Thanks for your help.
First, the transformation I used is as follows.
I confirmed that augmentation is applied to the same image and mask.
def data_augmentation(image, mask, aug=True):
    image = Image.fromarray(image)
    mask = Image.fromarray(mask)

    if aug:
        if random.random() > 0.5:
            alpha = random.randint(100, 200)
            augmented = HorizontalFlip(p=1)(image=np.array(image), mask=np.array(mask))
            image = Image.fromarray(augmented['image'])
            mask = Image.fromarray(augmented['mask'])
            
        if random.random() > 0.5:
            alpha = random.randint(100, 200)
            augmented = Rotate(p=1, limit=45)(image=np.array(image), mask=np.array(mask))
            image = Image.fromarray(augmented['image'])
            mask = Image.fromarray(augmented['mask'])
            
        if random.random() > 0.5:
            alpha = random.randint(100, 200)
            augmented = Blur(p=1, blur_limit=5)(image=np.array(image), mask=np.array(mask))
            image = Image.fromarray(augmented['image'])
            mask = Image.fromarray(augmented['mask'])
         

    image = TF.to_tensor(image).float()
    image = image/(image.max()+0.00001)
    mask = binarize(TF.to_tensor(mask)).float()
    return image, mask

Also, as you advised, I tried learning with a small sample.
As a result of training, I found that train loss is still constant even in a small sample.
However, when learning without applying augmentation, it was confirmed that learning was normally performed.
I wonder why learning is not possible when augmentation is applied.
When augmentation is applied, is it done in epoch with little learning?
Any help would be appreciated. Thanks!