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8539646 17c84c4 8539646 17c84c4 8539646 17c84c4 f844563 8539646 f844563 8539646 4aaac8f 8539646 f844563 8539646 045deae 8539646 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | import os
import importlib.util
import numpy as np
import torch
import gradio as gr
from huggingface_hub import hf_hub_download
REPO_ID = "c1tr0n75/VoxelPathFinder"
# 1) Make sure torch is imported, then define device BEFORE using it anywhere
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 2) Download model code and weights from the model repo
PY_PATH = hf_hub_download(repo_id=REPO_ID, filename="pathfinding_nn.py")
CKPT_PATH = hf_hub_download(repo_id=REPO_ID, filename="training_outputs/final_model.pth")
# 3) Dynamically import the model definitions
spec = importlib.util.spec_from_file_location("pathfinding_nn", PY_PATH)
mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(mod)
PathfindingNetwork = mod.PathfindingNetwork
create_voxel_input = mod.create_voxel_input
# 4) Build and load model
MODEL = PathfindingNetwork().to(DEVICE).eval()
ckpt = torch.load(CKPT_PATH, map_location=DEVICE)
state = ckpt.get("model_state_dict", ckpt)
MODEL.load_state_dict(state)
ACTION_NAMES = ['FORWARD','BACK','LEFT','RIGHT','UP','DOWN']
def decode(actions):
return [ACTION_NAMES[a] for a in actions if 0 <= a < 6]
def infer_random(obstacle_prob=0.2, seed=None):
if seed is not None:
np.random.seed(int(seed))
voxel_dim = MODEL.voxel_dim
D, H, W = voxel_dim
obstacles = (np.random.rand(D, H, W) < float(obstacle_prob)).astype(np.float32)
free = np.argwhere(obstacles == 0)
if len(free) < 2:
return {"error": "Not enough free cells; lower obstacle_prob."}
s_idx, g_idx = np.random.choice(len(free), size=2, replace=False)
start = tuple(free[s_idx]); goal = tuple(free[g_idx])
voxel_np = create_voxel_input(obstacles, start, goal, voxel_dim=voxel_dim)
voxel = torch.from_numpy(voxel_np).float().unsqueeze(0).to(DEVICE)
pos = torch.tensor([[start, goal]], dtype=torch.long, device=DEVICE)
with torch.no_grad():
actions = MODEL(voxel, pos)[0].tolist()
return {
"start": start,
"goal": goal,
"num_actions": len([a for a in actions if 0 <= a < 6]),
"actions_ids": actions,
"actions_decoded": decode(actions)[:50],
}
#def infer_npz(npz_file):
# if npz_file is None:
# return {"error": "Upload a .npz with 'voxel_data' and 'positions'."}
# data = np.load(npz_file.name)
# voxel = torch.from_numpy(data['voxel_data']).float().unsqueeze(0).to(DEVICE)
# pos = torch.from_numpy(data['positions']).long().unsqueeze(0).to(DEVICE)
# with torch.no_grad():
# actions = MODEL(voxel, pos)[0].tolist()
# return {
# "num_actions": len([a for a in actions if 0 <= a < 6]),
# "actions_ids": actions,
# "actions_decoded": decode(actions)[:50],
# }
with gr.Blocks(title="Voxel Path Finder") as demo:
gr.Markdown("## 3D Voxel Path Finder — Inference")
with gr.Tab("Random environment"):
obstacle = gr.Slider(0.0, 1.0, value=0.2, step=0.05, label="Obstacle probability")
seed = gr.Number(value=None, label="Seed (optional)")
btn = gr.Button("Run inference")
out = gr.JSON(label="Result")
btn.click(infer_random, inputs=[obstacle, seed], outputs=out)
# with gr.Tab("Upload .npz sample"):
# file = gr.File(file_types=[".npz"], label="Upload sample (voxel_data, positions)")
# btn2 = gr.Button("Run inference")
# out2 = gr.JSON(label="Result")
# btn2.click(infer_npz, inputs=file, outputs=out2)
if __name__ == "__main__":
demo.launch() |