def test(args):
print('[%s] Selected mode: Test' % appName)
# Local Variables
numSamples = None
# -----------------------------------------
# Network Testing Model - Importing Graph
# -----------------------------------------
# Loads the dataset and restores a specified trained model.
data = Dataloader(args)
# Searches dataset images filenames
if TEST_EVALUATE_SUBSET == 0:
_, _, _, _, numSamples = data.getTestData()
elif TEST_EVALUATE_SUBSET == 1:
data.test_image_filenames, data.test_depth_filenames, tf_test_image_filenames, tf_test_depth_filenames, numSamples = data.getTrainData(
)
model = Test(args, data)
with tf.Session() as sess:
print('\n[network/Testing] Loading the model...')
# Use to load from *.ckpt file
saver = tf.train.Saver()
saver.restore(sess, args.model_path)
# Use to load from npy file
# net.load(model_data_path, sess)
# ==============
# Testing Loop
# ==============
if args.show_test_results:
test_plotObj = Plot(args.mode, title='Test Predictions')
timer = -time.time()
pred_list, gt_list = [], []
for i in range(numSamples):
# for i in range(5): # Only for testing!
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 = sess.run(model.image_op, feed_test)
pred, pred_up = sess.run(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.getSize())
pred_80 = np.zeros((model.batch_size, ) +
model.output_size.getSize())
# Fill arrays for later on metrics evaluation
pred_list.append(pred_up[0, :, :, 0])
gt_list.append(depth[:, :, 0])
# Saves the Test Predictions as uint16 PNG Images
if SAVE_TEST_DISPARITIES:
# Convert the Predictions Images from float32 to uint16
pred_up_uint16 = exposure.rescale_intensity(pred_up[0],
out_range='float')
pred_up_uint16 = img_as_uint(pred_up_uint16)
depth_uint16 = exposure.rescale_intensity(depth,
out_range='float')
depth_uint16 = img_as_uint(depth_uint16)
# Save PNG Images
imageio.imsave("output/tmp/pred/pred" + str(i) + ".png",
pred_up_uint16)
imageio.imsave("output/tmp/gt/gt" + str(i) + ".png",
depth_uint16)
# Prints Testing Progress
timer2 += time.time()
print('step: %d/%d | t: %f | size(pred_list+gt_list): %d' %
(i + 1, numSamples, timer2,
total_size(pred_list) + total_size(gt_list)))
# break # Test
# Show Results
if args.show_test_results:
test_plotObj.showTestResults(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("\n[Network/Testing] Testing FINISHED! Time elapsed: %f s" %
timer)
# =========
# Results
# =========
# Calculate Metrics
if data.test_depth_filenames:
print(
"[Network/Testing] Calculating Metrics based on Testing Predictions..."
)
pred_array = np.array(pred_list)
gt_array = np.array(gt_list)
# LainaMetrics.evaluate(pred_array, gt_array) # FIXME:
# myMetrics.evaluate(pred_array, gt_array) # FIXME:
MonodepthMetrics.evaluate(pred_array, gt_array)
else:
print(
"[Network/Testing] It's not possible to calculate Metrics. There are no corresponding labels for Testing Predictions!"
)
