After successful converting my model detectron2 model to ONNX format I cant make predictions.
I am getting the following error:
failed: Fatal error: AliasWithName is not a registered function/op
My code:
import onnx
import onnxruntime as ort
import numpy as np
import glob
import cv2
onnx_model = onnx.load("test.onnx")
onnx.checker.check_model(onnx_model)
im = cv2.imread('img.png')
print(im.shape)
ort_sess = ort.InferenceSession('test.onnx',providers=[ 'CPUExecutionProvider'])
outputs = ort_sess.run(None, {'input': im})
print(outputs)
I am doing something wrong?
In documentation: https://detectron2.readthedocs.io/en/latest/modules/export.html#detectron2.export.Caffe2Tracer.export_onnx
They say:
"Export the model to ONNX format. Note that the exported model contains custom ops only available in caffe2, therefore it cannot be directly executed by another runtime (such as onnxruntime or TensorRT). Post-processing or transformation passes may be applied on the model to accommodate different runtimes, but we currently do not provide support for them."
What is that "Post-processing or transformation" that I should do?
When I use the pyLDAvis.gensim functoion to construct visualization at google colab, it shows this error:
a result has failed to un-serialize. please ensure that the objects
returned by the function are always picklable.
My codes are:
!pip install pyldavis
import pyLDAvis
import pyLDAvis.gensim_models as pg
pyLDAvis.enable_notebook()
vis = pg.prepare(lda, corpus, dictionary, sort_topics=False) # construct visualization
Upgrading pandas to '1.2.0' version worked for me.
I have an issue with calling Python code in MATLAB. My Python code involves predicting the battery state of charge using LSTM with attention ANN based on the inputs sent from MATLAB. The prediction is then sent back to MATLAB. I already have previously trained weights and biases saved in an h5 file, which is loaded and used in the Python code. Below is the Python code:
import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from tensorflow import keras
from tensorflow.keras import Sequential
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Dense
from tensorflow.keras import optimizers
import matplotlib.pyplot as plt
from tensorflow.keras.layers import *
from tensorflow.keras.models import *
from tensorflow.keras import backend as K
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.models import load_model
from tensorflow.keras.layers import Dropout, InputLayer
import h5py
#to create sequential data
def create_inout_sequences(input_data, tw):
inout_seq = []
L = len(input_data)
for i in range(L-tw):
train_seq = input_data[i:i+tw]
#train_out = output_data[i:i+tw]
inout_seq.append(train_seq)
return inout_seq
def search(inputs):
class attention(Layer):
def __init__(self, return_sequences=True,**kwargs):
self.return_sequences = return_sequences
super(attention,self).__init__()
def build(self, input_shape):
self.W=self.add_weight(name="att_weight", shape=(input_shape[-1],1),
initializer="normal")
self.b=self.add_weight(name="att_bias", shape=(input_shape[1],1),
initializer="zeros")
super(attention,self).build(input_shape)
def call(self, x):
e=(K.dot(x,self.W)+self.b)
a = K.softmax(e, axis=1)
output = x*a
if self.return_sequences:
return output
return K.sum(output, axis=1)
def get_config(self):
# For serialization with 'custom_objects'
config = super().get_config()
config['return_sequences'] = self.return_sequences
return config
#convert test data to sequential form
inputs=np.array(inputs)
inputs=np.tile(inputs, (36, 1))
inputs_new=create_inout_sequences(inputs, 35)
inputs_new=np.array(inputs_new)
model1 = Sequential()
model1.add(InputLayer(input_shape=(35,5)))
model1.add((LSTM(22, return_sequences=True)))
model1.add(attention(return_sequences=False))
model1.add(Dense(104, activation="relu"))
model1.add(Dropout(0.2))
model1.add(Dense(1, activation="sigmoid"))
lr_schedule = keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate=0.01,
decay_steps=10000,
decay_rate=0.99)
model1.compile(optimizer=tf.keras.optimizers.Adam(epsilon=1e-08,learning_rate=lr_schedule),loss='mse')
#call previously trained weights
model1.load_weights('SOC_weights.h5')
x=float(model1.predict(inputs_new, batch_size=100,verbose=0))
return x # send prediction to Matlab
Note: I am using Python 3.6, tensorflow version: 2.5, keras version: 2.4.3, h5py version: 3.1.0, cython version: 0.28
I am able to run this code without any error on Python, but have issues when used in MATLAB 2020a... below is my MATLAB code:
pyenv('Version','3.6');
py.importlib.import_module('tensorflow');
py.importlib.import_module('testingSOC'); % file containing the Python codes
inputs=[0.555555556,0.435139205,0.68313128,0.499987472,0.241225578];% test inputs
SOC_output=py.testingSOC.search(inputs)
Below is the error received on Matlab:
Error using training>load_weights (line 2312)
Python Error: ImportError: `load_weights` requires h5py when loading weights from HDF5.
