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