mirror of
https://github.com/invoke-ai/InvokeAI
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395 lines
15 KiB
Python
395 lines
15 KiB
Python
import os, yaml, pickle, shutil, tarfile, glob
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import cv2
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import albumentations
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import PIL
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import numpy as np
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import torchvision.transforms.functional as TF
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from omegaconf import OmegaConf
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from functools import partial
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from PIL import Image
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from tqdm import tqdm
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from torch.utils.data import Dataset, Subset
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import taming.data.utils as tdu
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from taming.data.imagenet import str_to_indices, give_synsets_from_indices, download, retrieve
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from taming.data.imagenet import ImagePaths
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from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light
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def synset2idx(path_to_yaml="data/index_synset.yaml"):
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with open(path_to_yaml) as f:
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di2s = yaml.load(f)
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return dict((v,k) for k,v in di2s.items())
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class ImageNetBase(Dataset):
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def __init__(self, config=None):
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self.config = config or OmegaConf.create()
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if not type(self.config)==dict:
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self.config = OmegaConf.to_container(self.config)
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self.keep_orig_class_label = self.config.get("keep_orig_class_label", False)
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self.process_images = True # if False we skip loading & processing images and self.data contains filepaths
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self._prepare()
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self._prepare_synset_to_human()
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self._prepare_idx_to_synset()
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self._prepare_human_to_integer_label()
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self._load()
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def __len__(self):
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return len(self.data)
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def __getitem__(self, i):
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return self.data[i]
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def _prepare(self):
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raise NotImplementedError()
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def _filter_relpaths(self, relpaths):
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ignore = set([
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"n06596364_9591.JPEG",
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])
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relpaths = [rpath for rpath in relpaths if not rpath.split("/")[-1] in ignore]
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if "sub_indices" in self.config:
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indices = str_to_indices(self.config["sub_indices"])
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synsets = give_synsets_from_indices(indices, path_to_yaml=self.idx2syn) # returns a list of strings
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self.synset2idx = synset2idx(path_to_yaml=self.idx2syn)
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files = []
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for rpath in relpaths:
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syn = rpath.split("/")[0]
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if syn in synsets:
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files.append(rpath)
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return files
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else:
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return relpaths
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def _prepare_synset_to_human(self):
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SIZE = 2655750
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URL = "https://heibox.uni-heidelberg.de/f/9f28e956cd304264bb82/?dl=1"
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self.human_dict = os.path.join(self.root, "synset_human.txt")
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if (not os.path.exists(self.human_dict) or
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not os.path.getsize(self.human_dict)==SIZE):
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download(URL, self.human_dict)
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def _prepare_idx_to_synset(self):
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URL = "https://heibox.uni-heidelberg.de/f/d835d5b6ceda4d3aa910/?dl=1"
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self.idx2syn = os.path.join(self.root, "index_synset.yaml")
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if (not os.path.exists(self.idx2syn)):
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download(URL, self.idx2syn)
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def _prepare_human_to_integer_label(self):
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URL = "https://heibox.uni-heidelberg.de/f/2362b797d5be43b883f6/?dl=1"
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self.human2integer = os.path.join(self.root, "imagenet1000_clsidx_to_labels.txt")
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if (not os.path.exists(self.human2integer)):
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download(URL, self.human2integer)
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with open(self.human2integer, "r") as f:
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lines = f.read().splitlines()
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assert len(lines) == 1000
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self.human2integer_dict = dict()
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for line in lines:
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value, key = line.split(":")
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self.human2integer_dict[key] = int(value)
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def _load(self):
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with open(self.txt_filelist, "r") as f:
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self.relpaths = f.read().splitlines()
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l1 = len(self.relpaths)
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self.relpaths = self._filter_relpaths(self.relpaths)
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print("Removed {} files from filelist during filtering.".format(l1 - len(self.relpaths)))
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self.synsets = [p.split("/")[0] for p in self.relpaths]
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self.abspaths = [os.path.join(self.datadir, p) for p in self.relpaths]
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unique_synsets = np.unique(self.synsets)
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class_dict = dict((synset, i) for i, synset in enumerate(unique_synsets))
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if not self.keep_orig_class_label:
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self.class_labels = [class_dict[s] for s in self.synsets]
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else:
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self.class_labels = [self.synset2idx[s] for s in self.synsets]
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with open(self.human_dict, "r") as f:
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human_dict = f.read().splitlines()
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human_dict = dict(line.split(maxsplit=1) for line in human_dict)
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self.human_labels = [human_dict[s] for s in self.synsets]
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labels = {
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"relpath": np.array(self.relpaths),
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"synsets": np.array(self.synsets),
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"class_label": np.array(self.class_labels),
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"human_label": np.array(self.human_labels),
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}
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if self.process_images:
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self.size = retrieve(self.config, "size", default=256)
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self.data = ImagePaths(self.abspaths,
