def test_inference(self):
"""
Like the testing function but this one is for calculate the inference time
and measure the frame per second
"""
print("INFERENCE mode will begin NOW..")
# load the best model checkpoint to test on it
self.load_best_model()
# init tqdm and get the epoch value
tt = tqdm(range(1000)) #self.test_data_len))
# idx of image
idx = 0
# create the FPS Meter
fps_meter = FPSMeter()
# loop by the number of iterations
for cur_iteration in tt:
# load mini_batches
x_batch = self.test_data['X'][idx:idx + 1]
flo_batch = self.test_data['Flo'][idx:idx + 1]
y_batch = self.test_data['Y'][idx:idx + 1]
# update idx of mini_batch
#idx += 1
# Feed this variables to the network
if self.args.random_cropping:
feed_dict = {
self.test_model.x_pl_before: x_batch,
self.test_model.flo_pl_before: flo_batch,
self.test_model.y_pl_before: y_batch,
self.test_model.is_training: False,
}
else:
feed_dict = {
self.test_model.x_pl: x_batch,
self.test_model.flo_pl: flo_batch,
self.test_model.y_pl: y_batch,
self.test_model.is_training: False
}
# calculate the time of one inference
start = time.time()
# run the feed_forward
_ = self.sess.run([self.test_model.out_argmax],
feed_dict=feed_dict)
# update the FPS meter
fps_meter.update(time.time() - start)
fps_meter.print_statistics()
def realsense_inference(self):
print("INFERENCE will begin NOW..")
# init tqdm and get the epoch value
tt = tqdm(range(self.test_data_len))
# idx of image
idx = 0
# create the FPS Meter
fps_meter = FPSMeter()
# loop by the number of iterations
for cur_iteration in tt:
# load mini_batches
x_batch = self.test_data['X'][idx:idx + 1]
# y_batch = self.test_data['Y'][idx:idx + 1]
# update idx of mini_batch
idx += 1
# Feed this variables to the network
feed_dict = {self.model.x_pl: x_batch,
self.model.is_training: False}
# calculate the time of one inference
start = time.time()
# run the feed_forward
_ = self.sess.run(
[self.model.out_argmax],
feed_dict=feed_dict)
# update the FPS meter
fps_meter.update(time.time() - start)
fps_meter.print_statistics()
def test_inference(self):
"""
Like the testing function but this one is for calculate the inference time
and measure the frame per second
"""
print("INFERENCE mode will begin NOW..")
# load the best model checkpoint to test on it
self.load_best_model()
# output_node: network/output/Argmax
# input_node: network/input/Placeholder
# for n in tf.get_default_graph().as_graph_def().node:
# if 'input' in n.name:#if 'Argmax' in n.name:
# import pdb; pdb.set_trace()
print("Saving graph...")
tf.train.write_graph(self.sess.graph_def, ".", 'graph.pb')
print("Graph saved successfully.\n\n")
exit(1)
# init tqdm and get the epoch value
tt = tqdm(range(self.test_data_len))
# idx of image
idx = 0
# create the FPS Meter
fps_meter = FPSMeter()
# loop by the number of iterations
for cur_iteration in tt:
# load mini_batches
x_batch = self.test_data['X'][idx:idx + 1]
y_batch = self.test_data['Y'][idx:idx + 1]
# update idx of mini_batch
idx += 1
# Feed this variables to the network
if self.args.random_cropping:
feed_dict = {self.test_model.x_pl_before: x_batch,
self.test_model.y_pl_before: y_batch
# self.test_model.is_training: False,
}
else:
feed_dict = {self.test_model.x_pl: x_batch,
self.test_model.y_pl: y_batch
# self.test_model.is_training: False
}
# calculate the time of one inference
start = time.time()
# run the feed_forward
_ = self.sess.run(
[self.test_model.out_argmax],
feed_dict=feed_dict)
# update the FPS meter
fps_meter.update(time.time() - start)
fps_meter.print_statistics()
def test_eval(self, pkl=False):
print("Testing mode will begin NOW..")
# load the best model checkpoint to test on it
if not pkl:
self.load_best_model()
# init tqdm and get the epoch value
tt = tqdm(range(self.test_data_len))
# idx of image
idx = 0
# create the FPS Meter
fps_meter = FPSMeter()
# loop by the number of iterations
for cur_iteration in tt:
# load mini_batches
x_batch = self.test_data['X'][idx:idx + 1]
# Feed this variables to the network
if self.args.random_cropping:
feed_dict = {self.test_model.x_pl_before: x_batch,
self.test_model.is_training: False,
}
else:
feed_dict = {self.test_model.x_pl: x_batch,
self.test_model.is_training: False
}
start = time.time()
# run the feed_forward
out_argmax, segmented_imgs = self.sess.run(
[self.test_model.out_argmax,
self.test_model.segmented_summary],
feed_dict=feed_dict)
fps_meter.update(time.time() - start)
if pkl:
out_argmax[0] = self.linknet_postprocess(out_argmax[0])
segmented_imgs = decode_labels(out_argmax, 20)
# Colored results for visualization
colored_save_path = self.args.out_dir + 'imgs/' + (self.names_mapper['Y'][idx]).decode()
if not os.path.exists(os.path.dirname(colored_save_path)):
os.makedirs(os.path.dirname(colored_save_path))
plt.imsave(colored_save_path, segmented_imgs[0])
# Results for official evaluation
save_path = self.args.out_dir + 'results/'+ (self.names_mapper['Y'][idx]).decode()
if not os.path.exists(os.path.dirname(save_path)):
os.makedirs(os.path.dirname(save_path))
output = postprocess(out_argmax[0])
misc.imsave(save_path, misc.imresize(output, [1024, 2048], 'nearest'))
idx += 1
# print in console
tt.close()
fps_meter.print_statistics()