Environnement:
Linux version: Ubuntu 18.04
GPU type: Titan X
CUDA version: 10.0
Framework: TensorRT 5.1.5-1, Tensorflow-gpu 1.12
Dear all,
We use Tensorflow Object Detection API to train models and we would like to convert them to uff and then use them in TensorRT.
Thanks to the script in UffSample provided by Nvidia we can convert the Tensorflow model zoo ssd_inception_v2 model to uff and then create an engine.
The problem is that if when we retrain the same model with the COCO dataset, this new model can’t be converted to TensortRT engine anymore.
The graph in Tensorboard seems to be slightly different from the model zoo 2018 frozen graph.
We have tried different versions of the config.py (see an example bellow used for the 2018 pretrained version) and we have remained each time with the same error.
This behavior might be in relation with some part of the graph that has to be additionally transformed when using Tensorflow Object Detection API.
For the Tensorflow export part we use the script as described in the github: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/exporting_models.md
The error is as follow in our PC with the above configuration:
[libprotobuf FATAL /home/erisuser/p4sw/sw/gpgpu/MachineLearning/DIT/externals/protobuf/x86_64/10.0/include/google/protobuf/repeated_field.h:1408] CHECK failed: (index) < (current_size_):
terminate called after throwing an instance of 'google_private::protobuf::FatalException'
what(): CHECK failed: (index) < (current_size_):
We obtain another error when we execute the same script on our Jetson AGX Xavier (Linux 18.04, TensorRT 5.0.3-1, CUDA 10.0):
UFFParser: Parser error: BoxPredictor_0/Reshape: Reshape: -1 dimension specified more than 1 time
config.py used to create the uff:
#
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import graphsurgeon as gs
import tensorflow as tf
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=91,
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)
namespace_plugin_map = {
"MultipleGridAnchorGenerator": PriorBox,
"Postprocessor": NMS,
"Preprocessor": Input,
"ToFloat": Input,
"image_tensor": Input,
"image_tensor:0": Input, #added for 2018
"MultipleGridAnchorGenerator/Concatenate": concat_priorbox,
"MultipleGridAnchorGenerator/Identity": 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") #added for 2018
Any help would be greatly appreciated,
Thank you