I am trying to modify an inference code for pruned SqueezeNet network
However, I faced the following error. Could anyone comment how to go around this cpu/gpu backend error ?
[kevin#linux SqueezeNet-Pruning]$ python predict.py --image “3_100.jpg” --model “model_prunned” --num_class “2”
prediction in progress
Traceback (most recent call last):
File “predict.py”, line 63, in
prediction = predict_image(imagepath)
File “predict.py”, line 47, in predict_image
output = model(input)
File “/usr/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 477, in call
result = self.forward(*input, **kwargs)
File “/home/kevin/Documents/Grive/Personal/Coursera/Machine_Learning/pruning/Pruning-CNN/SqueezeNet-Pruning/finetune.py”, line 39, in forward
x = self.features(x)
File “/usr/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 477, in call
result = self.forward(*input, **kwargs)
File “/usr/lib/python3.7/site-packages/torch/nn/modules/container.py”, line 92, in forward
input = module(input)
File “/usr/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 477, in call
result = self.forward(*input, **kwargs)
File “/usr/lib/python3.7/site-packages/torch/nn/modules/conv.py”, line 313, in forward
self.padding, self.dilation, self.groups)
RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 ‘weight’
[kevin#linux SqueezeNet-Pruning]$
I think it can relate to the use of GPU. I think the program might work with correct configuration with GPU. Or you can delete line 39, 40.
Related
I'm trying to use a pre-trained tensorflow model to classify an image.
I downloaded the efficientnet model from tensorflow hub.
The python code loads the model from the .pb file.
It then loads a sample image, resizes the image to 224x224, squishes the rgb values to [0,1] and adds another dimension to make it 4d (collection of images) as the model expects.
Use col_x for inference. The final input shape that is given to the model is (1, 224, 224, 3).
import os
import tensorflow as tf
from tensorflow import keras
print(tf.version.VERSION)
path = os.path.join(os.getcwd(), 'efficientnet')
model = keras.models.load_model(path)
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
from PIL import Image
import numpy as np
img = Image.open("data/zebra.jpg")
img = img.resize((224, 224), Image.ANTIALIAS)
x = tf.keras.preprocessing.image.img_to_array(img)
plt.imshow(img)
plt.show()
norm_x = x / 255
col_x = norm_x[np.newaxis,...]
plt.imshow(col_x[0])
plt.show()
model(col_x)
But I get this error:
Traceback (most recent call last):
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 926, in conv2d
"dilations", dilations)
tensorflow.python.eager.core._FallbackException: Expecting int64_t value for attr strides, got numpy.int32
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "<pyshell>", line 1, in <module>
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 968, in __call__
outputs = self.call(cast_inputs, *args, **kwargs)
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\keras\engine\network.py", line 719, in call
convert_kwargs_to_constants=base_layer_utils.call_context().saving)
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\keras\engine\network.py", line 888, in _run_internal_graph
output_tensors = layer(computed_tensors, **kwargs)
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 968, in __call__
outputs = self.call(cast_inputs, *args, **kwargs)
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\keras\layers\convolutional.py", line 207, in call
outputs = self._convolution_op(inputs, self.kernel)
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 1106, in __call__
return self.conv_op(inp, filter)
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 638, in __call__
return self.call(inp, filter)
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 237, in __call__
name=self.name)
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 2014, in conv2d
name=name)
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 933, in conv2d
data_format=data_format, dilations=dilations, name=name, ctx=_ctx)
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 1022, in conv2d_eager_fallback
ctx=ctx, name=name)
File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\eager\execute.py", line 60, in quick_execute
inputs, attrs, num_outputs)
tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [Op:Conv2D]
I was able could reproduce your error, Please the change the last line.
to either predict or evaluate or any function that you would want to infer.
model.predict(col_x)
You are using model(col_x) , which is sending the image directly to the model as an class attribute.
Also, for the other error I do not think your system is using the GPU if available, please install the correct version of Tensorflow and CUDA for that purpose.
