def load_inference_model(dirname, executor):
"""
Load inference model from a directory
:param dirname: directory path
:param executor: executor that load inference model
:return: [program, feed_var_names, fetch_var_names]
program: program especially for inference.
feeded_var_names: Names of variables that need to feed data
fetch_vars: Variables from which we can get inference results.
"""
if not os.path.isdir(dirname):
raise ValueError("There is no directory named '%s'", dirname)
model_file_name = dirname + "/__model__"
model = pickle.load(open(model_file_name, "r"))
program_desc_str = model["program_desc_str"]
feed_var_names = model["feed_var_names"]
fetch_var_names = model["fetch_var_names"]
program = Program.parse_from_string(program_desc_str)
load_persistables_if_exist(executor, dirname, program)
fetch_vars = [program.global_block().var(name) for name in fetch_var_names]
return [program, feed_var_names, fetch_vars]
def test_parse_program_from_string(self):
prog = Program()
x = prog.global_block().create_var(
name='X', shape=[1000, 784], dtype='float32')
y = prog.global_block().create_var(
name='Y', shape=[784, 100], dtype='float32')
out = prog.global_block().create_var(name='Out', dtype='float32')
prog.global_block().append_op(
type="mul", inputs={'X': [x],
'Y': [y]}, outputs={'Out': [out]})
binary_str = prog.desc.serialize_to_string()
prog_restored = Program.parse_from_string(binary_str)
print(prog)
print(prog_restored)
def test_parse_program_from_string(self):
prog = Program()
x = prog.global_block().create_var(name='X',
shape=[1000, 784],
dtype='float32')
y = prog.global_block().create_var(name='Y',
shape=[784, 100],
dtype='float32')
out = prog.global_block().create_var(name='Out', dtype='float32')
prog.global_block().append_op(type="mul",
inputs={
'X': [x],
'Y': [y]
},
outputs={'Out': [out]})
binary_str = prog.desc.serialize_to_string()
prog_restored = Program.parse_from_string(binary_str)
print(prog)
print(prog_restored)