def do_save_inference_model(args):
test_prog = fluid.default_main_program()
startup_prog = fluid.default_startup_program()
with fluid.program_guard(test_prog, startup_prog):
test_prog.random_seed = args.random_seed
startup_prog.random_seed = args.random_seed
with fluid.unique_name.guard():
context_wordseq = fluid.data(
name='context_wordseq',
shape=[-1, 1],
dtype='int64',
lod_level=1)
response_wordseq = fluid.data(
name='response_wordseq',
shape=[-1, 1],
dtype='int64',
lod_level=1)
labels = fluid.data(name='labels', shape=[-1, 1], dtype='int64')
input_inst = [context_wordseq, response_wordseq, labels]
input_field = InputField(input_inst)
data_reader = fluid.io.PyReader(
feed_list=input_inst, capacity=4, iterable=False)
logits = create_net(
is_training=False, model_input=input_field, args=args)
if args.use_cuda:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
assert (args.init_from_params) or (args.init_from_pretrain_model)
if args.init_from_params:
save_load_io.init_from_params(args, exe, test_prog)
elif args.init_from_pretrain_model:
save_load_io.init_from_pretrain_model(args, exe, test_prog)
# saving inference model
fluid.io.save_inference_model(
args.inference_model_dir,
feeded_var_names=[
input_field.context_wordseq.name,
input_field.response_wordseq.name,
],
target_vars=[logits, ],
executor=exe,
main_program=test_prog,
model_filename="model.pdmodel",
params_filename="params.pdparams")
print("save inference model at %s" % (args.inference_model_dir))
def do_predict(args):
"""
predict function
"""
test_prog = fluid.default_main_program()
startup_prog = fluid.default_startup_program()
with fluid.program_guard(test_prog, startup_prog):
test_prog.random_seed = args.random_seed
startup_prog.random_seed = args.random_seed
with fluid.unique_name.guard():
context_wordseq = fluid.data(name='context_wordseq',
shape=[-1, 1],
dtype='int64',
lod_level=1)
response_wordseq = fluid.data(name='response_wordseq',
shape=[-1, 1],
dtype='int64',
lod_level=1)
labels = fluid.data(name='labels', shape=[-1, 1], dtype='int64')
input_inst = [context_wordseq, response_wordseq, labels]
input_field = InputField(input_inst)
data_reader = fluid.io.PyReader(feed_list=input_inst,
capacity=4,
iterable=False)
logits = create_net(is_training=False,
model_input=input_field,
args=args)
logits.persistable = True
fetch_list = [logits.name]
#for_test is True if change the is_test attribute of operators to True
test_prog = test_prog.clone(for_test=True)
if args.use_cuda:
place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0')))
else:
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
assert (args.init_from_params) or (args.init_from_pretrain_model)
if args.init_from_params:
save_load_io.init_from_params(args, exe, test_prog)
if args.init_from_pretrain_model:
save_load_io.init_from_pretrain_model(args, exe, test_prog)
compiled_test_prog = fluid.CompiledProgram(test_prog)
processor = reader.DataProcessor(data_path=args.predict_file,
max_seq_length=args.max_seq_len,
batch_size=args.batch_size)
batch_generator = processor.data_generator(place=place,
phase="test",
shuffle=False,
sample_pro=1)
num_test_examples = processor.get_num_examples(phase='test')
data_reader.decorate_batch_generator(batch_generator)
data_reader.start()
scores = []
while True:
try:
results = exe.run(compiled_test_prog, fetch_list=fetch_list)
scores.extend(results[0])
except fluid.core.EOFException:
data_reader.reset()
break
scores = scores[:num_test_examples]
print("Write the predicted results into the output_prediction_file")
fw = io.open(args.output_prediction_file, 'w', encoding="utf8")
for index, score in enumerate(scores):
fw.write("%s\t%s\n" % (index, score))
print("finish........................................")