Show_batch with custom ItemList and ItemBase

(Kai Lichtenberg) #1

Hey!
I’m using a custom ItemList with a custom ItemBase (inherited from ImageList and Image) with the not really creative names TensorList and TensorImage. Training and everything else works like expected, when I plot my DataBunch I get:

ImageDataBunch;

Train: LabelList (1175 items)
x: TensorList
TensorImage (3, 224, 224),TensorImage (3, 224, 224),TensorImage (3, 224, 224),TensorImage (3, 224,  224),TensorImage (3, 224, 224)
y: CategoryList
NIO,NIO,NIO,NIO,NIO

Valid: LabelList (325 items)
x: TensorList
TensorImage (3, 224, 224),TensorImage (3, 224, 224),TensorImage (3, 224, 224),TensorImage (3, 224,  224),TensorImage (3, 224, 224)
y: CategoryList
NIO,NIO,NIO,NIO,NIO

Test: None

Those images need some exposure enhancement in order to have better visibility for the human eye so I also overloaded the show function in my custom Image class. That works if I directly call the function in my custom class. It is not working when I use show_batch in my DataBunch. The show_xys function is called to plot the images and it seems to use the show function I customized:

def show_xys(self, xs, ys, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, **kwargs):
        "Show the `xs` (inputs) and `ys` (targets) on a figure of `figsize`."
        rows = int(np.ceil(math.sqrt(len(xs))))
        axs = subplots(rows, rows, imgsize=imgsize, figsize=figsize)
        for x,y,ax in zip(xs, ys, axs.flatten()): x.show(ax=ax, y=y, **kwargs)
        for ax in axs.flatten()[len(xs):]: ax.axis('off')
        plt.tight_layout()

When I plot(type(x)) in this function it says x is an Image and not my custom TensorImage, which kind off confused me :thinking: Does someone know whats going on?

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#2

I do :wink:
Before going to show_xys, your xs and ys are passed to the reconstruct method of your ItemList. Since you inherited from ImageList, they go through ImageList.reconstruct which (as you can see here) converts them to an Image.

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(Kai Lichtenberg) #3

Now that’s an easy fix, thanks!! :grinning: Totally missed that

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