Transfer learning is a technique where you use a model trained on a very large dataset (usually ImageNet in computer vision) and then adapt it to your own dataset. The idea is that it has learned to recognize many features on all of this data, and that you will benefit from this knowledge, especially if your dataset is small, compared to starting from a randomly initialized model. It has been proved in this article on a wide range of tasks that transfer learning nearly always give better results.
In practice, you need to change the last part of your model to be adapted to your own number of classes. Most convolutional models end with a few linear layers (a part will call head). The last convolutional layer will have analyzed features in the image that went through the model, and the job of the head is to convert those in predictions for each of our classes. In transfer learning we will keep all the convolutional layers (called the body or the backbone of the model) with their weights pretrained on ImageNet but will define a new head initialized randomly.
Then we will train the model we obtain in two phases: first we freeze the body weights and only train the head (to convert those analyzed features into predictions for our own data), then we unfreeze the layers of the backbone (gradually if necessary) and fine-tune the whole model (possibly using differential learning rates).
cnn_learner factory method helps you to automatically get a pretrained model from a given architecture with a custom head that is suitable for your data.
Build convnet style learner.
This method creates a
Learner object from the
data object and model inferred from it with the backbone given in
arch. Specifically, it will cut the model defined by
arch (randomly initialized if
pretrained is False) at the last convolutional layer by default (or as defined in
cut, see below) and add:
- blocks of [
The blocks are defined by the
ps arguments. Specifically, the first block will have a number of inputs inferred from the backbone
arch and the last one will have a number of outputs equal to
data.c (which contains the number of classes of the data) and the intermediate blocks have a number of inputs/outputs determined by
lin_frts (of course a block has a number of inputs equal to the number of outputs of the previous block). The default is to have an intermediate hidden size of 512 (which makes two blocks
model_activation -> 512 ->
n_classes). If you pass a float then the final dropout layer will have the value
ps, and the remaining will be
ps/2. If you pass a list then the values are used for dropout probabilities directly.
Note that the very last block doesn't have a
nn.ReLU activation, to allow you to use any final activation you want (generally included in the loss function in pytorch). Also, the backbone will be frozen if you choose
pretrained=True (so only the head will train if you call
fit) so that you can immediately start phase one of training as described above.
Alternatively, you can define your own
custom_head to put on top of the backbone. If you want to specify where to split
arch you should so in the argument
cut which can either be the index of a specific layer (the result will not include that layer) or a function that, when passed the model, will return the backbone you want.
The final model obtained by stacking the backbone and the head (custom or defined as we saw) is then separated in groups for gradual unfreezing or differential learning rates. You can specify how to split the backbone in groups with the optional argument
split_on (should be a function that returns those groups when given the backbone).
path = untar_data(URLs.MNIST_SAMPLE) data = ImageDataBunch.from_folder(path)
learner = cnn_learner(data, models.resnet18, metrics=[accuracy]) learner.fit_one_cycle(1,1e-3)
Build Unet learner from
Once you've actually trained your model, you may want to use it on a single image. This is done by using the following method.
img = learner.data.train_ds learner.predict(img)
(Category 3, tensor(0), tensor([0.6472, 0.3528]))
Here the predict class for our image is '3', which corresponds to a label of 0. The probabilities the model found for each class are 99.65% and 0.35% respectively, so its confidence is pretty high.
Note that if you want to load your trained model and use it on inference mode with the previous function, you should export your
And then you can load it with an empty data object that has the same internal state like this:
learn = load_learner(path)
You can customize
cnn_learner for your own model's default
split_on functions by adding them to the dictionary
model_meta. The key should be your model and the value should be a dictionary with the keys
split_on (see the source code for examples). The constructor will call
create_head for you based on
cut; you can also call them yourself, which is particularly useful for testing.
Cut off the body of a typically pretrained
cut (int) or cut the model as specified by
Model head that takes
nf features, runs through
lin_ftrs, and ends with
ps is the probability of the dropouts, as documented above in
Tests found for
pytest -sv tests/test_vision_train.py::test_ClassificationInterpretation[source]
Some other tests where
ClassificationInterpretation is used:
pytest -sv tests/test_tabular_train.py::test_confusion_tabular[source]
pytest -sv tests/test_vision_train.py::test_interp[source]
To run tests please refer to this guide.
Interpretation methods for classification models.
This provides a confusion matrix and visualization of the most incorrect images. Pass in your
y, and your
losses, and then use the methods below to view the model interpretation results. For instance:
learn = cnn_learner(data, models.resnet18) learn.fit(1) preds,y,losses = learn.get_preds(with_loss=True) interp = ClassificationInterpretation(learn, preds, y, losses)
The following factory method gives a more convenient way to create an instance of this class:
Create an instance of
tta indicates if we want to use Test Time Augmentation.
You can also use a shortcut
learn.interpret() to do the same.
ClassificationInterpretation object from
Show images in
top_losses along with their prediction, actual, loss, and probability of predicted class.
k items are arranged as a square, so it will look best if
k is a square number (4, 9, 16, etc). The title of each image shows: prediction, actual, loss, probability of actual class. When
heatmap is True (by default it's True) , Grad-CAM heatmaps (http://openaccess.thecvf.com/content_ICCV_2017/papers/Selvaraju_Grad-CAM_Visual_Explanations_ICCV_2017_paper.pdf) are overlaid on each image.
plot_top_losses should be used with single-labeled datasets. See
plot_multi_top_losses below for a version capable of handling multi-labeled datasets.
k largest(/smallest) losses and indexes, defaulting to all losses (sorted by
Returns tuple of (losses,indices).
(tensor([6.9999, 5.2013, 5.0712, 4.6642, 4.1224, 3.8131, 3.6013, 3.5701, 3.4746]), tensor([1378, 764, 1811, 983, 36, 358, 859, 1965, 1023]))
Show images in
top_losses along with their prediction, actual, loss, and probability of predicted class in a multilabeled dataset.
plot_top_losses() but aimed at multi-labeled datasets. It plots misclassified samples sorted by their respective loss.
Since you can have multiple labels for a single sample, they can easily overlap in a grid plot. So it plots just one sample per row.
Note that you can pass
save_misclassified=True (by default it's
False). In such case, the method will return a list containing the misclassified images which you can use to debug your model and/or tune its hyperparameters.
Plot the confusion matrix, with
title and using
normalize, plots the percentages with
slice_size can be used to avoid out of memory error if your set is too big.
kwargs are passed to
Confusion matrix as an
array([[ 970, 40], [ 18, 1010]])
Sorted descending list of largest non-diagonal entries of confusion matrix, presented as actual, predicted, number of occurrences.
When working with large datasets, memory problems can arise when computing the confusion matrix. For example, an error can look like this:
RuntimeError: $ Torch: not enough memory: you tried to allocate 64GB. Buy new RAM!
In this case it is possible to force
ClassificationInterpretation to compute the confusion matrix for data slices and then aggregate the result by specifying slice_size parameter.
array([[ 970, 40], [ 18, 1010]])
[('3', '7', 40), ('7', '3', 18)]