complete_demo_app

app.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import os, glob, re, torch, numpy as np
+from typing import List, Tuple
+from safetensors.torch import load_file
+import gradio as gr
+import plotly.graph_objects as go
+import torch.nn as nn
+from huggingface_hub import hf_hub_download, list_repo_files
+
+# =========================
+# Config
+# =========================
+LOCAL_FILE_DEFAULT  = "assets/scene0073_00.safetensors"  # local safetensors file
+PM_KEY_DEFAULT      = "point_map"
+TOPK_VIEWS_DEFAULT  = 3
+
+VOXEL_SIZE          = 0.02
+DOWNSAMPLE_N_MAX    = 600_000
+POINT_SIZE_MIN      = 1.2
+POINT_SIZE_MAX      = 2.0
+
+CLR_RED             = "rgba(230,40,40,0.98)"
+BG_COLOR            = "#f7f9fb"
+GRID_COLOR          = "#e6ecf2"
+BOX_COLOR           = "rgba(80,80,80,0.6)"
+
+TOP_VIEW_IMAGE_PATH = "assets/scene0073_00.png"
+
+DEFAULT_CAM = dict(
+    eye=dict(x=1.35, y=1.35, z=0.95),
+    up=dict(x=0, y=0, z=1),
+    center=dict(x=0, y=0, z=0),
+    projection=dict(type="perspective"),
+)
+
+def _merge_safetensors_dicts(paths: List[str]):
+    merged = {}
+    for p in paths:
+        sd = load_file(p, device="cpu")
+        merged.update(sd)
+    return merged
+
+def _local_all_under(path: str) -> List[str]:
+    out = []
+    if os.path.isfile(path):
+        return [path]
+    for root, _, files in os.walk(path):
+        for f in files:
+            out.append(os.path.join(root, f))
+    return sorted(out)
+
+# =========================
+# Load pretrained (your existing loader)
+# =========================
+def load_pretrain(
+    model: torch.nn.Module,
+    ckpt_path: str,                 # e.g. "assets/ckpt_100.pth" or "assets/model.safetensors"
+    repo_id: str = "MatchLab/poma3d-demo",
+    revision: str = "main",
+    allow_local_fallback: bool = True,
+):
+    if allow_local_fallback and (os.path.isfile(ckpt_path) or os.path.isdir(ckpt_path)):
+        print(f"📂 Using local checkpoint(s): {ckpt_path}")
+        local_files = _local_all_under(ckpt_path)
+    else:
+        # 2) REMOTE: resolve file list from Space
+        print(f"📦 Resolving from HF Space: {repo_id}/{ckpt_path} (rev={revision})")
+        files = list_repo_files(repo_id=repo_id, repo_type='model', revision=revision)
+
+        # Exact file hit?
+        if ckpt_path in files:
+            to_fetch = [ckpt_path]
+        else:
+            # Treat ckpt_path as a folder prefix (ensure trailing slash for matching)
+            prefix = ckpt_path if ckpt_path.endswith("/") else ckpt_path + "/"
+            to_fetch = [f for f in files if f.startswith(prefix)]
+            if not to_fetch:
+                preview = "\n".join(files[:100])
+                raise FileNotFoundError(
+                    f"'{ckpt_path}' not found in Space '{repo_id}' (rev='{revision}').\n"
+                    f"Files present (first 100):\n{preview}"
+                )
+
+        # Download all matching files locally
+        local_files = []
+        for rel in to_fetch:
+            lp = hf_hub_download(repo_id=repo_id, filename=rel, repo_type='model', revision=revision)
+            local_files.append(lp)
+        local_files.sort()
+
+    # Filter by types we know how to load
+    safes = [p for p in local_files if p.endswith(".safetensors")]
+    pths  = [p for p in local_files if re.search(r"\.(?:pth|pt)$", p)]
+
+    if safes:
+        print(f"🧩 Found {len(safes)} .safetensors shard(s); merging…")
+        state = _merge_safetensors_dicts(safes)
+    elif pths:
+        # pick the largest .pth/.pt to avoid optimizer/state variants
+        pths_sorted = sorted(pths, key=lambda p: os.path.getsize(p), reverse=True)
+        pick = pths_sorted[0]
+        print(f"🧩 Using .pth/.pt: {os.path.basename(pick)} (largest of {len(pths)} candidates)")
+        state = torch.load(pick, map_location="cpu")
+        # strip common prefixes
+        if isinstance(state, dict) and any(k.startswith(("model.", "target_model.")) for k in state.keys()):
+            state = { (k.split(".", 1)[1] if k.startswith(("model.", "target_model.")) else k): v
+                      for k, v in state.items() }
+        # nested 'state_dict'
+        if isinstance(state, dict) and "state_dict" in state and isinstance(state["state_dict"], dict):
+            state = state["state_dict"]
+    else:
+        raise FileNotFoundError(
+            "No loadable checkpoint found. Expecting one or more of: "
+            ".safetensors or .pth/.pt under the given path."
