From 6a49074dce78615bce54777fb2be3bfd0dd8f780 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Fri, 14 Oct 2022 20:03:01 +0200
Subject: Removed aesthetic gradients; training improvements

---
 aesthetic_gradient.py | 137 --------------------------------------------------
 1 file changed, 137 deletions(-)
 delete mode 100644 aesthetic_gradient.py

(limited to 'aesthetic_gradient.py')

diff --git a/aesthetic_gradient.py b/aesthetic_gradient.py
deleted file mode 100644
index 5386d0f..0000000
--- a/aesthetic_gradient.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import argparse
-import datetime
-import logging
-import json
-from pathlib import Path
-
-import torch
-import torch.utils.checkpoint
-from torchvision import transforms
-import pandas as pd
-
-from accelerate.logging import get_logger
-from PIL import Image
-from tqdm import tqdm
-from transformers import CLIPModel
-from slugify import slugify
-
-logger = get_logger(__name__)
-
-
-torch.backends.cuda.matmul.allow_tf32 = True
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description="Simple example of a training script."
-    )
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--train_data_file",
-        type=str,
-        default=None,
-        help="A directory."
-    )
-    parser.add_argument(
-        "--token",
-        type=str,
-        default=None,
-        help="A token to use as a placeholder for the concept.",
-    )
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=224,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="output/aesthetic-gradient",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument(
-        "--config",
-        type=str,
-        default=None,
-        help="Path to a JSON configuration file containing arguments for invoking this script. If resume_from is given, its resume.json takes priority over this."
-    )
-
-    args = parser.parse_args()
-    if args.config is not None:
-        with open(args.config, 'rt') as f:
-            args = parser.parse_args(
-                namespace=argparse.Namespace(**json.load(f)["args"]))
-
-    if args.train_data_file is None:
-        raise ValueError("You must specify --train_data_file")
-
-    if args.token is None:
-        raise ValueError("You must specify --token")
-
-    if args.output_dir is None:
-        raise ValueError("You must specify --output_dir")
-
-    return args
-
-
-def main():
-    args = parse_args()
-
-    now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
-    basepath = Path(args.output_dir)
-    basepath.mkdir(parents=True, exist_ok=True)
-    target = basepath.joinpath(f"{slugify(args.token)}-{now}.pt")
-
-    logging.basicConfig(filename=basepath.joinpath("log.txt"), level=logging.DEBUG)
-
-    data_file = Path(args.train_data_file)
-    if not data_file.is_file():
-        raise ValueError("data_file must be a file")
-    data_root = data_file.parent
-    metadata = pd.read_csv(data_file)
-    image_paths = [
-        data_root.joinpath(item.image)
-        for item in metadata.itertuples()
-        if "skip" not in item or item.skip != "x"
-    ]
-
-    model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
-
-    image_transforms = transforms.Compose(
-        [
-            transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.LANCZOS),
-            transforms.RandomCrop(args.resolution),
-            transforms.ToTensor(),
-            transforms.Normalize([0.5], [0.5]),
-        ]
-    )
-
-    with torch.no_grad():
-        embs = []
-        for path in tqdm(image_paths):
-            image = Image.open(path)
-            if not image.mode == "RGB":
-                image = image.convert("RGB")
-            image = image_transforms(image).unsqueeze(0)
-            emb = model.get_image_features(image)
-            print(f">>>> {emb.shape}")
-            embs.append(emb)
-
-        embs = torch.cat(embs, dim=0).mean(dim=0, keepdim=True)
-
-        print(embs.shape)
-
-        torch.save(embs, target)
-
-
-if __name__ == "__main__":
-    main()
-- 
cgit v1.2.3-54-g00ecf