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@ -204,7 +204,7 @@ def is_persistable(var):
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@static_only
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def serialize_program(feed_vars, fetch_vars):
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def serialize_program(feed_vars, fetch_vars, **kwargs):
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"""
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:api_attr: Static Graph
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@ -213,6 +213,10 @@ def serialize_program(feed_vars, fetch_vars):
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Args:
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feed_vars(Variable | list[Variable]): Variables needed by inference.
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fetch_vars(Variable | list[Variable]): Variables returned by inference.
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kwargs: Supported keys including 'program'.
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Attention please, kwargs is used for backward compatibility mainly.
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- program(Program): specify a program if you don't want to use default main program.
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Returns:
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bytes: serialized program.
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@ -235,7 +239,6 @@ def serialize_program(feed_vars, fetch_vars):
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predict = paddle.static.nn.fc(image, 10, activation='softmax')
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loss = paddle.nn.functional.cross_entropy(predict, label)
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avg_loss = paddle.tensor.stat.mean(loss)
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exe = paddle.static.Executor(paddle.CPUPlace())
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exe.run(paddle.static.default_startup_program())
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@ -252,7 +255,7 @@ def serialize_program(feed_vars, fetch_vars):
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# verify fetch_vars
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_check_vars('fetch_vars', fetch_vars)
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program = _get_valid_program()
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program = _get_valid_program(kwargs.get('program', None))
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program = _normalize_program(program, feed_vars, fetch_vars)
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return _serialize_program(program)
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@ -265,7 +268,7 @@ def _serialize_program(program):
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@static_only
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def serialize_persistables(feed_vars, fetch_vars, executor):
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def serialize_persistables(feed_vars, fetch_vars, executor, **kwargs):
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"""
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:api_attr: Static Graph
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@ -274,6 +277,10 @@ def serialize_persistables(feed_vars, fetch_vars, executor):
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Args:
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feed_vars(Variable | list[Variable]): Variables needed by inference.
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fetch_vars(Variable | list[Variable]): Variables returned by inference.
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kwargs: Supported keys including 'program'.
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Attention please, kwargs is used for backward compatibility mainly.
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- program(Program): specify a program if you don't want to use default main program.
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Returns:
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bytes: serialized program.
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@ -296,7 +303,6 @@ def serialize_persistables(feed_vars, fetch_vars, executor):
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predict = paddle.static.nn.fc(image, 10, activation='softmax')
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loss = paddle.nn.functional.cross_entropy(predict, label)
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avg_loss = paddle.tensor.stat.mean(loss)
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exe = paddle.static.Executor(paddle.CPUPlace())
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exe.run(paddle.static.default_startup_program())
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@ -314,7 +320,7 @@ def serialize_persistables(feed_vars, fetch_vars, executor):
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# verify fetch_vars
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_check_vars('fetch_vars', fetch_vars)
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program = _get_valid_program()
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program = _get_valid_program(kwargs.get('program', None))
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program = _normalize_program(program, feed_vars, fetch_vars)
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return _serialize_persistables(program, executor)
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@ -380,7 +386,8 @@ def save_to_file(path, content):
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@static_only
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def save_inference_model(path_prefix, feed_vars, fetch_vars, executor):
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def save_inference_model(path_prefix, feed_vars, fetch_vars, executor,
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**kwargs):
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"""
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:api_attr: Static Graph
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@ -396,6 +403,9 @@ def save_inference_model(path_prefix, feed_vars, fetch_vars, executor):
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fetch_vars(Variable | list[Variable]): Variables returned by inference.
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executor(Executor): The executor that saves the inference model. You can refer
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to :ref:`api_guide_executor_en` for more details.
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kwargs: Supported keys including 'program'.
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Attention please, kwargs is used for backward compatibility mainly.
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- program(Program): specify a program if you don't want to use default main program.
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Returns:
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None
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@ -418,7 +428,6 @@ def save_inference_model(path_prefix, feed_vars, fetch_vars, executor):
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predict = paddle.static.nn.fc(image, 10, activation='softmax')
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loss = paddle.nn.functional.cross_entropy(predict, label)
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avg_loss = paddle.tensor.stat.mean(loss)
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exe = paddle.static.Executor(paddle.CPUPlace())
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exe.run(paddle.static.default_startup_program())
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@ -456,7 +465,7 @@ def save_inference_model(path_prefix, feed_vars, fetch_vars, executor):
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# verify fetch_vars
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_check_vars('fetch_vars', fetch_vars)
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program = _get_valid_program()
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program = _get_valid_program(kwargs.get('program', None))
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program = _normalize_program(program, feed_vars, fetch_vars)
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# serialize and save program
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program_bytes = _serialize_program(program)
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@ -475,8 +484,35 @@ def deserialize_program(data):
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Args:
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data(bytes): serialized program.
