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Merge pull request #38 from noprompt/push-dlma-into-main 

Push DLMA into `main`, improvements to `data.resolve`
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Victor Hall 2023-02-05 08:20:10 -05:00 committed by GitHub
parent 581396ac0e c8c658d181
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5 changed files with 161 additions and 179 deletions
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80
data/data_loader.py
View File

@ -15,15 +15,10 @@ limitations under the License.
"""
import bisect
import math
import os
import logging
import copy
import random
from data.image_train_item import ImageTrainItem
import data.aspects as aspects
import data.resolver as resolver
from colorama import Fore, Style
import PIL
PIL.Image.MAX_IMAGE_PIXELS = 715827880*4 # increase decompression bomb error limit to 4x default
@ -32,24 +27,23 @@ class DataLoaderMultiAspect():
"""
Data loader for multi-aspect-ratio training and bucketing
data_root: root folder of training data
image_train_items: list of `lImageTrainItem` objects
seed: random seed
batch_size: number of images per batch
flip_p: probability of flipping image horizontally (i.e. 0-0.5)
"""
def __init__(self, data_root, seed=555, debug_level=0, batch_size=1, flip_p=0.0, resolution=512, log_folder=None):
self.data_root = data_root
self.debug_level = debug_level
self.flip_p = flip_p
self.log_folder = log_folder
def __init__(self, image_train_items: list[ImageTrainItem], seed=555, batch_size=1):
self.seed = seed
self.batch_size = batch_size
self.has_scanned = False
self.aspects = aspects.get_aspect_buckets(resolution=resolution, square_only=False)
logging.info(f"* DLMA resolution {resolution}, buckets: {self.aspects}")
self.__prepare_train_data()
(self.rating_overall_sum, self.ratings_summed) = self.__sort_and_precalc_image_ratings()
# Prepare data
self.prepared_train_data = image_train_items
random.Random(self.seed).shuffle(self.prepared_train_data)
self.prepared_train_data = sorted(self.prepared_train_data, key=lambda img: img.caption.rating())
# Initialize ratings
self.rating_overall_sum: float = 0.0
self.ratings_summed: list[float] = []
for image in self.prepared_train_data:
self.rating_overall_sum += image.caption.rating()
self.ratings_summed.append(self.rating_overall_sum)
def __pick_multiplied_set(self, randomizer):
"""
@ -138,54 +132,6 @@ class DataLoaderMultiAspect():
return image_caption_pairs
def __sort_and_precalc_image_ratings(self) -> tuple[float, list[float]]:
self.prepared_train_data = sorted(self.prepared_train_data, key=lambda img: img.caption.rating())
rating_overall_sum: float = 0.0
ratings_summed: list[float] = []
for image in self.prepared_train_data:
rating_overall_sum += image.caption.rating()
ratings_summed.append(rating_overall_sum)
return rating_overall_sum, ratings_summed
def __prepare_train_data(self, flip_p=0.0) -> list[ImageTrainItem]:
"""
Create ImageTrainItem objects with metadata for hydration later
"""
if not self.has_scanned:
self.has_scanned = True
logging.info(" Preloading images...")
