In this tutorial, we'll see how to create custom subclasses of
ItemList while retaining everything the fastai library has to offer. To allow basic functions to work consistently across various applications, the fastai library delegates several tasks to one of those specific objects, and we'll see here which methods you have to implement to be able to have everything work properly. But first let's take a step back to see where you'll use your end result.
The data block API works by allowing you to pick a class that is responsible to get your items and another class that is charged with getting your targets. Combined together, they create a pytorch
Dataset that is then wrapped inside a
DataLoader. The training set, validation set and maybe test set are then all put in a
The data block API allows you to mix and match what class your inputs have, what class your targets have, how to do the split between train and validation set, then how to create the
DataBunch, but if you have a very specific kind of input/target, the fastai classes might no be sufficient to you. This tutorial is there to explain what is needed to create a new class of items and what methods are important to implement or override.
It goes in two phases: first we focus on what you need to create a custom
ItemBase class (which is the type of your inputs/targets) then on how to create your custom
ItemList (which is basically a set of
ItemBase) while highlighting which methods are called by the library.
The fastai library contains three basic types of
ItemBase that you might want to subclass:
Whether you decide to create your own item class or to subclass one of the above, here is what you need to implement:
Those are the more important attributes your custom
ItemBase needs as they're used everywhere in the fastai library:
ItemBase.datais the thing that is passed to pytorch when you want to create a
DataLoader. This is what needs to be fed to your model. Note that it might be different from the representation of your item since you might want something that is more understandable.
__str__representation: if applicable, this is what will be displayed when the fastai library has to show your item.
If we take the example of a
o for instance:
o.datais a tensor where the tags are one-hot encoded
str(o)returns the tags separated by ;
If you want to code the way data augmentation should be applied to your custom
Item, you should write an
apply_tfms method. This is what will be called if you apply a
transform block in the data block API.
For cycleGANs, we need to create a custom type of items since we feed the model tuples of images. Let's look at how to code this. The basis is to code the
data attribute that is what will be given to the model. Note that we still keep track of the initial object (usuall in an
obj attrivute) to be able to show nice representations later on. Here the object is the tuple of images and the data their underlying tensors normalized between -1 and 1.
class ImageTuple(ItemBase): def __init__(self, img1, img2): self.img1,self.img2 = img1,img2 self.obj,self.data = (img1,img2),[-1+2*img1.data,-1+2*img2.data]
Then we want to apply data augmentation to our tuple of images. That's done by writing and
apply_tfms method as we saw before. Here we just pass that call to the two underlying images then update the data.
def apply_tfms(self, tfms, **kwargs): self.img1 = self.img1.apply_tfms(tfms, **kwargs) self.img2 = self.img2.apply_tfms(tfms, **kwargs) self.data = [-1+2*self.img1.data,-1+2*self.img2.data] return self
We define a last method to stack the two images next ot each other, which we will use later for a customized
def to_one(self): return Image(0.5+torch.cat(self.data,2)/2)
This is the main class that allows you to group your inputs or your targets in the data block API. You can then use any of the splitting or labelling methods before creating a
DataBunch. To make sure everything is properly working, here is what you need to know.
Whether you're directly subclassing
ItemList or one of the particular fastai ones, make sure to know the content of the following three variables as you may need to adjust them:
_bunchcontains the name of the class that will be used to create a
_processorcontains a class (or a list of classes) of
PreProcessorthat will then be used as the default to create processor for this
_label_clscontains the class that will be used to create the labels by default
_label_cls is the first to be used in the data block API, in the labelling function. If this variable is set to
None, the label class will be set to
FloatList depending on the type of the first item. The default can be overridden by passing a
label_cls in the kwargs of the labelling function.
_processor is the second to be used. The processors are called at the end of the labelling to apply some kind of function on your items. The default processor of the inputs can be overriden by passing a
processor in the kwargs when creating the
ItemList, the default processor of the targets can be overridden by passing a
processor in the kwargs of the labelling function.
