Can not read weights.pb files

[Muffty]

Hi, I try to use the downloaded weights file (I got using download_latest_network.py).
It is not .txt.gz but .pb.gz and read_weights_file does not seem to be okay with that.

[advait]

@so-much-meta can you advise here? Very eager to continue progress on this project, but this seems to be a hard blocker. Happy to help update any code if you can point us in the right direction. Thanks!

[so-much-meta]

Sorry, it’s been a while since I’ve looked at this project. I’ll take a look this evening and respond, and if I can get some time soon will try to make some updates (eg, I know a big need is support for se-resnets).

…

On Tue, Apr 23, 2019 at 9:00 PM Advait Shinde ***@***.***> wrote: @so-much-meta <https://github.com/so-much-meta> can you advise here? Very eager to continue progress on this project, but this seems to be a hard blocker. Happy to help update any code if you can point us in the right direction. Thanks! — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub <#8 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AJGXKCLI23ISADXWYVME6C3PR6WLDANCNFSM4G3EXCJA> .

[Sumegh-git]

@so-much-meta could you provide some help with how to work with the .pb files which are currently available in LC0 page ?

I tried renaming that to .txt but then it throws a size mismatch error -

line 153, in from_weights_file
param.data.copy_(w.view_as(param))
RuntimeError: shape '[8, 112, 3, 3]' is invalid for input of size 8640

[ZararB]

You can convert the pb.gz to a txt.gz file using the save_txt function of the net class but this won't fix the issue because the nets on the website are a newer version and aren't compatible. You can try to make it compatible but I found it easier to do this:

Download the model and config yaml files from here :

https://www.comp.nus.edu.sg/~sergio-v/new/128x10-t60-2/

Load the model using the tfprocess module

`
import yaml
import tfprocess

class KerasNet:

def __init__(self, model_file="128x10-t60-2-5300.pb.gz", cfg_file="configs/128x10-t60-2.yaml"):

    with open(cfg_file, "rb") as f:
        cfg = f.read()

    cfg = yaml.safe_load(cfg)
    print(yaml.dump(cfg, default_flow_style=False))

    tfp = tfprocess.TFProcess(cfg)
    tfp.init_net_v2()
    tfp.replace_weights_v2(model_file)

    self.model = tfp.model 

def evaluate(self, leela_board):

    input_planes = leela_board.lcz_features()
    model_input = input_planes.reshape(1, 112, 64)
    policy, value, _ = self.model.predict(model_input)

    return policy, value

`

[lzanini]

@ZararB Thanks, this was very helpful!

Do you happen to have a solution to read current training data? Using TarTrainingFile yield following error

Exception: Only version 3 of training data supported

[ZararB]

@lzanini Sorry, I am only using the network for evaluation and haven't looked into training

[lzanini]

@ZararB thank you for your answer.

Did you manage to get correct evaluations using lczero-training network with inputs computed by this library?

I get the network to run, but output values are surprising to say the least, and very different from the ones I get with the engine limited to nodes=1. Which makes me wonder if the inputs are still valid for the latest networks.

[ZararB]

@lzanini yeah the code I wrote before was a bit misleading. The policy network output needs to be run through the softmax function to get probabilities. Here is the full thing:

`
import numpy as np
import yaml
import tfprocess
from collections import OrderedDict

class KerasNet:

def __init__(self, model_file="128x10-t60-2-5300.pb.gz", cfg_file="configs/example.yaml"):

    with open(cfg_file, "rb") as f:
        cfg = f.read()

    cfg = yaml.safe_load(cfg)
    print(yaml.dump(cfg, default_flow_style=False))

    tfp = tfprocess.TFProcess(cfg, gpu=True)
    tfp.init_net_v2()
    tfp.replace_weights_v2(model_file)
    self.model = tfp.model 

def _softmax(self, x, softmax_temp=1.0):
    e_x = np.exp((x - np.max(x))/softmax_temp)
    return e_x / e_x.sum(axis=0)

def _evaluate(self, leela_board):

    input_planes = leela_board.lcz_features()
    model_input = input_planes.reshape(1, 112, 64)
    model_output = self.model.predict(model_input)

    policy_logits = model_output[0][0]
    
    legal_uci = [m.uci() for m in leela_board.generate_legal_moves()]
    
    if legal_uci:
        legal_indexes = leela_board.lcz_uci_to_idx(legal_uci)
        softmaxed = self._softmax(policy_logits[legal_indexes])
        softmaxed_aspython = map(float, softmaxed)
        policy_legal = OrderedDict(sorted(zip(legal_uci, softmaxed_aspython),
                                    key = lambda mp: (mp[1], mp[0]),
                                    reverse=True))

    else:
        policy_legal = OrderedDict()

    return policy_legal

`

I copied most of this from the lcztools code and just modified it a little so it worked with mine

[lzanini]

@ZararB Where does the term x - np.max(x) come from in your softmax definition?

The usual definition (and the one defined in tensorflow) is simply

softmax = tf.exp(logits) / tf.reduce_sum(tf.exp(logits), axis)

Since the training script uses the standard tf.nn.softmax_cross_entropy_with_logits directly on the model output (here and here), I don't see why the values need to be normalized before going through the softmax

[ZararB]

@lzanini Both definitions are equivalent. The max is usually subtracted from all the logits so that we can work with smaller numbers and to prevent NaN issues. You can use either one

[lzanini]

You're right! Thanks again 👍