diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..d5ba5899a7cfe3677b8b9531dd64d920e42d1ba4 Binary files /dev/null and b/.DS_Store differ diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..4f4175e9ffb4854ad01e32b7d2e15761a86ad551 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +checkpoints/balanced288/text_tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text +checkpoints/balanced288/tokenizer.json filter=lfs diff=lfs merge=lfs -text +checkpoints/mixed240/text_tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text +checkpoints/mixed240/tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/LICENSE-CODE b/LICENSE-CODE new file mode 100644 index 0000000000000000000000000000000000000000..c99f2c223571bdb845f3344613ba1223e51eb1dc --- /dev/null +++ b/LICENSE-CODE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2026 Tancho contributors + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/NOTICE.md b/NOTICE.md new file mode 100644 index 0000000000000000000000000000000000000000..1cf1ec824ab05133388d3dcea20cb433bb93705a --- /dev/null +++ b/NOTICE.md @@ -0,0 +1,9 @@ +# Licensing and origin + +Published by Vox (Tokyo, Japan) under the v13s namespace. Tancho Generator is a derivative of NVIDIA Cosmos3-Edge, pinned revision a9d944e2c6a1bf9f48b92ad16348e70c5f1836ba. Copyright and origin notices in the original retained artifacts are preserved. The pinned upstream model card and available license/notice files are copied under licenses/upstream/ for attribution; its full-model capabilities are not claims about this Tancho release. Model weights and upstream model artifacts are distributed under the accompanying licenses/LICENSE.OpenMDW-1.1. Original Vox code, documentation, and synthetic evaluation records use LICENSE-CODE (MIT); that license does not replace third-party terms. + +The pinned NVIDIA Cosmos Framework is a separate dependency, not bundled source software. Install it under its own terms. The exported processor/tokenizer/configuration assets retain their original bytes. The checkpoint provenance files refer to the pinned upstream base snapshot; trained-export file identity is established by release-manifest.json and the checkpoint SHA-256 records. + +No GSI footage, extracted frames, client materials, credentials, or complete internal run archives are distributed. Public GSI source imagery informed training; see SOURCES.md. The packaged evidence contains synthetic numerical geometry, image hashes and saved generated text, with no images. These are developer-created records, not official GSI assessments. No NVIDIA, Nebius, or GSI endorsement is asserted. + +The dependency file deploy/nebius/edge-parent40-expected-weights.json supplies a historical fingerprint for checking the frozen vision encoder in the audited overlay helper. It is not a parent checkpoint and is not loaded as trained weights. The two released models were independently fine-tuned from the pretrained base. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f1df3cd03dab66c8f956993202529e87e2140cb3 --- /dev/null +++ b/README.md @@ -0,0 +1,102 @@ +--- +language: +- ja +- en +license: other +license_name: openmdw-1.1 +license_link: https://openmdw.ai/license/1-1/ +library_name: cosmos +base_model: nvidia/Cosmos3-Edge +base_model_relation: finetune +tags: +- physical-ai +- viewpoint-planning +- uav +- research +- cosmos3 +--- + +# Tancho Generator — router v5 research release + +**A reproducible research baseline for generating numeric observation viewpoints.** Developed in Japan by Vox, Tokyo; compute provided by Nebius AI Cloud. Released September 14, 2026. + +This release contains **two fine-tuned checkpoints and a deterministic input-based router**. “v5” is the routing-policy version, not a fifth checkpoint or the upstream model version. It is the current adopted Tancho research baseline. The older [Tancho release](https://huggingface.co/v13s/tancho) contains different weights and a different output protocol; its 21/40 result is not this release's metric. + +## What it does + +Given a reference image and trusted numeric context (current/target coordinates, target size, required pixels, viewing direction, approved volume, speed and mission-time limits), the Generator emits a JSON list: + +```json +[{"offset_m": [0.0, 20.0, 15.0], "dwell_ms": 2000, "speed_mps": 3.0}] +``` + +This is a **format illustration**, not a certified plan. `offset_m` is the displacement from the target in local ENU metres; add the target position to obtain the viewpoint. The other fields are dwell milliseconds and outbound speed in m/s. A separate validator checks the unchanged answer. + +This application Generator uses Cosmos's **autoregressive tower**. It does not train the application's Reasoner role (deciding what to observe), use the diffusion tower to produce a continuous camera trajectory, or execute flight control. The intended architecture remains observation objective → viewpoint sequence → detailed camera/aircraft trajectory → flight control. + +## Exactly what is released + +| Component | Path | Role | +|---|---|---| +| mixed240 | `checkpoints/mixed240/` | Default viewpoint generation | +| balanced288 | `checkpoints/balanced288/` | Eligible stationary observations only | +| router v5 | `tancho/viewpoint_router.py` | Select once before inference | +| validators and pinned-backend helpers | `tancho/`, `training/`, `run_request.py` | Preserve raw output and check geometry | +| saved-output evidence | `evidence/` | Re-score the reported v5 answers without new inference | + +Each checkpoint is the original 31-file retained export (4,905,905,551 bytes), including the fine-tuned AR tower and frozen vision encoder. The two exports total 9,811,811,102 bytes before code and documentation. Framework configuration/assets in the exports do not mean diffusion/VAE weights were fine-tuned or that the export alone is a full Cosmos multimodal snapshot. Loading uses the pinned Cosmos Framework and base-overlay procedure in [REPRODUCE.md](REPRODUCE.md), **not a plain `transformers.from_pretrained` call or an HF-hosted inference endpoint**. + +Router eligibility requires all of: + +- Remaining mission time after return margin is exactly 1, 2, or 3 seconds. +- Trusted-input preflight is undetermined rather than an established invalid/infeasible request. +- Conversion of the current position to relative coordinates and back is exact. +- A current-position, 1000 ms stationary validation probe passes every check. + +Eligible inputs use balanced288 with an explicit current-relative-position input. All other inputs use mixed240 and the original relative-coordinate prompt. The probe is only an eligibility check; it is not returned as a model answer. There is one generation per request, without answer repair, retry, or cross-model fallback. The **1/2/3 seconds describe remaining mission budget, not inference latency or flight-control frequency**. The included snapshot also contains older explicit policies and protocol helpers as dependencies; only `viewpoint-router-v5` is the advertised release. Neither move432 nor the experimental timing-input router is released here. + +## Measured results and unresolved failures + +Success means passing the contract and all evaluated numerical geometry/envelope checks together. These results are from the fixed v4-versus-v5 comparison on September 13, 2026. + +| Evaluation group | Cases | v4 accepted | v5 accepted | +|---|---:|---:|---:| +| New stationary, 1-second budget | 64 | 56 | 60 | +| New stationary, 2-second budget | 64 | 53 | 60 | +| New stationary, 3-second budget | 64 | 53 | 60 | +| Previously evaluated stationary regression cases | 128 | 111 | 122 | +| Movement required | 64 | 15 | 15 | +| Normal numeric conditions | 224 | 221 | 221 | +| Current viewpoint valid, 4-second boundary | 32 | 0 | 0 | +| Current position on the wrong side | 32 | 0 | 0 | + +New stationary success is **180/192 (93.75%)**, with 22 paired improvements and 4 regressions against v4. This is not overall accuracy. The 352 cases outside the stationary branch had identical raw outputs across policies. All 1,344 original generations and their input/model identities were audited; the original GPU and temporary disk were deleted. The adopted implementation passed 93 related CPU tests. + +**The movement and boundary failures remain unresolved.** balanced288 had previously failed a broad deployment comparison due to movement regression; v5 adopts it only within the stationary branch. Later movement/timing experiments do not change these weights or results. + +The 544 new numerical geometries were not in earlier packs; 128 cases are explicitly reused regression cases. Reference images reuse the existing evaluation split. Normal cases are 28 base scenes × 8 symmetry transformations, not 224 independent flights. Numerical geometry is synthetic and is not tied to reconstructed objects in the images. Camera scoring uses a fixed 3840-pixel width and 81-degree field of view. Passing does not establish image understanding, occlusion handling, actual image quality, field generalization, or flight safety. The evaluation informed adoption and is not an untouched external certification set. + +## Training provenance + +Both runs started independently from pinned pretrained `nvidia/Cosmos3-Edge` revision `a9d944e2c6a1bf9f48b92ad16348e70c5f1836ba`; neither continues the retired parent-40 fine-tune. + +| Item | mixed240 | balanced288 | +|---|---|---| +| Training examples | 480 mixed numeric cases | 576 cases: 480 normal + 96 time-constrained | +| Epochs / updates | 4 / 240 | 4 / 288 | +| Learning rate / gradient accumulation | 1e-6 / 8 | 1e-6 / 8 | +| Evaluated retained export | Final 240-update export | Final 288-update export | + +For each model, **233 AR tensors were trainable and 437 vision tensors were frozen**. Verification of 670 exported tensors is not a claim that all 670 were trained. Numerical teachers come from the project's geometry/planning code, not human-reviewed observation decisions. Training uses reference tiles from public GSI Omachi footage, with evaluation tiles separated within the same flight. The synthetic geometry varies independently of the reference imagery. See [SOURCES.md](SOURCES.md). + +## Use and release verification + +See [REPRODUCE.md](REPRODUCE.md) for download, CPU saved-answer rescoring, request preparation, and the pinned CUDA backend procedure. `smoke.py` re-scores 672 previously generated v5 answers; it makes **zero model calls** and does not measure fresh accuracy. Release verification checks exported bytes and downloaded package behavior; it does not add a new performance benchmark or a Jetson measurement. + +Intended runtime is Jetson Orin NX 16GB, but latency, memory use with two checkpoints, and device deployment remain unmeasured. No learned-model real-flight execution has been validated. Upstream capabilities cannot be transferred into claims about this fine-tune. + +## Licenses and attribution + +Weights and upstream model artifacts retain [OpenMDW 1.1](licenses/LICENSE.OpenMDW-1.1). Original Vox code, documentation, and synthetic evaluation records are under [MIT](LICENSE-CODE); third-party terms remain applicable to their artifacts. [NOTICE.md](NOTICE.md) records scope and attribution. No source footage, extracted frames, client materials, credentials, or complete internal run archives are distributed. NVIDIA, Nebius, and GSI do not endorse this release. + +Contact: tancho@vox.delivery diff --git a/REPRODUCE.md b/REPRODUCE.md new file mode 100644 index 0000000000000000000000000000000000000000..4e331e95d9b70131bb354a482d606abce622ce44 --- /dev/null +++ b/REPRODUCE.md @@ -0,0 +1,64 @@ +# Download and reproduce + +## Scope + +This is a custom Cosmos Framework research package. Python 3.11+ and Pillow suffice for the CPU steps. Actual generation requires Linux, an NVIDIA CUDA environment compatible with the pinned framework, the full pinned pretrained base snapshot, and an overlay of the selected export. A generic Transformers loader is not supported. The two checkpoint files are not merged weights. + +## Download + +```sh +hf download v13s/tancho-generator-v5 --local-dir tancho-generator-v5 +cd tancho-generator-v5 +python -m pip install -r requirements-cpu.txt +python verify_files.py +python smoke.py +``` + +For repeatable work, add `--revision COMMIT_SHA` using the immutable commit from the HF revision history. `verify_files.py` checks every file listed in `release-manifest.json`, including both weight exports. The manifest itself is anchored by the HF commit, not a self-signature. `smoke.py` reproduces the recorded group counts by routing and re-scoring 672 saved answers. It does not load model weights or generate new answers. + +## Prepare a request without a GPU + +Use an RGB observation image you are authorized to process. `examples/normal.json` and `examples/stationary.json` contain synthetic numeric contexts from the evaluated cases. They do not locate objects in your image. + +```sh +python run_request.py --request examples/stationary.json --image /absolute/path/observation.png --output prepared-stationary --prepare-only +python run_request.py --request examples/normal.json --image /absolute/path/observation.png --output prepared-normal --prepare-only +``` + +Expected selections are balanced288 and mixed240 respectively. This step writes the exact prompt, canonical image proof, and routing decision, with zero model calls. It converts the user image to RGB PNG without resizing. Output directories must be new. The route's `checkpoint_prefix` is an original retention identifier; local exports live at `checkpoints//`. + +## Pinned CUDA backend + +The original audited runtime used NVIDIA Cosmos Framework revision `0fe093eafbac390ffb7906a6f4ed9f558fe42c4c`, with a clean checkout at `/opt/cosmos-framework`. Install the framework and dependencies for that revision. One recorded environment recipe is: + +```sh +git clone https://github.com/NVIDIA/cosmos-framework.git /opt/cosmos-framework +git -C /opt/cosmos-framework checkout 0fe093eafbac390ffb7906a6f4ed9f558fe42c4c +cd /opt/cosmos-framework +uv sync --frozen --all-extras --group=cu130-train +export PATH=/opt/cosmos-framework/.venv/bin:$PATH +``` + +Run the next commands from the downloaded Tancho repository, using absolute destinations. Obtain the base once; it is larger than the two AR/vision exports and requires additional disk space for overlays. `prepare_model.py` checks the immutable upstream snapshot, and will not silently use a new `main` revision. + +```sh +python training/prepare_model.py --output /absolute/path/cosmos3-edge-base +python training/prepare_train_v2_overlay.py --base /absolute/path/cosmos3-edge-base --export "$PWD/checkpoints/mixed240" --overlay /absolute/path/overlays/mixed240 --output /absolute/path/proofs/mixed240 --name mixed240 +python training/prepare_train_v2_overlay.py --base /absolute/path/cosmos3-edge-base --export "$PWD/checkpoints/balanced288" --overlay /absolute/path/overlays/balanced288 --output /absolute/path/proofs/balanced288 --name balanced288 +``` + +The helpers preserve the base, replace and verify 670 AR/vision tensors, and confirm that 437 vision tensors are frozen. `expected-weights.json` records the overlay path and expected loaded tensor hashes. Historical parent-40 metadata is used only as a frozen-vision fingerprint; no parent-40 training weights are loaded. Paths in the generated proof are absolute, so regenerate the overlay proof if relocating it. + +Then run one request: + +```sh +python run_request.py --request examples/stationary.json --image /absolute/path/observation.png --output generated-stationary --proof-root /absolute/path/proofs +``` + +This invokes the included audited `training/detect_native.py` with one selected model and greedy decoding (seed 42, maximum 768 new tokens), saves the original output and independently validates it. No guard retry flag is passed. Each invocation loads one model; this wrapper does not claim an optimized two-model resident service. A rejected answer remains rejected. Backend errors propagate instead of selecting another model. There is no flight-command transport. + +The native backend and overlay procedure were used in the original GPU evaluations. The new public request wrapper is verified through request preparation; publication does not claim a fresh GPU execution of that wrapper or device deployment. Evidence generation used the original reference images, which are not distributed. Using another image is a new inference input, not an exact rerun of the reported score. + +## Evaluation records + +`evidence/evaluation-summary.json` gives the original paired v4/v5 counts and gates. `evidence/v5-saved-outputs.json` contains all 672 v5 raw answers with synthetic contexts, selected model, prompt hash, and reference-image fingerprint. `smoke.py` validates the router selection and rescoring against that summary. It cannot reconstruct the source image from its hash. diff --git a/SOURCES.md b/SOURCES.md new file mode 100644 index 0000000000000000000000000000000000000000..3dc42f86bb08e011ebdbb7e67996c71c6b1e3a43 --- /dev/null +++ b/SOURCES.md @@ -0,0 +1,12 @@ +# Sources for this release + +Both Generator training packs use reference-image tiles from the Geospatial Information Authority of Japan (GSI), Nagano City Omachi, October 13, 2019, Typhoon Hagibis. Source footage SHA-256 recorded locally: a112641a9335adc310f872963ce49a4e73d7a9451fe07fc06705286e32379ec2. This is a local provenance checksum, not one issued by GSI. + +Official source: https://www.gsi.go.jp/BOUSAI/R1.taihuu19gou.html +GSI content terms: https://www.gsi.go.jp/kikakuchousei/kikakuchousei40182.html + +Attribution: 出典：国土地理院ウェブサイト。動画の切り出し・縮小と合成観測条件・数値教師・評価はTancho開発側で作成。国土地理院の公式評価ではありません。 + +Training and evaluation tiles are separated within this one flight. Images are reference inputs; synthetic target coordinates have no provided image-to-3D correspondence. mixed240 uses 480 numeric examples; balanced288 uses 576. The released saved answers include newly generated numerical geometry and explicitly marked reused regression cases; they reuse reference evaluation images. No footage or image tiles are included in this repository. + +Earlier documents described Omachi as evaluation-only for a retired observation task. That statement does not apply to these Generator training packs. No client footage was used in these checkpoints. 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new file mode 100644 index 0000000000000000000000000000000000000000..15ff9ea5ad233da518fea09808397a3e6ebf0e5f --- /dev/null +++ b/checkpoints/balanced288/assets/example_i2v_prompt.json @@ -0,0 +1,124 @@ +{ + "subjects": [ + { + "description": "A car (viewed from the driver's perspective/dashcam) traveling along a coastal mountain road. The vehicle's grey dashboard and windshield frame are visible at the bottom of the frame.", + "appearance_details": "Grey/dark interior dashboard, windshield wipers visible, clean windshield providing clear view of the road ahead", + "relationship": "Primary subject and point-of-view vehicle navigating the mountain road", + "location": "Bottom foreground, providing the POV perspective", + "relative_size": "Large within frame (dashboard occupies bottom portion)", + "orientation": "Facing forward along the road direction", + "pose": "", + "action": "Driving fast along the curved coastal road, then making a sudden emergency stop", + "state_changes": "Transitions from fast forward motion to abrupt deceleration and complete stop", + "clothing": "", + "expression": "", + "gender": "", + "age": "", + "skin_tone_and_texture": "", + "facial_features": "", + "number_of_subjects": 1, + "number_of_arms": 0, + "number_of_legs": 0 + }, + { + "description": "Falling rocks and debris from the steep rocky mountain cliff face, creating a landslide that tumbles down toward the road surface", + "appearance_details": "Grey and brown angular rocks of various sizes, accompanied by dust and smaller debris, mixed with displaced green vegetation", + "relationship": "Obstacle that forces the car to make an emergency stop", + "location": "Center and center-right of frame, falling from the mountain cliff face toward the road", + "relative_size": "Medium within frame, growing larger as rocks approach the road", + "orientation": "Falling downward from the cliff face", + "pose": "", + "action": "Tumbling and cascading down the steep rocky mountainside toward the road", + "state_changes": "Begins as small movement on the cliff face, escalates into a significant rockfall with dust cloud", + "clothing": "", + "expression": "", + "gender": "", + "age": "", + "skin_tone_and_texture": "", + "facial_features": "", + "number_of_subjects": 0, + "number_of_arms": 0, + "number_of_legs": 0 + } + ], + "background_setting": "A dramatic coastal mountain highway carved along steep rocky cliffs. The road is a well-maintained two-lane highway with white dashed center lines, solid white edge lines, and raised reflective road markers. A metal guardrail runs along the right side protecting from a drop toward the ocean. The mountain rises steeply on the left with exposed grey and brown rock faces partially covered with lush green native vegetation including ferns and shrubs. The ocean is visible in the distance to the right under a partly cloudy blue sky. The road curves sharply to the left around the mountain headland.", + "lighting": { + "conditions": "Bright natural daylight with partly cloudy skies, strong ambient illumination", + "direction": "Top-lit and slightly front-lit from the sun positioned high in the sky", + "shadows": "Moderate shadows cast by the mountain cliff creating areas of shade on the road and vegetation, soft shadows from clouds", + "illumination_effect": "Bright, clear visibility with good contrast between the sunlit road surface and shaded mountain areas, natural coastal light" + }, + "aesthetics": { + "composition": "Driver's POV/dashcam perspective with the road curving ahead as the central leading line, mountain dominating the upper-left portion, ocean visible to the right, dashboard framing the bottom", + "color_scheme": "Dominant greens from vegetation, grey-brown rocky cliff faces, dark grey asphalt road with white markings, blue sky and ocean, yellow warning sign providing accent color", + "mood_atmosphere": "Initially scenic and adventurous, transitioning to tense and dangerous as the landslide occurs", + "patterns": "Repeating metal guardrail posts along the right side, dashed center line markings, layered rock strata on the cliff face" + }, + "cinematography": { + "camera_motion": "Forward-moving dashcam perspective with slight vibration from vehicle speed, sudden forward lurch during emergency braking", + "framing": "Wide shot from driver's POV through windshield", + "camera_angle": "Eye-level from driver's seated position", + "depth_of_field": "Deep", + "focus": "Road ahead and mountain cliff face in sharp focus", + "lens_focal_length": "Wide-angle (approximately 28-35mm equivalent)" + }, + "style_medium": "Live-action video", + "artistic_style": "Realistic dashcam footage, dramatic documentary style", + "context": "Dashcam footage capturing a dangerous landslide event on a coastal mountain highway, requiring emergency driving maneuver", + "actions": [ + { + "time": "0:00-0:02", + "description": "The car drives fast along the winding coastal mountain road, approaching the left curve with the road rushing beneath" + }, + { + "time": "0:02-0:04", + "description": "Rocks begin to dislodge from the steep cliff face ahead, tumbling down with increasing intensity as a landslide develops, dust rising from impacts" + }, + { + "time": "0:04-0:05", + "description": "The car makes a sudden emergency stop, the camera lurching forward from the deceleration as rocks and debris continue falling onto the road ahead" + } + ], + "text_and_signage_elements": [ + { + "text": "Left curve arrow", + "category": "scene_sign", + "appearance": "Black arrow on yellow diamond-shaped warning sign, mounted on a metal pole", + "spatial_temporal": "Left side of road, visible from 0:00 and passing by as car moves forward", + "context": "Road warning sign indicating a sharp left curve ahead, standard highway signage" + } + ], + "segments": [ + { + "segment_index": 0, + "time_range": "0:00-0:02", + "description": "The car speeds along the scenic coastal mountain highway, navigating the curve. The road surface rushes beneath, white lines blur slightly from speed, and the mountain looms ahead.", + "key_changes": "Road perspective shifts as car navigates the curve, passing the warning sign, guardrail moves in peripheral view", + "camera": "Forward-moving dashcam with slight road vibration, steady forward momentum" + }, + { + "segment_index": 1, + "time_range": "0:02-0:04", + "description": "Rocks begin breaking loose from the cliff face ahead. Small stones first, then larger boulders cascade down the mountainside creating a growing dust cloud and debris field on and near the road.", + "key_changes": "Mountain transitions from static backdrop to active hazard, dust cloud forms, debris appears on road surface ahead", + "camera": "Still moving forward but beginning to slow, slight camera shake increases" + }, + { + "segment_index": 2, + "time_range": "0:04-0:05", + "description": "The car performs an emergency stop. The camera pitches forward from sudden braking force. The vehicle comes to a complete halt with the landslide debris visible on the road ahead, dust still settling.", + "key_changes": "Rapid deceleration, camera lurches forward then settles, motion stops completely, dust and small rocks still visible falling", + "camera": "Abrupt forward pitch from braking, then stabilizes to static as vehicle stops" + } + ], + "transitions": [], + "temporal_caption": "The video begins with a dashcam view of a car driving fast along a scenic coastal mountain road, the asphalt rushing beneath and a sharp left curve sign visible on the left. The car navigates the curve at speed with the steep rocky cliff towering above and the ocean visible to the right. Around the 2-second mark, small rocks begin to dislodge from the cliff face ahead, quickly escalating into a significant rockfall with larger boulders tumbling down the mountainside and a dust cloud forming. By second 4, the driver reacts with emergency braking - the camera lurches forward dramatically from the sudden deceleration. The car comes to a complete stop between seconds 4 and 5, with the road ahead partially blocked by fallen rocks and debris, dust still settling in the air around the landslide zone.", + "audio_description": "Initially the sound of a car engine at moderate-high RPM and tire noise on asphalt with wind passing over the vehicle. Around second 2, the rumbling and cracking sounds of rocks breaking loose begin, growing louder with impacts of stones hitting the road surface and each other. Loud thuds and crashes as larger rocks land. At second 4, the sharp screech of tires braking hard on asphalt, followed by the settling sounds of smaller pebbles and dust, with distant rumbling of the remaining rockfall subsiding.", + "resolution": { + "W": 1280, + "H": 720 + }, + "aspect_ratio": "16,9", + "duration": "5s", + "fps": 24 + } \ No newline at end of file diff --git a/checkpoints/balanced288/assets/example_reasoning_prompt.json b/checkpoints/balanced288/assets/example_reasoning_prompt.json new file mode 100644 index 0000000000000000000000000000000000000000..3371076f17b1ff8bfff60fb24536b160f637809b --- /dev/null +++ b/checkpoints/balanced288/assets/example_reasoning_prompt.json @@ -0,0 +1,4 @@ +{ + "prompt": "The task is to put flower into the red bottle. Generate a plan consisting of subtasks for accomplish the task.", + "max_tokens": 4096 +} diff --git a/checkpoints/balanced288/assets/negative_prompt.json b/checkpoints/balanced288/assets/negative_prompt.json new file mode 100644 index 0000000000000000000000000000000000000000..b51ecb2195e2f16a89959aa3b757c7d66cac6f22 --- /dev/null +++ b/checkpoints/balanced288/assets/negative_prompt.json @@ -0,0 +1,131 @@ +{ + "subjects": [ + { + "description": "A subject suffering from extreme temporal instability — flickering outlines, morphing silhouette, and visible frame-to-frame discontinuities that make it look like a corrupted video file.", + "appearance_details": "Distorted features, visible compression artifacts, muddy textures lacking fine detail, color bleeding between elements, and unnatural skin tones or surface textures that appear artificial or computer-generated.", + "relationship": "Subjects appear disconnected from the environment, floating or improperly grounded in the scene without proper occlusion or spatial coherence.", + "location": "Subjects are poorly placed within the frame, appearing at awkward positions that violate basic compositional rules.", + "relative_size": "Inconsistent scale relationships between subjects and the environment, with objects appearing too large or too small relative to their surroundings.", + "orientation": "Unnatural orientations that defy physics and spatial logic.", + "pose": "Poses snap between positions with no interpolation, creating a strobe-like effect.", + "action": "Motion is completely incoherent — limbs teleport between positions, movement appears as discrete jumps rather than continuous flow, and speed varies wildly between frames.", + "state_changes": "The subject flickers between completely different visual states every few frames, as if multiple renders were randomly interleaved.", + "clothing": "Garments phase in and out of visibility, textures swim across surfaces untethered to the geometry.", + "expression": "Frozen, uncanny valley expressions or expressions that change abruptly without natural transition.", + "gender": "", + "age": "", + "skin_tone_and_texture": "Waxy, plastic-looking skin with visible artifacts and inconsistent texture resolution across the frame.", + "facial_features": "Asymmetric facial features, extra fingers or limbs, teeth that appear blurry or malformed.", + "number_of_subjects": 0, + "number_of_arms": 0, + "number_of_legs": 0 + }, + { + "description": "A subject with catastrophically broken surface rendering — visible polygon edges, texture seams, UV mapping errors, and normal map inversions.", + "appearance_details": "Distorted features, visible compression artifacts, muddy textures lacking fine detail, color bleeding between elements, and unnatural skin tones or surface textures that appear artificial or computer-generated.", + "relationship": "Subjects appear disconnected from the environment, floating or improperly grounded in the scene without proper occlusion or spatial coherence.", + "location": "Subjects are poorly placed within the frame, appearing at awkward positions that violate basic compositional rules.", + "relative_size": "Inconsistent scale relationships between subjects and the environment, with objects appearing too large or too small relative to their surroundings.", + "orientation": "Unnatural orientations that defy physics and spatial logic.", + "pose": "Static poses reveal texture swimming, moire patterns, and aliased edges on every contour.", + "action": "Movement reveals progressively worse texture breakdown — stretching, tearing, and resolution collapse in deformed regions.", + "state_changes": "Surface materials randomly swap between frames — metal becomes cloth, glass becomes stone, with no transition.", + "clothing": "Fabric renders as a flat solid color with no folds, wrinkles, or material response to form.", + "expression": "Frozen, uncanny valley expressions or expressions that change abruptly without natural transition.", + "gender": "", + "age": "", + "skin_tone_and_texture": "Waxy, plastic-looking skin with visible artifacts and inconsistent texture resolution across the frame.", + "facial_features": "Asymmetric facial features, extra fingers or limbs, teeth that appear blurry or malformed.", + "number_of_subjects": 0, + "number_of_arms": 0, + "number_of_legs": 0 + }, + { + "description": "A subject that appears composited into the scene rather than belonging to it — mismatched lighting direction, wrong color temperature, and edges that bleed into the background.", + "appearance_details": "Distorted features, visible compression artifacts, muddy textures lacking fine detail, color bleeding between elements, and unnatural skin tones or surface textures that appear artificial or computer-generated.", + "relationship": "Subjects appear disconnected from the environment, floating or improperly grounded in the scene without proper occlusion or spatial coherence.", + "location": "Subjects are poorly placed within the frame, appearing at awkward positions that violate basic compositional rules.", + "relative_size": "Inconsistent scale relationships between subjects and the environment, with objects appearing too large or too small relative to their surroundings.", + "orientation": "Unnatural orientations that defy physics and spatial logic.", + "pose": "Proportions shift between frames — limbs lengthen and shorten, heads resize, creating an unsettling instability.", + "action": "Movement creates visible matting artifacts — green screen fringe, edge chattering, and translucent boundaries.", + "state_changes": "The subject randomly changes size, opacity, and z-depth relative to other scene elements.", + "clothing": "Clothing appears to be from a different render pass — different noise level, different motion blur kernel, different color space.", + "expression": "Frozen, uncanny valley expressions or expressions that change abruptly without natural transition.", + "gender": "", + "age": "", + "skin_tone_and_texture": "Waxy, plastic-looking skin with visible artifacts and inconsistent texture resolution across the frame.", + "facial_features": "Asymmetric facial features, extra fingers or limbs, teeth that appear blurry or malformed.", + "number_of_subjects": 0, + "number_of_arms": 0, + "number_of_legs": 0 + } + ], + "background_setting": "A poorly rendered, flat background with visible seams, repeated textures, and inconsistent depth cues. The environment lacks volumetric depth and appears as a painted backdrop rather than a three-dimensional space. Vegetation looks like flat cutouts with no volumetric depth. The background appears to have been composited from multiple source materials at different resolutions, creating visible seams and edge artifacts where elements meet. Textures swim and shift across surfaces in a way that breaks the illusion of solidity — patterns drift laterally rather than staying anchored to the geometry they belong to. Background elements flicker in and out of existence between frames, particularly at the edges of the field of view. The rendering resolution is visibly lower for distant elements, creating a jarring transition between near and far objects. Cloud textures repeat obviously in the sky with visible tiling. Water surfaces lack proper reflection and refraction, appearing as flat animated textures. Fog and atmospheric effects pop in and out rather than smoothly transitioning. Trees and vegetation exhibit obvious LOD (level-of-detail) switching. Building facades have inconsistent window spacing and pattern repetition. The overall scene feels like a poorly assembled collage of individually rendered elements rather than a coherent whole.", + "lighting": { + "conditions": "Harsh, flat lighting with no natural variation. The scene appears uniformly lit as if by a single overhead fluorescent light, removing all sense of depth and atmosphere.", + "direction": "Inconsistent light sources — shadows point in multiple contradictory directions, breaking physical plausibility.", + "shadows": "Hard-edged, unrealistic shadows that pop in and out of existence between frames. Some objects cast no shadows while others have impossibly dark ones that don't animate smoothly with the object's motion. Shadow edges exhibit visible staircase aliasing artifacts. Shadow maps appear to have been rendered at extremely low resolution, creating blocky patterns. Self-shadowing on characters shows visible peter-panning artifacts where shadows detach from their source. Contact shadows between objects and the ground appear and disappear as objects move slightly. Shadow color is pure black with no ambient contribution, creating an unnaturally harsh contrast that flattens the image. Multiple shadow cascades have visible boundaries where resolution changes. The shadow rendering appears to be temporally unstable — even static objects have shadows that shimmer and crawl frame to frame, breaking the illusion of a stable light source.", + "illumination_effect": "No bounce light, no ambient occlusion, no subtle color interactions between surfaces. The scene looks like a poorly lit 3D render from the early 2000s." + }, + "aesthetics": { + "composition": "Cluttered, poorly framed composition with no clear focal point. Important elements are cut off by the frame edges. The rule of thirds is completely ignored, leading to an unbalanced and visually unpleasant arrangement.", + "color_scheme": "Oversaturated, garish colors that clash violently. Color banding is visible in gradient areas. The overall palette feels artificial and digitally processed rather than natural.", + "mood_atmosphere": "Unsettling, uncanny atmosphere that fails to evoke any intended emotional response. The scene feels lifeless and sterile despite attempting to portray dynamic action.", + "patterns": "Visible tiling artifacts in textures, moiré patterns, and aliasing on edges." + }, + "cinematography": { + "camera_motion": "Extremely shaky, unstable camera with visible rolling shutter artifacts. The motion is jerky and discontinuous, causing motion sickness and making the scene impossible to follow. The camera also executes an over-aggressive, unrelenting push-in: it dollies and optically zooms toward the subject far too fast — magnifying the scene roughly 2.5x to 6x within a few seconds — combined with a large, uncontrolled lateral drift, so the framing never settles. Instead of holding a stable composition, the viewpoint continuously accelerates forward and slides sideways, blowing past the intended framing into an unwanted extreme close-up by the end of the clip. This push-in is a distinct failure mode from the shake/jitter described above — it has little to no rotational wobble — a smooth but excessive, monotonic magnitude of forward dolly, zoom, and translation that never eases off, never holds, and never returns to a steady frame. On scenes with strong perspective lines (conveyor belts, quarries, aerial industrial sites, long corridors) the runaway zoom-and-translate makes the perspective rush inward, reading as erratic, claustrophobic, nauseating camerawork.", + "framing": "Poorly framed shots that cut off important elements and include unnecessary empty space. The shot also relentlessly tightens into an unintended extreme close-up as the camera barrels forward, cropping out the intended subject and context because the push-in never stops.", + "camera_angle": "Awkward, disorienting camera angles that provide no useful spatial information about the scene. The camera path exhibits visible mathematical artifacts suggesting simple interpolation between keyframes rather than natural camera operation. Camera motion is completely disconnected from the scene content — panning away from action, dollying during dialogue, and shaking during still moments. The camera appears to pass through solid objects occasionally. Zoom is applied digitally rather than optically, revealing progressively worse resolution. Camera motion exhibits non-physical acceleration profiles — instant starts and stops rather than smooth ease-in/ease-out. Rolling shutter simulation is applied inconsistently, present in some frames but not others. The camera occasionally exhibits impossible motion like teleporting between positions. Virtual camera stabilization creates an uncanny floating sensation disconnected from any physical camera rig.", + "depth_of_field": "Uniform focus throughout, creating a flat, documentary-like appearance with no cinematic depth separation.", + "focus": "Soft, out-of-focus imagery with visible chromatic aberration and lens distortion that was not corrected in post-processing.", + "lens_focal_length": "Inappropriate focal length causing barrel distortion and unnatural perspective compression." + }, + "style_medium": "Low quality compressed digital video with visible encoding artifacts", + "artistic_style": "Amateur, unpolished with inconsistent visual style", + "context": "A poorly produced video with numerous technical and artistic flaws that detract from any intended narrative or visual impact.", + "actions": [ + { + "time": "0:00-0:02", + "description": "As the camera starts moving, rigid foreground objects (cars, signs) warp and change shape; straight painted road lines bend and smear." + }, + { + "time": "0:02-0:04", + "description": "A clearly-present background object vanishes without occlusion while a new object pops into the frame edge; feet/wheels slide over the ground without contact." + }, + { + "time": "0:04-0:06", + "description": "A hand manipulating an object fuses with it into a morphing blob; poured/chopped material changes mass, portions disappearing and multiplying." + }, + { + "time": "0:06-0:08", + "description": "Thin structures (wires, lane lines, foliage) flicker and shimmer; object count and world geometry no longer match the opening frame." + }, + { + "time": "0:00-0:06", + "description": "Throughout, the camera pushes in far too aggressively — a fast continuous forward dolly and zoom (about 2.5x-6x) with a large lateral drift and no rotational shake — never holding a steady framing and ending in an unwanted extreme close-up." + } + ], + "text_and_signage_elements": [], + "segments": [ + { + "segment_index": 0, + "time_range": "0:00-0:04", + "description": "Camera-motion segment where rigid-body stability collapses: vehicles, signage, and road markings morph, warp, and slide as the viewpoint translates.", + "key_changes": "World geometry un-locks — ground plane and lane lines drift; a background object vanishes and another appears without cause.", + "camera": "Forward-moving camera that drives rigid-object deformation; simultaneously an over-fast forward dolly-and-zoom with lateral drift that magnifies the scene far too quickly and never holds a stable frame." + }, + { + "segment_index": 1, + "time_range": "0:04-0:08", + "description": "Manipulation and material-transfer segment where hand-object contact fails.", + "key_changes": "Hand and handled object merge into a morphing blob; transferred food/liquid violates mass conservation, portions vanishing and multiplying; thin high-frequency structures flicker.", + "camera": "Unstable framing with incorrect motion blur; the excessive push-in continues, sliding sideways and blowing past the composition into an extreme close-up." + } + ], + "transitions": [], + "temporal_caption": "The clip opens with objects appearing sharp and stable, but as the camera moves forward every rigid object begins to betray its geometry: parked cars warp and bulge, painted lane markings smear and slide across the asphalt, stop signs and poles bend and reshape frame to frame. Midway, a vehicle that was clearly present silently vanishes while a new object pops into existence at the frame edge without cause. Hands manipulating objects fuse with them into a morphing blob; poured or chopped food changes mass and volume, portions disappearing and multiplying. Thin structures — wires, foliage, railings, lane lines — flicker and shimmer throughout. Feet, wheels, and paws glide over the ground without firm contact. By the end, world geometry, object count, and material mass are all inconsistent with how the clip began. Over the whole clip the camera also barrels forward far too fast: a smooth, unrelenting push-in — fast forward dolly plus optical zoom, roughly 2.5x to 6x magnification in a few seconds — paired with a large lateral drift, with essentially no rotational shake. The framing never stabilizes; the viewpoint continuously accelerates past the intended composition and collapses into an uncomfortable extreme close-up by the end, which on industrial and aerial scenes with strong perspective lines reads as erratic, disorienting camerawork.", + "audio_description": "", + "physical_realism": "No adherence to physical laws. Objects defy gravity, pass through solid surfaces, and change mass and momentum without cause. Fluid dynamics, cloth simulation, and rigid body physics are all fundamentally broken. Furthermore, conservation of energy is violated as objects gain or lose kinetic energy spontaneously. Elastic collisions produce inelastic results and vice versa. Surface friction is inconsistent — objects slide on rough surfaces while sticking to smooth ones. Air resistance appears to affect only some objects while others move through the atmosphere unimpeded. Rigid bodies morph under camera motion; objects slide without ground contact; mass is not conserved in transfer; objects enter/vanish breaking permanence; thin structures flicker." +} diff --git a/checkpoints/balanced288/chat_template.jinja b/checkpoints/balanced288/chat_template.jinja new file mode 100644 index 0000000000000000000000000000000000000000..0a3d5967de71577bd2bb20994cee54c39bed63ba --- /dev/null +++ b/checkpoints/balanced288/chat_template.jinja @@ -0,0 +1,324 @@ + +{% macro render_extra_keys(json_dict, handled_keys) %} + {%- if json_dict is mapping %} + {%- for json_key in json_dict if json_key not in handled_keys %} + {%- if json_dict[json_key] is mapping or (json_dict[json_key] is sequence and json_dict[json_key] is not string) %} + {{- ' +<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '' }} + {%- else %} + {{-' +<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '' }} + {%- endif %} + {%- endfor %} + {%- endif %} +{% endmacro %} +{%- set enable_thinking = enable_thinking if enable_thinking is defined else True %} +{%- set truncate_history_thinking = truncate_history_thinking if truncate_history_thinking is defined else True %} + +{%- set ns = namespace(last_user_idx = -1) %} +{%- set loop_messages = messages %} +{%- for m in loop_messages %} + {%- if m["role"] == "user" %} + {%- set ns.last_user_idx = loop.index0 %} + {%- endif %} +{%- endfor %} + +{%- if messages[0]["role"] == "system" %} + {%- if messages[0]["content"] is string %} + {%- set system_message = messages[0]["content"] %} + {%- else %} + {%- set ns = namespace(system_text="") %} + {%- for part in messages[0]["content"] %} + {%- if 'text' in part %} + {%- set ns.system_text = ns.system_text + part.text %} + {%- endif %} + {%- endfor %} + {%- set system_message = ns.system_text %} + {%- endif %} + + {%- set loop_messages = messages[1:] %} +{%- else %} + {%- set system_message = "" %} + {%- set loop_messages = messages %} +{%- endif %} +{%- if not tools is defined %} + {%- set tools = [] %} +{%- endif %} +{# Recompute last_user_idx relative to loop_messages after handling system #} +{%- set ns = namespace(last_user_idx = -1) %} +{%- for m in loop_messages %} + {%- if m["role"] == "user" %} + {%- set ns.last_user_idx = loop.index0 %} + {%- endif %} +{%- endfor %} +{%- if system_message is defined %} + {{- "<|im_start|>system +" + system_message }} +{%- else %} + {%- if tools is iterable and tools | length > 0 %} + {{- "<|im_start|>system +" }} + {%- endif %} +{%- endif %} +{%- if tools is iterable and tools | length > 0 %} + {%- if system_message is defined and system_message | length > 0 %} + {{- " + +" }} + {%- endif %} + {{- "# Tools + +You have access to the following functions: + +" }} + {{- "" }} + {%- for tool in tools %} + {%- if tool.function is defined %} + {%- set tool = tool.function %} + {%- endif %} + {{- " + +" ~ tool.name ~ "" }} + {%- if tool.description is defined %} + {{- ' +' ~ (tool.description | trim) ~ '' }} + {%- endif %} + {{- ' +' }} + {%- if tool.parameters is defined and tool.parameters is mapping and tool.parameters.properties is defined and tool.parameters.properties is mapping %} + {%- for param_name, param_fields in tool.parameters.properties|items %} + {{- ' +' }} + {{- ' +' ~ param_name ~ '' }} + {%- if param_fields.type is defined %} + {{- ' +' ~ (param_fields.type | string) ~ '' }} + {%- endif %} + {%- if param_fields.description is defined %} + {{- ' +' ~ (param_fields.description | trim) ~ '' }} + {%- endif %} + {%- if param_fields.enum is defined %} + {{- ' +' ~ (param_fields.enum | tojson | safe) ~ '' }} + {%- endif %} + {%- set handled_keys = ['name', 'type', 'description', 'enum'] %} + {{- render_extra_keys(param_fields, handled_keys) }} + {{- ' +' }} + {%- endfor %} + {%- endif %} + {% set handled_keys = ['type', 'properties', 'required'] %} + {{- render_extra_keys(tool.parameters, handled_keys) }} + {%- if tool.parameters is defined and tool.parameters.required is defined %} + {{- ' +' ~ (tool.parameters.required | tojson | safe) ~ '' }} + {%- endif %} + {{- ' +' }} + {%- set handled_keys = ['type', 'name', 'description', 'parameters'] %} + {{- render_extra_keys(tool, handled_keys) }} + {{- ' +' }} + {%- endfor %} + {{- " +" }} + + {{- ' + +If you choose to call a function ONLY reply in the following format with NO suffix: + + + + +value_1 + + +This is the value for the second parameter +that can span +multiple lines + + + + + +Reminder: +- Function calls MUST follow the specified format: an inner block must be nested within XML tags +- Required parameters MUST be specified +- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after +- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls +' }} +{%- endif %} + + +{%- if system_message is defined %} + {{- '<|im_end|> +' }} +{%- else %} + {%- if tools is iterable and tools | length > 0 %} + {{- '<|im_end|> +' }} + {%- endif %} +{%- endif %} + +{%- set image_count = namespace(value=0) %} +{%- set video_count = namespace(value=0) %} +{%- for message in loop_messages %} + {%- if message.role == "assistant" %} + {# Add reasoning content in to content field for unified processing below. #} + {%- set text_content = namespace(text_content="") -%} + {%- if message.content is string %} + {%- set text_content.text_content = message.content %} + {%- else %} + {%- for content in message.content %} + {%- if 'text' in content %} + {%- set text_content.text_content = content.text %} + {%- break %} + {%- endif %} + {%- endfor %} + {%- endif %} + {%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %} + {%- set content = " +" ~ message.reasoning_content ~ " + +" ~ (text_content.text_content | default('', true)) %} + {%- else %} + {%- set content = text_content.text_content | default('', true) %} + {# Allow downstream logic to to take care of broken thought, only handle coherent reasoning here. #} + {%- if '' not in content and '' not in content %} + {%- set content = "" ~ content %} + {%- else %} + {%- set content = content %} + {%- endif -%} + {%- endif %} + {%- if message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %} + {# Assistant message has tool calls. #} + {{- '<|im_start|>assistant +' }} + {%- set include_content = not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %} + {%- if content is string and content | trim | length > 0 %} + {%- if include_content %} + {{- (content | trim) ~ ' +' -}} + {%- else %} + {%- set c = (content | string) %} + {%- if '' in c %} + {# Keep only content after the last closing think. Also generation prompt causes this. #} + {%- set c = c.split('')[-1] %} + {%- elif '' in c %} + {# If was opened but never closed, drop the trailing think segment #} + {%- set c = c.split('')[0] %} + {%- endif %} + {%- set c = "" ~ c | trim %} + {%- if c | length > 0 %} + {{- c ~ ' +' -}} + {%- endif %} + {%- endif %} + {%- else %} + {{- "" -}} + {%- endif %} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function is defined %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- ' + +' -}} + {%- if tool_call.arguments is defined %} + {%- for args_name, args_value in tool_call.arguments|items %} + {{- ' +' -}} + {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %} + {{- args_value ~ ' + +' -}} + {%- endfor %} + {%- endif %} + {{- ' + +' -}} + {%- endfor %} + {{- '<|im_end|> +' }} + {%- else %} + {# Assistant message doesn't have tool calls. #} + {%- if not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %} + {{- '<|im_start|>assistant +' ~ (content | default('', true) | string | trim) ~ '<|im_end|> +' }} + {%- else %} + {%- set c = (content | default('', true) | string) %} + {%- if '' in c and '' in c %} + {%- set c = "" ~ c.split('')[-1] %} + {%- endif %} + {%- set c = c | trim %} + {%- if c | length > 0 %} + {{- '<|im_start|>assistant +' ~ c ~ '<|im_end|> +' }} + {%- else %} + {{- '<|im_start|>assistant +<|im_end|> +' }} + {%- endif %} + {%- endif %} + {%- endif %} + {%- elif message.role == "user" or message.role == "system" %} + {{- '<|im_start|>' + message.role + ' +' }} + {%- if message.content is string %} + {{- message.content }} + {%- else %} + {%- for content in message.content %} + {%- if content.type == 'image' or 'image' in content or 'image_url' in content %} + {%- set image_count.value = image_count.value + 1 %} + {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%} + <|vision_start|><|image_pad|><|vision_end|> + {%- elif content.type == 'video' or 'video' in content %} + {%- set video_count.value = video_count.value + 1 %} + {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%} + <|vision_start|><|video_pad|><|vision_end|> + {%- elif 'text' in content %} + {{- content.text }} + {%- endif %} + {%- endfor %} + {%- endif %} + {{- '<|im_end|> +' }} + {%- elif message.role == "tool" %} + {%- if loop.previtem and loop.previtem.role != "tool" %} + {{- '<|im_start|>user +' }} + {%- endif %} + {{- ' +' }} + {{- message.content }} + {{- ' + +' }} + {%- if not loop.last and loop.nextitem.role != "tool" %} + {{- '<|im_end|> +' }} + {%- elif