items = resolver.resolve(self.data_root, self.aspects, flip_p=flip_p, seed=self.seed)
image_paths = set(map(lambda item: item.pathname, items))
print (f" * DLMA: {len(items)} images loaded from {len(image_paths)} files")
self.prepared_train_data = [item for item in items if item.error is None]
random.Random(self.seed).shuffle(self.prepared_train_data)
self.__report_errors(items)
def __report_errors(self, items: list[ImageTrainItem]):
for item in items:
if item.error is not None:
logging.error(f"{Fore.LIGHTRED_EX} *** Error opening {Fore.LIGHTYELLOW_EX}{item.pathname}{Fore.LIGHTRED_EX} to get metadata. File may be corrupt and will be skipped.{Style.RESET_ALL}")
logging.error(f" *** exception: {item.error}")
undersized_items = [item for item in items if item.is_undersized]
if len(undersized_items) > 0:
underized_log_path = os.path.join(self.log_folder, "undersized_images.txt")
logging.warning(f"{Fore.LIGHTRED_EX} ** Some images are smaller than the target size, consider using larger images{Style.RESET_ALL}")
logging.warning(f"{Fore.LIGHTRED_EX} ** Check {underized_log_path} for more information.{Style.RESET_ALL}")
with open(underized_log_path, "w") as undersized_images_file:
undersized_images_file.write(f" The following images are smaller than the target size, consider removing or sourcing a larger copy:")
for undersized_item in undersized_items:
message = f" *** {undersized_item.pathname} with size: {undersized_item.image_size} is smaller than target size: {undersized_item.target_wh}\n"
undersized_images_file.write(message)
def __pick_random_subset(self, dropout_fraction: float, picker: random.Random) -> list[ImageTrainItem]:
"""
Picks a random subset of all images

79
data/every_dream.py
View File

@ -16,108 +16,61 @@ limitations under the License.
import logging
import torch
from torch.utils.data import Dataset
from data.data_loader import DataLoaderMultiAspect as dlma
import math
import data.dl_singleton as dls
from data.data_loader import DataLoaderMultiAspect
from data.image_train_item import ImageTrainItem
import random
from torchvision import transforms
from transformers import CLIPTokenizer
import torch.nn.functional as F
import numpy
class EveryDreamBatch(Dataset):
"""
data_root: root path of all your training images, will be recursively searched for images
repeats: how many times to repeat each image in the dataset
flip_p: probability of flipping the image horizontally
data_loader: `DataLoaderMultiAspect` object
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
batch_size: how many images to return in a batch
conditional_dropout: probability of dropping the caption for a given image
resolution: max resolution (relative to square)
jitter: number of pixels to jitter the crop by, only for non-square images
crop_jitter: number of pixels to jitter the crop by, only for non-square images
seed: random seed
"""
def __init__(self,
data_root,
flip_p=0.0,
data_loader: DataLoaderMultiAspect,
debug_level=0,
batch_size=1,
conditional_dropout=0.02,
resolution=512,
crop_jitter=20,
seed=555,
tokenizer=None,
log_folder=None,
retain_contrast=False,
write_schedule=False,
shuffle_tags=False,
rated_dataset=False,
rated_dataset_dropout_target=0.5
):
self.data_root = data_root
self.batch_size = batch_size
self.data_loader = data_loader
self.batch_size = data_loader.batch_size
self.debug_level = debug_level
self.conditional_dropout = conditional_dropout
self.crop_jitter = crop_jitter
self.unloaded_to_idx = 0
self.tokenizer = tokenizer
self.log_folder = log_folder
#print(f"tokenizer: {tokenizer}")
self.max_token_length = self.tokenizer.model_max_length
self.retain_contrast = retain_contrast
self.write_schedule = write_schedule
self.shuffle_tags = shuffle_tags
self.seed = seed
self.rated_dataset = rated_dataset
self.rated_dataset_dropout_target = rated_dataset_dropout_target
if seed == -1:
seed = random.randint(0, 99999)
if not dls.shared_dataloader:
logging.info(" * Creating new dataloader singleton")
dls.shared_dataloader = dlma(data_root=data_root,
seed=seed,
debug_level=debug_level,
batch_size=self.batch_size,
flip_p=flip_p,
resolution=resolution,
log_folder=self.log_folder,