+        )
+
+    # Load into model
+    result = model.load_state_dict(state, strict=False)
+
+    # Report
+    weight_keys = set(state.keys()) if isinstance(state, dict) else set()
+    model_keys  = set(model.state_dict().keys())
+    loaded_keys = model_keys.intersection(weight_keys)
+    print("✅ Weights loaded")
+    print(f"   • Loaded keys: {len(loaded_keys)}")
+    print(f"   • Missing keys: {len(result.missing_keys)}")
+    print(f"   • Unexpected keys: {len(result.unexpected_keys)}")
+
+    return result
+# =========================
+# Representation model (fg-clip-base + LoRA)
+# =========================
+def build_model(device: torch.device):
+    from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
+    from peft import LoraConfig, get_peft_model
+
+    class RepModel(torch.nn.Module):
+        def __init__(self, model_root="qihoo360/fg-clip-base"):
+            super().__init__()
+            lora_cfg = LoraConfig(
+                r=32, lora_alpha=64,
+                target_modules=["q_proj","k_proj","v_proj","fc1","fc2"],
+                lora_dropout=0.05, bias="none",
+                task_type="FEATURE_EXTRACTION"
+            )
+            cfg = AutoConfig.from_pretrained(model_root, trust_remote_code=True)
+            base = AutoModelForCausalLM.from_config(cfg, trust_remote_code=True)
+            self.target_model = get_peft_model(base, lora_cfg)
+            self.tokenizer = AutoTokenizer.from_pretrained(model_root, trust_remote_code=True, use_fast=True)
+
+        @torch.no_grad()
+        def get_text_feature(self, texts, device):
+            tok = self.tokenizer(texts, padding="max_length", truncation=True, max_length=248, return_tensors="pt").to(device)
+            feats = self.target_model.get_text_features(tok["input_ids"], walk_short_pos=False)
+            feats = torch.nn.functional.normalize(feats.float(), dim=-1)
+            return feats
+
+        @torch.no_grad()
+        def get_image_feature(self, pm_batched):
+            feats = self.target_model.get_image_features(pm_batched)
+            feats = torch.nn.functional.normalize(feats.float(), dim=-1)
+            return feats
+
+    m = RepModel().to(device).eval()
+    print("Using fg-clip-base RepModel.")
+    return m
+
+# =========================
+# Data loading & helpers
+# =========================
+def load_scene_local(path: str, pm_key: str = PM_KEY_DEFAULT) -> torch.Tensor:
+    if not os.path.exists(path):
+        raise FileNotFoundError(f"Local file not found: {path}")
+    sd = load_file(path)
+    if pm_key not in sd:
+        raise KeyError(f"Key '{pm_key}' not found in {list(sd.keys())}")
+    pm = sd[pm_key]  # (V,H,W,3)
+    if pm.dim() != 4 or pm.shape[-1] != 3:
+        raise ValueError(f"Invalid shape {tuple(pm.shape)}, expected (V,H,W,3)")
+    return pm.permute(0, 3, 1, 2).contiguous()  # -> (V,3,H,W)
+
+def _xyz_to_numpy(xyz: torch.Tensor) -> np.ndarray:
+    pts = xyz.permute(1, 2, 0).reshape(-1, 3).cpu().numpy().astype(np.float32)
+    mask = np.isfinite(pts).all(axis=1)
+    return pts[mask]
+
+def stack_views(pm: torch.Tensor) -> Tuple[np.ndarray, np.ndarray]:
+    pts_all, vid_all = [], []
+    for v in range(pm.shape[0]):
+        pts = _xyz_to_numpy(pm[v])
+        if pts.size == 0: continue
+        pts_all.append(pts)
+        vid_all.append(np.full((pts.shape[0],), v, dtype=np.int32))
+    pts_all = np.concatenate(pts_all, axis=0)
+    vid_all = np.concatenate(vid_all, axis=0)
+    return pts_all, vid_all
+
+def voxel_downsample_with_ids(pts, vids, voxel: float):
+    if pts.shape[0] == 0: return pts, vids
+    grid = np.floor(pts / voxel).astype(np.int64)
+    key = np.core.records.fromarrays(grid.T, names="x,y,z", formats="i8,i8,i8")
+    _, uniq_idx = np.unique(key, return_index=True)
+    return pts[uniq_idx], vids[uniq_idx]