Visit this answer Which TensorFlow and CUDA version combinations are compatible? for correcting it.
Cheers.
I am really new to torch and machine learning. I am trying to run my project using GPU. I tried to have such modification to my code:
model = Challenge()
model = model.to(torch.device('cuda'))
However, I am still having following error:
Traceback (most recent call last):
File "C:/Users/ruidong/Desktop/YZR temp/Project2/train_challenge.py", line 112, in <module>
main()
File "C:/Users/ruidong/Desktop/YZR temp/Project2/train_challenge.py", line 91, in main
stats)
File "C:/Users/ruidong/Desktop/YZR temp/Project2/train_challenge.py", line 40, in _evaluate_epoch
output = model(X)
File "C:\Users\ruidong\Anaconda3\envs\EECS445\lib\site-packages\torch\nn\modules\module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "C:\Users\ruidong\Desktop\YZR temp\Project2\model\challenge.py", line 48, in forward
z = F.relu(self.conv1(x))
File "C:\Users\ruidong\Anaconda3\envs\EECS445\lib\site-packages\torch\nn\modules\module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "C:\Users\ruidong\Anaconda3\envs\EECS445\lib\site-packages\torch\nn\modules\conv.py", line 345, in forward
return self.conv2d_forward(input, self.weight)
File "C:\Users\ruidong\Anaconda3\envs\EECS445\lib\site-packages\torch\nn\modules\conv.py", line 342, in conv2d_forward
self.padding, self.dilation, self.groups)
RuntimeError: Expected object of device type cuda but got device type cpu for argument #1 'self' in call to _thnn_conv2d_forward
Any suggestions? really appreciate.
The model is correctly moved to GPU. However, for a model that is placed in GPU, you need to pass the tensors that are in GPU too. The error is because you are passing tensor that is placed in CPU in a model that is in GPU. Just do the same for inputs before passing them to model
I had a Tensorflow code that worked well with python 2. I need to switch to Python 3. besides, minor changes, this is the big error I am getting. Something is causing input feeding error.
Traceback (most recent call last):
File "main.py", line 530, in <module>
train(config)
File "main.py", line 337, in train
loss,train_op = trainer.step(sess,batch)
File "trainer.py", line 33, in step
loss, train_op = sess.run([self.loss,self.train_op],feed_dict=feed_dict)
File "anaconda2/envs/tf_gpu/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 905, in run
run_metadata_ptr)
File "anaconda2/envs/tf_gpu/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1116, in _run
str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (0,) for Tensor 'simple_lstm/pre_emb_mat:0', which has shape '(?, 100)'
How could I resolve it?
When I run the following code, I get the error:
E tensorflow/stream_executor/cuda/cuda_blas.cc:654] failed to run cuBLAS routine cublasSgemv_v2: CUBLAS_STATUS_EXECUTION_FAILED
Traceback (most recent call last):
File "modelAndLayer.py", line 16, in <module>
y_pred=model(X)
File "/home/cxsbg/anaconda3/envs/dl36/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/engine/base_layer.py", line 314, in __call__
output = super(Layer, self).__call__(inputs, *args, **kwargs)
File "/home/cxsbg/anaconda3/envs/dl36/lib/python3.6/site-packages/tensorflow/python/layers/base.py", line 717, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "modelAndLayer.py", line 10, in call
output=self.dense(input)
File "/home/cxsbg/anaconda3/envs/dl36/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/engine/base_layer.py", line 314, in __call__
output = super(Layer, self).__call__(inputs, *args, **kwargs)
File "/home/cxsbg/anaconda3/envs/dl36/lib/python3.6/site-packages/tensorflow/python/layers/base.py", line 717, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "/home/cxsbg/anaconda3/envs/dl36/lib/python3.6/site-packages/tensorflow/python/layers/core.py", line 163, in call
outputs = gen_math_ops.mat_mul(inputs, self.kernel)
File "/home/cxsbg/anaconda3/envs/dl36/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py", line 4305, in mat_mul
_six.raise_from(_core._status_to_exception(e.code, message), None)
File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.InternalError: Blas GEMV launch failed: m=3, n=2 [Op:MatMul]
My gpu is RTX2080 and the driver is v410. The cuda is v9.0, the cudnn is v7. The tensorflow-gpu is v1.8 (I tired on both v1.8 and v1.12). The python is v3.6 (I tried on both v3.6 and v2.7). The system is Ubuntu 16.04 (I also tired on win10).
