Medical UNet Example

  • Creator
    Topic
  • #55178
    Peter HerrmannPeter Herrmann
    Participant
      @pieth

      Dear Youssef,

      I ran your U-Net example.
      But unfortunately nothing is happens on my computer.
      Loss remains unchanged and the prediction image looks messy.

      unet-test2

      Is there anything else I need to consider?
      Is the GPU on by default?

      I use a Win10 workstation with Intel Xeon Gold processors, 384 GB RAM and a 48 GB Nvidia GPU

      Best regards

      Peter

    • Author
      Replies
    • #55197
      Youssef MENJOURYoussef MENJOUR
      Admin
        @youssefmenjour

        Dear Peter,

        We are currently making a lot of changes, so we apologize for any inconvenience.

        First of all we will verify if your CUDA and CuDNN and patch (we also patch windows for a cuda bug) are well installed.

        To do this, we’ll have to start from scratch.

        First you will have to download the latest version of HAIBAL plateform installer  you can found also the latest version on this page. Install it and if we are lucky just patch and our problem is solved :).

         

        Unfortunately, this shortcut didn’t work, so we’ll have to make sure that the installation of your machine is properly done.

        ————————————— Step 1 ——————————————————————————————————

         

        Uninstall the version of CUDA present on your machine.   I invite you to reboot your PC once this operation is done.

         

        Uninstall cuda

        ————————————— Step 2 ——————————————————————————————————

        Now we are going to make sure that the directory where the CuDNN DLLs are located is also properly deleted.
        So we have to delete this directory.

        repo to delete

        The folder is named :”NVIDIA GPU Computing Toolkit” (C:\Program Files)

        ————————————— Step 3 ——————————————————————————————————

        Now you will launch the HAIBAL installer that you already installed

        haibal installer

        and logically it will detect only your device.

        installer

        For me he has detected a Geforce RTX 3090.

        ————————————— Step 4 ——————————————————————————————————

        So Launch the CUDA installation.

        launch CUDA installation

        Once this installation is finished, restart the platform installer (a new version with an automatic refresh will come in the future – so for now restart the tools)

        launch patch installation

         

        ————————————— Step 5 ——————————————————————————————————

        and patch !!!

        launch patch installation

        When the operation is finish please restart your computer.

        Finished install

        Let’s first do that and tell me if it works now.

         

        #55492
        Peter HerrmannPeter Herrmann
        Participant
        Participant
          @pieth

          Dear Youssef,

          I’ve done all the steps outlined.

          Now it’s working.
          Thanks!

          Peter

          #55704
          Youssef MENJOURYoussef MENJOUR
          Admin
            @youssefmenjour

            Dear Peter,

            An example page was create for U-Net: Convolutional Networks for Biomedical Image Segmentation.

            https://graiphic.io/examples/u-net-biomedical/

            Be carefull,  this example is not optimized because the best practice to monitor your test is to” forward” the test image after a full batch – outside of the batch loop, we purposely did it like this to display the whole code on one page without subVI. Normaly you can divide by 2 the speed of train process 🙂 .

            A fit functionality will be propose before march aimed to train efficiently any model with limited monitoring.

             

             

             

            #56968
            Peter HerrmannPeter Herrmann
            Participant
            Participant
              @pieth

              Dear Youssef,

              nice page.

              The only thing that isn’t quite right is the HAIBAL architecture compared to the model design.
              In your architecture, the number of convolution filters is constant (16). I call this the simple variant, which I also started with.

              However, the model design starts with 32 conv filters and increases to 1024 filters at the bottom of the UNet.

              #56969
              Youssef MENJOURYoussef MENJOUR
              Admin
                @youssefmenjour

                ha! I didn’t realize it !
                the most important thing is that you can go ahead with your project 🙂

                Do not hesitate Peter if you have any question.

                #56985
                Peter HerrmannPeter Herrmann
                Participant
                Participant
                  @pieth

                  Thanks a lot Youssef!

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