import math import os import json from dataclasses import dataclass, field import random import imageio import numpy as np import pytorch_lightning as pl import torch import torch.nn.functional as F from torch.utils.data import DataLoader, Dataset from torchvision import transforms from PIL import Image from transformers import CLIPImageProcessor, CLIPTokenizer from craftsman import register from craftsman.utils.base import Updateable from craftsman.utils.config import parse_structured from craftsman.utils.typing import * def rot2eul(R): beta = -np.arcsin(R[2,0]) alpha = np.arctan2(R[2,1]/np.cos(beta),R[2,2]/np.cos(beta)) gamma = np.arctan2(R[1,0]/np.cos(beta),R[0,0]/np.cos(beta)) return np.array((alpha, beta, gamma)) def eul2rot(theta) : R = np.array([[np.cos(theta[1])*np.cos(theta[2]), np.sin(theta[0])*np.sin(theta[1])*np.cos(theta[2]) - np.sin(theta[2])*np.cos(theta[0]), np.sin(theta[1])*np.cos(theta[0])*np.cos(theta[2]) + np.sin(theta[0])*np.sin(theta[2])], [np.sin(theta[2])*np.cos(theta[1]), np.sin(theta[0])*np.sin(theta[1])*np.sin(theta[2]) + np.cos(theta[0])*np.cos(theta[2]), np.sin(theta[1])*np.sin(theta[2])*np.cos(theta[0]) - np.sin(theta[0])*np.cos(theta[2])], [-np.sin(theta[1]), np.sin(theta[0])*np.cos(theta[1]), np.cos(theta[0])*np.cos(theta[1])]]) return R @dataclass class ObjaverseDataModuleConfig: root_dir: str = None data_type: str = "occupancy" # occupancy or sdf n_samples: int = 4096 # number of points in input point cloud scale: float = 1.0 # scale of the input point cloud and target supervision noise_sigma: float = 0.0 # noise level of the input point cloud load_supervision: bool = True # whether to load supervision supervision_type: str = "occupancy" # occupancy, sdf, tsdf, tsdf_w_surface n_supervision: int = 10000 # number of points in supervision load_image: bool = False # whether to load images image_data_path: str = "" # path to the image data image_type: str = "rgb" # rgb, normal background_color: Tuple[float, float, float] = field( default_factory=lambda: (1.0, 1.0, 1.0) ) idx: Optional[List[int]] = None # index of the image to load n_views: int = 1 # number of views rotate_points: bool = False # whether to rotate the input point cloud and the supervision load_caption: bool = False # whether to load captions caption_type: str = "text" # text, clip_embeds tokenizer_pretrained_model_name_or_path: str = "" batch_size: int = 32 num_workers: int = 0 class ObjaverseDataset(Dataset): def __init__(self, cfg: Any, split: str) -> None: super().__init__() self.cfg: ObjaverseDataModuleConfig = cfg self.split = split self.uids = json.load(open(f'{cfg.root_dir}/{split}.json')) print(f"Loaded {len(self.uids)} {split} uids") if self.cfg.load_caption: self.tokenizer = CLIPTokenizer.from_pretrained(self.cfg.tokenizer_pretrained_model_name_or_path) self.background_color = torch.as_tensor(self.cfg.background_color) self.distance = 1.0 self.camera_embedding = torch.as_tensor([ [[1, 0, 0, 0], [0, 0, -1, -self.distance], [0, 1, 0, 0], [0, 0, 0, 1]], # front to back [[0, 0, 1, self.distance], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1]], # right to left [[-1, 0, 0, 0], [0, 0, 1, self.distance], [0, 1, 0, 0], [0, 0, 0, 1]], # back to front [[0, 0, -1, -self.distance], [-1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1]], # left to right ], dtype=torch.float32) if self.cfg.n_views != 1: assert self.cfg.n_views == self.camera_embedding.shape[0] def __len__(self): return len(self.uids) def _load_shape(self, index: int) -> Dict[str, Any]: if self.cfg.data_type == "occupancy": # for input point cloud pointcloud = np.load(f'{self.cfg.root_dir}/{self.uids[index]}/pointcloud.npz') surface = np.asarray(pointcloud['points']) * 2 # range from -1 to 1 normal = np.asarray(pointcloud['normals']) surface = np.concatenate([surface, normal], axis=1) elif self.cfg.data_type == "sdf": data = np.load(f'{self.cfg.root_dir}/{self.uids[index]}.npz') # for input point cloud surface = data["surface"] else: raise NotImplementedError(f"Data type {self.cfg.data_type} not implemented") # random sampling rng = np.random.default_rng() ind = rng.choice(surface.shape[0], self.cfg.n_samples, replace=False) surface = surface[ind] # rescale data surface[:, :3] = surface[:, :3] * self.cfg.scale # target scale # add noise to input point cloud surface[:, :3] += (np.random.rand(surface.shape[0], 3) * 2 - 1) * self.cfg.noise_sigma ret = { "uid": self.uids[index].split('/')[-1], "surface": surface.astype(np.float32), } return ret def _load_shape_supervision(self, index: int) -> Dict[str, Any]: # for supervision ret = {} if self.cfg.data_type == "occupancy": points = np.load(f'{self.cfg.root_dir}/{self.uids[index]}/points.npz') rand_points = np.asarray(points['points']) * 2 # range from -1.1 to 1.1 occupancies = np.asarray(points['occupancies']) occupancies = np.unpackbits(occupancies) elif self.cfg.data_type == "sdf": data = np.load(f'{self.cfg.root_dir}/{self.uids[index]}.npz') rand_points = data['rand_points'] sdfs = data['sdfs'] else: raise NotImplementedError(f"Data type {self.cfg.data_type} not implemented") # random sampling rng = np.random.default_rng() ind = rng.choice(rand_points.shape[0], self.cfg.n_supervision, replace=False) rand_points = rand_points[ind] rand_points = rand_points * self.cfg.scale ret["rand_points"] = rand_points.astype(np.float32) if self.cfg.data_type == "occupancy": assert self.cfg.supervision_type == "occupancy", "Only occupancy supervision is supported for occupancy data" occupancies = occupancies[ind] ret["occupancies"] = occupancies.astype(np.float32) elif self.cfg.data_type == "sdf": if self.cfg.supervision_type == "sdf": ret["sdf"] = sdfs[ind].flatten().astype(np.float32) elif self.cfg.supervision_type == "occupancy": ret["occupancies"] = np.where(sdfs[ind].flatten() < 1e-3, 0, 1).astype(np.float32) else: raise NotImplementedError(f"Supervision type {self.cfg.supervision_type} not implemented") return ret def _load_image(self, index: int) -> Dict[str, Any]: def _load_single_image(img_path): img = torch.from_numpy( np.asarray( Image.fromarray(imageio.v2.imread(img_path)) .convert("RGBA") ) / 255.0 ).float() mask: Float[Tensor, "H W 1"] = img[:, :, -1:] image: Float[Tensor, "H W 3"] = img[:, :, :3] * mask + self.background_color[ None, None, : ] * (1 - mask) return image ret = {} if self.cfg.image_type == "rgb" or self.cfg.image_type == "normal": assert self.cfg.n_views == 1, "Only single view is supported for single image" sel_idx = random.choice(self.cfg.idx) ret["sel_image_idx"] = sel_idx if self.cfg.image_type == "rgb": img_path = f'{self.cfg.image_data_path}/' + "/".join(self.uids[index].split('/')[-2:]) + f"/{sel_idx}.png" elif self.cfg.image_type == "normal": img_path = f'{self.cfg.image_data_path}/' + "/".join(self.uids[index].split('/')[-2:]) + f"/{sel_idx}_normal.png" ret["image"] = _load_single_image(img_path) ret["c2w"] = self.camera_embedding[sel_idx % 4] elif self.cfg.image_type == "mvrgb" or self.cfg.image_type == "mvnormal": sel_idx = random.choice(self.cfg.idx) ret["sel_image_idx"] = sel_idx mvimages = [] for i in range(self.cfg.n_views): if self.cfg.image_type == "mvrgb": img_path = f'{self.cfg.image_data_path}/' + "/".join(self.uids[index].split('/')[-2:]) + f"/{sel_idx+i}.png" elif