ci: deploy terramind_tim (cf4492f1e576)

README.package.md

@@ -6,6 +6,8 @@ Optional **GPU/CPU** batch tool under `inference/*` (not the thin game `server/`
 
 **Authority:** `rules/06-server-vlm-tim-and-on-device-ml.md`, `rules/12-python-gradio-terramind-server.md`, `docs/PRO-TAB-VLM-ORCHESTRATION-SPEC.md` (Coordinates → `ai_lat` / `ai_lon` for catalog pipelines).
 
+**Default backbone:** NU:TONIC pins the largest TerraMind-1.0 TIM variant as ``nutonic_terramind_tim_local.tim_defaults.DEFAULT_TIM_MODEL_ID`` (**``terramind_v1_large_tim``**). Example YAMLs and the HF Space / Gradio demos use that unless you override ``model_id``.
+
 ## Install (separate venv recommended)
 
 ```bash

nutonic_terramind_tim_local/gradio_panel.py

@@ -5,6 +5,7 @@ from typing import Any
 import gradio as gr
 
 from nutonic_terramind_tim_local.space_api import health, tim_infer
+from nutonic_terramind_tim_local.tim_defaults import DEFAULT_TIM_MODEL_ID
 
 
 def _infer_from_json(req: dict[str, Any]) -> dict[str, Any]:
@@ -26,7 +27,7 @@ def build_gradio_blocks() -> gr.Blocks:
             value={
                 "analysis_profile": "brief_only",
                 "config": {
-                    "model_id": "terramind_v1_tiny_tim",
+                    "model_id": DEFAULT_TIM_MODEL_ID,
                     "pretrained": True,
                     "modalities": ["RGB"],
                     "tim_modalities": ["LULC", "location"],

nutonic_terramind_tim_local/inputs_build.py

@@ -418,4 +418,10 @@ def _build_inputs(
             }
             for label, (_tensor, meta) in temporal_stac.items()
         }
+    try:
+        s2p = stac_s2_params_from_cfg(in_cfg, row)
+        if s2p.get("lat") is not None and s2p.get("lon") is not None:
+            aux["request_wgs84"] = {"latitude": float(s2p["lat"]), "longitude": float(s2p["lon"])}
+    except Exception:
+        pass
     return out, aux

nutonic_terramind_tim_local/run.py

@@ -2,6 +2,7 @@
 
 from __future__ import annotations
 
+import math
 import os
 import random
 import sys
@@ -73,6 +74,54 @@ def _set_ensemble_iteration_seed(seed: int) -> None:
         torch.cuda.manual_seed_all(s)
 
 
+def _haversine_km(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
+    r = 6371.0
+    p1, p2 = math.radians(lat1), math.radians(lat2)
+    dphi = math.radians(lat2 - lat1)
+    dl = math.radians(lon2 - lon1)
+    a = math.sin(dphi / 2) ** 2 + math.cos(p1) * math.cos(p2) * math.sin(dl / 2) ** 2
+    return 2 * r * math.asin(min(1.0, math.sqrt(max(0.0, a))))
+
+
+def _sanitize_coordinates_in_row(
+    row: dict[str, Any],
+    *,
+    inputs_aux: Mapping[str, Any] | None,
+    max_decode_drift_km: float = 250.0,
+) -> None:
+    """If decoded TiM WGS84 is far from the requested batch lat/lon, replace with request (bad decode guard)."""
+    if not isinstance(inputs_aux, Mapping):
+        return
+    req = inputs_aux.get("request_wgs84")
+    if not isinstance(req, dict):
+        return
+    try:
+        elat = float(req["latitude"])
+        elon = float(req["longitude"])
+    except (KeyError, TypeError, ValueError):
+        return
+    tmo = row.get("tim_modality_outputs")
+    if not isinstance(tmo, dict):
+        return
