View Python installation location
Enter in Terminal
where python
View PIP installation library file location
pip show <packagename>
Open vscode and set the library path
Ctrl+shift+p Open ` preference; Open Settings(JSON)
Add path
"python.analysis.extraPaths":[
"/root/miniconda3/lib/python3.9/site-packages",
"/root/.local/lib/python3.9/site-packages",
"...."
]To learn Python Network programming, I wrote two small programs on the server and the client according to the book, and found that an error was reported:
Traceback (most recent call last):
File “./tsTserv.py”, line 20, in
data = tcpCliSock.recv(BUFSIZ)
File “/usr/lib/python2.6/socket.py”, line 165, in _dummy
raise error(EBADF, ‘Bad file descriptor’)
socket.error: [Errno 9] Bad file descriptor
The source code of the server side is as follows:
while True:
print 'waiting for connection...'
tcpCliSock,addr = tcpSerSock.accept()
print '...connected from:',addr
while True:
data = tcpCliSock.recv(BUFSIZ)
if not data:
break
tcpCliSock.send('[%s] %s' %(ctime(),data))
tcpCliSock.close()
tcpSerSock.close()
Solution:
tcpCliSock.close() is placed in the second while loop, causing tcpCliSock to be closed after receiving data once, and this statement should be placed in the outer loop
color_t = sens_frame.color_t[self.computespeed_t_type]
KeyError: ‘frame_device_t’
Probably because two values are not manually added in the registry
Enable Metadata: Metadata contains important timestamp information and needs to be enabled manually. Please refer to:
https://dev.intelrealsense.com/docs/compiling-librealsense-for-windows-guide
Modifying the Windows Registry:
For each interface found (Steps 2 and 3) perform
Using Registry Editing tool such as “regedit” navigate to HKLM\SYSTEM\CurrentControlSet\Control\DeviceClasses{e5323777-f976-4f5b-9b55-b94699c46e44} branch.
Browse into the subdirectory with the name identical to the Device instance path obtained from the previous step
Expand the entry into #GLOBAL -> Device Parameters
Add DWORD 32bit value named MetadataBufferSizeInKB0 with value 5.
Add an additional DWORD 32bit value named MetadataBufferSizeInKB1 with value 5 for RS400 device zero interface ##?##USB#VID_8086&PID… MI_00…
Repeat the previous step for
HKLM\SYSTEM\CurrentControlSet\Control\DeviceClasses\{65E8773D-8F56-11D0-A3B9-00A0C9223196}branch
1. No tf data. Actual error: Fixed Frame [world] does not exist
When using rviz to view relevant coordinate information, you may encounter No tf data. Actual error: Fixed Frame [world] does not exist, the main reason is that there is no world coordinate system in the TF tree. The solution is to let rviz know where the world coordinate system is. You can manually publish using the following command
rosrun tf static_transform_publisher 0 0 0 0 0 0 1 map world 5
2. nvalid argument “/world” passed to canTransform argument source_frame in tf2 frame_ids cannot start with a ‘/’ like
After the first problem is solved, another problem may appear Invalid argument "/world" passed to canTransform argument source_frame in tf2 frame_ids cannot start with a '/' like: as shown in the figure:
this problem is mainly because “/ world” is used in the release code and “world” is used in our release It may be good to recompile the code from “/world” to “world”
report errors
RuntimeError: "unfolded2d_copy" not implemented for 'Half'
reason
Parameters use_half=true passed in by the model, that is, the CPU is reasoned by using fp16 mixed precision calculation. Fp16 is used to speed up the speed, but the pytorch CPU does not support fp16,
Solution:
So that the model can be calculated;
Modification of my error report:
I hope this article is useful to you!
Thank you for your comments!
