174 lines
6.7 KiB
Python
174 lines
6.7 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 logging
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import torch
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from torch.utils.data import Dataset
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from data.data_loader import DataLoaderMultiAspect as dlma
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import math
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import data.dl_singleton as dls
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from data.image_train_item import ImageTrainItem
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import random
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from torchvision import transforms
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from transformers import CLIPTokenizer
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import torch.nn.functional as F
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import numpy
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class EveryDreamBatch(Dataset):
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"""
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data_root: root path of all your training images, will be recursively searched for images
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repeats: how many times to repeat each image in the dataset
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flip_p: probability of flipping the image horizontally
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debug_level: 0=none, 1=print drops due to unfilled batches on aspect ratio buckets, 2=debug info per image, 3=save crops to disk for inspection
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batch_size: how many images to return in a batch
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conditional_dropout: probability of dropping the caption for a given image
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resolution: max resolution (relative to square)
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jitter: number of pixels to jitter the crop by, only for non-square images
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"""
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def __init__(self,
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data_root,
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flip_p=0.0,
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debug_level=0,
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batch_size=1,
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conditional_dropout=0.02,
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resolution=512,
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crop_jitter=20,
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seed=555,
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tokenizer=None,
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log_folder=None,
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retain_contrast=False,
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write_schedule=False,
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shuffle_tags=False,
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):
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self.data_root = data_root
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self.batch_size = batch_size
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self.debug_level = debug_level
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self.conditional_dropout = conditional_dropout
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self.crop_jitter = crop_jitter
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self.unloaded_to_idx = 0
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self.tokenizer = tokenizer
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self.log_folder = log_folder
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#print(f"tokenizer: {tokenizer}")
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self.max_token_length = self.tokenizer.model_max_length
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self.retain_contrast = retain_contrast
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self.write_schedule = write_schedule
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self.shuffle_tags = shuffle_tags
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self.seed = seed
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if seed == -1:
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seed = random.randint(0, 99999)
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if not dls.shared_dataloader:
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logging.info(" * Creating new dataloader singleton")
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dls.shared_dataloader = dlma(data_root=data_root,
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seed=seed,
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debug_level=debug_level,
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batch_size=self.batch_size,
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flip_p=flip_p,
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resolution=resolution,
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log_folder=self.log_folder,
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)
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self.image_train_items = dls.shared_dataloader.get_all_images()
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self.num_images = len(self.image_train_items)
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self._length = self.num_images
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logging.info(f" ** Trainer Set: {self._length / batch_size:.0f}, num_images: {self.num_images}, batch_size: {self.batch_size}")
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if self.write_schedule:
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self.write_batch_schedule(0)
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def write_batch_schedule(self, epoch_n):
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with open(f"{self.log_folder}/ep{epoch_n}_batch_schedule.txt", "w", encoding='utf-8') as f:
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for i in range(len(self.image_train_items)):
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try:
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f.write(f"step:{int(i / self.batch_size):05}, wh:{self.image_train_items[i].target_wh}, r:{self.image_train_items[i].runt_size}, path:{self.image_train_items[i].pathname}\n")
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except Exception as e:
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logging.error(f" * Error writing to batch schedule for file path: {self.image_train_items[i].pathname}")
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def get_runts():
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return dls.shared_dataloader.runts
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def shuffle(self, epoch_n):
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self.seed += 1
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if dls.shared_dataloader:
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dls.shared_dataloader.shuffle()
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self.image_train_items = dls.shared_dataloader.get_all_images()
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else:
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raise Exception("No dataloader singleton to shuffle")
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if self.write_schedule:
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self.write_batch_schedule(epoch_n)
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def __len__(self):
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return self._length
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def __getitem__(self, i):
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example = {}
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train_item = self.__get_image_for_trainer(self.image_train_items[i], self.debug_level)
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if self.retain_contrast:
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std_dev = 1.0
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mean = 0.0
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else:
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std_dev = 0.5
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mean = 0.5
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image_transforms = transforms.Compose(
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[
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transforms.ToTensor(),
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transforms.Normalize([mean], [std_dev]),
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]
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)
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if self.shuffle_tags:
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example["caption"] = train_item["caption"].get_shuffled_caption(self.seed)
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else:
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example["caption"] = train_item["caption"].get_caption()
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example["image"] = image_transforms(train_item["image"])
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if random.random() > self.conditional_dropout:
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example["tokens"] = self.tokenizer(example["caption"],
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truncation=True,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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).input_ids
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else:
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example["tokens"] = self.tokenizer(" ",
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truncation=True,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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).input_ids
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example["tokens"] = torch.tensor(example["tokens"])
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example["runt_size"] = train_item["runt_size"]
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return example
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def __get_image_for_trainer(self, image_train_item: ImageTrainItem, debug_level=0):
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example = {}
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save = debug_level > 2
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image_train_tmp = image_train_item.hydrate(crop=False, save=save, crop_jitter=self.crop_jitter)
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example["image"] = image_train_tmp.image
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example["caption"] = image_train_tmp.caption
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example["runt_size"] = image_train_tmp.runt_size
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return example
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