TensorRT Implementation for Edge devices
AsharFatmi opened this issue · comments
@sergiomsilva
How can I convert my trained/fine-tuned License plate detector in a TRT engine to deploy it on an edge device like Jetson nano or Jetson TX2 .
Currently, on TX2, run time is not acceptable when considering edge devices.
TensorRT implementation will help in reducing the runtime.
Please let me know if it is at all possible to implement TensorRT for this License Plate detection.
As the layers supported by TensorRT are limited
Thanks in Advance.
Hi, I think that for Yolo part you can use this github link https://github.com/jkjung-avt/tensorrt_demo. Good luck!
@tudorikas I am looking for the License Plate detection part.
Yolo is being used for vehicle detection and OCR.
ok, for detection Licence Plate you can change his code by training an yolo network on this set https://storage.googleapis.com/openimages/web/visualizer/index.html?set=train&type=detection&c=%2Fm%2F01jfm_ .
Yolo network does not give a perfect Bounding box just around the license plate instead it will give some extra region like bumpers in the region.
Since YOLO takes a format of [left, top, width, height].
Whereas this Keras model takes the box with just the license plate as in training it takes all the 4 coordinates of the LP region.
any thoughts?
All the layers that WPOD Net has are supported by TensorRT.
Do you have any idea as to how to convert the model to TensorRT.
I cannot convert to uff model as merge, switch, etc layers are not supported.
While converting to onnx I have to provide a fixed input shape do you know any workaround for this?
can i edit the model json file for the input shape ? without having to retrain the model?
@shuangyichen
Do you have any idea as to how to convert the model to TensorRT.
I cannot convert to uff model as merge, switch, etc layers are not supported.
While converting to onnx I have to provide a fixed input shape do you know any workaround for this?
can i edit the model json file for the input shape ? without having to retrain the model?
Input shape :(1,3,480,768)
I have already finished license-plate-detection/train part in pytorch. And I tried to convert pt to onnx, and then to trt.
Can I just edit the resulting model.json file for the input shape instead of retraining the model?
@AsharFatmi You can try it. I tried to convert keras model to TensorRT before but I failed. Most examples online are from torch to onnx to trt. And there are many tools for torch-to-trt(though not necessarily easy to use
@AsharFatmi You can try it. I tried to convert keras model to TensorRT before but I failed. Most examples online are from torch to onnx to trt. And there are many tools for torch-to-trt(though not necessarily easy to use
Can you share some links regarding the torch to trt conversions?
@AsharFatmi 1. https://github.com/NVIDIA-AI-IOT/torch2trt I havent tried this.
2.A more common idea is to torch.onnx.export first to generate onnx model
2.1 then use onnx-tensorrt to genarate trt engine.
2.2 then use TensorRT python API to convert onnx to trt. (examples in TensorRT/samples/python/yolov3_onnx
@shuangyichen Thank you for all the help. I will try a couple of different ways.
I was able to convert the model to TensorRT.
Keras to onnx using keras2onnx library.
onn2 to tensorRT using onnx2trt library.
But when I run inference on the .trt model the output from the model is different from the keras model.
KERAS Model OUTPUT:-
[[[ 5.35682165e-16 1.00000000e+00 1.02072847e+00 ... -2.07958460e-01
5.04563272e-01 -1.20855430e-02]
[ 8.77869645e-17 1.00000000e+00 7.72716165e-01 ... -1.96673796e-01
6.91386938e-01 -5.71288705e-01]
[ 1.62569552e-29 1.00000000e+00 1.13321781e+00 ... -1.17423847e-01
7.27890730e-01 -2.67761678e-01]
...
[ 9.93481616e-11 1.00000000e+00 8.08987558e-01 ... -1.09358504e-01
3.96312863e-01 -3.76817733e-02]
[ 5.05836795e-08 1.00000000e+00 6.38040662e-01 ... 2.58072745e-03
4.28376526e-01 -3.47221732e-01]
[ 2.63872035e-09 1.00000000e+00 8.12149942e-01 ... -1.26684764e-02
1.86289668e-01 -6.14305809e-02]]
[[ 1.15013890e-12 1.00000000e+00 1.06367087e+00 ... -5.53396583e-01
3.23190212e-01 -1.00925833e-01]
[ 7.17620210e-18 1.00000000e+00 8.54213297e-01 ... 4.44860458e-01
-6.65633440e-01 -1.41143531e-01]
[ 4.54133458e-24 1.00000000e+00 1.19984531e+00 ... 1.20644540e-01
-3.73397738e-01 -6.67934895e-01]
...