Problem description
Pycharm encountered SSL error while running websocket
Error: Connection error: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed
Problem-solving
Since I use Anaconda environment in pycharm, I need to configure it in the environment corresponding to anaconda
python -m certifi
Get certificate path
conda config --set ssl_verify <your-path>
Save the certificate path so that websocket can be opened normally
Use summary to view network parameters
If you need to view the specific parameters of the network, use the use summary
from torchsummary import summary
summary(model, (3, 448, 448))
Show results
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 224, 224] 9,408
BatchNorm2d-2 [-1, 64, 224, 224] 128
ReLU-3 [-1, 64, 224, 224] 0
MaxPool2d-4 [-1, 64, 112, 112] 0
Conv2d-5 [-1, 64, 112, 112] 4,096
BatchNorm2d-6 [-1, 64, 112, 112] 128
ReLU-7 [-1, 64, 112, 112] 0
Conv2d-8 [-1, 64, 112, 112] 36,864
BatchNorm2d-9 [-1, 64, 112, 112] 128
ReLU-10 [-1, 64, 112, 112] 0
Conv2d-11 [-1, 256, 112, 112] 16,384
BatchNorm2d-12 [-1, 256, 112, 112] 512
Conv2d-13 [-1, 256, 112, 112] 16,384
BatchNorm2d-14 [-1, 256, 112, 112] 512
ReLU-15 [-1, 256, 112, 112] 0
Bottleneck-16 [-1, 256, 112, 112] 0
Conv2d-17 [-1, 64, 112, 112] 16,384
BatchNorm2d-18 [-1, 64, 112, 112] 128
ReLU-19 [-1, 64, 112, 112] 0
Conv2d-20 [-1, 64, 112, 112] 36,864
BatchNorm2d-21 [-1, 64, 112, 112] 128
ReLU-22 [-1, 64, 112, 112] 0
Conv2d-23 [-1, 256, 112, 112] 16,384
BatchNorm2d-24 [-1, 256, 112, 112] 512
ReLU-25 [-1, 256, 112, 112] 0
Bottleneck-26 [-1, 256, 112, 112] 0
Conv2d-27 [-1, 64, 112, 112] 16,384
BatchNorm2d-28 [-1, 64, 112, 112] 128
ReLU-29 [-1, 64, 112, 112] 0
Conv2d-30 [-1, 64, 112, 112] 36,864
BatchNorm2d-31 [-1, 64, 112, 112] 128
ReLU-32 [-1, 64, 112, 112] 0
Conv2d-33 [-1, 256, 112, 112] 16,384
BatchNorm2d-34 [-1, 256, 112, 112] 512
ReLU-35 [-1, 256, 112, 112] 0
Bottleneck-36 [-1, 256, 112, 112] 0
Conv2d-37 [-1, 128, 112, 112] 32,768
BatchNorm2d-38 [-1, 128, 112, 112] 256
ReLU-39 [-1, 128, 112, 112] 0
Conv2d-40 [-1, 128, 56, 56] 147,456
BatchNorm2d-41 [-1, 128, 56, 56] 256
ReLU-42 [-1, 128, 56, 56] 0
Conv2d-43 [-1, 512, 56, 56] 65,536
BatchNorm2d-44 [-1, 512, 56, 56] 1,024
Conv2d-45 [-1, 512, 56, 56] 131,072
BatchNorm2d-46 [-1, 512, 56, 56] 1,024
ReLU-47 [-1, 512, 56, 56] 0
Bottleneck-48 [-1, 512, 56, 56] 0
Conv2d-49 [-1, 128, 56, 56] 65,536
BatchNorm2d-50 [-1, 128, 56, 56] 256
ReLU-51 [-1, 128, 56, 56] 0
Conv2d-52 [-1, 128, 56, 56] 147,456
BatchNorm2d-53 [-1, 128, 56, 56] 256
ReLU-54 [-1, 128, 56, 56] 0
Conv2d-55 [-1, 512, 56, 56] 65,536
BatchNorm2d-56 [-1, 512, 56, 56] 1,024
ReLU-57 [-1, 512, 56, 56] 0
Bottleneck-58 [-1, 512, 56, 56] 0
Conv2d-59 [-1, 128, 56, 56] 65,536
BatchNorm2d-60 [-1, 128, 56, 56] 256
ReLU-61 [-1, 128, 56, 56] 0
Conv2d-62 [-1, 128, 56, 56] 147,456
BatchNorm2d-63 [-1, 128, 56, 56] 256
ReLU-64 [-1, 128, 56, 56] 0
Conv2d-65 [-1, 512, 56, 56] 65,536
BatchNorm2d-66 [-1, 512, 56, 56] 1,024
ReLU-67 [-1, 512, 56, 56] 0
Bottleneck-68 [-1, 512, 56, 56] 0
Conv2d-69 [-1, 128, 56, 56] 65,536
BatchNorm2d-70 [-1, 128, 56, 56] 256
ReLU-71 [-1, 128, 56, 56] 0
Conv2d-72 [-1, 128, 56, 56] 147,456
BatchNorm2d-73 [-1, 128, 56, 56] 256
