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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/NOTICE b/NOTICE new file mode 100644 index 0000000000000000000000000000000000000000..212353f01c0c686a1fe747cc55c76b94991be4a7 --- /dev/null +++ b/NOTICE @@ -0,0 +1,13 @@ +Forma3D-Tiny v0.2.0, derived from Image3D Student v0.1.0 +Copyright 2026 Lucasgitguy-co. + +Original project source, documentation and procedural demonstration artifacts are released under Apache License 2.0. + +The optional Idol integration and Gaussian attribute conventions were developed with reference to +speridlabs/idol-sdk, revision 4702a7f00850e34b6e83a2170cc123c3e9570744 (Apache-2.0). +Its architecture is imported as an optional dependency; pretrained teacher weights are not redistributed. +Adaptations in this project: staged loading, query-chunk attention, explicit real-valued RoPE selection, +independent student training, coordinate-preserving Gaussian IO, and diagnostic rendering. +Upstream attribution is retained in licenses/Idol-APACHE-2.0.txt and THIRD_PARTY.md. + +ABO continuation data: Amazon.com, Amazon Berkeley Objects contributors; converted to GLB by CodePawl. CC BY 4.0. Training preprocessing decompresses geometry, normalizes objects, samples surface colors and renders Gaussian proxies. See ATTRIBUTIONS.json and licenses/LICENSE-ABO-CC-BY-4.0.txt. No endorsement implied. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..526666c8fc67235f0ef8a12599309838d2af8a25 --- /dev/null +++ b/README.md @@ -0,0 +1,127 @@ +--- +license: apache-2.0 +pipeline_tag: image-to-3d +base_model: Lucasgitguy-co/forma3d-tiny-3 +base_model_relation: finetune +model_name: Forma3D Tiny 4 +datasets: +- codepawl/abo-glb +tags: +- pytorch +- gaussian-splatting +- mesh-proxy-supervision +- research-prototype +--- +# Forma3D Tiny 4 + +A 306,321-parameter image-to-Gaussian research model continued from +[Forma3D Tiny 3](https://huggingface.co/Lucasgitguy-co/forma3d-tiny-3) at +`3469f527c5f6833d654f62653ee92e0f73854b33`. The architecture remains a 16³ grid, +width 64 and at most 4,096 Gaussian slots. Trained locally on an M4 MacBook Air +with 16 GB unified memory using PyTorch MPS, float32 and batch size 8. + +## Use + +Clone and install with Python 3.12. Git LFS is needed to fetch the checkpoint: + +```bash +git lfs install +git clone https://huggingface.co/Lucasgitguy-co/forma3d-tiny-4 +cd forma3d-tiny-4 +python3.12 -m venv .venv +source .venv/bin/activate +pip install . +image3d generate --checkpoint model.pt --image examples/abo-B071FJR479-v3-input.png --out object.ply --device mps --threshold 0.6 +image3d preview --ply object.ply --out preview.png +``` + +Use `--device cpu` on systems without Metal. Replace the sample input with an actual +image path to try other inputs. The recommended threshold is **0.6**; the inherited +CLI defaults to 0.5, so pass the flag explicitly. `generation_config.json` is +informational and is not automatically read by the CLI. Output is a Gaussian PLY, +not a textured mesh. No text prompting is supported. + +## Training and selection + +The expanded dataset contains 1,024 ABO objects across all splits, not 1,024 training +objects. Training uses 677 objects with 12 views each (8,124 inputs): four azimuths at +three elevations. The original 512-object split assignments are preserved; duplicate +identities are grouped before new objects are assigned. The common validation set +has 87 objects / 348 views. An additional 100 objects / 400 views are reserved for +final confirmation. The historical test set is excluded from tuning. + +Three variants start from the same v3 checkpoint: original data, 12-view data, +and expanded-object data. Each receives 6,000 AdamW steps at learning rate 5e-5. +Selection maximizes validation silhouette IoU subject to nonworsening occupancy IoU +and Chamfer distance, with thresholds 0.3, 0.4, 0.5 and 0.6 tuned on validation. +The expanded-object variant won. `model.pt` is seed 44, step 6000; `repeat/model.pt` +is its independently trained seed-45 confirmation. The initial winner was fixed +before inspecting final confirmation results. + +## Held-out confirmation results + +All rows use the same 100 held-out objects / 400 proxy views. Higher IoU and lower +Chamfer distance are better. The first two rows isolate the checkpoint change at +threshold 0.5; the third includes the validation-selected threshold change. + +| Checkpoint | Threshold | Silhouette IoU | Occupancy IoU | Chamfer distance | +|---|---:|---:|---:|---:| +| Original v3 | 0.5 | 0.463688 | 0.337398 | 0.069585 | +| Tiny 4, seed 44 | 0.5 | 0.512971 | 0.424836 | 0.053612 | +| Tiny 4, seed 44 (recommended) | 0.6 | 0.540550 | 0.434657 | 0.052380 | +| Tiny 4, seed 45 (repeat) | 0.6 | 0.546573 | 0.412526 | 0.054105 | + +For recommended seed 44 versus original v3 at 0.5, paired object-cluster bootstrap +95% intervals (2,000 resamples) for candidate-minus-baseline changes are: +silhouette IoU **+0.07686 [0.06621, 0.08926]**, occupancy IoU +**+0.09726 [0.07289, 0.12272]**, and Chamfer +**−0.01721 [−0.03762, −0.00622]**. These intervals include the threshold change. +Second-seed intervals also exclude zero in the favorable direction for all three +metrics. Detailed scores and per-example results are in `evaluation/` and +`evaluation.json`; splits and source metadata are in `splits/`. + +The 15-minute batch-8 training benchmark measured 650.02 samples/s; this excludes +preparation and validation overhead. It recorded about 496 MiB peak process RSS +and 141 MiB MPS driver allocation; these overlap in unified memory and are not a +measurement of total system memory. CPU/MPS loss and gradient parity passed. + +## Examples and limitations + +Each turntable shows the predicted Gaussian preview on the left and a reference +mesh silhouette on the right. The six examples include the two lowest, two median +and two highest silhouette-IoU views in the confirmation set; they are not random +samples. The reference image is a silhouette, not a photorealistic rendering. + +![abo-B071FJR479-v3: prediction and reference silhouette](examples/abo-B071FJR479-v3.gif) + +![abo-B073P1N45L-v0: prediction and reference silhouette](examples/abo-B073P1N45L-v0.gif) + +![abo-B07MBFDHMH-v3: prediction and reference silhouette](examples/abo-B07MBFDHMH-v3.gif) + +![abo-B07TX2D39D-v3: prediction and reference silhouette](examples/abo-B07TX2D39D-v3.gif) + +![abo-B082XLWY2S-v1: prediction and reference silhouette](examples/abo-B082XLWY2S-v1.gif) + +![abo-B082XLWY2S-v3: prediction and reference silhouette](examples/abo-B082XLWY2S-v3.gif) + +Inputs are orthographic Gaussian-proxy renders on black. Natural photographs, +photorealistic rendering, arbitrary viewpoints, thin structures and hidden surfaces +remain unvalidated. Centering an ordinary photo does not make it an in-domain input. +Chamfer measures symmetric mean Euclidean distance between predicted Gaussian centers +and mesh-surface samples in normalized coordinates, not reconstructed mesh quality. +Silhouettes compare alpha>=0.5 splat rasterization with triangle rasterization at +96 pixels from four fixed unseen viewpoints. Near duplicates may remain; ancestor +training exposure is not fully known. These measurements do not establish broad +real-world reconstruction quality. + +## Reproduction and attribution + +`recipe/` contains the training/evaluation implementation and dependency lock. +Original source and weight lineage is Apache 2.0; retain LICENSE, NOTICE and +THIRD_PARTY.md. ABO assets and derived example imagery are credited to Amazon.com +and Amazon Berkeley Objects contributors under CC BY 4.0, with GLB conversion by +CodePawl. Geometry was normalized, sampled and rendered into proxy inputs and +silhouettes. See ATTRIBUTIONS.json, attribution/INHERITED.json, +licenses/LICENSE-ABO-CC-BY-4.0.txt and the +[dataset](https://huggingface.co/datasets/codepawl/abo-glb). +Raw GLB meshes and the full training image cache are not bundled. diff --git a/SHA256SUMS b/SHA256SUMS new file mode 100644 index 0000000000000000000000000000000000000000..14abe5cfd1010cad81be3e701668a296a8f05de9 --- /dev/null +++ b/SHA256SUMS @@ -0,0 +1,135 @@ +11ad7efa24975ee4b0c3c3a38ed18737f0658a5f75a0a96787b576a78a023361 .gitattributes +774b71ed13f2801cac3d861943078155ebb89ae38b44ec5476a73aa1f31f021a ATTRIBUTIONS.json +cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30 LICENSE +d84c0de5fa230ed94d4a2c84c791ffe4a13a2f73ac2657b19bcf0b55f11726a7 NOTICE +9296d2f2d3d855b9d7af850cb8541964559d7bf9495f1f3a23acd0653237efc0 README.md +ef7c689f184a7921b02887eeb8d859e8cca77da6d2082999bb19bf29beda61f1 THIRD_PARTY.md +952b78774f6d41182b685271d6ed64abbca9b0c5ff4081e8e55ab92ce256e737 attribution/INHERITED.json 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src/image3d_student/engine.py +a18005f75a66846446b2574b4f1b085ebde5321e835c4e5b4507e15e812ee5ef src/image3d_student/model.py +41961fae62d2819c1e3f455e74539a66b8c4f57bedd9fb9379497b3a5764c692 src/image3d_student/splats.py +2e2439bc0a48c52e32a8e273f879ed7328fdb82632a996ed3e239e0391ca3a59 src/image3d_student/teacher_idol.py +4c3927059d014ead681f50489b42102a806aef712dad2f6be3e8dd9b0ec3648a training.json diff --git a/THIRD_PARTY.md b/THIRD_PARTY.md new file mode 100644 index 0000000000000000000000000000000000000000..bfee95299d47cbec44d2fc01ef0b0289c5d87bf6 --- /dev/null +++ b/THIRD_PARTY.md @@ -0,0 +1,13 @@ +# Third-party scope + +This training kit extends Lucasgitguy-co/image3d-student, Apache-2.0. The original +checkpoint is downloaded separately. No new trained weights are bundled in the kit. + +Optional Idol SDK integration is inherited from the original project, pinned to +`4702a7f00850e34b6e83a2170cc123c3e9570744`; its Apache notice is retained in +`licenses/Idol-APACHE-2.0.txt`. The Mac recipe does not install or run Idol, Hi3D, +InstantMesh, Hunyuan, or gated encoders. Their weights are not redistributed. + +Dependencies retain their respective licenses. Third-party dataset terms are +documented in DATASETS.md; per-asset attribution is generated during preparation +and copied into any published release. Code licensing does not relicense datasets. diff --git a/attribution/INHERITED.json b/attribution/INHERITED.json new file mode 100644 index 0000000000000000000000000000000000000000..21199f0e3f9d88c9ebeb2be6001f1e838ee594e7 --- /dev/null +++ b/attribution/INHERITED.json @@ -0,0 +1,13190 @@ +{ + "inherited": [ + { + "repo": "scenario-labs/3d-collection", + "revision": "55b4c002b4c1529fc48da3dff98516c3069bfa3f", + "path": "assets/scenario_asset_VTLjiPwXDDUaQYPENSAeCR5y/output.glb", + "sha256": "17b116da2c10a8a22ad121f852928296cf7c8d81cac84c36af83e5a55aec8cf7", + "asset_id": "scenario:asset_VTLjiPwXDDUaQYPENSAeCR5y", + "license": "cc-by-4.0", + "author": "Scenario", + "title": "scenario:asset_VTLjiPwXDDUaQYPENSAeCR5y", + "source_model": "model_scenario-claude-opus-5-5-3d", + "source_url": "https://huggingface.co/datasets/scenario-labs/3d-collection", + "object_id": "scenario-511b5291735b012642ba", + "source_object_id": "scenario-511b5291735b012642ba" + }, + { + "repo": 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Nothing in this Public License constitutes or may be interpreted + as a limitation upon, or waiver of, any privileges and immunities + that apply to the Licensor or You, including from the legal + processes of any jurisdiction or authority. diff --git a/model.pt b/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..58acfadefd9991645e757d21b0b93dec88aa19a3 --- /dev/null +++ b/model.