Exception: jetson.utils -- failed to create glDisplay device - nvidia-jetson-nano

i am working on jetson nano and utilizing the jetson library provided by hello ai world.
i have tested the basic algo which comes with the library all of them are working fine but
i did a few changings to make it run on a video
got an error
here is my code
import jetson.inference
import jetson.utils
import cv2
#import argparse
import sys
import numpy as np
width=720
height=480
vs=cv2.VideoCapture('b.m4v') #video input file
net = jetson.inference.detectNet("ssd-mobilenet-v2", threshold=0.5) #loading the model
#camera = jetson.utils.gstCamera(1280, 720, "/dev/video0") #using V4L2
display = jetson.utils.glDisplay() #initialting a display window
while display.IsOpen():
_,frame = vs.read() #reading a frmae
img = cv2.cvtColor(frame, cv2.COLOR_BGR2BGRA) #converting it to a bgra format for jetson util
img = jetson.utils.cudaFromNumpy(img) #converting image to cuda format from numpy array
#img, width, height = camera.CaptureRGBA()
detections = net.Detect(img, width, height) #running detections on each image and saving the results in detections
display.RenderOnce(img, width, height) #display the output frame with detection
display.SetTitle("Object Detection | Network {:.0f} FPS".format(net.GetNetworkFPS())) #display title for the output feed
this is the error:
[OpenGL] glDisplay -- X screen 0 resolution: 1280x1024
[OpenGL] failed to create X11 Window.
jetson.utils -- PyDisplay_Dealloc()
Traceback (most recent call last):
File "pool1.py", line 16, in <module>
display = jetson.utils.glDisplay() #initialting a display window
Exception: jetson.utils -- failed to create glDisplay device
PyTensorNet_Dealloc()

It's not technically an answer but I think here is the problem.
Here is the log of my faulty working code
[TRT] binding to input 0 Input binding index: 0
[TRT] binding to input 0 Input dims (b=1 c=3 h=300 w=300) size=1080000
[TRT] binding to output 0 NMS binding index: 1
[TRT] binding to output 0 NMS dims (b=1 c=1 h=100 w=7) size=2800
[TRT] binding to output 1 NMS_1 binding index: 2
[TRT] binding to output 1 NMS_1 dims (b=1 c=1 h=1 w=1) size=4
device GPU, /usr/local/bin/networks/SSD-Mobilenet-v2/ssd_mobilenet_v2_coco.uff initialized.
W = 7 H = 100 C = 1
detectNet – maximum bounding boxes: 100
detectNet – loaded 91 class info entries
detectNet – number of object classes: 91
jetson.utils – PyDisplay_New()
jetson.utils – PyDisplay_Init()
[OpenGL] glDisplay – X screen 0 resolution: 1280x1024
[OpenGL] failed to create X11 Window.
jetson.utils – PyDisplay_Dealloc()
Traceback (most recent call last):
File “pool1.py”, line 18, in
display = jetson.utils.glDisplay() #initialting a display window
Exception: jetson.utils – failed to create glDisplay device
PyTensorNet_Dealloc()
and here is the log of the builtin alog working
[TRT] binding to output 0 NMS dims (b=1 c=1 h=100 w=7) size=2800
[TRT] binding to output 1 NMS_1 binding index: 2
[TRT] binding to output 1 NMS_1 dims (b=1 c=1 h=1 w=1) size=4
device GPU, /usr/local/bin/networks/SSD-Mobilenet-v2/ssd_mobilenet_v2_coco.uff initialized.
W = 7 H = 100 C = 1
detectNet – maximum bounding boxes: 100
detectNet – loaded 91 class info entries
detectNet – number of object classes: 91
jetson.utils – PyCamera_New()
jetson.utils – PyCamera_Init()
[gstreamer] initialized gstreamer, version 1.14.5.0
[gstreamer] gstCamera attempting to initialize with GST_SOURCE_NVARGUS, camera /dev/video0
[gstreamer] gstCamera pipeline string:
v4l2src device=/dev/video0 ! video/x-raw, width=(int)1280, height=(int)720, format=YUY2 ! videoconvert ! video/x-raw, format=RGB ! videoconvert !appsink name=mysink
[gstreamer] gstCamera successfully initialized with GST_SOURCE_V4L2, camera /dev/video0
jetson.utils – PyDisplay_New()
jetson.utils – PyDisplay_Init()
[OpenGL] glDisplay – X screen 0 resolution: 1280x1024
[OpenGL] glDisplay – display device initialized
[gstreamer] opening gstCamera for streaming, transitioning pipeline to GST_STATE_PLAYING
[gstreamer] gstreamer changed state from NULL to READY ==> mysink
[gstreamer] gstreamer changed state from NULL to READY ==> videoconvert1
[gstreamer] gstreamer changed state from NULL to READY ==> capsfilter1
[gstreamer] gstreamer changed state from NULL to READY ==> videoconvert0
You can clearly see the problem.
My main query is is gstramer even supported by opencv 4.1.1 on nano

