.get_preds()/.predict() have different results

Part 1 (2019)
3

@JumpyJason
I tried that, but it seems not work ):

imgs = test_df['Image'].map(string_to_img)
preds = learn.get_preds(dl=learn.dls.test_dl(imgs))
decoded_preds = learn.get_preds(dl=learn.dls.test_dl(imgs), with_decoded=True)

print(preds)
print(decoded_preds)

output:

(tensor([[ 0.3652, -0.2001, -0.4073,  ...,  0.4606,  0.0130,  0.7156],
        [ 0.3540, -0.2094, -0.3962,  ...,  0.4514,  0.0024,  0.7101],
        [ 0.3500, -0.3029, -0.3497,  ...,  0.4191, -0.0114,  0.7321],
        ...,
        [ 0.3605, -0.2154, -0.3960,  ...,  0.4887,  0.0117,  0.7206],
        [ 0.3479, -0.2149, -0.3969,  ...,  0.5086,  0.0184,  0.7256],
        [ 0.3425, -0.2235, -0.3874,  ...,  0.4936,  0.0244,  0.7236]]), None)
(tensor([[ 0.3652, -0.2001, -0.4073,  ...,  0.4606,  0.0130,  0.7156],
        [ 0.3540, -0.2094, -0.3962,  ...,  0.4514,  0.0024,  0.7101],
        [ 0.3500, -0.3029, -0.3497,  ...,  0.4191, -0.0114,  0.7321],
        ...,
        [ 0.3605, -0.2154, -0.3960,  ...,  0.4887,  0.0117,  0.7206],
        [ 0.3479, -0.2149, -0.3969,  ...,  0.5086,  0.0184,  0.7256],
        [ 0.3425, -0.2235, -0.3874,  ...,  0.4936,  0.0244,  0.7236]]), None, tensor([[ 0.3652, -0.2001, -0.4073,  ...,  0.4606,  0.0130,  0.7156],
        [ 0.3540, -0.2094, -0.3962,  ...,  0.4514,  0.0024,  0.7101],
        [ 0.3500, -0.3029, -0.3497,  ...,  0.4191, -0.0114,  0.7321],
        ...,
        [ 0.3605, -0.2154, -0.3960,  ...,  0.4887,  0.0117,  0.7206],
        [ 0.3479, -0.2149, -0.3969,  ...,  0.5086,  0.0184,  0.7256],
        [ 0.3425, -0.2235, -0.3874,  ...,  0.4936,  0.0244,  0.7236]]))

In addition, I found a topic about how to decode by PointScaler:

sclr = PointScaler()
sclr(img)
dp = sclr.decode(TensorPoint.create(pred[1]))

This works, but I’m wondering why with_decoded=True doesn’t work as expected :face_with_raised_eyebrow:

2 Likes