loop.last %} + {{- '<|im_end|> +' }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + ' +' + message.content + '<|im_end|> +' }} + {%- endif %} +{%- endfor %} + +{%- if add_generation_prompt %} + {%- if enable_thinking %} + {{- '<|im_start|>assistant + +' }} + {%- else %} + {{- '<|im_start|>assistant +' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/checkpoints/balanced288/config.json b/checkpoints/balanced288/config.json new file mode 100644 index 0000000000000000000000000000000000000000..29fd08f0530a641349fd642d586f1c2aa6779e74 --- /dev/null +++ b/checkpoints/balanced288/config.json @@ -0,0 +1,69 @@ +{ + "allow_patterns_overrides": [ + "*/*.safetensors" + ], + "architectures": [ + "Cosmos3EdgeForConditionalGeneration" + ], + "image_token_id": 19, + "model_type": "cosmos3_edge", + "projector_config": { + "input_hidden_size": 1152, + "merger_intermediate_size": 11520, + "model_type": "cosmos3_edge_projector", + "out_hidden_size": 2048, + "spatial_merge_size": 2, + "use_postshuffle_norm": false + }, + "projector_hidden_size": 11520, + "text_config": { + "attention_bias": false, + "attention_dropout": 0.0, + "dtype": "bfloat16", + "head_dim": 128, + "hidden_act": "relu2", + "hidden_dropout": 0.0, + "hidden_size": 2048, + "initializer_range": 0.02, + "intermediate_size": 9216, + "max_position_embeddings": 131072, + "mlp_bias": false, + "model_type": "cosmos3_edge_text", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "residual_in_fp32": false, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "mrope_section": [ + 24, + 20, + 20 + ], + "rope_theta": 100000000, + "rope_type": "default" + }, + "sliding_window": null, + "use_cache": true, + "vocab_size": 131072 + }, + "tie_word_embeddings": false, + "transformers_version": "4.57.6", + "video_token_id": 18, + "vision_config": { + "attention_dropout": 0.0, + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_type": "cosmos3_edge_vision", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 27, + "num_patches": 256, + "patch_size": 16, + "spatial_merge_size": 2 + }, + "vision_end_token_id": 21, + "vision_start_token_id": 20 +} diff --git a/checkpoints/balanced288/generation_config.json b/checkpoints/balanced288/generation_config.json new file mode 100644 index 0000000000000000000000000000000000000000..43db172db8643a9306fc432d12536a5225cddbbd --- /dev/null +++ b/checkpoints/balanced288/generation_config.json @@ -0,0 +1,8 @@ +{ + "_from_model_config": true, + "bos_token_id": 1, + "do_sample": true, + "eos_token_id": 11, + "pad_token_id": 0, + "transformers_version": "4.57.6" +} diff --git a/checkpoints/balanced288/model.safetensors.index.json b/checkpoints/balanced288/model.safetensors.index.json new file mode 100644 index 0000000000000000000000000000000000000000..36288b02b48fbf26c5c62b5b088e002e6deed7a9 --- /dev/null +++ b/checkpoints/balanced288/model.safetensors.index.json @@ -0,0 +1,677 @@ +{ + "metadata": { + "total_size": 4871232992 + }, + "weight_map": { + "model.visual.embeddings.patch_embedding.weight": "00000.safetensors", + "model.visual.embeddings.patch_embedding.bias": "00000.safetensors", + 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"_id": "6a458c7ce6a31bbaf190d572", + "id": "nvidia/Cosmos3-Edge", + "private": false, + "library_name": "cosmos", + "tags": [ + "cosmos", + "diffusers", + "safetensors", + "cosmos3_edge", + "nvidia", + "cosmos3", + "arxiv:2405.14867", + "license:other", + "deploy:azure", + "deploy:sagemaker", + "region:us" + ], + "downloads": 1615933, + "likes": 199, + "modelId": "nvidia/Cosmos3-Edge", + "author": "nvidia", + "sha": "a9d944e2c6a1bf9f48b92ad16348e70c5f1836ba", + "lastModified": "2026-08-26T21:26:27.000Z", + "gated": false, + "disabled": false, + "model-index": null, + "config": { + "architectures": [ + "Cosmos3EdgeForConditionalGeneration" + ], + "model_type": "cosmos3_edge", + "tokenizer_config": { + "bos_token": "", + "eos_token": "<|im_end|>", + "pad_token": "<|im_end|>", + "unk_token": "" + }, + "chat_template_jinja": "\n{% macro render_extra_keys(json_dict, handled_keys) %}\n {%- if json_dict is mapping %}\n {%- for json_key in json_dict if json_key not in handled_keys %}\n {%- if json_dict[json_key] is mapping or (json_dict[json_key] is sequence and json_dict[json_key] is not string) %}\n {{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '' }}\n {%- else %}\n {{-'\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '' }}\n {%- endif %}\n {%- endfor %}\n {%- endif %}\n{% endmacro %}\n{%- set enable_thinking = enable_thinking if enable_thinking is defined else True %}\n{%- set truncate_history_thinking = truncate_history_thinking if truncate_history_thinking is defined else True %}\n\n{%- set ns = namespace(last_user_idx = -1) %}\n{%- set loop_messages = messages %}\n{%- for m in loop_messages %}\n {%- if m[\"role\"] == \"user\" %}\n {%- set ns.last_user_idx = loop.index0 %}\n {%- endif %}\n{%- endfor %}\n\n{%- if messages[0][\"role\"] == \"system\" %}\n {%- if messages[0][\"content\"] is string %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- else %}\n {%- set ns = namespace(system_text=\"\") %}\n {%- for part in messages[0][\"content\"] %}\n {%- if 'text' in part %}\n {%- set ns.system_text = ns.system_text + part.text %}\n {%- endif %}\n {%- endfor %}\n {%- set system_message = ns.system_text %}\n {%- endif %}\n\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = [] %}\n{%- endif %}\n{# Recompute last_user_idx relative to loop_messages after handling system #}\n{%- set ns = namespace(last_user_idx = -1) %}\n{%- for m in loop_messages %}\n {%- if m[\"role\"] == \"user\" %}\n {%- set ns.last_user_idx = loop.index0 %}\n {%- endif %}\n{%- endfor %}\n{%- if system_message is defined %}\n {{- \"<|im_start|>system\n\" + system_message }}\n{%- else %}\n {%- if tools is iterable and tools | length > 0 %}\n {{- \"<|im_start|>system\n\" }}\n {%- endif %}\n{%- endif %}\n{%- if tools is iterable and tools | length > 0 %}\n {%- if system_message is defined and system_message | length > 0 %}\n {{- \"\n\n\" }}\n {%- endif %}\n {{- \"# Tools\n\nYou have access to the following functions:\n\n\" }}\n {{- \"\" }}\n {%- for tool in tools %}\n {%- if tool.function is defined %}\n {%- set tool = tool.function %}\n {%- endif %}\n {{- \"\n\n\" ~ tool.name ~ \"\" }}\n {%- if tool.description is defined %}\n {{- '\n' ~ (tool.description | trim) ~ '' }}\n {%- endif %}\n {{- '\n' }}\n {%- if tool.parameters is defined and tool.parameters is mapping and tool.parameters.properties is defined and tool.parameters.properties is mapping %}\n {%- for param_name, param_fields in tool.parameters.properties|items %}\n {{- '\n' }}\n {{- '\n' ~ param_name ~ '' }}\n {%- if param_fields.type is defined %}\n {{- '\n' ~ (param_fields.type | string) ~ '' }}\n {%- endif %}\n {%- if param_fields.description is defined %}\n {{- '\n' ~ (param_fields.description | trim) ~ '' }}\n {%- endif %}\n {%- if param_fields.enum is defined %}\n {{- '\n' ~ (param_fields.enum | tojson | safe) ~ '' }}\n {%- endif %}\n {%- set handled_keys = ['name', 'type', 'description', 'enum'] %}\n {{- render_extra_keys(param_fields, handled_keys) }}\n {{- '\n' }}\n {%- endfor %}\n {%- endif %}\n {% set handled_keys = ['type', 'properties', 'required'] %}\n {{- render_extra_keys(tool.parameters, handled_keys) }}\n {%- if tool.parameters is defined and tool.parameters.required is defined %}\n {{- '\n' ~ (tool.parameters.required | tojson | safe) ~ '' }}\n {%- endif %}\n {{- '\n' }}\n {%- set handled_keys = ['type', 'name', 'description', 'parameters'] %}\n {{- render_extra_keys(tool, handled_keys) }}\n {{- '\n' }}\n {%- endfor %}\n {{- \"\n\" }}\n\n {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n\n\n\nvalue_1\n\n\nThis is the value for the second parameter\nthat can span\nmultiple lines\n\n\n\n\n\nReminder:\n- Function calls MUST follow the specified format: an inner block must be nested within XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n' }}\n{%- endif %}\n\n\n{%- if system_message is defined %}\n {{- '<|im_end|>\n' }}\n{%- else %}\n {%- if tools is iterable and tools | length > 0 %}\n {{- '<|im_end|>\n' }}\n {%- endif %}\n{%- endif %}\n\n{%- set image_count = namespace(value=0) %}\n{%- set video_count = namespace(value=0) %}\n{%- for message in loop_messages %}\n {%- if message.role == \"assistant\" %}\n {# Add reasoning content in to content field for unified processing below. #}\n {%- set text_content = namespace(text_content=\"\") -%}\n {%- if message.content is string %}\n {%- set text_content.text_content = message.content %}\n {%- else %}\n {%- for content in message.content %}\n {%- if 'text' in content %}\n {%- set text_content.text_content = content.text %}\n {%- break %}\n {%- endif %}\n {%- endfor %}\n {%- endif %}\n {%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %}\n {%- set content = \"\n\" ~ message.reasoning_content ~ \"\n\n\" ~ (text_content.text_content | default('', true)) %}\n {%- else %}\n {%- set content = text_content.text_content | default('', true) %}\n {# Allow downstream logic to to take care of broken thought, only handle coherent reasoning here. #}\n {%- if '' not in content and '' not in content %}\n {%- set content = \"\" ~ content %}\n {%- else %}\n {%- set content = content %}\n {%- endif -%}\n {%- endif %}\n {%- if message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %}\n {# Assistant message has tool calls. #}\n {{- '<|im_start|>assistant\n' }}\n {%- set include_content = not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %}\n {%- if content is string and content | trim | length > 0 %}\n {%- if include_content %}\n {{- (content | trim) ~ '\n' -}}\n {%- else %}\n {%- set c = (content | string) %}\n {%- if '' in c %}\n {# Keep only content after the last closing think. Also generation prompt causes this. #}\n {%- set c = c.split('')[-1] %}\n {%- elif '' in c %}\n {# If was opened but never closed, drop the trailing think segment #}\n {%- set c = c.split('')[0] %}\n {%- endif %}\n {%- set c = \"\" ~ c | trim %}\n {%- if c | length > 0 %}\n {{- c ~ '\n' -}}\n {%- endif %}\n {%- endif %}\n {%- else %}\n {{- \"\" -}}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\n\n' -}}\n {%- if tool_call.arguments is defined %}\n {%- for args_name, args_value in tool_call.arguments|items %}\n {{- '\n' -}}\n {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}\n {{- args_value ~ '\n\n' -}}\n {%- endfor %}\n {%- endif %}\n {{- '\n\n' -}}\n {%- endfor %}\n {{- '<|im_end|>\n' }}\n {%- else %}\n {# Assistant message doesn't have tool calls. #}\n {%- if not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %}\n {{- '<|im_start|>assistant\n' ~ (content | default('', true) | string | trim) ~ '<|im_end|>\n' }}\n {%- else %}\n {%- set c = (content | default('', true) | string) %}\n {%- if '' in c and '' in c %}\n {%- set c = \"\" ~ c.split('')[-1] %}\n {%- endif %}\n {%- set c = c | trim %}\n {%- if c | length > 0 %}\n {{- '<|im_start|>assistant\n' ~ c ~ '<|im_end|>\n' }}\n {%- else %}\n {{- '<|im_start|>assistant\n<|im_end|>\n' }}\n {%- endif %}\n {%- endif %}\n {%- endif %}\n {%- elif message.role == \"user\" or message.role == \"system\" %}\n {{- '<|im_start|>' + message.role + '\n' }}\n {%- if message.content is string %}\n {{- message.content }}\n {%- else %}\n {%- for content in message.content %}\n {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}\n {%- set image_count.value = image_count.value + 1 %}\n {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}\n 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file mode 100644 index 0000000000000000000000000000000000000000..15ff9ea5ad233da518fea09808397a3e6ebf0e5f --- /dev/null +++ b/checkpoints/mixed240/assets/example_i2v_prompt.json @@ -0,0 +1,124 @@ +{ + "subjects": [ + { + "description": "A car (viewed from the driver's perspective/dashcam) traveling along a coastal mountain road. The vehicle's grey dashboard and windshield frame are visible at the bottom of the frame.", + "appearance_details": "Grey/dark interior dashboard, windshield wipers visible, clean windshield providing clear view of the road ahead", + "relationship": "Primary subject and point-of-view vehicle navigating the mountain road", + "location": "Bottom foreground, providing the POV perspective", + "relative_size": "Large within frame (dashboard occupies bottom portion)", + "orientation": "Facing forward along the road direction", + "pose": "", + "action": "Driving fast along the curved coastal road, then making a sudden emergency stop", + "state_changes": "Transitions from fast forward motion to abrupt deceleration and complete stop", + "clothing": "", + "expression": "", + "gender": "", + "age": "", + "skin_tone_and_texture": "", + "facial_features": "", + "number_of_subjects": 1, + "number_of_arms": 0, + "number_of_legs": 0 + }, + { + "description": "Falling rocks and debris from the steep rocky mountain cliff face, creating a landslide that tumbles down toward the road surface", + "appearance_details": "Grey and brown angular rocks of various sizes, accompanied by dust and smaller debris, mixed with displaced green vegetation", + "relationship": "Obstacle that forces the car to make an emergency stop", + "location": "Center and center-right of frame, falling from the mountain cliff face toward the road", + "relative_size": "Medium within frame, growing larger as rocks approach the road", + "orientation": "Falling downward from the cliff face", + "pose": "", + "action": "Tumbling and cascading down the steep rocky mountainside toward the road", + "state_changes": "Begins as small movement on the cliff face, escalates into a significant rockfall with dust cloud", + "clothing": "", + "expression": "", + "gender": "", + "age": "", + "skin_tone_and_texture": "", + "facial_features": "", + "number_of_subjects": 0, + "number_of_arms": 0, + "number_of_legs": 0 + } + ], + "background_setting": "A dramatic coastal mountain highway carved along steep rocky cliffs. The road is a well-maintained two-lane highway with white dashed center lines, solid white edge lines, and raised reflective road markers. A metal guardrail runs along the right side protecting from a drop toward the ocean. The mountain rises steeply on the left with exposed grey and brown rock faces partially covered with lush green native vegetation including ferns and shrubs. The ocean is visible in the distance to the right under a partly cloudy blue sky. The road curves sharply to the left around the mountain headland.", + "lighting": { + "conditions": "Bright natural daylight with partly cloudy skies, strong ambient illumination", + "direction": "Top-lit and slightly front-lit from the sun positioned high in the sky", + "shadows": "Moderate shadows cast by the mountain cliff creating areas of shade on the road and vegetation, soft shadows from clouds", + "illumination_effect": "Bright, clear visibility with good contrast between the sunlit road surface and shaded mountain areas, natural coastal light" + }, + "aesthetics": { + "composition": "Driver's POV/dashcam perspective with the road curving ahead as the central leading line, mountain dominating the upper-left portion, ocean visible to the right, dashboard framing the bottom", + "color_scheme": "Dominant greens from vegetation, grey-brown rocky cliff faces, dark grey asphalt road with white markings, blue sky and ocean, yellow warning sign providing accent color", + "mood_atmosphere": "Initially scenic and adventurous, transitioning to tense and dangerous as the landslide occurs", + "patterns": "Repeating metal guardrail posts along the right side, dashed center line markings, layered rock strata on the cliff face" + }, + "cinematography": { + "camera_motion": "Forward-moving dashcam perspective with slight vibration from vehicle speed, sudden forward lurch during emergency braking", + "framing": "Wide shot from driver's POV through windshield", + "camera_angle": "Eye-level from driver's seated position", + "depth_of_field": "Deep", + "focus": "Road ahead and mountain cliff face in sharp focus", + "lens_focal_length": "Wide-angle (approximately 28-35mm equivalent)" + }, + "style_medium": "Live-action video", + "artistic_style": "Realistic dashcam footage, dramatic documentary style", + "context": "Dashcam footage capturing a dangerous landslide event on a coastal mountain highway, requiring emergency driving maneuver", + "actions": [ + { + "time": "0:00-0:02", + "description": "The car drives fast along the winding coastal mountain road, approaching the left curve with the road rushing beneath" + }, + { + "time": "0:02-0:04", + "description": "Rocks begin to dislodge from the steep cliff face ahead, tumbling down with increasing intensity as a landslide develops, dust rising from impacts" + }, + { + "time": "0:04-0:05", + "description": "The car makes a sudden emergency stop, the camera lurching forward from the deceleration as rocks and debris continue falling onto the road ahead" + } + ], + "text_and_signage_elements": [ + { + "text": "Left curve arrow", + "category": "scene_sign", + "appearance": "Black arrow on yellow diamond-shaped warning sign, mounted on a metal pole", + "spatial_temporal": "Left side of road, visible from 0:00 and passing by as car moves forward", + "context": "Road warning sign indicating a sharp left curve ahead, standard highway signage" + } + ], + "segments": [ + { + "segment_index": 0, + "time_range": "0:00-0:02", + "description": "The car speeds along the scenic coastal mountain highway, navigating the curve. The road surface rushes beneath, white lines blur slightly from speed, and the mountain looms ahead.", + "key_changes": "Road perspective shifts as car navigates the curve, passing the warning sign, guardrail moves in peripheral view", + "camera": "Forward-moving dashcam with slight road vibration, steady forward momentum" + }, + { + "segment_index": 1, + "time_range": "0:02-0:04", + "description": "Rocks begin breaking loose from the cliff face ahead. Small stones first, then larger boulders cascade down the mountainside creating a growing dust cloud and debris field on and near the road.", + "key_changes": "Mountain transitions from static backdrop to active hazard, dust cloud forms, debris appears on road surface ahead", + "camera": "Still moving forward but beginning to slow, slight camera shake increases" + }, + { + "segment_index": 2, + "time_range": "0:04-0:05", + "description": "The car performs an emergency stop. The camera pitches forward from sudden braking force. The vehicle comes to a complete halt with the landslide debris visible on the road ahead, dust still settling.", + "key_changes": "Rapid deceleration, camera lurches forward then settles, motion stops completely, dust and small rocks still visible falling", + "camera": "Abrupt forward pitch from braking, then stabilizes to static as vehicle stops" + } + ], + "transitions": [], + "temporal_caption": "The video begins with a dashcam view of a car driving fast along a scenic coastal mountain road, the asphalt rushing beneath and a sharp left curve sign visible on the left. The car navigates the curve at speed with the steep rocky cliff towering above and the ocean visible to the right. Around the 2-second mark, small rocks begin to dislodge from the cliff face ahead, quickly escalating into a significant rockfall with larger boulders tumbling down the mountainside and a dust cloud forming. By second 4, the driver reacts with emergency braking - the camera lurches forward dramatically from the sudden deceleration. The car comes to a complete stop between seconds 4 and 5, with the road ahead partially blocked by fallen rocks and debris, dust still settling in the air around the landslide zone.", + "audio_description": "Initially the sound of a car engine at moderate-high RPM and tire noise on asphalt with wind passing over the vehicle. Around second 2, the rumbling and cracking sounds of rocks breaking loose begin, growing louder with impacts of stones hitting the road surface and each other. Loud thuds and crashes as larger rocks land. At second 4, the sharp screech of tires braking hard on asphalt, followed by the settling sounds of smaller pebbles and dust, with distant rumbling of the remaining rockfall subsiding.", + "resolution": { + "W": 1280, + "H": 720 + }, + "aspect_ratio": "16,9", + "duration": "5s", + "fps": 24 + } \ No newline at end of file diff --git a/checkpoints/mixed240/assets/example_reasoning_prompt.json b/checkpoints/mixed240/assets/example_reasoning_prompt.json new file mode 100644 index 0000000000000000000000000000000000000000..3371076f17b1ff8bfff60fb24536b160f637809b --- /dev/null +++ b/checkpoints/mixed240/assets/example_reasoning_prompt.json @@ -0,0 +1,4 @@ +{ + "prompt": "The task is to put flower into the red bottle. Generate a plan consisting of subtasks for accomplish the task.", + "max_tokens": 4096 +} diff --git a/checkpoints/mixed240/assets/negative_prompt.json b/checkpoints/mixed240/assets/negative_prompt.json new file mode 100644 index 0000000000000000000000000000000000000000..b51ecb2195e2f16a89959aa3b757c7d66cac6f22 --- /dev/null +++ b/checkpoints/mixed240/assets/negative_prompt.json @@ -0,0 +1,131 @@ +{ + "subjects": [ + { + "description": "A subject suffering from extreme temporal instability — flickering outlines, morphing silhouette, and visible frame-to-frame discontinuities that make it look like a corrupted video file.", + "appearance_details": "Distorted features, visible compression artifacts, muddy textures lacking fine detail, color bleeding between elements, and unnatural skin tones or surface textures that appear artificial or computer-generated.", + "relationship": "Subjects appear disconnected from the environment, floating or improperly grounded in the scene without proper occlusion or spatial coherence.", + "location": "Subjects are poorly placed within the frame, appearing at awkward positions that violate basic compositional rules.", + "relative_size": "Inconsistent scale relationships between subjects and the environment, with objects appearing too large or too small relative to their surroundings.", + "orientation": "Unnatural orientations that defy physics and spatial logic.", + "pose": "Poses snap between positions with no interpolation, creating a strobe-like effect.", + "action": "Motion is completely incoherent — limbs teleport between positions, movement appears as discrete jumps rather than continuous flow, and speed varies wildly between frames.", + "state_changes": "The subject flickers between completely different visual states every few frames, as if multiple renders were randomly interleaved.", + "clothing": "Garments phase in and out of visibility, textures swim across surfaces untethered to the geometry.", + "expression": "Frozen, uncanny valley expressions or expressions that change abruptly without natural transition.", + "gender": "", + "age": "", + "skin_tone_and_texture": "Waxy, plastic-looking skin with visible artifacts and inconsistent texture resolution across the frame.", + "facial_features": "Asymmetric facial features, extra fingers or limbs, teeth that appear blurry or malformed.", + "number_of_subjects": 0, + "number_of_arms": 0, + "number_of_legs": 0 + }, + { + "description": "A subject with catastrophically broken surface rendering — visible polygon edges, texture seams, UV mapping errors, and normal map inversions.", + "appearance_details": "Distorted features, visible compression artifacts, muddy textures lacking fine detail, color bleeding between elements, and unnatural skin tones or surface textures that appear artificial or computer-generated.", + "relationship": "Subjects appear disconnected from the environment, floating or improperly grounded in the scene without proper occlusion or spatial coherence.", + "location": "Subjects are poorly placed within the frame, appearing at awkward positions that violate basic compositional rules.", + "relative_size": "Inconsistent scale relationships between subjects and the environment, with objects appearing too large or too small relative to their surroundings.", + "orientation": "Unnatural orientations that defy physics and spatial logic.", + "pose": "Static poses reveal texture swimming, moire patterns, and aliased edges on every contour.", + "action": "Movement reveals progressively worse texture breakdown — stretching, tearing, and resolution collapse in deformed regions.", + "state_changes": "Surface materials randomly swap between frames — metal becomes cloth, glass becomes stone, with no transition.", + "clothing": "Fabric renders as a flat solid color with no folds, wrinkles, or material response to form.", + "expression": "Frozen, uncanny valley expressions or expressions that change abruptly without natural transition.", + "gender": "", + "age": "", + "skin_tone_and_texture": "Waxy, plastic-looking skin with visible artifacts and inconsistent texture resolution across the frame.", + "facial_features": "Asymmetric facial features, extra fingers or limbs, teeth that appear blurry or malformed.", + "number_of_subjects": 0, + "number_of_arms": 0, + "number_of_legs": 0 + }, + { + "description": "A subject that appears composited into the scene rather than belonging to it — mismatched lighting direction, wrong color temperature, and edges that bleed into the background.", + "appearance_details": "Distorted features, visible compression artifacts, muddy textures lacking fine detail, color bleeding between elements, and unnatural skin tones or surface textures that appear artificial or computer-generated.", + "relationship": "Subjects appear disconnected from the environment, floating or improperly grounded in the scene without proper occlusion or spatial coherence.", + "location": "Subjects are poorly placed within the frame, appearing at awkward positions that violate basic compositional rules.", + "relative_size": "Inconsistent scale relationships between subjects and the environment, with objects appearing too large or too small relative to their surroundings.", + "orientation": "Unnatural orientations that defy physics and spatial logic.", + "pose": "Proportions shift between frames — limbs lengthen and shorten, heads resize, creating an unsettling instability.", + "action": "Movement creates visible matting artifacts — green screen fringe, edge chattering, and translucent boundaries.", + "state_changes": "The subject randomly changes size, opacity, and z-depth relative to other scene elements.", + "clothing": "Clothing appears to be from a different render pass — different noise level, different motion blur kernel, different color space.", + "expression": "Frozen, uncanny valley expressions or expressions that change abruptly without natural transition.", + "gender": "", + "age": "", + "skin_tone_and_texture": "Waxy, plastic-looking skin with visible artifacts and inconsistent texture resolution across the frame.", + "facial_features": "Asymmetric facial features, extra fingers or limbs, teeth that appear blurry or malformed.", + "number_of_subjects": 0, + "number_of_arms": 0, + "number_of_legs": 0 + } + ], + "background_setting": "A poorly rendered, flat background with visible seams, repeated textures, and inconsistent depth cues. The environment lacks volumetric depth and appears as a painted backdrop rather than a three-dimensional space. Vegetation looks like flat cutouts with no volumetric depth. The background appears to have been composited from multiple source materials at different resolutions, creating visible seams and edge artifacts where elements meet. Textures swim and shift across surfaces in a way that breaks the illusion of solidity — patterns drift laterally rather than staying anchored to the geometry they belong to. Background elements flicker in and out of existence between frames, particularly at the edges of the field of view. The rendering resolution is visibly lower for distant elements, creating a jarring transition between near and far objects. Cloud textures repeat obviously in the sky with visible tiling. Water surfaces lack proper reflection and refraction, appearing as flat animated textures. Fog and atmospheric effects pop in and out rather than smoothly transitioning. Trees and vegetation exhibit obvious LOD (level-of-detail) switching. Building facades have inconsistent window spacing and pattern repetition. The overall scene feels like a poorly assembled collage of individually rendered elements rather than a coherent whole.", + "lighting": { + "conditions": "Harsh, flat lighting with no natural variation. The scene appears uniformly lit as if by a single overhead fluorescent light, removing all sense of depth and atmosphere.", + "direction": "Inconsistent light sources — shadows point in multiple contradictory directions, breaking physical plausibility.", + "shadows": "Hard-edged, unrealistic shadows that pop in and out of existence between frames. Some objects cast no shadows while others have impossibly dark ones that don't animate smoothly with the object's motion. Shadow edges exhibit visible staircase aliasing artifacts. Shadow maps appear to have been rendered at extremely low resolution, creating blocky patterns. Self-shadowing on characters shows visible peter-panning artifacts where shadows detach from their source. Contact shadows between objects and the ground appear and disappear as objects move slightly. Shadow color is pure black with no ambient contribution, creating an unnaturally harsh contrast that flattens the image. Multiple shadow cascades have visible boundaries where resolution changes. The shadow rendering appears to be temporally unstable — even static objects have shadows that shimmer and crawl frame to frame, breaking the illusion of a stable light source.", + "illumination_effect": "No bounce light, no ambient occlusion, no subtle color interactions between surfaces. The scene looks like a poorly lit 3D render from the early 2000s." + }, + "aesthetics": { + "composition": "Cluttered, poorly framed composition with no clear focal point. Important elements are cut off by the frame edges. The rule of thirds is completely ignored, leading to an unbalanced and visually unpleasant arrangement.", + "color_scheme": "Oversaturated, garish colors that clash violently. Color banding is visible in gradient areas. The overall palette feels artificial and digitally processed rather than natural.", + "mood_atmosphere": "Unsettling, uncanny atmosphere that fails to evoke any intended emotional response. The scene feels lifeless and sterile despite attempting to portray dynamic action.", + "patterns": "Visible tiling artifacts in textures, moiré patterns, and aliasing on edges." + }, + "cinematography": { + "camera_motion": "Extremely shaky, unstable camera with visible rolling shutter artifacts. The motion is jerky and discontinuous, causing motion sickness and making the scene impossible to follow. The camera also executes an over-aggressive, unrelenting push-in: it dollies and optically zooms toward the subject far too fast — magnifying the scene roughly 2.5x to 6x within a few seconds — combined with a large, uncontrolled lateral drift, so the framing never settles. Instead of holding a stable composition, the viewpoint continuously accelerates forward and slides sideways, blowing past the intended framing into an unwanted extreme close-up by the end of the clip. This push-in is a distinct failure mode from the shake/jitter described above — it has little to no rotational wobble — a smooth but excessive, monotonic magnitude of forward dolly, zoom, and translation that never eases off, never holds, and never returns to a steady frame. On scenes with strong perspective lines (conveyor belts, quarries, aerial industrial sites, long corridors) the runaway zoom-and-translate makes the perspective rush inward, reading as erratic, claustrophobic, nauseating camerawork.", + "framing": "Poorly framed shots that cut off important elements and include unnecessary empty space. The shot also relentlessly tightens into an unintended extreme close-up as the camera barrels forward, cropping out the intended subject and context because the push-in never stops.", + "camera_angle": "Awkward, disorienting camera angles that provide no useful spatial information about the scene. The camera path exhibits visible mathematical artifacts suggesting simple interpolation between keyframes rather than natural camera operation. Camera motion is completely disconnected from the scene content — panning away from action, dollying during dialogue, and shaking during still moments. The camera appears to pass through solid objects occasionally. Zoom is applied digitally rather than optically, revealing progressively worse resolution. Camera motion exhibits non-physical acceleration profiles — instant starts and stops rather than smooth ease-in/ease-out. Rolling shutter simulation is applied inconsistently, present in some frames but not others. The camera occasionally exhibits impossible motion like teleporting between positions. Virtual camera stabilization creates an uncanny floating sensation disconnected from any physical camera rig.", + "depth_of_field": "Uniform focus throughout, creating a flat, documentary-like appearance with no cinematic depth separation.", + "focus": "Soft, out-of-focus imagery with visible chromatic aberration and lens distortion that was not corrected in post-processing.", + "lens_focal_length": "Inappropriate focal length causing barrel distortion and unnatural perspective compression." + }, + "style_medium": "Low quality compressed digital video with visible encoding artifacts", + "artistic_style": "Amateur, unpolished with inconsistent visual style", + "context": "A poorly produced video with numerous technical and artistic flaws that detract from any intended narrative or visual impact.", + "actions": [ + { + "time": "0:00-0:02", + "description": "As the camera starts moving, rigid foreground objects (cars, signs) warp and change shape; straight painted road lines bend and smear." + }, + { + "time": "0:02-0:04", + "description": "A clearly-present background object vanishes without occlusion while a new object pops into the frame edge; feet/wheels slide over the ground without contact." + }, + { + "time": "0:04-0:06", + "description": "A hand manipulating an object fuses with it into a morphing blob; poured/chopped material changes mass, portions disappearing and multiplying." + }, + { + "time": "0:06-0:08", + "description": "Thin structures (wires, lane lines, foliage) flicker and shimmer; object count and world geometry no longer match the opening frame." + }, + { + "time": "0:00-0:06", + "description": "Throughout, the camera pushes in far too aggressively — a fast continuous forward dolly and zoom (about 2.5x-6x) with a large lateral drift and no rotational shake — never holding a steady framing and ending in an unwanted extreme close-up." + } + ], + "text_and_signage_elements": [], + "segments": [ + { + "segment_index": 0, + "time_range": "0:00-0:04", + "description": "Camera-motion segment where rigid-body stability collapses: vehicles, signage, and road markings morph, warp, and slide as the viewpoint translates.", + "key_changes": "World geometry un-locks — ground plane and lane lines drift; a background object vanishes and another appears without cause.", + "camera": "Forward-moving camera that drives rigid-object deformation; simultaneously an over-fast forward dolly-and-zoom with lateral drift that magnifies the scene far too quickly and never holds a stable frame." + }, + { + "segment_index": 1, + "time_range": "0:04-0:08", + "description": "Manipulation and material-transfer segment where hand-object contact fails.", + "key_changes": "Hand and handled object merge into a morphing blob; transferred food/liquid violates mass conservation, portions vanishing and multiplying; thin high-frequency structures flicker.", + "camera": "Unstable framing with incorrect motion blur; the excessive push-in continues, sliding sideways and blowing past the composition into an extreme close-up." + } + ], + "transitions": [], + "temporal_caption": "The clip opens with objects appearing sharp and stable, but as the camera moves forward every rigid object begins to betray its geometry: parked cars warp and bulge, painted lane markings smear and slide across the asphalt, stop signs and poles bend and reshape frame to frame. Midway, a vehicle that was clearly present silently vanishes while a new object pops into existence at the frame edge without cause. Hands manipulating objects fuse with them into a morphing blob; poured or chopped food changes mass and volume, portions disappearing and multiplying. Thin structures — wires, foliage, railings, lane lines — flicker and shimmer throughout. Feet, wheels, and paws glide over the ground without firm contact. By the end, world geometry, object count, and material mass are all inconsistent with how the clip began. Over the whole clip the camera also barrels forward far too fast: a smooth, unrelenting push-in — fast forward dolly plus optical zoom, roughly 2.5x to 6x magnification in a few seconds — paired with a large lateral drift, with essentially no rotational shake. The framing never stabilizes; the viewpoint continuously accelerates past the intended composition and collapses into an uncomfortable extreme close-up by the end, which on industrial and aerial scenes with strong perspective lines reads as erratic, disorienting camerawork.", + "audio_description": "", + "physical_realism": "No adherence to physical laws. Objects defy gravity, pass through solid surfaces, and change mass and momentum without cause. Fluid dynamics, cloth simulation, and rigid body physics are all fundamentally broken. Furthermore, conservation of energy is violated as objects gain or lose kinetic energy spontaneously. Elastic collisions produce inelastic results and vice versa. Surface friction is inconsistent — objects slide on rough surfaces while sticking to smooth ones. Air resistance appears to affect only some objects while others move through the atmosphere unimpeded. Rigid bodies morph under camera motion; objects slide without ground contact; mass is not conserved in transfer; objects enter/vanish breaking permanence; thin structures flicker." +} diff --git a/checkpoints/mixed240/chat_template.jinja b/checkpoints/mixed240/chat_template.jinja new file mode 100644 index 0000000000000000000000000000000000000000..0a3d5967de71577bd2bb20994cee54c39bed63ba --- /dev/null +++ b/checkpoints/mixed240/chat_template.jinja @@ -0,0 +1,324 @@ + +{% macro render_extra_keys(json_dict, handled_keys) %} + {%- if json_dict is mapping %} + {%- for json_key in json_dict if json_key not in handled_keys %} + {%- if json_dict[json_key] is mapping or (json_dict[json_key] is sequence and json_dict[json_key] is not string) %} + {{- ' +<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '' }} + {%- else %} + {{-' +<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '' }} + {%- endif %} + {%- endfor %} + {%- endif %} +{% endmacro %} +{%- set enable_thinking = enable_thinking if enable_thinking is defined else True %} +{%- set truncate_history_thinking = truncate_history_thinking if truncate_history_thinking is defined else True %} + +{%- set ns = namespace(last_user_idx = -1) %} +{%- set loop_messages = messages %} +{%- for m in loop_messages %} + {%- if m["role"] == "user" %} + {%- set ns.last_user_idx = loop.index0 %} + {%- endif %} +{%- endfor %} + +{%- if messages[0]["role"] == "system" %} + {%- if messages[0]["content"] is string %} + {%- set system_message = messages[0]["content"] %} + {%- else %} + {%- set ns = namespace(system_text="") %} + {%- for part in messages[0]["content"] %} + {%- if 'text' in part %} + {%- set ns.system_text = ns.system_text + part.text %} + {%- endif %} + {%- endfor %} + {%- set system_message = ns.system_text %} + {%- endif %} + + {%- set loop_messages = messages[1:] %} +{%- else %} + {%- set system_message = "" %} + {%- set loop_messages = messages %} +{%- endif %} +{%- if not tools is defined %} + {%- set tools = [] %} +{%- endif %} +{# Recompute last_user_idx relative to loop_messages after handling system #} +{%- set ns = namespace(last_user_idx = -1) %} +{%- for m in loop_messages %} + {%- if m["role"] == "user" %} + {%- set ns.last_user_idx = loop.index0 %} + {%- endif %} +{%- endfor %} +{%- if system_message is defined %} + {{- "<|im_start|>system +" + system_message }} +{%- else %} + {%- if tools is iterable and tools | length > 0 %} + {{- "<|im_start|>system +" }} + {%- endif %} +{%- endif %} +{%- if tools is iterable and tools | length > 0 %} + {%- if system_message is defined and system_message | length > 0 %} + {{- " + +" }} + {%- endif %} + {{- "# Tools + +You have access to the following functions: + +" }} + {{- "" }} + {%- for tool in tools %} + {%- if tool.function is defined %} + {%- set tool = tool.function %} + {%- endif %} + {{- " + +" ~ tool.name ~ "" }} + {%- if tool.description is defined %} + {{- ' +' ~ (tool.description | trim) ~ '' }} + {%- endif %} + {{- ' +' }} + {%- if tool.parameters is defined and tool.parameters is mapping and tool.parameters.properties is defined and tool.parameters.properties is mapping %} + {%- for param_name, param_fields in tool.parameters.properties|items %} + {{- ' +' }} + {{- ' +' ~ param_name ~ '' }} + {%- if param_fields.type is defined %} + {{- ' +' ~ (param_fields.type | string) ~ '' }} + {%- endif %} + {%- if param_fields.description is defined %} + {{- ' +' ~ (param_fields.description | trim) ~ '' }} + {%- endif %} + {%- if param_fields.enum is defined %} + {{- ' +' ~ (param_fields.enum | tojson | safe) ~ '' }} + {%- endif %} + {%- set handled_keys = ['name', 'type', 'description', 'enum'] %} + {{- render_extra_keys(param_fields, handled_keys) }} + {{- ' +' }} + {%- endfor %} + {%- endif %} + {% set handled_keys = ['type', 'properties', 'required'] %} + {{- render_extra_keys(tool.parameters, handled_keys) }} + {%- if tool.parameters is defined and tool.parameters.required is defined %} + {{- ' +' ~ (tool.parameters.required | tojson | safe) ~ '' }} + {%- endif %} + {{- ' +' }} + {%- set handled_keys = ['type', 'name', 'description', 'parameters'] %} + {{- render_extra_keys(tool, handled_keys) }} + {{- ' +' }} + {%- endfor %} + {{- " +" }} + + {{- ' + +If you choose to call a function ONLY reply in the following format with NO suffix: + + + + +value_1 + + +This is the value for the second parameter +that can span +multiple lines + + + + + +Reminder: +- Function calls MUST follow the specified format: an inner block must be nested within XML tags +- Required parameters MUST be specified +- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after +- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls +' }} +{%- endif %} + + +{%- if system_message is defined %} + {{- '<|im_end|> +' }} +{%- else %} + {%- if tools is iterable and tools | length > 0 %} + {{- '<|im_end|> +' }} + {%- endif %} +{%- endif %} + +{%- set image_count = namespace(value=0) %} +{%- set video_count = namespace(value=0) %} +{%- for message in loop_messages %} + {%- if message.role == "assistant" %} + {# Add reasoning content in to content field for unified processing below. #} + {%- set text_content = namespace(text_content="") -%} + {%- if message.content is string %} + {%- set text_content.text_content = message.content %} + {%- else %} + {%- for content in message.content %} + {%- if 'text' in content %} + {%- set text_content.text_content = content.text %} + {%- break %} + {%- endif %} + {%- endfor %} + {%- endif %} + {%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %} + {%- set content = " +" ~ message.reasoning_content ~ " + +" ~ (text_content.text_content | default('', true)) %} + {%- else %} + {%- set content = text_content.text_content | default('', true) %} + {# Allow downstream logic to to take care of broken thought, only handle coherent reasoning here. #} + {%- if '' not in content and '' not in content %} + {%- set content = "" ~ content %} + {%- else %} + {%- set content = content %} + {%- endif -%} + {%- endif %} + {%- if message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %} + {# Assistant message has tool calls. #} + {{- '<|im_start|>assistant +' }} + {%- set include_content = not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %} + {%- if content is string and content | trim | length > 0 %} + {%- if include_content %} + {{- (content | trim) ~ ' +' -}} + {%- else %} + {%- set c = (content | string) %} + {%- if '' in c %} + {# Keep only content after the last closing think. Also generation prompt causes this. #} + {%- set c = c.split('')[-1] %} + {%- elif '' in c %} + {# If was opened but never closed, drop the trailing think segment #} + {%- set c = c.split('')[0] %} + {%- endif %} + {%- set c = "" ~ c | trim %} + {%- if c | length > 0 %} + {{- c ~ ' +' -}} + {%- endif %} + {%- endif %} + {%- else %} + {{- "" -}} + {%- endif %} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function is defined %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- ' + +' -}} + {%- if tool_call.arguments is defined %} + {%- for args_name, args_value in tool_call.arguments|items %} + {{- ' +' -}} + {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %} + {{- args_value ~ ' + +' -}} + {%- endfor %} + {%- endif %} + {{- ' + +' -}} + {%- endfor %} + {{- '<|im_end|> +' }} + {%- else %} + {# Assistant message doesn't have tool calls. #} + {%- if not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %} + {{- '<|im_start|>assistant +' ~ (content | default('', true) | string | trim) ~ '<|im_end|> +' }} + {%- else %} + {%- set c = (content | default('', true) | string) %} + {%- if '' in c and '' in c %} + {%- set c = "" ~ c.split('')[-1] %} + {%- endif %} + {%- set c = c | trim %} + {%- if c | length > 0 %} + {{- '<|im_start|>assistant +' ~ c ~ '<|im_end|> +' }} + {%- else %} + {{- '<|im_start|>assistant +<|im_end|> +' }} + {%- endif %} + {%- endif %} + {%- endif %} + {%- elif message.role == "user" or message.role == "system" %} + {{- '<|im_start|>' + message.role + ' +' }} + {%- if message.content is string %} + {{- message.content }} + {%- else %} + {%- for content in message.content %} + {%- if content.type == 'image' or 'image' in content or 'image_url' in content %} + {%- set image_count.value = image_count.value + 1 %} + {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%} + <|vision_start|><|image_pad|><|vision_end|> + {%- elif content.type == 'video' or 'video' in content %} + {%- set video_count.value = video_count.value + 1 %} + {%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%} + <|vision_start|><|video_pad|><|vision_end|> + {%- elif 'text' in content %} + {{- content.text }} + {%- endif %} + {%- endfor %} + {%- endif %} + {{- '<|im_end|> +' }} + {%- elif message.role == "tool" %} + {%- if loop.previtem and loop.previtem.role != "tool" %} + {{- '<|im_start|>user +' }} + {%- endif %} + {{- ' +' }} + {{- message.content }} + {{- ' + +' }} + {%- if not loop.last and loop.nextitem.role != "tool" %} + {{- '<|im_end|> +' }} + {%- elif loop.last %} + {{- '<|im_end|> +' }} + {%- endif %} + {%- else %} + {{- '<|im_start|>' + message.role + ' +' + message.content + '<|im_end|> +' }} + {%- endif %} +{%- endfor %} + +{%- if add_generation_prompt %} + {%- if enable_thinking %} + {{- '<|im_start|>assistant + +' }} + {%- else %} + {{- '<|im_start|>assistant +' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/checkpoints/mixed240/config.json b/checkpoints/mixed240/config.json new file mode 100644 index 0000000000000000000000000000000000000000..29fd08f0530a641349fd642d586f1c2aa6779e74 --- /dev/null +++ b/checkpoints/mixed240/config.json @@ -0,0 +1,69 @@ +{ + "allow_patterns_overrides": [ + "*/*.safetensors" + ], + "architectures": [ + "Cosmos3EdgeForConditionalGeneration" + ], + "image_token_id": 19, + "model_type": "cosmos3_edge", + "projector_config": { + "input_hidden_size": 1152, + "merger_intermediate_size": 11520, + "model_type": "cosmos3_edge_projector", + "out_hidden_size": 2048, + "spatial_merge_size": 2, + "use_postshuffle_norm": false + }, + "projector_hidden_size": 11520, + "text_config": { + "attention_bias": false, + "attention_dropout": 0.0, + "dtype": "bfloat16", + "head_dim": 128, + "hidden_act": "relu2", + "hidden_dropout": 0.0, + "hidden_size": 2048, + "initializer_range": 0.02, + "intermediate_size": 9216, + "max_position_embeddings": 131072, + "mlp_bias": false, + "model_type": "cosmos3_edge_text", + "num_attention_heads": 16, + "num_hidden_layers": 28, + "num_key_value_heads": 8, + "residual_in_fp32": false, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "mrope_section": [ + 24, + 20, + 20 + ], + "rope_theta": 100000000, + "rope_type": "default" + }, + "sliding_window": null, + "use_cache": true, + "vocab_size": 131072 + }, + "tie_word_embeddings": false, + "transformers_version": "4.57.6", + "video_token_id": 18, + "vision_config": { + "attention_dropout": 0.0, + "hidden_act": "gelu_pytorch_tanh", + "hidden_size": 1152, + "intermediate_size": 4304, + "layer_norm_eps": 1e-06, + "model_type": "cosmos3_edge_vision", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 27, + "num_patches": 256, + "patch_size": 16, + "spatial_merge_size": 2 + }, + "vision_end_token_id": 21, + "vision_start_token_id": 20 +} diff --git a/checkpoints/mixed240/generation_config.json b/checkpoints/mixed240/generation_config.json new file mode 100644 index 0000000000000000000000000000000000000000..43db172db8643a9306fc432d12536a5225cddbbd --- /dev/null +++ b/checkpoints/mixed240/generation_config.json @@ -0,0 +1,8 @@ +{ + "_from_model_config": true, + "bos_token_id": 1, + "do_sample": true, + "eos_token_id": 11, + "pad_token_id": 0, + "transformers_version": "4.57.6" +} diff --git a/checkpoints/mixed240/model.safetensors.index.json b/checkpoints/mixed240/model.safetensors.index.json new file mode 100644 index 0000000000000000000000000000000000000000..36288b02b48fbf26c5c62b5b088e002e6deed7a9 --- /dev/null +++ b/checkpoints/mixed240/model.safetensors.index.json @@ -0,0 +1,677 @@ +{ + "metadata": { + "total_size": 4871232992 + }, + "weight_map": { + "model.visual.embeddings.patch_embedding.weight": "00000.safetensors", + "model.visual.embeddings.patch_embedding.bias": "00000.safetensors", + 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string) ~ '' }}\n {%- endif %}\n {%- endfor %}\n {%- endif %}\n{% endmacro %}\n{%- set enable_thinking = enable_thinking if enable_thinking is defined else True %}\n{%- set truncate_history_thinking = truncate_history_thinking if truncate_history_thinking is defined else True %}\n\n{%- set ns = namespace(last_user_idx = -1) %}\n{%- set loop_messages = messages %}\n{%- for m in loop_messages %}\n {%- if m[\"role\"] == \"user\" %}\n {%- set ns.last_user_idx = loop.index0 %}\n {%- endif %}\n{%- endfor %}\n\n{%- if messages[0][\"role\"] == \"system\" %}\n {%- if messages[0][\"content\"] is string %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- else %}\n {%- set ns = namespace(system_text=\"\") %}\n {%- for part in messages[0][\"content\"] %}\n {%- if 'text' in part %}\n {%- set ns.system_text = ns.system_text + part.text %}\n {%- endif %}\n {%- endfor %}\n {%- set system_message = ns.system_text %}\n {%- endif %}\n\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = [] %}\n{%- endif %}\n{# Recompute last_user_idx relative to loop_messages after handling system #}\n{%- set ns = namespace(last_user_idx = -1) %}\n{%- for m in loop_messages %}\n {%- if m[\"role\"] == \"user\" %}\n {%- set ns.last_user_idx = loop.index0 %}\n {%- endif %}\n{%- endfor %}\n{%- if system_message is defined %}\n {{- \"<|im_start|>system\n\" + system_message }}\n{%- else %}\n {%- if tools is iterable and tools | length > 0 %}\n {{- \"<|im_start|>system\n\" }}\n {%- endif %}\n{%- endif %}\n{%- if tools is iterable and tools | length > 0 %}\n {%- if system_message is defined and system_message | length > 0 %}\n {{- \"\n\n\" }}\n {%- endif %}\n {{- \"# Tools\n\nYou have access to the following functions:\n\n\" }}\n {{- \"\" }}\n {%- for tool in tools %}\n {%- if tool.function is defined %}\n {%- set tool = tool.function %}\n {%- endif %}\n {{- 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}, + "requests": 672, + "generations": 1344, + "scope": "Synthetic geometry. v4 vs v5. Saved output rescoring is not fresh inference. Evaluation frames are not redistributed." +} diff --git a/evidence/v5-saved-outputs.json b/evidence/v5-saved-outputs.json new file mode 100644 index 0000000000000000000000000000000000000000..4cdf81c8d48967dd5b9a7d48616e18560661f9d7 --- /dev/null +++ b/evidence/v5-saved-outputs.json @@ -0,0 +1,54434 @@ +[ + { + "id": "current-2026091342-stay-000", + "context": { + "target_position_m": [ + -147.8, + -107.6, + 0.0 + ], + "current_position_m": [ + -147.8, + -96.28, + 11.38 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -177.89, + -137.69 + ], + [ + -117.71, + -137.69 + ], + [ + -117.71, + -77.51 + ], + [ + -177.89, + -77.51 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 1.0, + "max_mission_seconds": 121, + "return_margin_seconds": 120 + }, + "side": "north", + "side_ja": "北", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "dwell-stay", + "strict_speed_limit": true, + "target_size_m": 1.3, + "required_pixels": 146, + "preferred_pitch_deg": -45.15, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 20.016715869978754, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "evaluation_group": "regression-fresh-stay" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,11.32,11.38],\"dwell_ms\":1000,\"speed_mps\":1.0}]\n```", + "model": "balanced288", + "prompt_sha256": "a875f22929448fddce4c89381aa2a53f1e5dd5828b3cd58975a6f8107c789330" + }, + { + "id": "current-2026091342-stay-001", + "context": { + "target_position_m": [ + 171.2, + -86.6, + 0.0 + ], + "current_position_m": [ + 171.2, + -58.81999999999999, + 15.31 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 111.73, + -146.07 + ], + [ + 230.67, + -146.07 + ], + [ + 230.67, + -27.13 + ], + [ + 111.73, + -27.13 