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未验证 提交 db2e6cee 编写于 作者: S Shibo Tao 提交者： GitHub
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add two paddle-2.0 apis: paddle.static.io.save_inference_model and... 

add two paddle-2.0 apis: paddle.static.io.save_inference_model and paddle.static.io.load_inference_model (#28606)

* add two apis: paddle.static.io.save_inference_model and paddle.static.io.load_inference_mode, which are campatible with paddle.fluid.io.save_inference_model and paddle.fluid.io.load_inference_model respectively.

* add unittest for new save_inference_model and load_inference_model. test=develop

* enhance doc. test=develop

* add paddle.enable_static() to test_inference_model_io.py. test=develop
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python/paddle/fluid/io.py
浏览文件 @ db2e6cee
......@@ -43,6 +43,8 @@ from . import dataloader
from .dataloader import *
from . import core
from .. import compat as cpt
from paddle.utils import deprecated
from paddle.fluid.framework import static_only
batch = paddle.batch
......@@ -82,7 +84,10 @@ def is_parameter(var):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
param = fluid.default_main_program().global_block().var('fc.w')
res = fluid.io.is_parameter(param)
"""
......@@ -103,7 +108,10 @@ def is_persistable(var):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
param = fluid.default_main_program().global_block().var('fc.b')
res = fluid.io.is_persistable(param)
"""
......@@ -137,7 +145,10 @@ def get_program_parameter(program):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
data = fluid.data(name="img", shape=[64, 784])
w = fluid.layers.create_parameter(shape=[784, 200], dtype='float32', name='fc_w')
b = fluid.layers.create_parameter(shape=[200], dtype='float32', name='fc_b')
......@@ -162,7 +173,10 @@ def get_program_persistable_vars(program):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
data = fluid.data(name="img", shape=[64, 784])
w = fluid.layers.create_parameter(shape=[784, 200], dtype='float32', name='fc_w')
b = fluid.layers.create_parameter(shape=[200], dtype='float32', name='fc_b')
......@@ -202,7 +216,7 @@ def _load_program_scope(main=None, startup=None, scope=None):
yield
def _get_valid_program(main_program):
def _get_valid_program(main_program=None):
if main_program is None:
main_program = default_main_program()
elif isinstance(main_program, CompiledProgram):
......@@ -268,8 +282,10 @@ def save_vars(executor,
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
main_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(main_prog, startup_prog):
......@@ -417,8 +433,11 @@ def save_params(executor, dirname, main_program=None, filename=None):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
params_path = "./my_paddle_model"
image = fluid.data(name='img', shape=[None, 28, 28], dtype='float32')
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
......@@ -465,7 +484,10 @@ def _save_distributed_persistables(executor, dirname, main_program):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model"
t = distribute_transpiler.DistributeTranspiler()
......@@ -634,8 +656,10 @@ def save_persistables(executor, dirname, main_program=None, filename=None):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
dir_path = "./my_paddle_model"
file_name = "persistables"
image = fluid.data(name='img', shape=[None, 28, 28], dtype='float32')
......@@ -711,8 +735,10 @@ def load_vars(executor,
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
main_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(main_prog, startup_prog):
......@@ -946,8 +972,10 @@ def load_params(executor, dirname, main_program=None, filename=None):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model"
prog = fluid.default_main_program()
......@@ -995,8 +1023,10 @@ def load_persistables(executor, dirname, main_program=None, filename=None):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model"
prog = fluid.default_main_program()
......@@ -1034,7 +1064,10 @@ def _load_distributed_persistables(executor, dirname, main_program=None):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model"
t = distribute_transpiler.DistributeTranspiler()
......@@ -1160,7 +1193,8 @@ def append_fetch_ops(inference_program,
attrs={'col': i})
@dygraph_not_support
@static_only
@deprecated(since="2.0.0", update_to="paddle.static.save_inference_model")
def save_inference_model(dirname,
feeded_var_names,
target_vars,
......@@ -1226,8 +1260,10 @@ def save_inference_model(dirname,
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
path = "./infer_model"
# User defined network, here a softmax regession example
......@@ -1370,7 +1406,8 @@ def save_inference_model(dirname,
return target_var_name_list
@dygraph_not_support
@static_only
@deprecated(since="2.0.0", update_to="paddle.static.load_inference_model")
def load_inference_model(dirname,
executor,
model_filename=None,