+    coord = tmo.get("Coordinates")
+    if not isinstance(coord, dict):
+        return
+    try:
+        clat = float(coord["latitude"])  # type: ignore[arg-type]
+        clon = float(coord["longitude"])  # type: ignore[arg-type]
+    except (KeyError, TypeError, ValueError):
+        return
+    drift = _haversine_km(clat, clon, elat, elon)
+    if drift <= max_decode_drift_km:
+        return
+    coord["latitude"] = elat
+    coord["longitude"] = elon
+    coord["replaced_decode_invalid"] = True
+    coord["original_decode_km_drift"] = round(float(drift), 2)
+    coord["original_latitude"] = clat
+    coord["original_longitude"] = clon
+
+
 def _pair_from_wgs(wgs: Mapping[str, Any] | None) -> tuple[float | None, float | None]:
     if not wgs or not isinstance(wgs, dict):
         return None, None
@@ -165,6 +214,9 @@ def _export_row(
     tim_modality_outputs = build_tim_modality_outputs(
         model, tim_raw, tensor_sample_limit=sample_limit, policy=tim_policy
     )
+    row_prelim: dict[str, Any] = {"tim_modality_outputs": tim_modality_outputs}
+    _sanitize_coordinates_in_row(row_prelim, inputs_aux=inputs_aux)
+    tim_modality_outputs = row_prelim["tim_modality_outputs"]
     profile = str(
         analysis_profile
         or export_cfg.get("analysis_profile")

nutonic_terramind_tim_local/space_api.py

@@ -9,6 +9,7 @@ from fastapi import Body, FastAPI, HTTPException
 
 from nutonic_terramind_tim_local.spaces_zero import apply_zero_gpu
 from nutonic_terramind_tim_local.terramind_patches import terramind_patch_diagnostics
+from nutonic_terramind_tim_local.tim_defaults import DEFAULT_TIM_MODEL_ID
 
 app = FastAPI(title="NU:TONIC TerraMind TiM local (Space)", version="0.1.0")
 
@@ -39,7 +40,7 @@ def tim_export(
         examples=[
             {
                 "config": {
-                    "model_id": "terramind_v1_tiny_tim",
+                    "model_id": DEFAULT_TIM_MODEL_ID,
                     "pretrained": True,
                     "modalities": ["RGB"],
                     "tim_modalities": ["LULC", "location"],

nutonic_terramind_tim_local/tim_defaults.py

@@ -0,0 +1,6 @@
+"""Canonical TerraMind TiM backbone id for NU:TONIC (TerraTorch ``BACKBONE_REGISTRY``)."""
+
+# Largest TerraMind-1.0 TIM variant (IBM ``terratorch`` registry). Prefer this unless profiling or CI forces smaller.
+DEFAULT_TIM_MODEL_ID = "terramind_v1_large_tim"
+
+__all__ = ["DEFAULT_TIM_MODEL_ID"]

pyproject.toml

@@ -13,7 +13,7 @@ dependencies = [
     "terratorch>=1.1.1,<1.3",
     "PyYAML>=6.0.1",
     "numpy>=1.26.0",
-    "Pillow>=10.0.0",
+    "Pillow>=12.2.0",
     # Align with TerraTorch → peft: older peft imports HybridCache from transformers;
     # transformers 5.2+ reshaped exports — peft 0.18.1+ avoids the stale import path.
     "peft>=0.18.1",
@@ -31,7 +31,7 @@ s2 = ["pystac-client>=0.7.0", "rasterio>=1.3.9", "boto3>=1.34.0"]
 space = [
     "fastapi>=0.115.0",
     "uvicorn[standard]>=0.32.0",
-    "gradio>=4.44.0,<6",
+    "gradio>=6.7.0",
     "spaces>=0.44.0,<1",
 ]
 

requirements.txt

@@ -2,7 +2,7 @@ torch>=2.2.0
 terratorch>=1.1.1,<1.3
 PyYAML>=6.0.1
 numpy>=1.26.0
-Pillow>=10.0.0
+Pillow>=12.2.0
 peft>=0.18.1
 transformers>=4.48.0
 urllib3>=2.6.3