def test_augment_graph_with_zero_value_dimension(self):
'''TEST_CONFIG_5'''
# Conv
# |
# Conv
# |
# Resize
G = helper.make_tensor_value_info('G', TensorProto.FLOAT, [1, 1, 5, 5])
H = helper.make_tensor_value_info('H', TensorProto.FLOAT, [1, 1, 3, 3])
J = helper.make_tensor_value_info('J', TensorProto.FLOAT, [1, 1, 3, 3])
M = helper.make_tensor_value_info('M', TensorProto.FLOAT, [0])
N = helper.make_tensor_value_info('N', TensorProto.FLOAT, [0])
O = helper.make_tensor_value_info('O', TensorProto.FLOAT, [1, 1, 5, 5])
# O = helper.make_tensor_value_info('O', TensorProto.FLOAT, None)
conv_node_1 = onnx.helper.make_node('Conv', ['G', 'H'], ['I'],
name='Conv1',
kernel_shape=[3, 3],
pads=[1, 1, 1, 1])
conv_node_2 = onnx.helper.make_node('Conv', ['I', 'J'], ['K'],
name='Conv2',
kernel_shape=[3, 3],
pads=[1, 1, 1, 1])
resize_node_1 = onnx.helper.make_node('Resize', ['K', 'M', 'N'], ['O'],
name='Reize1')
graph = helper.make_graph([conv_node_1, conv_node_2, resize_node_1],
'test_graph_5', [G, H, J, M, N], [O])
model = helper.make_model(graph,
opset_imports=[helper.make_opsetid("", 13)])
test_model_path = './test_model_5.onnx'
onnx.save(model, test_model_path)
augmented_model_path = './augmented_test_model_5.onnx'
calibrater = MinMaxCalibrater(test_model_path, [],
augmented_model_path)
augmented_model = calibrater.get_augment_model()
augmented_model_node_names = [
node.name for node in augmented_model.graph.node
]
augmented_model_outputs = [
output.name for output in augmented_model.graph.output
]
added_node_names = [
'I_ReduceMin', 'I_ReduceMax', 'K_ReduceMin', 'K_ReduceMax',
'O_ReduceMin', 'O_ReduceMax'
]
added_outputs = [
'I_ReduceMin', 'I_ReduceMax', 'K_ReduceMin', 'K_ReduceMax',
'O_ReduceMin', 'O_ReduceMax'
]
# Original 3 nodes + added ReduceMin/Max nodes * 8
self.assertEqual(len(augmented_model_node_names), 19)
# Original 1 graph output + added outputs * 8
self.assertEqual(len(augmented_model_outputs), 17)
for name in added_node_names:
self.assertTrue(name in augmented_model_node_names)
for output in added_outputs:
self.assertTrue(output in augmented_model_outputs)
def test_augment_graph_config_2(self):
"""TEST_CONFIG_2"""
# Conv
# |
# Conv
G = helper.make_tensor_value_info("G", TensorProto.FLOAT, [1, 1, 5, 5])
H = helper.make_tensor_value_info("H", TensorProto.FLOAT, [1, 1, 3, 3])
J = helper.make_tensor_value_info("J", TensorProto.FLOAT, [1, 1, 3, 3])
K = helper.make_tensor_value_info("K", TensorProto.FLOAT, [1, 1, 5, 5])
conv_node_1 = onnx.helper.make_node(
"Conv",
["G", "H"],
["I"],
name="Conv1",
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
)
conv_node_2 = onnx.helper.make_node(
"Conv",
["I", "J"],
["K"],
name="Conv2",
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
)
graph = helper.make_graph([conv_node_1, conv_node_2], "test_graph_2",
[G, H, J], [K])
model = helper.make_model(graph,
opset_imports=[helper.make_opsetid("", 13)])
test_model_path = "./test_model_2.onnx"
onnx.save(model, test_model_path)
augmented_model_path = "./augmented_test_model_2.onnx"
calibrater = MinMaxCalibrater(test_model_path, ["Conv", "MatMul"],
augmented_model_path)
augmented_model = calibrater.get_augment_model()
augmented_model_node_names = [
node.name for node in augmented_model.graph.node
]
augmented_model_outputs = [
output.name for output in augmented_model.graph.output
]
added_node_names = [
