CHANGES.md

Changes

Most recent releases are shown at the top. Each release shows:

• New: New classes, methods, functions, etc
• Changed: Additional paramaters, changes to inputs or outputs, etc
• Fixed: Bug fixes that don't change documented behaviour

Note that the top-most release is changes in the unreleased master branch on Github. Parentheses after an item show the name or github id of the contributor of that change.

1.0.25.dev0 (Work In Progress)

New:

• FloatList to do regression

Changed:

Fixed:

1.0.24 (2018-11-13)

New:

Changed:

• Big refactor of the data block API

Fixed:

1.0.23 (2018-11-13)

New:

Changed:

• tools/build-docs and tools/update-nbs scripts combined into one script.

Fixed:

1.0.22 (2018-11-09)

Breaking changes:

• We no longer import submodule names automatically with import *
• Callbacks are now inside the callbacks namespace if you from fastai import *

Changed:

• All the DataBunch factory method use the data block API, the factory method of Datasets are deprecated and will be removed in a future version.

Fixed:

• learn.predict fixed

1.0.21 (2018-11-08)

New:

• CSVLogger callback (thanks to devorfu)
• Initial support for image regression problems.
• If a dataset class has learner_type then create_cnn uses that type to create the Learner.
• Introduce TaskType in DatasetBase to deal with single/multi-class or regression problems accross applications.

Changed:

• datasets() now can automatically figure out what class to use in many situations
• download_images() now saves images with their original extensions

1.0.20 (2018-11-07)

New:

• DataBunch.dl replaces the various holdout, is_test, and is_train approaches with a single consistent enum.
• fastai.text is fully compatible with the data block API.

Changed:

• download_url reads the get request with iter_content which is robust to 'content-length' errors. (thanks to Francisco Ingham and Zach Caceres)
• download_url has a timeout

Fixed:

• create_cnn correctly calculates # features in body correctly for more architectures
• TextDataset has now two subclasses for the preprocessing steps and doesn't do that preprocesing automatically.
• TextDataBunch doesn't save the result of preprocessing automatically, you have to use TextDataBunch.save.
• RNNLearner.classifier is now text_classifier_learner and RNN_Learner.language_model is now language_model_learner.
• pil2tensor is faster and works on more image types (thanks to kasparlund)
• Imports in the file picker widget (thanks to Hiromi)
• Batches of size 1 will be removed during training because of the issue with BatchNorm1d
• Confusion matrix show ints if normalize=False (default)
• RNNLearner.get_preds return the preds in the right order (thanks to StatisticDean)
• num_features_model now works with any model
• resize_method wasn't properly set when passed to ImageDataBunch
• reset the RNNs at the beginning of each epoch in RNNTrainer

1.0.19 (2018-11-03)

New:

• add an argument resize_method that tells apply_tfms how to resize the image to the desired size (crop, pad, squish or no).
• all the image dataset have an image_opener attribute (default open_image) that can be changed. The SegmentationDataset has a mask_opener attribute.
• add_test and add_test_folder in data block API.

Changed:

• jupyter et al no longer forced dependencies
• verify_images can now resize images on top of checking they're not broken.
• LR finder plot now uses python scientific notation instead of math superset notation

Fixed:

• ImageDataBunch.from_df doesn't change the dataframe.

1.0.18 (2018-10-30)

Fixed:

• Fix jupyter dep version

1.0.17 (2018-10-30)

New:

• Add tiny datasets

Changed:

• remove wrong Fbeta

Fixed:

• fix implementation of fbeta

1.0.16 (2018-10-30)

New:

• ImageDataBunch.single_from_classes to allow single image predictions
• DatasetBase has set_item and clear_item to force it to always return item
• DatasetBase uses abstract _get_x and _get_y
• batch_size property in DeviceDataLoader
• ClassificationLearner.predict to get prediction on a single item
• Monkey-patched torch.Tensor so matplotlib works
• Learner.create_unet
• Data block API

Changed:

• validate now takes optional n_batch
• create_cnn now returns a ClassificationLearner
• return_path flag to Learner.save
• ImageDataBunch.show_batch() now works for every type of dataset, removes show_images and show_xy_images as a result.
• Monkey-patched torch.utils.data.dataloader.DataLoader to create a passthrough to the dataset
• max_workers for download_images
• Change the arguments of ObjectDetectDataset to make it consistent with the rest of the API, changes the return of get_annotations to go with it.

