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| import os | |
| import sys | |
| import html | |
| import glob | |
| import uuid | |
| import hashlib | |
| import requests | |
| from tqdm import tqdm | |
| os.system("git clone https://github.com/FrozenBurning/SceneDreamer.git") | |
| os.system("cp -r SceneDreamer/* ./") | |
| os.system("bash install.sh") | |
| import os | |
| import torch | |
| import torch.nn as nn | |
| import importlib | |
| import argparse | |
| from imaginaire.config import Config | |
| from imaginaire.utils.cudnn import init_cudnn | |
| import gradio as gr | |
| from PIL import Image | |
| class WrappedModel(nn.Module): | |
| r"""Dummy wrapping the module. | |
| """ | |
| def __init__(self, module): | |
| super(WrappedModel, self).__init__() | |
| self.module = module | |
| def forward(self, *args, **kwargs): | |
| r"""PyTorch module forward function overload.""" | |
| return self.module(*args, **kwargs) | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description='Training') | |
| parser.add_argument('--config', type=str, default='./configs/scenedreamer_inference.yaml', help='Path to the training config file.') | |
| parser.add_argument('--checkpoint', default='./scenedreamer_released.pt', | |
| help='Checkpoint path.') | |
| parser.add_argument('--output_dir', type=str, default='./test/', | |
| help='Location to save the image outputs') | |
| parser.add_argument('--seed', type=int, default=8888, | |
| help='Random seed.') | |
| args = parser.parse_args() | |
| return args | |
| args = parse_args() | |
| cfg = Config(args.config) | |
| # Initialize cudnn. | |
| init_cudnn(cfg.cudnn.deterministic, cfg.cudnn.benchmark) | |
| # Initialize data loaders and models. | |
| lib_G = importlib.import_module(cfg.gen.type) | |
| net_G = lib_G.Generator(cfg.gen, cfg.data) | |
| net_G = net_G.to('cuda') | |
| net_G = WrappedModel(net_G) | |
| if args.checkpoint == '': | |
| raise NotImplementedError("No checkpoint is provided for inference!") | |
| # Load checkpoint. | |
| # trainer.load_checkpoint(cfg, args.checkpoint) | |
| checkpoint = torch.load(args.checkpoint, map_location='cpu') | |
| net_G.load_state_dict(checkpoint['net_G']) | |
| # Do inference. | |
| net_G = net_G.module | |
| net_G.eval() | |
| for name, param in net_G.named_parameters(): | |
| param.requires_grad = False | |
| torch.cuda.empty_cache() | |
| world_dir = os.path.join(args.output_dir) | |
| os.makedirs(world_dir, exist_ok=True) | |
| def get_bev(seed): | |
| print('[PCGGenerator] Generating BEV scene representation...') | |
| os.system('python terrain_generator.py --size {} --seed {} --outdir {}'.format(net_G.voxel.sample_size, seed, world_dir)) | |
| heightmap_path = os.path.join(world_dir, 'heightmap.png') | |
| semantic_path = os.path.join(world_dir, 'colormap.png') | |
| heightmap = Image.open(heightmap_path) | |
| semantic = Image.open(semantic_path) | |
| return semantic, heightmap | |
| def get_video(seed, num_frames, reso_h, reso_w): | |
| device = torch.device('cuda') | |
| rng_cuda = torch.Generator(device=device) | |
| rng_cuda = rng_cuda.manual_seed(seed) | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed(seed) | |
| net_G.voxel.next_world(device, world_dir, checkpoint) | |
| cam_mode = cfg.inference_args.camera_mode | |
| cfg.inference_args.cam_maxstep = num_frames | |
| cfg.inference_args.resolution_hw = [reso_h, reso_w] | |
| current_outdir = os.path.join(world_dir, 'camera_{:02d}'.format(cam_mode)) | |
| os.makedirs(current_outdir, exist_ok=True) | |
| z = torch.empty(1, net_G.style_dims, dtype=torch.float32, device=device) | |
| z.normal_(generator=rng_cuda) | |
| net_G.inference_givenstyle(z, current_outdir, **vars(cfg.inference_args)) | |
| return os.path.join(current_outdir, 'rgb_render.mp4') | |
| markdown=f''' | |
| # SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections | |
| Authored by Zhaoxi Chen, Guangcong Wang, Ziwei Liu | |
| ### Useful links: | |
| - [Official Github Repo](https://github.com/FrozenBurning/SceneDreamer) | |
| - [Project Page](https://scene-dreamer.github.io/) | |
| - [arXiv Link](https://arxiv.org/abs/2302.01330) | |
| Licensed under the S-Lab License. | |
| We offer a sampled scene whose BEVs are shown on the right. You can also use the button "Generate BEV" to randomly sample a new 3D world represented by a height map and a semantic map. But it requires a long time. | |
| To render video, push the button "Render" to generate a camera trajectory flying through the world. You can specify rendering options as shown below! | |
| ''' | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown(markdown) | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| semantic = gr.Image(value='./test/colormap.png',type="pil", shape=(512, 512)) | |
| with gr.Column(): | |
| height = gr.Image(value='./test/heightmap.png', type="pil", shape=(512, 512)) | |
| with gr.Row(): | |
| # with gr.Column(): | |
| # image = gr.Image(type='pil', shape(540, 960)) | |
| with gr.Column(): | |
| video = gr.Video() | |
| with gr.Row(): | |
| num_frames = gr.Slider(minimum=10, maximum=200, value=20, step=1, label='Number of rendered frames') | |
| user_seed = gr.Slider(minimum=0, maximum=999999, value=8888, step=1, label='Random seed') | |
| resolution_h = gr.Slider(minimum=256, maximum=2160, value=270, step=1, label='Height of rendered image') | |
| resolution_w = gr.Slider(minimum=256, maximum=3840, value=480, step=1, label='Width of rendered image') | |
| with gr.Row(): | |
| btn = gr.Button(value="Generate BEV") | |
| btn_2=gr.Button(value="Render") | |
| btn.click(get_bev,[user_seed],[semantic, height]) | |
| btn_2.click(get_video,[user_seed, num_frames, resolution_h, resolution_w], [video]) | |
| demo.launch(debug=True) |