"I_ReduceMin", "I_ReduceMax", "K_ReduceMin", "K_ReduceMax"
]
added_outputs = [
"I_ReduceMin", "I_ReduceMax", "K_ReduceMin", "K_ReduceMax"
]
# Original 2 nodes + added ReduceMin/Max nodes * 4
self.assertEqual(len(augmented_model_node_names), 12)
# Original 1 graph output + added outputs * 4
self.assertEqual(len(augmented_model_outputs), 11)
for name in added_node_names:
self.assertTrue(name in augmented_model_node_names)
for output in added_outputs:
self.assertTrue(output in augmented_model_outputs)
def test_augment_graph_config_1(self):
''' TEST_CONFIG_1'''
# Conv
# |
# Clip
# |
# MatMul
A = helper.make_tensor_value_info('A', TensorProto.FLOAT, [1, 1, 5, 5])
B = helper.make_tensor_value_info('B', TensorProto.FLOAT, [1, 1, 3, 3])
E = helper.make_tensor_value_info('E', TensorProto.FLOAT, [1, 1, 5, 1])
F = helper.make_tensor_value_info('F', TensorProto.FLOAT, [1, 1, 5, 1])
conv_node = onnx.helper.make_node('Conv', ['A', 'B'], ['C'],
name='Conv',
kernel_shape=[3, 3],
pads=[1, 1, 1, 1])
clip_node = onnx.helper.make_node('Clip', ['C'], ['D'], name='Clip')
matmul_node = onnx.helper.make_node('MatMul', ['D', 'E'], ['F'],
name='MatMul')
graph = helper.make_graph([conv_node, clip_node, matmul_node],
'test_graph_1', [A, B, E], [F])
model = helper.make_model(graph,
opset_imports=[helper.make_opsetid("", 13)])
test_model_path = './test_model_1.onnx'
onnx.save(model, test_model_path)
# Augmenting graph
augmented_model_path = './augmented_test_model_1.onnx'
calibrater = MinMaxCalibrater(test_model_path, ['Conv', 'MatMul'],
augmented_model_path)
augmented_model = calibrater.get_augment_model()
# Checking if each added ReduceMin and ReduceMax node and its output exists
augmented_model_node_names = [
node.name for node in augmented_model.graph.node
]
augmented_model_outputs = [
output.name for output in augmented_model.graph.output
]
added_node_names = [
'C_ReduceMin', 'C_ReduceMax', 'D_ReduceMin', 'D_ReduceMax',
'F_ReduceMin', 'F_ReduceMax'
]
added_outputs = [
'C_ReduceMin', 'C_ReduceMax', 'D_ReduceMin', 'D_ReduceMax',
'F_ReduceMin', 'F_ReduceMax'
]
# Original 3 nodes + added ReduceMin/Max nodes
self.assertEqual(len(augmented_model_node_names), 15)
# Original 1 graph output + added outputs * 6
self.assertEqual(len(augmented_model_outputs), 13)
for name in added_node_names:
self.assertTrue(name in augmented_model_node_names)
for output in added_outputs:
self.assertTrue(output in augmented_model_outputs)
def test_augment_graph_config_2(self):
'''TEST_CONFIG_2'''
# Conv
# |
# Conv
G = helper.make_tensor_value_info('G', TensorProto.FLOAT, [1, 1, 5, 5])
H = helper.make_tensor_value_info('H', TensorProto.FLOAT, [1, 1, 3, 3])
J = helper.make_tensor_value_info('J', TensorProto.FLOAT, [1, 1, 3, 3])
K = helper.make_tensor_value_info('K', TensorProto.FLOAT, [1, 1, 5, 5])
conv_node_1 = onnx.helper.make_node('Conv', ['G', 'H'], ['I'],
name='Conv1',
kernel_shape=[3, 3],
pads=[1, 1, 1, 1])
conv_node_2 = onnx.helper.make_node('Conv', ['I', 'J'], ['K'],
name='Conv2',
kernel_shape=[3, 3],
pads=[1, 1, 1, 1])
graph = helper.make_graph([conv_node_1, conv_node_2], 'test_graph_2',
[G, H, J], [K])
model = helper.make_model(graph,
opset_imports=[helper.make_opsetid("", 13)])
test_model_path = './test_model_2.onnx'
onnx.save(model, test_model_path)
augmented_model_path = './augmented_test_model_2.onnx'
calibrater = MinMaxCalibrater(test_model_path, ['Conv', 'MatMul'],