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ed7820ccd07b1a0fdbb17a95476f45892e2bd4ea7eba6c593e995845d9a9560d +size 4197723 diff --git a/preparation.json b/preparation.json new file mode 100644 index 0000000000000000000000000000000000000000..ca5e6a416bcef1891e3375fef935c2b9046a650b --- /dev/null +++ b/preparation.json @@ -0,0 +1,9 @@ +{ + "control": 1392, + "views12": 4176, + "objects1024": 8124, + "validation": 348, + "expanded-validation": 680, + "test": 308, + "confirmation": 400 +} diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..adc360cf3fc8fa8504f18ac8c2f1cb814f97d2b5 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,28 @@ +[build-system] +requires = ["setuptools>=77"] +build-backend = "setuptools.build_meta" + +[project] +name = "forma3d-tiny-4" +version = "0.5.0" +license = "Apache-2.0" +license-files = ["LICENSE", "NOTICE", "THIRD_PARTY.md", "licenses/*.txt"] +readme = "README.md" +authors = [{name = "Lucasgitguy-co"}] +description = "Experimental local image-to-Gaussian student with cached teacher supervision" +requires-python = ">=3.11,<3.14" +dependencies = ["torch>=2.5", "numpy>=1.26", "pillow>=10", "plyfile>=1", "trimesh>=4", "scipy>=1.11"] + +[project.optional-dependencies] +test = ["pytest>=8"] +data = ["huggingface_hub>=0.34", "pyarrow>=17"] +teacher = ["idol @ git+https://github.com/speridlabs/idol-sdk.git@4702a7f00850e34b6e83a2170cc123c3e9570744"] + +[project.scripts] +image3d = "image3d_student.cli:main" + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.pytest.ini_options] +testpaths = ["tests"] diff --git a/recipe/README.md b/recipe/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9f10c3a5b70c916a33057c1efc781881ddb66b77 --- /dev/null +++ b/recipe/README.md @@ -0,0 +1,16 @@ +# Reproduce the Forma3D experiments + +On an Apple Silicon Mac with uv and Node.js installed, from this directory: + +```bash +bash run.sh setup forma +bash run.sh forma +``` + +This downloads pinned source, weights and ABO assets, prepares the fixed splits, +benchmarks MPS batch sizes for 15 minutes each, and trains/evaluates the variants +sequentially. It can take hours. Rerun the same command to resume. No upload occurs. +Only the Forma commands are part of this release. Source and data pins are in setup.py +and forma3d/run.py. The original experiment implementation is preserved. Public +evaluation JSON replaces machine-specific paths with local-run/; those paths are +provenance references, not paths to download. diff --git a/recipe/common/__init__.py b/recipe/common/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c67053a6e07c053d21112316f67444539b135a3b --- /dev/null +++ b/recipe/common/__init__.py @@ -0,0 +1 @@ +"""Shared local experiment utilities.""" diff --git a/recipe/common/runtime.py b/recipe/common/runtime.py new file mode 100644 index 0000000000000000000000000000000000000000..ade37d75e03854521e74d73712ca33dd49617c3a --- /dev/null +++ b/recipe/common/runtime.py @@ -0,0 +1,141 @@ +import contextlib +import fcntl +import hashlib +import json +import os +from pathlib import Path +import resource +import subprocess +import time + +ROOT = Path(__file__).resolve().parents[1] +RUNS = ROOT / "runs" +VENDOR = ROOT / "vendor" +LIMIT = 10 * 1024**3 + +def read(path): + return json.loads(Path(path).read_text()) + +def write(path, value): + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + tmp = path.with_suffix(path.suffix + ".tmp") + tmp.write_text(json.dumps(value, indent=2, ensure_ascii=False, allow_nan=False) + "\n") + tmp.replace(path) + +def sha(path): + h = hashlib.sha256() + with open(path, "rb") as f: + for b in iter(lambda: f.read(1024 * 1024), b""): + h.update(b) + return h.hexdigest() + +def fingerprint(value): + return hashlib.sha256(json.dumps(value, sort_keys=True).encode()).hexdigest() + +def immutable(path, settings): + if Path(path).exists() and read(path) != settings: + raise RuntimeError(f"Settings changed: {path}. Use a new run directory.") + write(path, settings) + +@contextlib.contextmanager +def exclusive(): + RUNS.mkdir(exist_ok=True) + with (RUNS / ".training.lock").open("w") as f: + try: + fcntl.flock(f, fcntl.LOCK_EX | fcntl.LOCK_NB) + except BlockingIOError: + raise RuntimeError("Another kit command is running. Run one job at a time.") + yield + +def memory(): + # macOS ru_maxrss is bytes; current RSS includes mapped CPU memory. + peak = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss + rss = int(subprocess.check_output(["ps", "-o", "rss=", "-p", str(os.getpid())])) * 1024 + return {"rss_bytes": rss, "peak_rss_bytes": peak} + +def guard(extra=0): + m = memory() + # GPU allocations may overlap RSS. max avoids counting unified allocations twice. + if max(m["rss_bytes"], extra) > LIMIT: + raise MemoryError("10 GiB memory target exceeded; checkpoint retained. Reduce the batch size.") + return m + +def jsonl(path, rows): + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + tmp = path.with_suffix(".tmp") + with tmp.open("w") as f: + for row in rows: + f.write(json.dumps(row, ensure_ascii=False, allow_nan=False) + "\n") + tmp.replace(path) + +def rows(path): + return [json.loads(x) for x in Path(path).read_text().splitlines() if x.strip()] + +def assert_disjoint(splits, keys): + owner = {} + for split, records in splits.items(): + for row in records: + for key in keys: + value = row.get(key) + if value is None: + continue + token = (key, json.dumps(value, sort_keys=True)) + if token in owner and owner[token] != split: + raise ValueError(f"Cross-split overlap: {key} in {owner[token]} and {split}") + owner[token] = split + +def paired_ci(before, after, metric, ratio=False, seed=44, repeats=2000): + """Paired cluster bootstrap; ratio aggregates edits/reference units, not clip WER.""" + import numpy as np + a, b = {r["id"]: r for r in before}, {r["id"]: r for r in after} + if a.keys() != b.keys() or not a: + raise ValueError("Paired evaluation must have identical, nonempty IDs") + groups = {} + for key, row in a.items(): + group = row.get("group", key) + if group != b[key].get("group", key): + raise ValueError("Paired cluster mismatch") + groups.setdefault(group, []).append(key) + av, bv, den = [], [], [] + for ids in groups.values(): + av.append(sum(a[k][metric] for k in ids)) + bv.append(sum(b[k][metric] for k in ids)) + if ratio and any(a[k]["units"] != b[k]["units"] for k in ids): + raise ValueError("Reference units changed") + den.append(sum(a[k]["units"] for k in ids) if ratio else len(ids)) + av, bv, den = map(np.asarray, (av, bv, den)) + rng = np.random.default_rng(seed) + changes = [] + for _ in range(repeats): + ix = rng.integers(0, len(av), len(av)) + changes.append(float((bv[ix].sum() - av[ix].sum()) / den[ix].sum())) + return {"delta_candidate_minus_baseline": float((bv.sum()-av.sum())/den.sum()), + "ci95": np.quantile(changes, [.025, .975]).tolist(), "clusters": len(groups), + "bootstrap_repeats": repeats} + +def fleet_score(base, candidate): + if set(base) != set(candidate) or "ko" not in base or len(base) != 8: + raise ValueError("Expected all eight languages") + eligible = candidate["ko"]["error_rate"] <= base["ko"]["error_rate"] + eligible &= all(candidate[l]["error_rate"] <= base[l]["error_rate"] + .005 for l in base if l != "ko") + changes = [(base[l]["error_rate"] - candidate[l]["error_rate"]) / max(base[l]["error_rate"], 1e-12) for l in base] + return bool(eligible), sum(changes) / len(changes) + +def stable_object_splits(groups, old, confirmation=100): + assignment, new = {}, [] + for group, records in groups.items(): + prior = {old[r["metadata"]["asset_id"]] for r in records if r["metadata"]["asset_id"] in old} + if len(prior)>1: + raise ValueError("New duplicate links bridge original splits; cannot preserve split isolation") + if prior: + assignment[group] = prior.pop() + else: + new.append(group) + new.sort(key=lambda s: hashlib.sha256(("44/"+s).encode()).hexdigest()) + if len(new)0 + +def splat_mask(splats, view, size=96): + from image3d_student.splats import rotation_matrices + if splats is None: + return np.zeros((size,size),bool) + xyz=splats.xyz@view.T + rot=rotation_matrices(splats.quat) + cov=(rot*(splats.scale**2)[:,None,:])@rot.transpose(0,2,1) + cov=view[None]@cov@view.T[None] + alpha=np.zeros((size,size),np.float32) + flip=np.diag([1.,-1.]) + for i in range(len(xyz)): + center=xyz[i,:2]*np.array([1,-1])*(size/1.25)+size/2 + c=flip@cov[i,:2,:2]@flip*(size/1.25)**2+np.eye(2)*.3 + radius=min(size,int(np.ceil(3*np.sqrt(np.linalg.eigvalsh(c).max())))) + lo=np.maximum(0,np.floor(center-radius).astype(int)) + hi=np.minimum(size,np.ceil(center+radius+1).astype(int)) + if np.any(hi<=lo): continue + yy,xx=np.mgrid[lo[1]:hi[1],lo[0]:hi[0]] + delta=np.stack([xx-center[0],yy-center[1]],axis=-1) + a=(splats.opacity[i,0]*np.exp(-.5*np.einsum("...i,ij,...j->...",delta,np.linalg.inv(c),delta))).clip(0,.99) + patch=alpha[lo[1]:hi[1],lo[0]:hi[0]] + patch[:]=1-(1-patch)*(1-a) + return alpha>=.5 + +def iou(a,b): + return float(np.logical_and(a,b).sum()/max(1,np.logical_or(a,b).sum())) diff --git a/recipe/forma3d/run.py b/recipe/forma3d/run.py new file mode 100644 index 0000000000000000000000000000000000000000..157c7713da3a86037929d123ae27f535f5854e86 --- /dev/null +++ b/recipe/forma3d/run.py @@ -0,0 +1,443 @@ +"""MPS Forma3D continuation, stable splits, mesh metrics, resumable experiments.""" +import argparse +import importlib.util +import json +import math +import os +from pathlib import Path +import random +import shutil +import subprocess +import sys +import time +from types import SimpleNamespace + +sys.path.insert(0,str(Path(__file__).resolve().parents[1])) +from common.runtime import ROOT,RUNS,VENDOR,read,write,sha,immutable,exclusive,guard,memory,jsonl,rows,paired_ci,stable_object_splits +sys.path.insert(0,str(VENDOR/"forma/src")) +from geometry import rotation,rotate_splats,chamfer,mesh_mask,splat_mask,iou,VIEWS + +BASE=RUNS/"forma" +DATA=BASE/"data" +PIN="3469f527c5f6833d654f62653ee92e0f73854b33" +VARIANTS=("control","views12","objects1024") + +def load_module(name,path): + spec=importlib.util.spec_from_file_location(name,path) + mod=importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + return mod + +def starting_checkpoint(): + from huggingface_hub import hf_hub_download + return Path(hf_hub_download("Lucasgitguy-co/forma3d-tiny-3","model.pt",revision=PIN)) + +def prepare(): + import numpy as np + import trimesh + from image3d_student.data import cache_sample + from image3d_student.splats import preview + pipeline=load_module("published_pipeline",VENDOR/"forma/recipe/pipeline.py") + immutable(DATA/"settings.json",{"recipe":sha(VENDOR/"forma/recipe/pipeline.py"),"objects":1024,"seed":44,"views":12,"version":1}) + roots={} + for count in (512,1024): + root=BASE/f"original-{count}" + root.mkdir(parents=True,exist_ok=True) + source,helper=pipeline.upstream(root) + folder=pipeline.prepare(root,source,helper,SimpleNamespace(objects=count,seed=44)) + roots[count]=folder + old={} + for split in ("train","validation","test"): + actual=rows(roots[512]/f"{split}.jsonl") + expected=rows(VENDOR/f"forma/splits/{split}.jsonl") + if {r["id"] for r in actual}!={r["id"] for r in expected}: + raise ValueError("Original 512-object splits did not reproduce") + for r in actual: + old[r["metadata"]["asset_id"]]=split + # The larger original preparation has already unioned asset, item, image and target identities. + # Its progress rows precede grouping; use grouped split manifests instead. + all_rows=[r for s in ("train","validation","test") for r in rows(roots[1024]/f"{s}.jsonl")] + groups={} + for r in all_rows: + groups.setdefault(r["metadata"]["object_id"],[]).append(r) + assignment=stable_object_splits(groups,old) + immutable(DATA/"assignments.json",assignment) + manifests={"control":[],"views12":[],"objects1024":[],"validation":[],"expanded-validation":[],"test":[],"confirmation":[]} + # Keep the original validation/test inputs exactly; train gets twelve consistently framed views. + for r in all_rows: + uid=r["metadata"]["asset_id"]; split=assignment[r["metadata"]["object_id"]] + copied=dict(r) + for key in ("image","target"): + dest=DATA/r[key]; dest.parent.mkdir(parents=True,exist_ok=True) + if not dest.exists(): os.link(roots[1024]/r[key],dest) + if uid in old and split=="train": manifests["control"].append(copied) + if split=="validation": + manifests["expanded-validation"].append(copied) + if uid in old: manifests["validation"].append(copied) + if split in ("test","confirmation"): manifests[split].append(copied) + helper=load_module("prepare_mesh",VENDOR/"forma/prepare_hf.py") + fetch=helper.Downloader(BASE/"downloads",8,3072,4) + by_asset={r["metadata"]["asset_id"]:r for r in all_rows} + for index,(uid,r) in enumerate(sorted(by_asset.items())): + meta=r["metadata"]; split=assignment[meta["object_id"]] + reference=DATA/"reference"/f"{uid}.npz" + augmented=DATA/"augmentation"/f"{uid}.json" + if reference.exists() and (split!