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 1.0, + "max_mission_seconds": 121, + "return_margin_seconds": 120 + }, + "side": "north", + "side_ja": "北", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "dwell-stay", + "strict_speed_limit": true, + "target_size_m": 4.0, + "required_pixels": 227, + "preferred_pitch_deg": -28.85, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 39.61288399887358, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "evaluation_group": "regression-fresh-stay" + }, + "canonical_frame": { + 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+ 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[19.77,19.77,17.7],\"dwell_ms\":1000,\"speed_mps\":1.0}]\n```", + "model": "balanced288", + "prompt_sha256": "7b7bbff4551ea0219e43ce24195eacdade2e849462c2ec6993803381c1866c2b" + }, + { + "id": "current-2026091342-stay-003", + "context": { + "target_position_m": [ + 92.5, + 175.6, + 0.0 + ], + "current_position_m": [ + 110.56, + 193.66, + 19.55 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 32.2, + 115.3 + ], + [ + 152.8, + 115.3 + ], + [ + 152.8, + 235.9 + ], + [ + 32.2, + 235.9 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 1.0, + "max_mission_seconds": 121, + "return_margin_seconds": 120 + }, + "side": "northeast", + "side_ja": "北東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "dwell-stay", + "strict_speed_limit": true, + "target_size_m": 5.11, + "required_pixels": 286, + "preferred_pitch_deg": -37.44, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 40.16587154910261, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "evaluation_group": "regression-fresh-stay" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[18.06,18.06,19.55],\"dwell_ms\":1000,\"speed_mps\":1.0}]\n```", + "model": "balanced288", + "prompt_sha256": "b28ac142e2bffc0f581244e724377025c690b2a0d0b19ae319ceff76f81220a9" + }, + { + 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"tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[23.6,0.0,18.64],\"dwell_ms\":1000,\"speed_mps\":1.0}]\n```", + "model": "balanced288", + "prompt_sha256": "01d91694d87cf8e00776df7c76dd85c27b6500860dafd550a4f19a5f764e0ad2" + }, + { + "id": "current-2026091342-stay-005", + "context": { + "target_position_m": [ + -13.6, + -102.5, + 0.0 + ], + "current_position_m": [ + 8.000000000000002, + -102.5, + 23.14 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -72.96, + -161.86 + ], + [ + 45.76, + 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"strict_speed_limit": true, + "target_size_m": 1.45, + "required_pixels": 125, + "preferred_pitch_deg": -42.9, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 26.077161536458473, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "evaluation_group": "regression-fresh-stay" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[10.82,-10.82,14.22],\"dwell_ms\":1000,\"speed_mps\":1.0}]\n```", + "model": "balanced288", + "prompt_sha256": "575e72d4274e573b5b7384dfd665b2aa57516755141a922092786bc36eedd3db" + }, + { + "id": "current-2026091342-stay-007", + "context": { + "target_position_m": [ + 35.8, + 11.5, + 0.0 + ], + "current_position_m": [ + 53.209999999999994, + -5.91, + 16.02 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -19.28, + -43.58 + ], + [ + 90.88, + -43.58 + ], + [ + 90.88, + 66.58 + ], + [ + -19.28, + 66.58 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 1.0, + "max_mission_seconds": 121, + "return_margin_seconds": 120 + }, + "side": "southeast", + "side_ja": "南東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "dwell-stay", + "strict_speed_limit": true, + "target_size_m": 3.76, + "required_pixels": 230, + "preferred_pitch_deg": -33.05, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 36.750422555128885, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "evaluation_group": "regression-fresh-stay" + }, + "canonical_frame": { + 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+ "required_pixels": 89, + "preferred_pitch_deg": -45.27, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 53.801195343526295, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "evaluation_group": "regression-fresh-three-seconds" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "fcfe92060e5fced7c8918f3d8a2603bb08731a5863231b527b2d1f1985d9c9aa", + "rgb_sha256": "7c942456fbc8ad50f115c4a3bde47c7391f65106c79b5232d19428fad436faa9", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "4517afd3483b7f2d82a9a47312b02f94a39a657b843f5551932b31e7679c226b" + }, + "raw_text": "```json\n[{\"offset_m\":[21.3,21.3,30.41],\"dwell_ms\":1000,\"speed_mps\":2.0}]\n```", + "model": "balanced288", + "prompt_sha256": "cb5e421a027eedcfea27c67286221ec44d93f72af7845ce4690148fc422932ef" + }, + { + "id": "current-2026091344-stay-019", + 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+ "rgb_sha256": "7c942456fbc8ad50f115c4a3bde47c7391f65106c79b5232d19428fad436faa9", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "4517afd3483b7f2d82a9a47312b02f94a39a657b843f5551932b31e7679c226b" + }, + "raw_text": "```json\n[{\"offset_m\":[-33.06,-33.06,41.31],\"dwell_ms\":1000,\"speed_mps\":3.0}]\n```", + "model": "balanced288", + "prompt_sha256": "6ab9e74f01abf65b88e161743ff0737de1ed734ca91b8d5f37ec2705ad4d9663" + }, + { + "id": "current-2026091344-stay-043", + "context": { + "target_position_m": [ + -21.8, + 132.2, + 0.0 + ], + "current_position_m": [ + -59.260000000000005, + 94.73999999999998, + 39.5 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -145.7, + 8.3 + ], + [ + 102.1, + 8.3 + ], + [ + 102.1, + 256.1 + ], + [ + -145.7, + 256.1 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 3.0, + "max_mission_seconds": 123, + "return_margin_seconds": 120 + }, + "side": 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], + "current_position_m": [ + -39.9, + -123.6, + 39.57 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -120.04, + -272.74 + ], + [ + 178.24, + -272.74 + ], + [ + 178.24, + 25.54 + ], + [ + -120.04, + 25.54 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 3.0, + "max_mission_seconds": 123, + "return_margin_seconds": 120 + }, + "side": "west", + "side_ja": "西", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "dwell-stay", + "strict_speed_limit": true, + "target_size_m": 3.73, + "required_pixels": 84, + "preferred_pitch_deg": -29.83, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 99.82328872228048, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "evaluation_group": "regression-fresh-three-seconds" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "fcfe92060e5fced7c8918f3d8a2603bb08731a5863231b527b2d1f1985d9c9aa", + "rgb_sha256": "7c942456fbc8ad50f115c4a3bde47c7391f65106c79b5232d19428fad436faa9", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "4517afd3483b7f2d82a9a47312b02f94a39a657b843f5551932b31e7679c226b" + }, + "raw_text": "```json\n[{\"offset_m\":[-69.0,0.0,39.57],\"dwell_ms\":1000,\"speed_mps\":3.0}]\n```", + "model": "balanced288", + "prompt_sha256": "d4fea375e1ff61d222f98a8005c26391f7c7b694522ee19fa82a59ae84127afd" + }, + { + "id": "current-2026091344-stay-046", + "context": { + "target_position_m": [ + -66.2, + -0.4, + 0.0 + ], + "current_position_m": [ + -96.92, + 30.32, + 42.79 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -180.54, + -114.74 + ], + [ + 48.14, + -114.74 + ], + [ + 48.14, + 113.94 + ], + [ + -180.54, + 113.94 + ] + ], + "min_alt_m": 1.0, + 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+ "evaluation_group": "new-one-second" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "fcfe92060e5fced7c8918f3d8a2603bb08731a5863231b527b2d1f1985d9c9aa", + "rgb_sha256": "7c942456fbc8ad50f115c4a3bde47c7391f65106c79b5232d19428fad436faa9", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "4517afd3483b7f2d82a9a47312b02f94a39a657b843f5551932b31e7679c226b" + }, + "raw_text": "```json\n[{\"offset_m\":[-9.65,-9.65,7.1],\"dwell_ms\":1000,\"speed_mps\":4.0}]\n```", + "model": "balanced288", + "prompt_sha256": "765d5b80bf32f6e0d613cf98e627cc451f2b9030008ee02d467ff53425205f4b" + }, + { + "id": "current-2026091360-stay-060", + "context": { + "target_position_m": [ + -142.1, + -10.2, + 0.0 + ], + "current_position_m": [ + -153.89, + -10.2, + 9.56 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 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"current_position_m": [ + -44.8, + 56.559999999999995, + 24.64 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -132.96, + 8.44 + ], + [ + 43.36, + 8.44 + ], + [ + 43.36, + 184.76 + ], + [ + -132.96, + 184.76 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 2.0, + "max_mission_seconds": 124, + "return_margin_seconds": 120 + }, + "side": "south", + "side_ja": "南", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "dwell-stay", + "strict_speed_limit": true, + "target_size_m": 1.93, + "required_pixels": 74, + "preferred_pitch_deg": -31.61, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 58.6310831376571, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "evaluation_group": "boundary-four-seconds" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "be7b5122f4adc9b1d56a968bdd83f77ba006f29412b243554f1e0854a3c7af8f", + "rgb_sha256": "f40366198814d829f09c77d61a2d87364200b8d58033d42be31573805b9612b7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,-39.23,27.47],\"dwell_ms\":2000,\"speed_mps\":2.0}]\n```", + "model": "mixed240", + "prompt_sha256": "31ca65692aeab4d09005c5daf5d374aede649b95d3bcd51e9c57ca825c87549f" + }, + { + "id": "current-2026091366-stay-025", + "context": { + "target_position_m": [ + 130.0, + -63.3, + 0.0 + ], + "current_position_m": [ + 130.0, + -95.24, + 40.77 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 32.89, + -160.41 + ], + [ + 227.11, + -160.41 + ], + [ + 227.11, + 33.81 + ], + [ + 32.89, + 33.81 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + 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1.96, + "required_pixels": 47, + "preferred_pitch_deg": -37.53, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 93.74768270627038, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "evaluation_group": "boundary-wrong-side" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "be7b5122f4adc9b1d56a968bdd83f77ba006f29412b243554f1e0854a3c7af8f", + "rgb_sha256": "f40366198814d829f09c77d61a2d87364200b8d58033d42be31573805b9612b7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,60.21,42.67],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": "0e9289aeee0ff333a7b49d53dcd6c84abff22e0a34995b78431b074f7f7ef472" + }, + { + "id": "current-2026091366-stay-033", + "context": { + 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"resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "be7b5122f4adc9b1d56a968bdd83f77ba006f29412b243554f1e0854a3c7af8f", + "rgb_sha256": "f40366198814d829f09c77d61a2d87364200b8d58033d42be31573805b9612b7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,60.6,49.83],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": "34ef3621866e564bee59fc487a40960d0cd4ac7e96ff3ea54401a0f6992bc2a1" + }, + { + "id": "current-2026091366-stay-034", + "context": { + "target_position_m": [ + -90.2, + -158.3, + 0.0 + ], + "current_position_m": [ + -138.03, + -206.13, + 48.87 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -246.67, + -314.77 + ], + [ + 66.27, + -314.77 + ], + [ + 66.27, + -1.83 + ], + [ + -246.67, + -1.83 + ] + 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+ 23.87 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -23.36, + 55.16 + ], + [ + 85.36, + 55.16 + ], + [ + 85.36, + -53.56 + ], + [ + -23.36, + -53.56 + ] + ], + "min_alt_m": 20.44, + "max_alt_m": 24.44, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "south", + "side_ja": "南", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "altitude_band", + "strict_speed_limit": true, + "target_size_m": 1.7, + "required_pixels": 105, + "preferred_pitch_deg": -50.71, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 36.39669508372693, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-altitude_band-000", + "symmetry_turns": 2, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "be7b5122f4adc9b1d56a968bdd83f77ba006f29412b243554f1e0854a3c7af8f", + "rgb_sha256": "f40366198814d829f09c77d61a2d87364200b8d58033d42be31573805b9612b7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,-27.73,22.44],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "061d0d22e98fbe3cd87bdcccc59ca7f7705c7fd4dcd107a5e93470632c5a3c19" + }, + { + "id": "sym-2026091361-altitude_band-000-r3-f0", + "context": { + "target_position_m": [ + -0.8, + -31.0, + 0.0 + ], + "current_position_m": [ + -20.18, + -31.69, + 23.87 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -55.16, + 23.36 + ], + [ + -55.16, + -85.36 + ], + [ + 53.56, + -85.36 + ], + [ + 53.56, + 23.36 + ] + ], + "min_alt_m": 20.44, + "max_alt_m": 24.44, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "east", + "side_ja": "東", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "altitude_band", + "strict_speed_limit": true, + "target_size_m": 1.7, + "required_pixels": 105, + "preferred_pitch_deg": -50.71, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 36.39669508372693, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-altitude_band-000", + "symmetry_turns": 3, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "be7b5122f4adc9b1d56a968bdd83f77ba006f29412b243554f1e0854a3c7af8f", + "rgb_sha256": 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"max_viewpoints": 3, + "profile": "person", + "stress_family": "altitude_band", + "strict_speed_limit": true, + "target_size_m": 1.7, + "required_pixels": 105, + "preferred_pitch_deg": -50.71, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 36.39669508372693, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-altitude_band-000", + "symmetry_turns": 3, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "be7b5122f4adc9b1d56a968bdd83f77ba006f29412b243554f1e0854a3c7af8f", + "rgb_sha256": "f40366198814d829f09c77d61a2d87364200b8d58033d42be31573805b9612b7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": "```json\n[{\"offset_m\":[17.23,0.0,22.44],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "313ed6e25475f76d187aed9d782ef547f0821a519a7ad1850064b1418dbba04c" + }, + { + "id": "sym-2026091361-altitude_band-001-r0-f0", + "context": { + "target_position_m": [ + 86.6, + -72.1, + 0.0 + ], + "current_position_m": [ + 85.73, + -90.15, + 19.09 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 38.81, + -119.89 + ], + [ + 134.39, + -119.89 + ], + [ + 134.39, + -24.31 + ], + [ + 38.81, + -24.31 + ] + ], + "min_alt_m": 16.58, + "max_alt_m": 20.58, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "north", + "side_ja": "北", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "altitude_band", + "strict_speed_limit": true, + "target_size_m": 4.69, + "required_pixels": 331, + "preferred_pitch_deg": -46.8, + 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+ "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "be7b5122f4adc9b1d56a968bdd83f77ba006f29412b243554f1e0854a3c7af8f", + "rgb_sha256": "f40366198814d829f09c77d61a2d87364200b8d58033d42be31573805b9612b7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,20.58,18.58],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "748398c5aed049041031c2ece983c58e3b1e5adf24493204ad126305810202fa" + }, + { + "id": "sym-2026091361-altitude_band-001-r1-f0", + "context": { + "target_position_m": [ + 72.1, + 86.6, + 0.0 + ], + "current_position_m": [ + 90.15, + 85.73, + 19.09 + ], + "envelope": { + "revision": "e1", + "origin": { + 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+ "target_size_m": 4.69, + "required_pixels": 331, + "preferred_pitch_deg": -46.8, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 31.852767894048924, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-altitude_band-001", + "symmetry_turns": 2, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "be7b5122f4adc9b1d56a968bdd83f77ba006f29412b243554f1e0854a3c7af8f", + "rgb_sha256": "f40366198814d829f09c77d61a2d87364200b8d58033d42be31573805b9612b7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,-20.58,19.09],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": 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-85.73, + 19.09 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -119.89, + -38.81 + ], + [ + -119.89, + -134.39 + ], + [ + -24.31, + -134.39 + ], + [ + -24.31, + -38.81 + ] + ], + "min_alt_m": 16.58, + "max_alt_m": 20.58, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "east", + "side_ja": "東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "altitude_band", + "strict_speed_limit": true, + "target_size_m": 4.69, + "required_pixels": 331, + "preferred_pitch_deg": -46.8, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 31.852767894048924, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-altitude_band-001", + "symmetry_turns": 3, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "be7b5122f4adc9b1d56a968bdd83f77ba006f29412b243554f1e0854a3c7af8f", + "rgb_sha256": "f40366198814d829f09c77d61a2d87364200b8d58033d42be31573805b9612b7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": "```json\n[{\"offset_m\":[16.58,0.0,18.58],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "65a24da3301527504a3d24ff5bcb1c162a9e855a379b9cd288d5d8e8ac20710b" + }, + { + "id": "sym-2026091361-altitude_band-001-r3-f1", + "context": { + "target_position_m": [ + -72.1, + 86.6, + 0.0 + ], + "current_position_m": [ + -90.15, + 85.73, + 19.09 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -119.89, + 38.81 + ], + [ + -119.89, + 134.39 + ], + [ + -24.31, + 134.39 + ], + [ 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"f40366198814d829f09c77d61a2d87364200b8d58033d42be31573805b9612b7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": "```json\n[{\"offset_m\":[14.23,0.0,19.58],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "f6a6d24cecf814ad2817f8817d975cc589b0d89283f15456e7a5dc5ec9ce8a7c" + }, + { + "id": "sym-2026091361-altitude_band-002-r0-f0", + "context": { + "target_position_m": [ + -133.7, + 31.6, + 0.0 + ], + "current_position_m": [ + -147.41, + 18.02, + 13.06 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -185.43, + -20.13 + ], + [ + -81.97, + -20.13 + ], + [ + -81.97, + 83.33 + ], + [ + -185.43, + 83.33 + ] + ], + "min_alt_m": 10.73, + "max_alt_m": 14.73, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "northeast", + "side_ja": "北東", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "altitude_band", + "strict_speed_limit": true, + "target_size_m": 1.98, + "required_pixels": 129, + "preferred_pitch_deg": -27.48, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 34.504664422739765, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-altitude_band-002", + "symmetry_turns": 0, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "1679a5002dda3a3d085c6a36fa057829f2c6c4820285b6e359f5640d6f266fd5", + "rgb_sha256": "bf76e7bc224d57bb03245bf03f5c193c9ad0b14fc75662c464ef554dd10ed64a", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "817326de981baa6c387ef3ab9b9cfc80e5e89cf520ae3f8026a3ea54813856c7" + }, + "raw_text": "```json\n[{\"offset_m\":[12.29,12.29,13.73],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "e5fd6eff684b2453e7ab8eeb96f2d266cdeb30177e8f01eada7f7f9da0218733" + }, + { + "id": "sym-2026091361-altitude_band-002-r0-f1", + "context": { + "target_position_m": [ + 133.7, + 31.6, + 0.0 + ], + "current_position_m": [ + 147.41, + 18.02, + 13.06 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 185.43, + -20.13 + ], + [ + 81.97, + -20.13 + ], + [ + 81.97, + 83.33 + ], + [ + 185.43, + 83.33 + ] + ], + "min_alt_m": 10.73, + "max_alt_m": 14.73, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "northwest", + "side_ja": "北西", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "altitude_band", + "strict_speed_limit": true, + "target_size_m": 1.98, + "required_pixels": 129, + "preferred_pitch_deg": -27.48, + 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1, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "1679a5002dda3a3d085c6a36fa057829f2c6c4820285b6e359f5640d6f266fd5", + "rgb_sha256": "bf76e7bc224d57bb03245bf03f5c193c9ad0b14fc75662c464ef554dd10ed64a", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "817326de981baa6c387ef3ab9b9cfc80e5e89cf520ae3f8026a3ea54813856c7" + }, + "raw_text": "```json\n[{\"offset_m\":[-14.73,14.73,12.73],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "8ddb290ed191147a0e8817d7ccacfa91f1230c558be7e27af6bbcf7953b67aff" + }, + { + "id": "sym-2026091361-altitude_band-002-r1-f1", + "context": { + "target_position_m": [ + -31.6, + 133.7, + 0.0 + ], + "current_position_m": [ + -18.02, + 147.41, + 13.06 + ], + "envelope": { + "revision": "e1", + "origin": { + 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+ ], + "sha256": "817326de981baa6c387ef3ab9b9cfc80e5e89cf520ae3f8026a3ea54813856c7" + }, + "raw_text": "```json\n[{\"offset_m\":[-17.23,-17.23,12.73],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "1e0133832825600b5226506740a0bc3497b91f32b298bf62556f53dcab43e9bb" + }, + { + "id": "sym-2026091361-altitude_band-002-r2-f1", + "context": { + "target_position_m": [ + -133.7, + -31.6, + 0.0 + ], + "current_position_m": [ + -147.41, + -18.02, + 13.06 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -185.43, + 20.13 + ], + [ + -81.97, + 20.13 + ], + [ + -81.97, + -83.33 + ], + [ + -185.43, + -83.33 + ] + ], + "min_alt_m": 10.73, + "max_alt_m": 14.73, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "southeast", + "side_ja": "南東", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "altitude_band", + 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+ "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-altitude_band-002", + "symmetry_turns": 3, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "1679a5002dda3a3d085c6a36fa057829f2c6c4820285b6e359f5640d6f266fd5", + "rgb_sha256": "bf76e7bc224d57bb03245bf03f5c193c9ad0b14fc75662c464ef554dd10ed64a", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "817326de981baa6c387ef3ab9b9cfc80e5e89cf520ae3f8026a3ea54813856c7" + }, + "raw_text": "```json\n[{\"offset_m\":[12.21,-12.21,13.73],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "f582e362127090c3e93f179a326e6db3d18c8fb96416ee9abc596a8c556634cf" + }, + { + "id": "sym-2026091361-altitude_band-002-r3-f1", + "context": { + "target_position_m": [ + 31.6, + -133.7, + 0.0 + ], + "current_position_m": [ + 18.02, + -147.41, + 13.06 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -20.13, + -185.43 + ], + [ + -20.13, + -81.97 + ], + [ + 83.33, + -81.97 + ], + [ + 83.33, + -185.43 + ] + ], + "min_alt_m": 10.73, + "max_alt_m": 14.73, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "northeast", + "side_ja": "北東", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "altitude_band", + "strict_speed_limit": true, + "target_size_m": 1.98, + "required_pixels": 129, + "preferred_pitch_deg": -27.48, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 34.504664422739765, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-altitude_band-002", + "symmetry_turns": 3, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "1679a5002dda3a3d085c6a36fa057829f2c6c4820285b6e359f5640d6f266fd5", + "rgb_sha256": "bf76e7bc224d57bb03245bf03f5c193c9ad0b14fc75662c464ef554dd10ed64a", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "817326de981baa6c387ef3ab9b9cfc80e5e89cf520ae3f8026a3ea54813856c7" + }, + "raw_text": "```json\n[{\"offset_m\":[13.41,13.41,12.73],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "a5d4229160f1ae3c240f1aea42fef9b31e88796d1593a94c5880284db732ea05" + }, + { + "id": "sym-2026091361-altitude_band-003-r0-f0", + "context": { + "target_position_m": [ + 72.9, + 45.3, + 0.0 + ], + "current_position_m": [ + 63.71, + 33.81, + 13.87 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 13.61, + -13.99 + ], + [ + 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"max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "northwest", + "side_ja": "北西", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "altitude_band", + "strict_speed_limit": true, + "target_size_m": 4.45, + "required_pixels": 253, + "preferred_pitch_deg": -29.39, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 39.54046914176101, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-altitude_band-003", + "symmetry_turns": 0, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "1679a5002dda3a3d085c6a36fa057829f2c6c4820285b6e359f5640d6f266fd5", + "rgb_sha256": "bf76e7bc224d57bb03245bf03f5c193c9ad0b14fc75662c464ef554dd10ed64a", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "817326de981baa6c387ef3ab9b9cfc80e5e89cf520ae3f8026a3ea54813856c7" + }, + "raw_text": "```json\n[{\"offset_m\":[-14.2,14.2,15.52],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "408c6adc815ae8454c823693b24c5db7c28b52c0f03ed03dae9999a84f88f277" + }, + { + "id": "sym-2026091361-altitude_band-003-r1-f0", + "context": { + "target_position_m": [ + -45.3, + 72.9, + 0.0 + ], + "current_position_m": [ + -33.81, + 63.71, + 13.87 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 13.99, + 13.61 + ], + [ + 13.99, + 132.19 + ], + [ + -104.59, + 132.19 + ], + [ + -104.59, + 13.61 + ] + ], + "min_alt_m": 13.52, + "max_alt_m": 17.52, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "northwest", + "side_ja": "北西", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "altitude_band", + "strict_speed_limit": true, + 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false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "e0b640015b20099013ecbd4fcfeca8168f3cc5c56ee44a0058eae5f8ea713a03", + "rgb_sha256": "6b21e101e16694ea1a72b0ed8afee33724a5231e02ffe5b0ccd599db4ae84d42", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "3f4dc27b87e5353a52a4173c85199d055bf914968ff6ab72397733dcb74480a2" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,15.23,13.71],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "28f47339384baf809fc3eec15b73375333da5c769c252530ffc9019d6926b9d1" + }, + { + "id": "sym-2026091361-elevated-000-r1-f0", + "context": { + "target_position_m": [ + -42.7, + 86.1, + 7.0 + ], + "current_position_m": [ + -25.35, + 84.45, + 77.29 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -6.86, + 50.26 + ], + [ + -6.86, + 121.94 + ], + [ + -78.54, + 121.94 + ], + [ + -78.54, + 50.26 + ] + ], + "min_alt_m": 1.0, + 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+ "symmetry_orbit": "sym-2026091361-elevated-002", + "symmetry_turns": 0, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "be7b5122f4adc9b1d56a968bdd83f77ba006f29412b243554f1e0854a3c7af8f", + "rgb_sha256": "f40366198814d829f09c77d61a2d87364200b8d58033d42be31573805b9612b7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": "```json\n[{\"offset_m\":[-10.29,10.29,13.0],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "f3931ed2938ea13b17c10dd6c0bf2480473bc30ab1f03a93b2aa83b46875ff2e" + }, + { + "id": "sym-2026091361-elevated-002-r1-f0", + "context": { + "target_position_m": [ + 156.9, + 74.0, + 13.0 + ], + "current_position_m": [ + 165.7, + 66.67, + 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+ "max_viewpoints": 3, + "profile": "person", + "stress_family": "elevated", + "strict_speed_limit": true, + "target_size_m": 1.43, + "required_pixels": 121, + "preferred_pitch_deg": -51.28, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 26.56764106378998, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-elevated-002", + "symmetry_turns": 2, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "be7b5122f4adc9b1d56a968bdd83f77ba006f29412b243554f1e0854a3c7af8f", + "rgb_sha256": "f40366198814d829f09c77d61a2d87364200b8d58033d42be31573805b9612b7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": 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"frames": [ + { + "id": "F1", + "sha256": "be7b5122f4adc9b1d56a968bdd83f77ba006f29412b243554f1e0854a3c7af8f", + "rgb_sha256": "f40366198814d829f09c77d61a2d87364200b8d58033d42be31573805b9612b7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": "```json\n[{\"offset_m\":[12.71,12.71,13.06],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "452f442162591f1b78f3a56b166b247954493dd8516717fe36660c61a7dc50f7" + }, + { + "id": "sym-2026091361-elevated-003-r0-f0", + "context": { + "target_position_m": [ + -35.6, + -65.0, + 22.0 + ], + "current_position_m": [ + -43.46, + -71.7, + 6.23 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -68.38, + -97.78 + ], + [ + -2.82, + -97.78 + ], + [ + -2.82, + -32.22 + ], + [ + -68.38, + -32.22 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + 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"target_size_m": 5.03, + "required_pixels": 517, + "preferred_pitch_deg": -27.96, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 21.8715604829564, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-elevated-003", + "symmetry_turns": 0, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "be7b5122f4adc9b1d56a968bdd83f77ba006f29412b243554f1e0854a3c7af8f", + "rgb_sha256": "f40366198814d829f09c77d61a2d87364200b8d58033d42be31573805b9612b7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": "```json\n[{\"offset_m\":[-10.23,10.23,10.23],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": 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+ ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 97.78, + 68.38 + ], + [ + 97.78, + 2.82 + ], + [ + 32.22, + 2.82 + ], + [ + 32.22, + 68.38 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "southwest", + "side_ja": "南西", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "elevated", + "strict_speed_limit": true, + "target_size_m": 5.03, + "required_pixels": 517, + "preferred_pitch_deg": -27.96, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 21.8715604829564, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-elevated-003", + "symmetry_turns": 1, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "be7b5122f4adc9b1d56a968bdd83f77ba006f29412b243554f1e0854a3c7af8f", + "rgb_sha256": "f40366198814d829f09c77d61a2d87364200b8d58033d42be31573805b9612b7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": "```json\n[{\"offset_m\":[-10.6,10.6,10.27],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "f3d0805bb6cb1283a49aaf903c97efd68a1a39f2382df2e8d025fe89a460ed9e" + }, + { + "id": "sym-2026091361-elevated-003-r2-f0", + "context": { + "target_position_m": [ + 35.6, + 65.0, + 22.0 + ], + "current_position_m": [ + 43.46, + 71.7, + 6.23 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 68.38, + 97.78 + ], + [ + 2.82, + 97.78 + ], + [ + 2.82, + 32.22 + ], + [ + 68.38, + 32.22 + ] + ], + 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"size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": "```json\n[{\"offset_m\":[-10.23,-10.23,9.89],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "84b1eee4f0e898847c646c15992972288919e8b92245b07c0110df27037b3239" + }, + { + "id": "sym-2026091361-elevated-003-r2-f1", + "context": { + "target_position_m": [ + -35.6, + 65.0, + 22.0 + ], + "current_position_m": [ + -43.46, + 71.7, + 6.23 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -68.38, + 97.78 + ], + [ + -2.82, + 97.78 + ], + [ + -2.82, + 32.22 + ], + [ + -68.38, + 32.22 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "southeast", + "side_ja": "南東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "elevated", + "strict_speed_limit": true, + "target_size_m": 5.03, + "required_pixels": 517, + "preferred_pitch_deg": -27.96, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 21.8715604829564, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-elevated-003", + "symmetry_turns": 2, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "be7b5122f4adc9b1d56a968bdd83f77ba006f29412b243554f1e0854a3c7af8f", + "rgb_sha256": "f40366198814d829f09c77d61a2d87364200b8d58033d42be31573805b9612b7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "1ded72c693d95d0518996e2a99ca2df20d7e3b1b3ba5d26bfb9e14404b747a04" + }, + "raw_text": "```json\n[{\"offset_m\":[10.21,-10.21,13.21],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "6734f18ab570e9b74710817ba254e428673e277f0f67826a4101490d4c34368f" + }, + { + "id": "sym-2026091361-elevated-003-r3-f0", + "context": { + "target_position_m": [ + -65.0, + 35.6, + 22.0 + ], + "current_position_m": [ + -71.7, + 43.46, + 6.23 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -97.78, + 68.38 + ], + [ + -97.78, + 2.82 + ], + [ + -32.22, + 2.82 + ], + [ + -32.22, + 68.38 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "southeast", + "side_ja": "南東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "elevated", + "strict_speed_limit": true, + "target_size_m": 5.03, + "required_pixels": 517, + "preferred_pitch_deg": -27.96, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 21.8715604829564, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, 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5.44, + "required_pixels": 365, + "preferred_pitch_deg": -45.64, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 33.50490287159521, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-range-001", + "symmetry_turns": 2, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "e0b640015b20099013ecbd4fcfeca8168f3cc5c56ee44a0058eae5f8ea713a03", + "rgb_sha256": "6b21e101e16694ea1a72b0ed8afee33724a5231e02ffe5b0ccd599db4ae84d42", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "3f4dc27b87e5353a52a4173c85199d055bf914968ff6ab72397733dcb74480a2" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,-20.23,16.25],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": 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"sym-2026091361-range-001", + "symmetry_turns": 3, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "e0b640015b20099013ecbd4fcfeca8168f3cc5c56ee44a0058eae5f8ea713a03", + "rgb_sha256": "6b21e101e16694ea1a72b0ed8afee33724a5231e02ffe5b0ccd599db4ae84d42", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "3f4dc27b87e5353a52a4173c85199d055bf914968ff6ab72397733dcb74480a2" + }, + "raw_text": "```json\n[{\"offset_m\":[17.23,0.0,13.25],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "c01808944625508626b04409c9cf9e99afdd01ee3650afaa3366a7c85b0a1c75" + }, + { + "id": "sym-2026091361-range-001-r3-f1", + "context": { + "target_position_m": [ + -6.7, + -78.2, + 0.0 + ], + "current_position_m": [ + -25.69, + -80.15, + 95.16 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -56.94, + -128.44 + ], + [ + -56.94, + -27.96 + ], + [ + 43.54, + -27.96 + ], + [ + 43.54, + -128.44 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "east", + "side_ja": "東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "range", + "strict_speed_limit": true, + "target_size_m": 5.44, + "required_pixels": 365, + "preferred_pitch_deg": -45.64, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 33.50490287159521, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-range-001", + "symmetry_turns": 3, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "e0b640015b20099013ecbd4fcfeca8168f3cc5c56ee44a0058eae5f8ea713a03", + "rgb_sha256": "6b21e101e16694ea1a72b0ed8afee33724a5231e02ffe5b0ccd599db4ae84d42", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "3f4dc27b87e5353a52a4173c85199d055bf914968ff6ab72397733dcb74480a2" + }, + "raw_text": "```json\n[{\"offset_m\":[17.23,0.0,13.7],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "643459877a3bd11e97de4d898efd0df711f9ccf8852748e860f702ac5378bdd9" + }, + { + "id": "sym-2026091361-range-002-r0-f0", + "context": { + "target_position_m": [ + -150.6, + 33.5, + 0.0 + ], + "current_position_m": [ + -159.2, + 25.57, + 26.49 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -181.91, + 2.19 + ], + [ + -119.29, + 2.19 + ], + [ + -119.29, + 64.81 + ], + [ + -181.91, + 64.81 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 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0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "e0b640015b20099013ecbd4fcfeca8168f3cc5c56ee44a0058eae5f8ea713a03", + "rgb_sha256": "6b21e101e16694ea1a72b0ed8afee33724a5231e02ffe5b0ccd599db4ae84d42", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "3f4dc27b87e5353a52a4173c85199d055bf914968ff6ab72397733dcb74480a2" + }, + "raw_text": "```json\n[{\"offset_m\":[7.63,7.63,9.21],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "1b995b996f91948d0054ba73be8fde65c4510350014a1a2ad9b48413159f07ff" + }, + { + "id": "sym-2026091361-range-003-r0-f0", + "context": { + "target_position_m": [ + 160.4, + -70.1, + 0.0 + ], + "current_position_m": [ + 146.03, + -82.44, + 11.11 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 116.88, + -113.62 + ], + [ + 203.92, + -113.62 + ], + [ + 203.92, + -26.58 + ], + [ + 116.88, + 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"6b21e101e16694ea1a72b0ed8afee33724a5231e02ffe5b0ccd599db4ae84d42", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "3f4dc27b87e5353a52a4173c85199d055bf914968ff6ab72397733dcb74480a2" + }, + "raw_text": "```json\n[{\"offset_m\":[12.21,12.21,13.4],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "d61452f594b1da0a0cd31848b8434f691e9f3f778a9695eca7de2103c7fe4d1b" + }, + { + "id": "sym-2026091361-range-003-r0-f1", + "context": { + "target_position_m": [ + -160.4, + -70.1, + 0.0 + ], + "current_position_m": [ + -146.03, + -82.44, + 11.11 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -116.88, + -113.62 + ], + [ + -203.92, + -113.62 + ], + [ + -203.92, + -26.58 + ], + [ + -116.88, + -26.58 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "northwest", + "side_ja": "北西", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "range", + "strict_speed_limit": true, + "target_size_m": 3.8, + "required_pixels": 294, + "preferred_pitch_deg": -43.15, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 29.056185150874445, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-range-003", + "symmetry_turns": 0, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "e0b640015b20099013ecbd4fcfeca8168f3cc5c56ee44a0058eae5f8ea713a03", + "rgb_sha256": "6b21e101e16694ea1a72b0ed8afee33724a5231e02ffe5b0ccd599db4ae84d42", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "3f4dc27b87e5353a52a4173c85199d055bf914968ff6ab72397733dcb74480a2" + }, + "raw_text": "```json\n[{\"offset_m\":[-12.69,12.69,10.0],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "93ebb235c02866125fbcad6e05e4b4f1d1d508aadc9a91837eca4f9176c9a33a" + }, + { + "id": "sym-2026091361-range-003-r1-f0", + "context": { + "target_position_m": [ + 70.1, + 160.4, + 0.0 + ], + "current_position_m": [ + 82.44, + 146.03, + 11.11 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 113.62, + 116.88 + ], + [ + 113.62, + 203.92 + ], + [ + 26.58, + 203.92 + ], + [ + 26.58, + 116.88 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "northwest", + "side_ja": "北西", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "range", + "strict_speed_limit": true, + "target_size_m": 3.8, + "required_pixels": 294, + "preferred_pitch_deg": -43.15, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 29.056185150874445, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-range-003", + "symmetry_turns": 1, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "e0b640015b20099013ecbd4fcfeca8168f3cc5c56ee44a0058eae5f8ea713a03", + "rgb_sha256": "6b21e101e16694ea1a72b0ed8afee33724a5231e02ffe5b0ccd599db4ae84d42", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "3f4dc27b87e5353a52a4173c85199d055bf914968ff6ab72397733dcb74480a2" + }, + "raw_text": "```json\n[{\"offset_m\":[-13.64,13.64,13.64],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "4d60a4dcef39485f8ba96daf9233a78b962ebc8f4b80cf236b134deacbd3164b" + }, + { + "id": "sym-2026091361-range-003-r1-f1", + 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"fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "e0b640015b20099013ecbd4fcfeca8168f3cc5c56ee44a0058eae5f8ea713a03", + "rgb_sha256": "6b21e101e16694ea1a72b0ed8afee33724a5231e02ffe5b0ccd599db4ae84d42", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "3f4dc27b87e5353a52a4173c85199d055bf914968ff6ab72397733dcb74480a2" + }, + "raw_text": "```json\n[{\"offset_m\":[-13.64,-13.64,10.06],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "aa56bd71a5ed189c1d1fa209dee83a45903df75b546c98924837ecb9a16f2022" + }, + { + "id": "sym-2026091361-range-003-r2-f0", + "context": { + "target_position_m": [ + -160.4, + 70.1, + 0.0 + ], + "current_position_m": [ + -146.03, + 82.44, + 11.11 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + 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"e0b640015b20099013ecbd4fcfeca8168f3cc5c56ee44a0058eae5f8ea713a03", + "rgb_sha256": "6b21e101e16694ea1a72b0ed8afee33724a5231e02ffe5b0ccd599db4ae84d42", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "3f4dc27b87e5353a52a4173c85199d055bf914968ff6ab72397733dcb74480a2" + }, + "raw_text": "```json\n[{\"offset_m\":[-12.69,-12.69,13.09],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "d66dcbd71f02d6e68214040ab3327df5955756b8b85cb212f318afee00c16ab1" + }, + { + "id": "sym-2026091361-range-003-r2-f1", + "context": { + "target_position_m": [ + 160.4, + 70.1, + 0.0 + ], + "current_position_m": [ + 146.03, + 82.44, + 11.11 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 116.88, + 113.62 + ], + [ + 203.92, + 113.62 + ], + [ + 203.92, + 26.58 + ], + [ + 116.88, + 26.58 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "southeast", + "side_ja": "南東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "range", + "strict_speed_limit": true, + "target_size_m": 3.8, + "required_pixels": 294, + "preferred_pitch_deg": -43.15, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 29.056185150874445, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-range-003", + "symmetry_turns": 2, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "e0b640015b20099013ecbd4fcfeca8168f3cc5c56ee44a0058eae5f8ea713a03", + "rgb_sha256": "6b21e101e16694ea1a72b0ed8afee33724a5231e02ffe5b0ccd599db4ae84d42", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "3f4dc27b87e5353a52a4173c85199d055bf914968ff6ab72397733dcb74480a2" + }, + "raw_text": "```json\n[{\"offset_m\":[12.21,-12.21,13.26],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "07cd632d25d2eb41617f73a9512ab1bf214957a805a732d6651e5e3f1d43e1b4" + }, + { + "id": "sym-2026091361-range-003-r3-f0", + "context": { + "target_position_m": [ + -70.1, + -160.4, + 0.0 + ], + "current_position_m": [ + -82.44, + -146.03, + 11.11 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -113.62, + -116.88 + ], + [ + -113.62, + -203.92 + ], + [ + -26.58, + -203.92 + ], + [ + -26.58, + -116.88 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "southeast", + "side_ja": "南東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "range", + "strict_speed_limit": true, + "target_size_m": 3.8, + "required_pixels": 294, + "preferred_pitch_deg": -43.15, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 29.056185150874445, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-range-003", + "symmetry_turns": 3, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "e0b640015b20099013ecbd4fcfeca8168f3cc5c56ee44a0058eae5f8ea713a03", + "rgb_sha256": "6b21e101e16694ea1a72b0ed8afee33724a5231e02ffe5b0ccd599db4ae84d42", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "3f4dc27b87e5353a52a4173c85199d055bf914968ff6ab72397733dcb74480a2" + }, + "raw_text": "```json\n[{\"offset_m\":[12.71,-12.71,13.08],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "af56b7eafe089e26bd08680de017f4c3a88e30f43cb5b2fca77f2bb484092daa" + }, + { + "id": "sym-2026091361-range-003-r3-f1", + "context": { + "target_position_m": [ + -70.1, + 160.4, + 0.0 + ], + "current_position_m": [ + -82.44, + 146.03, + 11.11 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -113.62, + 116.88 + ], + [ + -113.62, + 203.92 + ], + [ + -26.58, + 203.92 + ], + [ + -26.58, + 116.88 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "northeast", + "side_ja": "北東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "range", + "strict_speed_limit": true, + "target_size_m": 3.8, + "required_pixels": 294, + "preferred_pitch_deg": -43.15, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 29.056185150874445, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-range-003", + "symmetry_turns": 3, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "e0b640015b20099013ecbd4fcfeca8168f3cc5c56ee44a0058eae5f8ea713a03", + "rgb_sha256": "6b21e101e16694ea1a72b0ed8afee33724a5231e02ffe5b0ccd599db4ae84d42", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "3f4dc27b87e5353a52a4173c85199d055bf914968ff6ab72397733dcb74480a2" + }, + "raw_text": "```json\n[{\"offset_m\":[12.69,12.69,19.0],\"dwell_ms\":2000,\"speed_mps\":5.0}]\n```", + "model": "mixed240", + "prompt_sha256": "c40e0c06d53f613d8baa789d2b486e3e32d1db158558f34f53804c90ffee757d" + }, + { + "id": "sym-2026091361-speed_time-000-r0-f0", + "context": { + "target_position_m": [ + -8.6, + 100.6, + 0.0 + ], + "current_position_m": [ + -6.48, + 85.67, + 41.41 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -28.81, + 80.39 + ], + [ + 11.61, + 80.39 + ], + [ + 11.61, + 120.81 + ], + [ + -28.81, + 120.81 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 1.0, + "max_mission_seconds": 107, + "return_margin_seconds": 15 + }, + "side": "north", + "side_ja": "北", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 1.27, + "required_pixels": 127, + "preferred_pitch_deg": -53.33, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 22.480311669360752, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-000", + "symmetry_turns": 0, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "1679a5002dda3a3d085c6a36fa057829f2c6c4820285b6e359f5640d6f266fd5", + "rgb_sha256": "bf76e7bc224d57bb03245bf03f5c193c9ad0b14fc75662c464ef554dd10ed64a", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "817326de981baa6c387ef3ab9b9cfc80e5e89cf520ae3f8026a3ea54813856c7" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,16.23,10.4],\"dwell_ms\":2000,\"speed_mps\":1.0}]\n```", + "model": "mixed240", + "prompt_sha256": "23e377a522b39fde8e2b7b29f303c12bcc9afbe879e6fd47b54bf162d8674556" + }, + { + "id": "sym-2026091361-speed_time-000-r0-f1", + "context": { + "target_position_m": [ + 8.6, + 100.6, + 0.0 + ], + "current_position_m": [ + 6.48, + 85.67, + 41.41 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 28.81, + 80.39 + ], + [ + -11.61, + 80.39 + ], + [ + -11.61, + 120.81 + ], + [ + 28.81, + 120.81 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 1.0, + "max_mission_seconds": 107, + "return_margin_seconds": 15 + }, + "side": "north", + "side_ja": "北", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 1.27, + "required_pixels": 127, + "preferred_pitch_deg": -53.33, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 22.480311669360752, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-000", + "symmetry_turns": 0, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "1679a5002dda3a3d085c6a36fa057829f2c6c4820285b6e359f5640d6f266fd5", + "rgb_sha256": "bf76e7bc224d57bb03245bf03f5c193c9ad0b14fc75662c464ef554dd10ed64a", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "817326de981baa6c387ef3ab9b9cfc80e5e89cf520ae3f8026a3ea54813856c7" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,16.23,10.4],\"dwell_ms\":2000,\"speed_mps\":1.0}]\n```", + "model": "mixed240", + "prompt_sha256": "ff18eab12407b5ae40c9c2484039bc2ea6c0d095874ed97e723a7622b6b82a95" + }, + { + "id": "sym-2026091361-speed_time-000-r1-f0", + "context": { + "target_position_m": [ + -100.6, + -8.6, + 0.0 + ], + "current_position_m": [ + -85.67, + -6.48, + 41.41 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -80.39, + -28.81 + ], + [ + -80.39, + 11.61 + ], + [ + -120.81, + 11.61 + ], + [ + -120.81, + -28.81 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 1.0, + "max_mission_seconds": 107, + "return_margin_seconds": 15 + }, + "side": "west", + "side_ja": "西", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 1.27, + "required_pixels": 127, + "preferred_pitch_deg": -53.33, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 22.480311669360752, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-000", + "symmetry_turns": 1, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "1679a5002dda3a3d085c6a36fa057829f2c6c4820285b6e359f5640d6f266fd5", + "rgb_sha256": "bf76e7bc224d57bb03245bf03f5c193c9ad0b14fc75662c464ef554dd10ed64a", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "817326de981baa6c387ef3ab9b9cfc80e5e89cf520ae3f8026a3ea54813856c7" + }, + "raw_text": "```json\n[{\"offset_m\":[-14.29,0.0,10.2],\"dwell_ms\":2000,\"speed_mps\":1.0}]\n```", + "model": "mixed240", + 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true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "094d09b4e79cfbabd482f04c0921e06da9bbaa1246618637ab49d9312655ac5d", + "rgb_sha256": "0cc9dddbd8668189a938af5b3b6ba6923d0b9b6dc8fba44a5a31c20b13917023", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "07a8509371bf169e4e8db7792a6bac5a582c0a5ee9259158df7b28da5b8e6df9" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,13.24,12.28],\"dwell_ms\":2000,\"speed_mps\":2.0}]\n```", + "model": "mixed240", + "prompt_sha256": "f784f1e7b852061e5f39276a2b6478a77f0c2d9a48a7292deb753999745f32bb" + }, + { + "id": "sym-2026091361-speed_time-002-r1-f0", + "context": { + "target_position_m": [ + -17.2, + 55.6, + 0.0 + ], + "current_position_m": [ + -10.64, + 56.12, + 83.73 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + 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+ "return_margin_seconds": 15 + }, + "side": "west", + "side_ja": "西", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 1.3, + "required_pixels": 140, + "preferred_pitch_deg": -31.95, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 20.874575121549274, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-002", + "symmetry_turns": 1, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "094d09b4e79cfbabd482f04c0921e06da9bbaa1246618637ab49d9312655ac5d", + "rgb_sha256": "0cc9dddbd8668189a938af5b3b6ba6923d0b9b6dc8fba44a5a31c20b13917023", + "size": [ + 640, + 640 + ] + } + ], + "sha256": 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1.3, + "required_pixels": 140, + "preferred_pitch_deg": -31.95, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 20.874575121549274, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-002", + "symmetry_turns": 2, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "094d09b4e79cfbabd482f04c0921e06da9bbaa1246618637ab49d9312655ac5d", + "rgb_sha256": "0cc9dddbd8668189a938af5b3b6ba6923d0b9b6dc8fba44a5a31c20b13917023", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "07a8509371bf169e4e8db7792a6bac5a582c0a5ee9259158df7b28da5b8e6df9" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,-14.09,12.91],\"dwell_ms\":2000,\"speed_mps\":2.0}]\n```", + "model": "mixed240", + "prompt_sha256": 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65.18 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -30.09, + 7.99 + ], + [ + -30.09, + 69.61 + ], + [ + -91.71, + 69.61 + ], + [ + -91.71, + 7.99 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 2.0, + "max_mission_seconds": 98, + "return_margin_seconds": 15 + }, + "side": "west", + "side_ja": "西", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 5.34, + "required_pixels": 351, + "preferred_pitch_deg": -32.24, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 34.20081604398474, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-003", + "symmetry_turns": 1, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + 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"```json\n[{\"offset_m\":[0.0,-20.24,16.67],\"dwell_ms\":2000,\"speed_mps\":2.0}]\n```", + "model": "mixed240", + "prompt_sha256": "2e11c764a013b33efe9f956707541800db47a7b39111d0a51c9ce9d5b205cff0" + }, + { + "id": "sym-2026091361-speed_time-003-r2-f1", + "context": { + "target_position_m": [ + 38.8, + -60.9, + 0.0 + ], + "current_position_m": [ + 38.2, + -40.07, + 65.18 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 7.99, + -30.09 + ], + [ + 69.61, + -30.09 + ], + [ + 69.61, + -91.71 + ], + [ + 7.99, + -91.71 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 2.0, + "max_mission_seconds": 98, + "return_margin_seconds": 15 + }, + "side": "south", + "side_ja": "南", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 5.34, + "required_pixels": 351, + "preferred_pitch_deg": -32.24, + "viewpoint_protocol": 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0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "094d09b4e79cfbabd482f04c0921e06da9bbaa1246618637ab49d9312655ac5d", + "rgb_sha256": "0cc9dddbd8668189a938af5b3b6ba6923d0b9b6dc8fba44a5a31c20b13917023", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "07a8509371bf169e4e8db7792a6bac5a582c0a5ee9259158df7b28da5b8e6df9" + }, + "raw_text": "```json\n[{\"offset_m\":[-34.29,0.0,32.69],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": "658d0695486179043149fd5c98c776a380acd4165d07a37ebc4aec3bc85da320" + }, + { + "id": "sym-2026091361-speed_time-004-r2-f0", + "context": { + "target_position_m": [ + -122.0, + -166.2, + 0.0 + ], + "current_position_m": [ + -127.67, + -137.08, + 109.37 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -62.58, + -106.78 + ], + [ + -181.42, + -106.78 + ], + [ + -181.42, + -225.62 + ], + [ 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"max_viewpoints": 3, + "profile": "person", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 2.07, + "required_pixels": 70, + "preferred_pitch_deg": -48.47, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 66.47749307939536, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-004", + "symmetry_turns": 2, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "094d09b4e79cfbabd482f04c0921e06da9bbaa1246618637ab49d9312655ac5d", + "rgb_sha256": "0cc9dddbd8668189a938af5b3b6ba6923d0b9b6dc8fba44a5a31c20b13917023", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "07a8509371bf169e4e8db7792a6bac5a582c0a5ee9259158df7b28da5b8e6df9" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,-42.79,39.28],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": "c2b62c83ec85998c930476b2513c0cf6f0dd69b7c572403974d45254e61afbbc" + }, + { + "id": "sym-2026091361-speed_time-004-r3-f0", + "context": { + "target_position_m": [ + 166.2, + -122.0, + 0.0 + ], + "current_position_m": [ + 137.08, + -127.67, + 109.37 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 106.78, + -62.58 + ], + [ + 106.78, + -181.42 + ], + [ + 225.62, + -181.42 + ], + [ + 225.62, + -62.58 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 3.0, + "max_mission_seconds": 93, + "return_margin_seconds": 15 + }, + "side": "east", + "side_ja": "東", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 2.07, + "required_pixels": 70, + "preferred_pitch_deg": -48.47, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 66.47749307939536, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-004", + "symmetry_turns": 3, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "094d09b4e79cfbabd482f04c0921e06da9bbaa1246618637ab49d9312655ac5d", + "rgb_sha256": "0cc9dddbd8668189a938af5b3b6ba6923d0b9b6dc8fba44a5a31c20b13917023", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "07a8509371bf169e4e8db7792a6bac5a582c0a5ee9259158df7b28da5b8e6df9" + }, + "raw_text": "```json\n[{\"offset_m\":[39.23,0.0,40.27],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": "1e53788efe73087fbfa56fae054ceed9b388dda511f3d956afe46b78018e219e" + }, + { + "id": 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"symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "094d09b4e79cfbabd482f04c0921e06da9bbaa1246618637ab49d9312655ac5d", + "rgb_sha256": "0cc9dddbd8668189a938af5b3b6ba6923d0b9b6dc8fba44a5a31c20b13917023", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "07a8509371bf169e4e8db7792a6bac5a582c0a5ee9259158df7b28da5b8e6df9" + }, + "raw_text": "```json\n[{\"offset_m\":[39.23,0.0,42.26],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": "97512230c1fdfedc41b050fbbe15d1183aea09daa6e86e13411798efc3f42399" + }, + { + "id": "sym-2026091361-speed_time-005-r0-f0", + "context": { + "target_position_m": [ + -171.3, + 138.4, + 0.0 + ], + "current_position_m": [ + -170.01, + 94.49, + 10.62 + ], + "envelope": { + "revision": "e1", + "origin": { + 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"sha256": "07a8509371bf169e4e8db7792a6bac5a582c0a5ee9259158df7b28da5b8e6df9" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,39.23,27.41],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": "207272a139ec56c0b1d53672189f51dd36cd9fe9d72ed62ef475caca3b0f389a" + }, + { + "id": "sym-2026091361-speed_time-005-r1-f0", + "context": { + "target_position_m": [ + -138.4, + -171.3, + 0.0 + ], + "current_position_m": [ + -94.49, + -170.01, + 10.62 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -82.04, + -227.66 + ], + [ + -82.04, + -114.94 + ], + [ + -194.76, + -114.94 + ], + [ + -194.76, + -227.66 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 3.0, + "max_mission_seconds": 85, + "return_margin_seconds": 15 + }, + "side": "west", + "side_ja": "西", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 4.13, + "required_pixels": 148, + "preferred_pitch_deg": -41.82, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 62.732221077337776, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-005", + "symmetry_turns": 1, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "094d09b4e79cfbabd482f04c0921e06da9bbaa1246618637ab49d9312655ac5d", + "rgb_sha256": "0cc9dddbd8668189a938af5b3b6ba6923d0b9b6dc8fba44a5a31c20b13917023", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "07a8509371bf169e4e8db7792a6bac5a582c0a5ee9259158df7b28da5b8e6df9" + }, + "raw_text": "```json\n[{\"offset_m\":[-30.41,0.0,30.41],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": 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"symmetry_orbit": "sym-2026091361-speed_time-005", + "symmetry_turns": 1, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "094d09b4e79cfbabd482f04c0921e06da9bbaa1246618637ab49d9312655ac5d", + "rgb_sha256": "0cc9dddbd8668189a938af5b3b6ba6923d0b9b6dc8fba44a5a31c20b13917023", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "07a8509371bf169e4e8db7792a6bac5a582c0a5ee9259158df7b28da5b8e6df9" + }, + "raw_text": "```json\n[{\"offset_m\":[-30.6,0.0,40.26],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": "511ea1eb977d9976738a76e486b0418bb6656903985e3e9227bdf806964b1e4e" + }, + { + "id": "sym-2026091361-speed_time-005-r2-f0", + "context": { + "target_position_m": [ + 171.3, + -138.4, + 0.0 + ], + "current_position_m": [ + 170.01, + -94.49, + 10.62 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 227.66, + -82.04 + ], + [ + 114.94, + -82.04 + ], + [ + 114.94, + -194.76 + ], + [ + 227.66, + -194.76 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 3.0, + "max_mission_seconds": 85, + "return_margin_seconds": 15 + }, + "side": "south", + "side_ja": "南", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 4.13, + "required_pixels": 148, + "preferred_pitch_deg": -41.82, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 62.732221077337776, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-005", + "symmetry_turns": 2, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "094d09b4e79cfbabd482f04c0921e06da9bbaa1246618637ab49d9312655ac5d", + "rgb_sha256": "0cc9dddbd8668189a938af5b3b6ba6923d0b9b6dc8fba44a5a31c20b13917023", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "07a8509371bf169e4e8db7792a6bac5a582c0a5ee9259158df7b28da5b8e6df9" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,-42.73,27.26],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": "48aa331f27ce46d1c4e06824afad595565397beb9811f8226e3c10014b93f71f" + }, + { + "id": "sym-2026091361-speed_time-005-r2-f1", + "context": { + "target_position_m": [ + -171.3, + -138.4, + 0.0 + ], + "current_position_m": [ + -170.01, + -94.49, + 10.62 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -227.66, + -82.04 + ], + [ + -114.94, + -82.04 + ], + [ + -114.94, + -194.76 + ], + [ + -227.66, 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"max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 4.13, + "required_pixels": 148, + "preferred_pitch_deg": -41.82, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 62.732221077337776, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-005", + "symmetry_turns": 3, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "094d09b4e79cfbabd482f04c0921e06da9bbaa1246618637ab49d9312655ac5d", + "rgb_sha256": "0cc9dddbd8668189a938af5b3b6ba6923d0b9b6dc8fba44a5a31c20b13917023", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "07a8509371bf169e4e8db7792a6bac5a582c0a5ee9259158df7b28da5b8e6df9" + }, + "raw_text": "```json\n[{\"offset_m\":[39.21,0.0,40.73],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": "04f4a1e578ece1d59d4933beb415133b4757c5c149794d97f83c8858c13fca08" + }, + { + "id": "sym-2026091361-speed_time-005-r3-f1", + "context": { + "target_position_m": [ + 138.4, + -171.3, + 0.0 + ], + "current_position_m": [ + 94.49, + -170.01, + 10.62 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 82.04, + -227.66 + ], + [ + 82.04, + -114.94 + ], + [ + 194.76, + -114.94 + ], + [ + 194.76, + -227.66 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 3.0, + "max_mission_seconds": 85, + "return_margin_seconds": 15 + }, + "side": "east", + "side_ja": "東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 4.13, + "required_pixels": 148, + "preferred_pitch_deg": -41.82, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 62.732221077337776, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-005", + "symmetry_turns": 3, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "094d09b4e79cfbabd482f04c0921e06da9bbaa1246618637ab49d9312655ac5d", + "rgb_sha256": "0cc9dddbd8668189a938af5b3b6ba6923d0b9b6dc8fba44a5a31c20b13917023", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "07a8509371bf169e4e8db7792a6bac5a582c0a5ee9259158df7b28da5b8e6df9" + }, + "raw_text": "```json\n[{\"offset_m\":[39.21,0.0,39.21],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": "0c5856d2b64e30761e67ab541afbd037a149fae96b76218a512b7f709056246b" + }, + { + "id": 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96.52 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 6.34, + 238.16 + ], + [ + 135.66, + 238.16 + ], + [ + 135.66, + 108.84 + ], + [ + 6.34, + 108.84 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 4.0, + "max_mission_seconds": 81, + "return_margin_seconds": 15 + }, + "side": "south", + "side_ja": "南", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 1.47, + "required_pixels": 46, + "preferred_pitch_deg": -32.84, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 71.83925685643544, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-006", + "symmetry_turns": 2, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "180e2f56260d0c291c48661940acc5ce397d86d1773649eb7404cb7b9a950b0b", + "rgb_sha256": "8edb2565f8c5fc2921cdb2fed2f55ca22d98a006ab80aa4a9368072818646691", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "c7b76fa69b1c5a7baa0bac517ccc346e73531fd0eab9e28d2cdc56fc5152153c" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,-40.24,40.24],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "a2f69d1e4289c1b97c47e57372c43d5c327d71611047d074edfaaeefb94f19a7" + }, + { + "id": "sym-2026091361-speed_time-006-r3-f0", + "context": { + "target_position_m": [ + -173.5, + -71.0, + 0.0 + ], + "current_position_m": [ + -208.7, + -70.51, + 96.52 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -238.16, + -6.34 + ], + [ + -238.16, + -135.66 + ], + [ + -108.84, + -135.66 + ], + [ + -108.84, + 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"max_viewpoints": 3, + "profile": "person", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 1.47, + "required_pixels": 46, + "preferred_pitch_deg": -32.84, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 71.83925685643544, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-006", + "symmetry_turns": 3, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "180e2f56260d0c291c48661940acc5ce397d86d1773649eb7404cb7b9a950b0b", + "rgb_sha256": "8edb2565f8c5fc2921cdb2fed2f55ca22d98a006ab80aa4a9368072818646691", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "c7b76fa69b1c5a7baa0bac517ccc346e73531fd0eab9e28d2cdc56fc5152153c" + }, + "raw_text": "```json\n[{\"offset_m\":[40.23,0.0,40.23],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "9a17dc240bd687759c7df68ddada324a311ff6f037b2b01725c7a6784248f071" + }, + { + "id": "sym-2026091361-speed_time-007-r0-f0", + "context": { + "target_position_m": [ + 64.2, + -42.1, + 0.0 + ], + "current_position_m": [ + 59.02, + -76.36, + 60.16 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 0.54, + -105.76 + ], + [ + 127.86, + -105.76 + ], + [ + 127.86, + 21.56 + ], + [ + 0.54, + 21.56 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 4.0, + "max_mission_seconds": 72, + "return_margin_seconds": 15 + }, + "side": "north", + "side_ja": "北", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 3.92, + "required_pixels": 125, + "preferred_pitch_deg": -27.13, + "viewpoint_protocol": 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"symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "180e2f56260d0c291c48661940acc5ce397d86d1773649eb7404cb7b9a950b0b", + "rgb_sha256": "8edb2565f8c5fc2921cdb2fed2f55ca22d98a006ab80aa4a9368072818646691", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "c7b76fa69b1c5a7baa0bac517ccc346e73531fd0eab9e28d2cdc56fc5152153c" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,39.23,40.8],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "e1184a1b0bf5233cd267177fb79625ebed0cb6e13477341cf33471436cfb19a4" + }, + { + "id": "sym-2026091361-speed_time-007-r1-f0", + "context": { + "target_position_m": [ + 42.1, + 64.2, + 0.0 + ], + "current_position_m": [ + 76.36, + 59.02, + 60.16 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 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+ "sha256": "180e2f56260d0c291c48661940acc5ce397d86d1773649eb7404cb7b9a950b0b", + "rgb_sha256": "8edb2565f8c5fc2921cdb2fed2f55ca22d98a006ab80aa4a9368072818646691", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "c7b76fa69b1c5a7baa0bac517ccc346e73531fd0eab9e28d2cdc56fc5152153c" + }, + "raw_text": "```json\n[{\"offset_m\":[-39.23,0.0,39.23],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "ae264693415bdbe496d6303c3cc19e7df1879f007c0cf9db4cdaa7ac42da90d8" + }, + { + "id": "sym-2026091361-speed_time-007-r1-f1", + "context": { + "target_position_m": [ + 42.1, + -64.2, + 0.0 + ], + "current_position_m": [ + 76.36, + -59.02, + 60.16 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 105.76, + -0.54 + ], + [ + 105.76, + -127.86 + ], + [ + -21.56, + -127.86 + ], + [ + -21.56, + -0.54 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 4.0, + 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"c7b76fa69b1c5a7baa0bac517ccc346e73531fd0eab9e28d2cdc56fc5152153c" + }, + "raw_text": "```json\n[{\"offset_m\":[-39.23,0.0,36.47],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "d91e6536f7c45a6dfdd4fa98f58d97e3908e78174f24cd140dbf752b9da34bac" + }, + { + "id": "sym-2026091361-speed_time-007-r2-f0", + "context": { + "target_position_m": [ + -64.2, + 42.1, + 0.0 + ], + "current_position_m": [ + -59.02, + 76.36, + 60.16 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -0.54, + 105.76 + ], + [ + -127.86, + 105.76 + ], + [ + -127.86, + -21.56 + ], + [ + -0.54, + -21.56 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 4.0, + "max_mission_seconds": 72, + "return_margin_seconds": 15 + }, + "side": "south", + "side_ja": "南", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 3.92, + "required_pixels": 125, + "preferred_pitch_deg": -27.13, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 70.49825739511532, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-007", + "symmetry_turns": 2, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "180e2f56260d0c291c48661940acc5ce397d86d1773649eb7404cb7b9a950b0b", + "rgb_sha256": "8edb2565f8c5fc2921cdb2fed2f55ca22d98a006ab80aa4a9368072818646691", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "c7b76fa69b1c5a7baa0bac517ccc346e73531fd0eab9e28d2cdc56fc5152153c" + }, + "raw_text": "```json\n[{\"offset_m\":[0.0,-49.23,49.23],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "e2bf222213b97d42f8b34ea462da55e6bb706c7a6b1b57af68a2eaddcad853da" + }, + { + "id": "sym-2026091361-speed_time-007-r2-f1", + "context": { + "target_position_m": [ + 64.2, + 42.1, + 0.0 + ], + "current_position_m": [ + 59.02, + 76.36, + 60.16 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 0.54, + 105.76 + ], + [ + 127.86, + 105.76 + ], + [ + 127.86, + -21.56 + ], + [ + 0.54, + -21.56 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 4.0, + "max_mission_seconds": 72, + "return_margin_seconds": 15 + }, + "side": "south", + "side_ja": "南", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 3.92, + "required_pixels": 125, + "preferred_pitch_deg": -27.13, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 70.49825739511532, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + 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60.16 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -105.76, + -0.54 + ], + [ + -105.76, + -127.86 + ], + [ + 21.56, + -127.86 + ], + [ + 21.56, + -0.54 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 4.0, + "max_mission_seconds": 72, + "return_margin_seconds": 15 + }, + "side": "east", + "side_ja": "東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 3.92, + "required_pixels": 125, + "preferred_pitch_deg": -27.13, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 70.49825739511532, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-007", + "symmetry_turns": 3, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "180e2f56260d0c291c48661940acc5ce397d86d1773649eb7404cb7b9a950b0b", + "rgb_sha256": "8edb2565f8c5fc2921cdb2fed2f55ca22d98a006ab80aa4a9368072818646691", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "c7b76fa69b1c5a7baa0bac517ccc346e73531fd0eab9e28d2cdc56fc5152153c" + }, + "raw_text": "```json\n[{\"offset_m\":[39.23,0.0,42.77],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "6fc6d9a66bba536e8bca6eab9b286d048c9554e2976bcf0b9a64d253eeba7094" + }, + { + "id": "sym-2026091361-speed_time-007-r3-f1", + "context": { + "target_position_m": [ + -42.1, + 64.2, + 0.0 + ], + "current_position_m": [ + -76.36, + 59.02, + 60.16 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -105.76, + 0.54 + ], + [ + -105.76, + 127.86 + ], + [ + 21.56, + 127.86 + ], + [ + 21.56, + 0.54 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 4.0, + "max_mission_seconds": 72, + "return_margin_seconds": 15 + }, + "side": "east", + "side_ja": "東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 3.92, + "required_pixels": 125, + "preferred_pitch_deg": -27.13, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 70.49825739511532, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-007", + "symmetry_turns": 3, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "180e2f56260d0c291c48661940acc5ce397d86d1773649eb7404cb7b9a950b0b", + "rgb_sha256": "8edb2565f8c5fc2921cdb2fed2f55ca22d98a006ab80aa4a9368072818646691", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "c7b76fa69b1c5a7baa0bac517ccc346e73531fd0eab9e28d2cdc56fc5152153c" + }, + "raw_text": "```json\n[{\"offset_m\":[39.23,0.0,43.86],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "b9c5c035dfa201bfb06198613a5463ab9119db31363dd4bab59492dc61ce0ee6" + }, + { + "id": "sym-2026091361-speed_time-008-r0-f0", + "context": { + "target_position_m": [ + 83.6, + 163.6, + 0.0 + ], + "current_position_m": [ + 37.45, + 126.59, + 101.35 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 6.01, + 86.01 + ], + [ + 161.19, + 86.01 + ], + [ + 161.19, + 241.19 + ], + [ + 6.01, + 241.19 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 1.0, + "max_mission_seconds": 282, + "return_margin_seconds": 15 + }, + "side": "northeast", + "side_ja": "北東", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 1.7, + "required_pixels": 44, + "preferred_pitch_deg": -53.41, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 86.8557496316211, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-008", + "symmetry_turns": 0, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "e35aaa2657583867469661a0e344fbd37c24782826565db3dbd74ceaf83ae187", + "rgb_sha256": "0988299edb4be23c9959b2ce10de86fb3437e25b9f612087386c1d9e8d01efd8", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "89446ee2944b02bca6968c9a74a7e309d787f143f76d7f2c57e8250a708b308d" + }, + "raw_text": "```json\n[{\"offset_m\":[33.64,33.64,43.28],\"dwell_ms\":2000,\"speed_mps\":1.0}]\n```", + "model": "mixed240", + "prompt_sha256": "c183dccbc8357b97d5e7e75c244a72127a65f6bd0f07ae1130e22673f95060aa" + }, + { + "id": "sym-2026091361-speed_time-008-r0-f1", + "context": { + "target_position_m": [ + -83.6, + 163.6, + 0.0 + ], + "current_position_m": [ + -37.45, + 126.59, + 101.35 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -6.01, + 86.01 + ], + [ + -161.19, + 86.01 + ], + [ + -161.19, + 241.19 + ], + [ + -6.01, + 241.19 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 1.0, + "max_mission_seconds": 282, + "return_margin_seconds": 15 + }, + "side": "northwest", + "side_ja": "北西", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 1.7, + "required_pixels": 44, + "preferred_pitch_deg": -53.41, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 86.8557496316211, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-008", + "symmetry_turns": 0, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "e35aaa2657583867469661a0e344fbd37c24782826565db3dbd74ceaf83ae187", + "rgb_sha256": "0988299edb4be23c9959b2ce10de86fb3437e25b9f612087386c1d9e8d01efd8", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "89446ee2944b02bca6968c9a74a7e309d787f143f76d7f2c57e8250a708b308d" + }, + "raw_text": "```json\n[{\"offset_m\":[-39.23,39.23,40.27],\"dwell_ms\":2000,\"speed_mps\":1.0}]\n```", + "model": "mixed240", + "prompt_sha256": "893ffbc2db3d7d29096dd0d41356c3f24e5da7d236d4e11c12373f51159023a4" + }, + { + "id": "sym-2026091361-speed_time-008-r1-f0", + "context": { + "target_position_m": [ + -163.6, + 83.6, + 0.0 + ], + 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"7cf825c95cdfcb9ca4b75a9d43ff7683c3a20d6872abc510179a2bca43f90df5" + }, + { + "id": "sym-2026091361-speed_time-011-r2-f0", + "context": { + "target_position_m": [ + 98.8, + -74.0, + 0.0 + ], + "current_position_m": [ + 126.65, + -41.16, + 1.88 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 168.09, + -4.71 + ], + [ + 29.51, + -4.71 + ], + [ + 29.51, + -143.29 + ], + [ + 168.09, + -143.29 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 2.0, + "max_mission_seconds": 135, + "return_margin_seconds": 15 + }, + "side": "southwest", + "side_ja": "南西", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 5.57, + "required_pixels": 163, + "preferred_pitch_deg": -39.4, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 76.81922453885853, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + 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"21ea302735f3a20e117b40829cf9c5c5950e0c345661731b210671d26d6e9400" + }, + { + "id": "sym-2026091361-speed_time-012-r2-f1", + "context": { + "target_position_m": [ + 152.8, + 4.1, + 0.0 + ], + "current_position_m": [ + 146.41, + 11.73, + 98.38 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 125.9, + 31.0 + ], + [ + 179.7, + 31.0 + ], + [ + 179.7, + -22.8 + ], + [ + 125.9, + -22.8 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 3.0, + "max_mission_seconds": 84, + "return_margin_seconds": 15 + }, + "side": "southeast", + "side_ja": "南東", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 2.1, + "required_pixels": 158, + "preferred_pitch_deg": -52.09, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 29.878895256745306, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + 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+ "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[12.64,-12.64,16.47],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": "b2a28b26fd9aa99677547d4d7f33ab6c73868266e1db208e847f5be0784e575c" + }, + { + "id": "sym-2026091361-speed_time-012-r3-f1", + "context": { + "target_position_m": [ + -4.1, + 152.8, + 0.0 + ], + "current_position_m": [ + -11.73, + 146.41, + 98.38 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -31.0, + 125.9 + ], + [ + -31.0, + 179.7 + ], + [ + 22.8, + 179.7 + ], + [ + 22.8, + 125.9 + ] + 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"symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[-17.74,17.74,13.86],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": "f1bdf1867842c4afd684066940da134cddf5c7479447ae03419ec86e5d5fe6d4" + }, + { + "id": "sym-2026091361-speed_time-013-r1-f1", + "context": { + "target_position_m": [ + -18.9, + -177.6, + 0.0 + ], + "current_position_m": [ + -8.34, + -171.25, + 16.77 + ], + "envelope": { + "revision": "e1", + "origin": { + 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4.43, + "required_pixels": 261, + "preferred_pitch_deg": -39.14, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 38.156237814278974, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-013", + "symmetry_turns": 2, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[12.23,-12.23,16.77],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": "3dde58e281c172095e9982fec9fc757a8f6910e08ead76bb31aa392366231f26" + }, + { + "id": "sym-2026091361-speed_time-013-r3-f0", + "context": { + "target_position_m": [ + 18.9, + -177.6, + 0.0 + ], + "current_position_m": [ + 8.34, + -171.25, + 16.77 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -15.5, + -143.2 + ], + [ + -15.5, + -212.0 + ], + [ + 53.3, + -212.0 + ], + [ + 53.3, + -143.2 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 3.0, + "max_mission_seconds": 46, + "return_margin_seconds": 15 + }, + "side": "southeast", + "side_ja": "南東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 4.43, + "required_pixels": 261, + "preferred_pitch_deg": -39.14, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 38.156237814278974, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-013", + "symmetry_turns": 3, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[12.64,-12.64,16.0],\"dwell_ms\":2000,\"speed_mps\":3.0}]\n```", + "model": "mixed240", + "prompt_sha256": "a7fedb5109ce8fde1562affec01059b2d54e0cf4280204dfcdd6896618b052fd" + }, + { + "id": "sym-2026091361-speed_time-013-r3-f1", + "context": { + "target_position_m": [ + 18.9, + 177.6, + 0.0 + ], + "current_position_m": [ + 8.34, + 171.25, + 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103.93 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -36.49, + 123.81 + ], + [ + -1.51, + 123.81 + ], + [ + -1.51, + 158.79 + ], + [ + -36.49, + 158.79 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 4.0, + "max_mission_seconds": 74, + "return_margin_seconds": 15 + }, + "side": "northeast", + "side_ja": "北東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 3.47, + "required_pixels": 401, + "preferred_pitch_deg": -46.48, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 19.453037778723644, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-015", + "symmetry_turns": 0, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[7.64,7.64,10.06],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "e4a425d93491a1b19a47ace22e6d63ef0260b36d58686b27378d5abce008f123" + }, + { + "id": "sym-2026091361-speed_time-015-r0-f1", + "context": { + "target_position_m": [ + 19.0, + 141.3, + 0.0 + ], + "current_position_m": [ + 25.57, + 135.54, + 103.93 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 36.49, + 123.81 + ], + [ + 1.51, + 123.81 + ], + [ + 1.51, + 158.79 + ], + [ + 36.49, + 158.79 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 4.0, + "max_mission_seconds": 74, + "return_margin_seconds": 15 + }, + "side": "northwest", + "side_ja": "北西", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 3.47, + "required_pixels": 401, + "preferred_pitch_deg": -46.48, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 19.453037778723644, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-015", + "symmetry_turns": 0, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[-10.23,10.23,9.2],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "402719edf36f0ae48e8d51d066024ec0a5342cfc83120809f0482a08387989fd" + }, + { + "id": "sym-2026091361-speed_time-015-r1-f0", + "context": { + "target_position_m": [ + -141.3, + -19.0, + 0.0 + ], + "current_position_m": [ + -135.54, + -25.57, + 103.93 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -123.81, + -36.49 + ], + [ + -123.81, + -1.51 + ], + [ + -158.79, + -1.51 + ], + [ + -158.79, + -36.49 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 4.0, + "max_mission_seconds": 74, + "return_margin_seconds": 15 + }, + "side": "northwest", + "side_ja": "北西", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 3.47, + "required_pixels": 401, + "preferred_pitch_deg": -46.48, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 19.453037778723644, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-015", + "symmetry_turns": 1, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[-9.23,9.23,10.29],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "124f3c359cec7e486d4cf23dd9b3795faca59424c0faffcb2c61056c88848a54" + }, + { + "id": "sym-2026091361-speed_time-015-r1-f1", + "context": { + "target_position_m": [ + -141.3, + 19.0, + 0.0 + ], + "current_position_m": [ + -135.54, + 25.57, + 103.93 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -123.81, + 36.49 + ], + [ + -123.81, + 1.51 + ], + [ + -158.79, + 1.51 + ], + [ + -158.79, + 36.49 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 4.0, + "max_mission_seconds": 74, + "return_margin_seconds": 15 + }, + "side": "southwest", + "side_ja": "南西", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 3.47, + "required_pixels": 401, + "preferred_pitch_deg": -46.48, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 19.453037778723644, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-015", + "symmetry_turns": 1, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[-9.23,-9.23,10.29],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "90e0d3559b0b8fe1aa3212011a83a830b84c807fc98c97a38dd988994f7dc59c" + }, + { + "id": "sym-2026091361-speed_time-015-r2-f0", + "context": { + "target_position_m": [ + 19.0, + -141.3, + 0.0 + ], + "current_position_m": [ + 25.57, + -135.54, + 103.93 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 36.49, + -123.81 + ], + [ + 1.51, + -123.81 + ], + [ + 1.51, + -158.79 + ], + [ + 36.49, + -158.79 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 4.0, + "max_mission_seconds": 74, + "return_margin_seconds": 15 + }, + "side": "southwest", + "side_ja": "南西", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 3.47, + "required_pixels": 401, + "preferred_pitch_deg": -46.48, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 19.453037778723644, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-015", + "symmetry_turns": 2, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[-10.23,-10.23,9.2],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "6f0a458208e2120c414be2e9e887fd0fc573174cac696653ea9fdacb1d3a2a52" + }, + { + "id": "sym-2026091361-speed_time-015-r2-f1", + "context": { + "target_position_m": [ + -19.0, + -141.3, + 0.0 + ], + "current_position_m": [ + -25.57, + -135.54, + 103.93 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -36.49, + -123.81 + ], + [ + -1.51, + -123.81 + ], + [ + -1.51, + -158.79 + ], + [ + -36.49, + -158.79 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 4.0, + "max_mission_seconds": 74, + "return_margin_seconds": 15 + }, + "side": "southeast", + "side_ja": "南東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 3.47, + "required_pixels": 401, + "preferred_pitch_deg": -46.48, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 19.453037778723644, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-015", + "symmetry_turns": 2, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[7.64,-7.64,10.29],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "dce074ef0c8b0f022f4cfa8d8e3a831a0099499e109e039d431ca7a06b9b00bb" + }, + { + "id": "sym-2026091361-speed_time-015-r3-f0", + "context": { + "target_position_m": [ + 141.3, + 19.0, + 0.0 + ], + "current_position_m": [ + 135.54, + 25.57, + 103.93 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 123.81, + 36.49 + ], + [ + 123.81, + 1.51 + ], + [ + 158.79, + 1.51 + ], + [ + 158.79, + 36.49 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 4.0, + "max_mission_seconds": 74, + "return_margin_seconds": 15 + }, + "side": "southeast", + "side_ja": "南東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 3.47, + "required_pixels": 401, + "preferred_pitch_deg": -46.48, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 19.453037778723644, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-015", + "symmetry_turns": 3, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[7.93,-7.93,9.26],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "dd38c9c681a5c63b9218d715ac1326b26ef9c11e605745b38529c8808f4a82ed" + }, + { + "id": "sym-2026091361-speed_time-015-r3-f1", + "context": { + "target_position_m": [ + 141.3, + -19.0, + 0.0 + ], + "current_position_m": [ + 135.54, + -25.57, + 103.93 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 123.81, + -36.49 + ], + [ + 123.81, + -1.51 + ], + [ + 158.79, + -1.51 + ], + [ + 158.79, + -36.49 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 4.0, + "max_mission_seconds": 74, + "return_margin_seconds": 15 + }, + "side": "northeast", + "side_ja": "北東", + "max_viewpoints": 3, + "profile": "vehicle", + "stress_family": "speed_time", + "strict_speed_limit": true, + "target_size_m": 3.47, + "required_pixels": 401, + "preferred_pitch_deg": -46.48, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 19.453037778723644, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-speed_time-015", + "symmetry_turns": 3, + "symmetry_reflected": true, + "evaluation_group": "fresh-normal" + }, + "canonical_frame": { + "schema_version": "tancho-detect-single-frame-1.0", + "fps": 0.5, + "resize_before_processor": false, + "video_decode": false, + "frames": [ + { + "id": "F1", + "sha256": "c2c02ce869b5b1144e21ec063900703332c04607e6682c6b7b10b2daedfe96a6", + "rgb_sha256": "ddc523f6354b099400eda4031020472d418b775641ee30ff2396817ee3c148c7", + "size": [ + 640, + 640 + ] + } + ], + "sha256": "e2741278b1d5114dfe86369fb71d27c81f9a3baea042ba8665929d14586a50dd" + }, + "raw_text": "```json\n[{\"offset_m\":[7.93,7.93,9.45],\"dwell_ms\":2000,\"speed_mps\":4.0}]\n```", + "model": "mixed240", + "prompt_sha256": "d5accd906de321db1b5b3952441f9aa4121d8c9d385849d58838c23a73c78ae2" + } +] diff --git a/examples/normal.json b/examples/normal.json new file mode 100644 index 0000000000000000000000000000000000000000..62cd6506b3cac98a3f3d854cbb2b3d621529aac7 --- /dev/null +++ b/examples/normal.json @@ -0,0 +1,62 @@ +{ + "id": "sym-2026091361-altitude_band-000-r0-f0", + "context": { + "target_position_m": [ + 31.0, + -0.8, + 0.0 + ], + "current_position_m": [ + 31.69, + -20.18, + 23.87 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + -23.36, + -55.16 + ], + [ + 85.36, + -55.16 + ], + [ + 85.36, + 53.56 + ], + [ + -23.36, + 53.56 + ] + ], + "min_alt_m": 20.44, + "max_alt_m": 24.44, + "max_speed_mps": 8.0, + "max_mission_seconds": 900, + "return_margin_seconds": 120 + }, + "side": "north", + "side_ja": "北", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "altitude_band", + "strict_speed_limit": true, + "target_size_m": 1.7, + "required_pixels": 105, + "preferred_pitch_deg": -50.71, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 36.39669508372693, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "symmetry_orbit": "sym-2026091361-altitude_band-000", + "symmetry_turns": 0, + "symmetry_reflected": false, + "evaluation_group": "fresh-normal" + } +} diff --git a/examples/stationary.json b/examples/stationary.json new file mode 100644 index 0000000000000000000000000000000000000000..3b797b6951c30611ca36a285d81efbda0b1d491c --- /dev/null +++ b/examples/stationary.json @@ -0,0 +1,59 @@ +{ + "id": "current-2026091360-stay-000", + "context": { + "target_position_m": [ + 95.6, + -161.8, + 0.0 + ], + "current_position_m": [ + 95.6, + -146.33, + 10.5 + ], + "envelope": { + "revision": "e1", + "origin": { + "latitude_deg": 36.5, + "longitude_deg": 137.85 + }, + "polygon_m": [ + [ + 60.55, + -196.85 + ], + [ + 130.65, + -196.85 + ], + [ + 130.65, + -126.75 + ], + [ + 60.55, + -126.75 + ] + ], + "min_alt_m": 1.0, + "max_alt_m": 110.0, + "max_speed_mps": 1.0, + "max_mission_seconds": 121, + "return_margin_seconds": 120 + }, + "side": "north", + "side_ja": "北", + "max_viewpoints": 3, + "profile": "person", + "stress_family": "dwell-stay", + "strict_speed_limit": true, + "target_size_m": 1.26, + "required_pixels": 121, + "preferred_pitch_deg": -34.16, + "viewpoint_protocol": "relative-v2", + "max_slant_range_m": 23.40925016809467, + "min_depression_deg": 20.0, + "max_depression_deg": 60.0, + "evaluation_group": "new-one-second" + } +} diff --git a/licenses/LICENSE.OpenMDW-1.1 b/licenses/LICENSE.OpenMDW-1.1 new file mode 100644 index 0000000000000000000000000000000000000000..ec297ac5456384786644013ec196da33b916be97 --- /dev/null +++ b/licenses/LICENSE.OpenMDW-1.1 @@ -0,0 +1,49 @@ +OpenMDW License Agreement, version 1.1 (OpenMDW-1.1) + +By exercising rights granted to you under this agreement, you accept and agree +to its terms. + +As used in this agreement, "Model Materials" means the materials provided to +you under this agreement, consisting of: (1) one or more machine learning +models (including architecture and parameters); and (2) all related artifacts +(including associated data, documentation and software) that are provided to +you hereunder. + +Subject to your compliance with this agreement, permission is hereby granted, +free of charge, to deal in the Model Materials without restriction, including +under all copyright, patent, database, and trade secret rights included or +embodied therein. + +If you distribute any portion of the Model Materials, you shall retain in your +distribution (1) a copy of this agreement, and (2) all copyright notices and +other notices of origin included in the Model Materials that are applicable to +your distribution. + +If you file, maintain, or voluntarily participate in a lawsuit against any +person or entity asserting that the Model Materials directly or indirectly +infringe any patent or copyright, then all rights and grants made to you +hereunder are terminated, unless that lawsuit was in response to a +corresponding lawsuit first brought against you. + +This agreement does not impose any restrictions or obligations with respect to +any use, modification, or sharing of any outputs generated by using the Model +Materials. + +THE MODEL MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE, TITLE, NONINFRINGEMENT, ACCURACY, OR THE +ABSENCE OF LATENT OR OTHER DEFECTS OR ERRORS, WHETHER OR NOT DISCOVERABLE, ALL +TO THE GREATEST EXTENT PERMISSIBLE UNDER APPLICABLE LAW. + +YOU ARE SOLELY RESPONSIBLE FOR (1) CLEARING RIGHTS OF OTHER PERSONS THAT MAY +APPLY TO THE MODEL MATERIALS OR ANY USE THEREOF, INCLUDING WITHOUT LIMITATION +ANY PERSON'S COPYRIGHTS OR OTHER RIGHTS INCLUDED OR EMBODIED IN THE MODEL +MATERIALS; (2) OBTAINING ANY NECESSARY CONSENTS, PERMISSIONS OR OTHER RIGHTS +REQUIRED FOR ANY USE OF THE MODEL MATERIALS; OR (3) PERFORMING ANY DUE +DILIGENCE OR UNDERTAKING ANY OTHER INVESTIGATIONS INTO THE MODEL MATERIALS OR +ANYTHING INCORPORATED OR EMBODIED THEREIN. + +IN NO EVENT SHALL THE PROVIDERS OF THE MODEL MATERIALS BE LIABLE FOR ANY CLAIM, +DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE MODEL MATERIALS, THE +USE THEREOF OR OTHER DEALINGS THEREIN. diff --git a/licenses/upstream/README.md b/licenses/upstream/README.md new file mode 100644 index 0000000000000000000000000000000000000000..512cbd148e89b9ed0738781c067e4885e1ad9e9c --- /dev/null +++ b/licenses/upstream/README.md @@ -0,0 +1,1093 @@ +--- +license: other +license_name: openmdw1.1-license +license_link: >- + https://openmdw.ai/license/1-1/ +library_name: cosmos +tags: + - nvidia + - cosmos + - cosmos3 +--- + +# **Cosmos 3: Omnimodal World Models for Physical AI** +**[Model Collection](https://huggingface.co/collections/nvidia/cosmos3)** | **[Code](https://github.com/nvidia/cosmos)** | **[White Paper](https://research.nvidia.com/labs/cosmos-lab/cosmos3/technical-report.pdf)** | **[Website](https://research.nvidia.com/labs/cosmos-lab/cosmos3/)** + +[NVIDIA Cosmos™](https://github.com/nvidia/cosmos) is a world foundation model platform designed to accelerate the development of Physical AI by enabling machines to understand, simulate, and interact with the physical world across robotics, autonomous driving, and smart space environments, including industrial and factory-scale applications. + +# Model Overview: Cosmos3-Edge + +## Description + +Cosmos3 is a collection of Omnimodal world models capable of generating dynamic, high-quality video, image, audio, and action commands from combinations of text, image, video, and action trajectory inputs. It serves as a foundational building block for a broad range of Physical AI applications and research spanning world understanding, world generation, simulation, and embodied policy learning. + +This model is ready for commercial and non-commercial use. + +> **Update — August 25, 2026:** The Cosmos3-Edge generator checkpoint, runtime defaults, usage examples, and benchmark results have been updated. Users pulling from `main` should refresh their local snapshot. See the [update announcement](https://huggingface.co/nvidia/Cosmos3-Edge/discussions/62) for compatibility and reproducibility details. + +**Model Developer:** NVIDIA + +### Model Versions + +**Released on: 07/20/2026** +- Cosmos3-Edge: + - Given multimodal inputs including text, images, video, and action trajectories, generate coherent text, images, video, and action outputs for multimodal understanding, world simulation, future prediction, action reasoning, and Physical AI applications. + +- Cosmos3-Edge-Policy-DROID: + - Given language instructions and visual observations from the DROID robot platform, generate robot action trajectories for manipulation and control tasks. + +- Cosmos3-Super-Image2Video-4Step: + - Given one or more input images and optional text instructions, generate temporally coherent video sequences that are consistent with the provided visual content. Distilled from Cosmos3-Super-Image2Video using [Improved Distribution Matching Distillation (DMD2)](https://arxiv.org/abs/2405.14867), enabling high-quality generation in 4 steps. + +- Cosmos3-Super-Text2Image-4Step: + - Given text input, generate high-fidelity images that are consistent with the provided description. Distilled from Cosmos3-Super-Text2Image using [Improved Distribution Matching Distillation (DMD2)](https://arxiv.org/abs/2405.14867), enabling high-quality generation in 4 steps. + +**Released on: 05/31/2026** +- Cosmos3-Nano: + - Given multimodal inputs including text, images, video, audio, and action trajectories, generate coherent text, images, video, audio, and action outputs for multimodal understanding, world simulation, future prediction, action reasoning, and Physical AI applications. + +- Cosmos3-Super: + - Given multimodal inputs including text, images, video, audio, and action trajectories, generate coherent text, images, video, audio, and action outputs for multimodal understanding, world simulation, future prediction, action reasoning, and Physical AI applications. + +- Cosmos3-Nano-Policy-DROID: + - Given language instructions and visual observations from the DROID robot platform, generate robot action trajectories for manipulation and control tasks. + +- Cosmos3-Super-Image2Video: + - Given one or more input images and optional text instructions, generate temporally coherent video sequences that are consistent with the provided visual content. + +- Cosmos3-Super-Text2Image: + - Given text input, generate high-fidelity images that are consistent with the provided description. + +### License + +This model is released under the [OpenMDW1.1](https://openmdw.ai/license/1-1/) + +### Deployment Geography + +Global + +### Use Case + +Physical AI: Encompassing robotics, autonomous vehicles (AV), and smart space environments, including industrial and factory-scale applications. + +### Release Date + +Hugging Face 07/20/2026 via [https://huggingface.co/collections/nvidia/cosmos3](https://huggingface.co/collections/nvidia/cosmos3) +GitHub 07/20/2026 via [https://github.com/nvidia/cosmos](https://github.com/nvidia/cosmos) + +## Model Architecture + +**Architecture Type:** Transformer + +**Network Architecture:** Mixture-of-Transformers (MoT) + +Cosmos3 is an Omni-modal foundation model built on a Mixture-of-Transformers (MoT) architecture consisting of two complementary transformer towers: an autoregressive transformer for discrete token generation and a diffusion transformer for continuous multimodal generation. During inference, text is generated through standard next-token autoregressive decoding, while non-text modalities, such as images, video, audio, and actions, are synthesized through iterative denoising. This unified architecture enables Cosmos3 to model heterogeneous modalities within a single framework while preserving generation mechanisms best suited to each modality. + +**This model was developed based on:** [Cosmos Framework](https://github.com/nvidia/cosmos-framework) + +**Number of trainable model parameters:** + +**Released on: 07/20/2026** +- Cosmos3-Edge: 4B +- Cosmos3-Edge-Policy-DROID: 4B +- Cosmos3-Super-Image2Video-4Step: 64B +- Cosmos3-Super-Text2Image-4Step: 64B + +**Released on: 05/31/2026** +- Cosmos3-Nano: 16B +- Cosmos3-Super: 64B +- Cosmos3-Nano-Policy-DROID: 16B +- Cosmos3-Super-Image2Video: 64B +- Cosmos3-Super-Text2Image: 64B + +## Input/Output Specifications + +- **Generator Input** + - **Input Type(s)**: Text, Image, Action Trajectory + - **Input Format(s)**: + - Text: String + - Image: jpg, png, jpeg, webp + - Action: JSON — 2D array shaped (T, D), where T is the number of frames and D is the embodiment-specific dimensionality + - **Input Parameters**: + - Text: One-dimensional (1D) + - Image: Two-dimensional (2D) + - Action trajectory: Two-dimensional (2D) + - **Other Properties Related to Input**: + - Image input is RGB color (8 bits per channel, sRGB color space); grayscale inputs are not supported. + - Action input is a per-frame sequence of robot/agent state or control values (e.g., joint positions, gripper state, camera pose). The full input is a 2D array shaped (T, D), where T is the number of frames and D is the embodiment-specific dimensionality listed below. + - Input action is only supported for compatible embodiments, including general camera motion (9D), autonomous vehicle (9D), egocentric motion (57D), single Franka Panda arm with RobotiQ gripper (10D), dual Franka Panda arm with RobotiQ gripper (20D), Agibot (29D), UR (10D), Google robot (10D), WidowX 250 (10D), UMI (10D). + - **Input Size and Length limits:** + - **Text:** 4096 tokens + - **Image:** 256p and 480p resolution at one of these aspect ratios (16:9, 4:3, 1:1, 3:4, 9:16) + - **Action:** 16 – 400 sequence length +- **Generator Output** + - **Output Type(s)**: Image, video, action, text + - **Output Format(s)**: + - Image: JPG + - Video: MP4 + - Action: JSON + - Text: string + - **Output Parameters**: + - Image: Two-dimensional (2D) + - Video: Three-dimensional (3D) + - Action: Two-dimensional (2D) + - Text: One-dimensional (1D) + - **Other Properties Related to Output**: + - The generated video is an MP4 file, with the resolution, frame rate, and duration specified in the input. + - Video generation supports 256p and 480p resolution, 12–30 fps, and 50–150 frames. These are set per request via the `size`, `fps`, and `num_frames` fields. + - The generated action is only supported for compatible embodiments, including general camera motion (9D), autonomous vehicle (9D), egocentric motion (57D), single Franka Panda arm with RobotiQ gripper (10D), dual Franka Panda arm with RobotiQ gripper (20D), Agibot (29D), UR (10D), Google robot (10D), WidowX 250 (10D), UMI (10D). + - Video: mp4 at the FPS specified in input + - Image: JPEG +- **Reasoner Input** + - **Input Type(s)**: Text, Text+Image, Text+Video + - **Input Format(s)**: + - Text: String + - Image: jpg, png, jpeg, webp + - Video: mp4 + - **Input Parameters**: + - Text: One-dimensional (1D) + - Image: Two-dimensional (2D) + - Video: Three-dimensional (3D) + - **Other Properties Related to Input**: + - Video inputs are recommended at a frame rate of 4 fps. + - Long-context inputs supported up to 256K tokens. + - **Input Size and Length limits:** + - **Text:** Up to 256K tokens (context window). + - **Image:** Standard input image formats; passed as file or URL. + - **Video:** mp4 at the recommended 4 fps. +- **Reasoner Output** + - **Output Type(s)**: Text + - **Output Format(s)**: + - Text: string + - **Output Parameters**: + - Text: One-dimensional (1D) + - **Other Properties Related to Output**: + - Default `max_tokens=4096+` is recommended for reasoning outputs; longer outputs may be requested. + - Reasoning outputs may include structured chain-of-thought, 2D/3D point localization, and bounding-box coordinates for vision-based tasks. + +The video content visualizes the input text description as a short animated scene, capturing key elements within the specified time constraints. + +Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g., GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. + +## Software Integration + +**Runtime Engine(s):** + +- [vLLM-Omni](https://github.com/vllm-project/vllm-omni) +- [vLLM](https://github.com/vllm-project/vllm) +- [PyTorch](https://github.com/nvidia/cosmos3) +- [Hugging Face Diffusers](https://github.com/huggingface/diffusers) + +**Supported Hardware Microarchitecture Compatibility:** + +- NVIDIA Ampere +- NVIDIA Blackwell +- NVIDIA Hopper + +**Operating System(s):** + +- Linux (We have not tested on other operating systems.) + +**Note:** Only BF16 precision is tested. Other precisions like FP4, FP8, and FP16 are not officially supported. + +The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment. + +## Training, Testing, and Evaluation Datasets + +### Dataset Overview + +- **Total Size:** 1.3B data points +- **Total Number of Datasets:** 393 dataset entries +- **Dataset partition:** Training [100%], Testing [N/A — evaluation benchmarks used separately], Validation [N/A — evaluation benchmarks used separately] +- **Time period for training data collection:** 2024–2026 +- **Time period for testing data collection:** N/A (standard public benchmarks) +- **Time period for validation data collection:** N/A (standard public benchmarks) + +Raw data from internal and external sources is transformed into training-ready data through multiple stages of curation, filtering, and quality review. Data acquisition spans diverse multimodal sources — robotics, autonomous driving, industrial environments, indoor and outdoor scenes, varied lighting and weather conditions, camera viewpoints, object categories, and human activities — to broaden coverage across Physical AI operating environments. Automated filtering pipelines remove corrupted, duplicate, low-quality, and restricted content. Metadata analysis, heuristic rules, and model-assisted classifiers are applied during preprocessing to flag anomalous distributions and low-diversity subsets. Human review supplements automated filtering for selected datasets, benchmark construction, and targeted quality analysis. Datasets are balanced across modalities and task categories — visual reasoning, text-to-image, text-to-video, image-to-video, video transfer, action-conditioned generation, and action command generation — to reduce overrepresentation of narrow domains. Synthetic and simulation-based augmentation supplements coverage of rare physical interactions and edge-case scenarios. Deduplication and provenance tracking are applied across the corpus. The resulting processed data is converted into model-ready tokenized or encoded representations through modality-specific preprocessors before training begins. + +Training datasets passed through multiple layers of automated and manual safeguards designed to reduce the presence of harmful or policy-violating content across categories including weapons and weapons-related instructional content, criminal planning, child sexual abuse material (CSAM), non-consensual intimate imagery (NCII), sexual content involving minors, harassment, hate speech, profanity, threats and incitement to violence, self-harm or suicide-related content, and graphic violence. Data sources are reviewed for licensing compatibility, provenance, and alignment with internal data governance and safety policies before admission into training corpora. Automated filtering pipelines combine multiple detection strategies: hash-matching against known CSAM and NCII reference databases; classifier-based moderation models trained for explicit sexual content, hate speech, violence, weapons imagery, and other restricted categories; keyword and regex-based screening for criminal-planning, threats, and self-harm phrases in text data; metadata and provenance heuristics for source-level risk signals; and embedding-based anomaly detection to surface samples that fall outside expected distributions. Human review and targeted audits supplement automated filtering for selected datasets, benchmark construction, and safety-sensitive evaluation. For multimodal Physical AI data (robotics, autonomous driving, industrial scenes), additional filtering targets invalid action trajectories, physically implausible interactions, and unsafe control sequences. Synthetic and simulation-generated data are evaluated through internal validation before inclusion. Benchmark evaluations and red-team testing are applied post-training to surface remaining safety gaps across world generation, reasoning, and action tasks. No large-scale data-filtering process can guarantee complete removal of all harmful content; residual risks may remain, particularly in rare edge cases or open-world deployment settings. Ongoing monitoring and dataset review continue post-release. + +- For more information about the datasets used to train this model, please see the [Public Summary of Training Content](https://docs.nvidia.com/cosmos/latest/_downloads/e482b7114ce8dbfbb07d2d4b42cafe4e/training-content-Cosmos-3.pdf). + +**Data Modality and Training Data Size** + +| Modality | Reasoning Data Sample Count | Generation Data Sample Count | +| -------- | ------------------- | -------------------- | +| Text | 22M | Not Applicable | +| Image | 19M | 767M | +| Video | 1M | 348M | +| Action | Not Applicable | 7M | + +**Data Collection Method by dataset** + +- Hybrid: Automatic/Sensors, Synthetic, Automated + +**Labeling Method by dataset** + +- Hybrid: Human, Automated + +**Properties:** The training, testing, and evaluation datasets consist of diverse multimodal video, image, action, synthetic, and sensor-conditioned data sourced from NVIDIA-owned data and publicly available, commercially permissive datasets. These datasets are curated to exclude known restricted content and to support building an Omni model that learns to generate and reason about dynamic physical environments across world reasoning and generation tasks. + +### Public Datasets + +| Dataset | Samples | +|---------------------------|-------------| +| OpenImage | 1.2M | +| Coyo700M | 100M | +| YouTube Video | 340M | +| UMI | 4.5M | + +### Private Datasets + +| Dataset | Samples | +|---------------------------|-------------| +| Egocentric | 7M | +| Nexar | 0.6M | +| AgiBot | 0.2M | +| HOI | 0.3M | + +### Synthetic Datasets + +| Dataset | Samples | +|-----------------------------------------|-------------| +| synthetic images generated using HiDream-I1 | 15M | +| synthetic images generated using Qwen-Image-2512 | 14M | +| synthetic captions generated using Qwen3-VL | 1115M | + +## Evaluation Datasets + +**Data Collection Method by dataset** + +- Hybrid: Automatic/Sensors, Synthetic, Automated + +**Labeling Method by dataset** + +- Hybrid: Human, Automated + +**Properties:** The training, testing, and evaluation datasets consist of diverse multimodal video, image, action, synthetic, and sensor-conditioned data sourced from NVIDIA-owned data and publicly available, commercially permissive datasets. These datasets are curated to exclude known restricted content and to support building an Omni model that learns to generate and reason about dynamic physical environments across world reasoning and generation tasks. + +## Benchmarks + +For detailed evaluations of the base model, see our [technical paper](https://research.nvidia.com/labs/cosmos-lab/cosmos3/technical-report.pdf). + +### Overall + +The table below summarizes Cosmos3-Edge across reasoning and generation. Each reasoning column (General, Robotics, Smart Infrastructure, Driving) reports the average score over that capability's benchmarks. For generation, **Image2Video** is the PAIBench overall score and **Policy: Robot** is the RoboLab success rate. In each column, the best result is in **bold** and the second-best is underlined. +\* denotes post-trained Cosmos3 variants: [Cosmos3-Nano-Policy-DROID](https://huggingface.co/nvidia/Cosmos3-Nano-Policy-DROID) and [Cosmos3-Edge-Policy-DROID](https://huggingface.co/nvidia/Cosmos3-Edge-Policy-DROID#robolab). + +![Overall benchmark results](assets/benchmark-overall.png) + +### Reasoning Benchmarks + +![Reasoning benchmarks](images/benchmark-reasoning.png) + +### Generation Benchmarks + +#### Image-to-Video Generation + +All models are evaluated on image-to-video generation at **480p, 24 fps**. Throughput is the number of frames generated per second, measured in eager mode on a single **NVIDIA H100** GPU. Cosmos3-Edge delivers the highest generation throughput while achieving competitive quality across PAIBench, RBench, and PhysicsIQ. + +![Image-to-Video benchmark results](assets/benchmark-image2video.png) + + +#### Action + +The Edge model is a strong initialization for downstream action tasks. For example, post-training it on the DROID dataset produces a policy whose RoboLab success rate is reported in the [Cosmos3-Edge-Policy-DROID model card](https://huggingface.co/nvidia/Cosmos3-Edge-Policy-DROID#robolab). + +### PBR (Performance Benchmark Reporting) + +The following tables report single-GPU or single-platform inference performance for the Cosmos3-Edge **Generator** and **Reasoner** towers. + +Generator results are measured using end-to-end or generation latency in seconds; lower is better. Reasoner results include serving and token-generation metrics, such as time to first token, request latency, and throughput. + +All results were measured using a single GPU and a batch size of 1. + +#### Generator + +Unless otherwise noted, visual-generation benchmarks use **480p resolution**. Image-to-video benchmarks generate **189 frames**. + +##### vLLM-Omni + +| GPU or Platform | Image-to-Video | Forward Dynamics | Inverse Dynamics | Policy DROID | +| --- | ---: | ---: | ---: | ---: | +| B200 SXM 192 GB | — | 2.44 s | 3.98 s | 0.99 s | +| H100 SXM 80 GB | 27.64 s | 3.91 s | 5.60 s | 1.41 s | +| H100 NVL 96 GB | 35.60 s | 4.73 s | 6.39 s | 1.37 s | +| H20 SXM 96 GB | 108.16 s | 12.77 s | 15.49 s | 3.41 s | +| RTX PRO 6000 Blackwell Server Edition | 36.29 s | 5.65 s | 7.46 s | 1.87 s | +| DGX Station | 12.17 s | 4.33 s | 6.34 s | 8.11 s | +| DGX Spark | 165.96 s | 26.43 s | 30.86 s | 7.66 s | +| Jetson AGX Thor T5000, 128 GB, MAXN | 137.50 s | 6.05 s | 7.19 s | 6.32 s | +| Jetson T3000, 32 GB, 1100 MHz | 194.76 s | 8.67 s | 10.25 s | 8.63 s | +| Jetson T2000, 16 GB, 702 MHz, THOR_NANO | 101.20 s | — | — | — | + +##### PyTorch + +| GPU or Platform | Image-to-Video | Forward Dynamics | Inverse Dynamics | Policy DROID | +| --- | ---: | ---: | ---: | ---: | +| H100 SXM 80 GB | 23.92 s | 3.69 s | 3.56 s | 1.25 s | +| H100 NVL 96 GB | 32.24 s | 4.64 s | 4.52 s | 1.28 s | +| H20 SXM 96 GB | 97.51 s | 12.78 s | 12.64 s | 2.92 s | +| RTX PRO 6000 Blackwell Server Edition | 38.98 s | 5.26 s | 5.66 s | 1.32 s | +| DGX Station | 10.57 s | 2.16 s | 2.26 s | 1.30 s | +| DGX Spark | 179.80 s | 24.59 s | 26.76 s | 5.44 s | +| Jetson AGX Thor T5000, 128 GB, MAXN | 153.00 s | — | — | — | +| Jetson T3000, 32 GB, 1100 MHz | 227.80 s | — | — | — | + +#### Reasoner + +The following tables report Cosmos3-Edge Reasoner performance. Reasoner workloads produce autoregressively generated text and therefore use different metrics from the Generator workloads: + +- **Time To First Token (TTFT):** Time from request submission until the first output token is produced. Lower is better. +- **Request Latency:** End-to-end latency for the complete request. Lower is better. +- **Request Throughput:** Completed requests per second. Higher is better. +- **Output Token Throughput:** Generated output tokens per second. Higher is better. + +##### vLLM serving benchmarks + +These measurements use the `nvidia/Cosmos3-Edge` checkpoint with one GPU. Metrics were collected at client-side concurrency levels of 1, 64, 128, and 256. + +The workload notation is **input sequence length / output sequence length / video FPS**. + +###### RTX PRO 4500 Blackwell Server Edition + +| Input / Output / Video FPS | Metric | Concurrency 1 | Concurrency 64 | Concurrency 128 | Concurrency 256 | +| --- | --- | ---: | ---: | ---: | ---: | +| 50 / 1 / 1 | Time To First Token (ms) ↓ | 165.79 | 8817.33 | 14702.20 | 29482.39 | +| | Request Latency (ms) ↓ | 165.79 | 8817.33 | 14702.20 | 29482.39 | +| | Request Count (requests) | 50 | 320 | 256 | 512 | +| | Request Throughput (req/s) ↑ | 6.00 | 6.55 | 6.55 | 6.52 | +| | Output Token Throughput (tok/s) ↑ | 6.00 | 6.55 | 6.55 | 6.52 | +| 50 / 1 / 2 | Time To First Token (ms) ↓ | 371.67 | 20375.98 | 33812.45 | 68201.55 | +| | Request Latency (ms) ↓ | 371.67 | 20375.98 | 33812.45 | 68201.55 | +| | Request Count (requests) | 50 | 313 | 249 | 492 | +| | Request Throughput (req/s) ↑ | 2.68 | 2.77 | 2.76 | 2.71 | +| | Output Token Throughput (tok/s) ↑ | 2.68 | 2.77 | 2.76 | 2.71 | +| 50 / 100 / 1 | Time To First Token (ms) ↓ | 166.86 | 6900.90 | 19625.83 | 45729.55 | +| | Request Latency (ms) ↓ | 764.15 | 16667.01 | 29196.84 | 55749.62 | +| | Request Count (requests) | 50 | 320 | 256 | 512 | +| | Request Throughput (req/s) ↑ | 1.31 | 3.73 | 3.74 | 3.70 | +| | Output Token Throughput (tok/s) ↑ | 130.63 | 372.40 | 373.98 | 369.87 | +| 50 / 100 / 2 | Time To First Token (ms) ↓ | 374.93 | 23526.65 | 47550.99 | 101553.31 | +| | Request Latency (ms) ↓ | 1041.29 | 33712.54 | 57641.53 | 111895.20 | +| | Request Count (requests) | 50 | 320 | 256 | 512 | +| | Request Throughput (req/s) ↑ | 0.96 | 1.79 | 1.79 | 1.78 | +| | Output Token Throughput (tok/s) ↑ | 95.74 | 178.73 | 178.89 | 178.15 | + +###### RTX PRO 6000 Blackwell Server Edition + +| Input / Output / Video FPS | Metric | Concurrency 1 | Concurrency 64 | Concurrency 128 | Concurrency 256 | +| --- | --- | ---: | ---: | ---: | ---: | +| 50 / 1 / 1 | Time To First Token (ms) ↓ | 141.99 | 3213.91 | 5384.51 | 10792.72 | +| | Request Latency (ms) ↓ | 141.99 | 3213.91 | 5384.51 | 10792.72 | +| | Request Count (requests) | 50 | 320 | 254 | 512 | +| | Request Throughput (req/s) ↑ | 6.96 | 18.00 | 17.95 | 17.89 | +| | Output Token Throughput (tok/s) ↑ | 6.96 | 18.00 | 17.95 | 17.89 | +| 50 / 1 / 2 | Time To First Token (ms) ↓ | 239.86 | 7483.22 | 12552.69 | 25259.11 | +| | Request Latency (ms) ↓ | 239.86 | 7483.22 | 12552.69 | 25259.11 | +| | Request Count (requests) | 49 | 303 | 249 | 491 | +| | Request Throughput (req/s) ↑ | 4.06 | 7.28 | 7.49 | 7.34 | +| | Output Token Throughput (tok/s) ↑ | 4.06 | 7.28 | 7.49 | 7.34 | +| 50 / 100 / 1 | Time To First Token (ms) ↓ | 138.74 | 943.46 | 2680.17 | 11599.63 | +| | Request Latency (ms) ↓ | 503.44 | 6188.90 | 13022.07 | 26388.89 | +| | Request Count (requests) | 50 | 320 | 256 | 512 | +| | Request Throughput (req/s) ↑ | 1.98 | 10.27 | 9.57 | 8.95 | +| | Output Token Throughput (tok/s) ↑ | 197.75 | 1026.14 | 956.47 | 893.91 | +| 50 / 100 / 2 | Time To First Token (ms) ↓ | 239.24 | 1798.96 | 11644.84 | 33293.32 | +| | Request Latency (ms) ↓ | 638.71 | 13599.89 | 26299.90 | 49165.91 | +| | Request Count (requests) | 50 | 320 | 256 | 512 | +| | Request Throughput (req/s) ↑ | 1.56 | 4.66 | 4.50 | 4.45 | +| | Output Token Throughput (tok/s) ↑ | 155.93 | 465.28 | 449.57 | 444.17 | + +##### Embedded-platform eager Transformers benchmarks + +These preliminary measurements use raw Hugging Face Transformers in eager mode rather than vLLM. They are presented separately because their runtime, workload, and metric definitions differ from the vLLM serving benchmarks above. + +| Board | Specification | Input | Prompt Tokens | Prefill Throughput | Prefill Latency | Decode Throughput | E2E Latency | +| --- | --- | --- | ---: | ---: | ---: | ---: | ---: | +| Jetson AGX Thor T5000 | 128 GB / MAXN | Text | 1705 | 8717 tok/s | 0.20 s | 37.3 tok/s | 3.60 s | +| Jetson AGX Thor T5000 | 128 GB / MAXN | Image | 911 | 4845 tok/s | 0.19 s | 42.6 tok/s | 3.17 s | +| Jetson AGX Thor T5000 | 128 GB / MAXN | Video | 1263 | 6032 tok/s | 0.21 s | 41.8 tok/s | 3.25 s | +| Jetson AGX Thor T4000 | 64 GB / MAXN, 1530 MHz | Text | 1705 | 6519 tok/s | 0.26 s | 34.1 tok/s | 3.99 s | +| Jetson AGX Thor T4000 | 64 GB / MAXN, 1530 MHz | Image | 911 | 3471 tok/s | 0.26 s | 40.3 tok/s | 3.41 s | +| Jetson AGX Thor T4000 | 64 GB / MAXN, 1530 MHz | Video | 1263 | 4164 tok/s | 0.30 s | 38.1 tok/s | 3.64 s | +| Jetson Thor T3000 | 32 GB / 1100 MHz | Text | 1705 | 5230 tok/s | 0.33 s | 29.7 tok/s | 4.61 s | +| Jetson Thor T3000 | 32 GB / 1100 MHz | Image | 911 | 2710 tok/s | 0.34 s | 36.3 tok/s | 3.83 s | +| Jetson Thor T3000 | 32 GB / 1100 MHz | Video | 1263 | 3388 tok/s | 0.37 s | 33.7 tok/s | 4.14 s | +| Jetson Thor T2000 | 16 GB / 702 MHz, THOR_NANO | Text | 1705 | 2355 tok/s | 0.72 s | 15.7 tok/s | 8.80 s | +| Jetson Thor T2000 | 16 GB / 702 MHz, THOR_NANO | Image | 911 | 1233 tok/s | 0.74 s | 19.6 tok/s | 7.21 s | +| Jetson Thor T2000 | 16 GB / 702 MHz, THOR_NANO | Video | 1263 | 1543 tok/s | 0.82 s | 18.0 tok/s | 7.87 s | +| Jetson AGX Orin | 64 GB | Text | 1705 | 3260 tok/s | 0.52 s | 12.3 tok/s | 10.83 s | +| Jetson AGX Orin | 64 GB | Image | 911 | 1840 tok/s | 0.50 s | 12.3 tok/s | 10.81 s | +| Jetson AGX Orin | 64 GB | Video | 1263 | 2103 tok/s | 0.60 s | 12.2 tok/s | 10.97 s | + +**Benchmark notes** + +- All Generator measurements use one GPU or one integrated computing platform. +- Generator values are average end-to-end or generation latency in seconds; lower is better. +- Unless otherwise specified, visual-generation measurements use **480p resolution**. +- Image-to-video measurements generate **189 output frames**. +- Jetson AGX Thor T5000 and Jetson T3000 visual-generation measurements use **832 × 480** resolution. +- Jetson T2000 visual-generation measurements use **448 × 256** resolution and therefore should not be compared directly with the 480p results. Its image-to-video values are warm-run measurements generating 189 frames. +- PyTorch Generator values report average generation latency rather than diffusion-only latency. +- Datacenter and enterprise forward- and inverse-dynamics results use the autonomous-driving (`AV`) configuration. +- Jetson AGX Thor T5000 and Jetson T3000 forward-dynamics, inverse-dynamics, and policy measurements use the DROID configuration with action chunk `[16, 8]`. + +## Usage + +- See [Cosmos](https://github.com/nvidia/cosmos) for details. + +### Prompt upsampling + +For optimal quality, prompts should be upsampled into a specific JSON structure. Description and code can be found [here](https://github.com/nvidia/cosmos-framework/blob/main/docs/prompt_upsampling.md). + +For example, for image-to-video upsampling using Opus-4.6: + +```bash +git clone https://github.com/NVIDIA/cosmos-framework.git packages/cosmos-framework +pip install -e packages/cosmos-framework + +export PROMPT_UPSAMPLER_ENDPOINT_URL="https://api.anthropic.com/v1/" +export PROMPT_UPSAMPLER_MODEL_NAME="claude-opus-4-6" +export PROMPT_UPSAMPLER_API_TOKEN="" + +python -m cosmos_framework.inference.prompt_upsampling \ + --input inputs/prompt_upsampler/prompts_i2v.txt \ + --image-list inputs/prompt_upsampler/images.txt \ + --output outputs/prompt_upsampler/upsampled_i2v_prompts_opus \ + --mode image2video \ + --endpoint-url "${PROMPT_UPSAMPLER_ENDPOINT_URL}" \ + --model "${PROMPT_UPSAMPLER_MODEL_NAME}" \ + --api-token "${PROMPT_UPSAMPLER_API_TOKEN}" \ + --resolution 480 \ + --aspect-ratio "16,9" \ + --duration "5s" \ + --fps 24 +``` + +For image-to-video, provide either one shared image via `--image-url` or one image per prompt via `--image-list` (the image-list file must have the same number of non-empty lines as the prompt file). Accepted image formats: local paths, HTTP(S) URLs, and `data:` URLs. + +### vLLM-Omni + +#### Container + +``` +docker pull vllm/vllm-omni:cosmos3 +``` + +#### General Invocation + +You can use the release-tested `vllm-omni` package for deploying an OpenAI-compatible API inference endpoint. +The recommended vLLM-Omni serving configuration for nvidia/Cosmos3-Edge on a single GPU is: + +```bash +vllm serve nvidia/Cosmos3-Edge \ + --omni \ + --host 0.0.0.0 \ + --port 8000 \ + --init-timeout 1800 +``` + +To speed up inference with additional GPUs, enable context parallelism with `--ulysses-degree` or switch to tensor parallelism with `--tensor-parallel-size`. Setting `--enable-layerwise-offload` can help reduce memory usage on GPUs with less available memory. + +#### Examples + +##### Download example prompts + +The example inputs (`assets/`) live in this model repo. Download just this folder with the Hugging Face CLI: + +```bash +pip install -U "huggingface_hub[cli]" +hf download nvidia/Cosmos3-Edge assets/ --local-dir Cosmos3-Edge +cd Cosmos3-Edge +``` + +Run all commands below from the downloaded repo root. + +--- + +##### Image to video generation + +```python +import json +import mimetypes +from pathlib import Path + +import requests + +# 1. Read JSON-upsampled prompt and negative prompt +json_prompt = json.load(open("assets/example_i2v_prompt.json")) +negative_prompt = json.load(open("assets/negative_prompt.json")) + +# 2. Build and send the multipart API request +url = "http://localhost:8000/v1/videos/sync" +image_path = Path("assets/example_i2v_input.jpg") +mime_type = mimetypes.guess_type(image_path)[0] or "image/png" +data = { + "prompt": json.dumps(json_prompt), + "negative_prompt": json.dumps(negative_prompt), + "size": "832x480", + "num_frames": "121", + "fps": "24", + "num_inference_steps": "20", + "guidance_scale": "6.0", + "max_sequence_length": "4096", + "flow_shift": "12.0", + "extra_params": json.dumps( + { + "use_resolution_template": False, + "use_duration_template": False, + "guardrails": True, + } + ), + "seed": "0", +} + +with image_path.open("rb") as image_file: + files = { + "input_reference": (image_path.name, image_file, mime_type), + } + print("Sending request to server...") + response = requests.post( + url, + data=data, + files=files, + headers={"Accept": "video/mp4"}, + ) + response.raise_for_status() + +# 3. Save the generated video +output_path = Path("/tmp/cosmos3_edge_i2v.mp4") +output_path.write_bytes(response.content) +print(f"Saved video to {output_path}") +``` + +Example output: + + + +--- + +##### Action generation + +The forward-dynamics example uses UMI robotics action trajectories, and the inverse-dynamics examples use autonomous-vehicle (AV) action trajectories. Source files: + +- Forward dynamics first frame: `assets/example_action_fd_umi_first_frame.png` +- Forward dynamics action chunks: `assets/example_action_fd_umi_action_chunks.json` +- Forward dynamics output video: `assets/edge_action_fd_umi_2chunk_output.mp4` +- Inverse dynamics source videos: `assets/example_action_id_av_0_input.mp4`, `assets/example_action_id_av_1_input.mp4` +- Inverse dynamics predicted actions: `assets/edge_action_id_av_0_output.json`, `assets/edge_action_id_av_1_output.json` + +###### Action forward dynamics + +The example below performs a 2-chunk UMI robotics rollout with the vLLM-Omni `/v1/videos/sync` inference endpoint. Each request sends one conditioning frame through `input_reference` and one 16-step normalized action chunk through `extra_params["action"]`. The request also sets the top-level `size` field to the input image resolution, so vLLM-Omni returns each chunk at the same resolution as the conditioning image without reflection padding. The stitched output drops each chunk's conditioning frame, producing 32 generated frames. The script extracts the last generated frame from each chunk and uses it as the next chunk's conditioning frame. + +```python +import json +import mimetypes +from pathlib import Path + +import imageio.v3 as iio +import numpy as np +import requests +from PIL import Image + +url = "http://localhost:8000/v1/videos/sync" +first_frame_path = Path("assets/example_action_fd_umi_first_frame.png") +action_spec = json.loads(Path("assets/example_action_fd_umi_action_chunks.json").read_text()) +action_chunks = action_spec["action_chunks"] + +prompt = action_spec.get("prompt", "mouse arrangement") +fps = int(action_spec.get("fps", 20)) +action_chunk_size = int(action_spec.get("action_chunk_size", 16)) +current_frame_path = first_frame_path +input_width, input_height = Image.open(first_frame_path).size +chunk_video_paths = [] +stitch_frames = [] + +for chunk_idx, action_chunk in enumerate(action_chunks): + mime_type = mimetypes.guess_type(current_frame_path)[0] or "image/png" + extra_params = { + "action_mode": "forward_dynamics", + "domain_name": action_spec.get("domain_name", "umi"), + "action_chunk_size": action_chunk_size, + "image_size": action_spec.get("image_size", 256), + "view_point": action_spec.get("view_point", "ego_view"), + "action": action_chunk, + "guardrails": True, + } + data = { + "prompt": prompt, + "num_frames": str(action_chunk_size + 1), # conditioning frame + generated frames + "fps": str(fps), + "size": f"{input_width}x{input_height}", # return chunks at input resolution + "num_inference_steps": "30", + "guidance_scale": "1.0", + "flow_shift": "10.0", + "seed": str(chunk_idx), + "extra_params": json.dumps(extra_params), + } + + with current_frame_path.open("rb") as image_file: + files = {"input_reference": (current_frame_path.name, image_file, mime_type)} + print(f"Sending action FD chunk {chunk_idx} to vLLM-Omni...") + response = requests.post( + url, + data=data, + files=files, + headers={"Accept": "video/mp4"}, + timeout=600, + ) + response.raise_for_status() + + chunk_video_path = Path(f"/tmp/cosmos3_edge_action_fd_chunk_{chunk_idx:02d}.mp4") + chunk_video_path.write_bytes(response.content) + chunk_video_paths.append(chunk_video_path) + + # The returned chunk contains the conditioning frame followed by generated frames. + # Drop the conditioning frame when stitching the generated-only rollout. + frames = iio.imread(chunk_video_path) + stitch_frames.extend(frames[1:]) + + # Autoregressive conditioning: use the final generated frame from this chunk + # as the input image for the next vLLM-Omni request. + if chunk_idx + 1 < len(action_chunks): + current_frame_path = Path(f"/tmp/cosmos3_edge_action_fd_ar_frame_{chunk_idx + 1:02d}.png") + iio.imwrite(current_frame_path, frames[-1]) + +stitched_path = Path("/tmp/cosmos3_edge_action_fd_umi_2chunk.mp4") +iio.imwrite(stitched_path, np.asarray(stitch_frames), fps=fps) +print("Generated chunk videos:", chunk_video_paths) +print("Saved stitched rollout:", stitched_path) +print("stitched resolution:", f"{input_width}x{input_height}") +``` + + +Example output: + + + +###### Action inverse dynamics + +```python +import json +import time +from pathlib import Path + +import requests + +base_url = "http://localhost:8000" +input_videos = { + "av_inverse_0": Path("assets/example_action_id_av_0_input.mp4"), + "av_inverse_1": Path("assets/example_action_id_av_1_input.mp4"), +} + +for name, video_path in input_videos.items(): + extra_params = { + "action_mode": "inverse_dynamics", + "domain_name": "av", + "action_chunk_size": 60, + "image_size": 480, + "view_point": "ego_view", + "raw_action_dim": 9, + "guardrails": True, + } + data = { + "prompt": "You are an autonomous vehicle planning system.", + "num_frames": "61", + "fps": "10", + "num_inference_steps": "30", + "guidance_scale": "1.0", + "flow_shift": "10.0", + "seed": "0", + "extra_params": json.dumps(extra_params), + } + + with video_path.open("rb") as video_file: + files = { + "input_reference": (video_path.name, video_file, "video/mp4"), + } + print(f"Submitting {name} request to server...") + response = requests.post(f"{base_url}/v1/videos", data=data, files=files) + response.raise_for_status() + initial = response.json() + + while True: + response = requests.get(f"{base_url}/v1/videos/{initial['id']}", timeout=30) + response.raise_for_status() + final = response.json() + print(initial["id"], final.get("status"), f"{final.get('progress', 0)}%") + if final.get("status") == "completed": + break + if final.get("status") in {"failed", "cancelled"}: + raise RuntimeError(json.dumps(final, indent=2)) + time.sleep(2) + + action = final.get("action") + if not action or "data" not in action: + raise RuntimeError(f"Response did not include action data: {json.dumps(final, indent=2)}") + + output_path = Path(f"/tmp/cosmos3_edge_action_id_{name}.json") + output_path.write_text(json.dumps(action, indent=2)) + print(f"Saved predicted action to {output_path}") + print("action shape:", action.get("shape"), "dtype:", action.get("dtype")) +``` + +Example outputs: + +- [av_inverse_0 predicted action JSON](https://huggingface.co/nvidia/Cosmos3-Edge/blob/main/assets/edge_action_id_av_0_output.json) +- [av_inverse_1 predicted action JSON](https://huggingface.co/nvidia/Cosmos3-Edge/blob/main/assets/edge_action_id_av_1_output.json) + + + + + +### Diffusers + +Cosmos3-Edge is supported in the Hugging Face Diffusers library through [`Cosmos3OmniPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cosmos3). The pipeline supports image-to-video generation and action-conditioned forward and inverse dynamics. + +#### Container + +To install Diffusers with `Cosmos3OmniPipeline`: + +```bash +uv venv --python 3.12 --seed --managed-python +source .venv/bin/activate +uv pip install \ + "diffusers @ git+https://github.com/huggingface/diffusers.git" \ + accelerate \ + av \ + cosmos_guardrail \ + huggingface_hub \ + imageio \ + imageio-ffmpeg \ + torch \ + torchvision \ + transformers +``` + +#### Examples + +The examples use the inputs in this repository's `assets/` directory. Run them from the model repository root. + +##### Image to video generation + +```python +import json +from pathlib import Path + +import torch +from diffusers import Cosmos3OmniPipeline +from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler +from diffusers.utils import export_to_video, load_image + +# Read the JSON-upsampled positive and negative prompts. +json_prompt = json.loads(Path("assets/example_i2v_prompt.json").read_text()) +negative_prompt = json.loads(Path("assets/negative_prompt.json").read_text()) + +pipe = Cosmos3OmniPipeline.from_pretrained( + "nvidia/Cosmos3-Edge", + torch_dtype=torch.bfloat16, + enable_safety_checker=True, +) +pipe.to("cuda") +pipe.scheduler = UniPCMultistepScheduler.from_config( + pipe.scheduler.config, flow_shift=12.0, use_karras_sigmas=False +) + +result = pipe( + prompt=json.dumps(json_prompt), + negative_prompt=json.dumps(negative_prompt), + image=load_image("assets/example_i2v_input.jpg"), + num_frames=121, + height=480, + width=832, + fps=24.0, + num_inference_steps=20, + guidance_scale=6.0, + generator=torch.Generator(device="cuda").manual_seed(0), + add_resolution_template=False, + add_duration_template=False, +) + +output_path = Path("assets/diffusers_outputs/edge_i2v_diffusers.mp4") +output_path.parent.mkdir(parents=True, exist_ok=True) +# macro_block_size=1 preserves the requested 832x480 resolution. +export_to_video(result.video, str(output_path), fps=24, macro_block_size=1) +print(f"Saved video to {output_path}") +``` + +Example output: + + + +--- + +##### Action generation + +The forward-dynamics example uses UMI robotics action trajectories, and the inverse-dynamics examples use autonomous-vehicle (AV) action trajectories. The pipeline's `CosmosActionCondition` groups the action task, embodiment, action chunk, and conditioning image or video. Unlike the image-to-video example, action prompts are plain task descriptions and should not be prompt-upsampled. + +###### Action forward dynamics + +This two-chunk UMI rollout conditions the first chunk on `assets/example_action_fd_umi_first_frame.png`, then conditions each subsequent chunk on the final generated frame of the previous chunk. Dropping each chunk's conditioning frame produces a stitched 32-frame rollout. + +```python +import json +from pathlib import Path + +import torch +from diffusers import Cosmos3OmniPipeline, CosmosActionCondition +from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler +from diffusers.utils import export_to_video, load_image + +action_spec = json.loads(Path("assets/example_action_fd_umi_action_chunks.json").read_text()) +output_dir = Path("assets/diffusers_outputs") +output_dir.mkdir(parents=True, exist_ok=True) + +pipe = Cosmos3OmniPipeline.from_pretrained( + "nvidia/Cosmos3-Edge", + torch_dtype=torch.bfloat16, + enable_safety_checker=True, +) +pipe.to("cuda") +pipe.scheduler = UniPCMultistepScheduler.from_config( + pipe.scheduler.config, flow_shift=10.0, use_karras_sigmas=False +) + +prompt = action_spec.get("prompt", "mouse arrangement") +fps = int(action_spec.get("fps", 20)) +chunk_size = int(action_spec.get("action_chunk_size", 16)) +current_frame = load_image("assets/example_action_fd_umi_first_frame.png") +stitched_frames = [] +chunk_paths = [] + +for chunk_idx, action_chunk in enumerate(action_spec["action_chunks"]): + result = pipe( + prompt=prompt, + action=CosmosActionCondition( + mode="forward_dynamics", + chunk_size=chunk_size, + domain_name=action_spec.get("domain_name", "umi"), + resolution_tier=int(action_spec.get("image_size", 256)), + raw_actions=torch.tensor(action_chunk, dtype=torch.float32), + image=current_frame, + view_point=action_spec.get("view_point", "ego_view"), + ), + fps=fps, + num_inference_steps=30, + guidance_scale=1.0, + generator=torch.Generator(device="cuda").manual_seed(chunk_idx), + use_system_prompt=False, + ) + + chunk_path = output_dir / f"edge_action_fd_diffusers_chunk_{chunk_idx:02d}.mp4" + export_to_video(result.video, str(chunk_path), fps=fps, macro_block_size=1) + chunk_paths.append(chunk_path) + stitched_frames.extend(result.video[1:]) + current_frame = result.video[-1] + +stitched_path = output_dir / "edge_action_fd_umi_2chunk_diffusers.mp4" +export_to_video(stitched_frames, str(stitched_path), fps=fps, macro_block_size=1) +print("Generated chunk videos:", chunk_paths) +print("Saved stitched rollout:", stitched_path) +``` + +Example output: + + + +###### Action inverse dynamics + +This example predicts the 60-step, 9-D AV action sequence that connects each 61-frame conditioning video. It writes one JSON output per input video. + +```python +import json +from pathlib import Path + +import torch +from diffusers import Cosmos3OmniPipeline, CosmosActionCondition +from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler +from diffusers.utils import load_video + +input_videos = { + "av_inverse_0": Path("assets/example_action_id_av_0_input.mp4"), + "av_inverse_1": Path("assets/example_action_id_av_1_input.mp4"), +} +output_dir = Path("assets/diffusers_outputs") +output_dir.mkdir(parents=True, exist_ok=True) + +pipe = Cosmos3OmniPipeline.from_pretrained( + "nvidia/Cosmos3-Edge", + torch_dtype=torch.bfloat16, + enable_safety_checker=True, +) +pipe.to("cuda") +pipe.scheduler = UniPCMultistepScheduler.from_config( + pipe.scheduler.config, flow_shift=10.0, use_karras_sigmas=False +) + +for name, video_path in input_videos.items(): + result = pipe( + prompt="You are an autonomous vehicle planning system.", + action=CosmosActionCondition( + mode="inverse_dynamics", + chunk_size=60, + domain_name="av", + resolution_tier=480, + video=load_video(str(video_path)), + view_point="ego_view", + ), + fps=10, + num_inference_steps=30, + guidance_scale=1.0, + generator=torch.Generator(device="cuda").manual_seed(0), + use_system_prompt=False, + ) + if result.action is None: + raise RuntimeError(f"{name} did not return an action tensor") + + action = result.action[0].cpu() + output_path = output_dir / f"edge_action_id_{name}_diffusers.json" + output_path.write_text( + json.dumps( + { + "data": action.tolist(), + "shape": list(action.shape), + "dtype": str(action.dtype), + "raw_action_dim": 9, + "action_mode": "inverse_dynamics", + "domain_id": 1, + }, + indent=2, + ) + + "\n" + ) + print(f"Saved predicted action to {output_path}; shape={tuple(action.shape)}") +``` + +Example outputs: + +- [av_inverse_0 predicted action JSON](https://huggingface.co/nvidia/Cosmos3-Edge/blob/main/assets/diffusers_outputs/edge_action_id_av_inverse_0_diffusers.json) +- [av_inverse_1 predicted action JSON](https://huggingface.co/nvidia/Cosmos3-Edge/blob/main/assets/diffusers_outputs/edge_action_id_av_inverse_1_diffusers.json) + +### vLLM + +#### Container + +```bash +docker pull vllm/vllm-openai:cosmos3 +``` + +#### General Invocation + +You can use the `vllm` package to deploy the Cosmos3-Edge reasoner as an OpenAI-compatible API endpoint. The recommended vLLM serving configuration for `nvidia/Cosmos3-Edge` on a single GPU is: + +```bash +vllm serve nvidia/Cosmos3-Edge \ + --host 0.0.0.0 \ + --port 8000 \ + --max-model-len 131072 \ + --allowed-local-media-path / \ + --mm-processor-kwargs '{"do_resize": true, "min_pixels": 4096, "max_pixels": 16777216}' \ + --media-io-kwargs '{"video": {"num_frames": 256}}' +``` + +#### Examples + +##### Reasoning + +Image input: + +Reasoning example input showing a tabletop robot-manipulation scene + +User prompt: + +```text +The task is to put flower into the red bottle. Generate a plan consisting of subtasks for accomplish the task. +``` + +```python +import base64 +import json +from pathlib import Path + +import openai + +# 1. Read the image reasoning prompt +example = json.load(open("assets/example_reasoning_prompt.json")) +image_path = Path("assets/example_reasoning_input.png").resolve() +image_url = ( + "data:image/png;base64," + base64.b64encode(image_path.read_bytes()).decode() +) + +# 2. Query the OpenAI-compatible vLLM server +client = openai.OpenAI( + api_key="EMPTY", + base_url="http://localhost:8000/v1", +) + +response = client.chat.completions.create( + model=client.models.list().data[0].id, + messages=[ + { + "role": "user", + "content": [ + {"type": "image_url", "image_url": {"url": image_url}}, + {"type": "text", "text": example["prompt"]}, + ], + }, + ], + max_tokens=example["max_tokens"], +) + +# 3. Print the default response (thinking is enabled) +print(response.choices[0].message.content) +``` + +Example output: + +```text +Got it, let's try to figure out how to put the flower into the red bottle. First, I need to identify the objects: the flower is the green one with the red center, and the red bottle is the red container. So the steps would involve moving the flower to the red bottle. + +First, the robot arm needs to locate the flower. Then, grasp the flower. After grasping, lift it, then move it towards the red bottle. Then, position the flower over the red bottle's opening, and finally release it into the bottle. Wait, but maybe I need to check if the red bottle has an opening that can accept the flower. Assuming the red bottle is a container with an opening, so the steps would be: move to flower, grasp, lift, move to red bottle, position over opening, release. Let me make sure each step is clear. + +First subtask: Position the robot arm above the flower. Then, lower the gripper to grasp the flower. Then, lift the flower from the table. Then, move the robot arm with the flower towards the red bottle. Then, position the flower over the red bottle's opening. Then, release the flower into the red bottle. That should cover the task. +To accomplish the task of putting the flower into the red bottle, the plan should follow these subtasks: + +1. **Position the robot arm above the flower** – Move the robot arm to a position directly over the green flower with the red center. +2. **Lower the gripper to grasp the flower** – Lower the gripper to make contact with the flower and close it around the stem. +3. **Lift the flower from the table** – Once the flower is securely grasped, lift it upward to clear the table surface. +4. **Move the robot arm with the flower toward the red bottle** – Transport the flower (still held by the gripper) from its current position to the location of the red bottle. +5. **Position the flower over the red bottle’s opening** – Align the flower so its stem is directly above the opening of the red bottle. +6. **Release the flower into the red bottle** – Open the gripper to let the flower fall into the red bottle, completing the task. + +Each step ensures the flower is picked up, transported, and placed into the red bottle in a controlled manner. +``` + +Thinking is enabled by default. To disable it, add the following argument to the `client.chat.completions.create(...)` call: + +```python +extra_body={"chat_template_kwargs": {"enable_thinking": False}} +``` + +## Limitations + +Cosmos3 may produce imperfect outputs in challenging scenarios. Generation artifacts include temporal inconsistency, unstable camera or object motion, imprecise physical interactions, and action-state drift — especially in long-horizon or high-resolution outputs. Reasoning may also be incorrect: object states, causal relationships, spatial geometry, temporal ordering, agent intent, and future outcomes can be misinferred, and complex or long-context inputs may yield hallucinated entities, inconsistent interpretations, or implausible predictions. Because the model lacks an explicit physics simulator, 3D geometry, 4D space-time evolution, object permanence, contact dynamics, and physical laws are only approximated — producing artifacts such as disappearing or morphing objects, unrealistic collisions, and physically implausible motions. Quality further degrades in out-of-distribution environments, safety-critical edge cases, and domains underrepresented in training. + +Cosmos3 outputs should not be treated as physically accurate simulation, reliable ground-truth reasoning, or safety-certified decision making. Applications involving robotics control, autonomous systems, scientific simulation, or safety-critical planning require additional validation, external constraints, system-level safety analysis, and domain-specific guardrails before deployment. + +## Inference + +**Acceleration Engine:** [vLLM-Omni](https://github.com/vllm-project/vllm-omni), [vLLM](https://github.com/vllm-project/vllm), [PyTorch](https://pytorch.org/) + +**Test Hardware:** B200, H100, H20, RTX PRO 6000, DGX Station, DGX Spark, Jetson Thor, Jetson AGX Orin + +## Ethical Considerations + +NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. + +Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included. + +Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment. + +For more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](EXPLAINABILITY.md), [Bias](BIAS.md), [Safety & Security](SAFETY.md), and [Privacy](PRIVACY.md) subcards. 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"bytes": 4905905551 + }, + "balanced288": { + "original_prefix": "keep/observation-viewpoint-balanced-current-train-v1/detect288", + "files": 31, + "bytes": 4905905551 + } + }, + "publication_checks": "Original retained SHA-256, remote blob identity, anonymous readback and CPU replay; no new GPU benchmark" +} diff --git a/requirements-cpu.txt b/requirements-cpu.txt new file mode 100644 index 0000000000000000000000000000000000000000..e7cc6c178b542946a555ad07fa55b5dde3fca566 --- /dev/null +++ b/requirements-cpu.txt @@ -0,0 +1 @@ +Pillow==12.3.0 diff --git a/run_request.py b/run_request.py new file mode 100644 index 0000000000000000000000000000000000000000..75e5343b43d53d562be0919e55120f4a59d66080 --- /dev/null +++ b/run_request.py @@ -0,0 +1,37 @@ +"""Prepare one trusted request, then run the selected pinned Cosmos backend once.""" +import argparse,io,json,subprocess,sys +from pathlib import Path +from PIL import Image +from tancho.viewpoint_router import prepare, BALANCED_VERSION +from tancho.viewpoint_eval import render,build_pack +from tancho.viewpoint_guard import assess +from tancho.detect_eval import frame_proof +ROOT=Path(__file__).resolve().parent + +def main(): + p=argparse.ArgumentParser(description=__doc__) + p.add_argument('--request',type=Path,required=True);p.add_argument('--image',type=Path,required=True) + p.add_argument('--output',type=Path,required=True);p.add_argument('--proof-root',type=Path) + p.add_argument('--prepare-only',action='store_true') + a=p.parse_args();case=json.loads(a.request.read_text());case['prompt']=render(case['context']) + image_bytes=io.BytesIO() + with Image.open(a.image) as im:im.convert('RGB').save(image_bytes,format='PNG') + case['canonical_frame']=frame_proof(image_bytes.getvalue())[1] + prepared,decision=prepare(case,version=BALANCED_VERSION) + if not case['id'].replace('-','').replace('_','').isalnum():raise ValueError('Use an alphanumeric request ID') + a.output.mkdir(parents=True,exist_ok=False);images=a.output/'input';images.mkdir() + (images/'frame.png').write_bytes(image_bytes.getvalue()) + pack=a.output/'pack';manifest=build_pack([(images,'frame',case['id'],prepared['context'])],pack,frame_directory='.') + (a.output/'routing.json').write_text(json.dumps(decision,indent=2)+'\n') + if a.prepare_only: + print(json.dumps(dict(prepared=True,model=decision['model'],model_generations=0)));return + if a.proof_root is None:raise ValueError('--proof-root from the overlay step is required') + proof=a.proof_root/decision['model']/'expected-weights.json' + result=subprocess.run([sys.executable,'-m','torch.distributed.run','--standalone','--nproc_per_node=1',str(ROOT/'training/detect_native.py'),'--pack',str(pack.resolve()),'--output',str((a.output/'native').resolve()),'--expected-weights',str(proof.resolve()),'--variant',decision['model']]) + result.check_returncode() + record=json.loads((a.output/'native/records'/case['id']/'record.json').read_text()) + check=assess(record['raw_text'],manifest['cases'][0]) + (a.output/'answer.json').write_text(json.dumps(dict(raw_text=record['raw_text'],validation=check),ensure_ascii=False,indent=2)+'\n') + print(json.dumps(dict(accepted=check['accepted'],reasons=check['reasons'],model_generations=1))) + +if __name__=='__main__':main() diff --git a/smoke.py b/smoke.py new file mode 100644 index 0000000000000000000000000000000000000000..9e4e2aed7ffbaf991ed258c1214afdc898137a3c --- /dev/null +++ b/smoke.py @@ -0,0 +1,20 @@ +"""CPU-only release check: rescore saved answers, never fabricate inference.""" +import json +from pathlib import Path +from collections import defaultdict +from tancho.viewpoint_router import prepare, BALANCED_VERSION +from tancho.viewpoint_eval import render +from tancho.viewpoint_guard import assess +ROOT=Path(__file__).resolve().parent +rows=json.loads((ROOT/'evidence/v5-saved-outputs.json').read_text()) +counts=defaultdict(lambda:dict(cases=0,accepted=0)) +for row in rows: + case=dict(id=row['id'],context=row['context'],canonical_frame=row['canonical_frame'],prompt=render(row['context'])) + prepared,decision=prepare(case,version=BALANCED_VERSION) + assert decision['model']==row['model'] and decision['prompt_sha256']==row['prompt_sha256'] + g=counts[row['context']['evaluation_group']];g['cases']+=1;g['accepted']+=assess(row['raw_text'],prepared)['accepted'] +expected=json.loads((ROOT/'evidence/evaluation-summary.json').read_text())['groups'] +for group,c in counts.items(): + assert c['cases']==expected[group]['cases'] and c['accepted']==expected[group]['new'],group +assert len(rows)==672 +print(json.dumps(dict(verified=True,saved_outputs=672,model_generations=0,groups=dict(counts)),indent=2)) diff --git a/tancho/__init__.py b/tancho/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bb5266c1111f486281870c51bf1671bb65c7772b --- /dev/null +++ b/tancho/__init__.py @@ -0,0 +1 @@ +"""Tancho development tools. No flight-control interface.""" diff --git a/tancho/contracts.py b/tancho/contracts.py new file mode 100644 index 0000000000000000000000000000000000000000..e947435d681e8071bde6f925ef327a4ce2299f09 --- /dev/null +++ b/tancho/contracts.py @@ -0,0 +1,115 @@ +"""Version 1 application contracts. Validation does not authorize flight.""" +import math + + +class ContractError(ValueError): + pass + + +def require(condition, message): + if not condition: + raise ContractError(message) + + +def fields(value, names): + require(isinstance(value, dict) and set(value) == set(names.split()), 'Unexpected or missing fields') + + +def number(value): + return type(value) in (int, float) and math.isfinite(value) + + +def text(value): + return isinstance(value, str) and 0 < len(value) <= 4096 + + +def strings(value): + return isinstance(value, list) and all(text(v) for v in value) and len(set(value)) == len(value) + + +def validate_context(c, now_ms): + fields(c, 'schema_version observation_id captured_at_ms expires_at_ms input_sha256 model_revision goal_revision envelope_revision evidence_ids target_ids') + require(c['schema_version'] == '1.0', 'Unknown context version') + for key in ('observation_id', 'model_revision', 'goal_revision', 'envelope_revision'): + require(text(c[key]), 'Missing identity or revision') + require(isinstance(c['input_sha256'], str) and len(c['input_sha256']) == 64 and all(v in '0123456789abcdef' for v in c['input_sha256']), 'Invalid input hash') + require(all(type(v) is int for v in (now_ms, c['captured_at_ms'], c['expires_at_ms'])), 'Timestamps must be integer milliseconds') + require(0 <= c['captured_at_ms'] <= now_ms < c['expires_at_ms'], 'Future or expired observation') + require(strings(c['evidence_ids']) and strings(c['target_ids']), 'Invalid evidence or target registry') + + +def validate_reasoner(output, context, *, now_ms): + """Context is constructed by the application, never trusted from model output.""" + validate_context(context, now_ms) + fields(output, 'schema_version observation_id target_id evidence_ids missing_information goal view_request priority abstain') + require(output['schema_version'] == '1.0', 'Unknown output version') + require(output['observation_id'] == context['observation_id'], 'Observation mismatch') + require(type(output['abstain']) is bool, 'abstain must be boolean') + require(type(output['priority']) is int and 0 <= output['priority'] <= 3, 'Invalid priority') + require(isinstance(output['missing_information'], str) and len(output['missing_information']) <= 4096, 'Invalid explanation') + require(strings(output['evidence_ids']) and set(output['evidence_ids']) <= set(context['evidence_ids']), 'Unknown or duplicate evidence') + require(output['goal'] in ('inspect', 'no_action', 'abstain'), 'Unknown goal') + target = output['target_id'] + require(target is None or (text(target) and target in context['target_ids']), 'Unknown target') + require(output['abstain'] == (output['goal'] == 'abstain'), 'Inconsistent abstention') + if output['goal'] == 'inspect': + require(target is not None and bool(output['evidence_ids']), 'Inspection requires target and evidence') + require(text(output['missing_information']), 'Inspection requires missing information') + fields(output['view_request'], 'side preferred_pitch_deg') + view = output['view_request'] + require(view['side'] in ('north','northeast','east','southeast','south','southwest','west','northwest','any'), 'Unknown view side') + require(number(view['preferred_pitch_deg']) and -90 <= view['preferred_pitch_deg'] <= 0, 'Invalid pitch request') + require(output['priority'] >= 1, 'Inspection priority must be positive') + else: + require(output['view_request'] is None and output['priority'] == 0, 'Non-action must not request a view') + if output['goal'] == 'abstain': + require(text(output['missing_information']), 'Abstention needs a reason') + if output['goal'] == 'no_action': + require(bool(output['evidence_ids']), 'No-action decision requires evidence') + return output + + +def rotation_columns(six): + """Gram-Schmidt, column-major 6D; returns columns, not rows.""" + require(isinstance(six, list) and len(six) == 6 and all(number(x) for x in six), 'Invalid rotation6d') + def dot(a,b): return sum(x*y for x,y in zip(a,b)) + def unit(v): + n = math.sqrt(dot(v,v)) + require(math.isfinite(n) and n > 1e-8, 'Degenerate rotation6d') + return [x/n for x in v] + a = unit(six[:3]); b0 = six[3:]; proj = dot(a,b0) + b = unit([b0[i]-proj*a[i] for i in range(3)]) + c = [a[1]*b[2]-a[2]*b[1], a[2]*b[0]-a[0]*b[2], a[0]*b[1]-a[1]*b[0]] + return [a,b,c] + + +def validate_trajectory(output, context, *, now_ms, goal_revision, envelope_revision): + validate_context(context, now_ms) + fields(output, 'schema_version observation_id goal_revision envelope_revision calibration_id coordinate_frame sample_period_ms poses') + require(output['schema_version'] == '1.0', 'Unknown trajectory version') + require(output['observation_id'] == context['observation_id'], 'Observation mismatch') + require(output['goal_revision'] == context['goal_revision'] == goal_revision, 'Goal changed') + require(output['envelope_revision'] == context['envelope_revision'] == envelope_revision, 'Envelope changed') + require(text(output['calibration_id']), 'Calibration reference missing') + require(output['coordinate_frame'] == 'camera_at_observation', 'Unknown frame') + require(type(output['sample_period_ms']) is int and output['sample_period_ms'] in (50, 500), 'Unsupported cadence') + poses = output['poses'] + require(isinstance(poses, list) and len(poses) == 32, 'Expected 32 poses') + for pose in poses: + require(isinstance(pose, list) and len(pose) == 9 and all(number(x) for x in pose), 'Expected finite 9D pose') + rotation_columns(pose[3:]) + return output + + +def validate_scene_splits(records): + seen = {} + sample_ids = set() + for row in records: + fields(row, 'sample_id leakage_group_id split') + require(text(row['sample_id']) and text(row['leakage_group_id']), 'Missing dataset identity') + require(row['sample_id'] not in sample_ids, 'Duplicate sample') + sample_ids.add(row['sample_id']) + require(row['split'] in ('train','validation','test'), 'Invalid split') + group = row['leakage_group_id'] + require(group not in seen or seen[group] == row['split'], 'Scene leakage across splits') + seen[group] = row['split'] diff --git a/tancho/detect_eval.py b/tancho/detect_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..763236d46a0675ae8a09383f3b2f5e19f6dd1ed0 --- /dev/null +++ b/tancho/detect_eval.py @@ -0,0 +1,218 @@ +"""機上のモデルに、1枚のタイルから矩形を出させる評価パック。 + +観測課題の経路は4枚1組の動画を前提にしているが、発見は1枚ごとに独立して測る。 +`perception_frame_eval` が単フレームの経路を既に通しているので、同じ形を使う。 +渡すのは 640×640 の PNG 1枚と、Gemini に渡したものと同一の指示文である。 + +**同一の指示文であることが、比較の前提**になる。文面を変えたら、それは別の測定になる。 +""" +import copy +import hashlib +import io +import json +from pathlib import Path + +VERSION = 'tancho-detect-eval-1.1' +FRAME_VERSION = 'tancho-detect-single-frame-1.0' +# 1.0 からの変更は2つ。生成の上限をパックごとに持てるようにしたのと、モデル自身の書式で +# 頼む指示文を足したこと。2026-09-11 の走行で、11/40 が768トークンで打ち切られ、 +# 指示した書式は40/40が無視された。どちらも測定の損失なので、条件として切り分ける。 +SETTINGS = {'max_new_tokens': 768, 'do_sample': False, 'seed': 42} +FPS = 0.5 +MAX_FRAME_BYTES = 2 * 1024 ** 2 + +# training/draft_detection_labels.py の BOX_PROMPT と同一。写した時点の sha256 を控える。 +PROMPT = """災害の空撮画像です。真上に近い角度から撮った 640×640 の切り出しで、 +地上分解能は1画素あたり数センチです。 + +次の3分類について、写っているものを漏れなく列挙してください。**1つの矩形に1つの物体**を +入れてください。複数をまとめて囲まないでください。 + +- vehicle: 乗用車・トラック・バス・重機。水に浸かっているかは問わない +- building: 建物。住家・倉庫・ビニールハウスを含む。**壊れているかどうかは判断しない** +- person: 人 + +矩形は画像の左上を [0,0]、右下を [1,1] とした相対座標で [x0,y0,x1,y1] と書いてください。 + +見えているものだけを挙げてください。木や屋根に隠れて確信が持てないものは confidence を +低くしてください。何も無ければ空の配列を返してください。 + +返すのはJSONオブジェクト1個だけです。キーは detections の1個。 +各要素は category、bbox、confidence の3個です。""" + + +# モデル自身の書式で頼む版。素の Cosmos3-Edge は上の指示を40件中0件しか使わず、 +# bbox_2d（0〜1000の整数）・label・裸の配列で答えた。書式を争っても情報は増えないので、 +# こちらが合わせた条件も測る。**課題の説明そのものは1文字も変えていない。** +PROMPT_EDGE = PROMPT.replace( + """矩形は画像の左上を [0,0]、右下を [1,1] とした相対座標で [x0,y0,x1,y1] と書いてください。""", + """矩形は bbox_2d として [x0,y0,x1,y1] の整数で書いてください。座標は画像の左上を 0、 +右下を 1000 とした尺度です。""").replace( + """返すのはJSONオブジェクト1個だけです。キーは detections の1個。 +各要素は category、bbox、confidence の3個です。""", + """返すのはJSONの配列1個だけです。各要素は bbox_2d と label、それに confidence の3個です。 +label は car、building、person のいずれかです。""") + +# 面をどう答えさせるか。400マスの列挙は3,000トークンを超えるので、2つの形を比べる。 +# box は矩形を出させて、こちらでマスへ落とす（モデルが既に使っている書式）。 +# runs は行ごとの列の区間で答えさせる（格子をそのまま、ただし短く）。 +_LANDSLIDE_HEAD = """災害の空撮画像です。640×640 の切り出しで、地上分解能は1画素あたり数センチです。 + +**崩れた土や岩が地表を覆っている範囲**を答えてください。斜面の裸地、流れ下った土砂の堆積、 +土砂で埋まった川底が対象です。元から裸地の農地や、工事中の造成地は含めません。 +""" + +PROMPT_LANDSLIDE_BOX = _LANDSLIDE_HEAD + """ +その範囲を四角で覆ってください。斜めに伸びる崩落は、複数の四角に分けて構いません。 + +返すのはJSONの配列1個だけです。各要素は bbox_2d と label の2個で、label は必ず +landslide です。bbox_2d は [x0,y0,x1,y1] の整数で、画像の左上を 0、右下を 1000 とした +尺度です。崩れた範囲が無ければ空の配列を返してください。""" + +PROMPT_LANDSLIDE_RUNS = _LANDSLIDE_HEAD + """ +画像を **20×20 の格子**（1マス32×32画素）に区切ります。左上のマスが row 0・column 0、 +右下が row 19・column 19 です。 + +崩れた土や岩が**そのマスの過半を占めている**マスを答えてください。1マスずつ並べるのではなく、 +**行ごとに、連続する列の区間**で書きます。たとえば row 5 の column 3 から 9 までと 12 から 14 までが +該当するなら {"row": 5, "runs": [[3, 9], [12, 14]]} です。 + +返すのはJSONの配列1個だけです。該当する行だけを挙げ、無い行は書かないでください。 +崩れた範囲が無ければ空の配列を返してください。""" + +PROMPTS = {'strict': PROMPT, 'edge': PROMPT_EDGE, + 'landslide_box': PROMPT_LANDSLIDE_BOX, 'landslide_runs': PROMPT_LANDSLIDE_RUNS} + + +def sha256(data): + return hashlib.sha256(data).hexdigest() + + +def encoded(value): + return json.dumps(value, ensure_ascii=False, sort_keys=True, separators=(',', ':')).encode() + + +def require(condition, message): + if not condition: + raise ValueError(message) + + +def frame_proof(data): + from PIL import Image + require(0 < len(data) <= MAX_FRAME_BYTES and data.startswith(b'\x89PNG\r\n\x1a\n'), + '入力は 2MB 以下の PNG である必要がある') + with Image.open(io.BytesIO(data)) as source: + require(source.format == 'PNG' and source.mode == 'RGB' + and getattr(source, 'n_frames', 1) == 1 and source.width * source.height <= 2048 ** 2 + and not any(k in source.info for k in ('exif', 'icc_profile', 'transparency')), + '解釈の余地を残す情報を持たない RGB PNG 1枚が要る') + source.load() + image = source.copy() + proof = {'schema_version': FRAME_VERSION, 'fps': FPS, 'resize_before_processor': False, + 'video_decode': False, + 'frames': [{'id': 'F1', 'sha256': sha256(data), 'rgb_sha256': sha256(image.tobytes()), + 'size': list(image.size)}]} + proof['sha256'] = sha256(encoded(proof)) + return image, proof + + +def enlarged(data, factor): + """画素を増やさずに大きさだけ変える。情報は増えないが、刻まれ方は変わる。""" + import io as _io + from PIL import Image + with Image.open(_io.BytesIO(data)) as source: + source.load() + image = source.convert('RGB') + image = image.resize((image.width * factor, image.height * factor), Image.LANCZOS) + buffer = _io.BytesIO() + image.save(buffer, format='PNG') + return buffer.getvalue() + + +def materialize(data, proof): + """凍結した proof と、いま読んだ画素が一致することを確かめてから渡す。""" + image, actual = frame_proof(data) + require(actual == proof, '凍結した1枚と画素または proof が違う') + return {'frames': [image], 'fps': FPS}, copy.deepcopy(proof) + + +def build_pack(sources, out, prompt_kind='strict', settings=None, upscale=1): + """`sources` は (タイルのパック, タイルIDの並び) の列。答えが2つのパックに分かれているため。 + + `upscale` は入力の拡大率。画素は増えないが、処理系が画像を何トークンへ刻むかは変わる。 + 2026-09-11 の走行では入力が40件とも721トークンで同一だったので、その依存を測る。 + """ + prompt = PROMPTS[prompt_kind] + settings = dict(settings or SETTINGS) + out = Path(out) + (out / 'frames').mkdir(parents=True, exist_ok=True) + cases, roots = [], [] + for tiles_root, tile_ids in sources: + tiles_root = Path(tiles_root) + roots.append(str(tiles_root)) + by_id = {r['tile_id']: r for r in + json.loads((tiles_root / 'tiles.json').read_text())['tiles']} + missing = [t for t in tile_ids if t not in by_id] + require(not missing, 'タイルが見つからない: ' + ','.join(missing)) + for tile_id in sorted(tile_ids): + data = (tiles_root / by_id[tile_id]['file']).read_bytes() + if upscale != 1: + data = enlarged(data, upscale) + _, proof = frame_proof(data) + name = tile_id + '.png' + require(not (out / 'frames' / name).exists(), '同じタイルIDが2つの出所にある: ' + tile_id) + (out / 'frames' / name).write_bytes(data) + cases.append({'id': tile_id, 'prompt': prompt, 'prompt_sha256': sha256(prompt.encode()), + 'frame': 'frames/' + name, 'canonical_frame': proof, + 'source_pack': str(tiles_root)}) + cases.sort(key=lambda c: c['id']) + manifest = {'schema_version': VERSION, 'settings': settings, + 'prompt_kind': prompt_kind, 'upscale': upscale, + 'prompt_sha256': sha256(prompt.encode()), 'source_packs': roots, + 'cases': cases} + manifest['sha256'] = sha256(encoded({k: v for k, v in manifest.items() if k != 'sha256'})) + (out / 'manifest.json').write_text(json.dumps(manifest, ensure_ascii=False, indent=2) + '\n') + return manifest + + +def verify_pack(root): + root = Path(root) + require(not root.is_symlink() and not any(p.is_symlink() for p in root.rglob('*')), + 'パックに symlink を置かない') + manifest = json.loads((root / 'manifest.json').read_text()) + require(manifest['schema_version'] == VERSION, 'パックの版が違う') + require(manifest['sha256'] == sha256(encoded({k: v for k, v in manifest.items() if k != 'sha256'})), + 'manifest が書き換えられている') + require(set(manifest['settings']) == set(SETTINGS), '生成の設定の項目が違う') + require(manifest['settings']['do_sample'] is False and manifest['settings']['seed'] == 42, + '貪欲・種42から外れている') + prompt = PROMPTS[manifest['prompt_kind']] + seen = set() + for case in manifest['cases']: + require(case['id'] not in seen, '同じタイルが2度入っている') + seen.add(case['id']) + require(case['prompt'] == prompt and case['prompt_sha256'] == sha256(prompt.encode()), + '指示文がパックの宣言と違う') + _, proof = frame_proof((root / case['frame']).read_bytes()) + require(proof == case['canonical_frame'], '凍結した画素と違う: ' + case['id']) + return manifest, manifest['cases'] + + +def read_case(root, case): + return materialize((Path(root) / case['frame']).read_bytes(), case['canonical_frame']) + + +def bound_reader(case, video, proof, calls): + def reader(sample, model): + require(sample.name == case['id'] and sample.prompt == case['prompt'] + and sample.model_mode.is_reasoner and sample.vision_path is None, + '生成が凍結した1件に結び付いていない') + require(len(calls) < 2, '媒体の読み出しが多すぎる') + pixels = [sha256(image.tobytes()) for image in video['frames']] + require(proof == case['canonical_frame'] and video['fps'] == proof['fps'] + and pixels == [f['rgb_sha256'] for f in proof['frames']], + '復号した画素が変わっている') + calls.append({'id': case['id'], 'rgb_sha256': pixels}) + return {model.input_caption_key: [case['prompt']], + 'reasoner_images': [None], 'reasoner_videos': [video]} + return reader diff --git a/tancho/edge_chat.py b/tancho/edge_chat.py new file mode 100644 index 0000000000000000000000000000000000000000..978883095e1c753e68fc9498447b491daa24f8d0 --- /dev/null +++ b/tancho/edge_chat.py @@ -0,0 +1,39 @@ +"""Use Cosmos3-Edge's native non-thinking template consistently for JSON tasks. + +Approved answer text is unchanged. Empty thinking delimiters are template +control tokens, excluded by the upstream assistant loss mask. +""" +import copy + +CHAT_MODE = 'edge-non-thinking-empty-prefix-v1' + + +def with_empty_thinking(conversation): + messages = copy.deepcopy(conversation) + for message in messages: + if message.get('role') != 'assistant': continue + content = message['content'] + if isinstance(content, str): + if not content.startswith(''): message['content']=''+content + else: + first = next((v for v in content if v.get('type')=='text'), None) + if first is not None and not first['text'].startswith(''): + first['text']=''+first['text'] + return messages + + +def install(): + from transformers.tokenization_utils_base import PreTrainedTokenizerBase + from cosmos_framework.data.generator.processors.cosmos3_edge_processing import NemotronNanoV3BridgeProcessor + if getattr(NemotronNanoV3BridgeProcessor, '_tancho_chat_mode', None) == CHAT_MODE: return + original_processor = NemotronNanoV3BridgeProcessor.apply_chat_template + original_tokenizer = PreTrainedTokenizerBase.apply_chat_template + def processor_chat(self, conversation, **kwargs): + kwargs['enable_thinking'] = False + return original_processor(self, with_empty_thinking(conversation), **kwargs) + def tokenizer_chat(self, conversation, *args, **kwargs): + kwargs['enable_thinking'] = False + return original_tokenizer(self, conversation, *args, **kwargs) + NemotronNanoV3BridgeProcessor.apply_chat_template = processor_chat + PreTrainedTokenizerBase.apply_chat_template = tokenizer_chat + NemotronNanoV3BridgeProcessor._tancho_chat_mode = CHAT_MODE diff --git a/tancho/eval_kind.py b/tancho/eval_kind.py new file mode 100644 index 0000000000000000000000000000000000000000..d140d7b9a70b198a0ed1bd2389a926b5bb8e6295 --- /dev/null +++ b/tancho/eval_kind.py @@ -0,0 +1,24 @@ +"""パックが名乗る版で、評価の読み方を選ぶ。学習側の `train_v2_pack.pack_kind` と同じ形。 + +版を1か所で選ばないと、司令は通ったのに容器の中の worker が古い検査のまま、という事故が起きる +（2026-09-10 に学習側で実際に起きた）。発見のパックと視点列のパックは、指示文の扱いが違うだけで +呼び出す関数の形は同じなので、選ぶ場所だけを共通にする。 +""" +import json +from pathlib import Path + + +class PackError(ValueError): + pass + + +def eval_kind(root): + """`verify_pack` / `read_case` / `bound_reader` を持つモジュールを返す。""" + declared = json.loads((Path(root) / 'manifest.json').read_text()).get('schema_version') + from . import detect_eval + if declared == detect_eval.VERSION: + return detect_eval + from . import viewpoint_eval + if declared == viewpoint_eval.VERSION: + return viewpoint_eval + raise PackError('知らない評価パックの版: ' + str(declared)) diff --git a/tancho/observation_api.py b/tancho/observation_api.py new file mode 100644 index 0000000000000000000000000000000000000000..8511b8fbfb1bdc87f049fd597284df81893ab006 --- /dev/null +++ b/tancho/observation_api.py @@ -0,0 +1,293 @@ +"""One explicitly selected Token Factory vision request, with durable review records. + +Run: python3 -m tancho.observation_api --help +No retries, model fallback, flight control or automatic approval. +""" +import argparse +import base64 +from datetime import datetime, timezone +import json +import os +from pathlib import Path +import time +import urllib.error +import urllib.request + +from .observation_guard import (EXPECTED_IDS, InferenceResponse, ObservationRequest, + preflight, review_observation, save_review, sha256) +from .probe import BASE_URL, KEY_FILE, NoRedirect, read_key, redact + +API_VERSION = 'observation-token-factory-1.0' +MAX_RESPONSE_BYTES = 2 * 1024 * 1024 +MINIMAX = 'MiniMaxAI/MiniMax-M3' + + +def encoded(value): + return json.dumps(value, ensure_ascii=False, sort_keys=True, allow_nan=False).encode() + + +def write_private(path, value): + """Exclusive create and fsync: prior attempts cannot be silently overwritten.""" + fd = os.open(path, os.O_WRONLY | os.O_CREAT | os.O_EXCL, 0o600) + with os.fdopen(fd, 'wb') as stream: + stream.write(encoded(value) + b'\n'); stream.flush(); os.fsync(stream.fileno()) + + +def strict_json(data): + def unique(pairs): + result = {} + for key, value in pairs: + if key in result: + raise ValueError('Duplicate JSON key') + result[key] = value + return result + def reject(_): + raise ValueError('Non-finite JSON') + return json.loads(data, object_pairs_hook=unique, parse_constant=reject) + + +def make_payload(request, model, max_tokens): + if not isinstance(model, str) or not model.strip() or any(c.isspace() for c in model): + raise ValueError('An explicit model ID is required') + if type(max_tokens) is not int or not 1 <= max_tokens <= 1024: + raise ValueError('max_tokens must be between 1 and 1024') + if tuple(f.evidence_id for f in request.frames) != EXPECTED_IDS: + raise ValueError('Expected ordered frames F1..F4') + content = [{'type': 'text', 'text': request.prompt}, + {'type': 'text', 'text': 'Usable evidence IDs: ' + ', '.join(request.usable_evidence_ids)}] + for frame in request.frames: + if frame.data.startswith(b'\x89PNG\r\n\x1a\n'): + mime = 'image/png' + elif frame.data.startswith(b'\xff\xd8\xff'): + mime = 'image/jpeg' + else: + raise ValueError('Unsupported image type') + content.extend([{'type': 'text', 'text': frame.evidence_id}, + {'type': 'image_url', 'image_url': {'url': 'data:' + mime + ';base64,' + base64.b64encode(frame.data).decode()}}]) + body = {'model': model, 'messages': [{'role': 'user', 'content': content}], + 'max_tokens': max_tokens, 'stream': False} + if model == MINIMAX: + body['chat_template_kwargs'] = {'thinking_mode': 'disabled'} + # Prompted JSON: do not use MiniMax's unverified constrained-output modes. + return body + + +def check_completion(body, model): + reasons = []; raw = None + if not isinstance(body, dict): + return None, ['provider_envelope_not_object'] + if body.get('error') or body.get('detail'): + reasons.append('provider_error') + if body.get('model') != model: + reasons.append('served_model_mismatch') + choices = body.get('choices') + if not isinstance(choices, list) or len(choices) != 1 or not isinstance(choices[0], dict): + return None, reasons + ['expected_one_choice'] + choice = choices[0]; message = choice.get('message') + if choice.get('index') != 0: + reasons.append('unexpected_choice_index') + if choice.get('finish_reason') != 'stop': + reasons.append('completion_not_stopped') + if isinstance(message, dict): + raw = message.get('content') + if message.get('role') != 'assistant': + reasons.append('unexpected_message_role') + if message.get('tool_calls') or message.get('function_call') or message.get('refusal'): + reasons.append('tool_call_or_refusal') + if not isinstance(raw, str) or not raw.strip(): + reasons.append('missing_answer_content') + raw = raw if isinstance(raw, str) else None + usage = body.get('usage') + if (not isinstance(usage, dict) or + any(type(usage.get(k)) is not int or usage[k] <= 0 for k in ('prompt_tokens', 'completion_tokens', 'total_tokens')) or + usage['prompt_tokens'] + usage['completion_tokens'] != usage['total_tokens']): + reasons.append('invalid_token_accounting') + return raw, reasons + + +class TokenFactoryAdapter: + def __init__(self, *, model, directory, max_tokens=768, timeout=45, key_file=KEY_FILE, + opener=None, key_loader=read_key): + if not 0 < timeout <= 60: + raise ValueError('Socket timeout must be greater than zero and at most 60 seconds') + # Validate settings before reserving outputs or accessing credentials. + if not isinstance(model, str) or not model.strip() or any(c.isspace() for c in model): + raise ValueError('An explicit model ID is required') + if type(max_tokens) is not int or not 1 <= max_tokens <= 1024: + raise ValueError('max_tokens must be between 1 and 1024') + self.model, self.directory, self.max_tokens = model, Path(directory), max_tokens + self.timeout, self.key_file = timeout, key_file + self.opener = opener + self.key_loader = key_loader + self.used = False + + def __call__(self, request): + if self.used: + raise ValueError('An adapter instance can make only one attempt') + self.used = True + body = make_payload(request, self.model, self.max_tokens); data = encoded(body) + audit = {'schema_version': API_VERSION, 'endpoint': BASE_URL + '/chat/completions', + 'requested_model': self.model, 'served_model': None, 'weights_revision_verified': False, + 'started_at': datetime.now(timezone.utc).isoformat(), 'http_attempted': False, + 'http_status': None, 'retry_count': 0, 'request_sha256': sha256(data), + 'socket_timeout_seconds': self.timeout, 'timeout_scope': 'socket inactivity; not a total wall-clock deadline', + 'response_limit_bytes': MAX_RESPONSE_BYTES, 'response_complete': False, + 'input_frames': [{'id': f.evidence_id, 'sha256': sha256(f.data), 'bytes': len(f.data)} for f in request.frames], + 'usage': None, 'provider_cost_usd': None, 'credential_redacted': False} + # Complete exact payload is durable before sending. No Authorization header. + write_private(self.directory / 'request.json', body) + write_private(self.directory / 'prepared.json', audit) + started = time.monotonic(); key = None; raw = None; reasons = [] + try: + key = self.key_loader(self.key_file) + req = urllib.request.Request(audit['endpoint'], data=data, method='POST', + headers={'Authorization': 'Bearer ' + key, 'Content-Type': 'application/json', + 'Accept': 'application/json', 'User-Agent': 'tancho-observation/0.1'}) + opener = self.opener if self.opener is not None else urllib.request.build_opener(NoRedirect()) + audit['http_attempted'] = True + write_private(self.directory / 'attempt.json', {'request_sha256': audit['request_sha256'], + 'dispatch_started_at': datetime.now(timezone.utc).isoformat(), + 'outcome': 'unknown_until_response_receipt; do not automatically resubmit'}) + try: + response = opener.open(req, timeout=self.timeout) + except urllib.error.HTTPError as exc: + response = exc + with response: + audit['http_status'] = response.code if hasattr(response, 'code') else response.status + payload = response.read(MAX_RESPONSE_BYTES + 1) + audit['response_bytes'] = len(payload) + audit['response_sha256'] = sha256(payload) + audit['response_hash_scope'] = 'complete_body' if len(payload) <= MAX_RESPONSE_BYTES else 'bounded_prefix_only' + if len(payload) > MAX_RESPONSE_BYTES: + reasons.append('provider_response_too_large') + else: + audit['response_complete'] = True + audit['response_body'] = payload.decode('utf-8', errors='replace') + try: + parsed = strict_json(payload) + audit['response'] = parsed + except (ValueError, UnicodeDecodeError): + parsed = None; reasons.append('invalid_provider_json') + if audit['http_status'] != 200: + reasons.append('http_status_' + str(audit['http_status'])) + elif 'invalid_provider_json' not in reasons: + raw, checked = check_completion(parsed, self.model); reasons.extend(checked) + if isinstance(parsed, dict): + audit['served_model'] = parsed.get('model'); audit['usage'] = parsed.get('usage') + except Exception as exc: + # Exception messages/headers may contain credentials; persist type only. + audit['error_type'] = type(exc).__name__ + if isinstance(exc, TimeoutError) or (isinstance(exc, urllib.error.URLError) and isinstance(exc.reason, TimeoutError)): + reasons.append('transport_timeout') + else: + reasons.append('transport_or_credential_failure') + audit['latency_seconds'] = round(time.monotonic() - started, 6) + if key: + sanitized = redact(audit, key) + if sanitized != audit or (raw is not None and key in raw): + reasons.append('credential_echo_redacted'); sanitized['credential_redacted'] = True + # A JSON value may spell the key with Unicode escapes. The + # decoded response is redacted recursively; omit the entire wire + # text so its alternate spelling cannot retain the credential. + if 'response_body' in sanitized: + sanitized['response_body'] = '[OMITTED: provider echoed active credential]' + audit = sanitized + raw = redact(raw, key) + audit['rejection_reasons'] = list(dict.fromkeys(reasons)) + audit['provider_contract_passed'] = not reasons + if 'response_body' in audit: + audit['response_body_text_sha256'] = sha256(audit['response_body'].encode()) + write_private(self.directory / 'response.json', audit) + return InferenceResponse(raw, audit, tuple(audit['rejection_reasons'])) + + +def observe(*, prompt, frame_paths, model, output, max_tokens=768, timeout=45, + key_file=KEY_FILE, opener=None, key_loader=read_key): + output = Path(output) + adapter = TokenFactoryAdapter(model=model, directory=output, max_tokens=max_tokens, + timeout=timeout, key_file=key_file, opener=opener, key_loader=key_loader) + output.mkdir(parents=True, mode=0o700, exist_ok=False) + write_private(output / 'observation-started.json', {'model': model, 'created_at': datetime.now(timezone.utc).isoformat(), + 'state': 'reserved; see record/record.json for final outcome', 'flight_authorized': False}) + record, frames = review_observation(prompt=prompt, frame_paths=frame_paths, + model={'id': model, 'revision': 'unpinned_remote_service'}, infer=adapter, adapter_kind='live') + path = save_review(output / 'record', record, frames) + # Parent directory is already private, including during child file creation. + path.parent.chmod(0o700) + for child in path.parent.iterdir(): child.chmod(0o600) + return path, record + + +def verify_api_record(path, record): + """Recheck the local request/response receipt against the saved observation.""" + from .observation_guard import Frame + directory = Path(path).parent.parent + audit = record['api'] + if audit.get('schema_version') != API_VERSION or audit != strict_json((directory / 'response.json').read_bytes()): + raise ValueError('API response receipt changed') + payload = strict_json((directory / 'request.json').read_bytes()) + if sha256(encoded(payload)) != audit['request_sha256']: + raise ValueError('API request changed') + frames = tuple(Frame(f['evidence_id'], (Path(path).parent / f['snapshot']).read_bytes()) for f in record['preflight']['frames']) + expected = make_payload(ObservationRequest(record['prompt'], frames, tuple(record['preflight']['usable_evidence_ids'])), + record['model']['id'], payload['max_tokens']) + if payload != expected or audit['requested_model'] != record['model']['id']: + raise ValueError('API request does not match inspected images, prompt or model') + if audit['input_frames'] != [{'id': f.evidence_id, 'sha256': sha256(f.data), 'bytes': len(f.data)} for f in frames]: + raise ValueError('API frame identity mismatch') + if record['new_inference_requested'] != audit['http_attempted']: + raise ValueError('API dispatch identity mismatch') + if 'response_body' in audit: + if sha256(audit['response_body'].encode()) != audit['response_body_text_sha256']: + raise ValueError('API response body changed') + if audit['http_status'] == 200 and isinstance(audit.get('response'), dict): + raw, reasons = check_completion(audit['response'], audit['requested_model']) + if record['raw_text'] != raw or (reasons and record['status'] == 'pending_human_review'): + raise ValueError('API completion disagrees with observation') + if record['status'] == 'pending_human_review' and not audit['provider_contract_passed']: + raise ValueError('Rejected provider completion cannot be accepted') + return True + + +def main(argv=None): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--model', required=True) + parser.add_argument('--prompt-file', type=Path, required=True) + parser.add_argument('--frame', action='append', default=[], metavar='F1=PATH') + parser.add_argument('--output', type=Path) + parser.add_argument('--max-tokens', type=int, default=768) + parser.add_argument('--timeout', type=float, default=45) + parser.add_argument('--key-file', type=Path, default=KEY_FILE) + parser.add_argument('--dry-run', action='store_true', help='Preflight only; no credentials, HTTP or output writes') + args = parser.parse_args(argv) + try: + frames = {} + for item in args.frame: + eid, path = item.split('=', 1) + if eid in frames: raise ValueError('Duplicate frame ID') + frames[eid] = Path(path) + prompt = args.prompt_file.read_text() + if not prompt.strip(): raise ValueError('A prompt is required') + if args.dry_run: + check, snapshots = preflight(frames) + result = {'dry_run': True, 'preflight': check, 'http_attempted': False} + if check['passed']: + body = make_payload(ObservationRequest(prompt, snapshots, tuple(check['usable_evidence_ids'])), args.model, args.max_tokens) + result.update(model=args.model, request_sha256=sha256(encoded(body)), max_tokens=args.max_tokens) + print(json.dumps(result, ensure_ascii=False, indent=2)); return 0 if check['passed'] else 2 + if args.output is None: raise ValueError('--output is required for a live attempt') + path, record = observe(prompt=prompt, frame_paths=frames, model=args.model, output=args.output, + max_tokens=args.max_tokens, timeout=args.timeout, key_file=args.key_file) + audit = record.get('api', {}) + print(json.dumps({'record': str(path), 'status': record['status'], 'model': record['model'], + 'http_status': audit.get('http_status'), 'usage': audit.get('usage'), + 'latency_seconds': audit.get('latency_seconds'), 'rejection_reasons': record['rejection_reasons'], + 'flight_authorized': False}, ensure_ascii=False, indent=2)) + return 0 if record['status'] == 'pending_human_review' else 2 + except (OSError, ValueError) as exc: + print(json.dumps({'error_type': type(exc).__name__, 'detail': 'Invalid input or output unavailable; inspect private attempt records.'})) + return 2 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/tancho/observation_events.py b/tancho/observation_events.py new file mode 100644 index 0000000000000000000000000000000000000000..0df7aa666d156557f31907db1fe4cacfdb00d82b --- /dev/null +++ b/tancho/observation_events.py @@ -0,0 +1,28 @@ +"""Explicit event-level partitions, including the historical seen-development exception.""" +from .contracts import require, text + + +def validate_event_partitions(sources, policy, *, preparing_data=True): + require(policy.get('schema_version') == 'observation-event-policy-1.0', 'Unknown event policy') + groups, hashes, events, source_ids = {}, {}, {}, set() + for source in sources: + sid = source['source_id'] + require(sid not in source_ids, 'Duplicate source') + source_ids.add(sid) + event, split = source.get('event_id'), source.get('split_proposal') + require(text(event) and event in policy['events'], 'Unregistered event') + rule = policy['events'][event] + require(split in rule['allowed_splits'], 'Event is reserved for another split') + require(not (preparing_data and rule.get('reserved_unopened')), + 'Reserved event is unopened; release a versioned evaluation plan first') + seen = events.setdefault(event, set()); seen.add(split) + require(len(seen) == 1 or (rule.get('allow_seen_development_overlap') is True + and seen <= {'train', 'validation'}), 'Event leakage across splits') + for key, registry in [('leakage_group_id', groups), ('sha256', hashes)]: + value = source.get(key) + if value is None and key == 'sha256': continue # Unfetched catalog entry. + require(text(value), 'Missing source group') + require(value not in registry or registry[value] == (event, split), + 'Media/group reused under a different event or split') + registry[value] = (event, split) + return {event: sorted(splits) for event, splits in events.items()} diff --git a/tancho/observation_evidence.py b/tancho/observation_evidence.py new file mode 100644 index 0000000000000000000000000000000000000000..91a4e2950308624f04f52c58e7fc8d7aace1fca8 --- /dev/null +++ b/tancho/observation_evidence.py @@ -0,0 +1,137 @@ +"""Mechanical triage and hash-bound human review; never automatic grounding approval.""" +from collections import Counter +import hashlib +import json +import re +from tancho.observation_task import parse_answer + + +DIMENSIONS = ('uncertainty', 'decision_goal', 'next_observation_goal') +VERDICTS = ('supported', 'unsupported', 'unclear') + + +def sha(value): + data = value.encode() if isinstance(value, str) else json.dumps(value, sort_keys=True, ensure_ascii=False, + separators=(',', ':'), allow_nan=False).encode() + return hashlib.sha256(data).hexdigest() + + +def triage(raw, condition, expected_decision, generation=None): + if condition not in ('visual', 'text_only', 'blank'): + raise ValueError('Unknown input condition') + flags = []; answer = None; error = None + try: + answer = parse_answer(raw) + except (ValueError, TypeError, AssertionError) as exc: + flags.append('invalid_json_contract'); error = str(exc) + if condition != 'visual' and re.search(r'\bF[1-4]\b', raw): + flags.append('cites_frames_without_usable_visual_evidence') + if answer: + facts = {v['text'] for v in answer['findings']} + if condition != 'visual' and facts: + flags.append('nonempty_findings_without_usable_visual_evidence') + if facts.intersection(answer['unknowns']): + flags.append('finding_repeated_as_unknown') + if answer['next_observation'] in facts: + flags.append('finding_repeated_as_next_observation') + if expected_decision is not None and answer['decision'] != expected_decision: + flags.append('decision_disagrees_with_reference') + repetition = None + # A bounded hint, including invalid/truncated output; this does not decide truth. + plain = re.sub(r'[\s\W\d_a-zA-Z]', '', raw[:20000]) + fragments = Counter(plain[i:i+6] for i in range(max(0, len(plain)-5))) + if fragments: + fragment, count = fragments.most_common(1)[0] + if count >= 8: + flags.append('repeated_character_fragment'); repetition = {'fragment': fragment, 'occurrences': count} + if generation: + tokens = generation['returned_token_ids']; settings = generation['settings'] + new = tokens if settings.get('return_only_new_tokens') else tokens[generation['input_tokens']:] + eos = settings.get('eos_token_id'); eos = eos if isinstance(eos, list) else [eos] + if len(new) >= settings['max_new_tokens'] and not any(t in eos for t in new): + flags.append('token_limit_reached_without_eos') + return {'flags': flags, 'answer': answer, 'parse_error': error, 'repetition_hint': repetition, + 'semantic_quality_evaluated': False} + + +def task_hash(task): + return sha({k: v for k, v in task.items() if k != 'task_sha256'}) + + +def manifest_hash(manifest): + return sha({k: v for k, v in manifest.items() if k != 'manifest_sha256'}) + + +def validate_manifest(manifest): + if manifest.get('schema_version') != 'observation-evidence-tasks-1.0' or manifest_hash(manifest) != manifest.get('manifest_sha256'): + raise ValueError('Stale or modified task manifest') + ids = set() + for task in manifest['tasks']: + if task['task_id'] in ids or task_hash(task) != task['task_sha256'] or sha(task['raw']) != task['raw_sha256']: + raise ValueError('Duplicate, stale or modified task') + ids.add(task['task_id']) + if task['condition'] == 'text_only' and task['frames']: + raise ValueError('Text-only task cannot display images as input') + if ('usable_evidence_ids' in task and not set(task['usable_evidence_ids']).issubset({f['id'] for f in task['frames']})): + raise ValueError('Usable evidence must be an input frame') + if task['condition'] not in ('visual', 'text_only', 'blank'): + raise ValueError('Unknown condition') + return ids + + +def validate_reviews(manifest, review): + """Validate a full inventory of pending/completed reviews; incomplete is not approval.""" + ids = validate_manifest(manifest) + if (review.get('schema_version') != 'observation-evidence-review-1.0' + or review.get('manifest_sha256') != manifest['manifest_sha256'] + or not isinstance(review.get('reviews'), list)): + raise ValueError('Review belongs to different inputs/answers') + records = review['reviews']; seen = set(); completed = 0 + by_id = {t['task_id']: t for t in manifest['tasks']} + for record in records: + task_id = record.get('task_id') + if task_id not in ids or task_id in seen: + raise ValueError('Unknown or duplicate review task') + seen.add(task_id); task = by_id[task_id] + if record.get('task_sha256') != task['task_sha256']: + raise ValueError('Review input/answer binding changed') + if record.get('status') == 'pending': + continue + if record.get('status') != 'reviewed' or not isinstance(review.get('reviewer'), str) or not review['reviewer'].strip(): + raise ValueError('Completed review needs reviewer identity') + answer = task['triage']['answer'] + findings = answer['findings'] if answer else [] + entries = record.get('findings', []) + if len(entries) != len(findings): + raise ValueError('Every finding needs a review') + available = set(task.get('usable_evidence_ids', [f['id'] for f in task['frames']])) if task['condition'] == 'visual' else set() + for i, (entry, finding) in enumerate(zip(entries, findings, strict=True)): + if entry.get('index') != i or entry.get('finding_sha256') != sha(finding): + raise ValueError('Finding review no longer matches the answer') + refs = entry.get('evidence_ids') + if not isinstance(refs, list) or len(set(refs)) != len(refs) or not set(refs).issubset(available): + raise ValueError('Unavailable evidence reference') + if entry.get('verdict') == 'supported' and not refs: + raise ValueError('Supported finding needs usable image evidence') + if entry.get('verdict') == 'supported' and not set(refs).issubset(finding['evidence_ids']): + raise ValueError('Supported finding must use evidence actually cited by the answer') + _check_verdict(entry) + dimensions = record.get('dimensions', {}) + if set(dimensions) != set(DIMENSIONS): + raise ValueError('Missing uncertainty/decision/purpose review') + for value in dimensions.values(): + _check_verdict(value) + if not answer and any(v['verdict'] == 'supported' for v in dimensions.values()): + raise ValueError('Invalid answer cannot pass its structured dimensions') + completed += 1 + if seen != ids: + raise ValueError('Missing review tasks') + return {'valid': True, 'reviewed': completed, 'pending': len(ids)-completed, + 'all_reviewed': completed == len(ids), 'automatic_model_promotion': False, + 'reviewer_identity_authenticated': False} + + +def _check_verdict(value): + if (not isinstance(value, dict) or value.get('verdict') not in VERDICTS + or not isinstance(value.get('note'), str) or not value['note'].strip()): + raise ValueError('Each review dimension needs a verdict and explanatory note') diff --git a/tancho/observation_guard.py b/tancho/observation_guard.py new file mode 100644 index 0000000000000000000000000000000000000000..35096e319cc1db6561365ec98c5c7ddd82790e83 --- /dev/null +++ b/tancho/observation_guard.py @@ -0,0 +1,197 @@ +"""Local image preflight and a human-review boundary; no provider or flight API. + +Adapters receive the exact immutable image bytes inspected here, not paths they +can re-open after validation. A structurally valid answer is still unapproved. +""" +from dataclasses import dataclass +from datetime import datetime, timezone +import hashlib +import json +from pathlib import Path +import statistics +import subprocess + +from .observation_task import parse_answer +from .observation_evidence import triage + +GUARD_VERSION = 'observation-review-1.1' +EXPECTED_IDS = ('F1', 'F2', 'F3', 'F4') +MAX_FRAME_BYTES = 16 * 1024 * 1024 +# Conservative low-detail heuristic at a declared resolution, not a scene model. +STAT_SIZE = 128 +UNIFORM_STD_MAX = 2.0 +UNIFORM_RANGE_MAX = 8 + + +def sha256(value): + return hashlib.sha256(value).hexdigest() + + +@dataclass(frozen=True) +class Frame: + evidence_id: str + data: bytes + + +@dataclass(frozen=True) +class ObservationRequest: + prompt: str + frames: tuple[Frame, ...] + usable_evidence_ids: tuple[str, ...] + + +@dataclass(frozen=True) +class InferenceResponse: + """Provider envelope checks accompany raw text; they cannot approve content.""" + raw_text: str | None + audit: dict + rejection_reasons: tuple[str, ...] = () + + +def image_statistics(data): + """Decode PNG/JPEG bytes with ffmpeg; inspect RGB channels separately.""" + if not (data.startswith(b'\x89PNG\r\n\x1a\n') or data.startswith(b'\xff\xd8\xff')): + raise ValueError('Expected a PNG or JPEG still frame') + result = subprocess.run([ + 'ffmpeg', '-hide_banner', '-loglevel', 'error', '-nostdin', + '-err_detect', 'explode', '-f', 'image2pipe', '-i', 'pipe:0', + '-frames:v', '1', '-vf', f'scale={STAT_SIZE}:{STAT_SIZE}', + '-threads', '1', '-f', 'rawvideo', '-pix_fmt', 'rgb24', 'pipe:1', + ], input=data, capture_output=True, timeout=15, check=True) + if len(result.stdout) != STAT_SIZE * STAT_SIZE * 3: + raise ValueError('Incomplete decoded frame') + channels = [result.stdout[i::3] for i in range(3)] + std = [statistics.pstdev(c) for c in channels] + ranges = [max(c) - min(c) for c in channels] + return {'rgb_std': std, 'rgb_range': ranges, + 'near_uniform': max(std) <= UNIFORM_STD_MAX and max(ranges) <= UNIFORM_RANGE_MAX} + + +def preflight(frame_paths): + """frame_paths maps F1..F4 to local still images; partial sets fail closed.""" + records, snapshots, reasons = [], [], [] + if set(frame_paths) != set(EXPECTED_IDS): + reasons.append('missing_or_unexpected_frame_ids') + for eid in EXPECTED_IDS: + if eid not in frame_paths: + records.append({'evidence_id': eid, 'status': 'missing'}) + continue + path = Path(frame_paths[eid]) + record = {'evidence_id': eid, 'source_path': str(path.resolve())} + records.append(record) + try: + # Bound reads and preserve the exact bytes passed to the adapter. + with path.open('rb') as stream: + data = stream.read(MAX_FRAME_BYTES + 1) + if len(data) > MAX_FRAME_BYTES: + record['status'] = 'oversize'; reasons.append('oversize_frame'); continue + record.update(sha256=sha256(data), bytes=len(data)) + stats = image_statistics(data) + record.update(stats) + record['status'] = 'near_uniform' if stats['near_uniform'] else 'readable' + snapshots.append(Frame(eid, data)) + except (OSError, ValueError, subprocess.SubprocessError) as exc: + record.update(status='unreadable', error_type=type(exc).__name__) + reasons.append('frame_decode_or_read_failed') + usable = tuple(r['evidence_id'] for r in records if r['status'] == 'readable') + if len(snapshots) == 4 and not usable: + reasons.append('all_frames_near_uniform') + return {'passed': not reasons, 'reasons': sorted(set(reasons)), 'frames': records, + 'usable_evidence_ids': list(usable), 'decoder': 'ffmpeg PNG/JPEG -> RGB24', + 'uniform_policy': {'resolution': [STAT_SIZE, STAT_SIZE], + 'max_channel_std': UNIFORM_STD_MAX, 'max_channel_range': UNIFORM_RANGE_MAX}}, tuple(snapshots) + + +def abstention(reason): + return {'findings': [], 'unknowns': [reason], 'decision': 'abstain', 'next_observation': None} + + +def review_observation(*, prompt, frame_paths, model, infer, adapter_kind='live'): + """Gate a single callback. The caller persists this record with save_review. + + model identifies the requested revision; actual loaded-weight proof belongs + to the model adapter. adapter_kind='replay' makes no fresh inference claim. + """ + if not isinstance(prompt, str) or not prompt.strip(): + raise ValueError('A prompt is required') + if not isinstance(model, dict) or not all(isinstance(model.get(k), str) and model[k] for k in ('id', 'revision')): + raise ValueError('Model identity and revision are required') + if adapter_kind not in ('live', 'replay', 'test'): + raise ValueError('Unknown adapter kind') + inspection, frames = preflight(frame_paths) + record = {'schema_version': GUARD_VERSION, 'created_at': datetime.now(timezone.utc).isoformat(), + 'prompt': prompt, 'prompt_sha256': sha256(prompt.encode()), 'model': dict(model), + 'model_identity_source': 'caller_supplied; adapter audit required for loaded-weight proof', + 'adapter_kind': adapter_kind, 'preflight': inspection, 'adapter_invoked': False, + 'new_inference_requested': False, 'raw_text': None, 'raw_sha256': None, + 'candidate_answer': None, 'rejection_reasons': [], 'review_status': 'pending', + 'response_checks': None, + 'semantic_truth_verified': False, 'flight_authorized': False} + if not inspection['passed']: + record.update(status='input_blocked', answer_origin='application', + answer=abstention('入力画像が欠落・読込不能、または全画像の視認性が不足しているため、観測目的を評価できない。'), + rejection_reasons=inspection['reasons']) + return record, frames + request = ObservationRequest(prompt, frames, tuple(inspection['usable_evidence_ids'])) + record['adapter_invoked'] = True + record['new_inference_requested'] = adapter_kind == 'live' + try: + raw = infer(request) + except Exception as exc: + # Do not copy provider exception bodies, which may contain credentials. + record.update(status='model_failed', answer_origin='application', error_type=type(exc).__name__, + rejection_reasons=['adapter_failed'], answer=abstention('モデル回答を取得できないため、観測判断を保留する。')) + return record, frames + provider_rejections = () + if isinstance(raw, InferenceResponse): + provider_rejections = raw.rejection_reasons + record['api'] = raw.audit + record['new_inference_requested'] = adapter_kind == 'live' and bool(raw.audit.get('http_attempted')) + record['model_identity_source'] = raw.audit.get('model_identity_source', + 'requested API ID and provider-reported identity; weights/revision unverified') + raw = raw.raw_text + if raw is None and provider_rejections: + record.update(status='model_failed', answer_origin='application', + rejection_reasons=list(provider_rejections), + answer=abstention('推論サービスの完全な回答を確認できないため、観測判断を保留する。')) + return record, frames + if isinstance(raw, str): + record.update(raw_text=raw, raw_sha256=sha256(raw.encode())) + # No reference label is available in a live request. Mechanical hints are + # review priorities; they do not establish truth or replace the raw output. + record['response_checks'] = triage(raw, 'visual', expected_decision=None) + try: + if not isinstance(raw, str): + raise ValueError('Adapter must return raw text') + candidate = parse_answer(raw) + record['candidate_answer'] = candidate + refs = {eid for f in candidate['findings'] for eid in f['evidence_ids']} + if not refs <= set(request.usable_evidence_ids): + raise ValueError('Evidence refers to an unavailable or uniform frame') + except (ValueError, TypeError, AssertionError) as exc: + record.update(status='output_rejected', answer_origin='application', + rejection_reasons=[str(exc)], answer=abstention('モデル回答の形式または根拠画像の参照を確認できないため、観測判断を保留する。')) + else: + record.update(status='pending_human_review', answer_origin='model_candidate', answer=candidate) + if provider_rejections: + record.update(status='output_rejected', answer_origin='application', + rejection_reasons=list(dict.fromkeys([*record['rejection_reasons'], *provider_rejections])), + answer=abstention('推論サービスの回答が完了したことを確認できないため、観測判断を保留する。')) + return record, frames + + +def save_review(directory, record, frames): + """New immutable directory containing raw output and exact inspected inputs.""" + directory = Path(directory) + directory.mkdir(parents=True, exist_ok=False) + by_id = {r['evidence_id']: r for r in record['preflight']['frames']} + for frame in frames: + if sha256(frame.data) != by_id[frame.evidence_id]['sha256']: + raise ValueError('Snapshot changed') + extension = 'png' if frame.data.startswith(b'\x89PNG') else 'jpg' + path = directory / f'{frame.evidence_id}.{extension}' + path.write_bytes(frame.data) + by_id[frame.evidence_id]['snapshot'] = path.name + with (directory / 'record.json').open('x', encoding='utf-8') as stream: + json.dump(record, stream, ensure_ascii=False, indent=2); stream.write('\n') + return directory / 'record.json' diff --git a/tancho/observation_holdout.py b/tancho/observation_holdout.py new file mode 100644 index 0000000000000000000000000000000000000000..b929d85dd6c32422771b9f8a056f14c2c8bbbb21 --- /dev/null +++ b/tancho/observation_holdout.py @@ -0,0 +1,79 @@ +"""Evaluation-only acquisition and review bindings for a previously reserved event.""" +import hashlib +import json +import math +from pathlib import Path +from .observation_events import validate_event_partitions + +EVENT = 'east-japan-typhoon-2019' +SOURCE = 'gsi-omachi-20191013' +MEDIA_URL = 'https://www.gsi.go.jp/common/000219420.mp4' +OFFSETS_MS = (500, 2500, 4500, 6500) + + +def digest(path): + with Path(path).open('rb') as stream: + return hashlib.file_digest(stream, 'sha256').hexdigest() + + +def windows(duration_seconds): + if (type(duration_seconds) not in (int, float) or not math.isfinite(duration_seconds) + or duration_seconds < 96): + raise ValueError('Protocol v1 requires at least 96 seconds') + duration_ms = math.floor(duration_seconds * 1000) + result = [] + for index in range(12): + # Compute in milliseconds, floor only the final start (less than 1 ms early). + start = math.floor((duration_seconds * 1000 - 8000) * index / 11) + end = start + 8000 + if end > duration_ms or (result and start < result[-1]['end_ms']): + raise ValueError('Overlapping or out-of-bounds interval') + result.append({'id': f'O{index + 1:02d}', 'start_ms': start, 'end_ms': end, + 'requested_frame_times_ms': [start + x for x in OFFSETS_MS]}) + return result + + +def validate_acquisition(plan, policy, protocol_path, historical_policy_path): + if plan.get('schema_version') != 'tancho-independent-acquisition-1.0': + raise ValueError('Unknown acquisition plan') + if plan.get('purpose') != 'evaluation_only' or plan.get('model_inference_permitted') is not False: + raise ValueError('Acquisition cannot authorize inference or training') + if plan['protocol_sha256'] != digest(protocol_path): + raise ValueError('Pre-access protocol changed') + if plan['historical_event_policy_sha256'] != digest(historical_policy_path): + raise ValueError('Historical event reservation changed') + source = plan['source'] + if (source.get('source_id') != SOURCE or source.get('event_id') != EVENT + or source.get('url') != MEDIA_URL or source.get('split_proposal') != 'test'): + raise ValueError('Unexpected source or non-test split') + if policy.get('purpose') != 'evaluation_only' or policy.get('authorized_source_ids') != [SOURCE]: + raise ValueError('Acquisition policy is not narrowly evaluation-only') + validate_event_partitions([source], policy) + return source + + +def validate_review_state(state, bundle_id, task_ids, reference_digest): + if (state.get('schema_version') != 'tancho-holdout-review-1.0' + or state.get('bundle_id') != bundle_id + or state.get('reference_sha256') != reference_digest): + raise ValueError('Review belongs to a different reference package') + entries = state.get('reviews', []) + if (len(entries) != len(task_ids) or [x.get('sample_id') for x in entries] != task_ids): + raise ValueError('Review inventory differs') + for row in entries: + if row.get('status') not in ('pending', 'accepted', 'needs_changes'): + raise ValueError('Invalid review status') + if not isinstance(row.get('note'), str): + raise ValueError('Review note must be text') + if row['status'] == 'needs_changes' and not row['note'].strip(): + raise ValueError('Corrections need a note') + complete = all(row['status'] == 'accepted' for row in entries) + if not isinstance(state.get('reviewer'), str): + raise ValueError('Reviewer must be text') + if any(row['status'] != 'pending' for row in entries) and not state['reviewer'].strip(): + raise ValueError('Reviewed entries require reviewer attribution') + return {'accepted': sum(x['status'] == 'accepted' for x in entries), + 'needs_changes': sum(x['status'] == 'needs_changes' for x in entries), + 'pending': sum(x['status'] == 'pending' for x in entries), + 'all_accepted': complete, + 'note': 'Self-reported review file; not an authenticated approval or evaluation result.'} diff --git a/tancho/observation_task.py b/tancho/observation_task.py new file mode 100644 index 0000000000000000000000000000000000000000..e9542660ded32fad8dba463fc8853410dc7821b7 --- /dev/null +++ b/tancho/observation_task.py @@ -0,0 +1,99 @@ +"""One label-free prompt and answer contract for the observation pilot.""" +import copy +import json +from .contracts import fields, require, text, strings + +PROMPT_VERSION = 'observation-ja-v1' +INSTRUCTION = '''災害の航空観測について、与えられた画像だけを根拠に答えてください。 +入力は短い観測区間から時刻順に抽出した4枚です。連続動画の全フレームではありません。 +順にF1、F2、F3、F4と呼び、元区間の開始から0.5、2.5、4.5、6.5秒の画像です。 +画像が無い、見えない、尺度が無い場合は、その限界を明示してください。飛行指示や数値の角度・距離を作らないでください。 +判断は観測目的に対して行います。inspectは不足情報を追加確認したい場合、no_actionはその目的を既に満たす場合、abstainは求められた結論を裏付けられない場合です。 +no_actionは安全や無被害を意味しません。目に見える事実と不明点を分けてください。 +JSONオブジェクトだけを返してください。Markdownや前置きは不要です。 +キーはfindings、unknowns、decision、next_observationの4つだけです。 +findingsは{text: 日本語の観測事実, evidence_ids: 根拠画像IDの配列}の配列。 +unknownsは日本語の不明点の文字列配列。decisionはinspect、no_action、abstainのいずれか。 +inspectの場合は根拠のあるfindingsとunknownsを示し、next_observationに追加確認したい内容を書く。それ以外はnext_observationをnullにする。 +根拠IDとして使えるのはF1、F2、F3、F4だけです。画像がないときに観測事実を作らないでください。 +観測目的：''' + + +def make_prompt(row): + # Intentionally whitelist only a task input. No source title, split, reviewer, + # expected decision, findings, uncertainty or next-view label enters inference. + return INSTRUCTION + row['observation_goal'] + + +def canonical_answer(row): + mapping = {e['evidence_id']: f'F{i}' for i, e in enumerate(row['evidence'], 1)} + answer = copy.deepcopy(row['answer']) + for finding in answer['findings']: + finding['evidence_ids'] = [mapping[eid] for eid in finding['evidence_ids']] + return answer + + +def validate_answer(answer): + fields(answer, 'findings unknowns decision next_observation') + require(isinstance(answer['findings'], list), 'Invalid findings') + for finding in answer['findings']: + fields(finding, 'text evidence_ids') + require(text(finding['text']) and strings(finding['evidence_ids']) + and bool(finding['evidence_ids']) + and set(finding['evidence_ids']) <= {'F1','F2','F3','F4'}, 'Invalid evidence reference') + require(strings(answer['unknowns']), 'Invalid unknowns') + require(answer['decision'] in ('inspect','no_action','abstain'), 'Invalid decision') + if answer['decision'] == 'inspect': + require(bool(answer['findings']) and bool(answer['unknowns']) + and text(answer['next_observation']), 'Inspection lacks evidence, uncertainty or purpose') + else: + require(answer['next_observation'] is None, 'Unexpected next observation') + require(bool(answer['unknowns']) if answer['decision'] == 'abstain' + else bool(answer['findings']), 'Missing basis for decision') + return answer + + +def parse_answer(raw): + def unique(pairs): + result = {} + for key, value in pairs: + if key in result: + raise ValueError(f'Duplicate JSON key: {key}') + result[key] = value + return result + def reject_constant(value): + raise ValueError(f'Non-finite JSON constant: {value}') + return validate_answer(json.loads(raw, object_pairs_hook=unique, parse_constant=reject_constant)) + + +def score_predictions(gold, raw_predictions): + """All expected samples remain in denominators; prose quality needs human review.""" + require(bool(gold), 'No evaluation samples') + require(len({r['sample_id'] for r in gold}) == len(gold), 'Duplicate evaluation sample') + require(set(raw_predictions) <= {r['sample_id'] for r in gold}, 'Unknown prediction sample') + labels = ('inspect','no_action','abstain') + confusion = {a: {b: 0 for b in (*labels, 'invalid', 'missing')} for a in labels} + details = [] + valid = correct = 0 + for row in gold: + sid = row['sample_id']; expected = row['answer']['decision'] + record = {'sample_id':sid, 'expected_decision':expected} + if sid not in raw_predictions: + predicted = 'missing'; record['error'] = 'No output' + else: + raw = raw_predictions[sid]; record['raw_text'] = raw + try: + answer = parse_answer(raw); predicted = answer['decision']; valid += 1 + record['answer'] = answer + except (ValueError, TypeError, AssertionError) as exc: + predicted = 'invalid'; record['error'] = str(exc) + confusion[expected][predicted] += 1 + correct += predicted == expected + record['predicted_decision'] = predicted; details.append(record) + recalls = [confusion[c][c] / sum(confusion[c].values()) for c in labels if sum(confusion[c].values())] + n = len(gold) + return {'samples':n,'received':len(raw_predictions),'schema_valid':valid, + 'schema_valid_rate':valid/n,'decision_accuracy':correct/n, + 'macro_recall':sum(recalls)/len(recalls),'confusion':confusion,'samples_detail':details, + 'semantic_quality_evaluated':False, + 'scope':'Decision agreement and structural validity only; not evidence truth, flight safety or generalization.'} diff --git a/tancho/observation_training.py b/tancho/observation_training.py new file mode 100644 index 0000000000000000000000000000000000000000..0a8204976b073ce22f7835f3ad8b0676edf16c7f --- /dev/null +++ b/tancho/observation_training.py @@ -0,0 +1,68 @@ +"""Versioned policy for deterministic visual SFT and its bounded integration check.""" +import hashlib +import json +from pathlib import Path + +VERSION = 'observation-strict-sft-1.0' +PROCESS_ENV = {'PYTHONHASHSEED': '42', 'CUBLAS_WORKSPACE_CONFIG': ':4096:8', + 'FLASH_ATTENTION_DETERMINISTIC': '1'} +INTEGRATION_STAGES = {'save': (0, 2), 'resume': (2, 4), 'continuous': (0, 4)} + + +def digest(path): + return hashlib.sha256(Path(path).read_bytes()).hexdigest() + + +def verify_integration_permit(permit, *, bundle, config, overrides, policy): + """A save/resume engineering test cannot authorize a candidate-training stage.""" + stage = permit.get('stage') + if (permit.get('schema_version') != 'observation-strict-integration-1.0' or stage not in INTEGRATION_STAGES + or permit.get('purpose') != 'native_save_resume_equivalence' + or permit.get('rng_advancement_probe') is not True or permit.get('retain_candidate') is not False): + raise ValueError('Invalid integration-only permit') + start, end = INTEGRATION_STAGES[stage] + if permit.get('start_iteration') != start or permit.get('step') != end: + raise ValueError('Unbounded integration stage') + for key, path in [('bundle_manifest_sha256', bundle / 'manifest.json'), ('config_sha256', config), + ('overrides_sha256', overrides), ('training_policy_sha256', policy)]: + if permit.get(key) != digest(path): + raise ValueError('Changed integration binding: ' + key) + if stage == 'resume': + previous = Path(permit['previous_audit_path']) + if digest(previous) != permit.get('previous_audit_sha256'): + raise ValueError('Previous audit changed') + record = json.loads(previous.read_text()) + if (not record.get('completed') or record.get('end_iteration') != 2 + or record.get('training_policy_version') != VERSION): + raise ValueError('Invalid resume parent') + return start, end + + +def compare_steps(continuous, save, resumed): + """Compare state transitions and every observed update, not only final loss.""" + if not all(a.get('completed') and a.get('training_policy_version') == VERSION for a in (continuous, save, resumed)): + raise ValueError('Incomplete deterministic SFT audits') + if [(a['start_iteration'], a['end_iteration']) for a in (continuous, save, resumed)] != [(0, 4), (0, 2), (2, 4)]: + raise ValueError('Unexpected integration intervals') + if [len(a['steps']) for a in (continuous, save, resumed)] != [4, 2, 2]: + raise ValueError('Incomplete update inventory') + transitions = {'parameters_restored': save['end_parameters'] == resumed['start_parameters'], + 'optimizer_scheduler_restored': save['end_optimizer'] == resumed['start_optimizer'], + 'rng_restored_before_data_fetch': save['checkpoints'][-1]['rng'] == resumed['resume_rng']['after_restore'], + 'nontrivial_rng_restore_exercised': resumed['resume_rng']['native_reseed_changed_loaded_rng'] is True, + 'initial_parameters_equal': continuous['start_parameters'] == save['start_parameters'], + 'initial_optimizer_equal': continuous['start_optimizer'] == save['start_optimizer'], + 'final_parameters_equal': continuous['end_parameters'] == resumed['end_parameters'], + 'final_optimizer_equal': continuous['end_optimizer'] == resumed['end_optimizer']} + fields = ('sample_ids', 'epoch', 'positions', 'tokens', 'loss', 'input', 'backward', + 'optimizer_entry', 'after_update', 'rng_probe') + steps = [] + for i, (a, b) in enumerate(zip(continuous['steps'], save['steps'] + resumed['steps'], strict=True)): + if a['iteration_before_update'] != i or b['iteration_before_update'] != i: + raise ValueError('Misordered updates') + steps.append({'update': i+1, 'equal': {k: a[k] == b[k] for k in fields}}) + passed = all(transitions.values()) and all(all(s['equal'].values()) for s in steps) + return {'passed': passed, 'transitions': transitions, 'steps': steps, + 'final_parameters_equal': continuous['end_parameters'] == resumed['end_parameters'], + 'scope': 'Four approved inputs, one GPU, instrumented RNG-advancement integration test.', + 'model_quality_evaluated': False, 'model_promotion_authorized': False} diff --git a/tancho/probe.py b/tancho/probe.py new file mode 100644 index 0000000000000000000000000000000000000000..0b9cef7de41607f038c1088e1152e491a5300bc8 --- /dev/null +++ b/tancho/probe.py @@ -0,0 +1,157 @@ +"""Bounded Token Factory connectivity probes using synthetic inputs only.""" +import argparse +import base64 +from datetime import datetime, timezone +import hashlib +import json +import os +from pathlib import Path +import struct +import time +import urllib.error +import urllib.request +import uuid +import zlib + +BASE_URL = "https://api.tokenfactory.nebius.com/v1" +KEY_FILE = Path.home() / ".config/tancho/token_factory_api_key" +COSMOS = "nvidia/Cosmos3-Super-Reasoner" +NEMOTRON = "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B" + + +def fixture_png(): + """64px RGB image: red square on white; deterministic, no external data.""" + def chunk(kind, data): + return struct.pack('!I', len(data)) + kind + data + struct.pack('!I', zlib.crc32(kind + data)) + pixels = b''.join(b'\0' + b''.join( + b'\xff\0\0' if 16 <= x < 48 and 16 <= y < 48 else b'\xff\xff\xff' + for x in range(64)) for y in range(64)) + return (b'\x89PNG\r\n\x1a\n' + chunk(b'IHDR', struct.pack('!2I5B', 64, 64, 8, 2, 0, 0, 0)) + + chunk(b'IDAT', zlib.compress(pixels)) + chunk(b'IEND', b'')) + + +def payload(kind, max_tokens): + if not 1 <= max_tokens <= 512: + raise ValueError('max-tokens must be between 1 and 512') + if kind == 'cosmos': + content = [ + {'type': 'text', 'text': 'This is a synthetic connectivity test, not a real scene. Describe the central shape and its color in one short sentence.'}, + {'type': 'image_url', 'image_url': {'url': 'data:image/png;base64,' + base64.b64encode(fixture_png()).decode()}} + ] + model = COSMOS + elif kind == 'nemotron': + content = ('Synthetic postflight log, not a real flight: observations=2; ' + 'accepted_updates=1; rejected_updates=1; injuries=unknown. ' + 'Write one short factual sentence summarizing these counts. Do not infer safety or issue commands.') + model = NEMOTRON + else: + raise ValueError('unknown probe') + return {'model': model, 'messages': [{'role': 'user', 'content': content}], + 'max_tokens': max_tokens, 'stream': False} + + +def read_key(path=KEY_FILE): + key = os.environ.get('NEBIUS_TOKEN_FACTORY_API_KEY') or os.environ.get('NEBIUS_API_KEY') + if not key: + if path.stat().st_mode & 0o077: + raise ValueError('Key file permissions must be 600 or stricter') + key = path.read_text().strip() + if not key.strip() or any(c.isspace() for c in key.strip()): + raise ValueError('Invalid API key format') + return key.strip() + + +def redact(value, key): + if isinstance(value, str): + return value.replace(key, '[REDACTED]') if key else value + if isinstance(value, list): + return [redact(v, key) for v in value] + if isinstance(value, dict): + return {redact(k, key): redact(v, key) for k, v in value.items()} + return value + + +class NoRedirect(urllib.request.HTTPRedirectHandler): + def redirect_request(self, req, fp, code, msg, headers, newurl): + return None # Never forward credentials to a different endpoint. + + +def request(path, key, body=None, opener=None): + encoded = json.dumps(body, sort_keys=True).encode() if body is not None else None + req = urllib.request.Request(BASE_URL + path, data=encoded, headers={ + 'Authorization': 'Bearer ' + key, 'Content-Type': 'application/json'}) + started = time.monotonic() + result = {'started_at': datetime.now(timezone.utc).isoformat(), + 'path': path, 'model': body.get('model') if body else None, + 'request_sha256': hashlib.sha256(encoded or path.encode()).hexdigest()} + opener = opener or urllib.request.build_opener(NoRedirect()) + try: + with opener.open(req, timeout=60) as response: + result['http_status'] = response.status + raw = response.read() + except urllib.error.HTTPError as exc: + result['http_status'] = exc.code + raw = exc.read() + except (urllib.error.URLError, TimeoutError, OSError): + result.update(http_status=None, error='Transport failure; no automatic retry. A timed-out request may still be billed.') + raw = b'{}' + try: + result['response'] = json.loads(raw) + if not isinstance(result['response'], dict): + result['error'] = 'Unexpected response: expected JSON object' + except (ValueError, UnicodeDecodeError): + result['error'] = 'Non-JSON response body omitted' + result['latency_seconds'] = round(time.monotonic() - started, 3) + return redact(result, key) + + +def save_result(result, directory): + directory.mkdir(parents=True, exist_ok=True, mode=0o700) + path = directory / (datetime.now(timezone.utc).strftime('%Y%m%dT%H%M%S') + '-' + uuid.uuid4().hex[:8] + '.json') + with path.open('x', encoding='utf-8') as f: + os.chmod(path, 0o600) + json.dump(result, f, ensure_ascii=False, indent=2) + f.write('\n') + return path + + +def main(argv=None): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--key-file', type=Path, default=KEY_FILE) + parser.add_argument('--output-dir', type=Path, default=Path('runs')) + sub = parser.add_subparsers(dest='command', required=True) + sub.add_parser('status') + sub.add_parser('models') + p = sub.add_parser('probe') + p.add_argument('kind', choices=['cosmos', 'nemotron']) + p.add_argument('--max-tokens', type=int, default=128) + p.add_argument('--dry-run', action='store_true', help='No key access and no network request') + args = parser.parse_args(argv) + if args.command == 'status': + print(json.dumps({'key_file_exists': args.key_file.is_file(), + 'key_env_set': bool(os.environ.get('NEBIUS_TOKEN_FACTORY_API_KEY') or os.environ.get('NEBIUS_API_KEY')), + 'inference_performed': False})) + return 0 + try: + body = payload(args.kind, args.max_tokens) if args.command == 'probe' else None + if args.command == 'probe' and args.dry_run: + print(json.dumps({'dry_run': True, 'request': body}, indent=2)) + return 0 + key = read_key(args.key_file) + result = request('/models?verbose=true' if args.command == 'models' else '/chat/completions', key, body) + path = save_result(result, args.output_dir) + summary = {k: result.get(k) for k in ['http_status', 'model', 'latency_seconds', 'error']} + response = result.get('response', {}) + if isinstance(response, dict): + summary['usage'] = response.get('usage') + if args.command == 'models': + summary['models'] = [m['id'] for m in response.get('data', [])] + else: + summary['choices'] = response.get('choices') + summary['api_error'] = response.get('error') or response.get('detail') + summary['record'] = str(path) + print(json.dumps(summary, ensure_ascii=False, indent=2)) + return 0 if result.get('http_status') == 200 and not result.get('error') and isinstance(response, dict) and not response.get('error') else 1 + except (OSError, ValueError) as exc: + print(json.dumps({'error': type(exc).