augmented_model_path)
augmented_model = calibrater.get_augment_model()
augmented_model_node_names = [
node.name for node in augmented_model.graph.node
]
augmented_model_outputs = [
output.name for output in augmented_model.graph.output
]
added_node_names = [
'I_ReduceMin', 'I_ReduceMax', 'K_ReduceMin', 'K_ReduceMax'
]
added_outputs = [
'I_ReduceMin', 'I_ReduceMax', 'K_ReduceMin', 'K_ReduceMax'
]
# Original 2 nodes + added ReduceMin/Max nodes * 4
self.assertEqual(len(augmented_model_node_names), 12)
# Original 1 graph output + added outputs * 4
self.assertEqual(len(augmented_model_outputs), 11)
for name in added_node_names:
self.assertTrue(name in augmented_model_node_names)
for output in added_outputs:
self.assertTrue(output in augmented_model_outputs)
def test_compute_range(self):
test_model_path = "./test_model_4.onnx"
self.construct_test_compute_range_model(test_model_path)
augmented_model_path = "./augmented_test_model_4.onnx"
calibrater = MinMaxCalibrater(
test_model_path, augmented_model_path=augmented_model_path)
data_reader = TestDataReader()
calibrater.collect_data(data_reader)
tensors_range = calibrater.compute_range()
sess_options = onnxruntime.SessionOptions()
sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_DISABLE_ALL
infer_session = onnxruntime.InferenceSession(
test_model_path,
sess_options=sess_options,
providers=["CPUExecutionProvider"],
)
data_reader.rewind()
rmin = np.array([np.inf, np.inf, np.inf, np.inf, np.inf, np.inf],
dtype=np.float32)
rmax = -1.0 * rmin
while True:
input = data_reader.get_next()
if not input:
break
output = np.asarray(infer_session.run(None, input)).reshape(6, -1)
rmin = np.minimum(rmin, np.amin(output, axis=1))
rmax = np.maximum(rmax, np.amax(output, axis=1))
min_max_pairs = list(zip(rmin, rmax))
output_names = [
infer_session.get_outputs()[i].name
for i in range(len(infer_session.get_outputs()))
]
output_min_max_dict = dict(zip(output_names, min_max_pairs))
for output_name in output_min_max_dict.keys():
self.assertEqual(output_min_max_dict[output_name],
tensors_range[output_name])
def test_augment_graph_config_3(self):
'''TEST_CONFIG_3'''
# (input)
# |
# Relu
# / \
# Conv \
# | |
# Clip |
# | /
# MatMul
# |
# (output)
L = helper.make_tensor_value_info('L', TensorProto.FLOAT, [1, 1, 5, 5])
N = helper.make_tensor_value_info('N', TensorProto.FLOAT, [1, 1, 3, 3])
Q = helper.make_tensor_value_info('Q', TensorProto.FLOAT, [1, 1, 5, 5])
relu_node = onnx.helper.make_node('Relu', ['L'], ['M'], name='Relu')
conv_node = onnx.helper.make_node('Conv', ['M', 'N'], ['O'],
name='Conv',
kernel_shape=[3, 3],
pads=[1, 1, 1, 1])
clip_node = onnx.helper.make_node('Clip', ['O'], ['P'], name='Clip')
matmul_node = onnx.helper.make_node('MatMul', ['P', 'M'], ['Q'],
name='MatMul')
graph = helper.make_graph(
[relu_node, conv_node, clip_node, matmul_node], 'test_graph_3',
[L, N], [Q])
model = helper.make_model(graph,
opset_imports=[helper.make_opsetid("", 13)])
test_model_path = './test_model_3.onnx'
onnx.save(model, test_model_path)
# Augmenting graph
augmented_model_path = './augmented_test_model_3.onnx'
calibrater = MinMaxCalibrater(test_model_path, ['Conv', 'MatMul'],
augmented_model_path)
augmented_model = calibrater.get_augment_model()
augmented_model_node_names = [
node.name for node in augmented_model.graph.node
]
augmented_model_outputs = [