="train" or augmented.exists()): + if split=="train": + aug=read(augmented); manifests["objects1024"].extend(aug) + if uid in old: manifests["views12"].extend(aug) + continue + raw=fetch.get(meta["repo"],meta["revision"],meta["path"]) + decoded=BASE/"decoded.glb" + subprocess.run(["node",str(VENDOR/"forma/recipe/decode.mjs"),str(raw),str(decoded)],check=True,capture_output=True) + splats,transform=helper.mesh_splats(decoded,4096,16,int(meta["sha256"][:8],16)^44) + scene=trimesh.load_scene(decoded,process=False) + parts=[] + for node in scene.graph.nodes_geometry: + matrix,name=scene.graph[node]; mesh=scene.geometry[name].copy() + if isinstance(mesh,trimesh.Trimesh) and len(mesh.faces): + mesh.apply_transform(matrix); mesh.apply_transform(np.asarray(transform)); parts.append(mesh) + mesh=trimesh.util.concatenate(parts) + samples,_=trimesh.sample.sample_surface(mesh,4096,seed=44) + reference.parent.mkdir(exist_ok=True) + np.savez_compressed(reference,vertices=mesh.vertices.astype(np.float32),faces=mesh.faces,samples=samples.astype(np.float32)) + if split=="train": + aug=[] + for j,(pitch,yaw) in enumerate((p,y) for p in (-20,0,20) for y in (0,90,180,270)): + mat=rotation(yaw,pitch) + # Fit rotated targets inside the fixed grid, applying identical framing to input and target. + scale=min(1.,.49/float(np.abs(splats.xyz@mat.T).max())) + rotated=rotate_splats(splats,mat,scale) + sid=f"abo-{uid}-aug{j}" + stage=BASE/"augmentation-stage" + if stage.exists(): shutil.rmtree(stage) + stage.mkdir() + preview(rotated,size=128).save(stage/"input.png") + item=cache_sample(stage,sid,stage/"input.png",rotated,16,source=meta.get("source","mesh-proxy:codepawl/abo-glb"), + extra={**meta,"input_rotation":mat.tolist(),"framing_scale":scale}) + for key in ("image","target"): + (stage/item[key]).replace(DATA/item[key]) + aug.append(item) + write(augmented,aug) + manifests["objects1024"].extend(aug) + if uid in old: manifests["views12"].extend(aug) + decoded.unlink(missing_ok=True) + print(f"Prepared geometry/views {index+1}/{len(by_asset)}",flush=True) + for name,rs in manifests.items(): jsonl(DATA/f"{name}.jsonl",rs) + write(DATA/"ready.json",{k:len(v) for k,v in manifests.items()}) + +def device(): + import torch + from image3d_student.engine import device_for + d=device_for("mps") + torch.set_num_threads(4) + if hasattr(torch.mps,"set_per_process_memory_fraction"): + torch.mps.set_per_process_memory_fraction(.65) + return d + +def checkpoint_model(path,dev): + import torch + from image3d_student.model import Student + ck=torch.load(path,map_location="cpu",weights_only=True) + if ck["config"]!={"grid":16,"width":64}: + raise ValueError("Expected unchanged tiny architecture") + model=Student(**ck["config"]).to(dev) + model.load_state_dict(ck["model"]) + return model + +def evaluate(model,manifest,threshold,out): + import numpy as np + import torch + from image3d_student.data import CachedDataset + from image3d_student.model import distillation_loss + from image3d_student.splats import Splats + dataset=CachedDataset(manifest) + model.eval(); predictions=[] + dev=next(model.parameters()).device + for idx,row in enumerate(dataset.rows): + image,target=dataset[idx] + with torch.no_grad(): + start=time.monotonic() + pred=model(image[None].to(dev)) + loss,terms=distillation_loss(pred,{k:v[None].to(dev) for k,v in target.items()},16) + pred={k:v[0].cpu().numpy() for k,v in pred.items()} + latency=time.monotonic()-start + occ=1/(1+np.exp(-np.clip(pred["occ"][:,0],-50,50)))>=threshold + truth=target["occ"].numpy()[:,0]>.5 + splats=Splats(**{k:pred[k][occ] for k in ("xyz","rgb","scale","quat","opacity")}) if occ.any() else None + meta=row["metadata"] + with np.load(DATA/"reference"/f"{meta['asset_id']}.npz",allow_pickle=False) as ref: + mat=np.asarray(meta.get("input_rotation",rotation(np.rad2deg(meta["yaw_radians"])))) + scale=meta.get("framing_scale",1.) + vertices=ref["vertices"]@mat.T*scale; samples=ref["samples"]@mat.T*scale + silhouettes=[] + for yaw,pitch in VIEWS: + view=rotation(yaw,pitch) + cache=DATA/"masks"/f"{row['id']}-{yaw}-{pitch}.npy" + if cache.exists(): mask=np.load(cache,allow_pickle=False) + else: + mask=mesh_mask(vertices,ref["faces"],view) + cache.parent.mkdir(exist_ok=True); np.save(cache,mask) + silhouettes.append(iou(splat_mask(splats,view),mask)) + predictions.append({"id":row["id"],"group":meta["object_id"],"loss":float(loss), + "occupancy_iou":iou(occ,truth),"silhouette_iou":float(np.mean(silhouettes)), + "chamfer":chamfer(pred["xyz"][occ],samples),"empty":not bool(occ.any()),"latency_seconds":latency, + **{k:float(v) for k,v in terms.items()}}) + summary={k:float(np.mean([r[k] for r in predictions])) for k in ("loss","occupancy_iou","silhouette_iou","chamfer","latency_seconds")} + summary.update(threshold=threshold,examples=len(predictions),empty_outputs=sum(r["empty"] for r in predictions),manifest_sha256=sha(manifest),**memory()) + jsonl(out.with_suffix(".jsonl"),predictions); write(out,summary) + return summary + +def eligible(base,candidate): + return candidate["chamfer"]<=base["chamfer"] and candidate["occupancy_iou"]>=base["occupancy_iou"] and candidate["silhouette_iou"]>base["silhouette_iou"] + +def benchmark(batch,seconds): + import torch + from torch.utils.data import default_collate + from image3d_student.data import CachedDataset + from image3d_student.model import distillation_loss + dev=device(); model=checkpoint_model(starting_checkpoint(),dev) + dataset=CachedDataset(DATA/"control.jsonl") + images,target=default_collate([dataset[i%len(dataset)] for i in range(batch)]) + images=images.to(dev); target={k:v.to(dev) for k,v in target.items()} + opt=torch.optim.AdamW(model.parameters(),lr=5e-5,weight_decay=1e-4) + start=None; count=0 + while start is None or time.monotonic()-startmeta["best_silhouette"]: + meta.update(best_step=meta["step"],best_silhouette=metric["silhouette_iou"]) + save(folder/"best.pt",model,optimizer,meta,rng) + save(folder/"last.pt",model,optimizer,meta,rng) + model.train() + images,target=default_collate([dataset[rng.randrange(len(dataset))] for _ in range(meta["batch"])]) + try: + guard(torch.mps.driver_allocated_memory()) + optimizer.zero_grad(set_to_none=True) + loss,_=distillation_loss(model(images.to(dev)),{k:v.to(dev) for k,v in target.items()},16) + if not torch.isfinite(loss): raise RuntimeError("Nonfinite loss") + loss.backward(); torch.nn.utils.clip_grad_norm_(model.parameters(),1.,error_if_nonfinite=True); optimizer.step() + except (MemoryError,RuntimeError) as exc: + if not isinstance(exc,MemoryError) and "out of memory" not in str(exc).lower(): raise + if meta["batch"]==1: raise + meta["batch"]=max(1,meta["batch"]//2) + optimizer.zero_grad(set_to_none=True); torch.mps.empty_cache() + save(folder/"last.pt",model,optimizer,meta,rng) + print(f"Memory pressure: reducing batch to {meta['batch']}",flush=True) + continue + meta["step"]+=1 + if meta["step"]%50==0: + print(f"{variant} seed={seed} step={meta['step']}/6000 loss={float(loss):.5f} batch={meta['batch']}",flush=True) + save(folder/"last.pt",model,optimizer,meta,rng) + # Evaluate final step explicitly (the loop stops at 6000). + if not any(h["step"]==6000 for h in meta["history"]): + metric=evaluate(model,DATA/"validation.jsonl",.5,folder/"validation-6000.json") + ok=eligible(baseline,metric); meta["history"].append({"step":6000,"eligible":ok,**metric}) + if ok and metric["silhouette_iou"]>meta["best_silhouette"]: + meta.update(best_step=6000,best_silhouette=metric["silhouette_iou"]) + save(folder/"best.pt",model,optimizer,meta,rng) + finally: + save(folder/"last.pt",model,optimizer,meta,rng) + write(folder/"complete.json",{**meta,"seconds_this_invocation":time.monotonic()-started,**memory()}) + +def threshold(path,folder): + model=checkpoint_model(path,device()); scores=[] + for t in (.3,.4,.5,.6): + out=folder/f"threshold-{t}.json" + if not out.exists(): evaluate(model,DATA/"validation.jsonl",t,out) + scores.append(read(out)) + baseline=read(BASE/"baseline-validation.json") + valid=[s for s in scores if s["chamfer"]<=baseline["chamfer"] and s["occupancy_iou"]>=baseline["occupancy_iou"]] + chosen=max(valid,key=lambda s:s["silhouette_iou"]) if valid else next(s for s in scores if s["threshold"]==.5) + write(folder/"threshold-selection.json",chosen) + return chosen + +def gallery(path,out,threshold_value): + import numpy as np + import torch + from PIL import Image,ImageDraw + from image3d_student.data import CachedDataset + from image3d_student.splats import Splats,preview,write_ply,read_ply + model=checkpoint_model(path,device()); model.eval() + dataset=CachedDataset(DATA/"confirmation.jsonl") + # Deterministic IDs; include quality extremes using final results without selecting a model on them. + result=rows(out/"confirmation.jsonl") + ranked=sorted(result,key=lambda r:r["silhouette_iou"]) + chosen={r["id"] for r in ranked[:2]+ranked[-2:]+ranked[len(ranked)//2:len(ranked)//2+2]} + for i,row in enumerate(dataset.rows): + if row["id"] not in chosen: continue + with torch.no_grad(): pred=model(dataset[i][0][None].to(device())) + keep=pred["occ"][0,:,0].sigmoid()>=threshold_value + if not bool(keep.any()): continue + splats=Splats(**{k:pred[k][0,keep].cpu().numpy() for k in ("xyz","rgb","scale","quat","opacity")}) + ply=out/(row["id"]+".ply"); write_ply(ply,splats); read_ply(ply) + with np.load(DATA/"reference"/f"{row['metadata']['asset_id']}.npz",allow_pickle=False) as ref: + vertices=ref["vertices"]@rotation(np.rad2deg(row["metadata"]["yaw_radians"])).T + frames=[] + for yaw in range(0,360,30): + left=preview(splats,size=192,yaw=np.deg2rad(yaw)) + right=Image.fromarray(mesh_mask(vertices,ref["faces"],rotation(yaw),192).astype(np.uint8)*255).convert("RGB") + frame=Image.new("RGB",(384,216)); frame.paste(left,(0,24)); frame.paste(right,(192,24)) + d=ImageDraw.Draw(frame); d.text((4,4),"Prediction",fill="white"); d.text((196,4),"Reference silhouette",fill="white") + frames.append(frame) + frames[0].save(out/(row["id"]+".gif"),save_all=True,append_images=frames[1:],duration=100,loop=0) + +def worker(action,*args,optional=False): + result=subprocess.run([sys.executable,str(Path(__file__).resolve()),action,*map(str,args)]) + if result.returncode and not optional: raise RuntimeError(f"{action} failed; rerun to resume") + return result.returncode==0 + +def run_all(seconds): + immutable(BASE/"run-config.json",{"script":sha(__file__),"geometry":sha(Path(__file__).with_name("geometry.py")), + "common":sha(ROOT/"common/runtime.py"),"lock":sha(ROOT/"locks/forma.txt"),"benchmark_seconds":seconds}) + if not (DATA/"ready.json").exists(): worker("prepare") + if not (BASE/"parity.json").exists() or not read(BASE/"parity.json")["passed"]: worker("audit") + benchmarks=[] + for batch in (4,8,16): + path=BASE/f"benchmark-{batch}.json" + if not path.exists(): worker("benchmark","--batch",batch,"--seconds",seconds,optional=True) + if path.exists(): benchmarks.append(read(path)) + if not benchmarks: raise RuntimeError("No batch size passed the memory benchmark") + batch=max(benchmarks,key=lambda x:x["samples_per_second"])["batch"] + write(BASE/"batch-selection.json",{"batch":batch}) + if not (BASE/"baseline-validation.json").exists(): worker("baseline") + for variant in VARIANTS: worker("train","--variant",variant,"--seed",44,"--batch",batch) + candidates=[] + # Threshold tuning also tests the untouched v3 baseline, so a threshold-only gain is visible. + worker("threshold","--checkpoint",starting_checkpoint(),"--out",BASE/"threshold-baseline") + for variant in VARIANTS: + folder=BASE/"experiments"/f"{variant}-seed44" + meta=read(folder/"complete.json") + if meta["best_step"]: + worker("threshold","--checkpoint",folder/"best.pt","--out",folder) + candidates.append((read(folder/"threshold-selection.json")["silhouette_iou"],variant)) + selection={"variant":None,"checkpoint":str(starting_checkpoint()),"threshold":read(BASE/"threshold-baseline/threshold-selection.json")["threshold"]} + baseline_quality=read(BASE/"threshold-baseline/threshold-selection.json")["silhouette_iou"] + if candidates and max(candidates)[0]>baseline_quality: + _,variant=max(candidates) + worker("train","--variant",variant,"--seed",45,"--batch",batch) + repeat=BASE/"experiments"/f"{variant}-seed45" + if read(repeat/"complete.json")["best_step"]: + worker("threshold","--checkpoint",repeat/"best.pt","--out",repeat) + if read(repeat/"threshold-selection.json")["silhouette_iou"]>baseline_quality: + folder=BASE/"experiments"/f"{variant}-seed44" + selection={"variant":variant,"checkpoint":str(folder/"best.pt"),"threshold":read(folder/"threshold-selection.json")["threshold"],"repeat_checkpoint":str(repeat/"best.pt"),"repeat_threshold":read(repeat/"threshold-selection.json")["threshold"]} + immutable(BASE/"selection.json",selection) + worker("final") + +def final(): + selected=read(BASE/"selection.json") + baseline_dir=BASE/"confirmation-baseline"; baseline_dir.mkdir(exist_ok=True) + evaluate(checkpoint_model(starting_checkpoint(),device()),DATA/"confirmation.jsonl",.5,baseline_dir/"confirmation.json") + before=rows(baseline_dir/"confirmation.jsonl") + report={"selection":selected,"runs":{},"baseline":read(baseline_dir/"confirmation.json"),"batch":read(BASE/"batch-selection.json")} + choices=[("selected",selected["checkpoint"],selected["threshold"])] + if selected.get("repeat_checkpoint"): choices.append(("repeat",selected["repeat_checkpoint"],selected["repeat_threshold"])) + for name,path,t in choices: + out=BASE/f"confirmation-{name}"; out.mkdir(exist_ok=True) + score=evaluate(checkpoint_model(path,device()),DATA/"confirmation.jsonl",t,out/"confirmation.json") + if t!