Update app.py

app.py

@@ -1 +1,151 @@
-SceneDreamer
+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", height=512, width=512)
+                with gr.Column():
+                    height = gr.Image(value='./test/heightmap.png', type="pil", height=512, width=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)