* Smart Masks - Training
- Reinstate smart mask training code
- Reinstate mask_type back to model.config
- change 'replicate_input_mask to 'learn_mask'
- Add learn mask option
- Add mask loading from alignments to plugins.train.trainer
- Add mask_blur and mask threshold options
- _base.py - Pass mask options through training_opts dict
- plugins.train.model - check for mask_type not None for learn_mask and penalized_mask_loss
- Limit alignments loading to just those faces that appear in the training folder
- Raise error if not all training images have an alignment, and alignment file is required
- lib.training_data - Mask generation code
- lib.faces_detect - cv2 dimension stripping bugfix
- Remove cv2 linting code
* Update mask helptext in cli.py
* Fix Warp to Landmarks
Remove SHA1 hashing from training data
* Update mask training config
* Capture missing masks at training init
* lib.image.read_image_batch - Return filenames with batch for ordering
* scripts.train - Documentation
* plugins.train.trainer - documentation
* Ensure backward compatibility.
Fix convert for new predicted masks
* Update removed masks to components for legacy models.
- Add new serializers (npy + compressed)
- Remove Serializer option from cli
- Revert get_aligned call in scripts/extract
- Default alignments to compressed
- Size masks to 128px and compress
- Remove mask thresholding/blur from generation code
- Add Mask class to lib/faces_detect
- Revert debug landmarks to aligned face
- Revert non-extraction code to staging version
* Standardize serialization
- Linting
- Standardize serializer use throughout code
- Extend serializer to load and save files
- Always load and save in utf-8
- Create documentation
* Move image utils to lib.image
* Add .pylintrc file
* Remove some cv2 pylint ignores
* TrainingData: Load images from disk in batches
* TrainingData: get_landmarks to batch
* TrainingData: transform and flip to batches
* TrainingData: Optimize color augmentation
* TrainingData: Optimize target and random_warp
* TrainingData - Convert _get_closest_match for batching
* TrainingData: Warp To Landmarks optimized
* Save models to threadpoolexecutor
* Move stack_images, Rename ImageManipulation. ImageAugmentation Docstrings
* Masks: Set dtype and threshold for lib.masks based on input face
* Docstrings and Documentation
- train/_config.py: PEP8 Fixes. Slight description change on coverage
- models/_base.py:
- Remove unused variables from Loss()
- Delete legacy config items from state file
- Save state file on Legacy update
- PEP8
- Remove _defaults.py for models with no config options
* documentation, pep8, style, clarity updates
* Update cli.py
* Update _config.py
remove extra mask and coverage
mask type as dropdown
* Update training_data.py
move coverage / LR to global
cut down on loss description
style change
losses working in PR
* simpler logging
* legacy update
- Name output nodes of models
- Add support for multiple outputs in models and in training
- Update loss output format for cli and gui
- Sort graphs tabs on training
- Fix analysis graph for new loss names
- Multi output support in convert
* Training: Add Convolutional Aware Initialization config option
* Centralize Conv2D layer for handling initializer
* Add 'is-output' to NNMeta to indicate that network is an output to the Model
Deprecation Warning: Rotation in Extract
Deprecation Warning: Multiple models within a single folder
Error Handling: Useful message for training size assertion error
Error Handling: Useful message for fewer images than batch size
Bugfix: BoundingBox object sometimes not available inside spawned process
Tool: Add tool to restore models from backup
Snapshot: Create snapshot based on total iterations rather than session iterations
Models: Move backup/snapshot functions to lib/model
Training: Output average loss since last save at each save iteration
GUI: display_page.py: minor logging update
Bugfix: Fully disable keypress monitor for GUI
Bugfix: Preview - Handle missing alignments file
Config changes:
- Separate plugin defaults into their own files
- Move mask_type to global training config
- Add ability to pass in custom config files
* Color Augmentation: Implement testing code
* Tweak Contrast and Lighting augmentation amounts
* Prevent color augmentation from entering timelapse
* Remove all augmentations from preview images