=.5: evaluate(checkpoint_model(path,device()),DATA/"confirmation.jsonl",.5,out/"historical-threshold.json") + after=rows(out/"confirmation.jsonl") + report["runs"][name]={"score":score,"checkpoint_sha256":sha(path),"ci":{m:paired_ci(before,after,m) for m in ("silhouette_iou","occupancy_iou","chamfer")}} + if name=="selected": gallery(path,out,t) + report["limitations"]="ABO Gaussian-proxy input only. Chamfer is center-to-mesh-surface-sample distance; silhouettes use orthographic alpha>=0.5 vs triangle rasterization at 96px. Not photorealistic or natural-photo evaluation." + write(BASE/"report.json",report) + (BASE/"MODEL_CARD.md").write_text("# Local Forma3D experiment\n\n"+report["limitations"]+"\n\nSee report.json and confirmation-selected/*.gif. Existing image3d CLI accepts the selected format=1 checkpoint. No publication performed.\n") + print(f"Finished. Report: {BASE / 'report.json'}",flush=True) + +def main(): + p=argparse.ArgumentParser(description=__doc__) + p.add_argument("action",choices=["all","prepare","audit","benchmark","baseline","train","threshold","final"]) + p.add_argument("--seconds",type=int,default=900); p.add_argument("--batch",type=int,default=4) + p.add_argument("--variant",choices=VARIANTS,default="control"); p.add_argument("--seed",type=int,default=44) + p.add_argument("--checkpoint",type=Path); p.add_argument("--out",type=Path,default=BASE/"threshold-baseline") + a=p.parse_args(); BASE.mkdir(parents=True,exist_ok=True) + if min(a.seconds,a.batch)<1: p.error("seconds and batch must be positive") + if a.action=="all": + with exclusive(): run_all(a.seconds) + elif a.action=="prepare": prepare() + elif a.action=="audit": audit() + elif a.action=="benchmark": benchmark(a.batch,a.seconds) + elif a.action=="baseline": evaluate(checkpoint_model(starting_checkpoint(),device()),DATA/"validation.jsonl",.5,BASE/"baseline-validation.json") + elif a.action=="train": train(a.variant,a.seed,a.batch) + elif a.action=="threshold": threshold(a.checkpoint,a.out) + else: final() + +if __name__=="__main__": main() diff --git a/recipe/locks/forma.txt b/recipe/locks/forma.txt new file mode 100644 index 0000000000000000000000000000000000000000..97cfb2f1b7b45e70edf57d4aaa09c145b5c96446 --- /dev/null +++ b/recipe/locks/forma.txt @@ -0,0 +1,28 @@ +anyio==4.16.0 +click==8.5.1 +filelock==4.2.9 +fsspec==2026.9.0 +h11==0.16.0 +hf-xet==1.8.0 +httpcore2==2.16.0 +httpx2==2.16.0 +huggingface-hub==2.4.0 +idna==3.20 +jinja2==3.1.6 +markupsafe==3.0.4 +mpmath==1.3.0 +networkx==3.7 +numpy==2.4.6 +packaging==26.3 +pillow==13.0.0 +plyfile==1.1.5 +pyarrow==26.0.0 +pyyaml==6.0.3 +scipy==1.17.1 +setuptools==84.2.1 +sympy==1.14.0 +torch==2.15.0 +tqdm==4.70.1 +trimesh==5.2.0 +truststore==0.10.5 +typing-extensions==4.16.0 diff --git a/recipe/run.sh b/recipe/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..30632322fdbd31bbe1eec0e729dbdbeec7bd792f --- /dev/null +++ b/recipe/run.sh @@ -0,0 +1,39 @@ +#!/bin/bash +set -euo pipefail +cd "$(dirname "$0")" +export HF_HOME="$PWD/.cache/huggingface" +export UV_CACHE_DIR="$PWD/.cache/uv" +export UV_LINK_MODE=copy +export PYTHONUNBUFFERED=1 +export CACTUS_TELEMETRY=0 +export PYTORCH_ENABLE_MPS_FALLBACK=0 +mkdir -p logs +case "${1:-help}" in + setup) + uv run --no-project --python 3.12 python setup.py "${2:-all}" + ;; + fleet) + test -x .venvs/fleet/bin/python || { echo "Run: bash run.sh setup fleet"; exit 1; } + caffeinate -i .venvs/fleet/bin/python fleet/run.py all 2>&1 | tee -a logs/fleet.log + ;; + forma) + test -x .venvs/forma/bin/python || { echo "Run: bash run.sh setup forma"; exit 1; } + caffeinate -i .venvs/forma/bin/python forma3d/run.py all 2>&1 | tee -a logs/forma.log + ;; + all) + bash run.sh fleet + bash run.sh forma + ;; + report) + uv run --no-project --python 3.12 python report.py + ;; + test) + .venvs/forma/bin/python -m unittest discover -s tests -v + if test -x .venvs/fleet/bin/python; then + JAX_PLATFORMS=cpu .venvs/fleet/bin/python -m unittest discover -s tests -p test_fleet.py -v + fi + ;; + *) + echo "Usage: bash run.sh {setup [all|fleet|forma]|fleet|forma|all|report|test}" + ;; +esac diff --git a/recipe/setup.py b/recipe/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..838b319ad672bd4a162e616caa923c69ee93cca6 --- /dev/null +++ b/recipe/setup.py @@ -0,0 +1,86 @@ +"""Download pinned source and create independent environments. Never trains/publishes.""" +import argparse +import json +import os +from pathlib import Path +import shutil +import subprocess +import urllib.request + +from common.runtime import ROOT, VENDOR, write, sha + +FORMA = "3469f527c5f6833d654f62653ee92e0f73854b33" +FLEET = "0cc581616f7e5493e2e3999e71c134cedc879a6d" +V2 = "1648582b07a0f7f9d482a8c0f4cf89e6ecd86972" +CODE = "ee2ffc67fe0f9537d4928e158440c7d228a63100" + +def get(url, dest): + if dest.exists(): + return + dest.parent.mkdir(parents=True, exist_ok=True) + req = urllib.request.Request(url, headers={"User-Agent": "m4-local-training/1"}) + with urllib.request.urlopen(req, timeout=120) as r, dest.with_suffix(dest.suffix+".part").open("wb") as f: + shutil.copyfileobj(r, f) + dest.with_suffix(dest.suffix+".part").replace(dest) + +def snapshot(repo, rev, folder): + url = f"https://huggingface.co/api/models/{repo}/revision/{rev}" + with urllib.request.urlopen(url, timeout=60) as r: + metadata = json.load(r) + for item in metadata["siblings"]: + name = item["rfilename"] + path = Path(name) + if path.is_absolute() or ".." in path.parts: + raise ValueError("Invalid remote path") + if path.suffix in (".pt", ".cact", ".safetensors"): + continue + get(f"https://huggingface.co/{repo}/resolve/{rev}/{name}", folder / name) + write(folder / "PIN.json", {"repo": repo, "revision": rev}) + +def run(*args, **kw): + print("+", " ".join(map(str,args)), flush=True) + subprocess.run(list(map(str,args)), check=True, **kw) + +def env(name, requirement): + dest = ROOT / ".venvs" / name + lock = ROOT / "locks" / (name + ".txt") + if not (dest / "bin/python").exists(): + run("uv", "venv", "--python", "3.12", dest) + args = ["uv", "pip", "install", "--link-mode", "copy", "--python", dest / "bin/python", "-r", lock if lock.exists() else requirement] + if not (dest / ".setup-complete").exists(): + args.append("--reinstall") + run(*args) + (dest / ".setup-complete").touch() + lock.parent.mkdir(exist_ok=True) + lock.write_bytes(subprocess.check_output(["uv", "pip", "freeze", "--python", str(dest / "bin/python")])) + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("model", choices=["forma", "fleet", "all"]) + args = parser.parse_args() + if not shutil.which("uv"): + raise SystemExit("Install uv first: brew install uv") + os.environ.setdefault("UV_CACHE_DIR", str(ROOT / ".cache/uv")) + if args.model in ("forma", "all"): + snapshot("Lucasgitguy-co/forma3d-tiny-3", FORMA, VENDOR / "forma") + get(f"https://huggingface.co/Lucasgitguy-co/forma3d-tiny-2/resolve/{V2}/scripts/prepare_hf.py", VENDOR / "forma/prepare_hf.py") + env("forma", VENDOR / "forma/recipe/requirements.txt") + run("npm", "ci", "--ignore-scripts", cwd=VENDOR / "forma/recipe") + if args.model in ("fleet", "all"): + snapshot("Lucasgitguy-co/Fleet-1", FLEET, VENDOR / "fleet") + env("fleet", VENDOR / "fleet/requirements.lock") + # The runtime explicitly pins JAX 0.4.38 for its Metal extra. + # An isolated older JAX/Flax stack avoids corrupting the reproduction environment. + metal = ROOT / "fleet/metal-requirements.txt" + try: + env("fleet-metal", metal) + write(ROOT / "locks/metal-setup.json", {"installed": True}) + except subprocess.CalledProcessError as exc: + write(ROOT / "locks/metal-setup.json", {"installed": False, "error": str(exc)}) + print("Metal environment unavailable; the runner will use the CPU environment.", flush=True) + files = {str(p.relative_to(VENDOR)): sha(p) for p in VENDOR.rglob("*") if p.is_file() and "node_modules" not in p.parts} + write(ROOT / "locks/source-hashes.json", files) + print("Setup complete. No weights trained or uploaded.") + +if __name__ == "__main__": + main() diff --git a/recipe/tests/test_quality.py b/recipe/tests/test_quality.py new file mode 100644 index 0000000000000000000000000000000000000000..5e5d3a1a8ea67a3730be8ae8dfe255e0d7ff5099 --- /dev/null +++ b/recipe/tests/test_quality.py @@ -0,0 +1,111 @@ +import importlib.util +from pathlib import Path +import sys +import tempfile +import unittest + +ROOT=Path(__file__).resolve().parents[1] +sys.path[:0]=[str(ROOT),str(ROOT/"forma3d"),str(ROOT/"vendor/forma/src")] +from common.runtime import assert_disjoint,fleet_score,immutable,read,paired_ci,stable_object_splits + +class SelectionTests(unittest.TestCase): + def scores(self,value=.2): + return {l:{"error_rate":value} for l in ("en","de","fr","es","it","nl","pl","ko")} + + def test_korean_regression_rejected_despite_mean_gain(self): + base=self.scores(); candidate=self.scores(.1); candidate["ko"]["error_rate"]=.201 + eligible,score=fleet_score(base,candidate) + self.assertFalse(eligible); self.assertGreater(score,0) + + def test_non_korean_tolerance(self): + base=self.scores(); candidate=self.scores(.19) + candidate["nl"]["error_rate"]=.206 + self.assertFalse(fleet_score(base,candidate)[0]) + candidate["nl"]["error_rate"]=.204 + self.assertTrue(fleet_score(base,candidate)[0]) + + def test_split_leakage(self): + with self.assertRaises(ValueError): + assert_disjoint({"train":[{"sha":"abc"}],"test":[{"sha":"abc"}]},("sha",)) + + def test_object_split_preservation_and_conflicts(self): + def row(uid): return {"metadata":{"asset_id":uid}} + groups={"oldgroup":[row("a"),row("new-duplicate")], **{str(i):[row(str(i))] for i in range(20)}} + assignment=stable_object_splits(groups,{"a":"test"},confirmation=5) + self.assertEqual(assignment["oldgroup"],"test") + self.assertEqual(list(assignment.values()).count("confirmation"),5) + groups["oldgroup"].append(row("b")) + with self.assertRaises(ValueError): stable_object_splits(groups,{"a":"test","b":"train"},confirmation=5) + + def test_resume_settings(self): + with tempfile.TemporaryDirectory() as d: + path=Path(d)/"settings.json" + immutable(path,{"seed":42}); immutable(path,{"seed":42}) + with self.assertRaises(RuntimeError): immutable(path,{"seed":43}) + self.assertEqual(read(path),{"seed":42}) + + def test_cluster_bootstrap_and_ratio(self): + a=[{"id":str(i),"group":str(i//2),"edits":2,"units":10,"metric":.3} for i in range(8)] + b=[{**r,"edits":1,"metric":.4} for r in a] + result=paired_ci(a,b,"edits",ratio=True,repeats=50) + self.assertEqual(result["clusters"],4) + self.assertAlmostEqual(result["delta_candidate_minus_baseline"],-.1) + self.assertAlmostEqual(paired_ci(a,b,"metric",repeats=50)["ci95"][0],.1) + with self.assertRaises(ValueError): paired_ci(a,b[:-1],"metric") + +class GeometryTests(unittest.TestCase): + def test_rotation_and_scale(self): + import numpy as np + from geometry import rotation,rotate_splats + from image3d_student.splats import Splats,rotation_matrices + q=np.array([[1,0,0,0]],np.float32) + a=Splats(np.array([[.1,.2,.3]]),np.ones((1,3)),np.array([[.01,.02,.03]]),q,np.ones((1,1))) + m=rotation(90,20); b=rotate_splats(a,m,.8) + np.testing.assert_allclose(b.xyz,a.xyz@m.T*.8,atol=1e-6) + np.testing.assert_allclose(rotation_matrices(b.quat)[0],m,atol=1e-6) + np.testing.assert_allclose(b.scale,a.scale*.8) + + def test_silhouette_black_objects_and_empty_outputs(self): + import numpy as np + from geometry import splat_mask,mesh_mask,rotation,iou,chamfer + from image3d_student.splats import Splats + a=Splats(np.zeros((1,3)),np.zeros((1,3)),np.full((1,3),.1),np.array([[1,0,0,0]]),np.ones((1,1))) + self.assertGreater(splat_mask(a,rotation(0)).sum(),0) + self.assertEqual(splat_mask(None,rotation(0)).sum(),0) + mask=mesh_mask(np.array([[-.2,-.2,0],[.2,-.2,0],[0,.2,0]]),np.array([[0,1,2]]),rotation(0)) + self.assertEqual(iou(mask,mask),1.) + self.assertEqual(chamfer(np.empty((0,3)),np.zeros((1,3))),2.) + self.assertEqual(chamfer(np.zeros((1,3)),np.zeros((1,3))),0.) + + def test_ply_round_trip(self): + import numpy as np + from image3d_student.splats import Splats,write_ply,read_ply + a=Splats(np.array([[.1,0,0]]),np.full((1,3),.4),np.full((1,3),.1),np.array([[1,0,0,0]]),np.full((1,1),.8)) + with tempfile.TemporaryDirectory() as d: + path=Path(d)/"test.ply"; write_ply(path,a); b=read_ply(path) + for key in a.