Processors are useful for pre-processing some data, but you also need to put in their state any variable you want to save for the call of
data.export() before creating a
Learner object for inference: the state of the
ItemList isn't saved there, only their processors. For instance
SegmentationProcessor's only reason to exist is to save the dataset classes, and during the process call, it doesn't do anything apart from setting the
c attributes to its dataset.
class SegmentationProcessor(PreProcessor): def __init__(self, ds:ItemList): self.classes = ds.classes def process(self, ds:ItemList): ds.classes,ds.c = self.classes,len(self.classes)
_bunch is the last class variable used in the data block. When you type the final
databunch(), the data block API calls the
_bunch.create method with the
_bunch of the inputs.
If you pass additional arguments in your
__init__ call that you save in the state of your
ItemList, we have to make sure they are also passed along in the
new method as this one is used to create your training and validation set when splitting. To do that, you just have to add their names in the
copy_new argument of your custom
ItemList, preferably during the
__init__. Here we will need two collections of filenames (for the two type of images) so we make sure the second one is copied like this:
def __init__(self, items, itemsB=None, **kwargs): super().__init__(items, **kwargs) self.itemsB = itemsB self.copy_new.append('itemsB')
Be sure to keep the kwargs as is, as they contain all the additional stuff you can pass to an
The most important method you have to implement is
get: this one will enable your custom
ItemList to generate an
ItemBase from the thing stored in its
items array. For instance an
ImageList has the following
def get(self, i): fn = super().get(i) res = self.open(fn) self.sizes[i] = res.size return res
The first line basically looks at
self.items[i] (which is a filename). The second line opens it since the
openmethod is just
def open(self, fn): return open_image(fn)
The third line is there for
ImageBBox targets that require the size of the input
Image to be created. Note that if you are building a custom target class and you need the size of an image, you should call
This is the method that is called in
learn.show_results() to transform a pytorch tensor back in an
ItemBase. In a way, it does the opposite of calling
ItemBase.data. It should take a tensor
t and return the same kind of thing as the
In some situations (
ImageBBox for instance) you need to have a look at the corresponding input to rebuild your item. In this case, you should have a second argument called
x (don't change that name). For instance, here is the
reconstruct method of
def reconstruct(self, t, x): return ImagePoints(FlowField(x.size, t), scale=False)
This is the method that is called in
learn.show_results() to transform predictions in an output tensor suitable for
reconstruct. For instance we may need to take the maximum argument (for
Category) or the predictions greater than a certain threshold (for
MultiCategory). It should take a tensor, along with optional kwargs and return a tensor.
For instance, here is the
analyze_pred method of
def analyze_pred(self, pred, thresh:float=0.5): return (pred >= thresh).float()
thresh can then be passed as kwarg during the calls to
If you want to use methods such a
learn.show_results() with a brand new kind of
ItemBase you will need to implement two other methods. In both cases, the generic function will grab the tensors of inputs, targets and predictions (if applicable), reconstruct the corresponding
ItemBase (as seen before) but it will delegate to the
ItemList the way to display the results.
def show_xys(self, xs, ys, **kwargs)->None: def show_xyzs(self, xs, ys, zs, **kwargs)->None:
In both cases
ys represent the inputs and the targets, in the second case
zs represent the predictions. They are lists of the same length that depend on the
rows argument you passed. The kwargs are passed from
learn.show_results(). As an example, here is the source code of those methods in
def show_xys(self, xs, ys, figsize:Tuple[int,int]=(9,10), **kwargs): "Show the `xs` and `ys` on a figure of `figsize`. `kwargs` are passed to the show method." rows = int(math.sqrt(len(xs))) fig, axs = plt.subplots(rows,rows,figsize=figsize) for i, ax in enumerate(axs.flatten() if rows > 1 else [axs]): xs[i].show(ax=ax, y=ys[i], **kwargs) plt.tight_layout() def show_xyzs(self, xs, ys, zs, figsize:Tuple[int,int]=None, **kwargs): """Show `xs` (inputs), `ys` (targets) and `zs` (predictions) on a figure of `figsize`. `kwargs` are passed to the show method.""" figsize = ifnone(figsize, (6,3*len(xs))) fig,axs = plt.subplots(len(xs), 2, figsize=figsize) fig.suptitle('Ground truth / Predictions', weight='bold', size=14) for i,(x,y,z) in enumerate(zip(xs,ys,zs)): x.show(ax=axs[i,0], y=y, **kwargs) x.show(ax=axs[i,1], y=z, **kwargs)
Linked to this method is the class variable
_show_square of an
ItemList. It defaults to
False but if it's
show_batch method will send
rows * rows
show_xys (so that it shows a square of inputs/targets), like here for images.
