267 lines
10 KiB
Python
267 lines
10 KiB
Python
"""
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Copyright [2022] Victor C Hall
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Licensed under the GNU Affero General Public License;
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You may not use this code except in compliance with the License.
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You may obtain a copy of the License at
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https://www.gnu.org/licenses/agpl-3.0.en.html
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import bisect
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import logging
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import math
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import os
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import random
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import typing
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import yaml
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import PIL
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import PIL.Image as Image
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import numpy as np
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from torchvision import transforms
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_RANDOM_TRIM = 0.04
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DEFAULT_MAX_CAPTION_LENGTH = 2048
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OptionalImageCaption = typing.Optional['ImageCaption']
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class ImageCaption:
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"""
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Represents the various parts of an image caption
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"""
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def __init__(self, main_prompt: str, rating: float, tags: list[str], tag_weights: list[float], max_target_length: int, use_weights: bool):
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"""
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:param main_prompt: The part of the caption which should always be included
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:param tags: list of tags to pick from to fill the caption
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:param tag_weights: weights to indicate which tags are more desired and should be picked preferably
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:param max_target_length: The desired maximum length of a generated caption
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:param use_weights: if ture, weights are considered when shuffling tags
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"""
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self.__main_prompt = main_prompt
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self.__rating = rating
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self.__tags = tags
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self.__tag_weights = tag_weights
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self.__max_target_length = max_target_length
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self.__use_weights = use_weights
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if use_weights and len(tags) > len(tag_weights):
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self.__tag_weights.extend([1.0] * (len(tags) - len(tag_weights)))
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if use_weights and len(tag_weights) > len(tags):
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self.__tag_weights = tag_weights[:len(tags)]
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def rating(self) -> float:
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return self.__rating
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def get_shuffled_caption(self, seed: int) -> str:
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"""
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returns the caption a string with a random selection of the tags in random order
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:param seed used to initialize the randomizer
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:return: generated caption string
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"""
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if self.__tags:
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max_target_tag_length = self.__max_target_length - len(self.__main_prompt)
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if self.__use_weights:
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tags_caption = self.__get_weighted_shuffled_tags(seed, self.__tags, self.__tag_weights, max_target_tag_length)
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else:
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tags_caption = self.__get_shuffled_tags(seed, self.__tags)
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return self.__main_prompt + ", " + tags_caption
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return self.__main_prompt
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def get_caption(self) -> str:
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if self.__tags:
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return self.__main_prompt + ", " + ", ".join(self.__tags)
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return self.__main_prompt
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@staticmethod
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def __get_weighted_shuffled_tags(seed: int, tags: list[str], weights: list[float], max_target_tag_length: int) -> str:
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picker = random.Random(seed)
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tags_copy = tags.copy()
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weights_copy = weights.copy()
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caption = ""
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while len(tags_copy) != 0 and len(caption) < max_target_tag_length:
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cum_weights = []
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weight_sum = 0.0
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for weight in weights_copy:
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weight_sum += weight
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cum_weights.append(weight_sum)
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point = picker.uniform(0, weight_sum)
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pos = bisect.bisect_left(cum_weights, point)
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weights_copy.pop(pos)
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tag = tags_copy.pop(pos)
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if caption:
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caption += ", "
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caption += tag
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return caption
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@staticmethod
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def __get_shuffled_tags(seed: int, tags: list[str]) -> str:
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random.Random(seed).shuffle(tags)
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return ", ".join(tags)
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class ImageTrainItem:
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"""
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image: PIL.Image
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identifier: caption,
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target_aspect: (width, height),
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pathname: path to image file
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flip_p: probability of flipping image (0.0 to 1.0)
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rating: the relative rating of the images. The rating is measured in comparison to the other images.