try #import cv2 or put it on the top, in front of import jetson.utils

Related

Julia CSV.read not recognizing "select" keyword

I am reading in a space-delimited file using the CSV library in Julia.
edgeList = CSV.read(
joinpath(dataDirectory, "out.file"),
types=[Int, Int],
header=["node1", "node2"],
skipto=3,
select=[1,2]
)
This yields the following error:
MethodError: no method matching CSV.File(::String; types=DataType[Int64, Int64], header=["node1", "node2"], skipto=3, select=[1, 2])
Closest candidates are:
CSV.File(::Any; header, normalizenames, datarow, skipto, footerskip, limit, transpose, comment, use_mmap, ignoreemptylines, missingstrings, missingstring, delim, ignorerepeated, quotechar, openquotechar, closequotechar, escapechar, dateformat, decimal, truestrings, falsestrings, type, types, typemap, categorical, pool, strict, silencewarnings, threaded, debug, parsingdebug, allowmissing) at /Users/n.jordanjameson/.julia/packages/CSV/4GOjG/src/CSV.jl:221 got unsupported keyword argument "select"
I am using Julia v. 1.6.2. Here is the output versioninfo():
Julia Version 1.6.2
Commit 1b93d53fc4 (2021-07-14 15:36 UTC)
Platform Info:
OS: macOS (x86_64-apple-darwin18.7.0)
CPU: Intel(R) Core(TM) i7-5650U CPU # 2.20GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-11.0.1 (ORCJIT, broadwell)
The version of CSV is 0.10.4. The wiki for this version of CSV is here: https://csv.juliadata.org/stable/reading.html#CSV.read, and it has a select / drop entry.
The file I am trying to read is from here: http://konect.cc/networks/moreno_crime/ (the file I'm using is called "out.moreno_crime_crime"). The first few lines are:
% bip unweighted
% 1476 829 551
1 1
1 2
1 3
1 4
2 5
2 6
2 7
2 8
2 9
2 10
I get a different error than you, can you restart Julia and make sure?
julia> CSV.read("/home/akako/Downloads/moreno_crime/out.moreno_crime_crime"; types=[Int, Int],
header=["node1", "node2"],
skipto=3,
select=[1,2]
)
ERROR: ArgumentError: provide a valid sink argument, like `using DataFrames; CSV.read(source, DataFrame)`
Stacktrace:
[1] read(source::String, sink::Nothing; copycols::Bool, kwargs::Base.Pairs{Symbol, Any, NTuple{4, Symbol}, NamedTuple{(:types, :header, :skipto, :select), Tuple{Vector{DataType}, Vector{String}, Int64, Vector{Int64}}}})
# CSV ~/.julia/packages/CSV/jFiCn/src/CSV.jl:89
[2] top-level scope
# REPL[8]:1
Stacktrace:
this error is telling you you can't CSV.read without a target sink, you might want to use CSV.File
julia> CSV.File("/home/akako/Downloads/moreno_crime/out.moreno_crime_crime"; types=[Int, Int],
header=["node1", "node2"],
skipto=3,
select=[1,2]
)
┌ Warning: thread = 1 warning: parsed expected 2 columns, but didn't reach end of line around data row: 1. Parsing extra columns and widening final columnset
└ # CSV ~/.julia/packages/CSV/jFiCn/src/file.jl:579
1476-element CSV.File:
CSV.Row: (node1 = 1, node2 = 1, Column3 = missing)
CSV.Row: (node1 = 1, node2 = 2, Column3 = missing)
CSV.Row: (node1 = 1, node2 = 3, Column3 = missing)
CSV.Row: (node1 = 1, node2 = 4, Column3 = missing)

How to minimise loss with constrain in PyTorch?