__name__, 'detail': 'Check key configuration, arguments, and output directory.'})) + return 2 diff --git a/tancho/px4_mission.py b/tancho/px4_mission.py new file mode 100644 index 0000000000000000000000000000000000000000..af4b00135900a3857af08df80f51331a10c22413 --- /dev/null +++ b/tancho/px4_mission.py @@ -0,0 +1,267 @@ +"""Viewpoint sequence to PX4 mission: the deterministic half of the flight path. + +The model proposes where to look from, in metres inside the approved envelope's +local ENU frame. It never emits latitude, longitude or a MAVLink item. This +module validates that proposal, checks it against the envelope (RFL stage 2) and +converts the survivor into mission items and gimbal commands. + +Horizons matter here. The camera9d contract in `tancho/contracts.py` covers +0.05 to 1.6 seconds (or 16 seconds at the slower cadence), which is far shorter +than a mission and, by its own spec, is not executable after a 60-second-old +prediction. A viewpoint sequence is the artifact that survives the 30-second +outer loop, so mission replanning is built on this contract, not on camera9d. + +PX4 SITL settled the item shape on 2026-09-08. A bare waypoint is accepted, and +so is a waypoint followed by DO_SET_ROI_LOCATION. DO_CHANGE_SPEED, +DO_GIMBAL_MANAGER_PITCHYAW and DO_MOUNT_CONTROL each came back as +MAV_MISSION_UNSUPPORTED in either order, so none of them may appear in a +mission. The camera is aimed at a point rather than by an angle, which suits a +viewpoint contract: the model already names the target it wants to see. + +Speed is therefore not a mission item. It belongs to the flight-plan parameters +set before the mission is uploaded, and the envelope check still clamps it. +""" +import math + +from tancho.contracts import ContractError, fields, number, require, text, validate_context + +SCHEMA = 'tancho-viewpoints-1.0' +FRAME = 'envelope_local_enu' +MAX_VIEWPOINTS = 8 +MAX_HORIZON_MS = 300_000 +MAX_LOCAL_OFFSET_M = 5000.0 +# WGS84. The local tangent plane is only used inside MAX_LOCAL_OFFSET_M. +WGS84_A = 6378137.0 +WGS84_F = 1 / 298.257223563 +# Read from the upstream MAVLink common.xml and confirmed against PX4 SITL. +MAVLINK_DEFINITION_SHA256 = '72fc17bf1615b7403160530e09ea38acad953a9f42e0446905241a575e31dd19' +MAV_FRAME_GLOBAL_RELATIVE_ALT_INT = 6 +# Items that carry no position use the mission frame; a global frame with zero +# coordinates is what the refused six-item form sent for the return to launch. +MAV_FRAME_MISSION = 2 +MAV_CMD_NAV_WAYPOINT = 16 +MAV_CMD_NAV_RETURN_TO_LAUNCH = 20 +MAV_CMD_NAV_TAKEOFF = 22 +MAV_CMD_DO_SET_ROI_LOCATION = 195 +# Refused by PX4 as mission items; kept here so nobody adds them back. +REFUSED_IN_MISSION = {'MAV_CMD_DO_CHANGE_SPEED': 178, + 'MAV_CMD_DO_GIMBAL_MANAGER_PITCHYAW': 1000, + 'MAV_CMD_DO_MOUNT_CONTROL': 205} + + +def validate_viewpoints(output, context, *, now_ms, goal_revision, envelope_revision): + """Accept only a bounded, ordered viewpoint sequence in the envelope frame.""" + validate_context(context, now_ms) + fields(output, 'schema_version observation_id goal_revision envelope_revision ' + 'calibration_id coordinate_frame viewpoints') + require(output['schema_version'] == SCHEMA, 'Unknown viewpoint version') + require(output['observation_id'] == context['observation_id'], 'Observation mismatch') + require(output['goal_revision'] == context['goal_revision'] == goal_revision, 'Goal changed') + require(output['envelope_revision'] == context['envelope_revision'] == envelope_revision, + 'Envelope changed') + require(text(output['calibration_id']), 'Calibration reference missing') + require(output['coordinate_frame'] == FRAME, 'Unknown frame') + rows = output['viewpoints'] + require(isinstance(rows, list) and 0 < len(rows) <= MAX_VIEWPOINTS, 'Unbounded viewpoint list') + total = 0 + for index, row in enumerate(rows): + fields(row, 'sequence position_m target_position_m dwell_ms speed_mps ' + 'target_id evidence_ids') + require(row['sequence'] == index, 'Viewpoint order is not the list order') + position, target = row['position_m'], row['target_position_m'] + for point in (position, target): + require(isinstance(point, list) and len(point) == 3 and all(number(v) for v in point) + and all(abs(v) <= MAX_LOCAL_OFFSET_M for v in point), 'Invalid local position') + # PX4 aims the camera at a point, so the contract names the point rather + # than an angle. Looking up is refused as before, now geometrically. + require(target[2] <= position[2], 'The viewpoint must look at or below the horizon') + require(math.dist(position[:2], target[:2]) >= 1.0 or position[2] - target[2] >= 1.0, + 'Viewpoint and target are the same place') + require(type(row['dwell_ms']) is int and 1000 <= row['dwell_ms'] <= 60_000, 'Invalid dwell') + require(number(row['speed_mps']) and 0.5 <= row['speed_mps'] <= 15.0, 'Invalid speed') + require(text(row['target_id']), 'Viewpoint without a target') + require(row['target_id'] in context['target_ids'], 'Unknown target reference') + evidence = row['evidence_ids'] + require(isinstance(evidence, list) and evidence + and all(text(e) and e in context['evidence_ids'] for e in evidence), + 'Viewpoint without resolvable evidence') + total += row['dwell_ms'] + require(total <= MAX_HORIZON_MS, 'Dwell alone exceeds the planning horizon') + return output + + +def validate_envelope(envelope): + """The approved volume: a surveyed origin, a polygon in metres and an altitude band.""" + fields(envelope, 'revision origin polygon_m min_alt_m max_alt_m max_speed_mps ' + 'max_mission_seconds return_margin_seconds') + origin = envelope['origin'] + fields(origin, 'latitude_deg longitude_deg') + require(number(origin['latitude_deg']) and -85.0 <= origin['latitude_deg'] <= 85.0, + 'Envelope origin latitude out of range') + require(number(origin['longitude_deg']) and -180.0 <= origin['longitude_deg'] <= 180.0, + 'Envelope origin longitude out of range') + polygon = envelope['polygon_m'] + require(isinstance(polygon, list) and len(polygon) >= 3, 'Envelope polygon needs three corners') + for point in polygon: + require(isinstance(point, list) and len(point) == 2 and all(number(v) for v in point) + and all(abs(v) <= MAX_LOCAL_OFFSET_M for v in point), 'Invalid envelope corner') + require(number(envelope['min_alt_m']) and number(envelope['max_alt_m']) + and 0.0 <= envelope['min_alt_m'] < envelope['max_alt_m'] <= 150.0, + 'Envelope altitude band must sit inside the 150 m limit') + require(number(envelope['max_speed_mps']) and 0.5 <= envelope['max_speed_mps'] <= 15.0, + 'Invalid envelope speed limit') + require(type(envelope['max_mission_seconds']) is int and 0 < envelope['max_mission_seconds'] <= 1800, + 'Invalid mission time limit') + require(type(envelope['return_margin_seconds']) is int and envelope['return_margin_seconds'] > 0, + 'Return margin required') + return envelope + + +def inside(polygon, point): + """Ray casting; a point on the boundary counts as outside, which is the safe side.""" + x, y = point + result = False + for index, (x1, y1) in enumerate(polygon): + x2, y2 = polygon[(index + 1) % len(polygon)] + if (y1 > y) != (y2 > y): + crossing = (x2 - x1) * (y - y1) / (y2 - y1) + x1 + if math.isclose(crossing, x, abs_tol=1e-9): + return False + if x < crossing: + result = not result + elif math.isclose(y1, y) and math.isclose(y2, y) and min(x1, x2) <= x <= max(x1, x2): + return False + return result + + +def segment_samples(start, end, step=2.0): + """Sample the straight leg so a path cannot leave and re-enter between corners.""" + distance = math.dist(start, end) + count = max(1, math.ceil(distance / step)) + return [tuple(start[axis] + (end[axis] - start[axis]) * k / count for axis in range(3)) + for k in range(count + 1)] + + +def check_envelope(viewpoints, envelope, *, start_position_m): + """RFL stage 2. Everything the aircraft would fly must sit inside the approved volume.""" + validate_envelope(envelope) + polygon = [tuple(point) for point in envelope['polygon_m']] + require(isinstance(start_position_m, (list, tuple)) and len(start_position_m) == 3 + and all(number(v) for v in start_position_m), 'Invalid current position') + legs, seconds, position = [], 0.0, tuple(start_position_m) + for row in viewpoints['viewpoints']: + target = tuple(row['position_m']) + speed = min(row['speed_mps'], envelope['max_speed_mps']) + for sample in segment_samples(position, target): + require(inside(polygon, (sample[0], sample[1])), 'Path leaves the approved polygon') + require(envelope['min_alt_m'] <= sample[2] <= envelope['max_alt_m'], + 'Path leaves the approved altitude band') + distance = math.dist(position, target) + seconds += distance / speed + row['dwell_ms'] / 1000.0 + legs.append({'sequence': row['sequence'], 'distance_m': distance, 'speed_mps': speed}) + position = target + # The way home has to fit inside the same budget as the way out. + home = math.dist(position, tuple(start_position_m)) / envelope['max_speed_mps'] + require(seconds + home + envelope['return_margin_seconds'] <= envelope['max_mission_seconds'], + 'Mission plus return margin exceeds the approved time') + return {'legs': legs, 'flight_seconds': seconds, 'return_seconds': home, + 'return_margin_seconds': envelope['return_margin_seconds'], + 'envelope_revision': envelope['revision'], 'inside_envelope': True} + + +def local_to_geodetic(origin, east_m, north_m): + """Local tangent plane, only valid inside MAX_LOCAL_OFFSET_M of the origin.""" + require(abs(east_m) <= MAX_LOCAL_OFFSET_M and abs(north_m) <= MAX_LOCAL_OFFSET_M, + 'Local offset exceeds the tangent plane limit') + latitude = math.radians(origin['latitude_deg']) + e2 = WGS84_F * (2 - WGS84_F) + denominator = math.sqrt(1 - e2 * math.sin(latitude) ** 2) + meridian = WGS84_A * (1 - e2) / denominator ** 3 + normal = WGS84_A / denominator + return (origin['latitude_deg'] + math.degrees(north_m / meridian), + origin['longitude_deg'] + math.degrees(east_m / (normal * math.cos(latitude)))) + + +def to_mission(viewpoints, envelope, *, start_position_m, wrap_flight=True): + """Convert a checked viewpoint sequence into mission items. + + `wrap_flight` adds a takeoff and a return to launch, and a flight needs both: + without the takeoff, SITL armed and sat at 2 m. The six-item form was once + refused, but that was a gap in the sequence numbers rather than an unsupported + command, and PX4 1.15.4 accepted all five shapes tried on 2026-09-08 and flew + this one to the commanded 60 m. + """ + report = check_envelope(viewpoints, envelope, start_position_m=start_position_m) + origin = envelope['origin'] + items, sequence = [], 0 + + def add(command, params, *, geodetic=None, frame=None): + """Parameters 5 to 7 travel as x, y and z in a MISSION_ITEM_INT. + + For a navigation command those three are latitude, longitude and + altitude; for a DO command they carry the remaining parameters, which + would otherwise never reach the autopilot. + """ + nonlocal sequence + require(len(params) == 7, 'A mission item carries exactly seven parameters') + if geodetic is None: + x, y, z = int(params[4]), int(params[5]), float(params[6]) + else: + latitude, longitude, altitude = geodetic + params = list(params[:4]) + [latitude, longitude, altitude] + x, y, z = round(latitude * 1e7), round(longitude * 1e7), float(altitude) + items.append({'seq': sequence, 'command': command, + 'frame': frame or MAV_FRAME_GLOBAL_RELATIVE_ALT_INT, 'autocontinue': 1, + 'params': [float(value) for value in params], 'x': x, 'y': y, 'z': z}) + sequence += 1 + + if wrap_flight: + first = viewpoints['viewpoints'][0] + start_latitude, start_longitude = local_to_geodetic(origin, start_position_m[0], + start_position_m[1]) + add(MAV_CMD_NAV_TAKEOFF, [0, 0, 0, 0, 0, 0, 0], + geodetic=(start_latitude, start_longitude, first['position_m'][2])) + legs = [] + for row in viewpoints['viewpoints']: + east, north, up = row['position_m'] + target_east, target_north, target_up = row['target_position_m'] + latitude, longitude = local_to_geodetic(origin, east, north) + target_latitude, target_longitude = local_to_geodetic(origin, target_east, target_north) + # PX4 stores the waypoint yaw in [0, 360), so emit it that way and the + # round trip stays exact. Sending -90 came back as 270 in SITL. + bearing = math.degrees(math.atan2(target_east - east, target_north - north)) % 360.0 + depression = math.degrees(math.atan2(up - target_up, + math.dist((east, north), (target_east, target_north)) + or 1e-9)) + # param4 is the vehicle yaw: face the target so the gimbal has the least to do. + add(MAV_CMD_NAV_WAYPOINT, [row['dwell_ms'] / 1000.0, 0, 0, bearing, 0, 0, 0], + geodetic=(latitude, longitude, up)) + # PX4 refuses gimbal commands in a mission but accepts a region of interest. + add(MAV_CMD_DO_SET_ROI_LOCATION, [0, 0, 0, 0, 0, 0, 0], + geodetic=(target_latitude, target_longitude, target_up)) + legs.append({'sequence': row['sequence'], 'vehicle_yaw_deg': round(bearing, 3), + 'camera_depression_deg': round(depression, 3), + 'speed_mps': min(row['speed_mps'], envelope['max_speed_mps'])}) + if wrap_flight: + add(MAV_CMD_NAV_RETURN_TO_LAUNCH, [0, 0, 0, 0, 0, 0, 0], frame=MAV_FRAME_MISSION) + return {'schema_version': 'tancho-px4-mission-1.0', + 'observation_id': viewpoints['observation_id'], + 'goal_revision': viewpoints['goal_revision'], + 'envelope_revision': viewpoints['envelope_revision'], + 'origin': dict(origin), 'items': items, 'envelope_check': report, + 'model_emitted_geodetic_position': False, + 'mavlink_definition_sha256': MAVLINK_DEFINITION_SHA256, + 'mavlink_ids_verified_against_upstream_xml': True, + 'mission_shape_accepted_by_px4_sitl': True, + 'commands_refused_by_px4_in_a_mission': dict(REFUSED_IN_MISSION), + # Speed is not a mission item here; it belongs to the flight plan. + 'flight_plan_parameters': {'legs': legs}, + 'camera_aimed_by_region_of_interest': True, + 'takeoff_and_return_included': wrap_flight, + # Legs alone upload cleanly and leave the vehicle on the ground. + 'flyable_as_uploaded': wrap_flight, + # PX4 1.15.4 SITL accepted this shape, armed and flew it to the + # commanded altitude on 2026-09-08. No real aircraft has flown it. + 'mission_execution_verified': 'px4-sitl-1.15.4' if wrap_flight else False, + 'vehicle_yaw_faces_the_target': True, + 'flown_by_a_real_aircraft': False} diff --git a/tancho/viewpoint_eval.py b/tancho/viewpoint_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..5d15f1542947b5d2a9c8db7c638900fc438c8098 --- /dev/null +++ b/tancho/viewpoint_eval.py @@ -0,0 +1,154 @@ +"""視点列の評価パック。**1件ごとに幾何が違うので、指示文は雛形＋場面で作る。** + +発見のパック（`detect_eval`）は40件が同じ指示文であることを検査する。指示文が黙って変わる事故を +止めるためで、あちらではそれが正しい。こちらは1件ごとに対象の位置と承認ボリュームが違うので、 +同じ規律を別の形で置く。**雛形を1つ凍結し、埋める値（場面）を1件ごとに記録し、出来上がった +指示文のハッシュも1件ごとに持つ。**どれが変わっても検査で落ちる。 + +モデルに出させるのは幾何だけである。観測の番号・改訂・較正の識別子は応用側が作る（contracts-v1 の +「Context is constructed by the application, never trusted from model output」）。**モデルの +出力をそのまま飛行の契約に入れない。**応用側が包んでから `px4_mission.validate_viewpoints` に掛ける。 +""" +import copy +import json +from pathlib import Path + +from .detect_eval import (FPS, encoded, frame_proof, materialize, sha256) +from .viewpoint_protocol import LEGACY, protocol, template, extra_fields + +VERSION = 'tancho-viewpoint-eval-1.0' +SETTINGS = {'max_new_tokens': 768, 'do_sample': False, 'seed': 42} + +TEMPLATE = """あなたは災害調査のドローンに載っている観測計画の部品です。 + +いまの状況です。座標は承認された飛行範囲の中だけで使う局所座標で、東をx、北をy、高さをzとし、 +単位はメートルです。 + +- 機体の現在位置: [{current}] +- 確認したい対象の位置: [{target}]（地表。画像の中央付近に写っています） +- 対象の実寸: 約{size_m}メートル +- 承認された飛行範囲: {polygon} の多角形の内側、高さ {min_alt} から {max_alt} メートル +- 速度の上限: 毎秒 {max_speed} メートル +- 希望する方角: 対象から見て{side}の側（向きは [{side_x}, {side_y}]。対象の位置にこの向きを掛けた + ぶんだけ足した所が、希望する視点の真下になります） +- 希望する見下ろす角度: 水平から下へ {pitch} 度 + +この対象を、あとで種類と状態を判別できる形で撮るための視点を決めてください。**対象が画面の中で +{required_px}画素以上になる距離まで近づき、承認された範囲の外へは出ないこと。** + +返すのはJSONの配列1個だけです。各要素は次の4つです。 + +- position_m: 視点の位置 [x, y, z] +- target_position_m: その視点から見る対象の位置 [x, y, z] +- dwell_ms: そこで留まる時間（1000から60000の整数） +- speed_mps: そこへ向かう速度（0.5から15.0） + +視点は{count}個までです。説明は書かず、JSONだけを返してください。""" + + +def require(condition, message): + if not condition: + raise ValueError(message) + + +def render(context): + """雛形に場面を埋める。埋める値は全て数か語で、文章を渡さない。 + + 方角は語と向きの両方で渡す（2026-09-11 に足した）。語だけで渡した版では、追加学習しても + 希望した側から見たのは40件中13件だった。「東をx、北をy」と書いたうえで「南の側」と言葉で + 指定していたので、**モデルは語を座標の向きへ翻訳してから位置を決める**必要があり、そこで + 落ちていた。向きを渡せば、翻訳が算数になる。 + """ + from .viewpoint_plan import SIDES + return template(TEMPLATE, protocol(context)).format( + current=', '.join(str(v) for v in context['current_position_m']), + target=', '.join(str(v) for v in context['target_position_m']), + size_m=context['target_size_m'], + polygon=' / '.join('[%g, %g]' % tuple(p) for p in context['envelope']['polygon_m']), + side_x='%g' % SIDES[context['side']][0], side_y='%g' % SIDES[context['side']][1], + min_alt=context['envelope']['min_alt_m'], max_alt=context['envelope']['max_alt_m'], + max_speed=context['envelope']['max_speed_mps'], side=context['side_ja'], + pitch=abs(context['preferred_pitch_deg']), required_px=context['required_pixels'], + count=context['max_viewpoints'], **extra_fields(context)) + + +def template_hash(cases): + modes = {protocol(c['context']) for c in cases} + require(len(modes) == 1, 'パック内で出力形式を混ぜない') + return sha256(template(TEMPLATE, modes.pop()).encode()) + + +def build_pack(cases_in, out, settings=None, *, frame_directory='tiles'): + """`cases_in` は (タイルのパック, タイルID, 件のID, 場面) の列。 + + **同じ画像を別の幾何で何度使ってもよい。**測るのは与えられた幾何の中で飛べる視点列を出せるか + なので、画像は場面の見た目を与えるためだけに置いている。件のIDは1件ごとに違う。 + """ + settings = dict(settings or SETTINGS) + out = Path(out) + (out / 'frames').mkdir(parents=True, exist_ok=True) + cases, roots = [], [] + for tiles_root, tile_id, case_id, context in cases_in: + tiles_root = Path(tiles_root) + if str(tiles_root) not in roots: + roots.append(str(tiles_root)) + source = tiles_root / frame_directory / (tile_id + '.png') + data = source.read_bytes() + _, proof = frame_proof(data) + name = tile_id + '.png' + (out / 'frames' / name).write_bytes(data) + prompt = render(context) + cases.append({'id': case_id, 'tile_id': tile_id, 'prompt': prompt, + 'prompt_sha256': sha256(prompt.encode()), + 'frame': 'frames/' + name, 'canonical_frame': proof, + 'context': context, 'source_pack': str(tiles_root)}) + cases.sort(key=lambda c: c['id']) + manifest = {'schema_version': VERSION, 'settings': settings, + 'template_sha256': template_hash(cases), 'source_packs': roots, 'cases': cases} + manifest['sha256'] = sha256(encoded({k: v for k, v in manifest.items() if k != 'sha256'})) + (out / 'manifest.json').write_text(json.dumps(manifest, ensure_ascii=False, indent=2) + '\n') + return manifest + + +def verify_pack(root): + root = Path(root) + require(not root.is_symlink() and not any(p.is_symlink() for p in root.rglob('*')), + 'パックに symlink を置かない') + manifest = json.loads((root / 'manifest.json').read_text()) + require(manifest['schema_version'] == VERSION, 'パックの版が違う') + require(manifest['sha256'] == sha256(encoded({k: v for k, v in manifest.items() if k != 'sha256'})), + 'manifest が書き換えられている') + require(manifest['template_sha256'] == template_hash(manifest['cases']), '雛形が変わっている') + require(set(manifest['settings']) == set(SETTINGS), '生成の設定の項目が違う') + require(manifest['settings']['do_sample'] is False and manifest['settings']['seed'] == 42, + '貪欲・種42から外れている') + seen = set() + for case in manifest['cases']: + require(case['id'] not in seen, '同じタイルが2度入っている') + seen.add(case['id']) + rendered = render(case['context']) + require(case['prompt'] == rendered and case['prompt_sha256'] == sha256(rendered.encode()), + '指示文が場面から作り直せない: ' + case['id']) + _, proof = frame_proof((root / case['frame']).read_bytes()) + require(proof == case['canonical_frame'], '凍結した画素と違う: ' + case['id']) + return manifest, manifest['cases'] + + +def read_case(root, case): + return materialize((Path(root) / case['frame']).read_bytes(), case['canonical_frame']) + + +def bound_reader(case, video, proof, calls): + def reader(sample, model): + require(sample.name == case['id'] and sample.prompt == case['prompt'] + and sample.model_mode.is_reasoner and sample.vision_path is None, + '生成が凍結した1件に結び付いていない') + require(len(calls) < 2, '媒体の読み出しが多すぎる') + pixels = [sha256(image.tobytes()) for image in video['frames']] + require(proof == case['canonical_frame'] and video['fps'] == proof['fps'] + and pixels == [f['rgb_sha256'] for f in proof['frames']], + '復号した画素が変わっている') + calls.append({'id': case['id'], 'rgb_sha256': pixels}) + return {model.input_caption_key: [case['prompt']], + 'reasoner_images': [None], 'reasoner_videos': [video]} + return reader diff --git a/tancho/viewpoint_guard.py b/tancho/viewpoint_guard.py new file mode 100644 index 0000000000000000000000000000000000000000..0f3e90babdf3daa02341cd1c9b24afc932f5ab9f --- /dev/null +++ b/tancho/viewpoint_guard.py @@ -0,0 +1,172 @@ +"""Bounded proposal validation for the synthetic geometry inference harness. + +Produces a selection/abstention decision, never a flight command. Live mission +revision, calibration, obstacle and freshness checks remain the application's +responsibility. No coordinate, speed or dwell repair and no planner fallback. +""" +import copy +import hashlib +import math +import re +from .px4_mission import validate_envelope +from .viewpoint_protocol import vector +from .contracts import ContractError +from .observation_api import strict_json +from .viewpoint_protocol import RELATIVE, CURRENT, TIME, protocol, requirement +from training.report_viewpoint_run import wrapped +from training.score_viewpoints import score + +VERSION = 'viewpoint-guard-v1' +PREFLIGHT_VERSION = 'viewpoint-guard-v2' +TIME_FEEDBACK_VERSION = 'viewpoint-guard-v3' +FEEDBACK = { + 'json': 'JSON配列の形式を守ってください。', + 'contract': 'offset_m、dwell_ms、speed_mpsの契約と数値範囲を守ってください。', + 'count': '視点の個数が許容範囲外です。', + 'target': '指定された対象を観測してください。', + 'pixels': '対象までの斜距離が長すぎて必要画素数を満たしていません。', + 'depression': '視点から対象への俯角が許容範囲外です。', + 'side': '対象から見た視点の方角が指定と異なります。', + 'route': '移動経路、高度帯、速度、滞在と帰還を含む時間上限を満たしていません。', +} + + +def preflight(context): + """Prove only two necessary-condition violations; otherwise undetermined. + + A positive minimum depression requires z_view > z_target. Every nonempty + valid proposal also requires at least one second of dwell, plus the trusted + return margin. No numerical search or estimated feasibility is used. + Invalid trusted inputs raise, rather than masquerading as model abstentions. + """ + if protocol(context) not in (RELATIVE, CURRENT, TIME): + raise ValueError('Preflight requires a supported relative protocol') + req = requirement(context) + box = validate_envelope(context['envelope']) + target = vector(context['target_position_m']) + vector(context['current_position_m']) + if type(context['max_viewpoints']) is not int or not 1 <= context['max_viewpoints'] <= 3: + raise ValueError('Invalid trusted viewpoint count') + proofs = [] + if box['max_alt_m'] <= target[2] and req['min_depression_deg'] > 0: + proofs.append({'rule': 'no_downward_view_in_altitude_band', + 'target_alt_m': target[2], 'maximum_alt_m': box['max_alt_m'], + 'minimum_depression_deg': req['min_depression_deg']}) + minimum_seconds = box['return_margin_seconds'] + 1 + if box['max_mission_seconds'] < minimum_seconds: + proofs.append({'rule': 'insufficient_dwell_and_return_budget', + 'minimum_dwell_seconds': 1, 'return_margin_seconds': box['return_margin_seconds'], + 'mission_seconds': box['max_mission_seconds']}) + return {'status': 'proven_infeasible' if proofs else 'undetermined', 'proofs': proofs} + + +def assess(raw_text, case, *, numeric_time_feedback=False): + """Fail closed on model output; malformed trusted context is an error.""" + if protocol(case['context']) not in (RELATIVE, CURRENT, TIME): + raise ValueError('Guard requires a supported relative protocol') + req = dict(requirement(case['context']), strict_speed_limit=True) + if not isinstance(raw_text, str): + return {'accepted': False, 'reasons': ['json']} + text = raw_text.strip() + match = re.fullmatch(r'```(?:json)?\s*([\s\S]*?)\s*```', text) + if match: text = match[1] + try: + rows = strict_json(text) + except (ValueError, TypeError): + return {'accepted': False, 'reasons': ['json']} + if not isinstance(rows, list) or not 1 <= len(rows) <= case['context']['max_viewpoints']: + return {'accepted': False, 'reasons': ['count']} + try: + output = wrapped(rows, case) + except ContractError: + return {'accepted': False, 'reasons': ['contract']} + wanted = case['context']['target_position_m'] + reasons = [] + if not all(r['target_position_m'] == wanted for r in output['viewpoints']): reasons.append('target') + result = score(output, case['context']['envelope'], + start_position_m=case['context']['current_position_m'], requirement=req) + for key in ('pixels', 'depression', 'side'): + if not all(r['checks'][key] for r in result['viewpoints']): reasons.append(key) + if not result['inside_envelope']: reasons.append('route') + decision = {'accepted': not reasons, 'reasons': reasons, 'score': result} + if numeric_time_feedback and result['envelope_reason'] == 'Mission plus return margin exceeds the approved time': + # Only contract-valid, speed-valid and path-valid proposals reach this + # branch. Match check_envelope's multi-leg timing without repairing it. + box = case['context']['envelope'] + start = case['context']['current_position_m']; position = start + travel = dwell = total = 0.0 + for row in output['viewpoints']: + leg = math.dist(position, row['position_m']) / row['speed_mps'] + stay = row['dwell_ms'] / 1000.0 + travel += leg; dwell += stay; total += leg + stay + position = row['position_m'] + home = math.dist(position, start) / box['max_speed_mps'] + available = box['max_mission_seconds'] - box['return_margin_seconds'] + decision['time_budget'] = { + 'mission_seconds': box['max_mission_seconds'], + 'return_margin_seconds': box['return_margin_seconds'], + 'available_seconds': available, 'proposed_travel_seconds': travel, + 'proposed_dwell_seconds': dwell, 'proposed_return_seconds': home, + 'proposed_total_seconds': total + home + box['return_margin_seconds'], + 'minimum_dwell_per_viewpoint_seconds': 1, + 'maximum_transit_seconds_with_one_minimum_dwell': available - 1, + } + return decision + + +def retry_prompt(case, decision): + if decision['accepted'] or not decision['reasons']: + raise ValueError('Only rejected proposals can be retried') + # Never echo untrusted text or supply a witness/corrected coordinates. + messages = [FEEDBACK[key] for key in decision['reasons']] + if 'time_budget' in decision: + t = decision['time_budget'] + messages.append( + f"時間超過の内訳（秒）: 上限={t['mission_seconds']:.9g}、帰還余裕={t['return_margin_seconds']:.9g}、" + f"移動・滞在・帰還に使える合計={t['available_seconds']:.9g}。" + f"前の提案の移動={t['proposed_travel_seconds']:.9g}、滞在={t['proposed_dwell_seconds']:.9g}、" + f"帰還={t['proposed_return_seconds']:.9g}、余裕込みの総時間={t['proposed_total_seconds']:.9g}。" + f"滞在は1視点につき最低1000ms必要です。1視点を最低時間だけ観測しても、" + f"移動と帰還に使える時間は最大{t['maximum_transit_seconds_with_one_minimum_dwell']:.9g}秒です。" + "滞在時間と移動量を時間上限に収めてください。") + return case['prompt']+'\n\n前の提案は検証で不合格でした。\n'+'\n'.join('- '+m for m in messages)+\ + '\n元の条件をすべて満たす新しい提案をJSON配列1個で返してください。説明は書かないでください。' + + +def generate_verified(case, generate, *, max_attempts=2, use_preflight=False, numeric_time_feedback=False): + """generate(prompt, attempt_number) returns a raw, unmodified record. + + Exceptions are propagated; infrastructure errors are not model abstentions. + Attempts are sequential with a hard bound; accepted answers are never retried. + """ + if type(max_attempts) is not int or max_attempts not in (1, 2): + raise ValueError('Use one or two attempts') + if type(use_preflight) is not bool: + raise ValueError('use_preflight must be boolean') + if type(numeric_time_feedback) is not bool or (numeric_time_feedback and not use_preflight): + raise ValueError('Numeric time feedback requires preflight and a boolean flag') + version = TIME_FEEDBACK_VERSION if numeric_time_feedback else PREFLIGHT_VERSION if use_preflight else VERSION + case = copy.deepcopy(case) + checked = preflight(case['context']) if use_preflight else None + if checked is not None and checked['status'] == 'proven_infeasible': + return {'version': version, 'case_id': case['id'], 'max_attempts': max_attempts, + 'preflight': checked, 'attempts': [], 'status': 'abstained', + 'selected_attempt': None, 'selected_raw_text': None, 'raw_first_accepted': None} + prompt = case['prompt']; attempts = [] + for number in range(1, max_attempts+1): + record = generate(prompt, number) + decision = assess(record['raw_text'], case, numeric_time_feedback=numeric_time_feedback) + attempts.append({'attempt': number, 'prompt': prompt, + 'prompt_sha256': hashlib.sha256(prompt.encode()).hexdigest(), + 'record': copy.deepcopy(record), 'validation': decision}) + if decision['accepted']: + break + if number < max_attempts: prompt = retry_prompt(case, decision) + accepted = attempts[-1]['validation']['accepted'] + result = {'version': version, 'case_id': case['id'], 'max_attempts': max_attempts, + 'attempts': attempts, 'status': 'accepted' if accepted else 'abstained', + 'selected_attempt': attempts[-1]['attempt'] if accepted else None, + 'selected_raw_text': attempts[-1]['record']['raw_text'] if accepted else None, + 'raw_first_accepted': attempts[0]['validation']['accepted']} + if checked is not None: result['preflight'] = checked + return result diff --git a/tancho/viewpoint_plan.py b/tancho/viewpoint_plan.py new file mode 100644 index 0000000000000000000000000000000000000000..f0f1e7c09892b277036dfc142712a2777c0586c7 --- /dev/null +++ b/tancho/viewpoint_plan.py @@ -0,0 +1,108 @@ +"""The deterministic planner that the model's viewpoint sequence is measured against. + +Two jobs, one geometry. It is the fallback the design already promises: when the +model's proposal fails RFL stage 2, a planner has to produce a sequence for the +same goal. And it is the baseline for the comparison, because a viewpoint +sequence is only worth a model if the model beats what a rule can compute from +the target position, the envelope and the requested side. + +The camera is the one in the flight plan: SIYI A8 mini, 4K at 3840x2160 with a +81 degree horizontal field of view (User Manual v1.10, recorded 2026-09-10). +Nothing here touches latitude or longitude; positions are metres in the +envelope's local ENU frame, the same frame `px4_mission` validates. +""" +import math + +from tancho.contracts import ContractError, require +from tancho.px4_mission import FRAME, MAX_VIEWPOINTS, SCHEMA, inside + +HORIZONTAL_FOV_DEG = 81.0 +# 高さの帯の端から取る余裕。補間した経路が丸め誤差で帯を出るのを防ぐ。 +ALTITUDE_MARGIN_M = 0.5 +IMAGE_WIDTH_PX = 3840 +IMAGE_HEIGHT_PX = 2160 +SIDES = {'north': (0.0, 1.0), 'northeast': (0.7071, 0.7071), 'east': (1.0, 0.0), + 'southeast': (0.7071, -0.7071), 'south': (0.0, -1.0), 'southwest': (-0.7071, -0.7071), + 'west': (-1.0, 0.0), 'northwest': (-0.7071, 0.7071)} + + +def ground_sample_distance_m(slant_range_m): + """Metres per pixel across the frame centre at that distance.""" + width = 2.0 * slant_range_m * math.tan(math.radians(HORIZONTAL_FOV_DEG) / 2.0) + return width / IMAGE_WIDTH_PX + + +def pixels_across(size_m, slant_range_m): + return size_m / ground_sample_distance_m(slant_range_m) + + +def range_for_pixels(size_m, pixels): + """How close the camera has to be for an object of that size to span that many pixels.""" + require(size_m > 0 and pixels > 0, 'Object size and pixel demand must be positive') + gsd = size_m / pixels + return gsd * IMAGE_WIDTH_PX / (2.0 * math.tan(math.radians(HORIZONTAL_FOV_DEG) / 2.0)) + + +def plan(target_position_m, envelope, *, side='any', pitch_deg=-35.0, target_size_m=4.5, + required_pixels=120.0, target_id='t1', evidence_ids=('e1',), dwell_ms=6000, + speed_mps=5.0, observation_id='obs', goal_revision='g', envelope_revision='e', + calibration_id='cal', count=1): + """One viewpoint per requested side, at the distance the pixel demand implies. + + `pitch_deg` is the optical axis elevation from the horizon, negative downward, + the same convention as the Reasoner's `view_request`. The planner keeps the + requested angle when the envelope allows it and lowers the altitude, then + shortens the range, when it does not. + """ + require(isinstance(target_position_m, (list, tuple)) and len(target_position_m) == 3, + 'Target position must be a local ENU triple') + require(-90.0 <= pitch_deg <= -5.0, 'Pitch must look downward and not straight down') + require(0 < count <= MAX_VIEWPOINTS, 'Viewpoint count out of range') + # 要求ちょうどの距離に置くと、採点の側で境目に乗る。5%だけ近づけて余裕を作る。 + wanted = range_for_pixels(target_size_m, required_pixels) / 1.05 + # 方角は希望であって指示ではない（contracts-v1 の `view_request.side`）。希望の側から + # 順に試し、承認ボリュームに入らなければ他の側へ回る。入らないまま返すより、別の側から + # 撮って採点で「方角は満たさない」と出るほうが読める。 + ordered = list(SIDES.values()) + if side in SIDES: + ordered = [SIDES[side]] + [v for k, v in SIDES.items() if k != side] + directions = ordered + rows = [] + for unit in directions: + if len(rows) >= count: + break + row = _one(target_position_m, envelope, unit, pitch_deg, wanted) + if row is None: + continue + row.update(sequence=len(rows), dwell_ms=dwell_ms, speed_mps=speed_mps, + target_id=target_id, evidence_ids=list(evidence_ids)) + rows.append(row) + if not rows: + raise ContractError('No viewpoint inside the envelope satisfies the request') + return {'schema_version': SCHEMA, 'observation_id': observation_id, + 'goal_revision': goal_revision, 'envelope_revision': envelope_revision, + 'calibration_id': calibration_id, 'coordinate_frame': FRAME, 'viewpoints': rows} + + +def _one(target, envelope, unit, pitch_deg, wanted_range): + """Place one viewpoint on that side, pulled inside the envelope if it has to be.""" + polygon = [tuple(p) for p in envelope['polygon_m']] + for scale in (1.0, 0.8, 0.6, 0.45, 0.3): + slant = wanted_range * scale + height = slant * math.sin(math.radians(-pitch_deg)) + ground = slant * math.cos(math.radians(-pitch_deg)) + altitude = target[2] + height + # 高さの帯のちょうど端に置かない。経路は端点まで補間して1点ずつ検査するので、 + # 端に乗せると丸め誤差で帯の外へ出る（2026-09-12 に1件、要求も俯角も方角も満たした + # 視点が「経路が高さの帯を出る」で落ちた）。画素数で同じ理由の余裕を取っているのと + # 同じ扱いにする。 + altitude = min(max(altitude, envelope['min_alt_m'] + ALTITUDE_MARGIN_M), + envelope['max_alt_m'] - ALTITUDE_MARGIN_M) + position = [target[0] + unit[0] * ground, target[1] + unit[1] * ground, altitude] + if not inside(polygon, (position[0], position[1])): + continue + if position[2] <= target[2]: + continue + return {'position_m': [round(v, 2) for v in position], + 'target_position_m': [round(v, 2) for v in target]} + return None diff --git a/tancho/viewpoint_protocol.py b/tancho/viewpoint_protocol.py new file mode 100644 index 0000000000000000000000000000000000000000..04425aa7afe68f6e5d4d2e05643ad0ac03673fdb --- /dev/null +++ b/tancho/viewpoint_protocol.py @@ -0,0 +1,183 @@ +"""Versioned geometry prompts and lossless target-relative action conversion. + +Only trusted application context selects the protocol. Decoding never clips a +model proposal to constraints; the ordinary flight and geometry checks follow it. +""" +import copy +import json +import math + +from .contracts import ContractError +from .viewpoint_plan import range_for_pixels + +LEGACY = 'absolute-v1' +DISTANCE = 'distance-v2' +RELATIVE = 'relative-v2' +AXES = 'relative-axes-v3' +CURRENT = 'relative-current-v4' +TIME = 'relative-time-v5' +RELATIVE_PROTOCOLS = (RELATIVE, AXES, CURRENT, TIME) +PROTOCOLS = (LEGACY, DISTANCE, *RELATIVE_PROTOCOLS) + +DISTANCE_TEMPLATE = """ + +撮影条件を数値で示します。 +- 最大斜距離（視点と対象の3次元距離）: {range_m:.6f} m +- 許容する俯角: 水平から下へ {min_angle:g}〜{max_angle:g} 度 +- 画素数の採点許容: 0.5画素 +- 移動と滞在と帰還を含む時間上限: {mission_seconds}秒 +- 帰還用に残す余裕: {return_seconds}秒 +画像は参考画像です。対象位置と画像の対応は与えられていません。座標と撮影条件に従ってください。""" + +RELATIVE_FIELDS = """- offset_m: 対象から視点への変位 [dx, dy, dz]（東、北、上、メートル） + 視点の絶対位置 = 対象の位置 + offset_m。対象の座標は出力しないでください。""" + + +AXES_TEMPLATE = """ + +offset_m の計算では、絶対位置と変位を区別してください。 +- 水平距離 h > 0、対象からの高さ v > 0 とすると、希望方角の中心は + offset_m = [h * ({unit_x:.6f}), h * ({unit_y:.6f}), v] です。 +- この方向に対象の絶対座標を掛けないでください。 +- 東は dx > 0、西は dx < 0、北は dy > 0、南は dy < 0 です。 +- 今回の希望方向の符号: {axis_signs}。 +- sqrt(dx*dx + dy*dy + dz*dz) が最大斜距離以下になるようにしてください。 +返すのは従来どおり offset_m、dwell_ms、speed_mps のみです。""" + +CURRENT_TEMPLATE = """ + +- 現在位置の対象相対座標（東、北、上、メートル）: {current_offset}""" + + +def current_offset(context): + """A derived input, not a proposed action; use only trusted state vectors. + + Match the existing relative coordinate precision (8 decimal places). This + does not establish that the current position is an acceptable viewpoint. + """ + current = vector(context['current_position_m']) + target = vector(context['target_position_m']) + delta = vector([a-b for a,b in zip(current, target)]) + return [round(v, 8) if v else 0.0 for v in delta] + + +def protocol(context): + value = context.get('viewpoint_protocol', LEGACY) + if value not in PROTOCOLS: + raise ValueError('Unknown viewpoint protocol: ' + str(value)) + return value + + +def requirement(context): + result = {'target_size_m': context['target_size_m'], + 'min_pixels': context['required_pixels'], 'side': context['side']} + if protocol(context) != LEGACY: + low = context['min_depression_deg'] + high = context['max_depression_deg'] + if not all(type(v) in (int, float) and math.isfinite(v) for v in (low, high)) \ + or not 0 < low <= high <= 90: + raise ValueError('Invalid depression range') + expected = range_for_pixels(context['target_size_m'], context['required_pixels']) + given = context['max_slant_range_m'] + if type(given) not in (int, float) or not math.isfinite(given) \ + or not math.isclose(given, expected, abs_tol=1e-6, rel_tol=0): + raise ValueError('Distance does not match the pixel requirement') + result.update(min_depression_deg=low, max_depression_deg=high) + if 'strict_speed_limit' in context: + if type(context['strict_speed_limit']) is not bool: + raise ValueError('strict_speed_limit must be a boolean') + result['strict_speed_limit'] = context['strict_speed_limit'] + return result + + +def upgraded(context, mode): + if mode not in (DISTANCE, *RELATIVE_PROTOCOLS): + raise ValueError('Choose an explicit-geometry protocol') + result = copy.deepcopy(context) + result.update(viewpoint_protocol=mode, + max_slant_range_m=range_for_pixels(context['target_size_m'], context['required_pixels']), + min_depression_deg=context.get('min_depression_deg', 20.0), + max_depression_deg=context.get('max_depression_deg', 60.0)) + requirement(result) + return result + + +def template(legacy, mode): + if mode not in PROTOCOLS: + raise ValueError('Unknown viewpoint protocol') + if mode == LEGACY: + return legacy + text = legacy.replace('（地表。画像の中央付近に写っています）', '（局所座標）') + if mode in RELATIVE_PROTOCOLS: + text = text.replace('各要素は次の4つです。', '各要素は次の3つです。').replace( + '- position_m: 視点の位置 [x, y, z]\n- target_position_m: その視点から見る対象の位置 [x, y, z]', + RELATIVE_FIELDS) + if mode == AXES: + text = text.replace( + '- 希望する方角: 対象から見て{side}の側（向きは [{side_x}, {side_y}]。対象の位置にこの向きを掛けた\n ぶんだけ足した所が、希望する視点の真下になります）', + '- 希望する方角: 対象から視点を見たときに{side}の側（水平の方向は [{side_x}, {side_y}]）') + text += AXES_TEMPLATE + return text + DISTANCE_TEMPLATE + (CURRENT_TEMPLATE if mode == CURRENT else '') + ('{time_constraints}' if mode == TIME else '') + + +def extra_fields(context): + if protocol(context) == LEGACY: + return {} + requirement(context) + fields = dict(range_m=context['max_slant_range_m'], min_angle=context['min_depression_deg'], + max_angle=context['max_depression_deg'], + mission_seconds=context['envelope']['max_mission_seconds'], + return_seconds=context['envelope']['return_margin_seconds']) + if protocol(context) == AXES: + from .viewpoint_plan import SIDES + x, y = SIDES[context['side']] + norm = math.hypot(x, y) + if not norm: + raise ValueError('Axes protocol requires a directional side') + fields.update(unit_x=x/norm, unit_y=y/norm, + axis_signs='、'.join(axis + (' > 0' if value > 0 else ' < 0' if value < 0 else ' = 0（方角の中心）') + for axis,value in (('dx',x),('dy',y)))) + if protocol(context) == CURRENT: + fields['current_offset'] = json.dumps(current_offset(context), allow_nan=False) + if protocol(context) == TIME: + from .viewpoint_time_constraints import prompt_suffix + fields['time_constraints'] = prompt_suffix(context) + return fields + + +def vector(value): + if not isinstance(value, list) or len(value) != 3 or not all( + type(v) in (int, float) and math.isfinite(v) for v in value): + raise ContractError('Expected a finite numeric triple') + return value + + +def decode(rows, context): + """Translate relative offsets without correcting invalid geometry.""" + if protocol(context) not in RELATIVE_PROTOCOLS: + return rows + if not isinstance(rows, list) or not 1 <= len(rows) <= context['max_viewpoints']: + raise ContractError('Invalid relative viewpoint count') + target = vector(context['target_position_m']) + decoded = [] + for row in rows: + if not isinstance(row, dict) or set(row) != {'offset_m', 'dwell_ms', 'speed_mps'}: + raise ContractError('Relative output requires offset_m, dwell_ms and speed_mps only') + delta = vector(row['offset_m']) + decoded.append({'position_m': [a + b for a, b in zip(target, delta)], + 'target_position_m': list(target), + 'dwell_ms': row['dwell_ms'], 'speed_mps': row['speed_mps']}) + return decoded + + +def encode(rows, context): + if protocol(context) not in RELATIVE_PROTOCOLS: + return rows + target = vector(context['target_position_m']) + result = [] + for row in rows: + if row['target_position_m'] != target: + raise ValueError('Teacher points to a different target') + result.append({'offset_m': [round(a - b, 8) for a, b in zip(row['position_m'], target)], + 'dwell_ms': row['dwell_ms'], 'speed_mps': row['speed_mps']}) + return result diff --git a/tancho/viewpoint_router.py b/tancho/viewpoint_router.py new file mode 100644 index 0000000000000000000000000000000000000000..8c58baa630fe042bdd2d6fbdf50709afca191f47 --- /dev/null +++ b/tancho/viewpoint_router.py @@ -0,0 +1,86 @@ +"""Opt-in routing for the synthetic viewpoint harness, never flight commands. + +Select once from trusted request state, before seeing model output. Checkpoint +labels are bindings the caller must resolve to the retained exports below. +""" +import copy +import hashlib +import json + +from .viewpoint_eval import render +from .viewpoint_guard import assess, generate_verified, preflight +from .viewpoint_protocol import CURRENT, RELATIVE, current_offset, decode, protocol + +VERSION = 'viewpoint-router-v1' +CURRENT_VERSION = 'viewpoint-router-v2' +TWO_SECOND_VERSION = 'viewpoint-router-v3' +THREE_SECOND_VERSION = 'viewpoint-router-v4' +BALANCED_VERSION = 'viewpoint-router-v5' +BALANCED_STATIONARY = 'balanced288' +RECOMMENDED_VERSION = BALANCED_VERSION +TRAINED_STATIONARY = 'current288' +BASE = 'mixed240' +STATIONARY = 'dwell288' +CHECKPOINTS = { + BALANCED_STATIONARY: 'keep/observation-viewpoint-balanced-current-train-v1/detect288', + BASE: 'keep/observation-viewpoint-mixed-v1/detect240', + STATIONARY: 'keep/observation-viewpoint-dwell-train-v1/detect288', + TRAINED_STATIONARY: 'keep/observation-viewpoint-current-train-v1/detect288', +} + + +def prepare(case, *, version=RECOMMENDED_VERSION): + """Return a copied request and an auditable, output-independent decision. + + A current-position probe checks eligibility only. It is never sent as the + model answer or as an example. Exact coordinate round-trip is required; + floating point timing tolerance must not authorize nonzero movement. + """ + if version not in (VERSION, CURRENT_VERSION, TWO_SECOND_VERSION, THREE_SECOND_VERSION, BALANCED_VERSION): + raise ValueError('Unknown routing policy') + case = copy.deepcopy(case) + ctx = case['context'] + if protocol(ctx) != RELATIVE: + raise ValueError('Router input must use relative-v2') + checked = preflight(ctx) # validate trusted geometry even on the base path + if case['prompt'] != render(ctx): + raise ValueError('Request prompt differs from trusted context') + box = ctx['envelope'] + available = box['max_mission_seconds'] - box['return_margin_seconds'] + probe_check = None + exact = None + eligible = False + budgets = (1, 2, 3) if version in (THREE_SECOND_VERSION, BALANCED_VERSION) else ((1, 2) if version == TWO_SECOND_VERSION else (1,)) + if available in budgets and checked['status'] == 'undetermined': + probe = [{'offset_m': current_offset(ctx), 'dwell_ms': 1000, + 'speed_mps': box['max_speed_mps']}] + exact = decode(probe, ctx)[0]['position_m'] == ctx['current_position_m'] + probe_check = assess(json.dumps(probe, allow_nan=False), case) + eligible = exact and probe_check['accepted'] + specialist = (BALANCED_STATIONARY if version == BALANCED_VERSION else + STATIONARY if version == VERSION else TRAINED_STATIONARY) + model = specialist if eligible else BASE + ctx['viewpoint_protocol'] = CURRENT if eligible else RELATIVE + case['prompt'] = render(ctx) + if 'prompt_sha256' in case: + case['prompt_sha256'] = hashlib.sha256(case['prompt'].encode()).hexdigest() + decision = {'version': version, 'case_id': case['id'], 'model': model, + 'checkpoint_prefix': CHECKPOINTS[model], + 'protocol': ctx['viewpoint_protocol'], 'available_seconds': available, + 'preflight': checked, 'exact_current_roundtrip': exact, + 'stationary_validation': probe_check, + 'prompt_sha256': hashlib.sha256(case['prompt'].encode()).hexdigest()} + return case, decision + + +def generate_routed(case, generate, *, version=RECOMMENDED_VERSION): + """generate(model_label, prepared_case) must use the checkpoint binding. + + Exactly one model call, no retry or cross-model fallback. Exceptions from + inference propagate. The raw answer is preserved and independently checked. + """ + prepared, decision = prepare(case, version=version) + result = generate_verified(prepared, + lambda prompt, attempt: generate(decision['model'], copy.deepcopy(prepared)), + max_attempts=1) + return {'version': version, 'routing': decision, 'generation': result} diff --git a/tancho/viewpoint_time_constraints.py b/tancho/viewpoint_time_constraints.py new file mode 100644 index 0000000000000000000000000000000000000000..ec7a78c0766abc98f0b7b883a10111fa2448a1ca --- /dev/null +++ b/tancho/viewpoint_time_constraints.py @@ -0,0 +1,42 @@ +"""Derived input constraints for one-viewpoint timing; never a generated action. + +Bounds assume maximum outbound and return speed. They are necessary upper +bounds for a slower outbound proposal, not a proof of full route feasibility. +""" +import math +from .viewpoint_guard import preflight +from .viewpoint_protocol import current_offset + +VERSION='viewpoint-time-constraints-v1' + + +def describe(context): + checked=preflight(context) + box=context['envelope'];speed=box['max_speed_mps'] + available_ms=1000*(box['max_mission_seconds']-box['return_margin_seconds']) + options=[] + for dwell in (1000,2000,6000): + possible=available_ms>=dwell + options.append(dict(dwell_ms=dwell,time_possible=possible, + max_one_way_distance_m=(available_ms-dwell)*speed/2000 if possible else None)) + return dict(version=VERSION,available_ms=available_ms,max_speed_mps=speed, + current_offset_m=current_offset(context),options=options, + preflight=checked,scope='one viewpoint; maximum speed in both directions; other geometry constraints still required') + + +def prompt_suffix(context): + info=describe(context) + lines=['','1視点を提案する場合の時間制約（滞在時間の単位はms）:', + f"- 移動・滞在・帰還に使える合計: {info['available_ms']} ms", + f"- 現在位置の対象相対座標: {info['current_offset_m']} m"] + for option in info['options']: + if option['time_possible']: + # Rounding down avoids overstating an allowed distance. + distance=math.floor(option['max_one_way_distance_m']*1e6)/1e6 + lines.append(f"- dwell_ms={option['dwell_ms']}: 現在位置から観測地点までの距離は最大 {distance:.6f} m（往復とも上限速度の場合）。") + else:lines.append(f"- dwell_ms={option['dwell_ms']}: 滞在だけで時間上限を超えるため使用不可。") + lines+=['- 距離は offset_m と現在位置の対象相対座標との差の3次元長さ。対象までの斜距離とは別です。', + '- 上記距離内でも、対象の画素数・方角・俯角・承認範囲をすべて満たしてください。', + '- 低い移動速度では使える距離が短くなります。複数視点では全区間の移動・全滞在・帰還を合算してください。', + '- 出力は従来のoffset_m、dwell_ms、speed_mpsだけです。滞在1000msをdwell_ms=1と書かないでください。'] + return '\n'.join(lines) diff --git a/training/detect_native.py b/training/detect_native.py new file mode 100644 index 0000000000000000000000000000000000000000..0599a3d643c2932d1206b34e9557230b1d07a455 --- /dev/null +++ b/training/detect_native.py @@ -0,0 +1,319 @@ +"""手を入れていない Cosmos3-Edge に、40タイルの矩形を出させる。学習はしない。 + +観測課題の SFT をかけた parent40 ではなく、**公開されている素の重み**をそのまま使う。 +次の段階が「原さんのラベルで追加学習して測る」なので、比べる相手は手を入れていない +状態でなければ基準線にならない（2026-09-11 のユーザー判断）。 + +重みの正しさは `prepare_model.py` が blob ごとに上流のハッシュと照合した記録で担保する。 +parent40 の 670テンソル照合はその指紋に固有なので、ここでは使わない。代わりに、走行の +あいだモデルが変化していないことを1件ごとに確かめる。 + +出力はそのままの文字列で保存する。通常の実行では解釈せず、手元で採点する。 +明示的な --viewpoint-guard では幾何を検証し、不合格時だけ1回再提案する。 +初回・再試行の生回答と選択を別々に保存し、値の修正はしない。 +""" +import argparse +import json +import signal +import subprocess +import sys +import time +import traceback +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +from tancho.eval_kind import eval_kind +from tancho.observation_api import write_private +from tancho.observation_holdout import digest +from tancho.observation_api import strict_json +from training.prepare_model import REPO, REVISION + +CASE_LIMIT_SECONDS = 120 +TOTAL_LIMIT_SECONDS = 2400 + + +def validate_framework(config): + """容器の中の native が、記録した版のまま手つかずであることを確かめる。""" + if sys.flags.optimize: + raise ValueError('native の assert を残したまま走らせる') + framework = Path(config['framework_root']) + revision = subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=framework, + text=True, timeout=10).strip() + if revision != config['framework_revision']: + raise ValueError('framework の版が違う') + dirty = subprocess.check_output(['git', 'status', '--porcelain', '--untracked-files=no'], + cwd=framework, text=True, timeout=10) + if dirty.strip(): + raise ValueError('framework が書き換えられている') + for name, expected in config['native_source_sha256'].items(): + if digest(framework / name) != expected: + raise ValueError('native の原本が違う: ' + name) + + +def validate_checkpoint(checkpoint, provenance_path): + """落としてきた素の重みが、固定したリビジョンのものであることを確かめる。""" + report = strict_json(Path(provenance_path).read_bytes()) + if report['repo'] != REPO or report['revision'] != REVISION: + raise ValueError('別のモデルまたはリビジョン') + if report.get('files') is None or not report['files']: + raise ValueError('取得の記録が空') + for item in report['files']: + path = Path(checkpoint) / item['path'] + if not path.exists() or path.stat().st_size != item['bytes']: + raise ValueError('取得したファイルが揃っていない: ' + item['path']) + return {'repo': report['repo'], 'revision': report['revision'], + 'files': len(report['files']), 'total_bytes': report['total_bytes']} + + +def verify_loaded(language, reference): + """載った670テンソルを1つずつ照合する。追加学習した重みには取得記録が無いので、 + 素の重みの `validate_checkpoint` の代わりにこちらを使う（eval_v3_native と同じ検査）。""" + from cosmos_framework.inference.model import _diffusers_to_net_key, _normalize_diffusers_target_key + from training.prepare_observation_export import tensor_hash + raw_state = language.state_dict() + state = {_normalize_diffusers_target_key(k): v for k, v in raw_state.items()} + if len(state) != len(raw_state): + raise ValueError('State key collision') + verified = {} + for raw, canonical in reference['raw_to_canonical'].items(): + key = (('visual.' + raw.removeprefix('model.')) + if raw.startswith(('model.visual.', 'model.projector.')) + else _diffusers_to_net_key(raw).removeprefix('language_model.')) + value = state[key] + value = value.to_local() if hasattr(value, 'to_local') else value + actual = tensor_hash(value) + if actual != reference['canonical_tensor_hashes'][canonical]: + raise ValueError('載った重みが違う: ' + canonical) + verified[canonical] = actual + if len(verified) != 670: + raise ValueError('重みの照合が全数そろっていない') + return verified + + +def run(pack, out, checkpoint, provenance_path=None, expected_path=None, variant=None, viewpoint_guard=False, viewpoint_preflight=False, viewpoint_time_feedback=False): + import torch + from cosmos_framework.inference.common.init import init_script, init_output_dir + from cosmos_framework.inference.args import (OmniSetupOverrides, OmniSampleOverrides, + reasoner_only_overrides) + from cosmos_framework.inference.inference import OmniInference + import cosmos_framework.inference.inference as native + from cosmos_framework.model.generator.mot.unified_mot import Nemotron3DenseVLTextForCausalLM + from cosmos_framework.inference.model import _normalize_diffusers_target_key + from tancho.edge_chat import install + + if not torch.cuda.is_available(): + raise RuntimeError('CUDA required') + # パックの版で読み方を選ぶ。