__dataclass_fields__: + np.testing.assert_allclose(getattr(a,key),getattr(b,key),atol=1e-6) + + def test_torch_resume_preserves_optimizer_and_rng(self): + import random + import torch + from image3d_student.model import Student + spec=importlib.util.spec_from_file_location("forma_run",ROOT/"forma3d/run.py") + mod=importlib.util.module_from_spec(spec); spec.loader.exec_module(mod) + torch.manual_seed(44); model=Student(16,64) + optimizer=torch.optim.AdamW(model.parameters(),lr=5e-5) + x=torch.rand(1,3,128,128) + model(x)["occ"].square().mean().backward(); optimizer.step() + rng=random.Random(44) + with tempfile.TemporaryDirectory() as d: + path=Path(d)/"last.pt"; mod.save(path,model,optimizer,{"step":1},rng) + ck=torch.load(path,map_location="cpu",weights_only=True) + resumed=Student(16,64); resumed.load_state_dict(ck["model"]) + opt2=torch.optim.AdamW(resumed.parameters(),lr=5e-5); opt2.load_state_dict(ck["optimizer"]) + for m,o in ((model,optimizer),(resumed,opt2)): + o.zero_grad(); m(x)["occ"].square().mean().backward(); o.step() + for a,b in zip(model.parameters(),resumed.parameters()): torch.testing.assert_close(a,b,rtol=0,atol=0) + other=random.Random(); other.setstate(ck["python_rng"]) + self.assertEqual(rng.random(),other.random()) + +if __name__=="__main__": unittest.main() diff --git a/repeat/model.pt b/repeat/model.pt new file mode 100644 index 0000000000000000000000000000000000000000..4197169247011019b7b358b66c89472771ea937f --- /dev/null +++ b/repeat/model.pt @@ -0,0 +1,3 @@ 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Amazon Berkeley Objects contributors", "conversion": "CodePawl GLB conversion; decompressed, normalized and sampled for training", "license": "cc-by-4.0", "license_url": "https://creativecommons.org/licenses/by/4.0/", "source_url": "https://huggingface.co/datasets/codepawl/abo-glb/blob/c7c8293fed3b7ab122ef5d7896521a187e0962d7/objects/B08GGQ9L9F/B08GGQ9L9F.glb", "normalization_matrix": [[1.3971167541096734, 0.0, 0.0, -0.0], [0.0, 1.3971167541096734, 0.0, -0.1692835250547045], [0.0, 0.0, 1.3971167541096734, -0.0008964411746499759], [0.0, 0.0, 0.0, 1.0]], "yaw_radians": 4.71238898038469, "render": "orthographic Gaussian proxy on black; camera +z", "target": "mesh-derived isotropic surface samples; no pretrained teacher", "input_rotation": [[-1.8369701465288538e-16, 0.0, -1.0], [0.0, 1.0, 0.0], [1.0, 0.0, -1.8369701465288538e-16]], "framing_scale": 1.0}, "image_sha256": "937214021b784851ca6b2b93fe616e9a3887fd6d6029dfeeacf2bdfde37331ff", "target_sha256": "e0d7858d89803c2a78fa5003ed05a5424f5680e81d7d3f06636b455c9a8e1683"} +{"id": "abo-B08GGQ9L9F-aug8", "image": "images/abo-B08GGQ9L9F-aug8.png", "target": "targets/abo-B08GGQ9L9F-aug8.npz", "source": "mesh-proxy:codepawl/abo-glb", "grid": 16, "teachers": {"appearance": "mesh-proxy:codepawl/abo-glb", "coarse": null, "fine": null}, "teacher_gaussians": 4096, "retained_gaussians": 270, "geometry_transforms": {"coarse": null, "fine": null}, "metadata": {"repo": "codepawl/abo-glb", "revision": "c7c8293fed3b7ab122ef5d7896521a187e0962d7", "path": "objects/B08GGQ9L9F/B08GGQ9L9F.glb", "sha256": "f323a4091112ed8720893afc5d40f8119a46addeac1002c242cd659e55698bcb", "decoded_sha256": "873b0e559baea016b73c42716fd02a2281146896e992e95f210769efacb97015", "asset_id": "B08GGQ9L9F", "object_id": "abo-B08GGQ9L9F", "amazon_item_ids": ["B08GGQ9L9F"], "title": "Amazon Brand - Solimo Rover Multi-purpose Laptop Table (Accacia)", "product_type": "table", "author": "Amazon.com; Amazon Berkeley Objects contributors", "conversion": "CodePawl GLB conversion; decompressed, normalized and sampled for training", "license": "cc-by-4.0", "license_url": "https://creativecommons.org/licenses/by/4.0/", "source_url": "https://huggingface.co/datasets/codepawl/abo-glb/blob/c7c8293fed3b7ab122ef5d7896521a187e0962d7/objects/B08GGQ9L9F/B08GGQ9L9F.glb", "normalization_matrix": [[1.3971167541096734, 0.0, 0.0, -0.0], [0.0, 1.3971167541096734, 0.0, -0.1692835250547045], [0.0, 0.0, 1.3971167541096734, -0.0008964411746499759], [0.0, 0.0, 0.0, 1.0]], "yaw_radians": 4.71238898038469, "render": "orthographic Gaussian proxy on black; camera +z", "target": "mesh-derived isotropic surface samples; no pretrained teacher", "input_rotation": [[1.0, 0.0, 0.0], [0.0, 0.9396926164627075, -0.3420201539993286], [0.0, 0.3420201539993286, 0.9396926164627075]], "framing_scale": 1.0}, "image_sha256": "1e0166474a71c69a7b301e329c8942ca3701bc3e8c8b262af73dca242bd44620", "target_sha256": "31e2d398859e4b1ae9e57fcaff6f059bc8463620570eb8fadc81ba74cb117a09"} +{"id": "abo-B08GGQ9L9F-aug9", "image": "images/abo-B08GGQ9L9F-aug9.png", "target": "targets/abo-B08GGQ9L9F-aug9.npz", "source": "mesh-proxy:codepawl/abo-glb", "grid": 16, "teachers": {"appearance": "mesh-proxy:codepawl/abo-glb", "coarse": null, "fine": null}, "teacher_gaussians": 4096, "retained_gaussians": 237, "geometry_transforms": {"coarse": null, "fine": null}, "metadata": {"repo": "codepawl/abo-glb", "revision": "c7c8293fed3b7ab122ef5d7896521a187e0962d7", "path": "objects/B08GGQ9L9F/B08GGQ9L9F.glb", "sha256": "f323a4091112ed8720893afc5d40f8119a46addeac1002c242cd659e55698bcb", "decoded_sha256": "873b0e559baea016b73c42716fd02a2281146896e992e95f210769efacb97015", "asset_id": "B08GGQ9L9F", "object_id": "abo-B08GGQ9L9F", "amazon_item_ids": ["B08GGQ9L9F"], "title": "Amazon Brand - Solimo Rover Multi-purpose Laptop Table (Accacia)", "product_type": "table", "author": "Amazon.com; Amazon Berkeley Objects contributors", "conversion": "CodePawl GLB conversion; decompressed, normalized and sampled for training", "license": "cc-by-4.0", "license_url": "https://creativecommons.org/licenses/by/4.0/", "source_url": "https://huggingface.co/datasets/codepawl/abo-glb/blob/c7c8293fed3b7ab122ef5d7896521a187e0962d7/objects/B08GGQ9L9F/B08GGQ9L9F.glb", "normalization_matrix": [[1.3971167541096734, 0.0, 0.0, -0.0], [0.0, 1.3971167541096734, 0.0, -0.1692835250547045], [0.0, 0.0, 1.3971167541096734, -0.0008964411746499759], [0.0, 0.0, 0.0, 1.0]], "yaw_radians": 4.71238898038469, "render": "orthographic Gaussian proxy on black; camera +z", "target": "mesh-derived isotropic surface samples; no pretrained teacher", "input_rotation": [[6.123234262925839e-17, 0.0, 1.0], [0.3420201539993286, 0.9396926164627075, -2.0942694406238013e-17], [-0.9396926164627075, 0.3420201539993286, 5.753957676425016e-17]], "framing_scale": 1.0}, "image_sha256": "323053ca989bb523234b41d268372838458f2d4969821ff421be672e7be826c2", "target_sha256": "bf787a167fab477e5ef35a10f8f56e139105686d741daf6a30f793bc06c0431c"} +{"id": "abo-B08GGQ9L9F-aug10", "image": "images/abo-B08GGQ9L9F-aug10.png", "target": "targets/abo-B08GGQ9L9F-aug10.npz", "source": "mesh-proxy:codepawl/abo-glb", "grid": 16, "teachers": {"appearance": "mesh-proxy:codepawl/abo-glb", "coarse": null, "fine": null}, "teacher_gaussians": 4096, "retained_gaussians": 267, "geometry_transforms": {"coarse": null, "fine": null}, "metadata": {"repo": "codepawl/abo-glb", "revision": "c7c8293fed3b7ab122ef5d7896521a187e0962d7", "path": "objects/B08GGQ9L9F/B08GGQ9L9F.glb", "sha256": "f323a4091112ed8720893afc5d40f8119a46addeac1002c242cd659e55698bcb", "decoded_sha256": "873b0e559baea016b73c42716fd02a2281146896e992e95f210769efacb97015", "asset_id": "B08GGQ9L9F", "object_id": "abo-B08GGQ9L9F", "amazon_item_ids": ["B08GGQ9L9F"], "title": "Amazon Brand - Solimo Rover Multi-purpose Laptop Table (Accacia)", "product_type": "table", "author": "Amazon.com; Amazon Berkeley Objects contributors", "conversion": "CodePawl GLB conversion; decompressed, normalized and sampled for training", "license": "cc-by-4.0", "license_url": "https://creativecommons.org/licenses/by/4.0/", "source_url": "https://huggingface.co/datasets/codepawl/abo-glb/blob/c7c8293fed3b7ab122ef5d7896521a187e0962d7/objects/B08GGQ9L9F/B08GGQ9L9F.glb", "normalization_matrix": [[1.3971167541096734, 0.0, 0.0, -0.0], [0.0, 1.3971167541096734, 0.0, -0.1692835250547045], [0.0, 0.0, 1.3971167541096734, -0.0008964411746499759], [0.0, 0.0, 0.0, 1.0]], "yaw_radians": 4.71238898038469, "render": "orthographic Gaussian proxy on black; camera +z", "target": "mesh-derived isotropic surface samples; no pretrained teacher", "input_rotation": [[-1.0, 0.0, 1.2246468525851679e-16], [4.1885388812476027e-17, 0.9396926164627075, 0.3420201539993286], [-1.1507915352850032e-16, 0.3420201539993286, -0.9396926164627075]], "framing_scale": 1.0}, "image_sha256": "9d39325e45205c2572d475127ac7648cd136a6b926768e00e94dcdc35f09596e", "target_sha256": "65a27517ee65ce77f0201686fcdb6b957081d4806c6afafd78a63bb9f9d4fdb2"} +{"id": "abo-B08GGQ9L9F-aug11", "image": "images/abo-B08GGQ9L9F-aug11.png", "target": "targets/abo-B08GGQ9L9F-aug11.npz", "source": "mesh-proxy:codepawl/abo-glb", "grid": 16, "teachers": {"appearance": "mesh-proxy:codepawl/abo-glb", "coarse": null, "fine": null}, "teacher_gaussians": 4096, "retained_gaussians": 238, "geometry_transforms": {"coarse": null, "fine": null}, "metadata": {"repo": "codepawl/abo-glb", "revision": "c7c8293fed3b7ab122ef5d7896521a187e0962d7", "path": "objects/B08GGQ9L9F/B08GGQ9L9F.glb", "sha256": "f323a4091112ed8720893afc5d40f8119a46addeac1002c242cd659e55698bcb", "decoded_sha256": "873b0e559baea016b73c42716fd02a2281146896e992e95f210769efacb97015", "asset_id": "B08GGQ9L9F", "object_id": "abo-B08GGQ9L9F", "amazon_item_ids": ["B08GGQ9L9F"], "title": "Amazon Brand - Solimo Rover Multi-purpose Laptop Table (Accacia)", "product_type": "table", "author": "Amazon.com; Amazon Berkeley Objects contributors", "conversion": "CodePawl GLB conversion; decompressed, normalized and sampled for training", "license": "cc-by-4.0", "license_url": "https://creativecommons.org/licenses/by/4.0/", "source_url": "https://huggingface.co/datasets/codepawl/abo-glb/blob/c7c8293fed3b7ab122ef5d7896521a187e0962d7/objects/B08GGQ9L9F/B08GGQ9L9F.glb", "normalization_matrix": [[1.3971167541096734, 0.0, 0.0, -0.0], [0.0, 1.3971167541096734, 0.0, -0.1692835250547045], [0.0, 0.0, 1.3971167541096734, -0.0008964411746499759], [0.0, 0.0, 0.0, 1.0]], "yaw_radians": 4.71238898038469, "render": "orthographic Gaussian proxy on black; camera +z", "target": "mesh-derived isotropic surface samples; no pretrained teacher", "input_rotation": [[-1.8369701465288538e-16, 0.0, -1.0], [-0.3420201539993286, 0.9396926164627075, 6.282807990999159e-17], [0.9396926164627075, 0.3420201539993286, -1.7261873691019539e-16]], "framing_scale": 1.0}, "image_sha256": "1efc8170bd8db05d1804df0b83afe211ae1ca9d5afea4dac84c0611bd539e9b7", "target_sha256": "4532f8ab044c0e752cbad6bce39ea73d3adf51cd85e1dd1ea8e7a96fbfecc0a9"} diff --git a/src/image3d_student/__init__.py b/src/image3d_student/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5f89157e6d7697fcbf7567b36cbf8a4182560d34 --- /dev/null +++ b/src/image3d_student/__init__.py @@ -0,0 +1,5 @@ +# SPDX-License-Identifier: Apache-2.0 +# Copyright 2026 Lucasgitguy-co +"""Research prototype; no pretrained general-purpose student weights are bundled.""" + +__version__ = "0.2.0" diff --git a/src/image3d_student/cli.py b/src/image3d_student/cli.py new file mode 100644 index 0000000000000000000000000000000000000000..23697882b645c34d872dee615649be0f16fc5f69 --- /dev/null +++ b/src/image3d_student/cli.py @@ -0,0 +1,126 @@ +# SPDX-License-Identifier: Apache-2.0 +# Copyright 2026 Lucasgitguy-co +import argparse +import importlib.util +import json +import platform +import shutil +import sys +from pathlib import Path + +import torch + + +def parser(): + p = argparse.ArgumentParser(description="Local image-to-Gaussian student research prototype") + sub = p.add_subparsers(dest="command", required=True) + sub.add_parser("doctor", help="Check dependencies, Metal availability and disk space; no downloads") + demo = sub.add_parser("demo-data", help="Create synthetic code-test fixtures, NOT teacher data") + demo.add_argument("--out", required=True) + demo.add_argument("--count", type=int, default=12) + demo.add_argument("--grid", type=int, choices=[8,16,32], default=8) + cache = sub.add_parser("cache", help="Cache Idol PLY with optional aligned geometry teacher meshes") + cache.add_argument("--image", required=True, help="Exact prepared/framed input used by teacher") + cache.add_argument("--idol-ply", required=True) + cache.add_argument("--out", required=True) + cache.add_argument("--id", required=True) + cache.add_argument("--object-id", help="Shared ID for all images of the same object, to prevent validation leakage") + cache.add_argument("--grid", type=int, choices=[8,16,32], default=16) + cache.add_argument("--coarse-mesh", help="InstantMesh output; this command imports, does not run InstantMesh") + cache.add_argument("--coarse-transform", help="JSON 4x4 mesh-to-Idol-PLY matrix") + cache.add_argument("--fine-mesh", help="Hi3D output; this command imports, does not run Hi3D") + cache.add_argument("--fine-transform", help="JSON 4x4 mesh-to-Idol-PLY matrix") + train = sub.add_parser("train", help="Train the student on cached targets") + train.add_argument("--manifest", required=True) + train.add_argument("--out", required=True) + train.add_argument("--device", choices=["auto","cuda","mps","cpu"], default="auto") + train.add_argument("--init-from", help="Post-train checkpoint weights; optimizer starts fresh") + train.add_argument("--steps", type=int, default=1000) + train.add_argument("--batch-size", type=int, default=1) + train.add_argument("--width", type=int, default=128) + train.add_argument("--lr", type=float, default=3e-4) + train.add_argument("--seed", type=int, default=42) + train.add_argument("--eval-every", type=int, default=50) + train.add_argument("--validation-fraction", type=float, default=.2) + train.add_argument("--allow-synthetic", action="store_true") + generate = sub.add_parser("generate", help="Generate standard 3DGS PLY with a trained student") + generate.add_argument("--checkpoint", required=True) + generate.add_argument("--image", required=True, help="Centered cutout or teacher-prepared image, black background") + generate.add_argument("--out", required=True) + generate.add_argument("--device", choices=["auto","cuda","mps","cpu"], default="auto") + generate.add_argument("--threshold", type=float, default=.5) + generate.add_argument("--max-splats", type=int, default=32768) + render = sub.add_parser("preview", help="CPU diagnostic turntable contact sheet") + render.add_argument("--ply", required=True) + render.add_argument("--out", required=True) + teacher = sub.add_parser("teacher-idol", help="EXPERIMENTAL staged Mac runner; weights not bundled") + teacher.add_argument("--image", required=True) + teacher.add_argument("--out", required=True) + teacher.add_argument("--cache", default=".cache/huggingface") + teacher.add_argument("--weights", default="speridlabs/idol") + teacher.add_argument("--device", choices=["mps","cpu"], default="mps") + teacher.add_argument("--mode", choices=["real","synthetic"], default="synthetic") + teacher.add_argument("--steps", type=int, default=20) + teacher.add_argument("--gaussians", type=int, default=32768) + teacher.add_argument("--query-chunk", type=int, default=128) + teacher.add_argument("--seed", type=int, default=42) + teacher.add_argument("--allow-download", action="store_true", help="Explicitly allow downloading model weights and gated encoders") + return p + + +def main(): + p = parser() + args = p.parse_args() + try: + if args.command == "doctor": + report = {"python": platform.python_version(), "platform": platform.platform(), "torch": str(torch.