Tutorial MNIST step 7

[cheahengsoon]

When I type in predict command.
The error shown
mnist> predict

[!] No index sample, setting random index.

EXCEPTION of type 'ValueError' occurred with message: empty range for randrange() (0,0, 0)
[!] To enable full traceback, run the following command: 'setg debug true'

the sample of my code.
C:\Users\Eng Soon Cheah\Desktop\counterfit-main\counterfit\commands\new.py

import questionary
import os
import importlib
import cmd2
import inspect
import argparse

from counterfit.core.state import CFState
from counterfit.core import config

parser = argparse.ArgumentParser()
# including this print the correct help message


def nix_path(path):
    new_path = "/".join(path.split("\\"))
    return new_path

@cmd2.with_argparser(parser)
@cmd2.with_category("Counterfit Commands")
def do_new(self, args):
    """Optional wizard to aid in creating a new attack target."""
    model_name = questionary.text("Target name:").ask()
    model_name = model_name.replace(" ", "")

    available_frameworks = list(CFState.get_instance().loaded_frameworks.keys())
    framework_choice = questionary.select("Which framework?", choices=available_frameworks).ask()

    if "textattack" in framework_choice:
        framework = "TextTarget"
    elif "art" in framework_choice:
        framework = "ArtTarget"
    else:
        raise ValueError("invalid framework")

    if framework == "TextTarget":
        model_data_type = "text"
    elif framework == "ArtTarget":
        model_data_type = questionary.select("What data type?", choices=["numpy", "image"]).ask()
    else:
        raise ValueError("invalid framework")

    if model_name not in os.listdir(config.targets_path):
        try:
            os.mkdir(f"{config.targets_path}/{model_name}")
            open(f"{config.targets_path}/{model_name}/__init__.py", "w").close()
            with open(f"{config.targets_path}/{model_name}/{model_name}.py", "w") as f:
                f.write(
                    f"""

# Generated by counterfit #

import pickle
import numpy as np
from counterfit.core.targets import ArtTarget


class Mnist(ArtTarget):
    model_name = "mnist"
    model_data_type = "image"
    model_endpoint = "counterfit/targets/tutorial/mnist_sklearn_pipeline.pkl"
    model_input_shape = (1, 28, 28)
    model_output_classes = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
    X = []

    def __init__(self):
        self.clip_values = (0, 255)
        with open(self.model_endpoint, "rb") as f:
            self.model = pickle.load(f)

        self.data_file = "counterfit/targets/tutorial/mnist_784.npz"
        self.sample_data = np.load(self.data_file, allow_pickle=True)

        self.X = self.sample_data["X"]

    def __call__(self, x):
        scores = self.model.predict_proba(x.reshape(x.shape[0], -1))
        return scores.tolist()
"""
                )

            CFState.get_instance().import_targets()
        except Exception as e:

            self.pwarning(f"\n [!] Failed to write target file: {e}.\n")

    else:
        self.pwarning(f"\n [!] {model_name} already exists. Choose a new name.\n")

[moohax]

Thank you for submitting an issue.

The new command will write a new target to the targets folder. For the tutorial, the new command should remain unaltered.
Check C:\Users\Eng Soon Cheah\Desktop\counterfit-main\counterfit\targets\ for the target after executing the new command.

1. Revert the new command back to the original.
2. Instead of using the new command. Create the following folder structure,

C:\Users\Eng Soon Cheah\Desktop\counterfit-main\counterfit\targets\mnist
C:\Users\Eng Soon Cheah\Desktop\counterfit-main\counterfit\targets\mnist\__init__.py
C:\Users\Eng Soon Cheah\Desktop\counterfit-main\counterfit\targets\mnist\mnist_issue.py

3. Paste the following code into mnist_issue.py

# Generated by counterfit #

import pickle
import numpy as np
from counterfit.core.targets import ArtTarget


class Mnist(ArtTarget):
    model_name = "mnist"
    model_data_type = "image"
    model_endpoint = "counterfit/targets/tutorial/mnist_sklearn_pipeline.pkl"
    model_input_shape = (1, 28, 28)
    model_output_classes = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
    X = []

    def __init__(self):
        self.clip_values = (0, 255)
        with open(self.model_endpoint, "rb") as f:
            self.model = pickle.load(f)

        self.data_file = "counterfit/targets/tutorial/mnist_784.npz"
        self.sample_data = np.load(self.data_file, allow_pickle=True)

        self.X = self.sample_data["X"]

    def __call__(self, x):
        scores = self.model.predict_proba(x.reshape(x.shape[0], -1))
        return scores.tolist()

5. Make sure all the files required exist and you should be good to go.

Le me know if that worked and I'll close this issue.

In case anyone sees the above error - it usually means that self.X is empty.

[cheahengsoon]

@moohax you can close the issue. Thank you.