The problem always occurs on tensorflow-gpu, but it works on tensorflow cpu.
Code is here (a simple linear model):
import tensorflow as tf
tf.enable_eager_execution()
X=tf.constant([[1.,2.,3,],[4.,5.,6.]])
Y=tf.constant([[10.],[20.]])
class Linear(tf.keras.Model):
def __init__(self):
super().__init__()
self.dense=tf.keras.layers.Dense(units=1,kernel_initializer=tf.zeros_initializer(),bias_initializer=tf.zeros_initializer())
def call(self,input):
output=self.dense(input)
return output
model=Linear()
optimizer=tf.train.GradientDescentOptimizer(learning_rate=1e-3)
for i in range(1000):
with tf.GradientTape() as tape:
y_pred=model(X)
loss=tf.reduce_mean(tf.square(y_pred-Y))
grads=tape.gradient(loss,model.variables)
optimizer.apply_gradients(zip(grads,model.variables))
print(model.variables)
I think the error is caused by the tf.enable_eager_execution() as I test it many times. Thanks to the author which-version-of-cuda-can-work-with-rtx-2080. When I use cuda9.2, the error is fixed.
I'm not so sure about the role of the steps parameter in predict_generator, and what I understand is that steps represents the amount of data generated by the generator, but someone denies my answer, and someone confirms my answer
I through the practice still couldn't find the right answer, my way is this, I use openslide to read a 5000x5000 size image, each produced a small map 100x100 to forecast the normal I can read 2500 100x100 the size of the picture, but when I set the steps=2500 is wrong
this is code:
# coding=utf-8
from __future__ import division
from keras.models import load_model
import openslide
import numpy as np
import Get_file_name
import generator
import matplotlib.pyplot as plt
def predict_model(img):
model = load_model(Get_file_name.model_path[0])
y= model.predict_generator(generator.pre_gen(img),steps=30)
print(y)
predict_model("cats_and_dogs_5")
This is wrong：
Exception in thread Thread-1:
Traceback (most recent call last):
File "/usr/lib/python3.5/threading.py", line 914, in _bootstrap_inner
self.run()
File "/usr/lib/python3.5/threading.py", line 862, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 612, in data_generator_task
generator_output = next(self._generator)
StopIteration
Traceback (most recent call last):
File "/home/zh/视频/MitosisDetection/mitosisDetection/predict.py", line 17, in <module>
predict_model("cats_and_dogs_5")
File "/home/zh/视频/MitosisDetection/mitosisDetection/predict.py", line 12, in predict_model
y= model.predict_generator(generator.pre_gen(img),steps=2500)
File "/usr/local/lib/python3.5/dist-packages/keras/legacy/interfaces.py", line 88, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 1183, in predict_generator
verbose=verbose)
File "/usr/local/lib/python3.5/dist-packages/keras/legacy/interfaces.py", line 88, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 2108, in predict_generator
outs = self.predict_on_batch(x)
File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 1696, in predict_on_batch
outputs = self.predict_function(ins)
File "/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py", line 2229, in __call__
feed_dict=feed_dict)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 778, in run
run_metadata_ptr)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 961, in _run
% (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape () for Tensor 'conv2d_1_input:0', which has shape '(?, 64, 64, 3)'
Process finished with exit code 1
If steps represents the amount of data generated by my generator, then why do I set steps=2500 wrong? If steps does not represent the amount of data generated by the generator, then what should I do? What should I do to set the amount of data generated by my generator?
Please God more advice, I'm around today, many people do not understand this thing!