def test_unknown_batchsize_shape(self):
'''
This test check the below case:
- when a module is built without specifying batch_norm size,
check whether the model output has a proper batch_size given by an input
'''
ch_in_num = 256
model_config = SupervisionTestConfig()
scope = 'unittest'
TEST_LAYER_NAME = 'supervision'
input_shape = [
None, model_config.input_output_height,
model_config.input_output_width, ch_in_num
]
batch_size = 1
inputs = create_test_input(batchsize=input_shape[0],
heightsize=input_shape[1],
widthsize=input_shape[2],
channelnum=input_shape[3])
layer_out, mid_points, heatmaps_out = get_layer(
ch_in=inputs,
model_config=model_config,
layer_index=0,
layer_type=TEST_LAYER_NAME,
scope=scope)
input_shape[0] = batch_size
expected_output_shape = [
batch_size, model_config.input_output_height,
model_config.input_output_width, model_config.num_of_channels_out
]
expected_input_name = 'unittest0/' + TEST_LAYER_NAME + '0_in'
expected_output_name = 'unittest0/' + TEST_LAYER_NAME + '0_out'
self.assertTrue(expected_input_name in mid_points)
self.assertTrue(expected_output_name in mid_points)
images = create_test_input(batchsize=input_shape[0],
heightsize=input_shape[1],
widthsize=input_shape[2],
channelnum=input_shape[3])
print('----------------------------------------------')
print('[tfTest] run test_unknown_batchsize_shape()')
print('[tfTest] midpoint name and shape')
print('[tfTest] layer_name = %s' % TEST_LAYER_NAME)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(layer_out, {inputs: images.eval()})
self.assertListEqual(list(output.shape), expected_output_shape)
print('[TfTest] output shape = %s' % list(output.shape))
print('[TfTest] expected_output_shape = %s' %
expected_output_shape)
def test_midpoint_name_shape(self):
'''
This test checks below:
- whether name and shape are correctly set.
'''
ch_in_num = 3
batch_size = None
model_config = ReceptionTestConfig()
scope = 'unittest'
TEST_LAYER_NAME = 'reception'
input_shape = [
batch_size, model_config.input_height, model_config.input_width,
ch_in_num
]
module_graph = tf.Graph()
with module_graph.as_default():
inputs = create_test_input(batchsize=input_shape[0],
heightsize=model_config.input_width,
widthsize=model_config.input_height,
channelnum=input_shape[3])
layer_out, mid_points, _ = get_layer(ch_in=inputs,
model_config=model_config,
layer_type=TEST_LAYER_NAME,
scope=scope)
init = tf.global_variables_initializer()
ckpt_saver = tf.train.Saver(tf.global_variables())
#----------------------------------------------------------
expected_output_shape = [
batch_size, model_config.output_height, model_config.output_width,
model_config.num_of_channels_out
]
expected_midpoint = LayerEndpointName(
layer_type=TEST_LAYER_NAME,
input_shape=input_shape,
output_shape=expected_output_shape,
conv_type=model_config.conv_config)
expected_input_name = 'unittest0/' + TEST_LAYER_NAME + '_in'
expected_output_name = 'unittest0/' + TEST_LAYER_NAME + '_out'
self.assertTrue(expected_input_name in mid_points)
self.assertTrue(expected_output_name in mid_points)
print('----------------------------------------------')
print('[tfTest] run test_midpoint_name_shape()')
print('[tfTest] midpoint name and shape')
print('[tfTest] layer_name = %s' % TEST_LAYER_NAME)
for name, shape in six.iteritems(expected_midpoint.shape_dict):
print('%s : shape = %s' % (name, shape))
self.assertListEqual(mid_points[name].get_shape().as_list(), shape)
# tensorboard graph summary =============
now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
tb_logdir_path = getcwd() + '/tf_logs'
tb_logdir = "{}/run-{}/".format(tb_logdir_path, now)
if not tf.gfile.Exists(tb_logdir_path):
tf.gfile.MakeDirs(tb_logdir_path)
# summary
tb_summary_writer = tf.summary.FileWriter(logdir=tb_logdir)
tb_summary_writer.add_graph(module_graph)
tb_summary_writer.close()
with self.test_session(graph=module_graph) as sess:
output_node_name = 'unittest0/' + TEST_LAYER_NAME + '/' + expected_output_name
pbsavedir, pbfilename, ckptfilename = \
save_pb_ckpt(module_name=TEST_LAYER_NAME,
init=init,
sess=sess,
ckpt_saver=ckpt_saver)
# frozen graph generation
convert_to_frozen_pb(module_name=TEST_LAYER_NAME,
pbsavedir=pbsavedir,
pbfilename=pbfilename,
ckptfilename=ckptfilename,
output_node_name=output_node_name,
input_shape=input_shape)
# # check tflite compatibility
print('------------------------------------------------')
print('[tfTest] convert to tflite')
tflitedir = getcwd() + '/tflite_files/'
if not tf.gfile.Exists(tflitedir):
tf.gfile.MakeDirs(tflitedir)
tflitefilename = TEST_LAYER_NAME + '.tflite'
toco = tf.contrib.lite.TocoConverter.from_session(
sess=sess, input_tensors=[inputs], output_tensors=[layer_out])
tflite_model = toco.convert()
open(tflitedir + '/' + tflitefilename, 'wb').write(tflite_model)
print('[tfTest] tflite conversion successful')