def test_load_graph_model_with_fused_depthwise_prelu(self):
"""load_graph_model should split fused depthwise conv2d with prelu"""
model_dir = testutils.get_path_to(testutils.DEPTHWISE_PRELU_PATH)
graph = api.load_graph_model(model_dir)
loaded_model = testutils.graph_to_model(graph)
original_model_name = testutils.get_path_to(
testutils.DEPTHWISE_PRELU_FILE)
original_model = testutils.graph_to_model(original_model_name)
# run both models and compare results
x = _load_image(testutils.get_path_to('./data/human1.jpg'))
y_from_loaded = _argmax(loaded_model(x))
y_from_original = _argmax(original_model(x))
# sanity check - should be reckognised as a human (class 1)
self.assertEqual(y_from_original[0], 1)
# same class
self.assertEqual(y_from_loaded[0], y_from_original[0])
# same confidence
self.assertAlmostEqual(y_from_loaded[1], y_from_original[1], 4)
def test_load_graph_model_with_simple_model(self):
"""load_graph_model should load simple model"""
model_dir = testutils.get_path_to(testutils.SIMPLE_MODEL_PATH_NAME)
graph = api.load_graph_model(model_dir)
self.assertIsInstance(graph, tf.Graph)
loaded_model = testutils.graph_to_model(graph)
original_model_name = testutils.get_path_to(
testutils.SIMPLE_MODEL_FILE_NAME)
original_model = testutils.graph_to_model(original_model_name)
# run both models and compare results
x_ = 4
x = tf.constant([[x_]], dtype=tf.float32)
y_from_loaded_model = as_scalar(loaded_model(x))
y_from_original_model = as_scalar(original_model(x))
# sanity check; fails if model is different from the one we expected:
# we want a model that predicts y = 5*x
self.assertAlmostEqual(y_from_original_model, x_*5, places=1)
# actual test
self.assertAlmostEqual(y_from_loaded_model, y_from_original_model,
places=4)
def test_load_graph_model_with_prelu(self):
"""load_graph_model should convert prelu operations"""
model_dir = testutils.get_path_to(testutils.PRELU_MODEL_PATH)
graph = api.load_graph_model(model_dir)
loaded_model = testutils.graph_to_model(graph)
original_model_name = testutils.get_path_to(testutils.PRELU_MODEL_FILE)
original_model = testutils.graph_to_model(original_model_name)
# run both models and compare results
cx, cy, cz, r = -0.12, 0.2, 0.1, 0.314158
px, py, pz = -0.4, 0.5, 0.4
x = tf.constant([[cx, cy, cz, r, px, py, pz]], dtype=tf.float32)
y_from_loaded_model = as_scalar(loaded_model(x))
y_from_original_model = as_scalar(original_model(x))
# sanity check; fails if model is different from the one we expected:
# we want a model that predicts whether a point (px,py,pz) is inside
# a sphere at (cx,cy,cz) of radius r
self.assertAlmostEqual(y_from_original_model, 1, places=1)
# actual test
self.assertAlmostEqual(y_from_loaded_model,
y_from_original_model,
places=4)
def test_rename_input_nodes(self):
"""rename_input_nodes should rename input nodes in-place"""
model_file = testutils.get_path_to(testutils.SIMPLE_MODEL_FILE_NAME)
graph_def = testutils.get_sample_graph_def(model_file)
updated = util.rename_input_nodes(graph_def, {'x': 'scalar'})
# update should be in-place
self.assertEqual(graph_def, updated)
# inputs should be renamed
self.assertEqual(util.get_input_nodes(updated)[0].name, 'scalar')
# model should still work
model = testutils.graph_to_model(updated)
s = 18
scalar = tf.constant([[s]], dtype=tf.float32)
result = model(scalar)
value = result[0].numpy()
# value = np.reshape(value, (1))
y = value[0]
self.assertAlmostEqual(y, s*5, delta=0.1)
def test_rename_output_nodes_append_identity(self):
"""rename_output_nodes should work for outputs that aren't Identity"""
model_file = testutils.get_path_to(testutils.SIMPLE_MODEL_FILE_NAME)
graph_def = testutils.get_sample_graph_def(model_file)
# some open-heart surgery on the model to remove the "Identity" output
idx = [i for (i, n) in enumerate(graph_def.node) if n.op == 'Identity']
del graph_def.node[idx[0]]
output = util.get_output_nodes(graph_def)[0].name
updated = util.rename_output_nodes(graph_def, {output: 'estimate'})
# update should be in-place
self.assertEqual(graph_def, updated)
# outputs should be renamed
self.assertEqual(util.get_output_nodes(updated)[0].name, 'estimate')
# model should still work
model = testutils.graph_to_model(updated)
s = 18
scalar = tf.constant([[s]], dtype=tf.float32)
result = model(scalar)
value = result[0].numpy()
# value = np.reshape(value, (1))
y = value[0]
self.assertAlmostEqual(y, s*5, delta=0.1)
