def testQuantizeTrain(self):
input_meta_name = "input_meta.meta"
with ops.Graph().as_default():
self._build_graph(is_freezed=False)
graph_def = ops.get_default_graph().as_graph_def()
input_meta_path = os.path.join(self.get_temp_dir(),
input_meta_name)
saver_lib.export_meta_graph(filename=input_meta_path)
original_graph_node = [node.name for node in graph_def.node]
meta_graph_def = MetaGraphDef()
meta_graph_def = self._parse_def_from_file(meta_graph_def,
input_meta_path)
q_config, _ = self._compose_config()
decent_q.quantize_train(meta_graph_def, q_config)
output_meta_graph_def = MetaGraphDef()
output_meta_graph_path = os.path.join(
self.get_temp_dir(), "quantize_train/quantize_train.ckpt.meta")
output_meta_graph_def = self._parse_def_from_file(
output_meta_graph_def, output_meta_graph_path)
quantize_train_graph_def = output_meta_graph_def.graph_def
for node in quantize_train_graph_def.node:
if node.name not in original_graph_node:
self.assertEqual(node.op, "FixNeuron")
def _parse_input_meta_graph_proto(input_graph, input_binary):
"""Parser input tensorflow graph into MetaGraphDef proto."""
if not gfile.Exists(input_graph):
print("Input meta graph file '" + input_graph + "' does not exist!")
return -1
input_meta_graph_def = MetaGraphDef()
mode = "rb" if input_binary else "r"
with gfile.FastGFile(input_graph, mode) as f:
if input_binary:
input_meta_graph_def.ParseFromString(f.read())
else:
text_format.Merge(f.read(), input_meta_graph_def)
print("Loaded meta graph file '" + input_graph)
return input_meta_graph_def
def _parse_input_meta_graph(input_meta_graph):
"""Parse input_meta_graph configurations"""
if not gfile.Exists(input_meta_graph):
raise ValueError("Input meta graph file '" + input_meta_graph +
"' does not exist.")
meta_graph_def = MetaGraphDef()
with gfile.GFile(input_meta_graph, "rb") as f:
meta_graph_def.ParseFromString(f.read())
return meta_graph_def
def testQuantizeEval(self):
input_meta_name = "original_meta.meta"
input_meta_path = os.path.join(self.get_temp_dir(), input_meta_name)
with ops.Graph().as_default():
self._build_graph(is_freezed=False)
graph_def = ops.get_default_graph().as_graph_def()
saver_lib.export_meta_graph(filename=input_meta_path)
original_meta_graph_def = MetaGraphDef()
original_meta_graph_def = self._parse_def_from_file(
original_meta_graph_def, input_meta_path)
q_config, _ = self._compose_config()
decent_q.quantize_train(original_meta_graph_def, q_config)
quant_train_meta_graph_def = MetaGraphDef()
quant_train_meta_graph_path = os.path.join(
self.get_temp_dir(), "quantize_train/quantize_train.ckpt.meta")
quant_train_meta_graph_def = self._parse_def_from_file(
quant_train_meta_graph_def, quant_train_meta_graph_path)
with session.Session() as sess:
new_saver = saver_lib.import_meta_graph(quant_train_meta_graph_def)
relu = sess.graph.get_tensor_by_name("relu/aquant:0")
input_fn = self._mock_input_fn("input:0", [1, 4, 4, 3])
init = variables.global_variables_initializer()
sess.run(init)
relu_val = sess.run([relu], feed_dict=input_fn(1))
decent_q.quantize_evaluate(quant_train_meta_graph_def, q_config)
quant_eval_meta_graph_def = MetaGraphDef()
quant_eval_meta_graph_path = os.path.join(
self.get_temp_dir(), "quantize_eval/quantize_eval.ckpt.meta")
quant_eval_meta_graph_def = self._parse_def_from_file(
quant_eval_meta_graph_def, quant_eval_meta_graph_path)
eval_quant_pos = [
node.attr["quantize_pos"].i
for node in quant_eval_meta_graph_def.graph_def.node
if node.op == "FixNeuron"
]
self.assertAllEqual([8, 7, 6, 4], eval_quant_pos)
def testDeployCheckpoint(self):
input_meta_name = "original_meta.meta"
input_meta_path = os.path.join(self.get_temp_dir(), input_meta_name)
q_config, _ = self._compose_config()
with ops.Graph().as_default():
self._build_graph(is_freezed=False)
graph_def = ops.get_default_graph().as_graph_def()
saver_lib.export_meta_graph(filename=input_meta_path)
original_meta_graph_def = MetaGraphDef()
original_meta_graph_def = self._parse_def_from_file(
original_meta_graph_def, input_meta_path)
decent_q.quantize_train(original_meta_graph_def, q_config)
quant_train_meta_graph_def = MetaGraphDef()
quant_train_meta_graph_path = os.path.join(
self.get_temp_dir(), "quantize_train/quantize_train.ckpt.meta")
quant_train_meta_graph_def = self._parse_def_from_file(
quant_train_meta_graph_def, quant_train_meta_graph_path)
with ops.Graph().as_default():
new_saver = saver_lib.import_meta_graph(quant_train_meta_graph_def)
with session.Session() as sess:
w_t = sess.graph.get_tensor_by_name("w/read/wquant:0")
b_t = sess.graph.get_tensor_by_name("b/read/wquant:0")
relu_t = sess.graph.get_tensor_by_name("relu/aquant:0")
input_fn = self._mock_input_fn("input:0", [1, 4, 4, 3])
init = variables.global_variables_initializer()
sess.run(init)
eval_relu, eval_w, eval_b = sess.run([relu_t, w_t, b_t],
feed_dict=input_fn(1))
checkpoint_prefix = os.path.join(self.get_temp_dir(),
"ckpt/saved_checkpoint")
checkpoint_state_name = "checkpoint_state"
checkpoint_path = new_saver.save(
sess,
checkpoint_prefix,
global_step=0,
latest_filename=checkpoint_state_name)
q_config.output_nodes = ["relu/aquant"]
decent_q.quantize_evaluate(quant_train_meta_graph_def, q_config)
quant_eval_meta_graph_def = MetaGraphDef()
quant_eval_meta_graph_path = os.path.join(
self.get_temp_dir(), "quantize_eval/quantize_eval.ckpt.meta")
quant_eval_meta_graph_def = self._parse_def_from_file(
quant_eval_meta_graph_def, quant_eval_meta_graph_path)
sess.close()
decent_q.deploy_checkpoint(quant_eval_meta_graph_def, checkpoint_path,
q_config)
deploy_graph_def = graph_pb2.GraphDef()
deploy_graph_path = os.path.join(self.get_temp_dir(),
"deploy/deploy_model.pb")
deploy_graph_def = self._parse_def_from_file(deploy_graph_def,
deploy_graph_path)
for node in deploy_graph_def.node:
if node.name == "conv2d":
# need to equal with quantize pos in quantize_eval_model.pb
self.assertAllEqual(node.attr['ipos'].list.i, [8, 6])
self.assertAllEqual(node.attr['wpos'].list.i, [8, 7])
self.assertAllEqual(node.attr['bpos'].list.i, [8, 8])
self.assertAllEqual(node.attr['opos'].list.i, [8, 4])
deploy_w = tensor_util.MakeNdarray(node.attr['weights'].tensor)
deploy_b = tensor_util.MakeNdarray(node.attr['bias'].tensor)
self.assertNDArrayNear(deploy_w, eval_w, 1e-6)
self.assertNDArrayNear(deploy_b, eval_b, 1e-6)