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onnx to trt conversion of ssd mobilenet v2 fpnlite tensorflow object detection API 2 #1205
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@VeeranjaneyuluToka Could you try reducing the See #795 (comment) |
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@VeeranjaneyuluToka How did you generate the ONNX model? Can you share the original model and the scripts you used to modify it? Also can you get a backtrace from |
@pranavm-nvidia I have savedmodel from tensorflow object detectionn API 2 which is trained using my own dataset, then i have used below command to convert from savedmodel to onnx model python -m tf2onnx.convert --saved-model /tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/saved_model --outputs 'Identity_6:0','Identity_7:0' --output /tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/ssd-mobnetv2_fpnlite_model.onnx --opset 12 --verbose i have sent an email with all the onnx models and script that i have used to modify it. and i am using the below command to convert from onnx to trt ./trtexec --onnx=/tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/ssd-mobnetv2_fpnlite_model_no_nms_latest.onnx --saveEngine=tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/ssd_mobilenet_v2_fpnlite.trt --verbose i will check with gdb and let you know if i find anything related to seg fault. |
@pranavm-nvidia , i have considered latest tf2onnx branch changes esp to fix this issues(onnx/tensorflow-onnx#1443) to avoid TensorListStack ops, then converted from savedmodel to onnx, then changed input-tensor datatype to float and tried to convert from onnx to trt, but getting the different error. Attached log for your reference. |
@pranavm-nvidia Thanks for all your help provided and prompt response. I could successfully convert from onnx to trt engine but results are quite different. wondering how can i debug this? would be great help if you can provide any suggestions or any references to debug further. |
@VeeranjaneyuluToka A good first step would be to try with Polygraphy to figure out which layer is the issue. I'd start with the ONNX model w/o the NMS node: polygraphy run </path/to/model.onnx> \
--trt --trt-outputs mark all \
--onnxrt --onnx-outputs mark all If that passes, then the problem must be in the NMS. |
polygraphy run /tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/280421_convert/ssd-mobnetv2-fpnlite-model-inploop-removed.onnx --trt --trt-outputs mark all --onnxrt --onnx-outputs mark all This is how the log looks like, @pranavm-nvidia :any inputs on this? |
@VeeranjaneyuluToka Seems like the ONNX model isn't valid. Looks to me like it could be a bug in |
@pranavm-nvidia, i have one doubt when i use the below command to remove the loop and attached input shape, it started giving shape information and could visualize in netron the same. polygraphy surgeon extract /tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/290421_convert/ssd-mobnetv2_fpnlite_model.onnx --inputs StatefulPartitionedCall/map/while_loop:2,1x3x640x640,float32 -o /tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/290421_convert/ssd-mobnetv2-fpnlite-model-inploop-removed.onnx But if you notice the attached graph, it shows 1x3x640x640 at first node and after that it shows 0x32x320x320, why it is showing 0 at first dimension? |
@VeeranjaneyuluToka Sounds like it might be a bug with ONNX shape inference. Is it causing any issues? TRT does its own shape inference, so it should ignore the intermediate shapes from the model |
@pranavm-nvidia , trt conversion goes fine. But when i run inference using trt engine, results are very bad. so i am not sure where it is going wrong? anything i can do to track or debug trt engine? |
@VeeranjaneyuluToka You can use Polygraphy to debug. I would start by checking if TRT results match ONNX-Runtime: polygraphy run \
/tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/290421_convert/ssd-mobnetv2-fpnlite-model-inploop-removed.onnx \
--trt --onnxrt |
@pranavm-nvidia, Thanks for quick reply!, I have done some thing like this, i have run inference of tf savedmodel and converted savedmodel to onnx with the below command python -m tf2onnx.convert --saved-model /tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/saved_model --output /tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/030521_convert/ssd-mobnetv2_fpnlite_model.onnx --opset 12 Then run the inference using onnx-Runtime, results are exactly same. But the onnx which got converted from the above command will have all the preprocessing, network, and post processing (includes NMS) and the input tensor data type is Uint8. So i need to modify the input tensor data type to float32, remove the loop nodes, consider only raw bounding boxes and get rid of NMS part from the saved model. I have used the below command for getting raw bounding box outputs python -m tf2onnx.convert --saved-model /tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/saved_model --output /tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/030521_convert/ssd-mobnetv2_fpnlite_model_no_NMS.onnx --opset 12 --inputs "input_tensor:0" --outputs "Identity_6:0","Identity_7:0" I can not run the inference using onnx runtime with the model created using the above command And then polygraphy to remove the loop node as you suggested polygraphy surgeon extract /tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/290421_convert/ssd-mobnetv2_fpnlite_model.onnx --inputs StatefulPartitionedCall/map/while_loop:2,1x3x640x640,float32 -o /tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/290421_convert/ssd-mobnetv2-fpnlite-model-inploop-removed.onnx And then i have used onnx graph surgeon to modify the input tensor data type and to add the NMS plugin. BTW, the above polygraphy command that you suggested gives the error as below Runner: trt-runner-N0-05/04/21-15:50:36 | Activating and starting inference |
@VeeranjaneyuluToka Could you try saving layerwise outputs from the first model (the one that works with ONNX-RT) and then comparing?