How to put constrain while minimising loss?
I am trying to minimise the mse loss with constrain loss but constrain was increasing instead of decreasing.then i tried to only minimise constrain then it throw following error.
class Edge_Detector(nn.Module):
def __init__(self,kernel_size,padding):
torch.manual_seed(1)
super(Edge_Detector,self).__init__()
self.sobelx=nn.Conv2d(1,1,kernel_size=kernel_size,padding=padding,bias=False)
self.relu=nn.ReLU()
self.sobely=nn.Conv2d(1,1,kernel_size=kernel_size,padding=padding,bias=False)
def forward(self,x):
x1=self.sobelx(x)
x2=self.sobely(x)
x=self.relu(x1+x2)
return x
def loss(self,x,y):
x=x.view(x.size(0),-1)
y=y.view(y.size(0),-1).float()
sobelx=self.sobelx.weight.data.squeeze().squeeze()
sobely=self.sobely.weight.data.squeeze().squeeze()
loss_mse=nn.MSELoss()(x,y)
loss_constrain=torch.matmul(sobelx,sobely.transpose(0,1)).trace()
#print('mse_loss : ',loss_mse)
#print('constrain_loss : ',loss_constrain)
#total_loss=loss_mse+loss_constrain
return loss_constrain
#Error Message:
RuntimeError Traceback (most recent call last)
<ipython-input-67-28b5b5719682> in <module>()
----> 1 learn.fit_one_cycle(15, 5e-2) #training for 4 epochs with lr=1e-3
13 frames
/usr/local/lib/python3.6/dist-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)
130 Variable._execution_engine.run_backward(
131 tensors, grad_tensors_, retain_graph, create_graph,
--> 132 allow_unreachable=True) # allow_unreachable flag
133
134
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
Since you did not post the whole stack trace I can't say for sure, but I am pretty certain what happends is that you call backward on loss_constrain which throws an error because it has requires_grad=False. This is because in you calculation of loss_constrain you call the data attribute of the Parameter class (self.sobelx.weight), the parameter itself has requires_grad set to True but after calling data it is set to False.
Just remove the .data part and see whether it works

firrtl_interpreter.InterpreterException: error: ConcreteSInt(303, 9) bad width 9 needs 10

I've got a module that generates the following FIRRTL
module Deep_1 :
input clock : Clock
input reset : UInt<1>
output io : {in : {flip data : SInt<8>[4]}, constIn : {flip data : SInt<8>[4]}, ...}
...
... (elided)
...
wire inputData : SInt<8>[4] #[Deep.scala 32:23]
wire constInputData : SInt<8>[4] #[Deep.scala 33:28]
reg outputData : SInt<8>[4], clock #[Deep.scala 34:23]
inputData[2] <= io.in.data[2] #[Deep.scala 37:18]
constInputData[2] <= io.constIn.data[2] #[Deep.scala 38:23]
node _T_209 = add(inputData[2], constInputData[2]) #[package.scala 32:44]
node _T_210 = tail(_T_209, 1) #[package.scala 32:44]
node _T_211 = asSInt(_T_210) #[package.scala 32:44]
outputData[2] <= _T_211 #[Deep.scala 39:19]
...
... (elided)
...
but when I try to run my tests through the firrtl interpreter I get
Exception during evaluation: error: ConcreteSInt(303, 9) bad width 9 needs 10 #[package.scala 32:44:#82.4]
Expression Evaluation stack
0 dut.outputData_2:Reg -> dut._T_211
1 dut._T_211:Node -> asSInt(dut._T_210)
2 dut._T_210:Node -> tail(dut._T_209, 1)
3 dut._T_209:Node -> add(dut.inputData_2, dut.constInputData_2)
If I understand this correctly, it is saying that the width of outputData[2] is 9 but 10 is needed. However, from what I can see, the width of both outputData[2] and _T_211 should be 8, the first by definition and the second as a result of the add() and tail() operations applied.
What am I missing?
How current is your software? I cannot reproduce the problem with current versions of the interpreter. I used the following firrtl test in the gist Attempt to reproduce firrtl-interpreter width error where I un-elided. As #Kamyar said, can you try using the Treadle backend. It is more modern and better supported at this point than is the interpreter.

