def calculate_score_adience(config, is_frontal=False):
if is_frontal == True:
print(f'Calculating accuracy for frontalized test set...')
else:
print(f'Calculating accuracy for test set...')
score_mean, score_one_off_mean, score_crops_mean, score_one_off_crops_mean = 0, 0, 0, 0
preds_cross, labels_cross = [], []
for i in range(config.NUM_FOLD_PATHS):
if is_frontal == True:
score, score_one_off, score_crops, score_one_off_crops = calculate_score(
config.DATASET_MEANS[i],
config.LABEL_ENCODER_PATHS[i],
config.BEST_WEIGHTS[i],
config.TEST_HDF5S_FRONTAL[i],
cross_val=i,
preds_cross=preds_cross,
labels_cross=labels_cross)
else:
score, score_one_off, score_crops, score_one_off_crops = calculate_score(
config.DATASET_MEANS[i],
config.LABEL_ENCODER_PATHS[i],
config.BEST_WEIGHTS[i],
config.TEST_HDF5S[i],
cross_val=i,
preds_cross=preds_cross,
labels_cross=labels_cross)
score_mean += score
score_crops_mean += score_crops
if config.DATASET_TYPE == 'age':
score_one_off_mean += score_one_off
score_one_off_crops_mean += score_one_off_crops
# load LabelEncoder
le = pickle.loads(open(config.LABEL_ENCODER_PATHS[0], 'rb').read())
cm_path = f'cm_{config.DATASET_TYPE}_frontal.png' if is_frontal == True else f'cm_{config.DATASET_TYPE}.png'
AgeGenderHelper.plot_confusion_matrix_from_data(
config,
labels_cross,
preds_cross,
le,
save_path=os.path.sep.join([config.OUTPUT_BASE, cm_path]))
print(
f'[INFO] rank 1 across {config.NUM_FOLD_PATHS} validations: {score_mean/config.NUM_FOLD_PATHS:.4f}'
)
print(
f'[INFO] rank 1 across {config.NUM_FOLD_PATHS} validations with crops: {score_crops_mean/config.NUM_FOLD_PATHS:.4f}'
)
if config.DATASET_TYPE == 'age':
print(
f'[INFO] one-off across {config.NUM_FOLD_PATHS} validations: {score_one_off_mean/config.NUM_FOLD_PATHS:.4f}'
)
print(
f'[INFO] one-off across {config.NUM_FOLD_PATHS} validations with crops: {score_one_off_crops_mean/config.NUM_FOLD_PATHS:.4f}'
)
def calculate_score(means_path,
label_encoder_path,
best_weight_path,
test_hdf5_path,
cross_val=None,
preds_cross=None,
labels_cross=None,
is_mapped=False):
# load RGB means for training set
means = json.loads(open(means_path).read())
# load LabelEncoder
le = pickle.loads(open(label_encoder_path, 'rb').read())
# initialize image preprocessors
sp, mp, cp, iap = SimplePreprocessor(
config.IMAGE_SIZE, config.IMAGE_SIZE), MeanPreprocessor(
means['R'], means['G'], means['B']), CropPreprocessor(
config.IMAGE_SIZE,
config.IMAGE_SIZE), ImageToArrayPreprocessor()
custom_objects = None
agh = AgeGenderHelper(config, deploy)
if config.DATASET_TYPE == 'age':
one_off_mappings = agh.build_oneoff_mappings(le)
one_off = OneOffAccuracy(one_off_mappings)
custom_objects = {'one_off_accuracy': one_off.one_off_accuracy}
# load model
print(f'[INFO] loading {best_weight_path}...')
model = load_model(best_weight_path, custom_objects=custom_objects)
# initialize testing dataset generator, then predict
if cross_val is None:
print(
f'[INFO] predicting in testing data (no crops){config.SALIENCY_INFO}...'
)
else:
print(
f'[INFO] predicting in testing data (no crops) for cross validation {cross_val}{config.SALIENCY_INFO}...'
