Closed aiorhiroki closed 4 years ago
augmentationとpreprocessingを明確に分けたいです(image_utilがpreprocessingを含んでいるので機能を分けたい...) こんな感じに
# apply augmentations
if self.augmentation:
sample = self.augmentation(image=image, mask=mask)
image, mask = sample['image'], sample['mask']
# apply preprocessing
if self.preprocessing:
sample = self.preprocessing(image=image, mask=mask)
image, mask = sample['image'], sample['mask']
callbacksの GenerateSampleResult
、 IoUHistory
をデータのlistじゃなくてgenerator(dataloader)にしたいです
そうするとこういうの
for sample_image_path in annotations:
input_image_path, mask_image_path = sample_image_path
sample_image = image_util.read_image(
input_image_path, anti_alias=True)
segmented = image_util.read_image(
mask_image_path, normalization=False, train_colors=train_colors)
が毎回書かれずにスッキリします
classificatoin (image, video両方)、segmentation w/ data augmentation、segmentationのoptunaが動くことを確認しました
classification(image,video),segmentation(aug, optuna)で動くことを確認しました〜
dataset, dataloaderの実装