Refactor Preference Optimization
Refactor preference dataset
Add iterator support for ImageInfo and ImageSetInfo
- Supporting iterating through either ImageInfo or ImageSetInfo to
clean up preference dataset implementation and support 2 or more
images more cleanly without needing to duplicate code
Add tests for all PO functions
Add metrics for process_batch
Add losses for gradient manipulation of loss parts
Add normalizing gradient for stabilizing gradients
Args added:
mapo_beta = 0.05
cpo_beta = 0.1
bpo_beta = 0.1
bpo_lambda = 0.2
sdpo_beta = 0.02
simpo_gamma_beta_ratio = 0.25
simpo_beta = 2.0
simpo_smoothing = 0.0
simpo_loss_type = "sigmoid"
ddo_alpha = 4.0
ddo_beta = 0.05
The original code had a mistake. It used 'lanczos' when the image got smaller (width > resized_width and height > resized_height) and 'area' when it stayed the same or got bigger. This was the wrong way. 'area' is better for big shrinking.
Currently the alpha channel is dropped by `pil_resize()` when `--alpha_mask` is supplied and the image width does not exceed the bucket.
This codepath is entered on the last line, here:
```
def trim_and_resize_if_required(
random_crop: bool, image: np.ndarray, reso, resized_size: Tuple[int, int]
) -> Tuple[np.ndarray, Tuple[int, int], Tuple[int, int, int, int]]:
image_height, image_width = image.shape[0:2]
original_size = (image_width, image_height) # size before resize
if image_width != resized_size[0] or image_height != resized_size[1]:
# リサイズする
if image_width > resized_size[0] and image_height > resized_size[1]:
image = cv2.resize(image, resized_size, interpolation=cv2.INTER_AREA) # INTER_AREAでやりたいのでcv2でリサイズ
else:
image = pil_resize(image, resized_size)
```
* Add get_my_logger()
* Use logger instead of print
* Fix log level
* Removed line-breaks for readability
* Use setup_logging()
* Add rich to requirements.txt
* Make simple
* Use logger instead of print
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Co-authored-by: Kohya S <52813779+kohya-ss@users.noreply.github.com>
