def generator_val_data(names, path, batch_size, img_ch, img_cols, img_rows):
while True:
x, y = load_batch_from_names_random(names, path, batch_size, img_ch, img_cols, img_rows)
yield x, y
def generator_val_data(names, path, batch_size, img_cols, img_rows, img_ch):
while True:
x, y = load_batch_from_names_random(names, path, batch_size, img_cols,
img_rows, img_ch)
yield x, y
def test_big(args):
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = args.dev
names_path = r"C:\Users\Aliab\PycharmProjects\data\test"
print("Names to test: {}".format(names_path))
dataset_path = r"D:\GazeCapture"
print("Dataset: {}".format(names_path))
weights_path = "weight_vgg.hdf5"
print("Weights: {}".format(weights_path))
# image parameter
img_cols = 128
img_rows = 128
img_ch = 3
# test parameter
batch_size = 64
chunk_size = 500
# model
model = get_eye_tracker_model(img_cols, img_rows, img_ch)
# model summary
model.summary()
# weights
print("Loading weights...")
model = load_model(weights_path)
model.load_weights(weights_path)
# data
test_names = load_data_names(names_path)
# limit amount of testing data
# test_names = test_names[:1000]
# results
err_x = []
err_y = []
print("Loading testing data...")
for it in list(range(0, len(test_names), chunk_size)):
x, y = load_batch_from_names_random(test_names[it:it + chunk_size],
dataset_path, batch_size, img_cols,
img_rows, img_ch)
# x, y = load_batch_from_names(test_names[it:it + chunk_size], dataset_path, img_ch, img_cols, img_rows)
predictions = model.predict(x=x, batch_size=batch_size, verbose=1)
# print and analyze predictions
for i, prediction in enumerate(predictions):
print("PR: {} {}".format(prediction[0], prediction[1]))
print("GT: {} {} \n".format(y[i][0], y[i][1]))
err_x.append(abs(prediction[0] - y[i][0]))
err_y.append(abs(prediction[1] - y[i][1]))
# mean absolute error
mae_x = np.mean(err_x)
mae_y = np.mean(err_y)
# standard deviation
std_x = np.std(err_x)
std_y = np.std(err_y)
# final results
print("MAE: {} {} ( samples)".format(mae_x, mae_y))
print("STD: {} {} ( samples)".format(std_x, std_y))