lxdv / ecg-classification Goto Github PK

It seems that there is a bug here

when i come to the third step (Generate 1D and 2D data files running), it shows this error

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "dataset-generation-pool.py", line 21, in
p.map(run, ecg_data)
File "C:\Users\Weber\AppData\Local\Programs\Python\Python37\lib\multiprocessing\pool.py", line 268, in map
return self._map_async(func, iterable, mapstar, chunksize).get()
File "C:\Users\Weber\AppData\Local\Programs\Python\Python37\lib\multiprocessing\pool.py", line 657, in get
raise self._value
subprocess.CalledProcessError: Command '['python3', 'dataset-generation.py', '--file', '../mit-bih\114']' returned non-zero exit status 9009.

hello,
when I run the file pipeline.py, FileNotFoundError happen : No such file or directory: 'data/class-mapper.json', so I wonder where can I find this file, Thank you.

I tried to ran inferency code for HeartNet2D model by changing few parameters but getting an error RuntimeError: Error(s) in loading state_dict for HeartNet: Missing key(s) in state_dict: "conv1.weight", "bn1.weight", "bn1.bias", "bn1.running_mean", "bn1.running_var", "layer0.0.conv1.weight", "layer0.0.bn1.weight", "layer0.0.bn1.bias", "layer0.0.bn1.running_mean", "layer0.0.bn1.running_var", "layer0.0.conv2.weight", "layer0.0.bn2.weight", "layer0.0.bn2.bias", "layer0.0.bn2.running_mean", "layer0.0.bn2.running_var", "layer0.0.downsample.0.weight", "layer1.0.conv1.weight", "layer1.0.bn1.weight", "layer1.0.bn1.bias", "layer1.0.bn1.running_mean", "layer1.0.bn1.running_var", "layer1.0.conv2.weight", "layer1.0.bn2.weight", "layer1.0.bn2.bias", "layer1.0.bn2.running_mean", "layer1.0.bn2.running_var", "layer1.0.downsample.0.weight", "layer1.1.conv1.weight", "layer1.1.bn1.weight", "layer1.1.bn1.bias", "layer1.1.bn1.running_mean", "layer1.1.bn1.running_var", "layer1.1.conv2.weight", "layer1.1.bn2.weight", "layer1.1.bn2.bias", "layer1.1.bn2.running_mean", "layer1.1.bn2.running_var", "layer2.0.conv1.weight", "layer2.0.bn1.weight", "layer2.0.bn1.bias", "layer2.0.bn1.running_mean", "layer2.0.bn1.running_var", "layer2.0.conv2.weight", "layer2.0.bn2.weight", "layer2.0.bn2.bias", "layer2.0.bn2.running_mean", "layer2.0.bn2.running_var", "layer2.0.downsample.0.weight", "layer2.1.conv1.weight", "layer2.1.bn1.weight", "layer2.1.bn1.bias", "layer2.1.bn1.running_mean", "layer2.1.bn1.running_var", "layer2.1.conv2.weight", "layer2.1.bn2.weight", "layer2.1.bn2.bias", "layer2.1.bn2.running_mean", "layer2.1.bn2.running_var", "layer2_.0.conv1.weight", "layer2_.0.bn1.weight", "layer2_.0.bn1.bias", "layer2_.0.bn1.running_mean", "layer2_.0.bn1.running_var", "layer2_.0.conv2.weight", "layer2_.0.bn2.weight", "layer2_.0.bn2.bias", "layer2_.0.bn2.running_mean", "layer2_.0.bn2.running_var", "layer2_.0.downsample.0.weight", "layer2_.1.conv1.weight", "layer2_.1.bn1.weight", "layer2_.1.bn1.bias", "layer2_.1.bn1.running_mean", "layer2_.1.bn1.running_var", "layer2_.1.conv2.weight", "layer2_.1.bn2.weight", "layer2_.1.bn2.bias", "layer2_.1.bn2.running_mean", "layer2_.1.bn2.running_var", "layer3.0.conv1.weight", "layer3.0.bn1.weight", "layer3.0.bn1.bias", "layer3.0.bn1.running_mean", "layer3.0.bn1.running_var", "layer3.0.conv2.weight", "layer3.0.bn2.weight", "layer3.0.bn2.bias", "layer3.0.bn2.running_mean", "layer3.0.bn2.running_var", "layer3.0.downsample.0.weight", "layer3.1.conv1.weight", "layer3.1.bn1.weight", "layer3.1.bn1.bias