dataset.py

import os
import math
import json
from pathlib import Path
import torch
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import pytorch_lightning as pl
from einops import rearrange
from PIL import Image
import numpy as np
import cv2
import random
import pickle
import webdataset as wds
from torch.utils.data.distributed import DistributedSampler
import matplotlib.pyplot as plt
import sys

class ObjaverseDataLoader():
    def __init__(self, root_dir, batch_size, total_view=12, num_workers=4):
        # super().__init__(self, root_dir, batch_size, total_view, num_workers)
        self.root_dir = root_dir
        self.batch_size = batch_size
        self.num_workers = num_workers
        self.total_view = total_view

        image_transforms = [torchvision.transforms.Resize((256, 256)),
                            transforms.ToTensor(),
                            transforms.Normalize([0.5], [0.5])]
        self.image_transforms = torchvision.transforms.Compose(image_transforms)

    def train_dataloader(self):
        dataset = ObjaverseData(root_dir=self.root_dir, total_view=self.total_view, validation=False,
                                image_transforms=self.image_transforms)
        # sampler = DistributedSampler(dataset)
        return wds.WebLoader(dataset, batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False)
                             # sampler=sampler)

    def val_dataloader(self):
        dataset = ObjaverseData(root_dir=self.root_dir, total_view=self.total_view, validation=True,
                                image_transforms=self.image_transforms)
        sampler = DistributedSampler(dataset)
        return wds.WebLoader(dataset, batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False)


class ObjaverseData(Dataset):
    def __init__(self,
                 root_dir='.objaverse/hf-objaverse-v1/views',
                 image_transforms=None,
                 total_view=12,
                 validation=False
                 ) -> None:
        """Create a dataset from a folder of images.
        If you pass in a root directory it will be searched for images
        ending in ext (ext can be a list)
        """
        self.root_dir = root_dir
        self.total_view = total_view

        # todo only partial data currently downloaded
        if os.path.exists(os.path.join(self.root_dir, 'valid_paths.json')):
            with open(os.path.join(self.root_dir, 'valid_paths.json')) as f:
                self.paths = json.load(f)
        else:
            self.paths = []
            # include all folders
            for folder in os.listdir(self.root_dir):
                if os.path.isdir(os.path.join(self.root_dir, folder)):
                    self.paths.append(folder)

        total_objects = len(self.paths)
        if validation:
            self.paths = self.paths[math.floor(total_objects / 100. * 99.):]  # used last 1% as validation
        else:
            self.paths = self.paths[:math.floor(total_objects / 100. * 99.)]  # used first 99% as training
        print('============= length of dataset %d =============' % len(self.paths))
        self.tform = image_transforms

    def __len__(self):
        return len(self.paths)

    def cartesian_to_spherical(self, xyz):
        ptsnew = np.hstack((xyz, np.zeros(xyz.shape)))
        xy = xyz[:, 0] ** 2 + xyz[:, 1] ** 2
        z = np.sqrt(xy + xyz[:, 2] ** 2)
        theta = np.arctan2(np.sqrt(xy), xyz[:, 2])  # for elevation angle defined from Z-axis down
        # ptsnew[:,4] = np.arctan2(xyz[:,2], np.sqrt(xy)) # for elevation angle defined from XY-plane up
        azimuth = np.arctan2(xyz[:, 1], xyz[:, 0])
        return np.array([theta, azimuth, z])

    def get_T(self, target_RT, cond_RT):
        R, T = target_RT[:3, :3], target_RT[:, -1]
        T_target = -R.T @ T

        R, T = cond_RT[:3, :3], cond_RT[:, -1]
        T_cond = -R.T @ T

        theta_cond, azimuth_cond, z_cond = self.cartesian_to_spherical(T_cond[None, :])
        theta_target, azimuth_target, z_target = self.cartesian_to_spherical(T_target[None, :])

        d_theta = theta_target - theta_cond
        d_azimuth = (azimuth_target - azimuth_cond) % (2 * math.pi)
        d_z = z_target - z_cond

        d_T = torch.tensor([d_theta.item(), math.sin(d_azimuth.item()), math.cos(d_azimuth.item()), d_z.item()])
        return d_T

    def load_im(self, path, color):
        '''
        replace background pixel with random color in rendering
        '''
        try:
            img = plt.imread(path)
        except:
            print(path)
            sys.exit()
        img[img[:, :, -1] == 0.] = color
        img = Image.fromarray(np.uint8(img[:, :, :3] * 255.))
        return img

    def __getitem__(self, index):
        data = {}
        total_view = 12
        index_target, index_cond = random.sample(range(total_view), 2)  # without replacement
        filename = os.path.join(self.root_dir, self.paths[index])

        # print(self.paths[index])

        color = [1., 1., 1., 1.]

        try:
            target_im = self.process_im(self.load_im(os.path.join(filename, '%03d.png' % index_target), color))
            cond_im = self.process_im(self.load_im(os.path.join(filename, '%03d.png' % index_cond), color))
            target_RT = np.load(os.path.join(filename, '%03d.npy' % index_target))
            cond_RT = np.load(os.path.join(filename, '%03d.npy' % index_cond))
        except:
            # very hacky solution, sorry about this
            filename = os.path.join(self.root_dir, '0a0c6d3b5f58499db8d6d649ba8de189')  # this one we know is valid
            target_im = self.process_im(self.load_im(os.path.join(filename, '%03d.png' % index_target), color))
            cond_im = self.process_im(self.load_im(os.path.join(filename, '%03d.png' % index_cond), color))
            target_RT = np.load(os.path.join(filename, '%03d.npy' % index_target))
            cond_RT = np.load(os.path.join(filename, '%03d.npy' % index_cond))
            target_im = torch.zeros_like(target_im)
            cond_im = torch.zeros_like(cond_im)

        data["image_target"] = target_im
        data["image_cond"] = cond_im
        data["T"] = self.get_T(target_RT, cond_RT)

        return data

    def process_im(self, im):
        im = im.convert("RGB")
        return self.tform(im)



# main
if __name__ == "__main__":
    # test dataloader
    dataloader = ObjaverseDataLoader(root_dir='/data/zero123/views_release', batch_size=2, num_workers=4)

    train_loader = dataloader.train_dataloader()
    import pdb; pdb.set_trace()
    for i, data in enumerate(dataloader.train_dataloader()):
        print(data)