__version__), + "mps_available": torch.backends.mps.is_available(), "mps_built": torch.backends.mps.is_built(), + "free_disk_gib": round(shutil.disk_usage(Path.cwd()).free / 1024**3, 2), + "idol_installed": importlib.util.find_spec("idol") is not None, + "teacher_status": {"Idol": "experimental adapter, real weights unverified", + "InstantMesh": "cached mesh import only; no Mac execution adapter", + "Hi3D": "cached mesh import only; no Mac execution adapter"}, + "weights_bundled": False, "quantization": "FP32 student; no binary kernels or 1-bit claims"} + print(json.dumps(report, indent=2)) + elif args.command == "demo-data": + from .demo import create_demo + print(create_demo(args.out, args.count, args.grid)) + elif args.command == "cache": + from .data import cache_sample + from .splats import read_ply + print(json.dumps(cache_sample(args.out, args.id, args.image, read_ply(args.idol_ply), args.grid, + coarse=args.coarse_mesh, coarse_transform=args.coarse_transform, + fine=args.fine_mesh, fine_transform=args.fine_transform, + extra={"object_id": args.object_id or args.id, "idol_ply": str(Path(args.idol_ply).resolve())}), indent=2)) + elif args.command == "train": + from .engine import train + train(args) + elif args.command == "generate": + from .engine import generate + print(json.dumps(generate(args.checkpoint, args.image, args.out, args.device, args.threshold, args.max_splats), indent=2)) + elif args.command == "preview": + from PIL import Image, ImageDraw + import numpy as np + from .splats import preview, read_ply + splats = read_ply(args.ply) + sheet = Image.new("RGB", (1024, 280), "#111111") + draw = ImageDraw.Draw(sheet) + for i in range(4): + sheet.paste(preview(splats, yaw=i*np.pi/2), (i*256, 24)) + draw.text((i*256+8, 6), f"{i*90} degrees - diagnostic view", fill="white") + out = Path(args.out) + if out.exists(): + raise ValueError("Preview already exists") + out.parent.mkdir(parents=True, exist_ok=True) + sheet.save(out) + elif args.command == "teacher-idol": + from .teacher_idol import run_idol + run_idol(args) + except (ValueError, RuntimeError, FileNotFoundError, ImportError) as exc: + print(f"Error: {exc}", file=sys.stderr) + return 2 + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/image3d_student/data.py b/src/image3d_student/data.py new file mode 100644 index 0000000000000000000000000000000000000000..239da27c8ab17b6954ca366ec9087e8b17ea9fca --- /dev/null +++ b/src/image3d_student/data.py @@ -0,0 +1,149 @@ +# SPDX-License-Identifier: Apache-2.0 +# Copyright 2026 Lucasgitguy-co +import hashlib +import json +from pathlib import Path + +import numpy as np +import torch +from PIL import Image, ImageOps +from torch.utils.data import Dataset + +from .splats import Splats, read_ply + + +def grid_centers(grid): + axis = (np.arange(grid, dtype=np.float32) + .5) / grid - .5 + return np.stack(np.meshgrid(axis, axis, axis, indexing="ij"), axis=-1).reshape(-1, 3) + + +def cell_indices(xyz, grid): + if not np.isfinite(xyz).all() or (np.abs(xyz) > .50001).any(): + raise ValueError("Targets must be aligned inside [-0.5,0.5]^3. No silent normalization is performed.") + ijk = np.floor((xyz + .5) * grid).astype(np.int64).clip(0, grid-1) + return (ijk[:, 0] * grid + ijk[:, 1]) * grid + ijk[:, 2] + + +def splat_targets(splats, grid): + """One highest-opacity teacher Gaussian per occupied cell; explicitly lossy.""" + cells = cell_indices(splats.xyz, grid) + order = np.argsort(-splats.opacity[:, 0], kind="stable") + _, first = np.unique(cells[order], return_index=True) + keep = order[first] + slots = cells[keep] + result = {"occ": np.zeros((grid**3, 1), np.float32), "xyz": grid_centers(grid), + "rgb": np.zeros((grid**3, 3), np.float32), + "scale": np.full((grid**3, 3), 1 / grid / 3, np.float32), + "quat": np.tile(np.array([1, 0, 0, 0], np.float32), (grid**3, 1)), + "opacity": np.zeros((grid**3, 1), np.float32), + "geometry": np.zeros((grid**3, 2), np.float32), + "geometry_valid": np.zeros(2, np.float32), "grid": np.array(grid, np.int32)} + result["occ"][slots] = 1 + for key in ("xyz", "rgb", "scale", "quat", "opacity"): + result[key][slots] = getattr(splats, key)[keep] + return result + + +def mesh_occupancy(path, transform_path, grid): + import trimesh + if not transform_path: + raise ValueError("Every geometry teacher needs an explicit --*-transform JSON matrix, even for identity.") + transform = np.asarray(json.loads(Path(transform_path).read_text()), dtype=float) + if transform.shape != (4, 4) or not np.isfinite(transform).all() or not np.allclose(transform[3], [0,0,0,1]): + raise ValueError("Transform must be a finite affine 4x4 matrix") + mesh = trimesh.load(str(path), force="mesh", process=False) + if not isinstance(mesh, trimesh.Trimesh) or len(mesh.faces) == 0: + raise ValueError("Geometry teacher must contain a triangle mesh") + mesh.apply_transform(transform) + cell_indices(np.asarray(mesh.vertices), grid) # validates the whole object + points, _ = trimesh.sample.sample_surface(mesh, min(200_000, grid**3 * 16), seed=42) + mask = np.zeros(grid**3, np.float32) + mask[np.unique(cell_indices(points, grid))] = 1 + return mask + + +def cache_sample(root, sample_id, image_path, splats, grid=16, source="idol", + coarse=None, coarse_transform=None, fine=None, fine_transform=None, extra=None): + root = Path(root) + if not sample_id or any(c not in "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-_" for c in sample_id): + raise ValueError("Sample ID may contain only letters, digits, - and _") + if grid not in (8, 16, 32): + raise ValueError("Grid must be 8, 16 or 32") + target = splat_targets(splats, grid) + for channel, mesh, transform in [(0, coarse, coarse_transform), (1, fine, fine_transform)]: + if mesh: + target["geometry"][:, channel] = mesh_occupancy(mesh, transform, grid) + target["geometry_valid"][channel] = 1 + row = {"id": sample_id, "image": f"images/{sample_id}.png", "target": f"targets/{sample_id}.npz", + "source": source, "grid": grid, "teachers": {"appearance": source, + "coarse": "InstantMesh" if coarse else None, "fine": "Hi3D" if fine else None}, + "teacher_gaussians": len(splats.xyz), "retained_gaussians": int(target["occ"].sum()), + "geometry_transforms": {"coarse": json.loads(Path(coarse_transform).read_text()) if coarse else None, + "fine": json.loads(Path(fine_transform).read_text()) if fine else None}, + "metadata": extra or {}} + manifest = root / "manifest.jsonl" + rows = load_manifest(manifest) if manifest.exists() else [] + if any(r["id"] == sample_id for r in rows): + raise ValueError(f"Sample {sample_id} already exists; choose a new ID") + if rows and any(r["grid"] != grid for r in rows): + raise ValueError("Do not mix grid resolutions in one dataset") + image = Image.open(image_path) + image.load() + if root.joinpath(row["image"]).exists() or root.joinpath(row["target"]).exists(): + raise ValueError("Sample files already exist; refusing to overwrite") + root.joinpath("images").mkdir(parents=True, exist_ok=True) + root.joinpath("targets").mkdir(parents=True, exist_ok=True) + image.save(root / row["image"]) + row["image_sha256"] = hashlib.sha256((root / row["image"]).read_bytes()).hexdigest() + np.savez_compressed(root / row["target"], **target) + row["target_sha256"] = hashlib.sha256((root / row["target"]).read_bytes()).hexdigest() + # Single-writer dataset. Atomic manifest replacement avoids partial JSON lines. + tmp = manifest.with_suffix(".tmp") + tmp.write_text("".join(json.dumps(r) + "\n" for r in [*rows, row])) + tmp.replace(manifest) + return row + + +def load_manifest(path): + rows = [json.loads(line) for line in Path(path).read_text().splitlines() if line.strip()] + if not rows or len({r["id"] for r in rows}) != len(rows): + raise ValueError("Manifest must contain nonempty, unique sample IDs") + return rows + + +def image_tensor(path, size=128): + """Already-framed RGB or RGBA cutout; black background, square letterbox.""" + with Image.open(path) as original: + rgba = ImageOps.exif_transpose(original).convert("RGBA") + bg = Image.new("RGBA", rgba.size, (0, 0, 0, 255)) + bg.alpha_composite(rgba) + image = ImageOps.pad(bg.convert("RGB"), (size, size), method=Image.Resampling.LANCZOS, color="black") + return torch.from_numpy(np.array(image, dtype=np.float32).transpose(2, 0, 1) / 255) + + +class CachedDataset(Dataset): + def __init__(self, manifest, rows=None): + self.root = Path(manifest).resolve().parent + self.rows = load_manifest(manifest) if rows is None else rows + if not self.rows: + raise ValueError("Empty dataset") + self.grid = self.rows[0]["grid"] + for row in self.rows: + if row["grid"] != self.grid: + raise ValueError("Mixed grids") + for key in ("image", "target"): + p = (self.root / row[key]).resolve() + if not p.is_relative_to(self.root) or not p.is_file(): + raise ValueError(f"Invalid dataset {key} path") + digest = hashlib.sha256(p.read_bytes()).hexdigest() + if digest != row[f"{key}_sha256"]: + raise ValueError(f"Dataset {key} hash mismatch for {row['id']}") + + def __len__(self): + return len(self.rows) + + def __getitem__(self, i): + row = self.rows[i] + with np.load(self.root / row["target"], allow_pickle=False) as cached: + target = {k: torch.from_numpy(cached[k].copy()).float() for k in cached.files if k != "grid"} + return image_tensor(self.root / row["image"]), target diff --git a/src/image3d_student/demo.py b/src/image3d_student/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..33dc1630c4db79ddf1e6e8e11ebb3ec50df3fd77 --- /dev/null +++ b/src/image3d_student/demo.py @@ -0,0 +1,31 @@ +# SPDX-License-Identifier: Apache-2.0 +# Copyright 2026 Lucasgitguy-co +from pathlib import Path + +import numpy as np + +from .data import cache_sample +from .splats import Splats, preview + + +def create_demo(root, count=12, grid=8): + """Procedural ellipsoids only. Never presented as outputs of any real teacher.""" + if count < 4: + raise ValueError("Use at least four fixtures") + root = Path(root) + root.mkdir(parents=True, exist_ok=True) + rng = np.random.default_rng(17) + phi, theta = np.meshgrid(np.linspace(.06, np.pi-.06, 14), np.linspace(0, 2*np.pi, 28, endpoint=False)) + unit = np.stack([np.sin(phi)*np.cos(theta), np.cos(phi), np.sin(phi)*np.sin(theta)], axis=-1).reshape(-1, 3) + for i in range(count): + radii = rng.uniform(.17, .36, 3) + xyz = (unit * radii).astype(np.float32) + colour = rng.uniform(.2, .95, 3) + rgb = np.clip(colour[None] * (.8 + .2 * unit[:, 1:2]), 0, 1) + splats = Splats(xyz, rgb, np.full_like(xyz, .035), np.tile([1,0,0,0], (len(xyz),1)), np.full((len(xyz),1), .9)) + image_path = root / f"fixture-{i:03}.png" + preview(splats, size=128).save(image_path) + cache_sample(root, f"fixture-{i:03}", image_path, splats, grid, source="synthetic", + extra={"purpose": "code verification only", "object_id": f"fixture-{i:03}"}) + image_path.unlink() + return root / "manifest.jsonl" diff --git a/src/image3d_student/engine.py b/src/image3d_student/engine.py new file mode 100644 index 