Model training

[zoelee70]

I am trying to install and run DeepFIR on a MAC. I am confused on how exactly to train the models and load them into the Results folder. Are there pre-trained models available for use?

[KeithGeorgeCiantar]

Hello, thank you for using our repository!

If you would like to use the GUI, I had a discussion to address certain issues in the comments of the instruction video on YouTube. You go through the comments here. Make sure to click 'Read More' on some of the comments because they're pretty long. They also contain some general information that might be helpful.

You can get the pre-trained models from the README here, the first link is for the models in the first paper, the second link is for models from our latest paper.

For tips to run the models in eval/inference mode, you should check out issue #3.

For tips regarding issues you might run into when training, kindly check out issue #4.

Hope this helps!

[zoelee70]

Thank you the comments on the video explained a lot. I was able to download the pre-trained models and edited the models.csv file to: name,label,location,epoch,group qrcan_ma_dan_blind_complex,"RCAN with DAN",/Users/zl/RUMpy-main/models/complex_pipeline_models,best,”Complex Data"

I was able to create the DeepFIR_GUI.app but am getting an error on the API connection and the models are not loading. I believe the problem has to do with my file structure. Should the RUMpy_main folder reside inside the virtual environment? Also I am unable to locate the .\resources\app folder in the downloaded code.

Thank you

[KeithGeorgeCiantar]

Good to hear you're making progress! I'll try to address your issues one by one to make sure everything is covered.

---

To start, the main use of the GUI is a demo application to quickly run the models on videos and images. The GUI can either be run by cloning the repository and then following the instructions here, or by downloading the packaged version from here.

If I have understood correctly, since you're using macOS, you have downloaded the file DeepFIR_GUI-darwin-x64.zip and have managed to run the application. For the .\resources\app, you should be able to find it in .\DeepFIR_GUI-darwin-x64\DeepFIR_GUI.app\Contents\Resources\app, or .\DeepFIR_GUI.app\Contents\Resources\app. It is only created when the GUI is packaged, so it is not found in the code itself.

It is important to note that the GUI is only the frontend, so to be able to load and run a model, the backend API would also need to be running. I will explain how to run the API in the next section.

---

Moving on to the API, this is the brains of the GUI application. The API backend for the GUI is run via the script RUMpy\GUI\deep_fir_server.py. This script uses the code in the repository to load and run the models. To run the script you need to follow these steps:

1. Clone the RUMpy repository.
2. Install Anaconda/Miniconda.
3. Open the terminal.
4. Follow the steps from here to setup the Python environment. Note: the Python environment does not need to be manually edited and should be managed through conda. The files in the environment and files in RUMpy_main are NOT dependent on each other and should be kept in separate locations.
5. On the same environment, follow the steps from here to install the remaining packages for the API.
6. In the terminal, navigate to the RUMpy_main\GUI folder.
7. Activate the environment, and run the command python deep_fir_server.py or python3 deep_fir_server.py. This might take a few seconds until the local server finishes setting up.
8. You should now have a local server running on your machine, take note of the output on the terminal to check the server's address.

Now that you have the backend API running, keep it running and open the DeepFIR_GUI.app. From here you can go to the Settings page and press the Test API Connection button to see if the GUI app can communicate with the backend server. If there is an error, make sure that the DeepFIR API URL has the same value as the output from the terminal (in general it should be http://127.0.0.1:5000/). If everything is working as expected, you now have all the components ready to be used.

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With everything set up, you should now be able to perform inference using the GUI. With all the pre-requisites from the previous two sections taken care of, the process to load and run the models should be as follows:

1. Open the terminal.
2. Activate the Python environment.
3. In the terminal, navigate to the RUMpy_main\GUI folder.
4. Run the command python deep_fir_server.py or python3 deep_fir_server.py.
5. In the file explorer, go the folder .\DeepFIR_GUI.app\Contents\Resources\app and check that the models.csv looks like what you've written in your previous comment.
6. Open the DeepFIR_GUI.app.
7. Go to the Settings page in the app.
8. Click the Test API Connection to make sure that the backend is working.
9. In the dropdown for Super-Resolution Model, you should be able to find RCAN with DAN and select it.
10. If you have a GPU, you can leave the Processor dropdown as is, otherwise you can switch to CPU.
11. Go to the Super-Resolution page, now you should be able to load an image or a video and use the app normally. Note: using the CPU will have a longer computation time to see the super resolved output.

