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):
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):
# 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):
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):
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)