Could not parsing from uff

Hello everyone.

I want to inference ssd_mobilenet_v2_coco.pb in jetson nano using TensorRT

So, I converted .pb to .uff using convert-to-uff like this:

# convert-to-uff --input-file ssd_mobilenet_v2_coco.pb -O NMS -p config.py

I got the ssd_mobilenet_v2_coco.uff file
and excute on this code:

#include <iostream>

#include "NvInfer.h"
#include "NvUffParser.h"

#include "common/logger.h"
#include "common/common.h"

#include <cuda_runtime_api.h>

int main()
{
    std::cout << "Hello TensorRT" << std::endl;
    
    initLibNvInferPlugins(&gLogger.getTRTLogger(), "");
    nvinfer1::IBuilder* builder = nvinfer1::createInferBuilder(gLogger);
    nvinfer1::INetworkDefinition* network = builder->createNetwork();

    auto parser = nvuffparser::createUffParser();
    
    parser->registerInput("Input_0", nvinfer1::DimsCHW(3, 300, 300), nvuffparser::UffInputOrder::kNCHW);
    parser->registerOutput("NMS");

    parser->parse("../model/ssd_mobilenet_v2_coco.uff", *network, nvinfer1::DataType::kFLOAT);

    return 0;
}

Then, I got some error like this
[01/11/2020-07:52:21] [E] [TRT] UffParser: Parser error: BoxPredictor_0/Reshape_1: Order size is not matching the number dimensions of TensorRT

I did everything can I do but I failed to createNetwork from .uff.

Do you knows how to fix it?

I attatch my files
https://drive.google.com/open?id=1MIzeNYmZZT6aIDgufMc9RpLspL2VNRbc

And I’m using
Docker Container nvcr.io/nvidia/tensorrt:19.10-py3
in Ubuntu 16.04

Thank you.

Hi,

Based on the error message it seems that Uffparser is trying to process the layer whose format is not NCHW.
Could you modify your model to make sure it’s all NCHW format and try again?

Thanks

Hi SunilJB

Thank you for your answer.
But, I think your answer is not a solution.
because that model is working well in TensorRT-Python environment.

This is my reference
https://jkjung-avt.github.io/tensorrt-ssd/

config.py

I noticed something.

It need to edit
config.py line: 80
inputOrder=[0, 2, 1] to inputOrder=[1, 0, 2]

but… same error accured

Hi,

Please refer below link, in case it helps:
https://devtalk.nvidia.com/default/topic/1066726/jetson-nano/convert-ssd-mobilenet-to-uff/post/5408296/#5408296
https://devtalk.nvidia.com/default/topic/1067688/deepstream-sdk/custom-trained-ssd-mobilenet-v2-output-detections-are-jittery-/

Thanks

I solved my problem.

The problem is inputName and config.py

#parser->registerInput("Input_0", nvinfer1::DimsCHW(3, 300, 300), nvuffparser::UffInputOrder::kNCHW);
parser->registerInput("Input", nvinfer1::DimsCHW(3, 300, 300), nvuffparser::UffInputOrder::kNCHW);
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import graphsurgeon as gs
import tensorflow as tf

Input = gs.create_node(
    name="Input",
    op="Placeholder",
    dtype=tf.float32,
    shape=[1, 3, 300, 300]
)

PriorBox = gs.create_plugin_node(
    #name="GridAnchor",
    name="MultipleGridAnchorGenerator",
    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=0.3,
    nmsThreshold=0.6,
    topK=100,
    keepTopK=100,
    numClasses=91,
    inputOrder=[1, 0, 2],
    confSigmoid=1,
    isNormalized=1
    #scoreConverter="SIGMOID"
)

concat_priorbox = gs.create_node(
    name="concat_priorbox",
    op="ConcatV2",
    #dtype=tf.float32,
    axis=2
)

concat_box_loc = gs.create_plugin_node(
    name="concat_box_loc",
    op="FlattenConcat_TRT", 
    #dtype=tf.float32,
    axis=1, 
    ignoreBatch=0
)

concat_box_conf = gs.create_plugin_node(
    name="concat_box_conf", 
    op="FlattenConcat_TRT", 
    #dtype=tf.float32, 
    axis=1, 
    ignoreBatch=0
)

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

def preprocess(dynamic_graph):
    # 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.find_nodes_by_op("NMS_TRT")[0].input.remove("Input")

Thank you.