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"""
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def __init__(self, image: PIL.Image, caption: ImageCaption, aspects: list[float], pathname: str, flip_p=0.0, multiplier: float=1.0, cond_dropout=None):
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self.caption = caption
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self.aspects = aspects
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self.pathname = pathname
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self.flip = transforms.RandomHorizontalFlip(p=flip_p)
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self.cropped_img = None
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self.runt_size = 0
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self.multiplier = multiplier
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self.cond_dropout = cond_dropout
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self.image_size = None
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if image is None or len(image) == 0:
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self.image = []
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else:
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self.image = image
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self.image_size = image.size
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self.target_size = None
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self.is_undersized = False
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self.error = None
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self.__compute_target_width_height()
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def hydrate(self, crop=False, save=False, crop_jitter=20):
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"""
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crop: hard center crop to 512x512
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save: save the cropped image to disk, for manual inspection of resize/crop
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crop_jitter: randomly shift cropp by N pixels when using multiple aspect ratios to improve training quality
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"""
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# print(self.pathname, self.image)
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try:
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# if not hasattr(self, 'image'):
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self.image = PIL.Image.open(self.pathname).convert('RGB')
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width, height = self.image.size
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if crop:
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cropped_img = self.__autocrop(self.image)
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self.image = cropped_img.resize((512, 512), resample=PIL.Image.BICUBIC)
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else:
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width, height = self.image.size
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jitter_amount = random.randint(0, crop_jitter)
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if self.target_wh[0] == self.target_wh[1]:
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if width > height:
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left = random.randint(0, width - height)
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self.image = self.image.crop((left, 0, height + left, height))
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width = height
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elif height > width:
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top = random.randint(0, height - width)
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self.image = self.image.crop((0, top, width, width + top))
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height = width
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elif width > self.target_wh[0]:
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slice = min(int(self.target_wh[0] * _RANDOM_TRIM), width - self.target_wh[0])
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slicew_ratio = random.random()
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left = int(slice * slicew_ratio)
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right = width - int(slice * (1 - slicew_ratio))
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sliceh_ratio = random.random()
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top = int(slice * sliceh_ratio)
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bottom = height - int(slice * (1 - sliceh_ratio))
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self.image = self.image.crop((left, top, right, bottom))
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else:
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image_aspect = width / height
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target_aspect = self.target_wh[0] / self.target_wh[1]
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if image_aspect > target_aspect:
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new_width = int(height * target_aspect)
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jitter_amount = max(min(jitter_amount, int(abs(width - new_width) / 2)), 0)
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left = jitter_amount
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right = left + new_width
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self.image = self.image.crop((left, 0, right, height))
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else:
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new_height = int(width / target_aspect)
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jitter_amount = max(min(jitter_amount, int(abs(height - new_height) / 2)), 0)
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top = jitter_amount
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bottom = top + new_height
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self.image = self.image.crop((0, top, width, bottom))
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self.image = self.image.resize(self.target_wh, resample=PIL.Image.BICUBIC)
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self.image = self.flip(self.image)
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except Exception as e:
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logging.error(f"Fatal Error loading image: {self.pathname}:")
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logging.error(e)
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exit()
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if type(self.image) is not np.ndarray:
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if save:
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base_name = os.path.basename(self.pathname)
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if not os.path.exists("test/output"):
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os.makedirs("test/output")
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self.image.save(f"test/output/{base_name}")
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self.image = np.array(self.image).astype(np.uint8)
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# self.image = (self.image / 127.5 - 1.0).astype(np.float32)
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# print(self.image.shape)
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return self
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def __compute_target_width_height(self):
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self.target_wh = None
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try:
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with Image.open(self.pathname) as image:
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width, height = image.size
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image_aspect = width / height
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target_wh = min(self.aspects, key=lambda aspects:abs(aspects[0]/aspects[1] - image_aspect))
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self.is_undersized = (width * height) < (target_wh[0] * target_wh[1])
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self.target_wh = target_wh
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except Exception as e:
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self.error = e
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@staticmethod
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def __autocrop(image: PIL.Image, q=.404):
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"""
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crops image to a random square inside small axis using a truncated gaussian distribution across the long axis
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"""
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x, y = image.size
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if x != y:
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if (x > y):
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rand_x = x - y
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sigma = max(rand_x * q, 1)
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else:
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rand_y = y - x
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sigma = max(rand_y * q, 1)
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if (x > y):
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x_crop_gauss = abs(random.gauss(0, sigma))
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x_crop = min(x_crop_gauss, (x - y) / 2)
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x_crop = math.trunc(x_crop)
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y_crop = 0
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else:
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y_crop_gauss = abs(random.gauss(0, sigma))
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x_crop = 0
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y_crop = min(y_crop_gauss, (y - x) / 2)
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y_crop = math.trunc(y_crop)
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min_xy = min(x, y)
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image = image.crop((x_crop, y_crop, x_crop + min_xy, y_crop + min_xy))
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return image
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