0000000000000000000000000000000000000000..5d748dd7f52497a444babe76b567081fe77690bc --- /dev/null +++ b/src/image3d_student/engine.py @@ -0,0 +1,204 @@ +# SPDX-License-Identifier: Apache-2.0 +# Copyright 2026 Lucasgitguy-co +import json +import hashlib +import random +import time +from pathlib import Path + +import numpy as np +import torch +from torch.utils.data import DataLoader + +from .data import CachedDataset, image_tensor, load_manifest +from .model import Student, distillation_loss +from .splats import Splats, write_ply + + +def device_for(name="auto"): + if name == "auto": + name = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu") + if name == "mps" and not torch.backends.mps.is_available(): + raise ValueError("Metal GPU unavailable in this process. Run in a normal macOS Terminal, or use --device cpu.") + if name == "cuda" and not torch.cuda.is_available(): + raise ValueError("CUDA requested but unavailable") + if name not in ("mps", "cpu", "cuda"): + raise ValueError("Supported devices: cpu, mps, cuda") + torch.set_num_threads(min(4, torch.get_num_threads())) + return torch.device(name) + + +def split_rows(rows, fraction, seed): + if not 0 <= fraction < 1: + raise ValueError("Validation fraction must lie in [0,1)") + # Keep repeated views/crops of the same object together. Supply object_id in cache metadata. + groups = {} + for row in rows: + group = row.get("metadata", {}).get("object_id", row["id"]) + groups.setdefault(group, []).append(row) + keys = sorted(groups) + random.Random(seed).shuffle(keys) + n = max(1, round(len(keys) * fraction)) if fraction else 0 + if n >= len(keys): + raise ValueError("Need at least two different objects for validation, or explicitly use --validation-fraction 0 for an overfit test") + val_keys = set(keys[:n]) + train = [r for k in keys if k not in val_keys for r in groups[k]] + val = [r for k in keys if k in val_keys for r in groups[k]] + if {r["image_sha256"] for r in train} & {r["image_sha256"] for r in val}: + raise ValueError("Identical images occur across training/validation. Assign the same object-id to related samples.") + return train, val + + +def on_device(target, device): + return {k: v.to(device) for k, v in target.items()} + + +@torch.no_grad() +def evaluate(model, loader, device): + model.eval() + sums = {} + for image, target in loader: + target = on_device(target, device) + pred = model(image.to(device)) + loss, terms = distillation_loss(pred, target, model.grid) + occupied = pred["occ"].sigmoid() > .5 + truth = target["occ"] > .5 + terms = {**terms, "loss": loss, "occupancy_iou": (occupied & truth).sum() / (occupied | truth).sum().clamp_min(1)} + for key, value in terms.items(): + sums[key] = sums.get(key, 0.) + float(value) + return {k: v / len(loader) for k, v in sums.items()} + + +def save_checkpoint(path, model, optimizer, step, metadata): + payload = {"format": 1, "config": model.config, "model": {k: v.detach().cpu() for k, v in model.state_dict().items()}, + "optimizer": optimizer.state_dict(), "step": step, "metadata": metadata} + tmp = Path(path).with_suffix(".tmp") + torch.save(payload, tmp) + tmp.replace(path) + + +def train(args): + if args.steps < 1 or args.batch_size < 1 or args.lr <= 0 or args.eval_every < 1: + raise ValueError("Steps, batch size, learning rate, and evaluation interval must be positive") + device = device_for(args.device) + random.seed(args.seed) + np.random.seed(args.seed) + torch.manual_seed(args.seed) + rows = load_manifest(args.manifest) + synthetic = any(r["source"] == "synthetic" for r in rows) + if synthetic and not args.allow_synthetic: + raise ValueError("Synthetic fixtures are code tests, not teacher data. Add --allow-synthetic for an explicit smoke test.") + train_rows, val_rows = split_rows(rows, args.validation_fraction, args.seed) + train_data = CachedDataset(args.manifest, train_rows) + generator = torch.Generator().manual_seed(args.seed) + loader = DataLoader(train_data, batch_size=args.batch_size, shuffle=True, num_workers=0, generator=generator) + val_loader = DataLoader(CachedDataset(args.manifest, val_rows), batch_size=1) if val_rows else None + model = Student(train_data.grid, args.width).to(device) + initialization = None + if getattr(args, "init_from", None): + initial_path = Path(args.init_from) + state = torch.load(initial_path, map_location="cpu", weights_only=True) + if state.get("format") != 1 or state["config"]["width"] != args.width: + raise ValueError("Initialization requires format=1 and matching --width") + # Coordinates are deterministic buffers, not learned weights. Rebuild them + # for the target grid while preserving every learned encoder/decoder weight. + learned = {k: v for k, v in state["model"].items() if k not in ("centers", "coords")} + result = model.load_state_dict(learned, strict=False) + if set(result.missing_keys) != {"centers", "coords"} or result.unexpected_keys: + raise ValueError("Checkpoint architecture mismatch") + initialization = {"sha256": hashlib.sha256(initial_path.read_bytes()).hexdigest(), + "config": state["config"], "step": state["step"], + "optimizer": "fresh AdamW; weights-only post-training"} + optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4) + run = Path(args.out) + if run.exists() and any(run.iterdir()): + raise ValueError("Run directory must be empty; choose a new --out directory") + run.mkdir(parents=True, exist_ok=True) + meta = {"synthetic_only": all(r["source"] == "synthetic" for r in rows), + "contains_synthetic": synthetic, "device": str(device), "torch": str(torch.__version__), + "seed": args.seed, "train_ids": [r["id"] for r in train_rows], "val_ids": [r["id"] for r in val_rows], + "parameters": sum(p.numel() for p in model.parameters()), + "teachers": sorted({v for r in rows for v in r["teachers"].values() if v}), + "validation": "held-out objects" if val_rows else "none; training overfit test only", + "dataset": str(Path(args.manifest).resolve()), + "manifest_sha256": hashlib.sha256(Path(args.manifest).read_bytes()).hexdigest(), + "initialization": initialization, "steps_requested": args.steps, + "learning_rate": args.lr, "batch_size": args.batch_size, + "validation_fraction": args.validation_fraction} + if val_loader: + meta["baseline_validation"] = evaluate(model, val_loader, device) + (run / "run.json").write_text(json.dumps(meta, indent=2)) + print(json.dumps(meta), flush=True) + step, best, started = 0, float("inf"), time.monotonic() + iterator = iter(loader) + try: + while step < args.steps: + try: + images, target = next(iterator) + except StopIteration: + iterator = iter(loader) + images, target = next(iterator) + model.train() + optimizer.zero_grad(set_to_none=True) + pred = model(images.to(device)) + loss, terms = distillation_loss(pred, on_device(target, device), model.grid) + if not torch.isfinite(loss): + raise RuntimeError("Nonfinite training loss; stopping") + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.) + optimizer.step() + step += 1 + if step == 1 or step % args.eval_every == 0 or step == args.steps: + record = {"step": step, "seconds": round(time.monotonic()-started, 3), + "train_loss": float(loss.detach()), **{k: float(v.detach()) for k, v in terms.items()}} + if val_loader: + record["validation"] = evaluate(model, val_loader, device) + score = record["validation"]["loss"] + if score < best: + best = score + save_checkpoint(run / "best.pt", model, optimizer, step, meta) + with (run / "metrics.jsonl").open("a") as handle: + handle.write(json.dumps(record) + "\n") + print(json.dumps(record), flush=True) + save_checkpoint(run / "last.pt", model, optimizer, step, meta) + except KeyboardInterrupt: + print("Interrupted; saving completed optimizer steps.", flush=True) + finally: + if step: + save_checkpoint(run / "last.pt", model, optimizer, step, meta) + return meta + + +@torch.no_grad() +def generate(checkpoint, image, out, device="auto", threshold=.5, max_splats=32768): + if not 0 < threshold < 1 or max_splats < 1: + raise ValueError("Threshold must be in (0,1) and max-splats positive") + device = device_for(device) + state = torch.load(checkpoint, map_location="cpu", weights_only=True) + if state.get("format") != 1 or state.get("step", 0) < 1: + raise ValueError("Expected a trained image3d-student checkpoint") + model = Student(**state["config"]) + model.load_state_dict(state["model"]) + model.to(device).eval() + if device.type == "mps": + torch.mps.synchronize() + started = time.monotonic() + pred = model(image_tensor(image)[None].to(device)) + score = pred["occ"][0, :, 0].sigmoid() + valid = torch.nonzero(score >= threshold).flatten() + if len(valid) == 0: + raise ValueError("No occupied cells above threshold. Train longer or inspect validation; no fabricated fallback splats were exported.") + valid = valid[torch.argsort(score[valid], descending=True)[:max_splats]] + values = {k: pred[k][0, valid].cpu().numpy() for k in ("xyz", "rgb", "scale", "quat", "opacity")} + elapsed = time.monotonic() - started + splats = Splats(**values) + out = Path(out) + if out.exists() or out.with_suffix(".json").exists(): + raise ValueError("Output already exists; choose a new filename") + write_ply(out, splats) + report = {"gaussians": len(valid), "seconds_preprocess_and_forward": elapsed, + "device": str(device), "checkpoint_step": state["step"], "training": state["metadata"], + "note": ("Synthetic-trained output is a pipeline demonstration, not a pretrained image-to-3D result." + if state["metadata"].get("contains_synthetic") else "Experimental cached-target student; inspect unseen-object quality.")} + out.with_suffix(".json").write_text(json.dumps(report, indent=2)) + return report diff --git a/src/image3d_student/model.py b/src/image3d_student/model.py new file mode 100644 index 0000000000000000000000000000000000000000..0284bde54f42a2aeba7d63aa401a52e993d02cff --- /dev/null +++ b/src/image3d_student/model.py @@ -0,0 +1,76 @@ +# SPDX-License-Identifier: Apache-2.0 +# Copyright 2026 Lucasgitguy-co +import math + +import torch +from torch import nn +from torch.nn import functional as F + +from .data import grid_centers + + +class Student(nn.Module): + """Small image-conditioned coordinate MLP; fixed cells avoid unordered point regression. + + 15 Gaussian outputs + 2 auxiliary geometry logits. This is a coarse baseline, + not an architecture expected to reproduce million-splat teacher detail. + """ + def __init__(self, grid=16, width=128): + super().__init__() + if grid not in (8, 16, 32) or width < 16: + raise ValueError("Unsupported grid or width") + self.config = {"grid": grid, "width": width} + self.grid = grid + blocks, channels = [], 3 + for out in (24, 48, 96, 128): + blocks.extend([nn.Conv2d(channels, out, 3, stride=2, padding=1), nn.GroupNorm(8, out), nn.SiLU()]) + channels = out + self.encoder = nn.Sequential(*blocks, nn.AdaptiveAvgPool2d(4), nn.Flatten(), + nn.Linear(128*4*4, width), nn.SiLU()) + centers = torch.from_numpy(grid_centers(grid)) + freq = 2**torch.arange(4, dtype=torch.float32) * math.pi * 2 + angles = centers[..., None] * freq + self.register_buffer("centers", centers) + self.register_buffer("coords", torch.cat([centers, angles.sin().flatten(1), angles.cos().flatten(1)], dim=1)) + self.query = nn.Sequential(nn.Linear(27, width), nn.SiLU()) + self.decoder = nn.Sequential(nn.Linear(width, width), nn.SiLU(), nn.Linear(width, width), nn.SiLU(), nn.Linear(width, 17)) + with torch.no_grad(): + self.decoder[-1].bias.zero_() + self.decoder[-1].bias[0] = -2 + + def forward(self, images): + features = self.encoder(images) + raw = self.decoder(features[:, None, :] + self.query(self.coords)[None]) + quat = raw[..., 10:14] + raw.new_tensor([1, 0, 0, 0]) + return {"occ": raw[..., :1], "xyz": self.centers[None] + raw[..., 1:4].tanh() / (2*self.grid), + "rgb": raw[..., 4:7].sigmoid(), + "scale": (raw[..., 7:10].clamp(-8, 5) - math.log(self.grid*3)).exp(), + "quat": F.normalize(quat, dim=-1, eps=1e-8), "opacity": raw[..., 14:15].sigmoid(), + "geometry": raw[..., 15:17]} + + +def balanced_bce(logits, target): + error = F.binary_cross_entropy_with_logits(logits, target, reduction="none") + positive = (error * target).sum() / target.sum().clamp_min(1) + negative = (error * (1-target)).sum() / (1-target).sum().clamp_min(1) + return (positive + negative) / 2 + + +def distillation_loss(pred, target, grid): + mask = target["occ"] + def masked(value): + return (value * mask).sum() / (mask.sum().clamp_min(1) * value.shape[-1]) + terms = {"occupancy": balanced_bce(pred["occ"], mask), + "position": masked(((pred["xyz"] - target["xyz"]) * grid).square()), + "colour": masked((pred["rgb"] - target["rgb"].clamp(0, 1)).square()), + "scale": masked((pred["scale"].log() - target["scale"].clamp_min(1e-8).log()).square()), + "rotation": masked(1 - (pred["quat"] * target["quat"]).sum(-1, keepdim=True).square().clamp(0, 