README.md

---
license: apache-2.0
tags:
- jax
- safetensors
---

# Parametric PerceptNet Fully Trained

## 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-fully-trained",
                              filename="config.json")
with open(config_path, "r") as f:
    config = ConfigDict(json.load(f))
```

### 3. Download the weights

#### 3.1. Using `safetensors`

```
from safetensors.flax import load_file

weights_path = hf_hub_download(repo_id="Jorgvt/ppnet-fully-trained",
                               filename="weights.safetensors")
variables = load_file(weights_path)
variables = flax.traverse_util.unflatten_dict(variables, sep=".")
state = variables["state"]
params = variables["params"]
```

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

### 4. Use the model

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