In the win7 system, under the command terminal, the two methods of installing pycallgraph report the exception of subprocess exited with error
Solution: downgrade setuptools
1. First check the current version of setuptools.
pip show setuptools
2. The version of setuptools has been downgraded. Here, I downgraded it to 57.5.0
pip install --upgrade setuptools==57.5.0
3. Try installing pycallgraph again
pip install pycallgraphPython selenium Error:
el = driver.find_element_by_xpath('//*[@id="changeCityBox"]/ul/li[2]/a')
driver.find_element_by_xpath('//*[@id="search_input"]').send_keys('python',Keys.ENTER)Solution: Modify the codes above to:
from selenium.webdriver.common.by import By
el = driver.find_element(By.XPATH,r'//*[@id="changeCityBox"]/ul/li[2]/a')
driver.find_element(By.XPATH,r'//*[@id="search_input"]').send_keys("python",Keys.ENTER)War here to use the browser driver for Google, other browsers can also be Edge to edge, modify the driver needs to configure the environment
from selenium.webdriver import Chrome
from selenium.webdriver.common.keys import Keys # Button commands for the keyboard
from selenium.webdriver.common.by import By
driver = Chrome()
driver.get("https://www.lagou.com/")
# Find the element copyxpath that the browser needs to operate on
el = driver.find_element(By.XPATH,r'//*[@id="changeCityBox"]/ul/li[2]/a')
# el = driver.find_element_by_xpath('//*[@id="changeCityBox"]/ul/li[2]/a')
el.click() # Click event
# find the input box F12 element copyxpath, enter python content, enter or search button xpath
driver.find_element(By.XPATH,r'//*[@id="search_input"]').send_keys("python",Keys.ENTER)
# driver.find_element_by_xpath('//*[@id="search_input"]').send_keys('python',Keys.ENTER)RuntimeError: stack expects each tensor to be equal size, but got [x] at entry 0 and [x] at entry 1
Problem description: When generating a dataloader, the training set can be run, but the test set has this error: RuntimeError: stack expects each tensor to be equal size, but got [200] at entry 0 and [116] at entry 1.
How to Solve: In generating the dataloader, I need to generate a dataset, so my error occurred because there is a minibatch in the dataset with a different number of data than the other minibatch, so I went into the custom dataset method to check, and through print debugging, I found that it was a problem with the dataset label.
Solution: Go into the dataset and print the output of the dataset.
How to Solve error: CUDA failure 999: unknown error
1. Error Message:
CUDA failure 999: unknown error ; GPU=-351697408 ; hostname=4f5e6dff58e6 ; expr=cudaSetDevice(info_.device_id);
2. Solution:
To reload the nvidia kernel module, enter the following command.
sudo rmmod nvidia_uvm
sudo modprobe nvidia_uvm
How to Solve Error: pyinstaller: error: unrecognized arguments: sklearn
Solution:
CMD sends an error after executing the command PIP istall librosa:
from numba.core.typeconv import _typeconv
ImportError: DLL load failed: not found
The reason is that the versions of numba, llvmlite and resampy are conflicting and incompatible
Solution:
reinstall the compatible package
pip install llvmlite==0.31.0
pip install numba==0.48.0
pip install resampy== 0.3.0
The final versions of all libraries are shown in the following figure:
The solutions to xgboost errors
Error 1: ‘Dict_ Items’ object has no attribute ‘copy’
there should be a problem with the form of a parameter. It must be in the list format. Solution: based on the source code
plst = params.items()
Add a sentence format conversion
plst = params.items()
plst = list(params.items())
Error 2: WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.6.0/src/learner.cc:627: Parameter…
Solution: Add the following code
xgb.set_config(verbosity=0)I found that every time I use the opencv package to process remote sensing images, it often reports an error, even though it has become uint format.
This time I found out that some of them only support three channels, my binary image is one channel, the error of storing the result is because I need to save the result of 1 channel, mine is three channels
so it is necessary to convert 1 channel to 3 channels back and forth ` img1 = np.array(b1)
# img2 = np.dtype(img1, np.uint8)
img = np.expand_dims(img1, axis=2)
img = np.concatenate((img, img, img), axis=-1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# contours = cv2.findContours(img)
dilate = cv2.fillPoly(img, [contours[1]],(255,15,15))
cv2.imshow("filled binary", dilate)`
AttributeError: module ‘PIL. Image’ has no attribute ‘open’. It means PIL.image does not has an open method. I have searched lots of solutions online, but they are not work. Finally, I inadvertently saw the address of image.py (c:\users\lenovo\pycharmprojects\kk\venv\lib\site packages\pil\image.py). I know the reason of the error.
from PIL import Image
import os
import csv
import timeReason: the image.py file under the PIL package was accidentally emptied, so image.open() cannot be realized.
temp_img_now = Image.open(temp_file)Solution: uninstall the pilot and pillow-PIL, and then reinstall them.
Preface: using yolov5-6.0 version, you want to detect several pictures, but there is a problem in the title. It can be seen that the upsampling function is not quite right. Now record the solution.
