bluestyle97
9 months ago
3 changed files with 326 additions and 1 deletions
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import os |
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import math |
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import json |
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from pathlib import Path |
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import cv2 |
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import numpy as np |
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from PIL import Image |
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import webdataset as wds |
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import pytorch_lightning as pl |
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import torch |
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import torch.nn.functional as F |
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from torch.utils.data import Dataset |
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from torch.utils.data import DataLoader |
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from torch.utils.data.distributed import DistributedSampler |
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from torchvision import transforms |
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from src.utils.train_util import instantiate_from_config |
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from src.utils.camera_util import ( |
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FOV_to_intrinsics, |
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center_looking_at_camera_pose, |
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get_circular_camera_poses, |
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) |
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class DataModuleFromConfig(pl.LightningDataModule): |
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def __init__( |
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self, |
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batch_size=8, |
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num_workers=4, |
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train=None, |
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validation=None, |
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test=None, |
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**kwargs, |
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): |
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super().__init__() |
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self.batch_size = batch_size |
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self.num_workers = num_workers |
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self.dataset_configs = dict() |
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if train is not None: |
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self.dataset_configs['train'] = train |
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if validation is not None: |
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self.dataset_configs['validation'] = validation |
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if test is not None: |
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self.dataset_configs['test'] = test |
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def setup(self, stage): |
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if stage in ['fit']: |
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self.datasets = dict((k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs) |
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else: |
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raise NotImplementedError |
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def train_dataloader(self): |
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sampler = DistributedSampler(self.datasets['train']) |
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return wds.WebLoader(self.datasets['train'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False, sampler=sampler) |
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def val_dataloader(self): |
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sampler = DistributedSampler(self.datasets['validation']) |
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return wds.WebLoader(self.datasets['validation'], batch_size=1, num_workers=self.num_workers, shuffle=False, sampler=sampler) |
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def test_dataloader(self): |
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return wds.WebLoader(self.datasets['test'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False) |
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class ObjaverseData(Dataset): |
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def __init__(self, |
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root_dir='objaverse/', |
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meta_fname='valid_paths.json', |
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input_image_dir='rendering_random_32views', |
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target_image_dir='rendering_random_32views', |
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input_view_num=6, |
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target_view_num=2, |
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total_view_n=32, |
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fov=50, |
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camera_rotation=True, |
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validation=False, |
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): |
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self.root_dir = Path(root_dir) |
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self.input_image_dir = input_image_dir |
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self.target_image_dir = target_image_dir |
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self.input_view_num = input_view_num |
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self.target_view_num = target_view_num |
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self.total_view_n = total_view_n |
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self.fov = fov |
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self.camera_rotation = camera_rotation |
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with open(os.path.join(root_dir, meta_fname)) as f: |
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filtered_dict = json.load(f) |
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paths = filtered_dict['good_objs'] |
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self.paths = paths |
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self.depth_scale = 5.0 |
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total_objects = len(self.paths) |
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print('============= length of dataset %d =============' % len(self.paths)) |
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def __len__(self): |
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return len(self.paths) |
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def load_im(self, path, color): |
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''' |
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replace background pixel with random color in rendering |
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''' |
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pil_img = Image.open(path) |
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image = np.asarray(pil_img, dtype=np.float32) / 255. |
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alpha = image[:, :, 3:] |
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image = image[:, :, :3] * alpha + color * (1 - alpha) |
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image = torch.from_numpy(image).permute(2, 0, 1).contiguous().float() |