self.cfg.image_type == "mvnormal": img_path = f'{self.cfg.image_data_path}/' + "/".join(self.uids[index].split('/')[-2:]) + f"/{sel_idx+i}_normal.png" mvimages.append(_load_single_image(img_path)) ret["mvimages"] = torch.stack(mvimages) ret["c2ws"] = self.camera_embedding else: raise NotImplementedError(f"Image type {self.cfg.image_type} not implemented") return ret def _load_caption(self, index: int, drop_text_embed: bool = False) -> Dict[str, Any]: ret = {} if self.cfg.caption_type == "text": caption = eval(json.load(open(f'{self.cfg.image_data_path}/' + "/".join(self.uids[index].split('/')[-2:]) + f'/annotation.json'))) texts = [v for k, v in caption.items()] sel_idx = random.randint(0, len(texts) - 1) ret["sel_caption_idx"] = sel_idx ret['text_input_ids'] = self.tokenizer( texts[sel_idx] if not drop_text_embed else "", max_length=self.tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt" ).input_ids.detach() else: raise NotImplementedError(f"Caption type {self.cfg.caption_type} not implemented") return ret def get_data(self, index): # load shape ret = self._load_shape(index) # load supervision for shape if self.cfg.load_supervision: ret.update(self._load_shape_supervision(index)) # load image if self.cfg.load_image: ret.update(self._load_image(index)) # load the rotation of the object and rotate the camera rots = np.load(f'{self.cfg.image_data_path}/' + "/".join(self.uids[index].split('/')[-2:]) + f'/rots.npy')[ret['sel_image_idx']].astype(np.float32) rots = torch.tensor(rots[:3, :3], dtype=torch.float32) if "c2ws" in ret.keys(): ret["c2ws"][:, :3, :3] = torch.matmul(rots, ret["c2ws"][:, :3, :3]) ret["c2ws"][:, :3, 3] = torch.matmul(rots, ret["c2ws"][:, :3, 3].unsqueeze(-1)).squeeze(-1) elif "c2w" in ret.keys(): ret["c2w"][:3, :3] = torch.matmul(rots, ret["c2w"][:3, :3]) ret["c2w"][:3, 3] = torch.matmul(rots, ret["c2w"][:3, 3].unsqueeze(-1)).squeeze(-1) # load caption if self.cfg.load_caption: ret.update(self._load_caption(index)) return ret def __getitem__(self, index): try: return self.get_data(index) except Exception as e: print(f"Error in {self.uids[index]}: {e}") return self.__getitem__(np.random.randint(len(self))) def collate(self, batch): batch = torch.utils.data.default_collate(batch) return batch @register("objaverse-datamodule") class ObjaverseDataModule(pl.LightningDataModule): cfg: ObjaverseDataModuleConfig def __init__(self, cfg: Optional[Union[dict, DictConfig]] = None) -> None: super().__init__() self.cfg = parse_structured(ObjaverseDataModuleConfig, cfg) def setup(self, stage=None) -> None: if stage in [None, "fit"]: self.train_dataset = ObjaverseDataset(self.cfg, "train") if stage in [None, "fit", "validate"]: self.val_dataset = ObjaverseDataset(self.cfg, "val") if stage in [None, "test", "predict"]: self.test_dataset = ObjaverseDataset(self.cfg, "test") def prepare_data(self): pass def general_loader(self, dataset, batch_size, collate_fn=None, num_workers=0) -> DataLoader: return DataLoader( dataset, batch_size=batch_size, collate_fn=collate_fn, num_workers=num_workers ) def train_dataloader(self) -> DataLoader: return self.general_loader( self.train_dataset, batch_size=self.cfg.batch_size, collate_fn=self.train_dataset.collate, num_workers=self.cfg.num_workers ) def val_dataloader(self) -> DataLoader: return self.general_loader(self.val_dataset, batch_size=1) def test_dataloader(self) -> DataLoader: return self.general_loader(self.test_dataset, batch_size=1) def predict_dataloader(self) -> DataLoader: return self.general_loader(self.test_dataset, batch_size=1)