Hyperparameter tuning using tensorboard.plugins.hparams api with custom loss function

I am building a neural network with my own custom loss function (pretty long and complicated). My network is unsupervised so my input and expected output are identical and also at the moment I am using one single input (just trying to optimize the loss for a single input).
I am trying to use tensorboard.plugins.hparams api for hyperparameter tuning and don't know how to incorporate my custom loss function there. I'm trying to follow the code suggested on the Tensorflow 2.0 website.
This is what the website suggests:
HP_NUM_UNITS = hp.HParam('num_units', hp.Discrete([16, 32]))
HP_DROPOUT = hp.HParam('dropout', hp.RealInterval(0.1, 0.2))
HP_OPTIMIZER = hp.HParam('optimizer', hp.Discrete(['adam', 'sgd']))
METRIC_ACCURACY = 'accuracy'
with tf.summary.create_file_writer('logs/hparam_tuning').as_default():
hp.hparams_config(
hparams=[HP_NUM_UNITS, HP_DROPOUT, HP_OPTIMIZER],
metrics=[hp.Metric(METRIC_ACCURACY, display_name='Accuracy')],
)
I need to change that as I don't want to use the dropout layer, so I can just delete that. In terms of the METRIC_ACCURACY, I don't want to use accuracy as that has no use in my model but rather use my custom loss function. If I were to do the regular fit model it would look like this:
model.compile(optimizer=adam,loss=dl_tf_loss, metrics=[dl_tf_loss])
So I tried to change the suggested code into the following code but I get an error and am wondering how I should change it so that it suits my needs. Here is what I tried:
HP_NUM_UNITS = hp.HParam('num_units', hp.Discrete([16, 32]))
HP_OPTIMIZER = hp.HParam('optimizer', hp.Discrete(['adam', 'sgd']))
#METRIC_LOSS = dl_tf_loss
with tf.summary.create_file_writer('logs/hparam_tuning').as_default():
hp.hparams_config(hparams=[HP_NUM_UNITS, HP_OPTIMIZER],metrics=
[hp.Metric(dl_tf_loss, display_name='Loss')])
It gives me the following error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-26-27d079c6be49> in <module>()
5
6 with tf.summary.create_file_writer('logs/hparam_tuning').as_default():
----> 7 hp.hparams_config(hparams=[HP_NUM_UNITS, HP_OPTIMIZER],metrics=[hp.Metric(dl_tf_loss, display_name='Loss')])
8
3 frames
/usr/local/lib/python3.6/dist-packages/tensorboard/plugins/hparams/summary_v2.py in hparams_config(hparams, metrics, time_created_secs)
127 hparams=hparams,
128 metrics=metrics,
--> 129 time_created_secs=time_created_secs,
130 )
131 return _write_summary("hparams_config", pb)
/usr/local/lib/python3.6/dist-packages/tensorboard/plugins/hparams/summary_v2.py in hparams_config_pb(hparams, metrics, time_created_secs)
161 domain.update_hparam_info(info)
162 hparam_infos.append(info)
--> 163 metric_infos = [metric.as_proto() for metric in metrics]
164 experiment = api_pb2.Experiment(
165 hparam_infos=hparam_infos,
/usr/local/lib/python3.6/dist-packages/tensorboard/plugins/hparams/summary_v2.py in <listcomp>(.0)
161 domain.update_hparam_info(info)
162 hparam_infos.append(info)
--> 163 metric_infos = [metric.as_proto() for metric in metrics]
164 experiment = api_pb2.Experiment(
165 hparam_infos=hparam_infos,
/usr/local/lib/python3.6/dist-packages/tensorboard/plugins/hparams/summary_v2.py in as_proto(self)
532 name=api_pb2.MetricName(
533 group=self._group,
--> 534 tag=self._tag,
535 ),
536 display_name=self._display_name,
TypeError: <tensorflow.python.eager.def_function.Function object at 0x7f9f3a78e5c0> has type Function, but expected one of: bytes, unicode
I also tried running the following code:
with tf.summary.create_file_writer('logs/hparam_tuning').as_default():
hp.hparams_config(hparams=[HP_NUM_UNITS, HP_OPTIMIZER],metrics=
[dl_tf_loss])
but got the following error:
AttributeError Traceback (most recent call last)
<ipython-input-28-6778bdf7f1b1> in <module>()
8
9 with tf.summary.create_file_writer('logs/hparam_tuning').as_default():
---> 10 hp.hparams_config(hparams=[HP_NUM_UNITS, HP_OPTIMIZER],metrics=[dl_tf_loss])
2 frames
/usr/local/lib/python3.6/dist-packages/tensorboard/plugins/hparams/summary_v2.py in <listcomp>(.0)
161 domain.update_hparam_info(info)
162 hparam_infos.append(info)
--> 163 metric_infos = [metric.as_proto() for metric in metrics]
164 experiment = api_pb2.Experiment(
165 hparam_infos=hparam_infos,
AttributeError: 'Function' object has no attribute 'as_proto'
Would greatly appreciate any help.
Thanks in advance!
I figured it out.
The original METRIC_ACCURACY that I changed to METRIC_LOSS is apparently just the name, I needed to write 'tf_dl_loss' as a string and not as the function.
In the proceeding parts of the tuning, I needed to anyway write my fit command, there I inserted the actual loss function as I showed in my example of the regular fit function.
Highly recommend this as a way of tuning the hyperparameters.
You might be interested by this demo. Compiling the model with dl_tf_loss in the metric will waste time. It is possible to let hp.Metric know about other recorded summaries in different directories using the group argument.

