README.md

metadata

license: apache-2.0
tags:
  - jax
  - safetensors

Parametric PerceptNet Bio-Fitted

Model Description

How to use it

Install the model's package from source:

git clone https://github.com/Jorgvt/paramperceptnet.git
cd paramperceptnet
pip install -e .

1.Import necessary libraries:

import json

from huggingface_hub import hf_hub_download
import flax
import orbax.checkpoint
from ml_collections import ConfigDict

from paramperceptnet.models import PerceptNet

2.Download the configuration

config_path = hf_hub_download(repo_id="Jorgvt/ppnet-bio-fitted",
                              filename="config.json")
with open(config_path, "r") as f:
    config = ConfigDict(json.load(f))

3. Download the weights

weights_path = hf_hub_download(repo_id="Jorgvt/ppnet-bio-fitted",
                               filename="weights.msgpack")
with open(weights_path, "rb") as f:
    variables = orbax.checkpoint.msgpack_utils.msgpack_restore(f.read())
state = variables["state"]
params = variables["params"]
state = jax.tree_util.tree_map(lambda x: jnp.array(x), state)
params = jax.tree_util.tree_map(lambda x: jnp.array(x), params)

4. Use the model

from jax import numpy as jnp
pred = model.apply({"params": params, **state}, jnp.ones((1,384,512,3)))