)
self.image_train_items = dls.shared_dataloader.get_shuffled_image_buckets(1.0) # First epoch always trains on all images
# First epoch always trains on all images
self.image_train_items = self.data_loader.get_shuffled_image_buckets(1.0)
num_images = len(self.image_train_items)
logging.info(f" ** Trainer Set: {num_images / batch_size:.0f}, num_images: {num_images}, batch_size: {self.batch_size}")
if self.write_schedule:
self.__write_batch_schedule(0)
def __write_batch_schedule(self, epoch_n):
with open(f"{self.log_folder}/ep{epoch_n}_batch_schedule.txt", "w", encoding='utf-8') as f:
for i in range(len(self.image_train_items)):
try:
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")
except Exception as e:
logging.error(f" * Error writing to batch schedule for file path: {self.image_train_items[i].pathname}")
def get_runts():
return dls.shared_dataloader.runts
logging.info(f" ** Trainer Set: {num_images / self.batch_size:.0f}, num_images: {num_images}, batch_size: {self.batch_size}")
def shuffle(self, epoch_n: int, max_epochs: int):
self.seed += 1
if dls.shared_dataloader:
if self.rated_dataset:
dropout_fraction = (max_epochs - (epoch_n * self.rated_dataset_dropout_target)) / max_epochs
else:
dropout_fraction = 1.0
self.image_train_items = dls.shared_dataloader.get_shuffled_image_buckets(dropout_fraction)
if self.rated_dataset:
dropout_fraction = (max_epochs - (epoch_n * self.rated_dataset_dropout_target)) / max_epochs
else:
raise Exception("No dataloader singleton to shuffle")
dropout_fraction = 1.0
if self.write_schedule:
self.__write_batch_schedule(epoch_n + 1)
self.image_train_items = self.data_loader.get_shuffled_image_buckets(dropout_fraction)
def __len__(self):
return len(self.image_train_items)

62
data/resolver.py
View File

@ -4,18 +4,21 @@ import os
import random
import typing
import zipfile
import argparse
import PIL.Image as Image
import tqdm
from colorama import Fore, Style
from data.image_train_item import ImageCaption, ImageTrainItem
class DataResolver:
def __init__(self, aspects: list[typing.Tuple[int, int]], flip_p=0.0, seed=555):
self.seed = seed
self.aspects = aspects
self.flip_p = flip_p
def __init__(self, args: argparse.Namespace):
"""
:param args: EveryDream configuration, an `argparse.Namespace` object.
"""
self.aspects = args.aspects
self.flip_p = args.flip_p
self.seed = args.seed
def image_train_items(self, data_root: str) -> list[ImageTrainItem]:
"""
@ -173,8 +176,11 @@ class DirectoryResolver(DataResolver):
if os.path.isdir(current):
yield from DirectoryResolver.recurse_data_root(current)
def strategy(data_root: str):
def strategy(data_root: str) -> typing.Type[DataResolver]:
"""
Determine the strategy to use for resolving the data.
:param data_root: The root directory or JSON file to resolve.
"""
if os.path.isfile(data_root) and data_root.endswith('.json'):
return JSONResolver
@ -183,41 +189,37 @@ def strategy(data_root: str):
raise ValueError(f"data_root '{data_root}' is not a valid directory or JSON file.")
def resolve_root(path: str, aspects: list[float], flip_p: float = 0.0, seed=555) -> list[ImageTrainItem]:
def resolve_root(path: str, args: argparse.Namespace) -> list[ImageTrainItem]:
"""
:param data_root: Directory or JSON file.
:param aspects: The list of aspect ratios to use
:param flip_p: The probability of flipping the image
Resolve the training data from the root path.
:param path: The root path to resolve.
:param args: EveryDream configuration, an `argparse.Namespace` object.
"""
if os.path.isfile(path) and path.endswith('.json'):
return JSONResolver(aspects, flip_p, seed).image_train_items(path)
if os.path.isdir(path):
return DirectoryResolver(aspects, flip_p, seed).image_train_items(path)
raise ValueError(f"data_root '{path}' is not a valid directory or JSON file.")
resolver = strategy(path)
return resolver(args).image_train_items(path)
def resolve(value: typing.Union[dict, str], aspects: list[float], flip_p: float=0.0, seed=555) -> list[ImageTrainItem]:
def resolve(value: typing.Union[dict, str], args: argparse.Namespace) -> list[ImageTrainItem]:
"""
Resolve the training data from the value.
