default/topic/1069027/tensorrt/parsing-gridanchor-op-gridanchor_trt-protobuf-repeated_field-h-1408-check-failed-index-lt-current_size-/?offset=3#5415537

Thanks.

Hello AastaLLL,

After doing that, got this error:

[TensorRT] ERROR: UffParser: Parser error: image_tensor: Invalid DataType value!
[TensorRT] ERROR: Network must have at least one output

Hi,

Sorry for the late.

Have you re-trained the model with the patch in comment#2?
If yes, would you mind to share the re-trained model with us for debugging?

Thanks.

Hi,

Yes I re-trained the model with the changes what you told in comment #2.

FYI- Model below-

https://drive.google.com/open?id=1Ki-Gyz4qL2lNkB1BYD_hhU-zA-XgoHTI

Thanks.

Hi,

We cannot download the model due to no permission.
Could you help to enable it?

Thanks.

Hi,

Check the update.
https://drive.google.com/file/d/1Ki-Gyz4qL2lNkB1BYD_hhU-zA-XgoHTI/view?usp=sharing

Hi,

The protobuf error can be fixed by appending a dummy constant tensor to the GridAnchor_TRT layer.
However, there is another issue in concatenate layer that still still under checking.

Attached the change we made for the error for your reference. topic_112757.txt (1.5 KB)

Thanks.

Thanks Aasta for updating.
Kindly keep update when the problem has been resolve.

Regards.

Hi,

Thanks for your patience.
We are still checking this issue.

To give further suggestion, could you tell us how many class are your model trained for?
Thanks.

Hi,

Three classes are there.

Thanks.

Hi,

Sorry that it takes us some time to fix this issue.

We confirmed that your .pb model can be converted into TensorRT with this config.py.

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import graphsurgeon as gs
import tensorflow as tf
import numpy as np

Input = gs.create_node("Input",
    op="Placeholder",
    dtype=tf.float32,
    shape=[1, 3, 300, 300])
PriorBox = gs.create_plugin_node(name="GridAnchor", op="GridAnchor_TRT",
    numLayers=6,
    minSize=0.2,
    maxSize=0.95,
    aspectRatios=[1.0, 2.0, 0.5, 3.0, 0.33],
    variance=[0.1,0.1,0.2,0.2],
    featureMapShapes=[19, 10, 5, 3, 2, 1])
NMS = gs.create_plugin_node(name="NMS", op="NMS_TRT",
    shareLocation=1,
    varianceEncodedInTarget=0,
    backgroundLabelId=0,
    confidenceThreshold=1e-8,
    nmsThreshold=0.6,
    topK=100,
    keepTopK=100,
    numClasses=3,
    inputOrder= [0, 2, 1],
    confSigmoid=1,
    isNormalized=1)
concat_priorbox = gs.create_node(name="concat_priorbox", op="ConcatV2", dtype=tf.float32, axis=2)
concat_box_loc = gs.create_plugin_node("concat_box_loc", op="FlattenConcat_TRT", dtype=tf.float32, axis=1, ignoreBatch=0)
concat_box_conf = gs.create_plugin_node("concat_box_conf", op="FlattenConcat_TRT", dtype=tf.float32, axis=1, ignoreBatch=0)
dummy_const = gs.create_node(name="dummy_const", op="Const", dtype=tf.float32, value=np.array([1, 1], dtype=np.float32))

namespace_plugin_map = {
    "Concatenate": concat_priorbox,
    "MultipleGridAnchorGenerator": PriorBox,
    "Postprocessor": NMS,
    "image_tensor": Input,
    "Cast": Input,
    "ToFloat": Input,
    "Preprocessor": Input,
    "concat": concat_box_loc,
    "concat_1": concat_box_conf
}

namespace_remove = {
    "ToFloat",
    "image_tensor",
    "Preprocessor/map/TensorArrayStack_1/TensorArrayGatherV3",
}

def preprocess(dynamic_graph):
    dynamic_graph.remove(dynamic_graph.find_nodes_by_path(namespace_remove), remove_exclusive_dependencies=False)
    # Now create a new graph by collapsing namespaces
    dynamic_graph.collapse_namespaces(namespace_plugin_map)
    # Remove the outputs, so we just have a single output node (NMS).
    dynamic_graph.remove(dynamic_graph.graph_outputs, remove_exclusive_dependencies=False)
    dynamic_graph.append(dummy_const)
    dynamic_graph.find_nodes_by_op("GridAnchor_TRT")[0].input.append("dummy_const")
$ sudo python3 /usr/lib/python3.6/dist-packages/uff/bin/convert_to_uff.py frozen_inference_graph.pb -o sample_ssd_relu6.uff -O NMS -p config.py
$ /usr/src/tensorrt/bin/trtexec --uff=sample_ssd_relu6.uff --uffInput=Input,3,300,300 --output=NMS

Please let us know your results.
Thanks.

Thank you AastaLLL for your response.
I’m not able to test now because of COVID-19 pandemic. Will update you back after testing.

Sure. Stay safe!