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ruv
e94f4d8f73
I'd shipped the Rust cog-pose --adapter *consumer* (+test) but there was no *producer* for cog-format adapters, leaving it a half-feature. cog_calibrate.py fits a rank-r LoRA on the cog conv+MLP head (pose_v1.safetensors, 56x20) from a labeled in-room capture and writes a safetensors with fc1.a/fc1.b/fc2.a/fc2.b (scale baked into b) — exactly what the Rust engine loads. Verified against the in-repo pose_v1.safetensors: correct keys/shapes, reduces fit error, active adapter, ~2.6KB. Adds test_cog_calibration.py (passes) + README documenting the two non-interchangeable producers (transformer .npz vs cog safetensors). Co-Authored-By: claude-flow <ruv@ruv.net>
76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
"""Regression test for the cog-pose adapter producer (cog_calibrate.py).
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Uses the in-repo `pose_v1.safetensors` (skips if absent). Verifies the produced adapter:
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- has the exact keys/shapes the Rust `cog-pose-estimation --adapter` loader expects,
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- reduces calibration fit error,
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- actually changes inference output,
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- is tiny.
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Run: python test_cog_calibration.py (or via pytest)
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"""
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import os
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import sys
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import tempfile
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from pathlib import Path
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import numpy as np
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import torch
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import torch.nn.functional as F
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HERE = Path(__file__).parent
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sys.path.insert(0, str(HERE))
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import cog_calibrate as C # noqa: E402
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BASE = HERE / "../../v2/crates/cog-pose-estimation/cog/artifacts/pose_v1.safetensors"
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def test_cog_adapter_producer():
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if not BASE.exists():
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print(f"(skip — {BASE} not present)")
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return
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from safetensors.torch import load_file
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rng = np.random.default_rng(0)
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n = 120
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X = rng.standard_normal((n, 56, 20)).astype("float32")
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Y = (0.5 + 0.1 * X[:, :34, 0].reshape(n, 34)).clip(0, 1).astype("float32")
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with tempfile.TemporaryDirectory() as d:
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calib = os.path.join(d, "calib.npz")
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adapter = os.path.join(d, "room.safetensors")
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np.savez(calib, X=X, Y=Y)
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net0 = C.CogPose()
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C.load_base(net0, str(BASE))
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net0.eval()
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with torch.no_grad():
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base_err = F.smooth_l1_loss(net0(torch.tensor(X)), torch.tensor(Y)).item()
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_, nparam, _ = C.fit(str(BASE), calib, adapter, rank=4, iters=400)
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t = load_file(adapter)
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# exact Rust loader contract: a:[in,r], b:[r,out]
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assert tuple(t["fc1.a"].shape) == (128, 4)
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assert tuple(t["fc1.b"].shape) == (4, 256)
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assert tuple(t["fc2.a"].shape) == (256, 4)
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assert tuple(t["fc2.b"].shape) == (4, 34)
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net = C.CogPose()
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C.load_base(net, str(BASE))
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net.add_lora(4)
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with torch.no_grad():
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net.fc1_lora[0].copy_(t["fc1.a"]); net.fc1_lora[1].copy_(t["fc1.b"] / (16 / 4))
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net.fc2_lora[0].copy_(t["fc2.a"]); net.fc2_lora[1].copy_(t["fc2.b"] / (16 / 4))
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net.eval()
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with torch.no_grad():
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cal_err = F.smooth_l1_loss(net(torch.tensor(X)), torch.tensor(Y)).item()
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changed = (net0(torch.tensor(X[:8])) - net(torch.tensor(X[:8]))).abs().sum().item()
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assert cal_err < base_err, f"calibration did not reduce error ({base_err} -> {cal_err})"
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assert changed > 1e-3, "adapter inert"
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assert nparam < 5000, f"adapter unexpectedly large ({nparam} params)"
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if __name__ == "__main__":
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test_cog_adapter_producer()
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print("PASS: cog adapter producer (Rust-loadable format, reduces error, active)")
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