stable-diffusion-finetune/ldm/data/coco.py
2022-06-10 10:54:14 +02:00

253 lines
11 KiB
Python

import os
import json
import albumentations
import numpy as np
from PIL import Image
from tqdm import tqdm
from torch.utils.data import Dataset
from abc import abstractmethod
class CocoBase(Dataset):
"""needed for (image, caption, segmentation) pairs"""
def __init__(self, size=None, dataroot="", datajson="", onehot_segmentation=False, use_stuffthing=False,
crop_size=None, force_no_crop=False, given_files=None, use_segmentation=True,crop_type=None):
self.split = self.get_split()
self.size = size
if crop_size is None:
self.crop_size = size
else:
self.crop_size = crop_size
assert crop_type in [None, 'random', 'center']
self.crop_type = crop_type
self.use_segmenation = use_segmentation
self.onehot = onehot_segmentation # return segmentation as rgb or one hot
self.stuffthing = use_stuffthing # include thing in segmentation
if self.onehot and not self.stuffthing:
raise NotImplemented("One hot mode is only supported for the "
"stuffthings version because labels are stored "
"a bit different.")
data_json = datajson
with open(data_json) as json_file:
self.json_data = json.load(json_file)
self.img_id_to_captions = dict()
self.img_id_to_filepath = dict()
self.img_id_to_segmentation_filepath = dict()
assert data_json.split("/")[-1] in [f"captions_train{self.year()}.json",
f"captions_val{self.year()}.json"]
# TODO currently hardcoded paths, would be better to follow logic in
# cocstuff pixelmaps
if self.use_segmenation:
if self.stuffthing:
self.segmentation_prefix = (
f"data/cocostuffthings/val{self.year()}" if
data_json.endswith(f"captions_val{self.year()}.json") else
f"data/cocostuffthings/train{self.year()}")
else:
self.segmentation_prefix = (
f"data/coco/annotations/stuff_val{self.year()}_pixelmaps" if
data_json.endswith(f"captions_val{self.year()}.json") else
f"data/coco/annotations/stuff_train{self.year()}_pixelmaps")
imagedirs = self.json_data["images"]
self.labels = {"image_ids": list()}
for imgdir in tqdm(imagedirs, desc="ImgToPath"):
self.img_id_to_filepath[imgdir["id"]] = os.path.join(dataroot, imgdir["file_name"])
self.img_id_to_captions[imgdir["id"]] = list()
pngfilename = imgdir["file_name"].replace("jpg", "png")
if self.use_segmenation:
self.img_id_to_segmentation_filepath[imgdir["id"]] = os.path.join(
self.segmentation_prefix, pngfilename)
if given_files is not None:
if pngfilename in given_files:
self.labels["image_ids"].append(imgdir["id"])
else:
self.labels["image_ids"].append(imgdir["id"])
capdirs = self.json_data["annotations"]
for capdir in tqdm(capdirs, desc="ImgToCaptions"):
# there are in average 5 captions per image
#self.img_id_to_captions[capdir["image_id"]].append(np.array([capdir["caption"]]))
self.img_id_to_captions[capdir["image_id"]].append(capdir["caption"])
self.rescaler = albumentations.SmallestMaxSize(max_size=self.size)
if self.split=="validation":
self.cropper = albumentations.CenterCrop(height=self.crop_size, width=self.crop_size)
else:
# default option for train is random crop
if self.crop_type in [None, 'random']:
self.cropper = albumentations.RandomCrop(height=self.crop_size, width=self.crop_size)
else:
self.cropper = albumentations.CenterCrop(height=self.crop_size, width=self.crop_size)
self.preprocessor = albumentations.Compose(
[self.rescaler, self.cropper],
additional_targets={"segmentation": "image"})
if force_no_crop:
self.rescaler = albumentations.Resize(height=self.size, width=self.size)
self.preprocessor = albumentations.Compose(
[self.rescaler],
additional_targets={"segmentation": "image"})
@abstractmethod
def year(self):
raise NotImplementedError()
def __len__(self):
return len(self.labels["image_ids"])
def preprocess_image(self, image_path, segmentation_path=None):
image = Image.open(image_path)
if not image.mode == "RGB":
image = image.convert("RGB")
image = np.array(image).astype(np.uint8)
if segmentation_path:
segmentation = Image.open(segmentation_path)
if not self.onehot and not segmentation.mode == "RGB":
segmentation = segmentation.convert("RGB")
segmentation = np.array(segmentation).astype(np.uint8)
if self.onehot:
assert self.stuffthing