", "layer3.1.bn1.running_mean", "layer3.1.bn1.running_var", "layer3.1.conv2.weight", "layer3.1.bn2.weight", "layer3.1.bn2.bias", "layer3.1.bn2.running_mean", "layer3.1.bn2.running_var", "layer3_.0.conv1.weight", "layer3_.0.bn1.weight", "layer3_.0.bn1.bias", "layer3_.0.bn1.running_mean", "layer3_.0.bn1.running_var", "layer3_.0.conv2.weight", "layer3_.0.bn2.weight", "layer3_.0.bn2.bias", "layer3_.0.bn2.running_mean", "layer3_.0.bn2.running_var", "layer3_.0.downsample.0.weight", "layer3_.1.conv1.weight", "layer3_.1.bn1.weight", "layer3_.1.bn1.bias", "layer3_.1.bn1.running_mean", "layer3_.1.bn1.running_var", "layer3_.1.conv2.weight", "layer3_.1.bn2.weight", "layer3_.1.bn2.bias", "layer3_.1.bn2.running_mean", "layer3_.1.bn2.running_var", "layer4.0.conv1.weight", "layer4.0.bn1.weight", "layer4.0.bn1.bias", "layer4.0.bn1.running_mean", "layer4.0.bn1.running_var", "layer4.0.conv2.weight", "layer4.0.bn2.weight", "layer4.0.bn2.bias", "layer4.0.bn2.running_mean", "layer4.0.bn2.running_var", "layer4.0.downsample.0.weight", "layer4.1.conv1.weight", "layer4.1.bn1.weight", "layer4.1.bn1.bias", "layer4.1.bn1.running_mean", "layer4.1.bn1.running_var", "layer4.1.conv2.weight", "layer4.1.bn2.weight", "layer4.1.bn2.bias", "layer4.1.bn2.running_mean", "layer4.1.bn2.running_var", "layer4_.0.conv1.weight", "layer4_.0.bn1.weight", "layer4_.0.bn1.bias", "layer4_.0.bn1.running_mean", "layer4_.0.bn1.running_var", "layer4_.0.conv2.weight", "layer4_.0.bn2.weight", "layer4_.0.bn2.bias", "layer4_.0.bn2.running_mean", "layer4_.0.bn2.running_var", "layer4_.0.downsample.0.weight", "layer4_.1.conv1.weight", "layer4_.1.bn1.weight", "layer4_.1.bn1.bias", "layer4_.1.bn1.running_mean", "layer4_.1.bn1.running_var", "layer4_.1.conv2.weight", "layer4_.1.bn2.weight", "layer4_.1.bn2.bias", "layer4_.1.bn2.running_mean", "layer4_.1.bn2.running_var", "layer5.0.conv1.weight", "layer5.0.bn1.weight", "layer5.0.bn1.bias", "layer5.0.bn1.running_mean", "layer5.0.bn1.running_var", "layer5.0.conv2.weight", "layer5.0.bn2.weight", "layer5.0.bn2.bias", "layer5.0.bn2.running_mean", "layer5.0.bn2.running_var", "layer5.0.downsample.0.weight", "fc.weight", "fc.bias". Unexpected key(s) in state_dict: "features.0.weight", "features.0.bias", "features.2.weight", "features.2.bias", "features.2.running_mean", "features.2.running_var", "features.2.num_batches_tracked", "features.3.weight", "features.3.bias", "features.5.weight", "features.5.bias", "features.5.running_mean", "features.5.running_var", "features.5.num_batches_tracked", "features.7.weight", "features.7.bias", "features.9.weight", "features.9.bias", "features.9.running_mean", "features.9.running_var", "features.9.num_batches_tracked", "features.10.weight", "features.10.bias", "features.12.weight", "features.12.bias", "features.12.running_mean", "features.12.running_var", "features.12.num_batches_tracked", "features.14.weight", "features.14.bias", "features.16.weight", "features.16.bias", "features.16.running_mean", "features.16.running_var", "features.16.num_batches_tracked", "features.17.weight", "features.17.bias", "features.19.weight", "features.19.bias", "features.19.running_mean", "features.19.running_var", "features.19.num_batches_tracked", "classifier.0.weight", "classifier.0.bias", "classifier.2.weight", "classifier.2.bias", "classifier.2.running_mean", "classifier.2.running_var", "classifier.2.num_batches_tracked", "classifier.4.weight", "classifier.4.bias".