1)), + "opacity": masked((pred["opacity"] - target["opacity"]).square())} + geometry = pred["geometry"].sum() * 0 + for i in range(2): + available = target["geometry_valid"][:, i] > 0 + if available.any(): + geometry = geometry + balanced_bce(pred["geometry"][available, :, i:i+1], target["geometry"][available, :, i:i+1]) + terms["geometry"] = geometry + total = terms["occupancy"] + terms["position"] + 2*terms["colour"] + .1*terms["scale"] + .1*terms["rotation"] + terms["opacity"] + .25*geometry + return total, terms diff --git a/src/image3d_student/splats.py b/src/image3d_student/splats.py new file mode 100644 index 0000000000000000000000000000000000000000..3f0e197bd80a1669f566a909b0198e901776c077 --- /dev/null +++ b/src/image3d_student/splats.py @@ -0,0 +1,108 @@ +# SPDX-License-Identifier: Apache-2.0 +# Copyright 2026 Lucasgitguy-co +"""Activated Gaussian attributes and standard degree-zero 3DGS PLY IO. + +Coordinates are preserved as supplied. Idol's *exported PLY* uses its y-up frame; +we do not apply Idol's internal z-up conversion a second time. +""" +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +from plyfile import PlyData, PlyElement + +C0 = 0.28209479177387814 + + +@dataclass +class Splats: + xyz: np.ndarray + rgb: np.ndarray + scale: np.ndarray + quat: np.ndarray # w,x,y,z + opacity: np.ndarray + + def __post_init__(self): + n = len(self.xyz) + if n == 0: + raise ValueError("No Gaussians: refusing to write or train on an empty object.") + for name, width in [("xyz", 3), ("rgb", 3), ("scale", 3), ("quat", 4), ("opacity", 1)]: + a = np.asarray(getattr(self, name), dtype=np.float32).reshape(-1, width) + if a.shape != (n, width) or not np.isfinite(a).all(): + raise ValueError(f"Invalid {name} shape or nonfinite values") + setattr(self, name, a) + if (self.scale <= 0).any(): + raise ValueError("Gaussian scales must be positive") + if (self.opacity < 0).any() or (self.opacity > 1).any(): + raise ValueError("Opacity must lie in [0,1]") + norm = np.linalg.norm(self.quat, axis=1, keepdims=True) + if (norm < 1e-8).any(): + raise ValueError("Zero quaternion") + self.quat = self.quat / norm + + def take(self, indices): + return Splats(**{k: getattr(self, k)[indices] for k in self.__dataclass_fields__}) + + +def read_ply(path): + v = PlyData.read(str(path))["vertex"].data + required = ["x", "y", "z", "f_dc_0", "f_dc_1", "f_dc_2", "opacity", + "scale_0", "scale_1", "scale_2", "rot_0", "rot_1", "rot_2", "rot_3"] + if not set(required).issubset(v.dtype.names): + raise ValueError("Expected a Gaussian-splat PLY, not a mesh or ordinary point-cloud PLY") + col = lambda names: np.column_stack([v[k] for k in names]).astype(np.float32) + return Splats(col(["x", "y", "z"]), 0.5 + C0 * col([f"f_dc_{i}" for i in range(3)]), + np.exp(np.clip(col([f"scale_{i}" for i in range(3)]), -20, 5)), + col([f"rot_{i}" for i in range(4)]), + 1 / (1 + np.exp(-np.clip(col(["opacity"]), -30, 30)))) + + +def write_ply(path, splats): + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + names = ["x", "y", "z", "nx", "ny", "nz", "f_dc_0", "f_dc_1", "f_dc_2", "opacity", + "scale_0", "scale_1", "scale_2", "rot_0", "rot_1", "rot_2", "rot_3"] + opacity = np.clip(splats.opacity, 1e-6, 1 - 1e-6) + values = np.concatenate([splats.xyz, np.zeros_like(splats.xyz), (splats.rgb - 0.5) / C0, + np.log(opacity / (1 - opacity)), np.log(splats.scale), splats.quat], axis=1) + table = np.empty(len(values), dtype=[(k, "f4") for k in names]) + for i, name in enumerate(names): + table[name] = values[:, i] + PlyData([PlyElement.describe(table, "vertex")], text=False).write(str(path)) + + +def rotation_matrices(q): + w, x, y, z = q.T + return np.stack([1-2*(y*y+z*z), 2*(x*y-z*w), 2*(x*z+y*w), + 2*(x*y+z*w), 1-2*(x*x+z*z), 2*(y*z-x*w), + 2*(x*z-y*w), 2*(y*z+x*w), 1-2*(x*x+y*y)], axis=-1).reshape(-1, 3, 3) + + +def preview(splats, size=256, yaw=0.0): + """CPU orthographic Gaussian preview; debugging only, not an evaluation renderer.""" + from PIL import Image + c, s = np.cos(yaw), np.sin(yaw) + view = np.array([[c, 0, s], [0, 1, 0], [-s, 0, c]], dtype=np.float32) + xyz = splats.xyz @ view.T + rot = rotation_matrices(splats.quat) + cov = (rot * (splats.scale ** 2)[:, None, :]) @ rot.transpose(0, 2, 1) + cov = view[None] @ cov @ view.T[None] + pixels = size / 1.25 + canvas = np.zeros((size, size, 3), dtype=np.float32) + # Back to front, camera on +z. Image y points down. + for i in np.argsort(xyz[:, 2]): + center = np.array([xyz[i, 0], -xyz[i, 1]]) * pixels + size / 2 + flip = np.diag([1., -1.]) + covariance = flip @ cov[i, :2, :2] @ flip * pixels ** 2 + np.eye(2) * 0.3 + radius = min(size, int(np.ceil(3 * np.sqrt(np.linalg.eigvalsh(covariance).max())))) + lo = np.maximum(0, np.floor(center - radius).astype(int)) + hi = np.minimum(size, np.ceil(center + radius + 1).astype(int)) + if np.any(hi <= lo): + continue + yy, xx = np.mgrid[lo[1]:hi[1], lo[0]:hi[0]] + delta = np.stack([xx - center[0], yy - center[1]], axis=-1) + exponent = np.einsum("...i,ij,...j->...", delta, np.linalg.inv(covariance), delta) + alpha = (splats.opacity[i, 0] * np.exp(-0.5 * exponent)).clip(0, .99)[..., None] + patch = canvas[lo[1]:hi[1], lo[0]:hi[0]] + patch[:] = patch * (1-alpha) + np.clip(splats.rgb[i], 0, 1) * alpha + return Image.fromarray((canvas.clip(0, 1) * 255).astype(np.uint8)) diff --git a/src/image3d_student/teacher_idol.py b/src/image3d_student/teacher_idol.py new file mode 100644 index 0000000000000000000000000000000000000000..0eceaa032171efaff7d01acb9eb4c0176e58d011 --- /dev/null +++ b/src/image3d_student/teacher_idol.py @@ -0,0 +1,141 @@ +# SPDX-License-Identifier: Apache-2.0 +# Copyright 2026 Lucasgitguy-co +"""Experimental, staged Idol inference for Apple MPS. + +Upstream revision: 4702a7f00850e34b6e83a2170cc123c3e9570744. +No weights are bundled. Full-weight inference has NOT been verified by this project. +""" +import gc +import json +import os +import shutil +from pathlib import Path + +import numpy as np +import torch +from PIL import Image + +from .engine import device_for + + +def patch_attention(layers, query_chunk=128): + """Bound attention working memory by splitting only the query dimension. + + Each query still sees ALL keys/values. Does not split or change softmax's key axis. + Disable CUDA-specific optimized sampling and avoid complex RoPE tensors on MPS. + """ + if query_chunk < 1: + raise ValueError("query_chunk must be positive") + from torch.nn import functional as F + + def attention(*args): + q, k, v = args[0].unbind(dim=2) if len(args) == 1 else args + q, k, v = (t.permute(0, 2, 1, 3) for t in (q, k, v)) + chunks = [F.scaled_dot_product_attention(q[:, :, start:start+query_chunk], k, v) + for start in range(0, q.shape[2], query_chunk)] + return torch.cat(chunks, dim=2).permute(0, 2, 1, 3) + + layers.scaled_dot_product_attention = attention + layers.RePo3DRotaryEmbedding.real_valued = True + return attention + + +def release(device): + gc.collect() + if device.type == "mps": + torch.mps.synchronize() + torch.mps.empty_cache() + + +@torch.no_grad() +def run_idol(args): + if args.steps < 1 or args.gaussians < 32768 or args.gaussians > 1048576 or args.gaussians % 32: + raise ValueError("Steps must be positive; Idol requires 32768..1048576 Gaussians, divisible by 32") + device = device_for(args.device) + cache = Path(args.cache).resolve() + cache.mkdir(parents=True, exist_ok=True) + os.environ["HF_HOME"] = str(cache) + os.environ["HF_HUB_OFFLINE"] = "0" if args.allow_download else "1" + os.environ["ATTN_BACKEND"] = "sdpa" + if args.allow_download and shutil.disk_usage(cache).free < 16 * 1024**3: + raise ValueError("Less than 16 GiB disk space available. This conservative download guard leaves room for teacher weights, encoders and temporary files. Free space, or use an existing complete cache without --allow-download.") + try: + from huggingface_hub import hf_hub_download, snapshot_download + from safetensors.torch import load_file + from tensordict import TensorDict + from idol.conditioning import ImageEncoder, require_encoder_access + from idol.models import BiRefNet, GaussianFixedlenDecoder, LatentSeqMMFlowModel, OctreeProbabilityFixedlenDecoder, layers + from idol.preprocess import CROP_MARGIN, preprocess_image, has_real_alpha + from idol.sampler import sample_flow + from idol.camera import decode_camera + except ImportError as exc: + raise RuntimeError("Install the optional teacher dependencies: python -m pip install '.[teacher]'") from exc + out = Path(args.out) + if out.exists(): + raise ValueError("Teacher output directory already exists; choose a new --out") + # Local-only by default. Hugging Face authentication is never written to project files. + config_path = hf_hub_download(args.weights, "config.json", local_files_only=not args.allow_download) + config = json.loads(Path(config_path).read_text()) + encoders = config["image_encoders"] + require_encoder_access([encoders["dinov3"], encoders["flux2_vae"]]) + weights = Path(snapshot_download(args.weights, allow_patterns=["config.json", "*.safetensors"], + local_files_only=not args.allow_download)) + patch_attention(layers, args.query_chunk) + torch.manual_seed(args.seed) + + def load(model, name): + state = load_file(str(weights / f"{name}.safetensors"), device="cpu") + model.load_state_dict(state, strict=True) + del state + return model.eval().requires_grad_(False).to(device) + + print("Experimental Idol MPS adapter: full-weight compatibility and quality remain unverified.", flush=True) + with Image.open(args.image) as opened: + image = opened.copy() + background = None + if not has_real_alpha(image): + print("Stage 1: background removal", flush=True) + background = load(BiRefNet(), "birefnet") + prepared = preprocess_image(image, background, config["image_size"], CROP_MARGIN[args.mode]) + del background + release(device) + pixels = torch.from_numpy(np.array(prepared)).permute(2, 0, 1).float()[None].div(255).to(device) + print("Stage 2: image conditioning", flush=True) + encoder = ImageEncoder(encoders["dinov3"], encoders["flux2_vae"], device="cpu") + # Both encoders initially reside on CPU; only the active encoder is moved to MPS. + encoder.dinov3.to(device) + feature1 = encoder._dinov3_tokens(pixels) + del encoder.dinov3 + release(device) + encoder.flux2_vae.to(device) + feature2 = encoder._flux2_tokens(pixels) + del encoder, pixels + release(device) + cond = TensorDict({"feature1": feature1, "feature2": feature2}, batch_size=1, device=device) + print("Stage 3: latent sampling", flush=True) + model = load(LatentSeqMMFlowModel(**config["denoiser"]["args"]), "denoiser") + noise = TensorDict({"latent": torch.randn(1, model.q_token_length, model.in_channels, device=device), + "camera": torch.randn(1, 1, model.cam_channels, device=device)}, batch_size=1, device=device) + result = sample_flow(model, noise, cond=cond, neg_cond=cond.apply(torch.zeros_like), steps=args.steps, + rescale_t=3., cfg_strength=3., verbose=True, batch_cfg=False) + latent, camera = result["latent"], decode_camera(result["camera"][0, 0]) + del model, noise, cond, feature1, feature2, result + release(device) + print("Stage 4: octree decoding", flush=True) + model = load(OctreeProbabilityFixedlenDecoder(**config["decoder"]["args"]), "decoder") + per_anchor = config["decoder_gs"]["args"]["representation_config"]["num_gaussians"] + anchors = OctreeProbabilityFixedlenDecoder.sample(model, latent, num_points=args.gaussians // per_anchor, + level=config["max_voxel_level"]) + del model + release(device) + print("Stage 5: Gaussian decoding", flush=True) + model = load(GaussianFixedlenDecoder(**config["decoder_gs"]["args"]), "decoder_gs") + gaussian = model.to_representation(anchors, model(anchors, latent))[0] + out.mkdir(parents=True) + gaussian.save_ply(str(out / "teacher.ply")) + prepared.save(out / "input.png") + (out / "metadata.json").write_text(json.dumps({"teacher": args.weights, "steps": args.steps, + "gaussians": args.gaussians, "seed": args.seed, "device": str(device), "camera": camera, + "sdk_revision": "4702a7f00850e34b6e83a2170cc123c3e9570744", "weights_snapshot": weights.name, + "coordinate_frame": "Idol exported PLY (y-up)", "adapter": "experimental staged MPS"}, indent=2)) + print(f"Teacher output saved to {out}", flush=True) diff --git a/training.json b/training.json new file mode 100644 index 0000000000000000000000000000000000000000..2475172ad9b98b1c75dad463813012e06a561a3f --- /dev/null +++ b/training.json @@ -0,0 +1,10 @@ +{ + "variant": "objects1024", + "seed": 44, + "initial_batch": 8, + "steps": 6000, + "lr": 5e-05, + "starting_sha256": "ec7c37e8ff293877149822150a5e08e8dde3e37fe0e6cfc1dc71e05adecd7790", + "manifest_sha256": "01e35e4806aebf7611397ead83342cb61d5726cd3de5bea5e03a03019f90264a", + "script": "7762c8ddc40bc3ba82b4f7d9ef9a1eb6fbb661f5be7d8f1d3068e07dcc301a5f" +}