def testQuantizeTraining(self):
with session.Session() as sess:
a = constant_op.constant(6.0, shape=[1, 1])
b = constant_op.constant(7.0, shape=[1, 1])
c = math_ops.matmul(a, b, name='matmul')
self.assertEquals(c.eval(), 42.0)
self.assertEquals(len(sess.graph_def.node), 3)
result = pywrap_tensorflow.do_quantize_training_on_graphdef(
sess.graph_def, 8)
# We just want to guarantee that some rewrite happened.
self.assertGreater(len(result.node), 3)
def testQuantizeTraining(self):
with session.Session() as sess:
a = constant_op.constant(6.0, shape=[1, 1])
b = constant_op.constant(7.0, shape=[1, 1])
c = math_ops.matmul(a, b, name='matmul')
self.assertEquals(c.eval(), 42.0)
self.assertEquals(len(sess.graph_def.node), 3)
result = pywrap_tensorflow.do_quantize_training_on_graphdef(
sess.graph_def, 8)
# We just want to guarantee that some rewrite happened.
self.assertGreater(len(result.node), 3)
def testQuantizeTraining(self):
with session.Session() as sess:
a = constant_op.constant(6.0, shape=[1, 1])
b = constant_op.constant(7.0, shape=[1, 1])
c = math_ops.matmul(a, b, name='matmul')
self.assertEquals(len(sess.graph_def.node), 3)
result = pywrap_tensorflow.do_quantize_training_on_graphdef(
sess.graph_def, 8)
# 2 convert ops are added so it should have 5 nodes now.
self.assertEquals(len(result.node), 5)
self.assertEquals(c.eval(), 42)
def testQuantizeTraining(self):
with session.Session() as sess:
a = constant_op.constant(6.0, shape=[1, 1])
b = constant_op.constant(7.0, shape=[1, 1])
c = math_ops.matmul(a, b, name='matmul')
self.assertEquals(len(sess.graph_def.node), 3)
result = pywrap_tensorflow.do_quantize_training_on_graphdef(
sess.graph_def, 8)
# 2 convert ops are added so it should have 5 nodes now.
self.assertEquals(len(result.node), 5)
self.assertEquals(c.eval(), 42)
def testQuantizedSaveRestore(self):
save_path = os.path.join(self.get_temp_dir(), 'quantized_save_restore')
g = ops.Graph()
with session.Session(graph=g) as sess:
a = constant_op.constant(6.0, shape=[1, 1], name='a')
b = variables.Variable(constant_op.constant(7.0, shape=[1, 1]),
name='b')
c = math_ops.matmul(a, b, name='matmul')
init_op = variables.global_variables_initializer()
saver = saver_module.Saver({'b': b})
result = pywrap_tensorflow.do_quantize_training_on_graphdef(
sess.graph_def, 8)
with ops.Graph().as_default() as g, session.Session(graph=g) as sess:
_ = importer.import_graph_def(result, name='')
# Initialize the variable.
sess.run(g.get_operation_by_name(init_op.name))
# Run the graph for one step to assign values to the quantization min/max
# variables.
sess.run(g.get_tensor_by_name(c.name))
saver.save(sess, save_path)
with ops.Graph().as_default() as g, session.Session(graph=g) as sess:
_ = importer.import_graph_def(result, name='')
# When we restore the saved variabled, the quantization variables should
# be restored as well.
saver.restore(sess, save_path)
self.assertEquals(7.0, sess.run(g.get_tensor_by_name('b:0')))
self.assertEquals(
6.0, sess.run(g.get_tensor_by_name('a/Min/Variable:0')))
self.assertEquals(
6.0, sess.run(g.get_tensor_by_name('a/Max/Variable:0')))
self.assertEquals(
7.0, sess.run(g.get_tensor_by_name('b/read/Min/Variable:0')))
self.assertEquals(
7.0, sess.run(g.get_tensor_by_name('b/read/Max/Variable:0')))
def testQuantizedSaveRestore(self):
save_path = os.path.join(self.get_temp_dir(), 'quantized_save_restore')
g = ops.Graph()
with session.Session(graph=g) as sess:
a = constant_op.constant(6.0, shape=[1, 1], name='a')
b = variables.VariableV1(
constant_op.constant(7.0, shape=[1, 1]), name='b')
c = math_ops.matmul(a, b, name='matmul')
init_op = variables.global_variables_initializer()
saver = saver_module.Saver({'b': b})
result = pywrap_tensorflow.do_quantize_training_on_graphdef(
sess.graph_def, 8)
with ops.Graph().as_default() as g, session.Session(graph=g) as sess:
_ = importer.import_graph_def(result, name='')
# Initialize the variable.
self.evaluate(g.get_operation_by_name(init_op.name))
# Run the graph for one step to assign values to the quantization min/max
# variables.
self.evaluate(g.get_tensor_by_name(c.name))
saver.save(sess, save_path)
with ops.Graph().as_default() as g, session.Session(graph=g) as sess:
_ = importer.import_graph_def(result, name='')
# When we restore the saved variabled, the quantization variables should
# be restored as well.
saver.restore(sess, save_path)
self.assertEquals(7.0, sess.run(g.get_tensor_by_name('b:0')))
self.assertEquals(6.0, sess.run(g.get_tensor_by_name('a/Min/Variable:0')))
self.assertEquals(6.0, sess.run(g.get_tensor_by_name('a/Max/Variable:0')))
self.assertEquals(7.0,
sess.run(g.get_tensor_by_name('b/read/Min/Variable:0')))
self.assertEquals(7.0,
sess.run(g.get_tensor_by_name('b/read/Max/Variable:0')))