app.py

import gradio as gr
import matplotlib.cm as cm
import numpy as np
import torch
import torch.nn.functional as F

torch.set_grad_enabled(False)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
ddpm, lidar_utils, _ = torch.hub.load(
    "kazuto1011/r2dm", "pretrained_r2dm", device=device
)


def colorize(tensor, cmap_fn=cm.turbo):
    colors = cmap_fn(np.linspace(0, 1, 256))[:, :3]
    colors = torch.from_numpy(colors).to(tensor)
    tensor = tensor.squeeze(1) if tensor.ndim == 4 else tensor
    ids = (tensor * 256).clamp(0, 255).long()
    tensor = F.embedding(ids, colors).permute(0, 3, 1, 2)
    tensor = tensor.mul(255).clamp(0, 255).byte()
    return tensor


@torch.no_grad()
def generate(num_steps) -> str:
    output = ddpm.sample(batch_size=1, num_steps=int(num_steps))
    output = lidar_utils.denormalize(output.clamp(-1, 1))
    range_image = lidar_utils.revert_depth(output[:, [0]])
    range_image = (range_image / lidar_utils.max_depth).clamp(0, 1)
    reflectance_image = output[:, [1]]
    range_image = colorize(range_image)[0].permute(1, 2, 0)
    reflectance_image = colorize(reflectance_image)[0].permute(1, 2, 0)
    return range_image.cpu().numpy(), reflectance_image.cpu().numpy()


with gr.Blocks() as demo:
    gr.Markdown(
        """
        # R2DM Demo
        **LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models**<br>
        Kazuto Nakashima, Ryo Kurazume<br>
        [[arXiv]](https://arxiv.org/abs/2309.09256) [[Code]](https://github.com/kazuto1011/r2dm)
        """
    )
    with gr.Row():
        with gr.Column():
            gr.Text(f"Device: {device}", label="device")
            num_steps = gr.Dropdown(
                choices=[2**i for i in range(3, 11)],
                value=8,
                label="number of sampling steps",
            )
            btn = gr.Button(value="Generate random samples")

        with gr.Column():
            range_view = gr.Image(
                type="numpy",
                image_mode="RGB",
                label="Range image",
                scale=1,
            )
            rflct_view = gr.Image(
                type="numpy",
                image_mode="RGB",
                label="Reflectance image",
                scale=1,
            )

    btn.click(
        generate,
        inputs=[num_steps],
        outputs=[range_view, rflct_view],
    )


demo.launch()