Error in testingSOC>search (line 87)
the error looks like h5py is not identified by MATLAB, so I have tried reinstalling h5py by using the command prompt (I am using Windows 10):
pip uninstall h5py
pip install h5py
but no changes...
I have also tried with tensorflow version: 2.2, keras version 2.4.3, h5py version 2.10 and cython version 0.29 but still get the same error.
I would really appreciate if you guys can provide an insight in solving this issue, and if there are any fundamental parts that I have missed. I would be glad to share more details if required.
Thanks!
Thanks to #TimRoberts for pointing out about including 'py.importlib.import_module('h5py')' which helped me in resolving this issue.Below is my solution, for those who would like to refer:
When I included 'py.importlib.import_module('h5py')' in my matlab codes, I received the following error:
Error using h5>init h5py.h5 (line 1)
Python Error: ImportError: DLL load failed: The specified procedure could not be found.
It looks like Python environment seems to use Matlab's h5 library in my case, which does not have the same features as Python's h5 library...I found that there is an option of running Python codes as a separate process which seems to be working for me (as seen in this link):
https://www.mathworks.com/help/matlab/matlab_external/out-of-process-execution-of-python-functionality.html?searchHighlight=out%20of%20process%20python&s_tid=srchtitle
I am using Google Colab and I want to use the weights of EfficientNet Noisy Student. https://www.kaggle.com/c/bengaliai-cv19/discussion/132894
First, I installed the package via:
!pip install git+https://github.com/qubvel/efficientnet
Then I tried the code found on the site mentioned above:
import efficientnet.keras as eff
model = eff.EfficientNetB0(weights='noisy-student')
And got this Value error:
ValueError: The `weights` argument should be either `None` (random initialization), `imagenet` (pre-training on ImageNet), or the path to the weights file to be loaded.
Does someone know how to fix this?
You could download the weights from here.
And load it manually like this:
path_to_weights = "/..your..path../efficientnet-b5_noisy-student_notop.h5"
model = EfficientNetB5(include_top=False)
model.load_weights(path_to_weights, by_name=True)
I face problems when trying to get a numpy array from a tensorflow tensor. I use a tensorflow hub module but I don't want to use tensorflow in downstream tasks but rather need a numpy array.
I know that I have to call the 'eval()' method on the tensor from within a tensorflow session. But unfortuantely I cannot get it to work... :( It tells me that the "tables are not initialized". I tried to add 'sess.run(tf.tables_initializer())' but then I get the error: 'NotFoundError: Resource localhost/module_1/embeddings_morph_specialized/class tensorflow::Var does not exist'. I am not sure what to try next. I have also tried 'sess.run()' but have also been unsuccessful.
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
embed = hub.Module("https://public.ukp.informatik.tu-darmstadt.de/arxiv2018-xling-sentence-embeddings/tf-hub/monolingual/1")
X = embed(["This is a test."])
# I tried:
#with tf.Session() as sess:
# sess.run(tf.tables_initializer())
# X.eval()
'X' is the tensor which I would like to convert to a numpy array.
Any help is appreciated. :)
Thank you.
Unfortunately, tf_hub modules are not yet supported in eager mode except in tf 2 (which is still in beta and I think needs slightly different hub modules anyway).
Therefore you'll need to run this in a session.
Something like:
embed = hub.Module("https://public.ukp.informatik.tu-darmstadt.de/arxiv2018-xling-sentence-embeddings/tf-hub/monolingual/1")
X = embed(["This is a test."])
with tf.Session() as session:
session.run([tf.global_variables_initializer(), tf.tables_initializer()])
numpy_arr = session.run(X)