[ 1.03462021e-08 1.00000000e+00 1.14307857e+00 ... -1.53487191e-01
2.21320093e-01 -2.01362371e-01]
[ 1.97145482e-08 1.00000000e+00 9.24914479e-01 ... 3.22847784e-01
-6.38035834e-02 -2.14298382e-01]
[ 1.70783192e-08 1.00000000e+00 9.65804636e-01 ... 4.09364104e-02
2.28743017e-01 -3.36741388e-01]]
[[ 8.94335161e-11 1.00000000e+00 7.75921345e-01 ... 1.04476353e-02
1.51742458e-01 5.28428927e-02]
[ 9.04731773e-16 1.00000000e+00 4.41512674e-01 ... 1.06468916e-01
-8.99115801e-02 2.96098143e-01]
[ 2.10258343e-17 1.00000000e+00 2.47518599e-01 ... 7.37828463e-02
-1.92303687e-01 4.42358196e-01]
...
[ 2.41078150e-16 1.00000000e+00 8.20632398e-01 ... -1.57306686e-01
1.35591239e-01 -4.33367584e-03]
[ 1.30340170e-13 1.00000000e+00 6.97541475e-01 ... -1.88214704e-01
8.80702883e-02 -1.52649814e-02]
[ 5.57648372e-09 1.00000000e+00 9.97833073e-01 ... -1.55004978e-01
2.76494384e-01 -6.27942905e-02]]
...
[[ 0.00000000e+00 1.00000000e+00 8.85395885e-01 ... 3.80443633e-02
4.81112301e-01 -1.28768587e+00]
[ 0.00000000e+00 1.00000000e+00 1.00841582e+00 ... -2.23223472e+00
-3.39565903e-01 -5.87081552e-01]
[ 0.00000000e+00 1.00000000e+00 1.37934232e+00 ... -1.70936894e+00
2.07115859e-01 -2.22187567e+00]
...
[ 2.83864250e-25 1.00000000e+00 1.45969176e+00 ... -7.09485292e-01
2.28834689e-01 -3.29626322e-01]
[ 1.24496172e-15 1.00000000e+00 1.31729507e+00 ... -6.66390240e-01
1.25542924e-01 -1.47483870e-01]
[ 2.48216048e-09 1.00000000e+00 1.15188110e+00 ... -1.47737026e-01
3.60481203e-01 -2.20017970e-01]]
[[ 0.00000000e+00 1.00000000e+00 1.48687065e-01 ... -8.53215158e-01
-1.15160823e+00 8.59936476e-01]
[ 0.00000000e+00 1.00000000e+00 6.86580241e-01 ... 2.05904245e-02
3.43396455e-01 -6.00309968e-01]
[ 0.00000000e+00 1.00000000e+00 -1.29906058e-01 ... 8.33660722e-01
6.93807364e-01 5.96281648e-01]
...
[ 2.00734995e-30 1.00000000e+00 8.45168352e-01 ... 4.50236648e-02
3.50932181e-01 -7.83286393e-02]
[ 2.55876199e-19 1.00000000e+00 9.60333586e-01 ... 2.57166326e-02
5.82121909e-02 -9.72378105e-02]
[ 4.64498925e-12 1.00000000e+00 9.78434443e-01 ... 6.36013597e-02
3.79364163e-01 -3.65832038e-02]]
[[ 0.00000000e+00 1.00000000e+00 -1.19233370e-01 ... -3.39587510e-01
8.02178085e-02 1.38728428e+00]
[ 0.00000000e+00 1.00000000e+00 -2.54251719e-01 ... 9.89254951e-01
-1.02365923e+00 1.74043214e+00]
[ 0.00000000e+00 1.00000000e+00 6.75196886e-01 ... 1.14767244e-02
8.67833734e-01 5.49621701e-01]
...