polygraphy run \
/tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/030521_convert/ssd-mobnetv2_fpnlite_model.onnx \
--onnxrt --onnx-outputs mark all \
--save-outputs golden.pkl
polygraphy run \
/tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/290421_convert/ssd-mobnetv2-fpnlite-model-inploop-removed.onnx \
--trt --trt-outputs mark all \
--load-outputs golden.pkl |
looks like no --save-outputs option in first command? |
@VeeranjaneyuluToka Which version of Polygraphy are you using? Could you try installing from source? |
@pranavm-nvidia after i build manually with the below commands polygraphy run /tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/280421_convert/ssd-mobnetv2_fpnlite_model.onnx --onnxrt --onnx-outputs mark all --save-outputs /tf_git_hubs/tensorflow/workspace/training_demo/exported-models/my_ssd_mobnetv2_fpnlite_model/280421_convert/ssd-mobnetv2_fpnlite_model.pkl |
@VeeranjaneyuluToka Did you install Polygraphy in your conda environment? It looks like there's a mismatch between the |
@pranavm-nvidia thanks for quick reply. If you remember earlier i was getting 1x51150x4 shape after squeeze operation(you can see the same in the attached graph After i remove squeeze node also, i was getting 1x51150x4 shape only as shown in the below attached graph And then i was able to convert from onnx to trt though trt results are quite different. Am i getting different results because of this? Today i manage to get 1x51150x1x4 shape as shown in the below graph But still i think i need to get 1x51150x6x4 as i have 6 classes. Do you have any suggestions on this? |
@VeeranjaneyuluToka You could insert a |
I modified using the onnxconverter_common.onnx2py and got as attached graph, is it correct? |
No, it shouldn't cause any difference in the output. To insert the Tile, you could do something like: import onnx
import onnx_graphsurgeon as gs
import numpy as np
graph = gs.import_onnx(onnx.load("/path/to/model.onnx"))
tmap = graph.tensors()
# Find boxes input and NMS node
boxes = tmap["Identity_6:0"]
nms = boxes.outputs[0]
# Make Tile layer and connect to NMS
tile_out = graph.layer(op="Tile",
inputs=[boxes, np.array([1, 1, 6, 1])],
outputs=["tile_out"])[0]
nms.inputs[0] = tile_out
# Re-export graph
onnx.save(gs.export_onnx(graph), "/path/to/new_model.onnx")) |
@pranavm-nvidia Thanks for sharing sample code snippet, here is the modified graph, would you mind have a look once and let me know this is how it should be? |
@VeeranjaneyuluToka Yup, looks right. Could you double check that the |
Make Tile layer and connect to NMStile_out = graph.layer(op="Tile", let me know if you want have a look into complete log? |
@VeeranjaneyuluToka Actually, you probably don't want to tile in this case (though if you do, you can set the |
@pranavm-nvidia ok, i will take off tile. Do you have any suggestions to investigate further on my accuracy issue? |
@VeeranjaneyuluToka Comparing layer-wise outputs would be a good next step, see #1205 (comment) This could be pretty tricky to debug though, given the number of modifications made to the ONNX model |
Closing since no activity for more than 3 weeks. please reopen if you still have question, thanks! |
Description
Trying to convert from onnx to tensorrt and getting [1] 23371 segmentation fault
adVariableOp:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_3/BatchNorm/feature_4/FusedBatchNormV3/ReadVariableOp_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_2/BatchNorm/feature_1/ReadVariableOp:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_2/BatchNorm/feature_1/ReadVariableOp_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_2/BatchNorm/feature_1/FusedBatchNormV3/ReadVariableOp:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_2/BatchNorm/feature_1/FusedBatchNormV3/ReadVariableOp_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/ssd_mobile_net_v2fpn_keras_feature_extractor/FeatureMaps/top_down/smoothing_1_batchnorm/ReadVariableOp:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/ssd_mobile_net_v2fpn_keras_feature_extractor/FeatureMaps/top_down/smoothing_1_batchnorm/ReadVariableOp_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/ssd_mobile_net_v2fpn_keras_feature_extractor/FeatureMaps/top_down/smoothing_1_batchnorm/FusedBatchNormV3/ReadVariableOp:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/ssd_mobile_net_v2fpn_keras_feature_extractor/FeatureMaps/top_down/smoothing_1_batchnorm/FusedBatchNormV3/ReadVariableOp_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_fold_opt__1312