:param value: The value to resolve, either a dict or a string.
:param aspects: The list of aspect ratios to use
:param flip_p: The probability of flipping the image
:param value: The value to resolve, either a dict, an array, or a string.
:param args: EveryDream configuration, an `argparse.Namespace` object.
"""
if isinstance(value, str):
return resolve_root(value, aspects, flip_p)
return resolve_root(value, args)
if isinstance(value, dict):
resolver = value.get('resolver', None)
match resolver:
case 'directory' | 'json':
path = value.get('path', None)
return resolve_root(path, aspects, flip_p, seed)
return resolve_root(path, args)
case 'multi':
items = []
for resolver in value.get('resolvers', []):
items += resolve(resolver, aspects, flip_p, seed)
return items
return resolve(value.get('resolvers', []), args)
case _:
raise ValueError(f"Cannot resolve training data for resolver value '{resolver}'")
raise ValueError(f"Cannot resolve training data for resolver value '{resolver}'")
if isinstance(value, list):
items = []
for item in value:
items += resolve(item, args)
return items

36
test/test_data_resolver.py
View File

@ -2,6 +2,7 @@ import json
import glob
import os
import unittest
import argparse
import PIL.Image as Image
@ -10,13 +11,18 @@ import data.resolver as resolver
DATA_PATH = os.path.abspath('./test/data')
JSON_ROOT_PATH = os.path.join(DATA_PATH, 'test_root.json')
ASPECTS = aspects.get_aspect_buckets(512)
IMAGE_1_PATH = os.path.join(DATA_PATH, 'test1.jpg')
CAPTION_1_PATH = os.path.join(DATA_PATH, 'test1.txt')
IMAGE_2_PATH = os.path.join(DATA_PATH, 'test2.jpg')
IMAGE_3_PATH = os.path.join(DATA_PATH, 'test3.jpg')
ARGS = argparse.Namespace(
aspects=aspects.get_aspect_buckets(512),
flip_p=0.5,
seed=42,
)
class TestResolve(unittest.TestCase):
@classmethod
def setUpClass(cls):
@ -51,7 +57,7 @@ class TestResolve(unittest.TestCase):
os.remove(file)
def test_directory_resolve_with_str(self):
items = resolver.resolve(DATA_PATH, ASPECTS)
items = resolver.resolve(DATA_PATH, ARGS)
image_paths = [item.pathname for item in items]
image_captions = [item.caption for item in items]
captions = [caption.get_caption() for caption in image_captions]
@ -69,7 +75,7 @@ class TestResolve(unittest.TestCase):
'path': DATA_PATH,
}
items = resolver.resolve(data_root_spec, ASPECTS)
items = resolver.resolve(data_root_spec, ARGS)
image_paths = [item.pathname for item in items]
image_captions = [item.caption for item in items]
captions = [caption.get_caption() for caption in image_captions]
@ -82,7 +88,7 @@ class TestResolve(unittest.TestCase):
self.assertEqual(len(undersized_images), 1)
def test_json_resolve_with_str(self):
items = resolver.resolve(JSON_ROOT_PATH, ASPECTS)
items = resolver.resolve(JSON_ROOT_PATH, ARGS)
image_paths = [item.pathname for item in items]
image_captions = [item.caption for item in items]
captions = [caption.get_caption() for caption in image_captions]
@ -100,7 +106,7 @@ class TestResolve(unittest.TestCase):
'path': JSON_ROOT_PATH,
}
items = resolver.resolve(data_root_spec, ASPECTS)
items = resolver.resolve(data_root_spec, ARGS)
image_paths = [item.pathname for item in items]
image_captions = [item.caption for item in items]
captions = [caption.get_caption() for caption in image_captions]
@ -110,4 +116,22 @@ class TestResolve(unittest.TestCase):
self.assertEqual(captions, ['caption for test1', 'caption for test2', 'test3'])
undersized_images = list(filter(lambda i: i.is_undersized, items))
self.assertEqual(len(undersized_images), 1)
self.assertEqual(len(undersized_images), 1)
def test_resolve_with_list(self):
data_root_spec = [
DATA_PATH,
JSON_ROOT_PATH,
]
items = resolver.resolve(data_root_spec, ARGS)
image_paths = [item.pathname for item in items]
image_captions = [item.caption for item in items]
captions = [caption.get_caption() for caption in image_captions]
self.assertEqual(len(items), 6)
self.assertEqual(image_paths, [IMAGE_1_PATH, IMAGE_2_PATH, IMAGE_3_PATH] * 2)
self.assertEqual(captions, ['caption for test1', 'test2', 'test3', 'caption for test1', 'caption for test2', 'test3'])
undersized_images = list(filter(lambda i: i.is_undersized, items))
self.assertEqual(len(undersized_images), 2)

83
train.py
View File

@ -15,6 +15,7 @@ limitations under the License.
