evaluate_appa_real.py

import os
import cv2
import numpy as np
import pandas as pd
import argparse
from tqdm import tqdm
from pathlib import Path
from wide_resnet import WideResNet
from keras.utils.data_utils import get_file

pretrained_model = "https://github.com/yu4u/age-gender-estimation/releases/download/v0.5/weights.28-3.73.hdf5"
modhash = "fbe63257a054c1c5466cfd7bf14646d6"


def get_args():
    parser = argparse.ArgumentParser(description="This script evaluate age estimation model "
                                                 "using the APPA-REAL validation data.",
                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument("--weight_file", type=str, default=None,
                        help="path to weight file (e.g. weights.28-3.73.hdf5)")
    parser.add_argument("--depth", type=int, default=16,
                        help="depth of network")
    parser.add_argument("--width", type=int, default=8,
                        help="width of network")
    args = parser.parse_args()
    return args


def main():
    args = get_args()
    depth = args.depth
    k = args.width
    weight_file = args.weight_file

    if not weight_file:
        weight_file = get_file("weights.28-3.73.hdf5", pretrained_model, cache_subdir="pretrained_models",
                               file_hash=modhash, cache_dir=os.path.dirname(os.path.abspath(__file__)))

    # load model and weights
    img_size = 64
    batch_size = 32
    model = WideResNet(img_size, depth=depth, k=k)()
    model.load_weights(weight_file)
    dataset_root = Path(__file__).parent.joinpath("appa-real", "appa-real-release")
    validation_image_dir = dataset_root.joinpath("valid")
    gt_valid_path = dataset_root.joinpath("gt_avg_valid.csv")
    image_paths = list(validation_image_dir.glob("*_face.jpg"))

    faces = np.empty((batch_size, img_size, img_size, 3))
    ages = []
    image_names = []

    for i, image_path in tqdm(enumerate(image_paths)):
        faces[i % batch_size] = cv2.resize(cv2.imread(str(image_path), 1), (img_size, img_size))
        image_names.append(image_path.name[:-9])

        if (i + 1) % batch_size == 0 or i == len(image_paths) - 1:
            results = model.predict(faces)
            ages_out = np.arange(0, 101).reshape(101, 1)
            predicted_ages = results[1].dot(ages_out).flatten()
            ages += list(predicted_ages)
            # len(ages) can be larger than len(image_names) due to the last batch, but it's ok.

    name2age = {image_names[i]: ages[i] for i in range(len(image_names))}
    df = pd.read_csv(str(gt_valid_path))
    appa_abs_error = 0.0
    real_abs_error = 0.0

    for i, row in df.iterrows():
        appa_abs_error += abs(name2age[row.file_name] - row.apparent_age_avg)
        real_abs_error += abs(name2age[row.file_name] - row.real_age)

    print("MAE Apparent: {}".format(appa_abs_error / len(image_names)))
    print("MAE Real: {}".format(real_abs_error / len(image_names)))


if __name__ == '__main__':
    main()