+
+def hard_cap(pts, vids, cap: int):
+    N = pts.shape[0]
+    if N <= cap: return pts, vids
+    idx = np.random.choice(N, size=cap, replace=False)
+    return pts[idx], vids[idx]
+
+def adaptive_point_size(n: int) -> float:
+    ps = 2.4 * (150_000 / max(n, 10)) ** 0.25
+    return float(np.clip(ps, POINT_SIZE_MIN, POINT_SIZE_MAX))
+
+def scene_bbox(pts: np.ndarray):
+    mn, mx = pts.min(axis=0), pts.max(axis=0)
+    x0,y0,z0 = mn; x1,y1,z1 = mx
+    corners = np.array([
+        [x0,y0,z0],[x1,y0,z0],[x1,y1,z0],[x0,y1,z0],
+        [x0,y0,z1],[x1,y0,z1],[x1,y1,z1],[x0,y1,z1]
+    ])
+    edges = [(0,1),(1,2),(2,3),(3,0),(4,5),(5,6),(6,7),(7,4),(0,4),(1,5),(2,6),(3,7)]
+    xs,ys,zs=[],[],[]
+    for a,b in edges:
+        xs += [corners[a,0], corners[b,0], None]
+        ys += [corners[a,1], corners[b,1], None]
+        zs += [corners[a,2], corners[b,2], None]
+    return xs,ys,zs
+
+@torch.no_grad()
+def rank_views_for_text(model, text, pm, device, topk: int):
+    img_feats = model.get_image_feature(pm.float().to(device))
+    txt_feat  = model.get_text_feature([text], device=device)[0]
+    sims = torch.matmul(img_feats, txt_feat)
+    order = torch.argsort(sims, descending=True)[:max(1, int(topk))]
+    return order.tolist()
+
+# =========================
+# Visualization
+# =========================
+def depth_values(pts: np.ndarray) -> np.ndarray:
+    z = pts[:, 2]
+    z_min, z_max = z.min(), z.max()
+    return (z - z_min) / (z_max - z_min + 1e-9)
+
+def base_figure_gray_depth(pts: np.ndarray, point_size: float, camera=DEFAULT_CAM) -> go.Figure:
+    depth = depth_values(pts)
+    fig = go.Figure(go.Scatter3d(
+        x=pts[:,0], y=pts[:,1], z=pts[:,2],
+        mode="markers",
+        marker=dict(size=point_size, color=depth, colorscale="Greys", reversescale=True, opacity=0.50),
+        hoverinfo="skip"
+    ))
+    bx,by,bz = scene_bbox(pts)
+    fig.add_trace(go.Scatter3d(x=bx,y=by,z=bz,mode="lines",line=dict(color=BOX_COLOR,width=2),hoverinfo="skip"))
+    fig.update_layout(scene=dict(aspectmode="data",camera=camera),
+                      margin=dict(l=0,r=0,b=0,t=0),
+                      paper_bgcolor=BG_COLOR,
+                      showlegend=False)
+    return fig
+
+def highlight_views_3d(pts, view_ids, selected, point_size, camera=DEFAULT_CAM):
+    depth = depth_values(pts)
+    colors = np.stack([depth, depth, depth], axis=1)
+    if selected:
+        sel_mask = np.isin(view_ids, np.array(selected, dtype=np.int32))
+        colors[sel_mask] = np.array([1, 0, 0])
+    fig = go.Figure(go.Scatter3d(
+        x=pts[:,0], y=pts[:,1], z=pts[:,2],
+        mode="markers",
+        marker=dict(size=point_size,
+                    color=[f"rgb({int(r*255)},{int(g*255)},{int(b*255)})"
+                           for r,g,b in colors],
+                    opacity=0.98),
+        hoverinfo="skip"
+    ))
+    bx,by,bz = scene_bbox(pts)
+    fig.add_trace(go.Scatter3d(x=bx,y=by,z=bz,mode="lines",
+                               line=dict(color=BOX_COLOR,width=2),hoverinfo="skip"))
+    fig.update_layout(scene=dict(aspectmode="data",camera=camera),
+                      margin=dict(l=0,r=0,b=0,t=0),
+                      paper_bgcolor=BG_COLOR,
+                      showlegend=False)
+    return fig
+
+# =========================
+# App setup
+# =========================
+device = 'cpu'
+model  = build_model(device)
+load_pretrain(model, "assets/ckpt_100.pth")
+
+with gr.Blocks(
+    title="POMA-3D: Text-conditioned 3D Scene Visualization",
+    css="#plot3d, #img_ref {height: 450px !important;}"
+) as demo:
+    gr.Markdown("### POMA-3D: The Point Map Way to 3D Scene Understanding - Embodied Localization Demo\n"
+                "Enter agent's situation text and choose **Top-K**; the most relevant views will turn **red**.")
