upload: example_usage.py

example_usage.py

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+"""
+example_usage.py — Minimal inference with BeMAE-Hα.
+
+This script loads a checkpoint from HuggingFace Hub, retrieves a spectrum
+de test depuis le dataset BeSS-foundation, et calcule son embedding z_halpha.
+"""
+import json
+import sys
+
+import numpy as np
+import torch
+from huggingface_hub import snapshot_download
+
+MODEL_ID = "anonym-submit-26/bemae-halpha-v1"
+
+# 1. Download the snapshot
+ckpt_dir = snapshot_download(MODEL_ID)
+sys.path.insert(0, ckpt_dir)
+
+from model import SpectralEncoderHalpha, ModelConfig  # noqa: E402
+
+# 2. Charger la config et les poids
+with open(f"{ckpt_dir}/config.json") as f:
+    meta = json.load(f)
+cfg = ModelConfig(**meta["model_config"])
+
+encoder = SpectralEncoderHalpha(cfg)
+state = torch.load(f"{ckpt_dir}/pytorch_model.bin", map_location="cpu")
+encoder.load_state_dict(state)
+encoder.eval()
+
+# 3. Prepare a dummy spectrum (Hα-centred, 128 bins, pseudo-continuum at 1.0)
+B = 4
+flux = torch.ones(B, 128) + 0.3 * torch.randn(B, 128) * 0.01
+wavelengths = torch.linspace(6512.8, 6612.8, 128).unsqueeze(0).expand(B, -1)
+validity = torch.ones(B, 128)
+
+# 4. Inference
+with torch.no_grad():
+    z_halpha, *_ = encoder(flux, wavelengths, validity, mask=None)
+
+print(f"z_halpha shape : {tuple(z_halpha.shape)}")
+print(f"z_halpha[0, :8] : {z_halpha[0, :8].numpy()}")