発見と視点列で指示文の作り方が違う。 + kind = eval_kind(pack) + manifest, cases = kind.verify_pack(pack) + # 生成の設定はパックが持つ。条件ごとに上限を変えるので、モジュール定数に固定しない。 + settings = dict(manifest['settings']) + if viewpoint_time_feedback and not viewpoint_preflight: + raise ValueError('Time feedback requires preflight') + if viewpoint_preflight and not viewpoint_guard: + raise ValueError('Preflight requires guarded inference') + if viewpoint_guard: + from tancho.viewpoint_guard import generate_verified, VERSION, PREFLIGHT_VERSION, TIME_FEEDBACK_VERSION + if manifest['schema_version'] != 'tancho-viewpoint-eval-1.0': + raise ValueError('Guard requires a viewpoint pack') + config = strict_json((ROOT / 'deploy/nebius/edge-inference-config.json').read_bytes()) + validate_framework(config) + reference = None + if expected_path is not None: + reference = strict_json(Path(expected_path).read_bytes()) + if len(reference['canonical_tensor_hashes']) != 670: + raise ValueError('期待する重みの数が Reasoner のものでない') + checkpoint = Path(reference['checkpoint']) + identity = {'expected_weights': str(expected_path), + 'expected_weights_sha256': digest(expected_path), + 'checkpoint': str(checkpoint)} + else: + identity = validate_checkpoint(checkpoint, provenance_path) + if not Path(native.__file__).resolve().is_relative_to(Path(config['framework_root']).resolve()): + raise ValueError('別の framework を読み込んでいる') + + out = Path(out) + out.mkdir(exist_ok=False) + started = time.monotonic() + variant = variant or 'cosmos3-edge-base' + status = {'completed': False, 'variant': variant, 'generations': 0, 'cases': [], + 'model_loads': 0, 'pack_sha256': manifest['sha256'], + 'prompt_sha256': manifest.get('prompt_sha256') or manifest.get('template_sha256'), 'model': identity, + 'prompt_kind': manifest.get('prompt_kind') or manifest['schema_version'], + 'upscale': manifest.get('upscale'), + 'settings': settings} + old_hook = native._get_reasoner_sample_data + original = None + try: + init_script() + install() + setup = OmniSetupOverrides( + checkpoint_path=str(checkpoint), output_dir=out / 'native-startup', + parallelism_preset='latency', guardrails=False, diffusion_cache=False, warmup=0, + num_iterations=1, max_num_seqs=1, keep_going=False, skip_invalid_samples=False, + ).build_setup() + setup.experiment_overrides.extend(reasoner_only_overrides()) + setup.video_guardrail = False + init_output_dir(setup.output_dir) + pipe = OmniInference.create(setup) + lm = pipe.model.net.language_model + if not isinstance(lm, Nemotron3DenseVLTextForCausalLM): + raise ValueError('別の Reasoner が載っている') + lm._ensure_vision_tower() + lm.eval() + for parameter in lm.parameters(): + parameter.requires_grad_(False) + if reference is not None: + identity['verified_tensors'] = len(verify_loaded(lm, reference)) + raw_state = lm.state_dict() + state = {_normalize_diffusers_target_key(k): v for k, v in raw_state.items()} + if len(state) != len(raw_state): + raise ValueError('State key collision') + + def versions(): + return {k: (id(v), v.data_ptr(), v._version, tuple(v.shape), str(v.dtype), str(v.device)) + for k, v in lm.state_dict(keep_vars=True).items()} + + initial_versions = versions() + status['model_loads'] = 1 + write_private(out / 'startup.json', { + 'variant': variant, 'checkpoint': str(checkpoint), 'model': identity, + 'loaded_tensors': len(state), 'model_loads': 1, + 'startup_seconds': time.monotonic() - started, 'torch': torch.__version__, + 'cuda': torch.version.cuda, 'device': torch.cuda.get_device_name(0)}) + + original = Nemotron3DenseVLTextForCausalLM.generate_reasoner_text + active = {} + + def audited(owner, *args, **kwargs): + if owner is not lm or not active or active.get('generated'): + raise ValueError('結び付いていない生成、または2度目の生成') + ids = kwargs.get('input_ids', args[0] if args else None) + if ids is None or not 0 < int(ids.shape[-1]) <= 8192: + raise ValueError('入力トークン数が範囲外') + settings = {k: v for k, v in kwargs.items() if not torch.is_tensor(v)} + for key, value in settings.items(): + if settings.get(key) != value: + raise ValueError('生成の設定が変わっている: ' + key) + entry = {'settings': settings, 'input_tokens': int(ids.shape[-1])} + active['generated'] = True + torch.cuda.reset_peak_memory_stats() + began = time.monotonic() + result = original(owner, *args, **kwargs) + torch.cuda.synchronize() + entry.update(generation_seconds=time.monotonic() - began, + output_tokens=int(result.detach().cpu().reshape(-1).shape[0]), + peak_allocated_bytes=torch.cuda.max_memory_allocated()) + write_private(active['dir'] / 'generation.json', entry) + active['result'] = entry + return result + + Nemotron3DenseVLTextForCausalLM.generate_reasoner_text = audited + + def on_timeout(*_): + raise TimeoutError('1件あたりの時間を超えた') + + signal.signal(signal.SIGALRM, on_timeout) + for case in cases: + if time.monotonic() - started > TOTAL_LIMIT_SECONDS: + raise TimeoutError('走行全体の時間を超えた') + directory = out / 'records' / case['id'] + directory.mkdir(parents=True, exist_ok=False) + video, proof = kind.read_case(pack, case) + first_directory = directory + def generate_once(prompt, attempt): + nonlocal active + directory = first_directory if attempt == 1 else first_directory / ('retry-'+str(attempt-1)) + directory.mkdir(parents=True, exist_ok=True) + attempt_case = dict(case, prompt=prompt, prompt_sha256=__import__('hashlib').sha256(prompt.encode()).hexdigest()) + calls = [] + active = {'case': case, 'dir': directory, 'generated': False} + if versions() != initial_versions: + raise ValueError('モデルの記憶域が変わっている') + native._get_reasoner_sample_data = kind.bound_reader(attempt_case, video, proof, calls) + sample = OmniSampleOverrides( + name=case['id'], model_mode='reasoner', prompt=attempt_case['prompt'], **settings, + output_dir=directory / 'native' / case['id'], + ).build_sample(model_config=pipe.model_config) + began = time.monotonic() + signal.alarm(min(CASE_LIMIT_SECONDS, max(1, int(TOTAL_LIMIT_SECONDS - (began - started))))) + try: + pipe.generate([sample]) + finally: + signal.alarm(0) + write_private(directory / 'media-readbacks.json', calls) + if not active.get('result') or not 1 <= len(calls) <= 2 or versions() != initial_versions: + raise ValueError('生成が不完全、またはモデルが変化した') + native_file = directory / 'native' / case['id'] / 'sample_outputs.json' + raw = json.loads(native_file.read_text()) + if raw['status'] != 'success' or len(raw['outputs']) != 1: + raise ValueError('native の出力が失敗している') + record = { + 'id': case['id'], 'variant': variant, + 'raw_text': raw['outputs'][0]['content']['reasoner_text'], + 'prompt_sha256': attempt_case['prompt_sha256'], 'canonical_frame': proof, + 'input_tokens': active['result']['input_tokens'], + 'output_tokens': active['result']['output_tokens'], + 'generation_seconds': active['result']['generation_seconds'], + 'worker_seconds': time.monotonic() - began, + 'native_output_sha256': digest(native_file)} + write_private(directory / 'record.json', record) + status['generations'] += 1 + return record + + if viewpoint_guard: + guarded = generate_verified(case, generate_once, use_preflight=viewpoint_preflight, numeric_time_feedback=viewpoint_time_feedback) + write_private(first_directory / 'guard.json', guarded) + else: + generate_once(case['prompt'], 1) + status['cases'].append(case['id']) + active = {} + print('CASE ' + case['id'] + ' ' + str(len(status['cases'])), flush=True) + status['completed'] = len(status['cases']) == len(cases) + if viewpoint_guard: status['viewpoint_guard'] = TIME_FEEDBACK_VERSION if viewpoint_time_feedback else PREFLIGHT_VERSION if viewpoint_preflight else VERSION + finally: + native._get_reasoner_sample_data = old_hook + if original is not None: + Nemotron3DenseVLTextForCausalLM.generate_reasoner_text = original + status['wall_seconds'] = time.monotonic() - started + write_private(out / 'status.json', status) + print('NATIVE ' + json.dumps({k: v for k, v in status.items() if k != 'cases'}), flush=True) + return status + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--pack', required=True) + parser.add_argument('--output', required=True) + parser.add_argument('--checkpoint', help='落としてきた素の Cosmos3-Edge') + parser.add_argument('--provenance', help='prepare_model.py の取得記録') + parser.add_argument('--expected-weights', + help='追加学習した重ね合わせの期待値。--provenance の代わりに使う') + parser.add_argument('--viewpoint-guard', action='store_true') + parser.add_argument('--viewpoint-preflight', action='store_true') + parser.add_argument('--viewpoint-time-feedback', action='store_true') + parser.add_argument('--variant', help='受領書に残す名前') + arguments = parser.parse_args() + if arguments.viewpoint_time_feedback and not arguments.viewpoint_preflight: + parser.error('--viewpoint-time-feedback requires --viewpoint-preflight') + if arguments.viewpoint_preflight and not arguments.viewpoint_guard: + parser.error('--viewpoint-preflight requires --viewpoint-guard') + if bool(arguments.provenance) == bool(arguments.expected_weights): + parser.error('--provenance か --expected-weights のどちらか一方が要る') + if arguments.provenance and not arguments.checkpoint: + parser.error('--provenance には --checkpoint が要る') + try: + status = run(Path(arguments.pack), Path(arguments.output), + Path(arguments.checkpoint) if arguments.checkpoint else None, + Path(arguments.provenance) if arguments.provenance else None, + Path(arguments.expected_weights) if arguments.expected_weights else None, + arguments.variant, arguments.viewpoint_guard, arguments.viewpoint_preflight, arguments.viewpoint_time_feedback) + except BaseException: + traceback.print_exc() + return 1 + return 0 if status['completed'] else 1 + + +if __name__ == '__main__': + raise SystemExit(main()) diff --git a/training/observation_baseline.py b/training/observation_baseline.py new file mode 100644 index 0000000000000000000000000000000000000000..20013c8538ae0153ea20dbe8caf50032d7a7cd9e --- /dev/null +++ b/training/observation_baseline.py @@ -0,0 +1,195 @@ +"""Plan/run pinned local Cosmos inference, then score its unedited JSON output. + +The run subcommand is a dry-run unless --execute is supplied. It never creates +cloud resources. Run it in the prepared Linux GPU environment after preflight. +""" +import argparse +from datetime import datetime, timezone +import hashlib +import json +import os +from pathlib import Path +import subprocess +import sys +import time + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +from tancho.observation_task import score_predictions, validate_answer +from tancho.edge_chat import CHAT_MODE + +CONDITIONS = ('visual', 'text_only', 'blank') + + +def digest(path): + with path.open('rb') as f: + return hashlib.file_digest(f, 'sha256').hexdigest() + + +def read_lines(path): + return [json.loads(line) for line in path.read_text().splitlines() if line.strip()] + + +def write_json(path, value): + path.write_text(json.dumps(value, ensure_ascii=False, indent=2)+'\n') + + +def verify_bundle(bundle): + """Verify the portable artifact before inference or scoring.""" + bundle = bundle.resolve() + manifest = json.loads((bundle/'manifest.json').read_text()) + for relative, expected in manifest['files'].items(): + path = (bundle/relative).resolve() + if not path.is_relative_to(bundle) or digest(path) != expected: + raise ValueError(f'Bundle integrity mismatch: {relative}') + gold = read_lines(bundle/'labels/validation.jsonl') + ids = {r['sample_id'] for r in gold} + if not ids or len(ids) != len(gold): + raise ValueError('Missing or duplicate validation IDs') + for row in gold: + validate_answer(row['answer']) + for condition in CONDITIONS: + inputs = read_lines(bundle/'inference'/f'{condition}.jsonl') + if len(inputs) != len(gold) or {r['name'] for r in inputs} != {f'{sid}-{condition}' for sid in ids}: + raise ValueError('Inference inputs and gold do not match') + return manifest, gold + + +def make_commands(bundle, checkpoint, output, seed, conditions): + return {condition: [sys.executable, str(ROOT/'training/edge_observation_inference.py'), + '--parallelism-preset=latency', + '-i', str(bundle/'inference'/f'{condition}.jsonl'), + '-o', str(output/condition), '--checkpoint-path', str(checkpoint), + f'--seed={seed}', '--no-guardrails'] for condition in conditions} + + +def run(args): + bundle = args.bundle.resolve(); framework = args.framework.resolve() + checkpoint = args.checkpoint.resolve(); output = args.output.resolve() + manifest, _ = verify_bundle(bundle) + if output.exists(): + raise ValueError('Use a new output directory; existing outputs could be skipped by upstream inference.') + revision = subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=framework, text=True).strip() + dirty = subprocess.check_output(['git', 'status', '--porcelain', '--untracked-files=no'], cwd=framework, text=True) + if revision != manifest['framework_revision'] or dirty.strip(): + raise ValueError('Framework must be the pinned, unmodified revision') + provenance_path = checkpoint/'tancho-model-provenance.json' + if not (checkpoint/'config.json').is_file() or not provenance_path.is_file(): + raise ValueError('Expected a local, verified Cosmos3-Edge snapshot') + model_provenance = json.loads(provenance_path.read_text()) + if model_provenance['repo'] != 'nvidia/Cosmos3-Edge' or model_provenance['revision'] != manifest['model_revision']: + raise ValueError('Model provenance does not match the pinned baseline') + if len(set(args.conditions)) != len(args.conditions): + raise ValueError('Duplicate inference condition') + # Full model blob verification is a separate runtime preflight. This record + # identifies the prepared snapshot; it is not proof that all weights load. + record = {'created_at':datetime.now(timezone.utc).isoformat(), 'executed':False, + 'bundle_manifest_sha256':digest(bundle/'manifest.json'), + 'framework_revision':revision, 'checkpoint_path':str(checkpoint), + 'model_provenance_sha256':digest(provenance_path), + 'expected_model_revision':manifest['model_revision'], 'seed':manifest['seed'], + 'conditions':args.conditions, 'framework_root':str(framework), + 'chat_mode':CHAT_MODE, 'chat_adapter_sha256':digest(ROOT/'tancho/edge_chat.py'), + 'offline':True, 'guardrails':False, + 'runtime_preprocessing_and_model_integrity_preflight_required':True, + 'commands':make_commands(bundle, checkpoint, output, manifest['seed'], args.conditions)} + if not args.execute: + print(json.dumps(record, ensure_ascii=False, indent=2)); return + output.mkdir(parents=True) + record['executed'] = True; record['runs'] = [] + write_json(output/'run.json', record) + env = os.environ | {'HF_HUB_OFFLINE':'1', 'TRANSFORMERS_OFFLINE':'1', 'LD_LIBRARY_PATH':''} + for condition, command in record['commands'].items(): + started = time.monotonic() + with (output/f'{condition}.log').open('w') as log: + result = subprocess.run(command, cwd=framework, env=env, stdout=log, stderr=subprocess.STDOUT) + record['runs'].append({'condition':condition, 'exit_code':result.returncode, + 'elapsed_seconds_including_model_load':time.monotonic()-started}) + write_json(output/'run.json', record) + if result.returncode: + raise SystemExit(f'{condition} failed; inspect the log. No automatic retry.') + print(json.dumps({'output':str(output), 'runs':record['runs']})) + + +def load_predictions(bundle, output, condition, seed, *, input_directory='inference'): + """Reject mismatched/stale run arguments; retain model failures in denominators.""" + if input_directory not in ('inference','inference_train'):raise ValueError('Unknown inference input directory') + expected = read_lines(bundle/input_directory/f'{condition}.jsonl') + predictions = {}; failures = {} + known_names = {r['name'] for r in expected} + if any(p.parent.name not in known_names for p in (output/condition).glob('*/sample_outputs.json')): + raise ValueError('Unexpected sample output directory') + for item in expected: + sid = item['name'].removesuffix('-'+condition) + path = output/condition/item['name']/'sample_outputs.json' + if not path.exists(): + failures[sid] = 'No sample_outputs.json'; continue + data = json.loads(path.read_text()); actual = data['args'] + for key in ('name','prompt','model_mode','max_new_tokens','do_sample','video_fps'): + if actual.get(key) != item.get(key): + raise ValueError(f'{item["name"]}: mismatched {key}') + if actual.get('seed') != seed: + raise ValueError(f'{item["name"]}: mismatched seed') + if condition == 'text_only': + if actual.get('vision_path') is not None: + raise ValueError('Text-only output unexpectedly has visual input') + else: + # Upstream may resolve/copy inputs. Check bytes, not a path string. + actual_media = Path(actual['vision_path']) + expected_media = (bundle/input_directory/item['vision_path']).resolve() + if not actual_media.is_absolute() or digest(actual_media) != digest(expected_media): + raise ValueError(f'{item["name"]}: visual input identity mismatch') + if data.get('status') != 'success': + failures[sid] = {'status':data.get('status'), 'message':data.get('message')} + continue + outputs = data.get('outputs', []) + if len(outputs) != 1 or not isinstance(outputs[0].get('content', {}).get('reasoner_text'), str): + failures[sid] = 'Missing single reasoner_text output'; continue + predictions[sid] = outputs[0]['content']['reasoner_text'] + return predictions, failures + + +def score(args): + bundle = args.bundle.resolve(); output = args.output.resolve() + manifest, gold = verify_bundle(bundle) + record = json.loads((output/'run.json').read_text()) + if not record['executed'] or record['bundle_manifest_sha256'] != digest(bundle/'manifest.json'): + raise ValueError('Missing executed run record or changed bundle') + if record['framework_revision'] != manifest['framework_revision'] or record['seed'] != manifest['seed']: + raise ValueError('Run revision or seed mismatch') + if record.get('chat_mode') != CHAT_MODE or record.get('chat_adapter_sha256') != digest(ROOT/'tancho/edge_chat.py'): + raise ValueError('Run chat adapter mismatch') + report = {'bundle_manifest_sha256':digest(bundle/'manifest.json'), + 'run_record_sha256':digest(output/'run.json'), 'conditions':{}, + 'validation_scope':'12 intervals from one development-validation site in the same earthquake; not an independent final test.'} + for condition in record['conditions']: + if condition not in CONDITIONS: + raise ValueError('Unknown condition') + raw, failures = load_predictions(bundle, output, condition, manifest['seed']) + metrics = score_predictions(gold, raw); metrics['upstream_failures'] = failures + report['conditions'][condition] = metrics + path = output/'scores.json' + write_json(path, report) + print(json.dumps({'scores':str(path), 'semantic_quality_evaluated':False})) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + sub = parser.add_subparsers(dest='command', required=True) + run_parser = sub.add_parser('run') + run_parser.add_argument('--bundle', type=Path, required=True) + run_parser.add_argument('--framework', type=Path, required=True) + run_parser.add_argument('--checkpoint', type=Path, required=True) + run_parser.add_argument('--output', type=Path, required=True) + run_parser.add_argument('--conditions', nargs='+', choices=CONDITIONS, default=list(CONDITIONS)) + run_parser.add_argument('--execute', action='store_true') + run_parser.set_defaults(func=run) + score_parser = sub.add_parser('score') + score_parser.add_argument('--bundle', type=Path, required=True) + score_parser.add_argument('--output', type=Path, required=True) + score_parser.set_defaults(func=score) + args = parser.parse_args(); args.func(args) + + +if __name__ == '__main__': + main() diff --git a/training/prepare_model.py b/training/prepare_model.py new file mode 100644 index 0000000000000000000000000000000000000000..830b4d2ee6b3689ef58aeeb4c95a204568bd94b1 --- /dev/null +++ b/training/prepare_model.py @@ -0,0 +1,111 @@ +"""Download the immutable public Edge snapshot and verify every upstream blob.""" +import argparse +from concurrent.futures import ThreadPoolExecutor +import hashlib +import json +import os +from pathlib import Path, PurePosixPath +import shutil +import urllib.parse +import urllib.request +import urllib.error +import time + +REVISION = 'a9d944e2c6a1bf9f48b92ad16348e70c5f1836ba' +REPO = 'nvidia/Cosmos3-Edge' + + +def open_source(url): + for attempt in range(4): + try: + return urllib.request.urlopen(url, timeout=120) + except urllib.error.HTTPError as e: + if e.code not in (429, 503) or attempt == 3: + raise + retry = e.headers.get('Retry-After', '') + if retry and not retry.isdigit(): + raise RuntimeError('Server requested a dated retry; rerun later') from None + delay = int(retry) if retry else 60 * (attempt + 1) + if delay > 300: + raise RuntimeError('Server requested a long retry; rerun later') from None + print('Rate limited; retry in', delay, 'seconds', flush=True) + time.sleep(delay) + + +def verify(path, item): + if not path.is_file() or path.stat().st_size != item['size']: + return None + sha = hashlib.sha256() + git = hashlib.sha1(b'blob ' + str(item['size']).encode() + b'\0') + with path.open('rb') as f: + while chunk := f.read(8 * 1024 * 1024): + sha.update(chunk) + git.update(chunk) + digest = sha.hexdigest() + expected = item.get('lfs', {}).get('sha256') + if expected and digest != expected: + return None + if not expected and git.hexdigest() != item['blobId']: + return None + return digest + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument('--output', type=Path, required=True) + p.add_argument('--metadata-cache', type=Path) + a = p.parse_args() + a.output.mkdir(parents=True, exist_ok=True) + url = f'https://huggingface.co/api/models/{REPO}/revision/{REVISION}?blobs=true' + with open_source(url) as r: + manifest = json.load(r) + if manifest['sha'] != REVISION or manifest.get('gated'): + raise ValueError('Unexpected revision or gated model') + items = manifest['siblings'] + for item in items: + name = PurePosixPath(item['rfilename']) + if name.is_absolute() or '..' in name.parts or not isinstance(item['size'], int): + raise ValueError('Unsafe manifest path/size') + needed = sum(i['size'] for i in items if not (a.output / i['rfilename']).is_file()) + if shutil.disk_usage(a.output).free < needed + 8 * 2**30: + raise ValueError('Insufficient free disk; retain 8 GiB reserve') + (a.output / 'tancho-source-manifest.json').write_text(json.dumps(manifest, indent=2) + '\n') + + def acquire(item): + dest = a.output / item['rfilename'] + dest.parent.mkdir(parents=True, exist_ok=True) + digest = verify(dest, item) + if digest: + return {'path': item['rfilename'], 'bytes': item['size'], 'sha256': digest} + tmp = dest.with_name(dest.name + '.partial') + cache = a.metadata_cache / item['rfilename'] if a.metadata_cache else None + if cache and verify(cache, item): + shutil.copyfile(cache, tmp) + else: + source = f'https://huggingface.co/{REPO}/resolve/{REVISION}/' + urllib.parse.quote(item['rfilename'], safe='/') + with open_source(source) as r, tmp.open('wb') as f: + written = 0 + while chunk := r.read(8 * 1024 * 1024): + written += len(chunk) + if written > item['size']: + raise ValueError('Response larger than expected blob') + f.write(chunk) + digest = verify(tmp, item) + if not digest: + raise ValueError('Blob integrity failure: ' + item['rfilename']) + os.replace(tmp, dest) + print('Verified', item['rfilename'], item['size'], flush=True) + return {'path': item['rfilename'], 'bytes': item['size'], 'sha256': digest} + + with ThreadPoolExecutor(max_workers=2) as pool: + results = list(pool.map(acquire, items)) + report = {'repo': REPO, 'revision': REVISION, 'files': results, + 'total_bytes': sum(i['bytes'] for i in results), + 'integrity': 'upstream LFS SHA256 or Git blob SHA1, plus local SHA256', + 'model_loaded': False} + (a.output / 'tancho-model-provenance.json').write_text(json.dumps(report, indent=2) + '\n') + print('COMPLETE', len(results), report['total_bytes'], flush=True) + + +if __name__ == '__main__': + main() diff --git a/training/prepare_observation_export.py b/training/prepare_observation_export.py new file mode 100644 index 0000000000000000000000000000000000000000..0522aee0db97292640edc0b15cc457221a60d399 --- /dev/null +++ b/training/prepare_observation_export.py @@ -0,0 +1,188 @@ +"""Verify trained HF reload and build a lossless Reasoner-only Omni overlay. + +The original snapshot/export are immutable. Generator tensors are preserved; +this overlay is evaluated only in reasoner mode, not for generation/actions. +""" +import argparse +import gc +import json +from pathlib import Path +import shutil +import sys +import time + +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) +from training.observation_baseline import digest, write_json, verify_bundle + +BUNDLE_SHA = '8aab3db21d4775afcb1067328622dd1685c40cdb96d8598a0f748da70f6f7a70' +INDEX_SHA = 'da25fad9eb3354f3f3b78777662e9d678ad6718bcd7e55435fdfa1a96c83cdd8' +AUDIT_SHA = '96f09363a609872cbd4b6e2e43293ab6231b35dde79748ad5bc5620f16cb8e7e' + + +def indexed_paths(root, index): + paths = {} + for relative in set(index.values()): + path = (root / relative).resolve() + if not path.is_relative_to(root.resolve()) or not path.is_file(): + raise ValueError('Invalid indexed shard: ' + relative) + paths[relative] = path + return paths + + +def tensor_hash(tensor): + import hashlib + import torch + tensor = tensor.detach().contiguous().cpu() + h = hashlib.sha256(str((tuple(tensor.shape), str(tensor.dtype))).encode()) + flat = tensor.reshape(-1).view(torch.uint8).numpy() + for i in range(0, flat.size, 1024 * 1024): + h.update(memoryview(flat[i:i + 1024 * 1024])) + return h.hexdigest() + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + for name in ('base', 'export', 'training-root', 'bundle', 'overlay', 'output'): + p.add_argument('--' + name, type=Path, required=True) + p.add_argument('--expected-index-sha256', default=INDEX_SHA) + p.add_argument('--expected-audit-sha256', default=AUDIT_SHA) + p.add_argument('--training-bundle-sha256', default=BUNDLE_SHA) + p.add_argument('--evaluation-bundle-sha256', default=BUNDLE_SHA) + p.add_argument('--training-audit', type=Path) + p.add_argument('--completed-updates', type=int, default=40) + a = p.parse_args(); a.output.mkdir(parents=True, exist_ok=False) + import torch + from safetensors import safe_open + from safetensors.torch import load_file, save_file + from transformers import AutoModelForImageTextToText + import cosmos_framework.model.generator.reasoner.cosmos3_edge # registers Auto class + from cosmos_framework.data.generator.processors import build_processor + from cosmos_framework.inference.inference import _decode_reasoner_video + from cosmos_framework.model.generator.utils.safetensors_loader import ( + _detect_indexed_snapshot, _verify_edge_vision_shard_hash) + from tancho.edge_chat import install, CHAT_MODE + install(); torch.manual_seed(42) + manifest, _ = verify_bundle(a.bundle) + assert digest(a.bundle / 'manifest.json') == a.evaluation_bundle_sha256 + provenance = json.loads((a.base / 'tancho-model-provenance.json').read_text()) + assert provenance['revision'] == manifest['model_revision'] + for item in provenance['files']: + path = a.base / item['path'] + assert path.stat().st_size == item['bytes'] and digest(path) == item['sha256'] + assert digest(a.export / 'model.safetensors.index.json') == a.expected_index_sha256 + training_audit = a.training_audit or a.training_root / 'results/finish/audit/audit.json' + assert digest(training_audit) == a.expected_audit_sha256 + export_record = json.loads((a.export / 'tancho-sft-provenance.json').read_text()) + assert export_record['completed_updates'] == a.completed_updates + assert export_record['bundle_manifest_sha256'] == a.training_bundle_sha256 + assert export_record['training_audit_sha256'] == a.expected_audit_sha256 + assert export_record['index_sha256'] == a.expected_index_sha256 + inventory = json.loads((a.training_root / 'results/checkpoint-inventory.json').read_text()) + checked = [] + for item in inventory: + path = Path(item['path']) + if path.is_relative_to(a.export): + assert path.stat().st_size == item['bytes'] and digest(path) == item['sha256'], path + checked.append(item) + indexed = _detect_indexed_snapshot(str(a.base)); assert indexed is not None + _verify_edge_vision_shard_hash(str(a.base), indexed.files) + base_index = json.loads((a.base / 'model.safetensors.index.json').read_text())['weight_map'] + export_index = json.loads((a.export / 'model.safetensors.index.json').read_text())['weight_map'] + indexed_paths(a.base, base_index); export_paths = indexed_paths(a.export, export_index) + assert set(export_paths.values()) <= {Path(item['path']).resolve() for item in checked}, 'Unverified export shard' + assert set(indexed.key_map.values()) == set(export_index) and len(export_index) == 670 + weights = {} + for relative, path in export_paths.items(): + shard = load_file(str(path)) + assert all(export_index[k] == relative and k not in weights for k in shard) + weights.update(shard) + assert set(weights) == set(export_index) + assert all(v.dtype == torch.bfloat16 and torch.isfinite(v).all() for v in weights.values()) + hashes = {k: tensor_hash(v) for k, v in weights.items()} + # Native HF reload: no missing/unexpected tensors or silent initialization. + started = time.monotonic() + model, info = AutoModelForImageTextToText.from_pretrained( + str(a.export), torch_dtype=torch.bfloat16, device_map='cuda:0', + attn_implementation='sdpa', local_files_only=True, output_loading_info=True) + model.eval() + assert not any(info.get(k) for k in ('missing_keys', 'unexpected_keys', 'mismatched_keys', 'error_msgs')), info + state = model.state_dict() + assert set(state) == set(weights) + assert all(tensor_hash(state[k]) == hashes[k] for k in weights), 'HF reload changed weights' + # The same wrapper and decoded four frames used in SFT and Omni inference. + processor = build_processor(tokenizer_type=str(a.base), config_variant='hf') + conversation = json.loads((a.bundle / 'train/text/A01.json').read_text())['conversations'] + video = _decode_reasoner_video(str(a.bundle / 'media/A01.mp4'), 0.5) + messages = [{'role': 'user', 'content': [ + {'type': 'video', 'video': video['frames'], 'fps': video['fps']}, + {'type': 'text', 'text': conversation[0]['content'][1]['text']}]}] + inputs = processor.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') + input_hashes = {k: tensor_hash(v) for k, v in inputs.items() if torch.is_tensor(v)} + inputs = {k: (v.unsqueeze(0) if k in ('input_ids', 'attention_mask') else v).to('cuda:0') + for k, v in inputs.items() if torch.is_tensor(v)} + with torch.inference_mode(): + generated = model.generate(**inputs, max_new_tokens=16, do_sample=False, use_cache=False, + eos_token_id=11, pad_token_id=0) + tokens = generated[0, inputs['input_ids'].shape[-1]:].tolist() + assert tokens + hf_report = {'passed': True, 'weights_verified': len(weights), 'loading_info': info, + 'train_probe_id': 'A01', 'generated_token_ids': tokens, + 'text': processor.processor.tokenizer.decode(tokens, skip_special_tokens=True), + 'input_tensor_hashes': input_hashes, 'chat_mode': CHAT_MODE, + 'elapsed_seconds_including_load_and_hash': time.monotonic() - started} + write_json(a.output / 'hf-reload.json', hf_report) + del state, model, inputs; gc.collect(); torch.cuda.empty_cache() + print('HF_RELOAD_VERIFIED ' + json.dumps(hf_report, ensure_ascii=False), flush=True) + # Reuse unchanged files with symlinks, replace all three weight shards. + a.overlay.mkdir(parents=True, exist_ok=False) + for path in a.base.rglob('*'): + if not path.is_file(): continue + relative = path.relative_to(a.base) + if str(relative) in indexed.files or relative.name == 'tancho-model-provenance.json': continue + target = a.overlay / relative; target.parent.mkdir(parents=True, exist_ok=True) + target.symlink_to(path.resolve()) + (a.overlay / 'tancho-base-model-provenance.json').symlink_to((a.base / 'tancho-model-provenance.json').resolve()) + base_hashes = {}; changed = []; frozen = [] + output_shards = [] + for relative in indexed.files: + source = a.base / relative + state = load_file(str(source)) + original_hashes = {k: tensor_hash(v) for k, v in state.items()} + replacement = {raw: canonical for raw, canonical in indexed.key_map.items() if base_index[raw] == relative} + for raw, canonical in replacement.items(): + assert state[raw].shape == weights[canonical].shape and state[raw].dtype == weights[canonical].dtype + base_hashes[canonical] = original_hashes[raw] + if canonical.startswith('model.visual.'): + assert original_hashes[raw] == hashes[canonical], 'Frozen vision changed' + frozen.append(canonical) + elif original_hashes[raw] != hashes[canonical]: changed.append(canonical) + state[raw] = weights[canonical] + target = a.overlay / relative; target.parent.mkdir(parents=True, exist_ok=True) + save_file(state, str(target), metadata={'format': 'pt'}) + # Read saved bytes again: all replacements and all generator weights. + with safe_open(str(target), framework='pt', device='cpu') as saved: + assert set(saved.keys()) == set(state) + for key in saved.keys(): + expected = hashes[replacement[key]] if key in replacement else original_hashes[key] + assert tensor_hash(saved.get_tensor(key)) == expected, key + output_shards.append({'path': str(target), 'bytes': target.stat().st_size, 'sha256': digest(target)}) + del state; gc.collect() + assert len(base_hashes) == 670 and len(frozen) == 437 and changed + for variant, values in (('base', base_hashes), ('sft', hashes)): + write_json(a.output / (variant + '-expected-weights.json'), { + 'variant': variant, 'canonical_tensor_hashes': values, 'raw_to_canonical': indexed.key_map, + 'checkpoint': str(a.base if variant == 'base' else a.overlay)}) + report = {'passed': True, 'bundle_sha256': a.evaluation_bundle_sha256, + 'training_bundle_sha256': a.training_bundle_sha256, + 'source_export_index_sha256': a.expected_index_sha256, + 'source_training_audit_sha256': a.expected_audit_sha256, 'source_export_files_verified': checked, + 'replaced_tensors': 670, 'frozen_vision_tensors_identical': len(frozen), + 'trainable_tensors_changed_after_bf16_export': len(changed), + 'all_other_shard_tensors_preserved': True, 'shards': output_shards, + 'reasoner_only': True, 'generator_or_action_model_validated': False} + write_json(a.overlay / 'tancho-sft-overlay-provenance.json', report) + write_json(a.output / 'overlay-verification.json', report) + print('OVERLAY_VERIFIED ' + json.dumps({k: v for k, v in report.items() if k not in ('source_export_files_verified', 'shards')}), flush=True) + + +if __name__ == '__main__': main() diff --git a/training/prepare_train_v2_overlay.py b/training/prepare_train_v2_overlay.py new file mode 100644 index 0000000000000000000000000000000000000000..6b0fa46657defe8a2939a4e6804ef0a225181dc8 --- /dev/null +++ b/training/prepare_train_v2_overlay.py @@ -0,0 +1,134 @@ +"""Rebuilds a model directory from the base snapshot and a freshly written export. + +The same overlay as `prepare_eval_v3_overlay.py`, for checkpoints that no earlier +run has seen. That one refuses unless the rebuilt shards come to bytes a previous +run recorded, which is the right check for reproducing parent-40 and the pilot +stages, and impossible for a checkpoint written an hour ago. Here the hashes are +read out of the export and written down, so the next run has something to match. + +Two checks stay, because they do not depend on a prior record: the base snapshot +must be the pinned revision byte for byte, and the 437 vision tensors must be +identical to parent-40's, which is what a frozen vision tower means. +""" + +import argparse +import gc +import json +from pathlib import Path +import sys + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +from tancho.observation_training import digest +from training.prepare_observation_export import indexed_paths, tensor_hash + +BASE_REVISION = 'a9d944e2c6a1bf9f48b92ad16348e70c5f1836ba' +FROZEN_VISION_TENSORS = 437 + + +def shard_key(path): + """transformer/diffusion_pytorch_model-00001-of-00002.safetensors, from any prefix.""" + parts = Path(str(path)).parts + return '/'.join(parts[-2:]) if len(parts) > 1 else str(path) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + for key in ('base', 'export', 'overlay', 'output'): + parser.add_argument('--' + key.replace('_', '-'), type=Path, required=True) + parser.add_argument('--name', required=True) + args = parser.parse_args() + if args.overlay.exists() or args.output.exists(): + raise ValueError('Keep existing export evidence') + from safetensors import safe_open + from safetensors.torch import load_file, save_file + from cosmos_framework.model.generator.utils.safetensors_loader import ( + _detect_indexed_snapshot, _verify_edge_vision_shard_hash) + + parent = json.loads((ROOT / 'deploy/nebius/edge-parent40-expected-weights.json').read_text()) + + provenance = json.loads((args.base / 'tancho-model-provenance.json').read_text()) + if provenance['revision'] != BASE_REVISION: + raise ValueError('Base model revision changed') + for item in provenance['files']: + path = args.base / item['path'] + if path.stat().st_size != item['bytes'] or digest(path) != item['sha256']: + raise ValueError('Base snapshot changed: ' + item['path']) + indexed = _detect_indexed_snapshot(str(args.base)) + if indexed is None or indexed.key_map != parent['raw_to_canonical']: + raise ValueError('Native raw/canonical tensor mapping changed') + _verify_edge_vision_shard_hash(str(args.base), indexed.files) + base_index = json.loads((args.base / 'model.safetensors.index.json').read_text())['weight_map'] + export_index = json.loads((args.export / 'model.safetensors.index.json').read_text())['weight_map'] + + weights = {} + for path in indexed_paths(args.export, export_index).values(): + weights.update(load_file(str(path))) + hashes = {key: tensor_hash(value) for key, value in weights.items()} + if len(hashes) != 670: + raise ValueError('Export tensor inventory is not the Reasoner') + frozen = [key for key in hashes if key.startswith('model.visual.')] + if len(frozen) != FROZEN_VISION_TENSORS or any( + hashes[k] != parent['canonical_tensor_hashes'][k] for k in frozen): + raise ValueError('Frozen vision changed relative to parent40') + + args.output.mkdir(parents=True) + args.overlay.mkdir(parents=True) + for path in args.base.rglob('*'): + if not path.is_file(): + continue + relative = path.relative_to(args.base) + if str(relative) in indexed.files or relative.name == 'tancho-model-provenance.json': + continue + target = args.overlay / relative + target.parent.mkdir(parents=True, exist_ok=True) + target.symlink_to(path.resolve()) + + shards, differences = [], [] + for relative in indexed.files: + state = load_file(str(args.base / relative)) + original = {key: tensor_hash(value) for key, value in state.items()} + replacements = {raw: canonical for raw, canonical in indexed.key_map.items() + if base_index[raw] == relative} + for raw, canonical in replacements.items(): + if state[raw].shape != weights[canonical].shape or state[raw].dtype != weights[canonical].dtype: + raise ValueError('Overlay tensor shape or dtype differs') + state[raw] = weights[canonical] + target = args.overlay / relative + target.parent.mkdir(parents=True, exist_ok=True) + save_file(state, str(target), metadata={'format': 'pt'}) + with safe_open(str(target), framework='pt', device='cpu') as saved: + if set(saved.keys()) != set(state): + raise ValueError('Overlay shard inventory differs') + for key in saved.keys(): + expected = hashes[replacements[key]] if key in replacements else original[key] + if tensor_hash(saved.get_tensor(key)) != expected: + raise ValueError('Overlay readback differs: ' + key) + actual = digest(target) + # `path` has to stay the absolute path the shard sits at: the evaluation + # runner re-hashes exactly this string before it loads any weights, and + # the first build of this script wrote the short key there instead, which + # cost a run at the preflight. `shard_key` is what a later run matches on, + # so both are recorded. + shards.append({'path': str(target), 'shard_key': shard_key(relative), + 'bytes': target.stat().st_size, 'sha256': actual}) + del state + gc.collect() + + if differences: + raise ValueError(json.dumps(differences)) + reference = {'variant': args.name, 'canonical_tensor_hashes': hashes, + 'raw_to_canonical': indexed.key_map, 'checkpoint': str(args.overlay)} + (args.output / 'expected-weights.json').write_text(json.dumps(reference, indent=2) + '\n') + (args.output / 'overlay-verification.json').write_text(json.dumps( + {'passed': True, 'name': args.name, 'replaced_tensors': 670, + 'frozen_vision_tensors_identical': FROZEN_VISION_TENSORS, + 'shards': shards, 'matched_recorded_shards': False, + 'first_build': True, + 'note': 'no earlier run recorded these bytes; the hashes here are the record'}, + indent=2) + '\n') + print('OVERLAY_BUILT ' + args.name) + + +if __name__ == '__main__': + main() diff --git a/training/report_viewpoint_run.py b/training/report_viewpoint_run.py new file mode 100644 index 0000000000000000000000000000000000000000..51a49c8e204fabf379aa3e6b1bad8e76a582b81b --- /dev/null +++ b/training/report_viewpoint_run.py @@ -0,0 +1,156 @@ +"""視点列の走行を採点する。**モデルの出力は、応用側が包んでから契約に掛ける。** + +モデルに出させるのは幾何（位置・対象・滞在・速度）だけである。観測の番号・改訂・較正の識別子は +応用側が作る（contracts-v1）。ここではその包みを作り、`px4_mission.validate_viewpoints` と +`check_envelope` に掛け、`score_viewpoints` で撮影の要件を数える。 + +**読み方は寄せない。**配列で返っても1件の辞書で返っても受けるが、値の直しはしない。位置を承認 +ボリュームの中へ丸めたり、速度を上限へ切り詰めたりすると、モデルが守れたのか採点側が直したのかが +分からなくなる。 +""" +import argparse +import json +import re +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +from tancho.contracts import ContractError +from tancho.px4_mission import FRAME, SCHEMA, check_envelope, validate_viewpoints +from tancho.viewpoint_eval import verify_pack +from training.score_viewpoints import score +from tancho.viewpoint_protocol import LEGACY, decode, requirement, protocol + +FENCE = re.compile(r'```(?:json)?\s*(.+?)\s*```', re.S) + + +def parsed(text): + """素直なJSON、囲みつきのJSON、1件だけの辞書のどれでも受ける。""" + body = text or '' + match = FENCE.search(body) + if match: + body = match.group(1) + value = json.loads(body) + if isinstance(value, dict) and 'viewpoints' in value: + value = value['viewpoints'] + return value if isinstance(value, list) else [value] + + +def wrapped(rows, case, now_ms=1000): + """応用側の包み。モデルは番号も改訂も持たない。""" + rows = decode(rows, case['context']) + context = {'schema_version': '1.0', 'observation_id': case['id'], + 'captured_at_ms': now_ms - 100, 'expires_at_ms': now_ms + 60_000, + 'input_sha256': case['canonical_frame']['sha256'], 'model_revision': 'cosmos3-edge', + 'goal_revision': 'g1', 'envelope_revision': case['context']['envelope']['revision'], + 'evidence_ids': ['F1'], 'target_ids': ['t1']} + viewpoints = [] + for index, row in enumerate(rows): + if not isinstance(row, dict): + raise ContractError('視点が辞書でない') + viewpoints.append({'sequence': index, + 'position_m': row.get('position_m'), + 'target_position_m': row.get('target_position_m'), + 'dwell_ms': row.get('dwell_ms'), 'speed_mps': row.get('speed_mps'), + 'target_id': 't1', 'evidence_ids': ['F1']}) + output = {'schema_version': SCHEMA, 'observation_id': case['id'], 'goal_revision': 'g1', + 'envelope_revision': context['envelope_revision'], 'calibration_id': 'cal1', + 'coordinate_frame': FRAME, 'viewpoints': viewpoints} + validate_viewpoints(output, context, now_ms=now_ms, goal_revision='g1', + envelope_revision=context['envelope_revision']) + return output + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--records', required=True) + parser.add_argument('--pack', required=True) + parser.add_argument('--planner', help='比べる計画器の答え（viewpoint-planner.json）') + parser.add_argument('--out', required=True) + arguments = parser.parse_args() + _, cases = verify_pack(arguments.pack) + by_id = {case['id']: case for case in cases} + counts = {'cases': len(cases), 'answered': 0, 'json_read': 0, 'unreadable': 0, + 'contract_ok': 0, 'contract_error': {}, 'inside_envelope': 0, + 'target_matches': 0, 'all_requirements': 0, + 'by_check': {'pixels': 0, 'depression': 0, 'side': 0}} + rows = {} + for path in sorted(Path(arguments.records).glob('*/record.json')): + record = json.loads(path.read_text()) + if record['id'] not in by_id or record['id'] in rows: + raise ValueError('Unknown or duplicate evaluation id: ' + record['id']) + case = by_id[record['id']] + # A paired id can occur in multiple prompt ablations. Never silently + # re-score a legacy answer as an answer to an explicit-distance prompt. + for key in ('prompt_sha256', 'canonical_frame'): + if key in record or protocol(case['context']) != LEGACY: + if record.get(key) != case[key]: + raise ValueError('Inference input differs from the evaluation pack: ' + key) + counts['answered'] += 1 + entry = {'id': record['id'], 'output_tokens': record['output_tokens']} + try: + proposal = parsed(record['raw_text']) + counts['json_read'] += 1 + except Exception as error: + counts['unreadable'] += 1 + entry['error'] = 'json: ' + type(error).__name__ + rows[record['id']] = entry + continue + context = case['context'] + try: + output = wrapped(proposal, case) + counts['contract_ok'] += 1 + except ContractError as error: + name = str(error)[:60] + counts['contract_error'][name] = counts['contract_error'].get(name, 0) + 1 + entry['error'] = 'contract: ' + name + rows[record['id']] = entry + continue + # モデルが見るべき対象を見ているか。与えた座標と一致しなければ、別の場所を見ている。 + wanted = [round(v, 1) for v in context['target_position_m']] + entry['target_matches'] = all( + [round(v, 1) for v in row['target_position_m']] == wanted for row in output['viewpoints']) + counts['target_matches'] += int(entry['target_matches']) + report = score(output, context['envelope'], start_position_m=context['current_position_m'], + requirement=requirement(context)) + entry['score'] = report + counts['inside_envelope'] += int(report['inside_envelope']) + if report['inside_envelope'] and report['viewpoints_meeting_all'] == report['count'] \ + and entry['target_matches']: + counts['all_requirements'] += 1 + for name in counts['by_check']: + if all(row['checks'][name] for row in report['viewpoints']): + counts['by_check'][name] += 1 + rows[record['id']] = entry + by_profile = {} + for case in cases: + entry = rows.setdefault(case['id'], {'id': case['id'], 'error': 'missing output'}) + profile = by_profile.setdefault(case['context'].get('profile', 'unspecified'), + {'cases': 0, 'all_requirements': 0, + 'pixels': 0, 'depression': 0, 'side': 0}) + profile['cases'] += 1 + report = entry.get('score', {}) + points = report.get('viewpoints', []) + good = bool(points) and entry.get('target_matches') and report.get('inside_envelope') \ + and report.get('viewpoints_meeting_all') == report.get('count') + profile['all_requirements'] += int(bool(good)) + for check in ('pixels', 'depression', 'side'): + profile[check] += int(bool(points) and all(p['checks'][check] for p in points)) + result = {'counts': counts, 'cases': rows, 'by_profile': by_profile} + if arguments.planner: + planner = json.loads(Path(arguments.planner).read_text()) + good = [k for k, v in planner.items() if 'score' in v + and v['score']['inside_envelope'] + and v['score']['viewpoints_meeting_all'] == v['score']['count']] + result['planner'] = {'cases': len(planner), 'all_requirements': len(good)} + Path(arguments.out).write_text(json.dumps(result, ensure_ascii=False, indent=2) + '\n') + print(json.dumps({k: counts[k] for k in + ('cases', 'json_read', 'unreadable', 'contract_ok', 'target_matches', + 'inside_envelope', 'all_requirements', 'by_check')}, ensure_ascii=False)) + if arguments.planner: + print(json.dumps({'planner': result['planner']}, ensure_ascii=False)) + + +if __name__ == '__main__': + main() diff --git a/training/score_viewpoints.py b/training/score_viewpoints.py new file mode 100644 index 0000000000000000000000000000000000000000..5b1273424a98fe3485b50038fd2fa983bcfc9c34 --- /dev/null +++ b/training/score_viewpoints.py @@ -0,0 +1,106 @@ +"""視点列を、撮影の要件で採点する。モデルの提案と決定論的な計画器を同じ尺度で並べるため。 + +要件はミッション定義（`ミッション定義と課題設計_2026-09-10.md` の「分類から逆算した撮影の要件」） +から来る。あとでクラウドで分類できる形に撮れているか、を幾何で確かめられる範囲まで書き下したものである。 + +1. **対象の画素数**。対象の実寸と斜距離から、画面上で何画素になるかを出す。 +2. **見下ろす角度**。浸水深のように外壁と地面の接点が要るものは、真下から撮ると軒に隠れる。 + 地平からの伏角に上下の許容を置く。 +3. **要求した方角**。Reasoner が `view_request.side` で指定した側から見ているか。 +4. **承認ボリューム**。`px4_mission.check_envelope` をそのまま使う。経路も含めて外へ出ないこと。 + +**採点は合否の数え上げで、点数を作らない。**要件ごとに満たしたかどうかを数え、飛行の秒数を添える。 +重みを付けて1つの値にすると、どの要件で負けたのかが読めなくなる。 +""" +import argparse +import json +import math +import sys +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +from tancho.contracts import ContractError +from tancho.px4_mission import check_envelope +from tancho.viewpoint_plan import SIDES, pixels_across + +DEFAULT_REQUIREMENT = {'target_size_m': 4.5, 'min_pixels': 120.0, + 'min_depression_deg': 20.0, 'max_depression_deg': 60.0, 'side': 'any'} + + +def depression_deg(position, target): + """地平から見下ろす角度。真下が90度、水平が0度。""" + ground = math.dist(position[:2], target[:2]) + height = position[2] - target[2] + return math.degrees(math.atan2(height, ground)) if (ground or height) else 0.0 + + +def side_of(position, target): + """対象から見て、視点がどちらの側にあるか。8方位のうち最も近いもの。""" + east, north = position[0] - target[0], position[1] - target[1] + if math.hypot(east, north) < 1e-6: + return 'any' + return min(SIDES, key=lambda name: math.dist( + (east / math.hypot(east, north), north / math.hypot(east, north)), SIDES[name])) + + +def score_one(row, requirement): + position, target = row['position_m'], row['target_position_m'] + slant = math.dist(position, target) + pixels = pixels_across(requirement['target_size_m'], slant) if slant else float('inf') + depression = depression_deg(position, target) + seen_from = side_of(position, target) + checks = { + # 画素の判定は0.5画素の余裕を持たせる。計画器が要求ちょうどを狙うと、 + # 浮動小数の丸めで 120.0 が 119.999… になり、満たしたものが不合格になる（実測）。 + 'pixels': pixels >= requirement['min_pixels'] - 0.5, + 'depression': requirement['min_depression_deg'] <= depression <= requirement['max_depression_deg'], + 'side': requirement['side'] in ('any', seen_from), + } + return {'sequence': row['sequence'], 'slant_range_m': round(slant, 1), + 'pixels_across': round(pixels, 1), 'depression_deg': round(depression, 1), + 'seen_from': seen_from, 'checks': checks, 'all_met': all(checks.values())} + + +def score(viewpoints, envelope, *, start_position_m, requirement=None): + requirement = dict(DEFAULT_REQUIREMENT, **(requirement or {})) + rows = [score_one(row, requirement) for row in viewpoints['viewpoints']] + try: + if requirement.get('strict_speed_limit') and any( + row['speed_mps'] > envelope['max_speed_mps'] for row in viewpoints['viewpoints']): + raise ContractError('Proposed speed exceeds the approved limit') + envelope_result = check_envelope(viewpoints, envelope, start_position_m=start_position_m) + inside_envelope, reason = True, None + except ContractError as error: + envelope_result, inside_envelope, reason = None, False, str(error) + met = {name: sum(1 for r in rows if r['checks'][name]) for name in DEFAULT_REQUIREMENT + if name in rows[0]['checks']} if rows else {} + return {'viewpoints': rows, 'requirement': requirement, + 'viewpoints_meeting_all': sum(1 for r in rows if r['all_met']), 'count': len(rows), + 'met_by_check': met, 'inside_envelope': inside_envelope, + 'envelope_reason': reason, + 'flight_seconds': round(envelope_result['flight_seconds'], 1) if envelope_result else None} + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--viewpoints', required=True) + parser.add_argument('--envelope', required=True) + parser.add_argument('--start', default='0,0,30', help='現在位置（東,北,高さ）メートル') + parser.add_argument('--requirement', help='要件のJSON。省略すると既定値') + parser.add_argument('--out') + arguments = parser.parse_args() + viewpoints = json.loads(Path(arguments.viewpoints).read_text()) + envelope = json.loads(Path(arguments.envelope).read_text()) + requirement = json.loads(Path(arguments.requirement).read_text()) if arguments.requirement else None + start = [float(v) for v in arguments.start.split(',')] + report = score(viewpoints, envelope, start_position_m=start, requirement=requirement) + if arguments.out: + Path(arguments.out).write_text(json.dumps(report, ensure_ascii=False, indent=2) + '\n') + print(json.dumps({k: report[k] for k in ('count', 'viewpoints_meeting_all', 'met_by_check', + 'inside_envelope', 'flight_seconds')}, + ensure_ascii=False)) + + +if __name__ == '__main__': + main() diff --git a/verify_files.py b/verify_files.py new file mode 100644 index 0000000000000000000000000000000000000000..86ee568f245849ec954c7e298266b62a27056511 --- /dev/null +++ b/verify_files.py @@ -0,0 +1,11 @@ +"""Verify release-manifest.json against all advertised payload bytes.""" +import hashlib,json +from pathlib import Path +root=Path(__file__).resolve().parent +manifest=json.loads((root/'release-manifest.json').read_text()) +for item in manifest['files']: + p=root/item['path'] + if not p.resolve().is_relative_to(root.resolve()) or p.is_symlink():raise ValueError('Unsafe release path') + with p.open('rb') as f:h=hashlib.file_digest(f,'sha256').hexdigest() + if p.stat().st_size!=item['bytes'] or h!=item['sha256']:raise ValueError('File differs: '+item['path']) +print(json.dumps(dict(verified=True,files=len(manifest['files']),bytes=sum(x['bytes'] for x in manifest['files']))))