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labels=labels,
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size=self.size,
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random_crop=self.random_crop,
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)
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else:
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self.data = self.abspaths
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class ImageNetTrain(ImageNetBase):
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NAME = "ILSVRC2012_train"
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URL = "http://www.image-net.org/challenges/LSVRC/2012/"
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AT_HASH = "a306397ccf9c2ead27155983c254227c0fd938e2"
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FILES = [
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"ILSVRC2012_img_train.tar",
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]
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SIZES = [
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147897477120,
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]
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def __init__(self, process_images=True, data_root=None, **kwargs):
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self.process_images = process_images
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self.data_root = data_root
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super().__init__(**kwargs)
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def _prepare(self):
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if self.data_root:
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self.root = os.path.join(self.data_root, self.NAME)
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else:
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cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
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self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
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self.datadir = os.path.join(self.root, "data")
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self.txt_filelist = os.path.join(self.root, "filelist.txt")
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self.expected_length = 1281167
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self.random_crop = retrieve(self.config, "ImageNetTrain/random_crop",
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default=True)
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if not tdu.is_prepared(self.root):
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# prep
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print("Preparing dataset {} in {}".format(self.NAME, self.root))
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datadir = self.datadir
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if not os.path.exists(datadir):
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path = os.path.join(self.root, self.FILES[0])
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if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
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import academictorrents as at
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atpath = at.get(self.AT_HASH, datastore=self.root)
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assert atpath == path
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print("Extracting {} to {}".format(path, datadir))
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os.makedirs(datadir, exist_ok=True)
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with tarfile.open(path, "r:") as tar:
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tar.extractall(path=datadir)
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print("Extracting sub-tars.")
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subpaths = sorted(glob.glob(os.path.join(datadir, "*.tar")))
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for subpath in tqdm(subpaths):
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subdir = subpath[:-len(".tar")]
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os.makedirs(subdir, exist_ok=True)
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with tarfile.open(subpath, "r:") as tar:
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tar.extractall(path=subdir)
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filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
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filelist = [os.path.relpath(p, start=datadir) for p in filelist]
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filelist = sorted(filelist)
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filelist = "\n".join(filelist)+"\n"
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with open(self.txt_filelist, "w") as f:
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f.write(filelist)
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tdu.mark_prepared(self.root)
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class ImageNetValidation(ImageNetBase):
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NAME = "ILSVRC2012_validation"
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URL = "http://www.image-net.org/challenges/LSVRC/2012/"
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AT_HASH = "5d6d0df7ed81efd49ca99ea4737e0ae5e3a5f2e5"
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VS_URL = "https://heibox.uni-heidelberg.de/f/3e0f6e9c624e45f2bd73/?dl=1"
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FILES = [
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"ILSVRC2012_img_val.tar",
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"validation_synset.txt",
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]
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SIZES = [
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6744924160,
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1950000,
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]
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def __init__(self, process_images=True, data_root=None, **kwargs):
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self.data_root = data_root
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self.process_images = process_images
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super().__init__(**kwargs)
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def _prepare(self):
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if self.data_root:
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self.root = os.path.join(self.data_root, self.NAME)
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else:
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cachedir = os.environ.get("XDG_CACHE_HOME", os.path.expanduser("~/.cache"))
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self.root = os.path.join(cachedir, "autoencoders/data", self.NAME)
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self.datadir = os.path.join(self.root, "data")
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self.txt_filelist = os.path.join(self.root, "filelist.txt")
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self.expected_length = 50000
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self.random_crop = retrieve(self.config, "ImageNetValidation/random_crop",
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default=False)
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if not tdu.is_prepared(self.root):
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# prep
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print("Preparing dataset {} in {}".format(self.NAME, self.root))
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datadir = self.datadir
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if not os.path.exists(datadir):
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path = os.path.join(self.root, self.FILES[0])
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if not os.path.exists(path) or not os.path.getsize(path)==self.SIZES[0]:
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import academictorrents as at
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atpath = at.get(self.AT_HASH, datastore=self.root)
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assert atpath == path
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print("Extracting {} to {}".format(path, datadir))
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os.makedirs(datadir, exist_ok=True)
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with tarfile.open(path, "r:") as tar:
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tar.extractall(path=datadir)
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vspath = os.path.join(self.root, self.FILES[1])
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if not os.path.exists(vspath) or not os.path.getsize(vspath)==self.SIZES[1]:
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download(self.VS_URL, vspath)
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with open(vspath, "r") as f:
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synset_dict = f.read().splitlines()
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synset_dict = dict(line.split() for line in synset_dict)
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print("Reorganizing into synset folders")
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synsets = np.unique(list(synset_dict.values()))
