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import os
import json
import numpy as np
import webdataset as wds
import pytorch_lightning as pl
import torch
from torch.utils.data import Dataset
from torch.utils.data.distributed import DistributedSampler
from PIL import Image
from pathlib import Path
from src.utils.train_util import instantiate_from_config
class DataModuleFromConfig(pl.LightningDataModule):
def __init__(
self,
batch_size=8,
num_workers=4,
train=None,
validation=None,
test=None,
**kwargs,
):
super().__init__()
self.batch_size = batch_size
self.num_workers = num_workers
self.dataset_configs = dict()
if train is not None:
self.dataset_configs['train'] = train
if validation is not None:
self.dataset_configs['validation'] = validation
if test is not None:
self.dataset_configs['test'] = test
def setup(self, stage):
if stage in ['fit']:
self.datasets = dict((k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs)
else:
raise NotImplementedError
def train_dataloader(self):
sampler = DistributedSampler(self.datasets['train'])
return wds.WebLoader(self.datasets['train'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False, sampler=sampler)
def val_dataloader(self):
sampler = DistributedSampler(self.datasets['validation'])
return wds.WebLoader(self.datasets['validation'], batch_size=4, num_workers=self.num_workers, shuffle=False, sampler=sampler)
def test_dataloader(self):
return wds.WebLoader(self.datasets['test'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False)
class ObjaverseData(Dataset):
def __init__(self,
root_dir='objaverse/',
meta_fname='valid_paths.json',
image_dir='rendering_zero123plus',
validation=False,
):
self.root_dir = Path(root_dir)
self.image_dir = image_dir
with open(os.path.join(root_dir, meta_fname)) as f:
lvis_dict = json.load(f)
paths = []
for k in lvis_dict.keys():
paths.extend(lvis_dict[k])
self.paths = paths
total_objects = len(self.paths)
if validation:
self.paths = self.paths[-16:] # used last 16 as validation
else:
self.paths = self.paths[:-16]
print('============= length of dataset %d =============' % len(self.paths))
def __len__(self):
return len(self.paths)
def load_im(self, path, color):
pil_img = Image.open(path)
image = np.asarray(pil_img, dtype=np.float32) / 255.
alpha = image[:, :, 3:]
image = image[:, :, :3] * alpha + color * (1 - alpha)
image = torch.from_numpy(image).permute(2, 0, 1).contiguous().float()
alpha = torch.from_numpy(alpha).permute(2, 0, 1).contiguous().float()
return image, alpha
def __getitem__(self, index):
while True:
image_path = os.path.join(self.root_dir, self.image_dir, self.paths[index])
'''background color, default: white'''
bkg_color = [1., 1., 1.]
img_list = []
try:
for idx in range(7):
img, alpha = self.load_im(os.path.join(image_path, '%03d.png' % idx), bkg_color)
img_list.append(img)
except Exception as e:
print(e)
index = np.random.randint(0, len(self.paths))
continue
break
imgs = torch.stack(img_list, dim=0).float()
data = {
'cond_imgs': imgs[0], # (3, H, W)
'target_imgs': imgs[1:], # (6, 3, H, W)
}
return data
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