[ 1.01715141e-30 1.00000000e+00 1.23101699e+00 ... -3.87017936e-01
1.34929419e-01 1.46293178e-01]
[ 5.34217413e-22 1.00000000e+00 1.08855963e+00 ... -6.93247654e-04
1.25452936e-01 7.26785064e-02]
[ 2.12459472e-13 1.00000000e+00 9.25891340e-01 ... -5.17582297e-02
1.55512884e-01 -1.80828243e-01]]]
TRT model OUPUT
[ 0. 1. 148.38953 ... -299.67487 -987.18317 254.93916]
Any idea why this is??
@AsharFatmi I have the same problem. I found the onnx model parsing was not successful. In my case, my trt engine only has the first conv layer. You can check your trt engine. If you solved the problem, Please tell me.
@AsharFatmi I have the same problem. I found the onnx model parsing was not successful. In my case, my trt engine only has the first conv layer. You can check your trt engine. If you solved the problem, Please tell me.
I cannot open the trt engine in neutron to check. How did you check your engine.
NVIDIA/TensorRT#375
Please try the way he mentioned.
I did what he said, Getting the same thing as you only have one layer in the resulting trt file.
{
"0": {
"inputs": {
"0": {
"dtype": "DataType.FLOAT",
"name": "'input'",
"shape": "(600, 845, 3)"
}
},
"name": "'(Unnamed Layer* 0) [Shuffle]'",
"num_inputs": "1",
"num_outputs": "1",
"outputs": {
"0": {
"dtype": "DataType.FLOAT",
"name": "'adjusted_input25'",
"shape": "(3, 600, 845)"
}
},
"precision": "DataType.FLOAT",
"precision_is_set": "False",
"type": "LayerType.SHUFFLE"
}
}
The error that I am getting is a bit different than yours.
kingashar@kingashar:~/tensorRT$ python3 network_check.py
Loading ONNX file from path ~/YOLOV3-LP/keras_input_model.onnx...
Beginning ONNX file parsing
In node 1 (importModel): INVALID_GRAPH: Assertion failed: tensors.count(input_name)
Writing /home/kingashar/network.json
Completed parsing of ONNX file
Building an engine from file ~/YOLOV3-LP/keras_input_model.onnx; this may take a while...
Completed creating Engine
You are using TensorRT5? I got the same problem when I use TensorRT5. You can try TensorRT7.
I am using TensorRT 6.0.1.5 i will try using TensorRT 7.
Did it work for you??
No...Still working on it
I tried my onnx model that keras2onnx generate, It shows:
In node -1 (importInput): UNSUPPORTED_GRAPH: Assertion failed: convertOnnxDims(onnxDtype.shape().dim(), trt_dims)
Let me know if you are able to crack it. I will do the same.
I have similar error:-
In node 1 (importModel): INVALID_GRAPH: Assertion failed: tensors.count(input_name)
No...Still working on it
I tried my onnx model that keras2onnx generate, It shows:
In node -1 (importInput): UNSUPPORTED_GRAPH: Assertion failed: convertOnnxDims(onnxDtype.shape().dim(), trt_dims)
Maybe the input shape is the issue for you as you mentioned earlier:-
@shuangyichen
Do you have any idea as to how to convert the model to TensorRT.
I cannot convert to uff model as merge, switch, etc layers are not supported.
While converting to onnx I have to provide a fixed input shape do you know any workaround for this?
can i edit the model json file for the input shape ? without having to retrain the model?Input shape :(1,3,480,768)
I have already finished license-plate-detection/train part in pytorch. And I tried to convert pt to onnx, and then to trt.
for keras the format is (Batch, width, hieght, 3).
Maybe try the process with (1, 480, 768, 3) ..
Good Luck
@AsharFatmi Sorry, of course the input shape for keras model is (1,480,768,3), what I mentioned before is input shape for torch model.
And I remember that when converting toch to onnx, input shape is needed. But when converting keras model to onnx, I just used keras2onnx. Could you plz share your keras2onnx code, and onnx model in Netron? Thank you!
Yeah,
I meant input of the Keras model initially. The input layer of the Keras model.
Like for eg in the file attached, I have specified the input layer shape.
Keras to onnx code:-
import keras2onnx
import onnxruntime
from src.keras_utils import load_model
model = load_model('/home/kingashar/YOLOV3-LP/lp_detector/uae-1392_final.h5')
onnx_model = keras2onnx.convert_keras(model, name=None, doc_string='', target_opset=None, channel_first_inputs=None)
temp_model_file = 'keras_input_model_JK.onnx'
keras2onnx.save_model(onnx_model, temp_model_file)
sess = onnxruntime.InferenceSession(temp_model_file)
print('DONE')
I am not able to export the png file from neutron.
