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Natively-TS-Understanding/
lenepa-cauker2m-5000-patchnorm-d256-steps200k

Name: lenepa-cauker2m-5000-patchnorm-d256-steps200k
Availability: InStock

pytorch

time-series

synthetic

representation-learning

nepa

cc-by-nc-4.0

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LeNEPA encoder (CauKer2M, encoder-only, CONT200K_LOCAL)

This repository contains an encoder-only LeNEPA checkpoint exported to safetensors for minimal inference.

This variant uses the local continuation checkpoint

pretrain/CAUKER2M_L5000_LENEPA_SIGREGT2p5_L0-8_PD0_PROJ_LR2x_MSSE_PATCHNORM_D256_OPTOYTSBCBAL_s0_UCRinterp5000_CONT200K_LOCAL/chkpt_220000.pt

, continued from the base W&B-tracked run recorded in provenance.json.

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, no repo dependency)
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: 1
channels: ["c0"]

Tokenizer:

causal conv_patch_embed with patch_size=8
per-patch normalization inside the tokenizer
MSSE scalar embeddings for patch mean and patch std

Outputs:

patch_tokens: [B, 625, 256] (post-final-norm tokens)
embedding: [B, 256] (mean pooled over tokens)

Preparing inputs

The exported inference.py expects x_waveform.shape == [B, 1, 5000] and does not resample internally.

Two common options:

No resampling

Use this when your waveform is already length 5000 or you just if it works better without resampling:

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=5000]
out = encode_lenepa(model=model, x_waveform=x)
print(out.patch_tokens.shape)

Interpolate to 5000

Validation on UCR shows, that resampling samples that have different length to 5000 generates noticably better classification quality:

python

from pathlib import Path

import torch
from torch.nn import functional as F

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_raw = torch.randn(2, 1, 137, device=device, dtype=torch.float32)  # [B, C, L_raw]
x = F.interpolate(x_raw, size=5000, mode="linear", align_corners=False)  # [B, C, 5000]
out = encode_lenepa(model=model, x_waveform=x)
print(out.embedding.shape)

Usage

Smoke test (loads lenepa_encoder.safetensors from the current directory and prints output shapes):

python inference.py

Programmatic usage:

python

from pathlib import Path

import torch
from torch.nn import functional as F

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_raw = torch.randn(2, 1, 137, device=device, dtype=torch.float32)  # [B, C, L_raw]
x = F.interpolate(x_raw, size=5000, mode="linear", align_corners=False)  # [B, C, 5000]
out = encode_lenepa(model=model, x_waveform=x)
print(out.embedding.shape)

Natively-TS-Understanding/lenepa-cauker2m-5000-patchnorm-d256-steps200k

LeNEPA encoder (CauKer2M, encoder-only, CONT200K_LOCAL)

IO contract

Preparing inputs

Usage

Natively-TS-Understanding/
lenepa-cauker2m-5000-patchnorm-d256-steps200k