Category Archives: Python

[Solved] Using summary to View network parameters Error: RuntimeError: Input type (torch.cuda.FloatTensor)

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))

[ncclUnhandledCudaError] unhandled cuda error, NCCL version xx.x.x

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+Selenium Error: AttributeError: ‘WebDriver‘ NameError: name ‘By‘ is not defined

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")

[Solved] error indicates that your module has parameters that were not used in producing loss

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.

[Solved] Mindspore 1.3.0 Compile Error: CMake Error at cmake/utils

[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.

[Solved] pycallgraph Install Error: subprocess-exited-with-error

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 pycallgraph

[Solved] Python Selenium Error: AttributeError: ‘WebDriver‘ object has no attribute ‘find_element_by_xpath‘

Python 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

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.

[Solved] TensorFlow Error: UnknownError (see above for traceback): Failed to get convolution algorithm.

[Python/Pytorch – Bug] –UnknownError (see above for traceback): Failed to get convolution algorithm.

 

Question

Problem: TensorFlow reports an error: unknown error (see above for traceback): failed to get revolution algorithm

 

analysis

Analysis: the reason is that the memory of the graphics card is not enough. Selecting the appropriate memory of the graphics card can solve the problem.

 

Solution:
1. Gpustat checks the usage of the graphics card
2. Select a graphics card with enough memory;

[Solved] CUDA failure 999: unknown error ; GPU=-351697408 ; hostname=4f5e6dff58e6 ; expr=cudaSetDevice(info_.device_id);

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