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_3/BatchNorm/feature_1/ReadVariableOp:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_3/BatchNorm/feature_1/ReadVariableOp_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_3/BatchNorm/feature_1/FusedBatchNormV3/ReadVariableOp:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_3/BatchNorm/feature_1/FusedBatchNormV3/ReadVariableOp_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_0/BatchNorm/feature_0/ReadVariableOp:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_0/BatchNorm/feature_0/ReadVariableOp_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_0/BatchNorm/feature_0/FusedBatchNormV3/ReadVariableOp:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_0/BatchNorm/feature_0/FusedBatchNormV3/ReadVariableOp_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_fold_opt__1307
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_1/BatchNorm/feature_0/ReadVariableOp:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_1/BatchNorm/feature_0/ReadVariableOp_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_1/BatchNorm/feature_0/FusedBatchNormV3/ReadVariableOp:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_1/BatchNorm/feature_0/FusedBatchNormV3/ReadVariableOp_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_fold_opt__1402
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_2/BatchNorm/feature_0/ReadVariableOp:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_2/BatchNorm/feature_0/ReadVariableOp_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_2/BatchNorm/feature_0/FusedBatchNormV3/ReadVariableOp:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_2/BatchNorm/feature_0/FusedBatchNormV3/ReadVariableOp_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_3/BatchNorm/feature_0/ReadVariableOp:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_3/BatchNorm/feature_0/ReadVariableOp_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_3/BatchNorm/feature_0/FusedBatchNormV3/ReadVariableOp:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/WeightSharedConvolutionalBoxPredictor/PredictionTower/conv2d_3/BatchNorm/feature_0/FusedBatchNormV3/ReadVariableOp_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_fold_opt__1354
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_fold_opt__1324
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_starts__757
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_ends__758
[04/20/2021-15:38:38] [V] [TRT] onnx2trt_utils.cpp:236: Weight at index 0: 9223372036854775807 is out of range. Clamping to: 2147483647
[04/20/2021-15:38:38] [V] [TRT] onnx2trt_utils.cpp:236: Weight at index 1: 9223372036854775807 is out of range. Clamping to: 2147483647
[04/20/2021-15:38:38] [V] [TRT] onnx2trt_utils.cpp:236: Weight at index 2: 9223372036854775807 is out of range. Clamping to: 2147483647
[04/20/2021-15:38:38] [W] [TRT] onnx2trt_utils.cpp:246: One or more weights outside the range of INT32 was clamped
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/Decode/truediv_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/Decode/truediv:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: ConstantFolding/StatefulPartitionedCall/Postprocessor/Decode/truediv_2_recip:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/Decode/get_center_coordinates_and_sizes/add_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/Decode/get_center_coordinates_and_sizes/add:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_starts__760
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_ends__761
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_starts__763
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_ends__764
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_starts__766
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_ends__767
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_starts__769
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_ends__770
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_starts__772
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_ends__773
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/PadOrClipBoxList/Pad__1118
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/Decode/get_center_coordinates_and_sizes/sub:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/Decode/get_center_coordinates_and_sizes/sub_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: ConstantFolding/StatefulPartitionedCall/Postprocessor/Decode/truediv_7_recip:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_fold_opt__1323
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/Minimum_5/x:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_fold_opt__1406
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/mul:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/Reshape_3:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/Concatenate/concat_5:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/mul_5/x:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/range_6/delta:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/PadOrClipBoxList/Select/e:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const__1218
