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Zero
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from typing import Dict
import webdataset as wds
import numpy as np
from omegaconf import DictConfig, ListConfig
import torch
from torch.utils.data import Dataset
from pathlib import Path
import json
from PIL import Image
from torchvision import transforms
import torchvision
from einops import rearrange
from ..util import instantiate_from_config
from datasets import load_dataset
import pytorch_lightning as pl
import copy
import csv
import cv2
import random
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
import json
import os, sys
import webdataset as wds
import math
from torch.utils.data.distributed import DistributedSampler
class ObjaverseDataModuleFromConfig(pl.LightningDataModule):
def __init__(self, root_dir, batch_size, total_view, train=None, validation=None,
test=None, num_workers=4, **kwargs):
super().__init__(self)
self.root_dir = root_dir
self.batch_size = batch_size
self.num_workers = num_workers
self.total_view = total_view
if train is not None:
dataset_config = train
if validation is not None:
dataset_config = validation
if 'image_transforms' in dataset_config:
image_transforms = [torchvision.transforms.Resize(dataset_config.image_transforms.size)]
else:
image_transforms = []
image_transforms.extend([transforms.ToTensor(),
transforms.Lambda(lambda x: rearrange(x * 2. - 1., 'c h w -> h w c'))])
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)
def test_dataloader(self):
return wds.WebLoader(ObjaverseData(root_dir=self.root_dir, total_view=self.total_view, validation=self.validation),\
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=[],
ext="png",
default_trans=torch.zeros(3),
postprocess=None,
return_paths=False,
total_view=4,
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 = Path(root_dir)
self.default_trans = default_trans
self.return_paths = return_paths
if isinstance(postprocess, DictConfig):
postprocess = instantiate_from_config(postprocess)
self.postprocess = postprocess
self.total_view = total_view
if not isinstance(ext, (tuple, list, ListConfig)):
ext = [ext]
with open(os.path.join(root_dir, 'valid_paths.json')) as f:
self.paths = json.load(f)
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 = {}
if self.paths[index][-2:] == '_1': # dirty fix for rendering dataset twice
total_view = 8
else:
total_view = 4
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])
if self.return_paths:
data["path"] = str(filename)
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, '692db5f2d3a04bb286cb977a7dba903e_1') # 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)
if self.postprocess is not None:
data = self.postprocess(data)
return data
def process_im(self, im):
im = im.convert("RGB")
return self.tform(im)
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