I am really new here but I have managed to train (i think) my own DeepLab model 
with my custom dataset in coco format:
(Epoch 3, training loss 0.165: 100%|█████████████████████████████| 70/70 
[12:10<00:00, 10.44s/it]
Epoch 3, validation pixAcc: 0.872, mIoU: 0.436: 
100%|█████████████████████████████| 5/5 [01:04<00:00, 12.89s/it]
Epoch 3 validation pixAcc: 0.872, mIoU: 0.436)

Howewer I cannot run it successfully
I've not been able to find tutorial about training and using my own DeepLab 
model so I have adapdet some found logic to get it processing my images on cpu 
without exeptions
But now I am stuck
Every time a get the same empty result
![изображение|316x332](upload://bQPNzo0F7nHTvaM6HKIB6Ajuld5.png) 
Maybe the problem is the same as here:
https://discuss.mxnet.apache.org/t/batch-dot-one-hot-vectors-with-embeddings-results-in-nan/3732/2
About running on CPU and stuff, but I have managed to use GPUs only in training 
process, not in the running. And I don't know how to do that
Maybe I have done all wrong
Could you get me some advice?
Thank you

Running script:

    import mxnet as mx
    from mxnet import image
    from mxnet.gluon.data.vision import transforms
    import gluoncv
    # using cpu
    ctx = mx.cpu(0)

    #%%

    filepath = r"F:\mxnet-cu100\gebrei(2)-1-2_06.jpg"

    #%%

    img = image.imread(filepath)

    from matplotlib import pyplot as plt
    plt.imshow(img.asnumpy())
    plt.show()

    #%%

    from gluoncv.data.transforms.presets.segmentation import test_transform
    img = test_transform(img, ctx)

    #%%

    CAT_LIST = [0, 1]
    NUM_CLASS = 2
    CLASSES = ("background", "building")

    model = gluoncv.model_zoo.get_model(
        'deeplab_resnet50_coco', pretrained=False, num_class=NUM_CLASS, 
classes=CLASSES)
    
model.load_parameters(r"F:\mxnet-cu100\runs\coco\deeplab\resnet50\epoch_0001_mIoU_0.4396.params",
                          allow_missing=True)

    #%%

    output = model.predict(img)
    predict = mx.nd.squeeze(mx.nd.argmax(output, 1)).asnumpy()


    #%%

    from gluoncv.utils.viz import get_color_pallete
    import matplotlib.image as mpimg
    mask = get_color_pallete(predict, 'ade20k')
    mask.save('output.png')

    #%%

    mmask = mpimg.imread('output.png')
    plt.imshow(mmask)
    plt.show()





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