def test_mat_mul(m, n, k, a, b):
"""Tests a MatMul replacement."""
a_constant_name = "a_constant"
b_constant_name = "b_constant"
mat_mul_name = "mat_mul"
float_graph_def = tf.GraphDef()
a_constant = quantize_graph.create_constant_node(a_constant_name,
value=a,
dtype=tf.float32,
shape=[m, k])
float_graph_def.node.extend([a_constant])
b_constant = quantize_graph.create_constant_node(b_constant_name,
value=b,
dtype=tf.float32,
shape=[k, n])
float_graph_def.node.extend([b_constant])
mat_mul_node = quantize_graph.create_node(
"MatMul", mat_mul_name, [a_constant_name, b_constant_name])
quantize_graph.set_attr_dtype(mat_mul_node, "T", tf.float32)
quantize_graph.set_attr_bool(mat_mul_node, "transpose_a", False)
quantize_graph.set_attr_bool(mat_mul_node, "transpose_b", False)
float_graph_def.node.extend([mat_mul_node])
test_graph(float_graph_def, {}, [mat_mul_name])
def test_mat_mul(m, n, k, a, b):
"""Tests a MatMul replacement."""
a_constant_name = "a_constant"
b_constant_name = "b_constant"
mat_mul_name = "mat_mul"
float_graph_def = tf.GraphDef()
a_constant = quantize_graph.create_constant_node(a_constant_name,
value=a,
dtype=tf.float32,
shape=[m, k])
float_graph_def.node.extend([a_constant])
b_constant = quantize_graph.create_constant_node(b_constant_name,
value=b,
dtype=tf.float32,
shape=[k, n])
float_graph_def.node.extend([b_constant])
mat_mul_node = quantize_graph.create_node("MatMul", mat_mul_name,
[a_constant_name, b_constant_name])
quantize_graph.set_attr_dtype(mat_mul_node, "T", tf.float32)
quantize_graph.set_attr_bool(mat_mul_node, "transpose_a", False)
quantize_graph.set_attr_bool(mat_mul_node, "transpose_b", False)
float_graph_def.node.extend([mat_mul_node])
test_graph(float_graph_def, {}, [mat_mul_name])
def test_batch_norm(self):
input_constant_name = "input_constant"
mean_constant_name = "mean_constant"
variance_constant_name = "variance_constant"
beta_constant_name = "beta_constant"
gamma_constant_name = "gamma_constant"
batch_norm_name = "batch_norm"
float_graph_def = tf.GraphDef()
input_constant = quantize_graph.create_constant_node(
input_constant_name,
value=[1, 4, 2, 5, 3, 6, -1, -4, -2, -5, -3, -6],
dtype=tf.float32,
shape=[1, 1, 6, 2])
float_graph_def.node.extend([input_constant])
mean_constant = quantize_graph.create_constant_node(mean_constant_name,
value=[10, 20],
dtype=tf.float32,
shape=[2])
float_graph_def.node.extend([mean_constant])
variance_constant = quantize_graph.create_constant_node(
variance_constant_name,
value=[0.25, 0.5],
dtype=tf.float32,
shape=[2])
float_graph_def.node.extend([variance_constant])
beta_constant = quantize_graph.create_constant_node(beta_constant_name,
value=[0.1, 0.6],
dtype=tf.float32,
shape=[2])
float_graph_def.node.extend([beta_constant])
gamma_constant = quantize_graph.create_constant_node(
gamma_constant_name, value=[0, 0], dtype=tf.float32, shape=[2])
float_graph_def.node.extend([gamma_constant])
batch_norm_node = quantize_graph.create_node(
"BatchNormWithGlobalNormalization", batch_norm_name, [
input_constant_name, mean_constant_name,
variance_constant_name, beta_constant_name, gamma_constant_name
])
quantize_graph.set_attr_dtype(batch_norm_node, "T", tf.float32)
quantize_graph.set_attr_bool(batch_norm_node,
"scale_after_normalization", False)
quantize_graph.set_attr_float(batch_norm_node, "variance_epsilon",
0.001)
float_graph_def.node.extend([batch_norm_node])
test_graph(float_graph_def, {}, [batch_norm_name])
def test_batch_norm(self):
input_constant_name = "input_constant"
mean_constant_name = "mean_constant"
variance_constant_name = "variance_constant"
beta_constant_name = "beta_constant"
gamma_constant_name = "gamma_constant"
batch_norm_name = "batch_norm"
float_graph_def = tf.GraphDef()
input_constant = quantize_graph.create_constant_node(input_constant_name,
value=[1, 4, 2, 5, 3,
6, -1, -4, -2,
-5, -3, -6],
dtype=tf.float32,
shape=[1, 1, 6, 2])
float_graph_def.node.extend([input_constant])
mean_constant = quantize_graph.create_constant_node(mean_constant_name,
value=[10, 20],
dtype=tf.float32,
shape=[2])
float_graph_def.node.extend([mean_constant])
variance_constant = quantize_graph.create_constant_node(
variance_constant_name, value=[0.25, 0.5], dtype=tf.float32, shape=[2])
float_graph_def.node.extend([variance_constant])
beta_constant = quantize_graph.create_constant_node(beta_constant_name,
value=[0.1, 0.6],
dtype=tf.float32,
shape=[2])
float_graph_def.node.extend([beta_constant])
gamma_constant = quantize_graph.create_constant_node(gamma_constant_name,
value=[0, 0],
dtype=tf.float32,
shape=[2])
float_graph_def.node.extend([gamma_constant])
batch_norm_node = quantize_graph.create_node(
"BatchNormWithGlobalNormalization", batch_norm_name,
[input_constant_name, mean_constant_name, variance_constant_name,
beta_constant_name, gamma_constant_name])
quantize_graph.set_attr_dtype(batch_norm_node, "T", tf.float32)
quantize_graph.set_attr_bool(batch_norm_node, "scale_after_normalization",
False)
quantize_graph.set_attr_float(batch_norm_node, "variance_epsilon", 0.001)
float_graph_def.node.extend([batch_norm_node])
test_graph(float_graph_def, {}, [batch_norm_name])