Continuing our custom item example, we create a custom
ItemList class that will wrap those
ImageTuples properly. The first thing is to write a custom
__init__ method (since we need a list of filenames here) which means we also have to change the
class ImageTupleList(ImageList): def __init__(self, items, itemsB=None, **kwargs): super().__init__(items, **kwargs) self.itemsB = itemsB self.copy_new.append('itemsB')
We then specify how to get one item. Here we pass the image in the first list of items, and pick one randomly in the second list.
def get(self, i): img1 = super().get(i) fn = self.itemsB[random.randint(0, len(self.itemsB)-1)] return ImageTuple(img1, open_image(fn))
We also add a custom factory method to directly create an
ImageTupleList from two folders.
@classmethod def from_folders(cls, path, folderA, folderB, **kwargs): itemsB = ImageList.from_folder(path/folderB).items res = super().from_folder(path/folderA, itemsB=itemsB, **kwargs) res.path = path return res
Finally, we have to specify how to reconstruct the
ImageTuple from tensors if we want
show_batch to work. We recreate the images and denormalize.
def reconstruct(self, t:Tensor): return ImageTuple(Image(t/2+0.5),Image(t/2+0.5))
There is no need to write a
analyze_preds method since the default behavior (returning the output tensor) is what we need here. However
show_results won't work properly unless the target (which we don't really care about here) has the right
reconstruct method: the fastai library uses the
reconstruct method of the target on the outputs. That's why we create another custom
ItemList with just that
reconstruct method. The first line is to reconstruct our dummy targets, and the second one is the same as in
class TargetTupleList(ItemList): def reconstruct(self, t:Tensor): if len(t.size()) == 0: return t return ImageTuple(Image(t/2+0.5),Image(t/2+0.5))
To make sure our
ImageTupleList uses that for labelling, we pass it in
_label_cls and this is what the result looks like.
class ImageTupleList(ImageList): _label_cls=TargetTupleList def __init__(self, items, itemsB=None, **kwargs): super().__init__(items, **kwargs) self.itemsB = itemsB self.copy_new.append('itemsB') def get(self, i): img1 = super().get(i) fn = self.itemsB[random.randint(0, len(self.itemsB)-1)] return ImageTuple(img1, open_image(fn)) def reconstruct(self, t:Tensor): return ImageTuple(Image(t/2+0.5),Image(t/2+0.5)) @classmethod def from_folders(cls, path, folderA, folderB, **kwargs): itemsB = ImageList.from_folder(path/folderB).items res = super().from_folder(path/folderA, itemsB=itemsB, **kwargs) res.path = path return res
Lastly, we want to customize the behavior of
show_results. Remember the
to_one method just puts the two images next to each other.
def show_xys(self, xs, ys, figsize:Tuple[int,int]=(12,6), **kwargs): "Show the `xs` and `ys` on a figure of `figsize`. `kwargs` are passed to the show method." rows = int(math.sqrt(len(xs))) fig, axs = plt.subplots(rows,rows,figsize=figsize) for i, ax in enumerate(axs.flatten() if rows > 1 else [axs]): xs[i].to_one().show(ax=ax, **kwargs) plt.tight_layout() def show_xyzs(self, xs, ys, zs, figsize:Tuple[int,int]=None, **kwargs): """Show `xs` (inputs), `ys` (targets) and `zs` (predictions) on a figure of `figsize`. `kwargs` are passed to the show method.""" figsize = ifnone(figsize, (12,3*len(xs))) fig,axs = plt.subplots(len(xs), 2, figsize=figsize) fig.suptitle('Ground truth / Predictions', weight='bold', size=14) for i,(x,z) in enumerate(zip(xs,zs)): x.to_one().show(ax=axs[i,0], **kwargs) z.to_one().show(ax=axs[i,1], **kwargs)