- instead of just comparing the number of connected components,
calculate the GT/pred label incidence matrix and retrieve the
share of singular values (i.e. nearly diagonal under reordering)
over total counts as similarity score
- also, suppress artificial class in that
(Functions cannot be both generators and procedures,
so make this a pure generator and save the image files
on the caller's side; also avoids passing output
directories)
Moreover, simplify by moving the `os.listdir` into the function
body (saving lots of extra variable bindings).
instead of looping over file pairs indefinitely, yielding
Numpy arrays: re-use `keras.utils.image_dataset_from_directory`
here as well, but with img/label generators zipped together
(thus, everything will already be loaded/prefetched on the GPU)
- use relative imports
- use tf.keras everywhere (and ensure v2)
- `weights_ensembling`:
* use `Patches` and `PatchEncoder` from .models
* drop TF1 stuff
* make function / CLI more flexible (expect list of
checkpoint dirs instead of single top-level directory)
- train for `classification`: delegate to `weights_ensembling.run_ensembling`
major conflicts resolved manually:
- branches for non-`light` segmentation already removed in main
- Keras/TF setup and no TF1 sessions, esp. in new ModelZoo
- changes to binarizer and its CLI (`mode`, `overwrite`, `run_single()`)
- writer: `build...` w/ kwargs instead of positional
- training for segmentation/binarization/enhancement tasks:
* drop unused `generate_data_from_folder()`
* simplify `preprocess_imgs()`: turn `preprocess_img()`, `get_patches()`
and `get_patches_num_scale_new()` into generators, only writing
result files in the caller (top-level loop) instead of passing
output directories and file counter
- training for new OCR task:
* `train`: put keys into additional `config_params` where they belong,
resp. (conditioned under existing keys), and w/ better documentation
* `train`: add new keys as kwargs to `run()` to make usable
* `utils`: instead of custom data loader `data_gen_ocr()`, re-use
existing `preprocess_imgs()` (for cfg capture and top-level loop),
but extended w/ new kwargs and calling new `preprocess_img_ocr()`;
the latter as single-image generator (also much simplified)
* `train`: use tf.data loader pipeline from that generator w/ standard
mechanisms for batching, shuffling, prefetching etc.
* `utils` and `train`: instead of `vectorize_label`, use `Dataset.padded_batch`
* add TensorBoard callback and re-use our checkpoint callback
* also use standard Keras top-level loop for training
still problematic (substantially unresolved):
- `Patches` now only w/ fixed implicit size
(ignoring training config params)
- `PatchEncoder` now only w/ fixed implicit num patches and projection dim
(ignoring training config params)
