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evaluate_appa_real.py
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evaluate_appa_real.py
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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.18-4.06.hdf5"
modhash = '89f56a39a78454e96379348bddd78c0d'
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.18-4.06.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.18-4.06.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()