# stored in caffe format: unlabeled==255. stuff and thing from
# 0-181. to be compatible with the labels in
# https://github.com/nightrome/cocostuff/blob/master/labels.txt
# we shift stuffthing one to the right and put unlabeled in zero
# as long as segmentation is uint8 shifting to right handles the
# latter too
assert segmentation.dtype == np.uint8
segmentation = segmentation + 1
processed = self.preprocessor(image=image, segmentation=segmentation)
image, segmentation = processed["image"], processed["segmentation"]
else:
image = self.preprocessor(image=image,)['image']
image = (image / 127.5 - 1.0).astype(np.float32)
if segmentation_path:
if self.onehot:
assert segmentation.dtype == np.uint8
# make it one hot
n_labels = 183
flatseg = np.ravel(segmentation)
onehot = np.zeros((flatseg.size, n_labels), dtype=np.bool)
onehot[np.arange(flatseg.size), flatseg] = True
onehot = onehot.reshape(segmentation.shape + (n_labels,)).astype(int)
segmentation = onehot
else:
segmentation = (segmentation / 127.5 - 1.0).astype(np.float32)
return image, segmentation
else:
return image
def __getitem__(self, i):
img_path = self.img_id_to_filepath[self.labels["image_ids"][i]]
if self.use_segmenation:
seg_path = self.img_id_to_segmentation_filepath[self.labels["image_ids"][i]]
image, segmentation = self.preprocess_image(img_path, seg_path)
else:
image = self.preprocess_image(img_path)
captions = self.img_id_to_captions[self.labels["image_ids"][i]]
# randomly draw one of all available captions per image
caption = captions[np.random.randint(0, len(captions))]
example = {"image": image,
#"caption": [str(caption[0])],
"caption": caption,
"img_path": img_path,
"filename_": img_path.split(os.sep)[-1]
}
if self.use_segmenation:
example.update({"seg_path": seg_path, 'segmentation': segmentation})
return example
class CocoImagesAndCaptionsTrain2017(CocoBase):
"""returns a pair of (image, caption)"""
def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False,):
super().__init__(size=size,
dataroot="data/coco/train2017",
datajson="data/coco/annotations/captions_train2017.json",
onehot_segmentation=onehot_segmentation,
use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop)
def get_split(self):
return "train"
def year(self):
return '2017'
class CocoImagesAndCaptionsValidation2017(CocoBase):
"""returns a pair of (image, caption)"""
def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False,
given_files=None):
super().__init__(size=size,
dataroot="data/coco/val2017",
datajson="data/coco/annotations/captions_val2017.json",
onehot_segmentation=onehot_segmentation,
use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop,
given_files=given_files)
def get_split(self):
return "validation"
def year(self):
return '2017'
class CocoImagesAndCaptionsTrain2014(CocoBase):
"""returns a pair of (image, caption)"""
def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False,crop_type='random'):
super().__init__(size=size,
dataroot="data/coco/train2014",
datajson="data/coco/annotations2014/annotations/captions_train2014.json",
onehot_segmentation=onehot_segmentation,
use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop,
use_segmentation=False,
crop_type=crop_type)
def get_split(self):
return "train"
def year(self):
return '2014'
class CocoImagesAndCaptionsValidation2014(CocoBase):
"""returns a pair of (image, caption)"""
def __init__(self, size, onehot_segmentation=False, use_stuffthing=False, crop_size=None, force_no_crop=False,
given_files=None,crop_type='center',**kwargs):
super().__init__(size=size,
dataroot="data/coco/val2014",
datajson="data/coco/annotations2014/annotations/captions_val2014.json",
onehot_segmentation=onehot_segmentation,
use_stuffthing=use_stuffthing, crop_size=crop_size, force_no_crop=force_no_crop,
given_files=given_files,
use_segmentation=False,
crop_type=crop_type)
def get_split(self):
return "validation"
def year(self):
return '2014'
if __name__ == '__main__':
with open("data/coco/annotations2014/annotations/captions_val2014.json", "r") as json_file:
json_data = json.load(json_file)
capdirs = json_data["annotations"]
import pudb; pudb.set_trace()
#d2 = CocoImagesAndCaptionsTrain2014(size=256)
d2 = CocoImagesAndCaptionsValidation2014(size=256)
print("constructed dataset.")
print(f"length of {d2.__class__.__name__}: {len(d2)}")
ex2 = d2[0]
# ex3 = d3[0]
# print(ex1["image"].shape)
print(ex2["image"].shape)
# print(ex3["image"].shape)
# print(ex1["segmentation"].shape)
print(ex2["caption"].__class__.__name__)