Fixed:

• remove empty classes in ImageDataBunch.from_folder

1.0.15 (2018-10-28)

Breaking changes:

• ConvLearner ctor is replaced by a function called create_cnn

New:

• Learner objects now determine from the loss function if there is something to add on top of the models to get the true predictions

Changed:

• Add recurse flag to get_image_files
• show_xy_images takes tensors instead of Image
• Add classes to SegmentationDataset
• get_preds now return the true probabilities
• TTA averages the probabilities and not the last activations of the model
• ClassificationInterpretation has been changed accordingly and the sigmoid argument has been deprecated

Fixed:

• Make pred_batch faster and remove redundent *
• Bug in Learner.pred_batch
• Bug in model_sizes (thanks to dienhoa)
• Bug in RNNLearner.classifier when used on a multilabel dataset

1.0.14 (2018-10-25)

New:

• download_images: multi-process download of a file or URLs
• verify_images: multi-process verification of directory of images with optional deletion

Changed:

• ImageDataBunch.from_folder now takes valid_pct
• master bar support in download_url
• various fixes to support the latest of fastprogress
• Learner.normalize() (without args) stores calculated stats in Learner.stats
• pred_batch moved to basic_train and fixed for multiple inputs
• lr_find() prints the next step to type when completed
• New version of fastprogress used; doesn't require ipywidgets
• Removed cifar_norm,cifar_denorm,imagenet_norm,imagenet_denorm

Fixed:

1.0.13 (2018-10-24)

New:

• pretrained language model is now downloaded directly in the .fastai/models/ folder. Use pretrained_model=URLs.WT103
• add an argument stop_div to Learner.lr_find() to prevent early stopping, useful for negative losses.
• add an argument convert_mode to open_mask and SegmentationDataset to choose the PIL conversion mode of the masks.

Changed:

• URLs.download_wt103() has been removed

1.0.12 (2018-10-23)

Fixed:

• change TextDataBunchClass method [from_ids_files, from_tokens, from_df, from_csv, from_folder] so that classes argument is passed to the call to TextDataset
• Strip space from file name when CSV has spaces
• Handle missing loss_func attr
• Pass on the use_bn parameter in get_tabular_learner
• Bad handling when final batch has size of 1
• rolled back numpy dependency to >=1.12 (anaconda package has a upper pin on it) and to pip>=9.0.1, the old version are buggy but should be ok for fastai

1.0.11 (2018-10-20)

Fixed:

• Added missing pyyaml dependency to conda too

Changed:

• Use spacy.blank instead of spacy.load to avoid having to download english model

1.0.10 (2018-10-20)

Fixed:

• Added missing pyyaml dependency

1.0.9 (2018-10-20)

New:

• EarlyStoppingCallback, SaveModelCallback, TerminateOnNaNCallback (initial draft: fredguth)
• datapath4file(filename) returns suitable path to store or find data file called filename, using config file ~/.fastai/config.yml, and default data directory ~/.fastai/data, unless ./data exists and contains that file
• MSELossFlat() loss function
• Simple integration tests for all applications

Changed:

• data is now called basic_data to avoid weird conflicts when naming our data objects data.
• datasets.untar_data and datasets.download_data will now download to fastai home directory ~/.fastai/data if the dataset does not already exist locally ./data.

Fixed:

• add dep_var column in test_df if it doesn't exists (Kevin Bird)
• backwards=True when creating a LanguageModelLoader (mboyanov)

1.0.8 (2018-10-20)

• Not released

1.0.7 (2018-10-19)

New:

• New class ImagePoints for targets that are a set of point coordinates
• New function Image.predict(learn:Learner) to get the activations of the model in Learner for an image
• New function Learner.validate to validate on a given dl (default valid_dl), with maybe new metrics or callbacks
• New function error_rate which is just 1-accuracy()

Changed:

• All vision models are now in the models module, including torchvision models (where tested and supported). So use models instead of tvm now. If your preferred torchvision model isn't imported, feel free to test it out and tell us on the forum if it works. And if it doesn't, a PR with a test and a fix would be appreciated!
• ImageBBox is now a subclass of ImagePoints
• All metrics are now Callback. You can pass a regular function like accuracy that will get averaged over batch or a full Callback that can do more complex things
• All datasets convenience functions and paths are inside the URLs class
• URLs that are a sample have name now suffixed with _SAMPLE

Fixed:

• Fix WeightDropout in RNNs when p=0
• pad_collate gets its kwargs from TextClasDataBunch
• Add small eps to std in TabularDataset to avoid division by zero
• fit_one_cycle doesn't take other callbacks
• Many broken docs links fixed

1.0.6 (2018-10-01)

• Last release without CHANGES updates