---

Hope this helps!

[zoelee70]

Thank you for taking the time to help me out. I have been able to create the DeepFIR_GUI.app and the models.csv was correctly installed.

I am having trouble when I run the python3 deep_fir_server.py command. I am getting a number of errors. Do you have any suggestions? Thank you

[KeithGeorgeCiantar]

Great to hear you've almost managed to get everything working!

According to the screenshot you sent, you were attempting to run python3 deep_fir_backend_script.py. At the moment, that script is not used when running the API backend, but it was something that we were testing and didn't fully finalise. You can get further details on it here.

For the API backend, you need to run python3 deep_fir_server.py or python deep_fir_server.py. This should give you the functionality you need to be able to load and run models.

Hope this helps!

[zoelee70]

I get a bus error with the python3 deep_fir_server.py command

zsh: bus error python3 deep_fir_server.py

[zoelee70]

I have no idea how to solve this error do you have any suggestions. Thank you

[KeithGeorgeCiantar]

Sorry to hear about this issue! This is a new one to me, but I might have some ideas to try.

1. Check that you're using the correct Python environment. If you installed everything on base, use conda list and pip list to ensure all the required packages are installed. Otherwise, you need to activate the environment. The main package lists can be found here and here, and the Flask package is also required.
2. Try commenting out all the code expect for the imports and re-try running the script. This should narrow it down to see whether the issue is with the packages or the code.
3. Try creating a conda environment with an older version of Python. During our development, with mainly worked with Python 3.7 and 3.8, so to create the environment we would use the command conda create -n *environment_name* python=3.7. I strongly suggest creating a separate environment instead of re-installing everything on base.
4. Check if certain packages/dependencies have issues with the Mac products. The custom Apple chips might need special attention when installing packages.

Let me know if any of these help!

[zoelee70]

I am making progress. I installed python 3.7 in a conda environment and the bus error disappeared. When I would run: python deep_fir_server.py I was getting a module not found error on the deep_fir_server.py line 12 (from rumpy.SISR.models.interface import SISRInterface) I added two lines to deep_fir_server.py before line 12: import sys sys.path.append('../')

Now when I run python deep_fir_server.py I do not get any errors and I can run along with the front end app. However, the models are not being installed. The models.csv is in the resources folder and has been updated. Any suggestions?

Thank you

[KeithGeorgeCiantar]

Ok great, we're almost there!

I think I know why you were getting an error at line 12. In the installation instructions here, point 4 says to do pip install -e ., which is a command that installs the rumpy code as a package.

When you have the terminal open, navigate to the RUMpy_main directory, activate the conda environment, and run the command pip install -e .. This should install the rumpy code as a package in the environment. Then when you run the command python deep_fir_server.py, the script should be able to use the rumpy code as a package without any issues. I'm not sure whether you need the sys.path.append('../'), but it may be an OS-related thing.

Hopefully this should be the last thing that's required!

[zoelee70]

I am able to see the models in the DeepFIR_GUI and am able to open the API URL:

when I try to load a model I get the following error message:

my models.csv file is:

Do you think there is a path issue similar to the error I was getting in the deep_fir_server.py module above?

Thank you for your help - as you can most likely guess I am not a programmer

[KeithGeorgeCiantar]

Could you try setting the processor to CPU and then pressing Load Model?

[zoelee70]

I tried two models selecting the CPU and got errors with both.

[KeithGeorgeCiantar]

Ok it seems that there's an issue with the version of einops. Could you do conda list or pip list, and tell me the version you have installed? According to an issue I found here, it might be there was an update that caused issues.

