def test():
# Local Variables
num_test_images = None
# -----------------------------------------
# Network Testing Model - Importing Graph
# -----------------------------------------
# Loads the dataset and restores a specified trained model.
data = Dataloader()
# Searches dataset images filenames
if SELECTED_EVALUATION_SUBSET == 'test':
_, _, _, _, num_test_images = data.get_test_data(
test_split=args.test_split, test_file_path=args.test_file_path)
elif SELECTED_EVALUATION_SUBSET == 'train':
data.test_image_filenames, data.test_depth_filenames, _, _, num_test_images = data.get_train_data(
)
model = Test(data)
with tf.Session() as sess:
print('\n[Test] Loading the model...')
load_model(saver=tf.train.Saver(), sess=sess)
# ==============
# Testing Loop
# ==============
if args.debug:
num_test_images = 5 # Only for testing!
# TODO: Criar uma classe de test assim como fiz para train e valid, e declarar este objeto dentro dela
if args.show_test_results:
test_plot_obj = Plot(args.mode, title='Test Predictions')
print("[Test] Generating Predictions...")
pred_list, gt_list = [], []
timer = -time.time()
for i in tqdm(range(num_test_images)):
timer2 = -time.time()
# Evalute the network for the given image
# data.test_depth_filenames = [] # Only for testing the following condition!!! # FIXME: Atualmente, o código não dá suporte para esta situação
if data.test_depth_filenames: # It's not empty
feed_test = {
model.tf_image_key: data.test_image_filenames[i],
model.tf_depth_key: data.test_depth_filenames[i]
}
_, depth, depth_resized = sess.run(model.depth_op, feed_test)
else:
feed_test = {model.tf_image_key: data.test_image_filenames[i]}
_, image, image_resized, pred, pred_up = sess.run(
model.image_op + model.pred_op, feed_test)
# Clips Predictions at 50, 80 meters
try:
pred_50, pred_80 = sess.run(
[model.tf_pred_50, model.tf_pred_80], feed_test)
except AttributeError:
pred_50 = np.zeros((model.batch_size, ) +
model.output_size.get_size())
pred_80 = np.zeros((model.batch_size, ) +
model.output_size.get_size())
# Fill arrays for later on metrics evaluation
if args.eval_tool == 'monodepth':
pred_list.append(pred_up[0, :, :, 0])
gt_list.append(depth[:, :, 0])
# Saves the predictions and ground truth depth images as uint16 PNG Images
if SAVE_TEST_DISPARITIES or args.eval_tool == 'monodepth' or args.eval_tool == 'kitti_depth':
# Convert the Predictions Images from float32 to uint16
# print(data.test_image_filenames[i])
imsave_as_uint16_png(
settings.output_tmp_pred_dir + 'pred' + str(i) + '.png',
pred_up[0])
imsave_as_uint16_png(
settings.output_tmp_gt_dir + 'gt' + str(i) + '.png', depth)
timer2 += time.time()
# Show Results
if args.show_test_results:
# TODO: Fazer um lista the 'images_to_display' e dar append das imagens na lista
test_plot_obj.show_test_results(
image=image,
depth=depth[:, :, 0],
image_resized=image_resized,
depth_resized=depth_resized[:, :, 0],
pred=pred[0, :, :, 0],
pred_up=pred_up[0, :, :, 0],
pred_50=pred_50[0, :, :, 0],
pred_80=pred_80[0, :, :, 0],
i=i + 1)
# input("Continue...")
# Testing Finished.
timer += time.time()
print("Time elapsed: {} s\n".format(timer))
# =========
# Results
# =========
# Calculates Metrics
if args.eval_tool:
if data.test_depth_filenames:
print(
"[Test/Metrics] Calculating Metrics based on Test Predictions..."
)
print('args.test_split:', args.test_split)
print('args.test_file_path:', args.test_file_path)
print('dataset_path:', data.dataset.dataset_path)
print()
# Invokes Evaluation Tools
if args.eval_tool == 'monodepth':
pred_depths, gt_depths = metrics.generate_depth_maps(
pred_list, gt_list, data.dataset.dataset_path)
metrics.evaluation_tool_monodepth(pred_depths, gt_depths)
elif args.eval_tool == 'kitti_depth':
metrics.evaluation_tool_kitti_depth(num_test_images)
else:
print(
"[Test/Metrics] It's not possible to calculate metrics. There are no corresponding labels for generated predictions!"
)
else:
print("[Test/Metrics] Metrics calculation wasn't requested!")
