提交 6a79ba2f 编写于 作者: Q Qiao Longfei 提交者: Tao Luo

Add save inference model to trainer (#12682)

* add save_inference_model for Trainer

* add comment

* update comment

* fix mac compile

* add unit test

* update API.spec

* revert cpplint-cpp-source
上级 01b68954
...@@ -43,6 +43,7 @@ paddle.fluid.Executor.run ArgSpec(args=['self', 'program', 'feed', 'fetch_list', ...@@ -43,6 +43,7 @@ paddle.fluid.Executor.run ArgSpec(args=['self', 'program', 'feed', 'fetch_list',
paddle.fluid.global_scope ArgSpec(args=[], varargs=None, keywords=None, defaults=None) paddle.fluid.global_scope ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.scope_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None) paddle.fluid.scope_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.Trainer.__init__ ArgSpec(args=['self', 'train_func', 'optimizer_func', 'param_path', 'place', 'parallel', 'checkpoint_config'], varargs=None, keywords=None, defaults=(None, None, False, None)) paddle.fluid.Trainer.__init__ ArgSpec(args=['self', 'train_func', 'optimizer_func', 'param_path', 'place', 'parallel', 'checkpoint_config'], varargs=None, keywords=None, defaults=(None, None, False, None))
paddle.fluid.Trainer.save_inference_model ArgSpec(args=['self', 'param_path', 'feeded_var_names', 'target_var_indexes'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Trainer.save_params ArgSpec(args=['self', 'param_path'], varargs=None, keywords=None, defaults=None) paddle.fluid.Trainer.save_params ArgSpec(args=['self', 'param_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Trainer.stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None) paddle.fluid.Trainer.stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Trainer.test ArgSpec(args=['self', 'reader', 'feed_order'], varargs=None, keywords=None, defaults=None) paddle.fluid.Trainer.test ArgSpec(args=['self', 'reader', 'feed_order'], varargs=None, keywords=None, defaults=None)
......
...@@ -47,14 +47,14 @@ def train_program(): ...@@ -47,14 +47,14 @@ def train_program():
loss = fluid.layers.square_error_cost(input=y_predict, label=y) loss = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_loss = fluid.layers.mean(loss) avg_loss = fluid.layers.mean(loss)
return avg_loss return [avg_loss, y_predict]
def optimizer_func(): def optimizer_func():
return fluid.optimizer.SGD(learning_rate=0.001) return fluid.optimizer.SGD(learning_rate=0.001)
def train(use_cuda, train_program, params_dirname): def train(use_cuda, train_program, params_dirname, inference_model_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = fluid.Trainer( trainer = fluid.Trainer(
...@@ -74,6 +74,8 @@ def train(use_cuda, train_program, params_dirname): ...@@ -74,6 +74,8 @@ def train(use_cuda, train_program, params_dirname):
''' '''
if params_dirname is not None: if params_dirname is not None:
trainer.save_params(params_dirname) trainer.save_params(params_dirname)
trainer.save_inference_model(inference_model_dirname,
['x'], [1])
trainer.stop() trainer.stop()
trainer.train( trainer.train(
...@@ -99,15 +101,55 @@ def infer(use_cuda, inference_program, params_dirname=None): ...@@ -99,15 +101,55 @@ def infer(use_cuda, inference_program, params_dirname=None):
print("infer results: ", results[0]) print("infer results: ", results[0])
def infer_by_saved_model(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# The input's dimension should be 2-D and the second dim is 13
# The input data should be >= 0
batch_size = 10
test_reader = paddle.batch(
paddle.dataset.uci_housing.test(), batch_size=batch_size)
test_data = next(test_reader())
test_feat = numpy.array(
[data[0] for data in test_data]).astype("float32")
test_label = numpy.array(
[data[1] for data in test_data]).astype("float32")
assert feed_target_names[0] == 'x'
results = exe.run(inference_program,
feed={feed_target_names[0]: numpy.array(test_feat)},
fetch_list=fetch_targets)
print("infer shape: ", results[0].shape)
print("infer results: ", results[0])
print("ground truth: ", test_label)
def main(use_cuda): def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda(): if use_cuda and not fluid.core.is_compiled_with_cuda():
return return
# Directory for saving the trained model # Directory for saving the trained model
params_dirname = "fit_a_line.inference.model" params_dirname = "fit_a_line.model"
inference_model_dirname = "fit_a_line.inference_model"
train(use_cuda, train_program, params_dirname) train(use_cuda, train_program, params_dirname, inference_model_dirname)
infer(use_cuda, inference_program, params_dirname) infer(use_cuda, inference_program, params_dirname)
infer_by_saved_model(use_cuda, inference_model_dirname)
class TestFitALine(unittest.TestCase): class TestFitALine(unittest.TestCase):
......
...@@ -431,6 +431,28 @@ class Trainer(object): ...@@ -431,6 +431,28 @@ class Trainer(object):
exe = executor.Executor(self.place) exe = executor.Executor(self.place)
io.save_persistables(exe, dirname=param_path) io.save_persistables(exe, dirname=param_path)
def save_inference_model(self, param_path, feeded_var_names,
target_var_indexes):
"""
Save model for cpp inference into :code:`param_path`.
Args:
param_path(str): The path to save parameters.
feeded_var_names(list(str)): The name of the vars that you
need to feed in before run program.
target_var_indexes(list(int)): the index of target var that
you need to return in trainer.train_func.
Returns:
None
"""
with self._prog_and_scope_guard():
exe = executor.Executor(self.place)
target_vars = [
self.train_func_outputs[index] for index in target_var_indexes
]
io.save_inference_model(param_path, feeded_var_names, target_vars,
exe)
@contextlib.contextmanager @contextlib.contextmanager
def _prog_and_scope_guard(self): def _prog_and_scope_guard(self):
with framework.program_guard( with framework.program_guard(
......
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