output.name for output in augmented_model.graph.output
]
added_node_names = [
'M_ReduceMin', 'M_ReduceMax', 'O_ReduceMin', 'O_ReduceMax',
'P_ReduceMin', 'P_ReduceMax', 'Q_ReduceMin', 'Q_ReduceMax'
]
added_outputs = [
'M_ReduceMin', 'M_ReduceMax', 'O_ReduceMin', 'O_ReduceMax',
'P_ReduceMin', 'P_ReduceMax', 'Q_ReduceMin', 'Q_ReduceMax'
]
# Original 4 nodes + added ReduceMin/Max nodes
self.assertEqual(len(augmented_model_node_names), 14)
# Original 1 graph output + added outputs
self.assertEqual(len(augmented_model_outputs), 11)
for name in added_node_names:
self.assertTrue(name in augmented_model_node_names)
for output in added_outputs:
self.assertTrue(output in augmented_model_outputs)
def test_augment_graph_config_1(self):
"""TEST_CONFIG_1"""
# Conv
# |
# Clip
# |
# MatMul
A = helper.make_tensor_value_info("A", TensorProto.FLOAT, [1, 1, 5, 5])
B = helper.make_tensor_value_info("B", TensorProto.FLOAT, [1, 1, 3, 3])
E = helper.make_tensor_value_info("E", TensorProto.FLOAT, [1, 1, 5, 1])
F = helper.make_tensor_value_info("F", TensorProto.FLOAT, [1, 1, 5, 1])
conv_node = onnx.helper.make_node(
"Conv",
["A", "B"],
["C"],
name="Conv",
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
)
clip_node = onnx.helper.make_node("Clip", ["C"], ["D"], name="Clip")
matmul_node = onnx.helper.make_node("MatMul", ["D", "E"], ["F"],
name="MatMul")
graph = helper.make_graph([conv_node, clip_node, matmul_node],
"test_graph_1", [A, B, E], [F])
model = helper.make_model(graph,
opset_imports=[helper.make_opsetid("", 13)])
test_model_path = "./test_model_1.onnx"
onnx.save(model, test_model_path)
# Augmenting graph
augmented_model_path = "./augmented_test_model_1.onnx"
calibrater = MinMaxCalibrater(test_model_path, ["Conv", "MatMul"],
augmented_model_path)
augmented_model = calibrater.get_augment_model()
# Checking if each added ReduceMin and ReduceMax node and its output exists
augmented_model_node_names = [
node.name for node in augmented_model.graph.node
]
augmented_model_outputs = [
output.name for output in augmented_model.graph.output
]
added_node_names = [
"C_ReduceMin",
"C_ReduceMax",
"D_ReduceMin",
"D_ReduceMax",
"F_ReduceMin",
"F_ReduceMax",
]
added_outputs = [
"C_ReduceMin",
"C_ReduceMax",
"D_ReduceMin",
"D_ReduceMax",
"F_ReduceMin",
"F_ReduceMax",
]
# Original 3 nodes + added ReduceMin/Max nodes
self.assertEqual(len(augmented_model_node_names), 15)
# Original 1 graph output + added outputs * 6
self.assertEqual(len(augmented_model_outputs), 13)
for name in added_node_names:
self.assertTrue(name in augmented_model_node_names)
for output in added_outputs:
self.assertTrue(output in augmented_model_outputs)
def test_augment_graph_with_zero_value_dimension(self):
"""TEST_CONFIG_5"""
# Conv
# |
# Conv
# |
# Resize
G = helper.make_tensor_value_info("G", TensorProto.FLOAT, [1, 1, 5, 5])
H = helper.make_tensor_value_info("H", TensorProto.FLOAT, [1, 1, 3, 3])
J = helper.make_tensor_value_info("J", TensorProto.FLOAT, [1, 1, 3, 3])
M = helper.make_tensor_value_info("M", TensorProto.FLOAT, [0])
N = helper.make_tensor_value_info("N", TensorProto.FLOAT, [0])
O = helper.make_tensor_value_info("O", TensorProto.FLOAT, [1, 1, 5, 5])
# O = helper.make_tensor_value_info('O', TensorProto.FLOAT, None)
conv_node_1 = onnx.helper.make_node(
"Conv",
["G", "H"],
["I"],
name="Conv1",
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
)