+
+    with gr.Row():
+        text_in = gr.Textbox(label="Text query", value="I am sleeping on the bed.", scale=4)
+        topk_in = gr.Number(label="Top-K views", value=TOPK_VIEWS_DEFAULT, precision=0, minimum=1, maximum=12)
+        submit_btn = gr.Button("Locate", variant="primary")
+
+    with gr.Row(equal_height=True):
+        with gr.Column(scale=1, min_width=500):
+            plot3d = gr.Plot(label="3D Point Cloud (rotatable)", elem_id="plot3d")
+        with gr.Column(scale=1, min_width=500):
+            img_ref = gr.Image(label="Top-Down Reference View", value=TOP_VIEW_IMAGE_PATH, elem_id="img_ref")
+
+    status = gr.Markdown()
+
+    pm_state   = gr.State(None)
+    pts_state  = gr.State(None)
+    vids_state = gr.State(None)
+
+    # Load scene automatically from LOCAL_FILE_DEFAULT
+    def on_load():
+        pm = load_scene_local(LOCAL_FILE_DEFAULT)
+        pts_all, vids_all = stack_views(pm)
+        pts_vx, vids_vx   = voxel_downsample_with_ids(pts_all, vids_all, VOXEL_SIZE)
+        pts_vx, vids_vx   = hard_cap(pts_vx, vids_vx, DOWNSAMPLE_N_MAX)
+        ps = adaptive_point_size(pts_vx.shape[0])
+        fig3d = base_figure_gray_depth(pts_vx, ps, camera=DEFAULT_CAM)
+        msg = f"✅ Loaded {os.path.basename(LOCAL_FILE_DEFAULT)} | Views: {pm.shape[0]} | Points: {pts_vx.shape[0]:,}"
+        return fig3d, TOP_VIEW_IMAGE_PATH, msg, pm, pts_vx, vids_vx
+
+    def on_submit(text, topk, pm, pts_vx, vids_vx):
+        if pm is None:
+            return gr.update(), TOP_VIEW_IMAGE_PATH, "⚠️ Scene not loaded yet."
+        k = int(max(1, min(12, int(topk)))) if topk else TOPK_VIEWS_DEFAULT
+        top_views = rank_views_for_text(model, text, pm, device, topk=k)
+        ps = adaptive_point_size(pts_vx.shape[0])
+        fig = highlight_views_3d(pts_vx, vids_vx, top_views, ps, camera=DEFAULT_CAM)
+        msg = f"Highlighted views (top-{k}): {top_views}"
+        return fig, TOP_VIEW_IMAGE_PATH, msg
+
+    demo.load(on_load, inputs=[], outputs=[plot3d, img_ref, status, pm_state, pts_state, vids_state])
+    submit_btn.click(on_submit, inputs=[text_in, topk_in, pm_state, pts_state, vids_state],
+                     outputs=[plot3d, img_ref, status])
+
+if __name__ == "__main__":
+    demo.launch()

assets/scene0073_00.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1f99163e32be5d157c264a54d35b2514b57377713150c2da5fd1dd71eb4ad957
+size 169390352

requirements.txt

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+# --- Core runtime ---
+numpy>=1.24,<2.3
+torch==2.2.2
+safetensors>=0.4.3
+
+# --- Hugging Face stack (compatible pins) ---
+transformers==4.44.2
+huggingface_hub==0.24.6
+accelerate>=0.33.0
+peft==0.12.0
+sentencepiece>=0.1.99
+protobuf>=4.25.0
+
+# --- App / UI ---
+gradio==4.44.0
+plotly>=5.22.0
+
+# --- Nice-to-have for many HF vision/text models ---
+timm>=0.9.16