- drop ad-hoc configuration parameter `reload_weights`
(used for conversion/export of models for inference,
to be replaced by extra CLI)
- re-interprete `dir_of_start_model` to also load weights
if not `continue_training`
- models: add new `get_model()`, passing in Sacred config
to capture builder function arguments
- train: fewer imports
- train: no need to pass `custom_objects` if loading with
`compile=False` (and we custom-compile later, anyway)
- growth strategy is more flexible, but uses much more VRAM
- limit strategy needs to be calibrated to models (currently fixed),
and batch size, but needs much less VRAM and is faster
- re-use Eynollah base class
- use `ModelZoo.load_models()` instead of `load_model()`
- pass in `device` init kwarg, delegate to `ModelZoo.load_models()`
- `device`: return Torch device at loaded model tensors
instead of ad-hoc selection
- make numeric init kwargs non-optional (only numeric)
- `load_models()`: uniformly handle arg types
- `load_model()`: move handling of non-model categories
to `load_models()`
- `load_model()`: move SavedModel preference over HDF5 to `model_path()`
- `_load_ocr_model()`: add user-selected device handling and reporting
for Torch (as for TF)
- `_load_ocr_model()`: move (TF-based) CNN-RNN case to `load_model()`
(including Keras layer mapping)
- `shutdown()`: only apply `shutdown()` to Predictor model types
- found positive and negative peaks, and even more so their
relative offsets, may overflow in the cropped image,
causing fake textlines; avoid that by clipping to the valid
y coordinates
- calculation for number of tiles: sometimes one less
tile is needed by making the previous last tile
half-full on the right side
- add some (commented) plotting
- simplify (a lot, but only partially)
- relative images now need larger relative min_area
(i.e. compensation factors)
- do not attempt (even) single-line skew estimation
(via linear regression) if there is no (large enough)
contour at all
- avoid re-computing `mask_parent`
- add some (commented) plotting
- re-use Eynollah base class, drop copied code
- simplify `run()` and `run_single()`
- delegate to `do_prediction()`
instead of custom (old) tiling loop
- drop `predict()`
- add `--device` option to CLI as well
- re-use Eynollah base class, drop copied code
- write usable `run()` and `run_single()`
- delegate to `resize_image_with_column_classifier()`
for column classifier, resizing and enhancement,
instead of `resize_and_enhance_image_with_column_classifier()`
(which does _not_ actually enhance)
- drop unused `predict_enhancement()`
- add defaults to `num_col` options (always numeric)
- add `--device` option to CLI as well
use rules from `resize_and_enhance_image_with_column_classifier()`
and apply them to `resize_image_with_column_classifier()` as well
(to be used by enhancer CLI)
instead of hard cut-offs between overlapping window tiles,
apply sigmoid attenuation to slide from one to the next
(apply all postprocessing in the end)
- calculation for number of tiles: sometimes one less
tile is needed by making the previous last tile
half-full on the right side
- calculation of window margins: fix case if dimension
extends to full image shape
- simplify (identifiers, slicing etc)