Version: yolov5-6.0, python3.8, pytorch1.11.0
1. Problem recurrence
2. Official website solution
This problem first appeared in yolov5 and is related to pytoch 1.11.0.
In other words, this problem may be encountered in both train and detect. The following is the solution to reduce the pytoch version to less than 10.
Then the blogger made a fix for PyTorch version 1.11.0
But it doesn’t seem to be solved. The my torch version is 1.11.0, but this problem still occurs.
The solution of modifying the upper sampling function given by netizens.
Just Comment out this part below.
It’s really solved.
Project scenario [with errtype = cudaerror; bool thrw = true] CUDA failure 999: unknown error; GPU=24 ：
The old program needs to be upgraded. The previous CUDA is 10.2
Problem Description:
environment
CUDA 11.2 (previously 10.2)
onnxruntime-gpu 1.10
python 3.9.7
When starting the program
Traceback (most recent call last):
File "/home/aiuser/cover/liheng-foggun/app.py", line 15, in <module>
model = DetectMultiBackend(weights=config.paddle.model_file)
File "/home/aiuser/miniconda3/envs/cover/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 28, in decorate_context
return func(*args, **kwargs)
File "/home/aiuser/cover/liheng-foggun/models/yolo.py", line 37, in __init__
self.session = onnxruntime.InferenceSession(weights, providers=['CUDAExecutionProvider'])
File "/home/aiuser/miniconda3/envs/cover/lib/python3.9/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 335, in __init__
self._create_inference_session(providers, provider_options, disabled_optimizers)
File "/home/aiuser/miniconda3/envs/cover/lib/python3.9/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 379, in _create_inference_session
sess.initialize_session(providers, provider_options, disabled_optimizers)
RuntimeError: /onnxruntime_src/onnxruntime/core/providers/cuda/cuda_call.cc:122 bool onnxruntime::CudaCall(ERRTYPE, const char*, const char*, ERRTYPE, const char*) [with ERRTYPE =
cudaError; bool THRW = true] /onnxruntime_src/onnxruntime/core/providers/cuda/cuda_call.cc:116 bool onnxruntime::CudaCall(ERRTYPE, const char*, const char*
, ERRTYPE, const char*) [with ERRTYPE = cudaError; bool THRW = true] CUDA failure 999: unknown error ; GPU=24 ; hostname=aiserver-sl-01 ; expr=cudaSetDevice(info_.device_id);
Cause analysis:
1. At first, I thought it was the onnxruntime GPU version problem, upgraded to 1.12 it still reports an error.
2. It is said that it is incompatible.
3. Try to reinstall the driver. When 11.2 is uninstalled, nvidia-smi finds that the previous 10.2 driver still exists.
4. The reason is that the previous drive was not unloaded completely
Solution:
1. Uninstall 10.2
sudo /usr/local/cuda-10.2/bin/cuda-uninstaller
2. Install a new drive
#install 515.57 offline
sudo ./NVIDIA-Linux-x86_64-515.57.run -no-x-check -no-nouveau-check
VIDIA-Linux-x86_64-515.57.run -no-x-check -no-nouveau-check
The reason for the above error when compiling custom functions is that tf2.x’s keras.compile does not support specific values by default
Questions
When using the wrapping method to customize the loss function of the keras model and need to calculate accuracy metrics such as precision or recall, or need to extract the specific values of the inputs y_true and y_prd (operations such as y_true.numpy()), an error message appears:
OperatorNotAllowedInGraphError: using a `tf.Tensor` as a Python `bool` is not allowed: AutoGraph did convert this function. This might indicate you are trying to use an unsupported feature.
Or
AttributeError: 'Tensor' object has no attribute 'numpy'
Solution:
Pass in parameters in the compile function:
run_eagerly=True
Reason:
Tf2.x enables eager mode by default, namely eager execution, that is, dynamic calculation graph. Compared with the static calculation graph of tf1.x, the advantage of eager mode is that it is convenient for debugging, which can easily print tensor values and evaluate results; and Numpy interacts well, and the conversion between tensor and ndarray is convenient and even universal. The tradeoff is that it runs significantly slower. After the static calculation graph is defined, it is almost always executed with C++ code on the tensorflow core, so the calculation efficiency is higher and the speed is faster.
Even so, run_eagerly defaults to False in the model.compile method, which means that the logic of the model is encapsulated in tf.function, which achieves faster computational efficiency (the autograph mechanism converts the dynamic computational graph through the @tf.function wrapper). is a static computation graph). But the @tf.function wrapper requires the function to use basic tf operations, not other operations in python or even functions from other packages, so the first error occurs when calling functions such as sklearn.metrics’ accuracy_score or imblearn.metrcis’ geometric_mean_score function. The second error occurs when using the y_true.numpy() method. The fundamental reason is that the model.compile method does not support the above operations after the static calculation graph converted by the @tf.function wrapper, although tf2.x enables the use of dynamic calculation graphs by default.
After passing run_eagerly=True to the model.compile method, the dynamic calculation graph is used to run, and the above operations can be performed normally. The disadvantage is that the dynamic calculation graph has the disadvantage of low operation efficiency.