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Returns:
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Program: deserialized program.
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Examples:
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.. code-block:: python
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import paddle
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paddle.enable_static()
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path_prefix = "./infer_model"
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# User defined network, here a softmax regession example
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image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
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label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
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predict = paddle.static.nn.fc(image, 10, activation='softmax')
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loss = paddle.nn.functional.cross_entropy(predict, label)
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exe = paddle.static.Executor(paddle.CPUPlace())
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exe.run(paddle.static.default_startup_program())
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# serialize the default main program to bytes.
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serialized_program = paddle.static.serialize_program([image], [predict])
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# deserialize bytes to program
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deserialized_program = paddle.static.deserialize_program(serialized_program)
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"""
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program = Program.parse_from_string(data)
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if not core._is_program_version_supported(program._version()):
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@ -496,8 +532,37 @@ def deserialize_persistables(program, data, executor):
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program(Program): program that contains parameter names (to deserialize).
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data(bytes): serialized parameters.
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executor(Executor): executor used to run load op.
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Returns:
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Program: deserialized program.
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Examples:
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.. code-block:: python
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import paddle
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paddle.enable_static()
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path_prefix = "./infer_model"
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# User defined network, here a softmax regession example
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image = paddle.static.data(name='img', shape=[None, 28, 28], dtype='float32')
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label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
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predict = paddle.static.nn.fc(image, 10, activation='softmax')
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loss = paddle.nn.functional.cross_entropy(predict, label)
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exe = paddle.static.Executor(paddle.CPUPlace())
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exe.run(paddle.static.default_startup_program())
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# serialize parameters to bytes.
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serialized_params = paddle.static.serialize_persistables([image], [predict], exe)
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# deserialize bytes to parameters.
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main_program = paddle.static.default_main_program()
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deserialized_params = paddle.static.deserialize_persistables(main_program, serialized_params, exe)
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"""
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if not isinstance(program, Program):
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raise TypeError(
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@ -567,7 +632,7 @@ def load_from_file(path):
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@static_only
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def load_inference_model(path_prefix, executor, **configs):
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def load_inference_model(path_prefix, executor, **kwargs):
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"""
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:api_attr: Static Graph
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@ -580,6 +645,10 @@ def load_inference_model(path_prefix, executor, **configs):
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- Set to None when reading the model from memory.
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executor(Executor): The executor to run for loading inference model.
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See :ref:`api_guide_executor_en` for more details about it.
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kwargs: Supported keys including 'model_filename', 'params_filename'.
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Attention please, kwargs is used for backward compatibility mainly.
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- model_filename(str): specify model_filename if you don't want to use default name.
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- params_filename(str): specify params_filename if you don't want to use default name.
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Returns:
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list: The return of this API is a list with three elements:
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@ -631,17 +700,17 @@ def load_inference_model(path_prefix, executor, **configs):
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# fetch_targets, we can use an executor to run the inference
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# program to get the inference result.
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"""
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# check configs
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# check kwargs
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supported_args = ('model_filename', 'params_filename')
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deprecated_args = ('pserver_endpoints', )
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caller = inspect.currentframe().f_code.co_name
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_check_args(caller, configs, supported_args, deprecated_args)
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_check_args(caller, kwargs, supported_args, deprecated_args)
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# load from memory
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if path_prefix is None:
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_logger.warning("Load inference model from memory is deprecated.")
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model_filename = configs.get('model_filename', None)
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params_filename = configs.get('params_filename', None)
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model_filename = kwargs.get('model_filename', None)
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params_filename = kwargs.get('params_filename', None)
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if params_filename is None:
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raise ValueError(
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"params_filename cannot be None when path_prefix is None.")
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@ -655,14 +724,14 @@ def load_inference_model(path_prefix, executor, **configs):
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# set model_path and params_path in new way,
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# path_prefix represents a file path without suffix in this case.
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if not configs:
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if not kwargs:
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model_path = path_prefix + ".pdmodel"
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params_path = path_prefix + ".pdiparams"
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# set model_path and params_path in old way for compatible,
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# path_prefix represents a directory path.
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else:
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model_filename = configs.get('model_filename', None)
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params_filename = configs.get('params_filename', None)
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model_filename = kwargs.get('model_filename', None)
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params_filename = kwargs.get('params_filename', None)
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# set model_path
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if model_filename is None:
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model_path = os.path.join(path_prefix, "__model__")
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Shibo Tao
5 years ago
committed by
GitHub