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: largest_int_val__1219
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/PadOrClipBoxList/zeros_10:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/PadOrClipBoxList/zeros:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/PadOrClipBoxList/sub_17/x:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/PadOrClipBoxList/sub_3/x:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_fold_opt__1301
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:90: Importing initializer: const_fold_opt__1350
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:103: Parsing node: __inference_map_while_cond_8144_19055_map/while/Less_1 [Less]
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:119: Searching for input: const_fold_opt__1415
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:119: Searching for input: StatefulPartitionedCall/add/y:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:125: __inference_map_while_cond_8144_19055_map/while/Less_1 [Less] inputs: [const_fold_opt__1415 -> ()], [StatefulPartitionedCall/add/y:0 -> ()],
[04/20/2021-15:38:38] [V] [TRT] ImporterContext.hpp:141: Registering layer: __inference_map_while_cond_8144_19055_map/while/Less_1 for ONNX node: __inference_map_while_cond_8144_19055_map/while/Less_1
[04/20/2021-15:38:38] [V] [TRT] ImporterContext.hpp:116: Registering tensor: __inference_map_while_cond_8144_19055_map/while/Less_1:0 for ONNX tensor: __inference_map_while_cond_8144_19055_map/while/Less_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:179: __inference_map_while_cond_8144_19055_map/while/Less_1 [Less] outputs: [__inference_map_while_cond_8144_19055_map/while/Less_1:0 -> ()],
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:103: Parsing node: __inference_map_while_cond_8144_19055_map/while/LogicalAnd [And]
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:119: Searching for input: __inference_map_while_cond_8144_19055_map/while/Less_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:119: Searching for input: __inference_map_while_cond_8144_19055_map/while/Less_1:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:125: __inference_map_while_cond_8144_19055_map/while/LogicalAnd [And] inputs: [__inference_map_while_cond_8144_19055_map/while/Less_1:0 -> ()], [__inference_map_while_cond_8144_19055_map/while/Less_1:0 -> ()],
[04/20/2021-15:38:38] [V] [TRT] ImporterContext.hpp:141: Registering layer: __inference_map_while_cond_8144_19055_map/while/LogicalAnd for ONNX node: __inference_map_while_cond_8144_19055_map/while/LogicalAnd
[04/20/2021-15:38:38] [V] [TRT] ImporterContext.hpp:116: Registering tensor: __inference_map_while_cond_8144_19055_map/while/LogicalAnd:0 for ONNX tensor: __inference_map_while_cond_8144_19055_map/while/LogicalAnd:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:179: __inference_map_while_cond_8144_19055_map/while/LogicalAnd [And] outputs: [__inference_map_while_cond_8144_19055_map/while/LogicalAnd:0 -> ()],
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:103: Parsing node: StatefulPartitionedCall/map/while_loop [Loop]
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:119: Searching for input: StatefulPartitionedCall/map/while/maximum_iterations:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:119: Searching for input: __inference_map_while_cond_8144_19055_map/while/LogicalAnd:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:119: Searching for input: StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/range_5/start:0
[04/20/2021-15:38:38] [V] [TRT] ModelImporter.cpp:125: StatefulPartitionedCall/map/while_loop [Loop] inputs: [StatefulPartitionedCall/map/while/maximum_iterations:0 -> ()], [__inference_map_while_cond_8144_19055_map/while/LogicalAnd:0 -> ()], [StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/range_5/start:0 -> ()],
[04/20/2021-15:38:38] [V] [TRT] ImporterContext.hpp:116: Registering tensor: map_while_map_while_loop_counter:0 for ONNX tensor: map_while_map_while_loop_counter:0
[04/20/2021-15:38:38] [V] [TRT] ImporterContext.hpp:116: Registering tensor: map_while_placeholder:0 for ONNX tensor: map_while_placeholder:0
[1] 23371 segmentation fault (core dumped) ./trtexec --verbose
Environment
TensorRT Version: 7.1.3.0
NVIDIA GPU: Jetson AGX Xavier (16 gb)
NVIDIA Driver Version: Jetpack 4.4.1
CUDA Version: 10.2
CUDNN Version: 8
Operating System: Ubuntu 18.04
Python Version (if applicable): 3.6.9
Tensorflow Version (if applicable): 2.4.0
PyTorch Version (if applicable):
Baremetal or Container (if so, version):
Relevant Files
Steps To Reproduce
./trtexec --onnx=/data/Veeru/models/regression/120421-ed1-onnx/ssd-mobnetv2_fpnlite_model_no_nms.onnx --saveEngine=/data/Veeru/models/regression/120421-ed1-onnx/ssd-mobnetv2_fpnlite_model_no_nms.trt --verbose
Please include:
#795
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