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alpha = torch.from_numpy(alpha).permute(2, 0, 1).contiguous().float() |
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return image, alpha |
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def __getitem__(self, index): |
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# load data |
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while True: |
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input_image_path = os.path.join(self.root_dir, self.input_image_dir, self.paths[index]) |
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target_image_path = os.path.join(self.root_dir, self.target_image_dir, self.paths[index]) |
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indices = np.random.choice(range(self.total_view_n), self.input_view_num + self.target_view_num, replace=False) |
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input_indices = indices[:self.input_view_num] |
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target_indices = indices[self.input_view_num:] |
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'''background color, default: white''' |
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bg_white = [1., 1., 1.] |
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bg_black = [0., 0., 0.] |
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image_list = [] |
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alpha_list = [] |
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depth_list = [] |
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normal_list = [] |
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pose_list = [] |
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try: |
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input_cameras = np.load(os.path.join(input_image_path, 'cameras.npz'))['cam_poses'] |
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for idx in input_indices: |
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image, alpha = self.load_im(os.path.join(input_image_path, '%03d.png' % idx), bg_white) |
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normal, _ = self.load_im(os.path.join(input_image_path, '%03d_normal.png' % idx), bg_black) |
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depth = cv2.imread(os.path.join(input_image_path, '%03d_depth.png' % idx), cv2.IMREAD_UNCHANGED) / 255.0 * self.depth_scale |
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depth = torch.from_numpy(depth).unsqueeze(0) |
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pose = input_cameras[idx] |
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pose = np.concatenate([pose, np.array([[0, 0, 0, 1]])], axis=0) |
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image_list.append(image) |
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alpha_list.append(alpha) |
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depth_list.append(depth) |
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normal_list.append(normal) |
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pose_list.append(pose) |
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target_cameras = np.load(os.path.join(target_image_path, 'cameras.npz'))['cam_poses'] |
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for idx in target_indices: |
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image, alpha = self.load_im(os.path.join(target_image_path, '%03d.png' % idx), bg_white) |
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normal, _ = self.load_im(os.path.join(target_image_path, '%03d_normal.png' % idx), bg_black) |
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depth = cv2.imread(os.path.join(target_image_path, '%03d_depth.png' % idx), cv2.IMREAD_UNCHANGED) / 255.0 * self.depth_scale |
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depth = torch.from_numpy(depth).unsqueeze(0) |
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pose = target_cameras[idx] |
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pose = np.concatenate([pose, np.array([[0, 0, 0, 1]])], axis=0) |
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image_list.append(image) |
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alpha_list.append(alpha) |
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depth_list.append(depth) |
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normal_list.append(normal) |
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pose_list.append(pose) |
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except Exception as e: |
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print(e) |
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index = np.random.randint(0, len(self.paths)) |
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continue |
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break |
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images = torch.stack(image_list, dim=0).float() # (6+V, 3, H, W) |
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alphas = torch.stack(alpha_list, dim=0).float() # (6+V, 1, H, W) |
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depths = torch.stack(depth_list, dim=0).float() # (6+V, 1, H, W) |
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normals = torch.stack(normal_list, dim=0).float() # (6+V, 3, H, W) |
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w2cs = torch.from_numpy(np.stack(pose_list, axis=0)).float() # (6+V, 4, 4) |
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c2ws = torch.linalg.inv(w2cs).float() |
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normals = normals * 2.0 - 1.0 |
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normals = F.normalize(normals, dim=1) |
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normals = (normals + 1.0) / 2.0 |
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normals = torch.lerp(torch.zeros_like(normals), normals, alphas) |
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# random rotation along z axis |
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if self.camera_rotation: |
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degree = np.random.uniform(0, math.pi * 2) |
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rot = torch.tensor([ |
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[np.cos(degree), -np.sin(degree), 0, 0], |
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[np.sin(degree), np.cos(degree), 0, 0], |
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[0, 0, 1, 0], |
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[0, 0, 0, 1], |
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]).unsqueeze(0).float() |
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c2ws = torch.matmul(rot, c2ws) |
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# rotate normals |
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N, _, H, W = normals.shape |
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normals = normals * 2.0 - 1.0 |
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normals = torch.matmul(rot[:, :3, :3], normals.view(N, 3, -1)).view(N, 3, H, W) |
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normals = F.normalize(normals, dim=1) |
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normals = (normals + 1.0) / 2.0 |
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normals = torch.lerp(torch.zeros_like(normals), normals, alphas) |
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# random scaling |
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if np.random.rand() < 0.5: |
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scale = np.random.uniform(0.8, 1.0) |
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c2ws[:, :3, 3] *= scale |
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depths *= scale |
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# instrinsics of perspective cameras |
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K = FOV_to_intrinsics(self.fov) |
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Ks = K.unsqueeze(0).repeat(self.input_view_num + self.target_view_num, 1, 1).float() |