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for s in synsets:
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os.makedirs(os.path.join(datadir, s), exist_ok=True)
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for k, v in synset_dict.items():
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src = os.path.join(datadir, k)
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dst = os.path.join(datadir, v)
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shutil.move(src, dst)
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filelist = glob.glob(os.path.join(datadir, "**", "*.JPEG"))
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filelist = [os.path.relpath(p, start=datadir) for p in filelist]
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filelist = sorted(filelist)
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filelist = "\n".join(filelist)+"\n"
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with open(self.txt_filelist, "w") as f:
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f.write(filelist)
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tdu.mark_prepared(self.root)
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class ImageNetSR(Dataset):
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def __init__(self, size=None,
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degradation=None, downscale_f=4, min_crop_f=0.5, max_crop_f=1.,
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random_crop=True):
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"""
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Imagenet Superresolution Dataloader
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Performs following ops in order:
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1. crops a crop of size s from image either as random or center crop
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2. resizes crop to size with cv2.area_interpolation
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3. degrades resized crop with degradation_fn
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:param size: resizing to size after cropping
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:param degradation: degradation_fn, e.g. cv_bicubic or bsrgan_light
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:param downscale_f: Low Resolution Downsample factor
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:param min_crop_f: determines crop size s,
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where s = c * min_img_side_len with c sampled from interval (min_crop_f, max_crop_f)
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:param max_crop_f: ""
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:param data_root:
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:param random_crop:
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"""
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self.base = self.get_base()
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assert size
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assert (size / downscale_f).is_integer()
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self.size = size
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self.LR_size = int(size / downscale_f)
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self.min_crop_f = min_crop_f
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self.max_crop_f = max_crop_f
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assert(max_crop_f <= 1.)
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self.center_crop = not random_crop
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self.image_rescaler = albumentations.SmallestMaxSize(max_size=size, interpolation=cv2.INTER_AREA)
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self.pil_interpolation = False # gets reset later if incase interp_op is from pillow
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if degradation == "bsrgan":
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self.degradation_process = partial(degradation_fn_bsr, sf=downscale_f)
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elif degradation == "bsrgan_light":
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self.degradation_process = partial(degradation_fn_bsr_light, sf=downscale_f)
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else:
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interpolation_fn = {
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"cv_nearest": cv2.INTER_NEAREST,
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"cv_bilinear": cv2.INTER_LINEAR,
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"cv_bicubic": cv2.INTER_CUBIC,
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"cv_area": cv2.INTER_AREA,
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"cv_lanczos": cv2.INTER_LANCZOS4,
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"pil_nearest": PIL.Image.NEAREST,
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"pil_bilinear": PIL.Image.BILINEAR,
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"pil_bicubic": PIL.Image.BICUBIC,
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"pil_box": PIL.Image.BOX,
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"pil_hamming": PIL.Image.HAMMING,
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"pil_lanczos": PIL.Image.LANCZOS,
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}[degradation]
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self.pil_interpolation = degradation.startswith("pil_")
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if self.pil_interpolation:
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self.degradation_process = partial(TF.resize, size=self.LR_size, interpolation=interpolation_fn)
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else:
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self.degradation_process = albumentations.SmallestMaxSize(max_size=self.LR_size,
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interpolation=interpolation_fn)
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def __len__(self):
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return len(self.base)
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def __getitem__(self, i):
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example = self.base[i]
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image = Image.open(example["file_path_"])
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if not image.mode == "RGB":
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image = image.convert("RGB")
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image = np.array(image).astype(np.uint8)
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min_side_len = min(image.shape[:2])
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crop_side_len = min_side_len * np.random.uniform(self.min_crop_f, self.max_crop_f, size=None)
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crop_side_len = int(crop_side_len)
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if self.center_crop:
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self.cropper = albumentations.CenterCrop(height=crop_side_len, width=crop_side_len)
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else:
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self.cropper = albumentations.RandomCrop(height=crop_side_len, width=crop_side_len)
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image = self.cropper(image=image)["image"]
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image = self.image_rescaler(image=image)["image"]
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if self.pil_interpolation:
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image_pil = PIL.Image.fromarray(image)
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LR_image = self.degradation_process(image_pil)
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LR_image = np.array(LR_image).astype(np.uint8)
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else:
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LR_image = self.degradation_process(image=image)["image"]
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example["image"] = (image/127.5 - 1.0).astype(np.float32)
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example["LR_image"] = (LR_image/127.5 - 1.0).astype(np.float32)
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return example
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class ImageNetSRTrain(ImageNetSR):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def get_base(self):
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with open("data/imagenet_train_hr_indices.p", "rb") as f:
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indices = pickle.load(f)
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dset = ImageNetTrain(process_images=False,)
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return Subset(dset, indices)
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class ImageNetSRValidation(ImageNetSR):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def get_base(self):
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with open("data/imagenet_val_hr_indices.p", "rb") as f:
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indices = pickle.load(f)
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dset = ImageNetValidation(process_images=False,)
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return Subset(dset, indices)
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