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Running
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T4
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from __future__ import annotations
import json
import math
from pathlib import Path
from typing import Any
import numpy as np
import torch
import torchvision
from einops import rearrange
from PIL import Image
from torch.utils.data import Dataset
class EditDataset(Dataset):
def __init__(
self,
path: str,
split: str = "train",
splits: tuple[float, float, float] = (0.9, 0.05, 0.05),
min_resize_res: int = 256,
max_resize_res: int = 256,
crop_res: int = 256,
flip_prob: float = 0.0,
):
assert split in ("train", "val", "test")
assert sum(splits) == 1
self.path = path
self.min_resize_res = min_resize_res
self.max_resize_res = max_resize_res
self.crop_res = crop_res
self.flip_prob = flip_prob
with open(Path(self.path, "seeds.json")) as f:
self.seeds = json.load(f)
split_0, split_1 = {
"train": (0.0, splits[0]),
"val": (splits[0], splits[0] + splits[1]),
"test": (splits[0] + splits[1], 1.0),
}[split]
idx_0 = math.floor(split_0 * len(self.seeds))
idx_1 = math.floor(split_1 * len(self.seeds))
self.seeds = self.seeds[idx_0:idx_1]
def __len__(self) -> int:
return len(self.seeds)
def __getitem__(self, i: int) -> dict[str, Any]:
name, seeds = self.seeds[i]
propt_dir = Path(self.path, name)
seed = seeds[torch.randint(0, len(seeds), ()).item()]
with open(propt_dir.joinpath("prompt.json")) as fp:
prompt = json.load(fp)["edit"]
image_0 = Image.open(propt_dir.joinpath(f"{seed}_0.jpg"))
image_1 = Image.open(propt_dir.joinpath(f"{seed}_1.jpg"))
reize_res = torch.randint(self.min_resize_res, self.max_resize_res + 1, ()).item()
image_0 = image_0.resize((reize_res, reize_res), Image.Resampling.LANCZOS)
image_1 = image_1.resize((reize_res, reize_res), Image.Resampling.LANCZOS)
image_0 = rearrange(2 * torch.tensor(np.array(image_0)).float() / 255 - 1, "h w c -> c h w")
image_1 = rearrange(2 * torch.tensor(np.array(image_1)).float() / 255 - 1, "h w c -> c h w")
crop = torchvision.transforms.RandomCrop(self.crop_res)
flip = torchvision.transforms.RandomHorizontalFlip(float(self.flip_prob))
image_0, image_1 = flip(crop(torch.cat((image_0, image_1)))).chunk(2)
return dict(edited=image_1, edit=dict(c_concat=image_0, c_crossattn=prompt))
class EditDatasetEval(Dataset):
def __init__(
self,
path: str,
split: str = "train",
splits: tuple[float, float, float] = (0.9, 0.05, 0.05),
res: int = 256,
):
assert split in ("train", "val", "test")
assert sum(splits) == 1
self.path = path
self.res = res
with open(Path(self.path, "seeds.json")) as f:
self.seeds = json.load(f)
split_0, split_1 = {
"train": (0.0, splits[0]),
"val": (splits[0], splits[0] + splits[1]),
"test": (splits[0] + splits[1], 1.0),
}[split]
idx_0 = math.floor(split_0 * len(self.seeds))
idx_1 = math.floor(split_1 * len(self.seeds))
self.seeds = self.seeds[idx_0:idx_1]
def __len__(self) -> int:
return len(self.seeds)
def __getitem__(self, i: int) -> dict[str, Any]:
name, seeds = self.seeds[i]
propt_dir = Path(self.path, name)
seed = seeds[torch.randint(0, len(seeds), ()).item()]
with open(propt_dir.joinpath("prompt.json")) as fp:
prompt = json.load(fp)
edit = prompt["edit"]
input_prompt = prompt["input"]
output_prompt = prompt["output"]
image_0 = Image.open(propt_dir.joinpath(f"{seed}_0.jpg"))
reize_res = torch.randint(self.res, self.res + 1, ()).item()
image_0 = image_0.resize((reize_res, reize_res), Image.Resampling.LANCZOS)
image_0 = rearrange(2 * torch.tensor(np.array(image_0)).float() / 255 - 1, "h w c -> c h w")
return dict(image_0=image_0, input_prompt=input_prompt, edit=edit, output_prompt=output_prompt)
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