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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 | |
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 | |
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) |