"""
import os
import pprint
import sys
import math
import signal
@ -48,11 +49,15 @@ from accelerate.utils import set_seed
import wandb
from torch.utils.tensorboard import SummaryWriter
from data.data_loader import DataLoaderMultiAspect
from data.every_dream import EveryDreamBatch
from data.image_train_item import ImageTrainItem
from utils.huggingface_downloader import try_download_model_from_hf
from utils.convert_diff_to_ckpt import convert as converter
from utils.gpu import GPU
import data.aspects as aspects
import data.resolver as resolver
_SIGTERM_EXIT_CODE = 130
_VERY_LARGE_NUMBER = 1e9
@ -265,6 +270,8 @@ def setup_args(args):
logging.info(logging.info(f"{Fore.CYAN} * Activating rated images learning with a target rate of {args.rated_dataset_target_dropout_percent}% {Style.RESET_ALL}"))
args.aspects = aspects.get_aspect_buckets(args.resolution)
return args
def update_grad_scaler(scaler: GradScaler, global_step, epoch, step):
@ -288,6 +295,49 @@ def update_grad_scaler(scaler: GradScaler, global_step, epoch, step):
scaler.set_growth_factor(factor)
scaler.set_backoff_factor(1/factor)
scaler.set_growth_interval(100)
def report_image_train_item_problems(log_folder: str, items: list[ImageTrainItem]) -> None:
for item in items:
if item.error is not None:
logging.error(f"{Fore.LIGHTRED_EX} *** Error opening {Fore.LIGHTYELLOW_EX}{item.pathname}{Fore.LIGHTRED_EX} to get metadata. File may be corrupt and will be skipped.{Style.RESET_ALL}")
logging.error(f" *** exception: {item.error}")
undersized_items = [item for item in items if item.is_undersized]
if len(undersized_items) > 0:
underized_log_path = os.path.join(log_folder, "undersized_images.txt")
logging.warning(f"{Fore.LIGHTRED_EX} ** Some images are smaller than the target size, consider using larger images{Style.RESET_ALL}")
logging.warning(f"{Fore.LIGHTRED_EX} ** Check {underized_log_path} for more information.{Style.RESET_ALL}")
with open(underized_log_path, "w") as undersized_images_file:
undersized_images_file.write(f" The following images are smaller than the target size, consider removing or sourcing a larger copy:")
for undersized_item in undersized_items:
message = f" *** {undersized_item.pathname} with size: {undersized_item.image_size} is smaller than target size: {undersized_item.target_wh}\n"
undersized_images_file.write(message)
def resolve_image_train_items(args: argparse.Namespace, log_folder: str) -> list[ImageTrainItem]:
logging.info(f"* DLMA resolution {args.resolution}, buckets: {args.aspects}")
logging.info(" Preloading images...")
