| | --- |
| | license: cc-by-nc-4.0 |
| | library_name: pytorch |
| | tags: |
| | - time-series |
| | - ecg |
| | - representation-learning |
| | --- |
| | |
| | # LeNEPA encoder (balanced, encoder-only) |
| |
|
| | This repository contains an **encoder-only** LeNEPA checkpoint exported to `safetensors` for minimal inference. |
| |
|
| | What is included: |
| |
|
| | - `lenepa_encoder.safetensors` — **encoder weights only** (no projector, no training/probe state) |
| | - `inference.py` — minimal end-to-end inference (no Hydra, no W&B) |
| | - `lenepa_encoder_config.json` — fixed IO + architecture contract |
| | - `provenance.json` — original `.pt` checkpoint path + W&B URL |
| |
|
| | ## IO contract |
| |
|
| | Inputs: |
| |
|
| | - `x_waveform`: `torch.float32` with shape `[B, 1, 5000]` |
| | - sampling frequency: `500` Hz |
| | - channels: `["I"]` (so `C=1`) |
| |
|
| | Outputs: |
| |
|
| | - `patch_tokens`: `[B, 200, 192]` (post-final-norm tokens) |
| | - `embedding`: `[B, 192]` (mean pooled over tokens) |
| |
|
| | ## Usage |
| |
|
| | Smoke test (loads `lenepa_encoder.safetensors` from the current directory and prints output shapes): |
| |
|
| | ```bash |
| | python inference.py |
| | ``` |
| |
|
| | Programmatic usage: |
| |
|
| | ```python |
| | from pathlib import Path |
| | |
| | import torch |
| | |
| | from inference import encode_lenepa, load_lenepa_encoder |
| | |
| | device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| | model = load_lenepa_encoder(weights_path=Path("lenepa_encoder.safetensors"), device=device) |
| | x = torch.randn(2, 1, 5000, device=device, dtype=torch.float32) # [B, C, L] |
| | out = encode_lenepa(model=model, x_waveform=x) |
| | print(out.embedding.shape) |
| | ``` |
| |
|
| |
|