ReLU-74 [-1, 128, 56, 56] 0
Conv2d-75 [-1, 512, 56, 56] 65,536
BatchNorm2d-76 [-1, 512, 56, 56] 1,024
ReLU-77 [-1, 512, 56, 56] 0
Bottleneck-78 [-1, 512, 56, 56] 0
Conv2d-79 [-1, 256, 56, 56] 131,072
BatchNorm2d-80 [-1, 256, 56, 56] 512
ReLU-81 [-1, 256, 56, 56] 0
Conv2d-82 [-1, 256, 28, 28] 589,824
BatchNorm2d-83 [-1, 256, 28, 28] 512
ReLU-84 [-1, 256, 28, 28] 0
Conv2d-85 [-1, 1024, 28, 28] 262,144
BatchNorm2d-86 [-1, 1024, 28, 28] 2,048
Conv2d-87 [-1, 1024, 28, 28] 524,288
BatchNorm2d-88 [-1, 1024, 28, 28] 2,048
ReLU-89 [-1, 1024, 28, 28] 0
Bottleneck-90 [-1, 1024, 28, 28] 0
Conv2d-91 [-1, 256, 28, 28] 262,144
BatchNorm2d-92 [-1, 256, 28, 28] 512
ReLU-93 [-1, 256, 28, 28] 0
Conv2d-94 [-1, 256, 28, 28] 589,824
BatchNorm2d-95 [-1, 256, 28, 28] 512
ReLU-96 [-1, 256, 28, 28] 0
Conv2d-97 [-1, 1024, 28, 28] 262,144
BatchNorm2d-98 [-1, 1024, 28, 28] 2,048
ReLU-99 [-1, 1024, 28, 28] 0
Bottleneck-100 [-1, 1024, 28, 28] 0
Conv2d-101 [-1, 256, 28, 28] 262,144
BatchNorm2d-102 [-1, 256, 28, 28] 512
ReLU-103 [-1, 256, 28, 28] 0
Conv2d-104 [-1, 256, 28, 28] 589,824
BatchNorm2d-105 [-1, 256, 28, 28] 512
ReLU-106 [-1, 256, 28, 28] 0
Conv2d-107 [-1, 1024, 28, 28] 262,144
BatchNorm2d-108 [-1, 1024, 28, 28] 2,048
ReLU-109 [-1, 1024, 28, 28] 0
Bottleneck-110 [-1, 1024, 28, 28] 0
Conv2d-111 [-1, 256, 28, 28] 262,144
BatchNorm2d-112 [-1, 256, 28, 28] 512
ReLU-113 [-1, 256, 28, 28] 0
Conv2d-114 [-1, 256, 28, 28] 589,824
BatchNorm2d-115 [-1, 256, 28, 28] 512
ReLU-116 [-1, 256, 28, 28] 0
Conv2d-117 [-1, 1024, 28, 28] 262,144
BatchNorm2d-118 [-1, 1024, 28, 28] 2,048
ReLU-119 [-1, 1024, 28, 28] 0
Bottleneck-120 [-1, 1024, 28, 28] 0
Conv2d-121 [-1, 256, 28, 28] 262,144
BatchNorm2d-122 [-1, 256, 28, 28] 512
ReLU-123 [-1, 256, 28, 28] 0
Conv2d-124 [-1, 256, 28, 28] 589,824
BatchNorm2d-125 [-1, 256, 28, 28] 512
ReLU-126 [-1, 256, 28, 28] 0
Conv2d-127 [-1, 1024, 28, 28] 262,144
BatchNorm2d-128 [-1, 1024, 28, 28] 2,048
ReLU-129 [-1, 1024, 28, 28] 0
Bottleneck-130 [-1, 1024, 28, 28] 0
Conv2d-131 [-1, 256, 28, 28] 262,144
BatchNorm2d-132 [-1, 256, 28, 28] 512
ReLU-133 [-1, 256, 28, 28] 0
Conv2d-134 [-1, 256, 28, 28] 589,824
BatchNorm2d-135 [-1, 256, 28, 28] 512
ReLU-136 [-1, 256, 28, 28] 0
Conv2d-137 [-1, 1024, 28, 28] 262,144
BatchNorm2d-138 [-1, 1024, 28, 28] 2,048
ReLU-139 [-1, 1024, 28, 28] 0
Bottleneck-140 [-1, 1024, 28, 28] 0
Conv2d-141 [-1, 512, 28, 28] 524,288
BatchNorm2d-142 [-1, 512, 28, 28] 1,024
ReLU-143 [-1, 512, 28, 28] 0
Conv2d-144 [-1, 512, 14, 14] 2,359,296
BatchNorm2d-145 [-1, 512, 14, 14] 1,024
ReLU-146 [-1, 512, 14, 14] 0
Conv2d-147 [-1, 2048, 14, 14] 1,048,576
BatchNorm2d-148 [-1, 2048, 14, 14] 4,096
Conv2d-149 [-1, 2048, 14, 14] 2,097,152
BatchNorm2d-150 [-1, 2048, 14, 14] 4,096
ReLU-151 [-1, 2048, 14, 14] 0
Bottleneck-152 [-1, 2048, 14, 14] 0
Conv2d-153 [-1, 512, 14, 14] 1,048,576
BatchNorm2d-154 [-1, 512, 14, 14] 1,024
ReLU-155 [-1, 512, 14, 14] 0
Conv2d-156 [-1, 512, 14, 14] 2,359,296
BatchNorm2d-157 [-1, 512, 14, 14] 1,024
ReLU-158 [-1, 512, 14, 14] 0
Conv2d-159 [-1, 2048, 14, 14] 1,048,576
BatchNorm2d-160 [-1, 2048, 14, 14] 4,096
ReLU-161 [-1, 2048, 14, 14] 0
Bottleneck-162 [-1, 2048, 14, 14] 0