Sorry, I still dont get it.
Where should I specifeid the input shape? In the onnx-to-trt?
My keras2onnx model in Netron shows that input shape is (N00*3), maybe that's the problem. But I still dont know where I should specify the input shape.
How have you saved your Keras model?
I have saved the weights and the config separately so, I specified the input shape in the config file.
My keras2onnx model in Netron shows that input shape is (N_0_0*3), maybe that's the problem. But I still dont know where I should specify the input shape.
I believe that is the issue.
I have attached screenshot of my Netron and json config file below for reference.
Keras model
@shuangyichen
Did it work ??
Now my TRT model is working.
Thank you for your help I solved this issue.
Hi, I'm trying to convert the 'license-plate-detection' code to c++ code. But to convert the whole keras code to c++ seems like huge work. Instead i'm trying to convert the keras model(json and h5 files) to tensorflow model. (my plan is -then use the new tensorflow model in tensorflow based c++ code). In below code i have converted the keras model to tensorflow model. Then i try to make object detection using '.pb file' and below code. The output from prediction is a matrix with shape - (1, 42, 64, 8). Is there way i can get detection box coordinates? Please help me.
from keras import backend as K
K.set_learning_phase(0)
import tensorflow as tf
from keras.models import model_from_json
jsonfile = open('wpod-net_update1.json', 'r')
model_json = jsonfile.read()
jsonfile.close()
model = model_from_json(model_json)
model.load_weights('wpod-net_update1.h5')
print(model.outputs)
#Our Output tensor : [<tf.Tensor 'concatenate_1/concat:0' shape=(?, ?, ?, 8) dtype=float32>]
print(model.inputs)
#Our Input tensor : [<tf.Tensor 'input:0' shape=(?, ?, ?, 3) dtype=float32>]
def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
graph = session.graph
with graph.as_default():
freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
output_names = output_names or []
output_names += [v.op.name for v in tf.global_variables()]
# Graph -> GraphDef ProtoBuf
input_graph_def = graph.as_graph_def()
if clear_devices:
for node in input_graph_def.node:
node.device = ""
#frozen_graph = convert_variables_to_constants(session, input_graph_def, output_names, freeze_var_names)
frozen_graph = tf.compat.v1.graph_util.convert_variables_to_constants(session, input_graph_def, output_names, freeze_var_names)
return frozen_graph
frozen_graph = freeze_session(K.get_session(), output_names=[out.op.name for out in model.outputs])
tf.train.write_graph(frozen_graph, "latest-pbfile", "wpod_tf_model.pb", as_text=False)
#Above code converted the keras to tensorflow pb file
#Below code is i'm trying to use on image to get detection coordinates
import os
import cv2
import numpy as np
import sys
from tensorflow.python.platform import gfile
sess=tf.InteractiveSession()
f = gfile.FastGFile("wpod_tf_model.pb", 'rb')
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
f.close()
sess.graph.as_default()
tf.import_graph_def(graph_def)
output_tensor = sess.graph.get_tensor_by_name('import/concatenate_1/concat:0')
#Our Output tensor : [<tf.Tensor 'concatenate_1/concat:0' shape=(?, ?, ?, 8) dtype=float32>]
image = cv2.imread("car.png")
image_expanded = np.expand_dims(image, axis = 0)
predictions = sess.run(softmax_tensor, {'import/input:0': image_expanded})
print(predictions)
print(predictions.shape)
Output from above code:
[[[[ 0.00000000e+00 1.00000000e+00 -3.34710617e+01 ... 1.94643288e+01
3.68301353e+01 9.24516773e+00]
[ 0.00000000e+00 1.00000000e+00 2.37034202e+00 ... -2.75982304e+01
3.61542473e+01 5.73606300e+00]
...
[ 0.00000000e+00 1.00000000e+00 1.56508980e+01 ... -5.42593575e+00
1.62256050e+01 -1.86855240e+01]]
[[ 0.00000000e+00 1.00000000e+00 -7.79394388e+00 ... 3.09290199e+01
8.63655853e+01 1.36856709e+01]
[ 0.00000000e+00 1.00000000e+00 2.93666458e+01 ... -3.08292675e+01
1.09749474e+02 2.12900391e+01]
[ 0.00000000e+00 1.00000000e+00 4.60487442e+01 ... -4.87868652e+01
1.08859711e+02 5.81501389e+00]
...