conv_node_2 = onnx.helper.make_node(
"Conv",
["I", "J"],
["K"],
name="Conv2",
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
)
resize_node_1 = onnx.helper.make_node("Resize", ["K", "M", "N"], ["O"],
name="Reize1")
graph = helper.make_graph(
[conv_node_1, conv_node_2, resize_node_1],
"test_graph_5",
[G, H, J, M, N],
[O],
)
model = helper.make_model(graph,
opset_imports=[helper.make_opsetid("", 13)])
test_model_path = "./test_model_5.onnx"
onnx.save(model, test_model_path)
augmented_model_path = "./augmented_test_model_5.onnx"
calibrater = MinMaxCalibrater(test_model_path, [],
augmented_model_path)
augmented_model = calibrater.get_augment_model()
augmented_model_node_names = [
node.name for node in augmented_model.graph.node
]
augmented_model_outputs = [
output.name for output in augmented_model.graph.output
]
added_node_names = [
"I_ReduceMin",
"I_ReduceMax",
"K_ReduceMin",
"K_ReduceMax",
"O_ReduceMin",
"O_ReduceMax",
]
added_outputs = [
"I_ReduceMin",
"I_ReduceMax",
"K_ReduceMin",
"K_ReduceMax",
"O_ReduceMin",
"O_ReduceMax",
]
# Original 3 nodes + added ReduceMin/Max nodes * 8
self.assertEqual(len(augmented_model_node_names), 19)
# Original 1 graph output + added outputs * 8
self.assertEqual(len(augmented_model_outputs), 17)
for name in added_node_names:
self.assertTrue(name in augmented_model_node_names)
for output in added_outputs:
self.assertTrue(output in augmented_model_outputs)
def test_augment_graph_config_3(self):
"""TEST_CONFIG_3"""
# (input)
# |
# Relu
# / \
# Conv \
# | |
# Clip |
# | /
# MatMul
# |
# (output)
L = helper.make_tensor_value_info("L", TensorProto.FLOAT, [1, 1, 5, 5])
N = helper.make_tensor_value_info("N", TensorProto.FLOAT, [1, 1, 3, 3])
Q = helper.make_tensor_value_info("Q", TensorProto.FLOAT, [1, 1, 5, 5])
relu_node = onnx.helper.make_node("Relu", ["L"], ["M"], name="Relu")
conv_node = onnx.helper.make_node(
"Conv",
["M", "N"],
["O"],
name="Conv",
kernel_shape=[3, 3],
pads=[1, 1, 1, 1],
)
clip_node = onnx.helper.make_node("Clip", ["O"], ["P"], name="Clip")
matmul_node = onnx.helper.make_node("MatMul", ["P", "M"], ["Q"],
name="MatMul")
graph = helper.make_graph(
[relu_node, conv_node, clip_node, matmul_node], "test_graph_3",
[L, N], [Q])
model = helper.make_model(graph,
opset_imports=[helper.make_opsetid("", 13)])
test_model_path = "./test_model_3.onnx"
onnx.save(model, test_model_path)
# Augmenting graph
augmented_model_path = "./augmented_test_model_3.onnx"
calibrater = MinMaxCalibrater(test_model_path, ["Conv", "MatMul"],
augmented_model_path)
augmented_model = calibrater.get_augment_model()
augmented_model_node_names = [
node.name for node in augmented_model.graph.node
]
augmented_model_outputs = [
output.name for output in augmented_model.graph.output
]
added_node_names = [
"M_ReduceMin",
"M_ReduceMax",
"O_ReduceMin",
"O_ReduceMax",
"P_ReduceMin",
"P_ReduceMax",
"Q_ReduceMin",
"Q_ReduceMax",
]
added_outputs = [
"M_ReduceMin",
"M_ReduceMax",
"O_ReduceMin",
"O_ReduceMax",
"P_ReduceMin",
"P_ReduceMax",
"Q_ReduceMin",
"Q_ReduceMax",
]
# Original 4 nodes + added ReduceMin/Max nodes
self.assertEqual(len(augmented_model_node_names), 14)
# Original 1 graph output + added outputs
self.assertEqual(len(augmented_model_outputs), 11)
for name in added_node_names:
self.assertTrue(name in augmented_model_node_names)
for output in added_outputs:
self.assertTrue(output in augmented_model_outputs)