Conv2d-163 [-1, 512, 14, 14] 1,048,576
BatchNorm2d-164 [-1, 512, 14, 14] 1,024
ReLU-165 [-1, 512, 14, 14] 0
Conv2d-166 [-1, 512, 14, 14] 2,359,296
BatchNorm2d-167 [-1, 512, 14, 14] 1,024
ReLU-168 [-1, 512, 14, 14] 0
Conv2d-169 [-1, 2048, 14, 14] 1,048,576
BatchNorm2d-170 [-1, 2048, 14, 14] 4,096
ReLU-171 [-1, 2048, 14, 14] 0
Bottleneck-172 [-1, 2048, 14, 14] 0
Conv2d-173 [-1, 256, 14, 14] 524,288
BatchNorm2d-174 [-1, 256, 14, 14] 512
Conv2d-175 [-1, 256, 14, 14] 589,824
BatchNorm2d-176 [-1, 256, 14, 14] 512
Conv2d-177 [-1, 256, 14, 14] 65,536
BatchNorm2d-178 [-1, 256, 14, 14] 512
Conv2d-179 [-1, 256, 14, 14] 524,288
BatchNorm2d-180 [-1, 256, 14, 14] 512
detnet_bottleneck-181 [-1, 256, 14, 14] 0
Conv2d-182 [-1, 256, 14, 14] 65,536
BatchNorm2d-183 [-1, 256, 14, 14] 512
Conv2d-184 [-1, 256, 14, 14] 589,824
BatchNorm2d-185 [-1, 256, 14, 14] 512
BatchNorm2d-197 [-1, 30, 14, 14] 60
================================================================
Error reported:
RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same
Run the model in the graphics card:
from torchsummary import summary
summary(net.cuda(), (3, 448, 448))Problem description
Problems encountered during distributed training
RuntimeError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:47, unhandled cuda error, NCCL version 21.0.3
ncclUnhandledCudaError: Call to CUDA function failed.
The specific errors are as follows:
Problem-solving
According to the analysis of error reporting information, an error is reported during initialization during distributed training, not during training. Therefore, the problem is located on the initialization of distributed training.
Enter the following command to check the card of the current server
nvidia-smi -L
The first card found is 3070
GPU 0: NVIDIA GeForce RTX 2080 Ti (UUID: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx)
GPU 1: NVIDIA GeForce RTX 3070 (UUID: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx)
GPU 2: NVIDIA GeForce RTX 2080 Ti (UUID: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx)
GPU 3: NVIDIA GeForce RTX 2080 Ti (UUID: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx)
GPU 4: NVIDIA GeForce RTX 2080 Ti (UUID: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx)
GPU 5: NVIDIA GeForce RTX 2080 Ti (UUID: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx)
GPU 6: NVIDIA GeForce RTX 2080 Ti (UUID: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx)
GPU 7: NVIDIA GeForce RTX 2080 Ti (UUID: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx)
Therefore, here I directly try to use 2-7 cards for training.
Correct solution!
python 3.10.1
selenium 4.4.3
Old version Package:
from selenium import webdriver
New version Package:
from selenium import webdriver from selenium.webdriver.common.by import By
You need to import one more, otherwise the ‘By’ will report an error
Positioning statement
drive.find_element(By.NAME,"username").send_keys("astudy")
Error Messages:
RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument find_unused_parameters=True to torch.nn.parallel.DistributedDataParallel, and by
making sure all forward function outputs participate in calculating loss.