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data = { |
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'input_images': images[:self.input_view_num], # (6, 3, H, W) |
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'input_alphas': alphas[:self.input_view_num], # (6, 1, H, W) |
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'input_depths': depths[:self.input_view_num], # (6, 1, H, W) |
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'input_normals': normals[:self.input_view_num], # (6, 3, H, W) |
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'input_c2ws': c2ws[:self.input_view_num], # (6, 4, 4) |
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'input_Ks': Ks[:self.input_view_num], # (6, 3, 3) |
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# lrm generator input and supervision |
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'target_images': images[self.input_view_num:], # (V, 3, H, W) |
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'target_alphas': alphas[self.input_view_num:], # (V, 1, H, W) |
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'target_depths': depths[self.input_view_num:], # (V, 1, H, W) |
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'target_normals': normals[self.input_view_num:], # (V, 3, H, W) |
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'target_c2ws': c2ws[self.input_view_num:], # (V, 4, 4) |
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'target_Ks': Ks[self.input_view_num:], # (V, 3, 3) |
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'depth_available': 1, |
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} |
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return data |
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class ValidationData(Dataset): |
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def __init__(self, |
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root_dir='objaverse/', |
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input_view_num=6, |
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input_image_size=256, |
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fov=30, |
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): |
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self.root_dir = Path(root_dir) |
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self.input_view_num = input_view_num |
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self.input_image_size = input_image_size |
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self.fov = fov |
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self.paths = sorted(os.listdir(self.root_dir)) |
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print('============= length of dataset %d =============' % len(self.paths)) |
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cam_distance = 4.0 |
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azimuths = np.array([30, 90, 150, 210, 270, 330]) |
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elevations = np.array([20, -10, 20, -10, 20, -10]) |
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azimuths = np.deg2rad(azimuths) |
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elevations = np.deg2rad(elevations) |
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x = cam_distance * np.cos(elevations) * np.cos(azimuths) |
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y = cam_distance * np.cos(elevations) * np.sin(azimuths) |
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z = cam_distance * np.sin(elevations) |
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cam_locations = np.stack([x, y, z], axis=-1) |
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cam_locations = torch.from_numpy(cam_locations).float() |
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c2ws = center_looking_at_camera_pose(cam_locations) |
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self.c2ws = c2ws.float() |
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self.Ks = FOV_to_intrinsics(self.fov).unsqueeze(0).repeat(6, 1, 1).float() |
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render_c2ws = get_circular_camera_poses(M=8, radius=cam_distance, elevation=20.0) |
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render_Ks = FOV_to_intrinsics(self.fov).unsqueeze(0).repeat(render_c2ws.shape[0], 1, 1) |
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self.render_c2ws = render_c2ws.float() |
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self.render_Ks = render_Ks.float() |
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def __len__(self): |
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return len(self.paths) |
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def load_im(self, path, color): |
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''' |
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replace background pixel with random color in rendering |
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''' |
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pil_img = Image.open(path) |
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pil_img = pil_img.resize((self.input_image_size, self.input_image_size), resample=Image.BICUBIC) |
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image = np.asarray(pil_img, dtype=np.float32) / 255. |
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if image.shape[-1] == 4: |
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alpha = image[:, :, 3:] |
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image = image[:, :, :3] * alpha + color * (1 - alpha) |
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else: |
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alpha = np.ones_like(image[:, :, :1]) |
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image = torch.from_numpy(image).permute(2, 0, 1).contiguous().float() |
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alpha = torch.from_numpy(alpha).permute(2, 0, 1).contiguous().float() |
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return image, alpha |
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def __getitem__(self, index): |
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# load data |
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input_image_path = os.path.join(self.root_dir, self.paths[index]) |
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'''background color, default: white''' |
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bkg_color = [1.0, 1.0, 1.0] |
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image_list = [] |
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alpha_list = [] |
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for idx in range(self.input_view_num): |
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image, alpha = self.load_im(os.path.join(input_image_path, f'{idx:03d}.png'), bkg_color) |
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image_list.append(image) |
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alpha_list.append(alpha) |
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images = torch.stack(image_list, dim=0).float() |
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alphas = torch.stack(alpha_list, dim=0).float() |
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data = { |
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'input_images': images, |
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'input_alphas': alphas, |
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'input_c2ws': self.c2ws, |
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'input_Ks': self.Ks, |
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'render_c2ws': self.render_c2ws, |
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'render_Ks': self.render_Ks, |
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} |
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return data |
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