I tried to download data from given link in README but couldn't download from there...
Can you send updated link?

Branch: exp-025
file : models/models_1d.py
defination:
def conv_subsumpling(in_planes, out_planes)
reference:
conv_subsumpling(self.inplanes, planes*block.expansion, stride)
Error:
conv_subsumpling() takes 2 positional arguments but 3 were given

Which is right, and how to fix this ?

as same like title ,because i have a dependencies version problem can author provide the full version like Python 3.7.3 or other
i download Python 3.7.9 but it can install pytorch 1.1.0 because Pytorch 1.1.0 it can not match 3.7.9

The problem occours both in windows 10 and ubuntu 18 .
And also can't finish data processing normally.

Secondly, branch exp-026 don't have requirement.txt for env.
I suppose it's the same as branch master.
but when i execute ''python main.py'' after half-processed data, it run out with "can't find moudle: torchsampler"
and i can't pip install torchsampler, orz.
pls help me!
it's a great work ! BTW

What should I do if I want to use the 2D model 1804.0681 for testing?

Hi, thanks a lot for your work!

I've come across some issues when trying to annotate the raw data on my windows machine. Taking a peek into your code, I believe the label name "" is real problematic. I guess the root of the evil is the difference in the naming convention of file system between Windows and macOS/Linux... So when trying to create a file with "" or "\" in its name, the system gives an error. (Actually os.path.join will give an erroneous result "/" on my machine).

FYI, I am using python 3.7.3 and windows 10 1903.

THanks for your code! However, i got the error here, what happened? I use colab to run it.

Trainer: Pipeline1D <class 'pipelines.pipelines.Pipeline1D'>
/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 3 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
cpuset_checked))
Checkpoint ./experiments/EcgResNet34/checkpoints/00000635.pth successfully loaded
18it [00:04, 3.99it/s]
Traceback (most recent call last):
File "pipeline.py", line 20, in
pipeline.run_pipeline()
File "/content/drive/MyDrive/researchHub/ecg-classification/pipelines/base_pipeline.py", line 69, in run_pipeline
and self.mapper[label] != "N"
KeyError: 5

What does it mean of each category

while the training of train.py file it shows me error related to no train.json file found.

So, how have you generate these json file: 1707.01836.json , 1804.06812.json, 1911.IEEE.json, 2002.00254.json, EcgResNet34.json EfficientNetB4.json, MobileNet.json? those files found at "configs/training/" folder.

Hello,

If my understanding is right:
The dimension of the output of the model is (batch size * number of classes). Logically, I would expect the elements in the second dimension (with dimension of number of classes) sum up to 1, i.e., they should represent the probability of belonging to that class. So the index of the maximum of the elements would identify the class of the input.

but, in my case at least, the elements do not sum up to 1 and I believe one normalization step (softmax function) should be added
as the last layer to make the sum equal to 1.

Thanks for your input,

E

I use Python 3.9 but console tell me C:\Users\xxxx\AppData\Roaming\Python\Python39\site-packages\pkg_resources_init_.py:122: PkgResourcesDeprecationWarning: p is an invalid version and will not be supported in a future release
warnings.warn(

I think is Python and dependencies version problem

Sorry sir i find it is python 3.7