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clip_iqa.py
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clip_iqa.py
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# MIT License
# Copyright Dave Lage (rockerBOO)
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import argparse
from collections import defaultdict
from pathlib import Path
import PIL
import torch
from accelerate.utils import set_seed
from torchmetrics.multimodal import CLIPImageQualityAssessment
from torchvision import transforms
from sd_ext.sd import generate_dataset, setup_sd_generator, sd_arguments
from sd_ext.dataset import load_image_dataset_from_dir
from sd_ext.format import to_csv, to_json, format_args
TRANSFORMS = transforms.Compose(
[
transforms.RandomResizedCrop(size=(299, 299), antialias=True),
transforms.PILToTensor(),
]
)
def collate_image_file(data):
return [
{
"image": PIL.Image.open(d["image"]["path"]),
"image_file": d["image"]["path"],
}
for d in data
]
def get_clip_iqa_prompts(prompts):
"""
CLIP IQA wants a pair of prompts with positive and a negative
"""
prompts = args.prompts.split(";")
prompts = [
[r.strip() for r in p.strip().split(".") if r != ""] for p in prompts
]
results = []
for p in prompts:
# we are dealing with built in options
if len(p) == 1:
results.append(p[0])
else:
results.append(tuple(p))
prompts = tuple(results)
print(f"Prompts: {prompts}")
return prompts
def main(args):
seed = args.seed
set_seed(seed)
device = torch.device(
args.device
if args.device
else ("cuda" if torch.cuda.is_available() else "cpu")
)
metric = CLIPImageQualityAssessment(
model_name_or_path=args.clip_model_name_or_path,
prompts=get_clip_iqa_prompts(args.prompts),
)
metric.to(device)
print(f"Loading {args.data_dir}")
if args.data_dir is not None:
ds = load_image_dataset_from_dir(args.data_dir)
else:
sd_generator = setup_sd_generator(args)
ds = generate_dataset(sd_generator)
dataloader = torch.utils.data.DataLoader(
ds,
batch_size=1,
# worker_init_fn=seed_worker,
# generator=generator,
collate_fn=collate_image_file,
)
print(f"Images: {len(ds)} batches: {len(dataloader)}")
scores = get_scores(dataloader, metric, device)
# average_scores = defaultdict()
# for k, (image_file, prompt_scores) in scores.items():
# for prompt, score in prompt_scores:
# average_scores.setdefault(prompt, []).append(score)
print(f"Average CLIP IQA scores for {len(dataloader)} in {args.data_dir}")
for score in scores:
print(f"{Path(score['image_file']).name} {score['prompt']} {score['score']}")
# print(f"{score['prompt']:<20} {sum(scores) / len(scores)}")
if args.csv:
to_csv(scores, args.csv)
if args.json:
to_json(scores, args.json)
def get_scores(dataloader, metric, device):
# clip_iqa_scores = defaultdict()
scores = []
for i, batch in enumerate(dataloader):
images = []
for image in batch:
images.append(TRANSFORMS(image["image"]))
results = metric(torch.stack(images).to(device))
for prompt, score in results.items():
scores.append(
{
"image_file": image["image_file"],
"prompt": prompt,
"score": score.item(),
}
)
return scores
if __name__ == "__main__":
argparser = argparse.ArgumentParser(
description="""CLIP Image Quality Assessment
Can use a premade set of images to score
--data_dir "/path/to/images"
Directory of images to run the prompts on.
*OR*
Generate images using Stable Diffusion
--num_images_to_generate 25
Number of images to generate
--lora_files "/path/to/lora.safetensors"
Path to LoRA file to use when generating
--prompts "quality;brightness;A super good photo. A super bad photo;"
Prompts as contrast with "positive" and "negative".
Positive and negative separated by `.` Eg: Good. Bad.
Prompts separated by `;` quality;brightness;
Prompts input is a string so make sure to "quote" the prompt.
See <https://torchmetrics.readthedocs.io/en/stable/multimodal/clip_iqa.html>
for more details.
Built in options (use as a single):
quality: “Good photo.” vs “Bad photo.”
brightness: “Bright photo.” vs “Dark photo.”
noisiness: “Clean photo.” vs “Noisy photo.”
colorfullness: “Colorful photo.” vs “Dull photo.”
sharpness: “Sharp photo.” vs “Blurry photo.”
contrast: “High contrast photo.” vs “Low contrast photo.”
complexity: “Complex photo.” vs “Simple photo.”
natural: “Natural photo.” vs “Synthetic photo.”
happy: “Happy photo.” vs “Sad photo.”
scary: “Scary photo.” vs “Peaceful photo.”
new: “New photo.” vs “Old photo.”
warm: “Warm photo.” vs “Cold photo.”
real: “Real photo.” vs “Abstract photo.”
beutiful: “Beautiful photo.” vs “Ugly photo.”
lonely: “Lonely photo.” vs “Sociable photo.”
relaxing: “Relaxing photo.” vs “Stressful photo.”
""",
formatter_class=argparse.RawTextHelpFormatter,
)
argparser.add_argument(
"--data_dir",
required=True,
help="Data dir",
)
argparser.add_argument(
"--clip_model_name_or_path",
default="openai/clip-vit-base-patch16",
help="CLIP Model to get the CLIP score from",
)
argparser.add_argument(
"--to_generate_prompts",
help="Prompts to generate with. Default we chose a variety of prompts",
)
argparser.add_argument("--output_scores", help="Output scores to")
argparser.add_argument(
"--num_images_to_generate",
type=int,
default=25,
help="Number of images to generate if using generated images",
)
argparser.add_argument(
"--prompts",
type=str,
default="sharpness;brightness;quality;contrast;colorfullness;happy;beutiful",
help="list of prompts separated by comma",
)
argparser = sd_arguments(argparser)
argparser = format_args(argparser)
args = argparser.parse_args()
main(args)