如何将 tensorflow.js model 权重转换为 pytorch 张量，然后返回？

Question

I am using ml5.js , a wrapper around tensorflowjs. I want to train a neural network in the browser, download the weights, process them as tensors in pyTorch, and load them back into the browser's tensorflowjs model. How do I convert between these formats tfjs <-> pytorch ?

The browser model has a save() function which generates three files. A metadata file specific to ml5.js (json), a topology file describing model architecture (json), and a binary weights file (bin).

// Browser
model.save()

// HTTP/Download
model_meta.json   (needed by ml5.js)
model.json        (needed by tfjs)
model.weights.bin (needed by tfjs)

# python backend
import json

with open('model.weights.bin', 'rb') as weights_file:
    with open('model.json', 'rb') as model_file:
        weights = weights_file.read()
        model = json.loads(model_file.read())
        ####
        pytorch_tensor = convert2tensor(weights, model) # whats in this function?
        ####
        # Do some processing in pytorch

        ####
        new_weights_bin = convert2bin(pytorch_tensor, model) # and in this?
        ####

Here is sample javascript code to generate and load the 3 files in the browser. To load, select all 3 files at once in the dialog box. If they are correct, a popup will show a sample prediction.

Answer 1

I was able to find a way to convert from tfjs model.weights.bin to numpy's ndarrays . It is trivial to convert from numpy arrays to pytorch state_dict which is a dictionary of tensors and their names.

Theory

First, the tfjs representation of the model should be understood. model.json describes the model. In python, it can be read as a dictionary. It has the following keys:

1. The model architecture is described as another json/dictionary under the key modelTopology .

2. It also has a json/dictionary under the key weightsManifest which describes the type/shape/location of each weight wrapped up in the corresponding model.weights.bin file. As an aside, the weights manifest allows for multiple .bin files to store weights.

Tensorflow.js has a companion python package tensorflowjs , which comes with utility functions to read and write weights between the tf.js binary and numpy array format.

Each weight file is read as a "group". A group is a list of dictionaries with keys name and data which refer to the weight name and the numpy array containing weights. There are optionally other keys too.

group = [{'name': weight_name, 'data': np.ndarray}, ...]   # 1 *.bin file

Application

Install tensorflowjs. Unfortunately, it will also install tensorflow.

pip install tensorflowjs

Use these functions. Note that I changed the signatures for convenience.

from typing import Dict, ByteString
import torch
from tensorflowjs.read_weights import decode_weights
from tensorflowjs.write_weights import write_weights

def convert2tensor(weights: ByteString, model: Dict) -> Dict[str, torch.Tensor]:
    manifest = model['weightsManifest']
    # If flatten=False, returns a list of groups equal to the number of .bin files.
    # Use flatten=True to convert to a single group
    group = decode_weights(manifest, weights, flatten=True)
    # Convert dicts in tfjs group format into pytorch's state_dict format:
    # {name: str, data: ndarray} -> {name: tensor}
    state_dict = {d['name']: torch.from_numpy(d['data']) for d in group}
    return state_dict

def convert2bin(state_dict: Dict[str: np.ndarray], model: Dict, directory='./'):
    # convert state_dict to groups (list of 1 group)
    groups = [[{'name': key, 'data': value} for key, value in state_dict.items()]]
    # this library function will write to .bin file[s], but you can read it back
    # or change the function internals my copying them from source
    write_weights(groups, directory, write_manifest=False)