How to output result image from output?

When i finished the model training. & load model & input a single image by:

# Image preprocessing
    image = cv2.imread(image_path, cv2.IMREAD_COLOR).astype(float)
    image = cv2.resize(image, dsize=(512,512))
    image_original = image.astype(np.uint8)
    image = torch.from_numpy(image.transpose(2, 0, 1)).float().unsqueeze(0)
    image = image.to(device)

    # Inference
    output = Model(image)

output is a tensor & shape is (5,512,512). like:

[[[  4916.116    5713.0825   6510.0493 ...   8198.43     7449.5635
     6700.7207]
  [  5698.8423   6637.15     7575.4585 ...   9693.137    8787.99
     7882.872 ]
  [  6481.569    7561.218    8640.867  ...  11187.843   10126.417
     9065.023 ]
  ...

if i want to invert the tensor to labelimage ,What should i do?

if i process output by softmax or argmax or torch.max ,the output will be null image like

[[0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]

It’s a weird question!

[jfzhang95]

What is your model setting? When I run:

    model = DeepLab(backbone='resnet', output_stride=16, num_classes=21).cuda()
    model.eval()
    input = torch.rand(1, 3, 513, 513).cuda()
    output = model(input)
    print(output.size())

The output size is (1, 21, 513, 513). Then run the following code

    prediction = output.data.cpu().numpy()
    prediction = np.argmax(prediction, axis=1)

I could get the final prediction and the shape of prediction is (513, 513).

Based on the example you provided, I guess the number of classes in your example is 5. So, when you get output, you could run pred=output.data.cpu().numpy() and pred=np.argmax(pred, axis=0) to get the prediction labels, just like what I do above.

Hope this could help you 😃

[albertchristianto]

@leedoge hi, I think i have solved this problem. but, maybe no. i forgot because it has already 1 month. You can check your input processing. like have you substract your image with imagenet mean ?

best regards, Albert Christianto