[zoelee70]

(base) zl@iMac-Pro ~ % cd a_project (base) zl@iMac-Pro a_project % conda activate my_env (my_env) zl@iMac-Pro a_project % conda list

packages in environment at /Users/zl/miniconda3/envs/my_env:

#

Name Version Build Channel

anyio 3.7.1 pyhd8ed1ab_0 conda-forge aom 3.5.0 hf0c8a7f_0 conda-forge appnope 0.1.3 pyhd8ed1ab_0 conda-forge argon2-cffi 23.1.0 pyhd8ed1ab_0 conda-forge argon2-cffi-bindings 21.2.0 py37h69ee0a8_2 conda-forge attrs 23.1.0 pyh71513ae_1 conda-forge backcall 0.2.0 pyh9f0ad1d_0 conda-forge backports 1.0 pyhd8ed1ab_3 conda-forge backports.functools_lru_cache 1.6.5 pyhd8ed1ab_0 conda-forge beautifulsoup4 4.12.2 pyha770c72_0 conda-forge blas 2.120 mkl conda-forge blas-devel 3.9.0 20_osx64_mkl conda-forge bleach 6.1.0 pyhd8ed1ab_0 conda-forge blosc 1.21.5 heccf04b_0 conda-forge bottleneck 1.3.5 py37h4de8ad1_0 conda-forge brotli 1.0.9 hb7f2c08_9 conda-forge brotli-bin 1.0.9 hb7f2c08_9 conda-forge brotli-python 1.0.9 py37h0582d14_7 conda-forge brunsli 0.1 h046ec9c_0 conda-forge bzip2 1.0.8 h10d778d_5 conda-forge c-ares 1.23.0 h10d778d_0 conda-forge c-blosc2 2.11.3 h354e526_0 conda-forge ca-certificates 2023.11.17 h8857fd0_0 conda-forge cached-property 1.5.2 hd8ed1ab_1 conda-forge cached_property 1.5.2 pyha770c72_1 conda-forge cairo 1.16.0 h904041c_1014 conda-forge certifi 2023.11.17 pyhd8ed1ab_0 conda-forge cffi 1.15.1 py37h7346b73_1 conda-forge cfitsio 4.0.0 hb20e66c_0 conda-forge charls 2.3.4 he49afe7_0 conda-forge charset-normalizer 3.3.2 pyhd8ed1ab_0 conda-forge click 8.1.3 py37hf985489_0 conda-forge click-config-file 0.6.0 pyhd8ed1ab_1 conda-forge cloudpickle 2.2.1 pyhd8ed1ab_0 conda-forge colorama 0.4.6 pyhd8ed1ab_0 conda-forge comm 0.1.4 pyhd8ed1ab_0 conda-forge configobj 5.0.8 pyhd8ed1ab_0 conda-forge cycler 0.11.0 pyhd8ed1ab_0 conda-forge cytoolz 0.12.0 py37h994c40b_0 conda-forge dask-core 2022.2.0 pyhd8ed1ab_0 conda-forge debugpy 1.6.3 py37hf6dfe07_0 conda-forge decorator 5.1.1 pyhd8ed1ab_0 conda-forge deepdiff 6.7.1 pyhd8ed1ab_0 conda-forge defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge einops 0.7.0 pyhd8ed1ab_0 conda-forge entrypoints 0.4 pyhd8ed1ab_0 conda-forge exceptiongroup 1.2.0 pyhd8ed1ab_0 conda-forge expat 2.5.0 hf0c8a7f_1 conda-forge ffmpeg 5.1.2 gpl_h8b4fe81_106 conda-forge filelock 3.13.1 pyhd8ed1ab_0 conda-forge flask 2.2.2 py37hecd8cb5_0
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(my_env) zl@iMac-Pro a_project % pip list Package Version Editable project location