)
test_gen = HDF5DatasetGenerator(test_hdf5_path,
batch_size=config.BATCH_SIZE,
preprocessors=[sp, mp, iap],
classes=config.NUM_CLASSES)
preds = model.predict_generator(test_gen.generator(),
steps=test_gen.num_images //
config.BATCH_SIZE)
# compute rank-1 and one-off accuracies
labels = to_categorical(
test_gen.db['labels'][0:config.BATCH_SIZE *
(test_gen.num_images // config.BATCH_SIZE)],
num_classes=config.NUM_CLASSES)
preds_mapped = preds.argmax(axis=1)
if is_mapped == True:
preds_mapped = agh.build_mapping_to_iog_labels()[preds_mapped]
if cross_val is None:
print(
'[INFO] serializing all images classified incorrectly for testing dataset...'
)
prefix_path = os.path.sep.join(
[config.WRONG_BASE, config.DATASET_TYPE])
agh.plot_confusion_matrix_from_data(config,
labels.argmax(axis=1),
preds_mapped,
le=le,
save_path=os.path.sep.join([
config.OUTPUT_BASE,
f'cm_{config.DATASET_TYPE}.png'
]))
else:
print(
f'[INFO] serializing all images classified incorrectly for cross validation {cross_val} of testing dataset...'
)
prefix_path = os.path.sep.join(
[config.WRONG_BASE, f'Cross{cross_val}', config.DATASET_TYPE])
preds_cross.extend(preds_mapped.tolist())
labels_cross.extend(labels.argmax(axis=1).tolist())
if os.path.exists(prefix_path):
shutil.rmtree(prefix_path)
os.makedirs(prefix_path)
for i, (pred, label) in enumerate(zip(preds_mapped,
labels.argmax(axis=1))):
if pred != label:
image = test_gen.db['images'][i]
if config.DATASET_TYPE == 'age':
real_label, real_pred = le.classes_[label], le.classes_[pred]
real_label = real_label.replace('_', '-')
real_label = real_label.replace('-inf', '+')
real_pred = real_pred.replace('_', '-')
real_pred = real_pred.replace('-inf', '+')
elif config.DATASET_TYPE == 'gender':
real_label = 'Male' if label == 0 else 'Female'
real_pred = 'Male' if pred == 0 else 'Female'
cv2.putText(image, f'Actual: {real_label}, Predict: {real_pred}',
(15, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 0),
2)
cv2.imwrite(os.path.sep.join([prefix_path, f'{i:05d}.jpg']), image)
score = accuracy_score(labels.argmax(axis=1), preds_mapped)
print(f'[INFO] rank-1: {score:.4f}')
score_one_off = None
if config.DATASET_TYPE == 'age':
score_one_off = one_off.one_off_compute(
labels, to_categorical(preds_mapped,
num_classes=config.NUM_CLASSES))
print(f'[INFO] one-off: {score_one_off:.4f}')
test_gen.close()
# re-initialize testing generator, now excluding SimplePreprocessor
test_gen = HDF5DatasetGenerator(test_hdf5_path,
config.BATCH_SIZE,
preprocessors=[mp],
classes=config.NUM_CLASSES)
preds = []
labels = to_categorical(test_gen.db['labels'],
num_classes=config.NUM_CLASSES)
print('[INFO] predicting in testing data (with crops)...')
# initialize progress bar
widgets = [
'Evaluating: ',
progressbar.Percentage(), ' ',
progressbar.Bar(), ' ',
progressbar.ETA()
]
pbar = progressbar.ProgressBar(maxval=math.ceil(test_gen.num_images /
config.BATCH_SIZE),
widgets=widgets).start()
for i, (images, _) in enumerate(test_gen.generator(passes=1)):
for image in images:
crops = cp.preprocess(image)
crops = np.array([iap.preprocess(c) for c in crops])
pred = model.predict(crops)
preds.append(pred.mean(axis=0))
pbar.update(i)
pbar.finish()
test_gen.close()
# compute rank-1 accuracy
preds_mapped = np.argmax(preds, axis=1)
if is_mapped == True:
preds_mapped = agh.build_mapping_to_iog_labels()[preds_mapped]
score_crops = accuracy_score(labels.argmax(axis=1), preds_mapped)
print(f'[INFO] rank-1: {score_crops:.4f}')
score_one_off_crops = None
if config.DATASET_TYPE == 'age':
score_one_off_crops = one_off.one_off_compute(
labels, to_categorical(preds_mapped,
num_classes=config.NUM_CLASSES))
print(f'[INFO] one-off: {score_one_off_crops:.4f}')
return score, score_one_off, score_crops, score_one_off_crops