Stanford NER Tagger and NLTK - not working [OSError: Java command failed ]

Trying to run Stanford NER Taggerand NLTK from a jupyter notebook.
I am continuously getting
OSError: Java command failed
I have already tried the hack at
https://gist.github.com/alvations/e1df0ba227e542955a8a
and thread
Stanford Parser and NLTK
I am using
NLTK==3.3
Ubuntu==16.04LTS
Here is my python code:
Sample_text = "Google, headquartered in Mountain View, unveiled the new Android phone"
sentences = sent_tokenize(Sample_text)
tokenized_sentences = [word_tokenize(sentence) for sentence in sentences]
PATH_TO_GZ = '/home/root/english.all.3class.caseless.distsim.crf.ser.gz'
PATH_TO_JAR = '/home/root/stanford-ner.jar'
sn_3class = StanfordNERTagger(PATH_TO_GZ,
path_to_jar=PATH_TO_JAR,
encoding='utf-8')
annotations = [sn_3class.tag(sent) for sent in tokenized_sentences]
I got these files using following commands:
wget http://nlp.stanford.edu/software/stanford-ner-2015-04-20.zip
wget http://nlp.stanford.edu/software/stanford-postagger-full-2015-04-20.zip
wget http://nlp.stanford.edu/software/stanford-parser-full-2015-04-20.zip
# Extract the zip file.
unzip stanford-ner-2015-04-20.zip
unzip stanford-parser-full-2015-04-20.zip
unzip stanford-postagger-full-2015-04-20.zip
I am getting the following error:
CRFClassifier invoked on Thu May 31 15:56:19 IST 2018 with arguments:
-loadClassifier /home/root/english.all.3class.caseless.distsim.crf.ser.gz -textFile /tmp/tmpMDEpL3 -outputFormat slashTags -tokenizerFactory edu.stanford.nlp.process.WhitespaceTokenizer -tokenizerOptions "tokenizeNLs=false" -encoding utf-8
tokenizerFactory=edu.stanford.nlp.process.WhitespaceTokenizer
Unknown property: |tokenizerFactory|
tokenizerOptions="tokenizeNLs=false"
Unknown property: |tokenizerOptions|
loadClassifier=/home/root/english.all.3class.caseless.distsim.crf.ser.gz
encoding=utf-8
Unknown property: |encoding|
textFile=/tmp/tmpMDEpL3
outputFormat=slashTags
Loading classifier from /home/root/english.all.3class.caseless.distsim.crf.ser.gz ... Error deserializing /home/root/english.all.3class.caseless.distsim.crf.ser.gz
Exception in thread "main" java.lang.RuntimeException: java.lang.ClassCastException: java.util.ArrayList cannot be cast to [Ledu.stanford.nlp.util.Index;
at edu.stanford.nlp.ie.AbstractSequenceClassifier.loadClassifierNoExceptions(AbstractSequenceClassifier.java:1380)
at edu.stanford.nlp.ie.AbstractSequenceClassifier.loadClassifierNoExceptions(AbstractSequenceClassifier.java:1331)
at edu.stanford.nlp.ie.crf.CRFClassifier.main(CRFClassifier.java:2315)
Caused by: java.lang.ClassCastException: java.util.ArrayList cannot be cast to [Ledu.stanford.nlp.util.Index;
at edu.stanford.nlp.ie.crf.CRFClassifier.loadClassifier(CRFClassifier.java:2164)