If you already have done the above, then the distributed data parallel module wasn’t able to locate the output tensors in the return value of your module’s forward function. Please include the loss function and the structure of the return value of forward of your module when reporting this issue (e.g. list, dict, iterable).
Parameter indices which did not receive grad for rank 0: 160 161 182 183 204 205 230 231 252 253 274 275 330 331 414 415 438 439 462 463 486 487 512 513 536 537 560 561 584 585
In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error
Solution:
Original Code
class AttentionBlock(nn.Module):
def __init__(
self,
):
super().__init__()
self.encoder_kv = conv_nd(1, 512, channels * 2, 1) #这行没有注释掉
self.encoder_qkv = conv_nd(1, 512, channels * 3, 1)
self.trans = nn.Linear(resolution*resolution*9+128,resolution*resolution*9)
def forward(self, x, encoder_out=None):
b, c, *spatial = x.shape
x = x.reshape(b, c, -1)
qkv = self.qkv(self.norm(x))
if encoder_out is not None:
# encoder_out = self.encoder_kv(encoder_out) #这行代码注释了，没有用self.encoder_kv
encoder_out = self.encoder_qkv(encoder_out)
return encode_out
Error reason:
self.encoder_kv is written in def__init__, but not used in forward, resulting in an error in torch.nn.parallel.DistributedDataParallel. Correction method
Modified code
class AttentionBlock(nn.Module):
def __init__(
self,
):
super().__init__()
#self.encoder_kv = conv_nd(1, 512, channels * 2, 1) #这行在forward中没有用到注释掉
self.encoder_qkv = conv_nd(1, 512, channels * 3, 1)
self.trans = nn.Linear(resolution*resolution*9+128,resolution*resolution*9)
def forward(self, x, encoder_out=None):
b, c, *spatial = x.shape
x = x.reshape(b, c, -1)
qkv = self.qkv(self.norm(x))
if encoder_out is not None:
# encoder_out = self.encoder_kv(encoder_out)
encoder_out = self.encoder_qkv(encoder_out)
return encode_out
Comment out the function self.encoder_kv = conv_nd(1, 512, channels * 2, 1) that is not used in the forward, and the program will run normally.
Problem:
Run Python setup.py install error: identifier “at_check” is undefined
Solution:
Add the following codes in the corresponding error report file
#ifndef AT_CHECK
#define AT_CHECK TORCH_CHECK
#endif
Reference source:
https://github.com/mrlooi/rotated_maskrcnn/issues/31
[Function Module].
Compile mindspore 1.3.0 with error CMake Error at cmake/utils.cmake:301 (message): Failed patch:
[Steps & Problems]
1. The compilation process after executing bash build.sh -e ascend gives an error, as follows:
-- Build files have been written to: /root/mindspore-v1.3.0/build/mindspore/_deps/icu4c-subbuild [100%] Built target icu4c-populate icu4c_SOURCE_DIR : /root/mindspore-v1.3.0/build/mindspore/_deps/icu4c-src patching /root/mindspore-v1.3.0/build/mindspore/_deps/icu4c-src -p1 < /root/mindspore-v1.3.0/build/mindspore/_ms_patch/icu4c.patch01 patching file icu4c/source/runConfigureICU Reversed (or previously applied) patch detected! Assume -R?[n] Apply anyway?[n] Skipping patch. 1 out of 1 hunk ignored -- saving rejects to file icu4c/source/runConfigureICU.rej CMake Error at cmake/utils.cmake:301 (message): Failed patch: /root/mindspore-v1.3.0/build/mindspore/_ms_patch/icu4c.patch01 Call Stack (most recent call first): cmake/external_libs/icu4c.cmake:36 (mindspore_add_pkg) cmake/mind_expression.cmake:85 (include) CMakeLists.txt:51 (include) -- Configuring incomplete, errors occurred! See also "/root/mindspore-v1.3.0/build/mindspore/CMakeFiles/CMakeOutput.log". See also "/root/mindspore-v1.3.0/build/mindspore/CMakeFiles/CMakeError.log".
——————————————————————————————————————————-
This situation is usually due to the failure of the last compilation on the way, try to compile again, please try to deal with the following.
rm -rf mindspore/build/mindspore/_deps/icu*Then retry the compilation.