[ 0.00000000e+00 1.00000000e+00 4.77344284e+01 ... -6.47512360e+01
1.09733971e+02 -2.87336864e+01]
[ 0.00000000e+00 1.00000000e+00 4.25194588e+01 ... -3.57531586e+01
4.50154076e+01 -3.05396996e+01]]
...
[[ 5.99232838e-18 1.00000000e+00 1.49527061e+00 ... 5.97574651e-01
-2.52342284e-01 -1.89448029e-01]
...
[ 1.31391238e-17 1.00000000e+00 1.93043900e+00 ... 7.44476855e-01
-7.56041348e-01 8.58260393e-02]]]]
(1, 42, 64, 8)
You are using TensorRT5? I got the same problem when I use TensorRT5. You can try TensorRT7.
Hi i'm using TensorRT5.1.6 in my jetson TX2 (jetpack 4.2.2) to keras model to onnx and then create a tensorrt engine. I managed to convert to onnx using keras2onnx as mentioned by @AsharFatmi but with some kind of warning like given below
Then i decide to give a try and ran onnx_to_tensorrt.py mentioned above by @AsharFatmi .But it resulted in the following errors
2020-04-07 14:22:32 - main - INFO - TRT_LOGGER Verbosity: Severity.ERROR
Traceback (most recent call last):
File "onnx_to_tensorrt.py", line 141, in
main()
File "onnx_to_tensorrt.py", line 79, in main
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(network_flags) as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
TypeError: create_network(): incompatible function arguments. The following argument types are supported:
1. (self: tensorrt.tensorrt.Builder) -> tensorrt.tensorrt.INetworkDefinition
Invoked with: <tensorrt.tensorrt.Builder object at 0x7f5ee71228>, 0
I would really appreciate a solution to convert it to trt using the existing TensorRT5.
@shuangyichen Thanks for sharing your work!! I am using your repo to convert model to TRT but the results differ for keras and TRT. Have you been able to successfully convert keras model to TRT? Please share some insights on how to get correct results from TRT engine.
@shuangyichen Thanks for sharing your work!! I am using your repo to convert model to TRT but the results differ for keras and TRT. Have you been able to successfully convert keras model to TRT? Please share some insights on how to get correct results from TRT engine.
You mean output size or ..?
@shuangyichen I mean the outputs(values) of keras and TRT do not match! Does the code in TensorRT 7 branch work for you!!
First of all, I had to change the input shape in model.json form [1, null, null, 3] to [1, 600, 845, 3] for the onnx to TRT conversion to work. But the outputs for keras / TRT are different.
Thanks!
@shuangyichen If the repo is not tested please let me know! Thanks. I havent been able to make much progress in getting correct results from TensorRT using your code. So, yeah I could use your help
@shuangyichen If the repo is not tested please let me know! Thanks. I havent been able to make much progress in getting correct results from TensorRT using your code. So, yeah I could use your help
Sure it works. My keras output and TRT output are almost the same, only the exact number of decimal places is different
@shuangyichen okay! Sure I must have been doing something wrong! I'll try again. Can you share the script that you used to verify numerical results please?
@makaveli10 I just subtract two outputs. And the result was like x.xx e-14. So I default to two outputs equal.
@shuangyichen and you used the same pretrained models as in this repo?
@makaveli10 Sure
Hi !
Have you guys succeeded to convert OCR-NET model in to tenssorrt model (ONNX / TRT ) ??
@bhargavravat
Hi,
Yes i was able to convert it. You need darknet2onnx , pytorch2onnx to do this task.
https://github.com/Tianxiaomo/pytorch-YOLOv4
you can refer to the above link
Hey @AsharFatmi :
I tried running the code , but it is giving me error.
RuntimeError: There were no tensor arguments to this function (e.g., you passed an empty list of Tensors), but no fallback function is registered for schema aten::_cat. This usually means that this function requires a non-empty list of Tensors. Available functions are [CPU, CUDA, QuantizedCPU, Autograd, Profiler, Tracer, Autocast]