at edu.stanford.nlp.ie.AbstractSequenceClassifier.loadClassifier(AbstractSequenceClassifier.java:1249)
at edu.stanford.nlp.ie.AbstractSequenceClassifier.loadClassifier(AbstractSequenceClassifier.java:1366)
at edu.stanford.nlp.ie.AbstractSequenceClassifier.loadClassifierNoExceptions(AbstractSequenceClassifier.java:1377)
... 2 more
---------------------------------------------------------------------------
OSError Traceback (most recent call last)
<ipython-input-15-5621d0f8177d> in <module>()
----> 1 ne_annot_sent_3c = [sn_3class.tag(sent) for sent in tokenized_sentences]
/home/root1/.virtualenv/demos/local/lib/python2.7/site-packages/nltk/tag/stanford.pyc in tag(self, tokens)
79 def tag(self, tokens):
80 # This function should return list of tuple rather than list of list
---> 81 return sum(self.tag_sents([tokens]), [])
82
83 def tag_sents(self, sentences):
/home/root1/.virtualenv/demos/local/lib/python2.7/site-packages/nltk/tag/stanford.pyc in tag_sents(self, sentences)
102 # Run the tagger and get the output
103 stanpos_output, _stderr = java(cmd, classpath=self._stanford_jar,
--> 104 stdout=PIPE, stderr=PIPE)
105 stanpos_output = stanpos_output.decode(encoding)
106
/home/root1/.virtualenv/demos/local/lib/python2.7/site-packages/nltk/__init__.pyc in java(cmd, classpath, stdin, stdout, stderr, blocking)
134 if p.returncode != 0:
135 print(_decode_stdoutdata(stderr))
--> 136 raise OSError('Java command failed : ' + str(cmd))
137
138 return (stdout, stderr)
OSError: Java command failed : [u'/usr/bin/java', '-mx1000m', '-cp', '/home/root/stanford-ner.jar', 'edu.stanford.nlp.ie.crf.CRFClassifier', '-loadClassifier', '/home/root/english.all.3class.caseless.distsim.crf.ser.gz', '-textFile', '/tmp/tmpMDEpL3', '-outputFormat', 'slashTags', '-tokenizerFactory', 'edu.stanford.nlp.process.WhitespaceTokenizer', '-tokenizerOptions', '"tokenizeNLs=false"', '-encoding', 'utf-8']
Download Stanford Named Entity Recognizer version 3.9.1: see ‘Download’ section from The Stanford NLP website.
Unzip it and move 2 files "ner-tagger.jar" and "english.all.3class.distsim.crf.ser.gz" to your folder
Open jupyter notebook or ipython prompt in your folder path and run the following python code:
import nltk
from nltk.tag.stanford import StanfordNERTagger
sentence = u"Twenty miles east of Reno, Nev., " \
"where packs of wild mustangs roam free through " \
"the parched landscape, Tesla Gigafactory 1 " \
"sprawls near Interstate 80."
jar = './stanford-ner.jar'
model = './english.all.3class.distsim.crf.ser.gz'
ner_tagger = StanfordNERTagger(model, jar, encoding='utf8')
words = nltk.word_tokenize(sentence)
# Run NER tagger on words
print(ner_tagger.tag(words))
I tested this on NLTK==3.3 and Ubuntu==16.0.6LTS