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anyio 3.7.1 appnope 0.1.3 argon2-cffi 23.1.0 argon2-cffi-bindings 21.2.0 attrs 23.1.0 backcall 0.2.0 backports.functools-lru-cache 1.6.5 beautifulsoup4 4.12.2 bleach 6.1.0 Bottleneck 1.3.5 Brotli 1.0.9 cached-property 1.5.2 certifi 2023.11.17 cffi 1.15.1 charset-normalizer 3.3.2 click 8.1.3 click-config-file 0.6.0 cloudpickle 2.2.1 colorama 0.4.6 comm 0.1.4 configobj 5.0.8 cycler 0.11.0 cytoolz 0.12.0 dask 2022.2.0 debugpy 1.6.3 decorator 5.1.1 deepdiff 6.7.1 defusedxml 0.7.1 einops 0.7.0 entrypoints 0.4 exceptiongroup 1.2.0 fastjsonschema 2.19.0 filelock 3.13.1 Flask 2.2.2 fonttools 4.38.0 fsspec 2023.1.0 h5py 3.7.0 huggingface-hub 0.16.4 idna 3.6 imagecodecs 2021.11.20 imageio 2.31.5 imageio-ffmpeg 0.4.9 importlib-metadata 4.11.4 importlib-resources 6.0.0 ipykernel 6.16.2 ipython 7.33.0 ipython-genutils 0.2.0 ipywidgets 8.1.1 itsdangerous 2.0.1 jedi 0.19.1 Jinja2 3.1.2 joblib 1.3.2 jsonschema 4.17.3 jupyter 1.0.0 jupyter_client 7.4.9 jupyter-console 6.5.1 jupyter_core 4.11.1 jupyter-server 1.23.4 jupyterlab_pygments 0.3.0 jupyterlab-widgets 3.0.9 kiwisolver 1.4.4 llvmlite 0.39.1 locket 1.0.0 lpips 0.1.2 MarkupSafe 2.1.1 matplotlib 3.5.3 matplotlib-inline 0.1.6 mistune 3.0.2 moviepy 1.0.3 munkres 1.1.4 nbclassic 1.0.0 nbclient 0.7.0 nbconvert 7.6.0 nbformat 5.8.0 nest-asyncio 1.5.8 networkx 2.6.3 notebook 6.5.6 notebook_shim 0.2.3 numba 0.56.3 numexpr 2.8.3 numpy 1.21.6 opencv-python 4.6.0 ordered-set 4.1.0 orjson 3.8.1 packaging 23.2 pandas 1.3.5 pandocfilters 1.5.0 parso 0.8.3 partd 1.4.1 pexpect 4.8.0 pickleshare 0.7.5 Pillow 9.2.0 pip 22.3.1 pkgutil_resolve_name 1.3.10 proglog 0.1.9 prometheus-client 0.17.1 prompt-toolkit 3.0.41 psutil 5.9.3 ptyprocess 0.7.0 pycparser 2.21 Pygments 2.17.2 pynndescent 0.5.11 pyobjc-core 8.5.1 pyobjc-framework-Cocoa 8.5 pyparsing 3.1.1 pyrsistent 0.18.1 PySocks 1.7.1 python-dateutil 2.8.2 pytz 2023.3.post1 PyWavelets 1.3.0 PyYAML 6.0 pyzmq 24.0.1 qtconsole 5.4.4 QtPy 2.4.1 requests 2.31.0 rptree 0.1.1 RUMpy 1.0 /Users/zl/a_project/RUMpy scikit-image 0.19.3 scikit-learn 1.0.2 scikit-video 1.1.11 scipy 1.7.3 Send2Trash 1.8.2 setuptools 68.2.2 six 1.16.0 sniffio 1.3.0 soupsieve 2.3.2.post1 terminado 0.17.1 threadpoolctl 3.1.0 tifffile 2021.11.2 timm 0.6.13 tinycss2 1.2.1 toml 0.10.2 toolz 0.12.0 torch 1.13.1 torchinfo 1.8.0 torchvision 0.14.1 tornado 6.2 tqdm 4.66.1 traitlets 5.9.0 typing_extensions 4.7.1 umap-learn 0.5.3 unicodedata2 14.0.0 urllib3 2.1.0 wcwidth 0.2.10 webencodings 0.5.1 websocket-client 1.6.1 Werkzeug 2.2.2 wheel 0.38.4 widgetsnbextension 4.0.9 zipp 3.15.0 (my_env) zl@iMac-Pro a_project %

[zoelee70]

(my_env) zl@iMac-Pro a_project % conda --version conda 23.10.0 (my_env) zl@iMac-Pro a_project % pip --version pip 22.3.1 from /Users/zl/miniconda3/envs/my_env/lib/python3.7/site-packages/pip (python 3.7) (my_env) zl@iMac-Pro a_project %

[KeithGeorgeCiantar]

Ok I think I've figured it out now.

Please do: conda install einops=0.6.1 pip install prefetch_generator

These should fix both the errors.

[zoelee70]

That worked! Thank you for your help. I will begin to try out some models in the morning.

[KeithGeorgeCiantar]

Great to hear! Good luck!