resolved_items = resolver.resolve(args.data_root, args)
report_image_train_item_problems(log_folder, resolved_items)
image_paths = set(map(lambda item: item.pathname, resolved_items))
# Remove erroneous items
image_train_items = [item for item in resolved_items if item.error is None]
print (f" * DLMA: {len(image_train_items)} images loaded from {len(image_paths)} files")
return image_train_items
def write_batch_schedule(args: argparse.Namespace, log_folder: str, train_batch: EveryDreamBatch, epoch: int):
if args.write_schedule:
with open(f"{log_folder}/ep{epoch}_batch_schedule.txt", "w", encoding='utf-8') as f:
for i in range(len(train_batch.image_train_items)):
try:
item = train_batch.image_train_items[i]
f.write(f"step:{int(i / train_batch.batch_size):05}, wh:{item.target_wh}, r:{item.runt_size}, path:{item.pathname}\n")
except Exception as e:
logging.error(f" * Error writing to batch schedule for file path: {item.pathname}")
def read_sample_prompts(sample_prompts_file_path: str):
@ -556,23 +606,26 @@ def main(args):
)
log_optimizer(optimizer, betas, epsilon)
image_train_items = resolve_image_train_items(args, log_folder)
data_loader = DataLoaderMultiAspect(
image_train_items=image_train_items,
seed=seed,
batch_size=args.batch_size,
)
train_batch = EveryDreamBatch(
data_root=args.data_root,
flip_p=args.flip_p,
data_loader=data_loader,
debug_level=1,
batch_size=args.batch_size,
conditional_dropout=args.cond_dropout,
resolution=args.resolution,
tokenizer=tokenizer,
seed = seed,
log_folder=log_folder,
write_schedule=args.write_schedule,
shuffle_tags=args.shuffle_tags,
rated_dataset=args.rated_dataset,
rated_dataset_dropout_target=(1.0 - (args.rated_dataset_target_dropout_percent / 100.0))
)
torch.cuda.benchmark = False
epoch_len = math.ceil(len(train_batch) / args.batch_size)
@ -592,10 +645,11 @@ def main(args):
if args.wandb is not None and args.wandb:
wandb.init(project=args.project_name, sync_tensorboard=True, )
log_writer = SummaryWriter(log_dir=log_folder,
flush_secs=5,
comment="EveryDream2FineTunes",
)
log_writer = SummaryWriter(
log_dir=log_folder,
flush_secs=5,
comment="EveryDream2FineTunes",
)
def log_args(log_writer, args):
arglog = "args:\n"
@ -732,6 +786,8 @@ def main(args):
# # discard the grads, just want to pin memory
# optimizer.zero_grad(set_to_none=True)
write_batch_schedule(args, log_folder, train_batch, 0)
for epoch in range(args.max_epochs):
loss_epoch = []
epoch_start_time = time.time()
@ -883,6 +939,7 @@ def main(args):
epoch_pbar.update(1)
if epoch < args.max_epochs - 1:
train_batch.shuffle(epoch_n=epoch, max_epochs = args.max_epochs)
write_batch_schedule(args, log_folder, train_batch, epoch + 1)
loss_local = sum(loss_epoch) / len(loss_epoch)
log_writer.add_scalar(tag="loss/epoch", scalar_value=loss_local, global_step=global_step)
@ -909,7 +966,6 @@ def main(args):
logging.info(f"{Fore.LIGHTWHITE_EX} **** Finished training ****{Style.RESET_ALL}")
logging.info(f"{Fore.LIGHTWHITE_EX} ***************************{Style.RESET_ALL}")
def update_old_args(t_args):
"""
Update old args to new args to deal with json config loading and missing args for compatibility
@ -947,7 +1003,6 @@ if __name__ == "__main__":
t_args = argparse.Namespace()
t_args.__dict__.update(json.load(f))
update_old_args(t_args) # update args to support older configs
print(f" args: \n{t_args.__dict__}")
args = argparser.parse_args(namespace=t_args)
else:
print("No config file specified, using command line args")
@ -996,4 +1051,6 @@ if __name__ == "__main__":
args, _ = argparser.parse_known_args()
print(f" Args:")
pprint.pprint(args.__dict__)
main(args)