......@@ -1422,9 +1459,11 @@ def load_inference_model(dirname,
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
paddle.enable_static()
# Build the model
main_prog = fluid.Program()
startup_prog = fluid.Program()
......@@ -1540,7 +1579,10 @@ def get_parameter_value(para, executor):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
exe = fluid.Executor(fluid.CPUPlace())
param = fluid.default_main_program().global_block().var('fc.w')
p = fluid.io.get_parameter_value(param, exe)
......@@ -1578,7 +1620,10 @@ def get_parameter_value_by_name(name, executor, program=None):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
exe = fluid.Executor(fluid.CPUPlace())
p = fluid.io.get_parameter_value('fc.w', exe)
"""
......@@ -1686,8 +1731,10 @@ def save(program, model_path):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
prog = fluid.default_main_program()
fluid.save( prog, "./temp")
......@@ -1753,8 +1800,10 @@ def load(program, model_path, executor=None, var_list=None):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
prog = fluid.default_main_program()
fluid.save( prog, "./temp")
......@@ -1914,7 +1963,10 @@ def load_program_state(model_path, var_list=None):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
x = fluid.data( name="x", shape=[10, 10], dtype='float32')
y = fluid.layers.fc( x, 10)
z = fluid.layers.fc( y, 10)
......@@ -2047,7 +2099,10 @@ def set_program_state(program, state_dict):
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
x = fluid.data( name="x", shape=[10, 10], dtype='float32')
y = fluid.layers.fc( x, 10)
z = fluid.layers.fc( y, 10)
......
python/paddle/fluid/tests/unittests/rnn/test_rnn_nets.py
浏览文件 @ db2e6cee
......@@ -323,10 +323,7 @@ def predict_test_util(place, mode):
exe = paddle.static.Executor(place)
[inference_program, feed_target_names,
fetch_targets] = paddle.static.load_inference_model(
dirname="./inference",
executor=exe,
model_filename="%s_infer.pdmodel" % mode,
params_filename="%s_infer.pdiparams" % mode)
"./inference/%s_infer" % mode, exe)
results = exe.run(inference_program,
feed={feed_target_names[0]: x.numpy()},
fetch_list=fetch_targets)
......@@ -345,3 +342,6 @@ def load_tests(loader, tests, pattern):
for test_class in [TestSimpleRNN, TestLSTM, TestGRU]:
suite.addTest(test_class(time_major, direction, device))
return suite
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_inference_model_io.py
浏览文件 @ db2e6cee
......@@ -23,6 +23,7 @@ import paddle.fluid.core as core
import paddle.fluid as fluid
import warnings
import paddle
import paddle.fluid.executor as executor
import paddle.fluid.layers as layers
import paddle.fluid.optimizer as optimizer
......@@ -30,15 +31,17 @@ from paddle.fluid.compiler import CompiledProgram
from paddle.fluid.framework import Program, program_guard
from paddle.fluid.io import save_inference_model, load_inference_model, save_persistables
from paddle.fluid.transpiler import memory_optimize
paddle.enable_static()
class TestBook(unittest.TestCase):
class InferModel(object):
class InferModel(object):
def __init__(self, list):
self.program = list[0]
self.feed_var_names = list[1]
self.fetch_vars = list[2]
class TestBook(unittest.TestCase):
def test_fit_line_inference_model(self):
MODEL_DIR = "./tmp/inference_model"
UNI_MODEL_DIR = "./tmp/inference_model1"
......@@ -88,10 +91,10 @@ class TestBook(unittest.TestCase):
six.moves.reload_module(executor) # reload to build a new scope
model_0 = self.InferModel(load_inference_model(MODEL_DIR, exe))
model_0 = InferModel(load_inference_model(MODEL_DIR, exe))
with open(os.path.join(UNI_MODEL_DIR, 'model'), "rb") as f:
model_str = f.read()
model_1 = self.InferModel(
model_1 = InferModel(
load_inference_model(None, exe, model_str, params_str))
for model in [model_0, model_1]:
......@@ -192,6 +195,103 @@ class TestInstance(unittest.TestCase):
[MODEL_DIR, ["x", "y"], [avg_cost], [], cp_prog])
class TestSaveInferenceModelNew(unittest.TestCase):
def test_save_and_load_inference_model(self):
MODEL_DIR = "./tmp/inference_model5"
init_program = fluid.default_startup_program()
program = fluid.default_main_program()
# fake program without feed/fetch
with program_guard(program, init_program):
x = layers.data(name='x', shape=[2], dtype='float32')
y = layers.data(name='y', shape=[1], dtype='float32')
y_predict = layers.fc(input=x, size=1, act=None)
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(cost)
sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost, init_program)
place = core.CPUPlace()
exe = executor.Executor(place)
exe.run(init_program, feed={}, fetch_list=[])
tensor_x = np.array([[1, 1], [1, 2], [5, 2]]).astype("float32")
tensor_y = np.array([[-2], [-3], [-7]]).astype("float32")
for i in six.moves.xrange(3):
exe.run(program,
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost])
self.assertRaises(ValueError, paddle.static.save_inference_model,
None, ['x', 'y'], [avg_cost], exe)
self.assertRaises(ValueError, paddle.static.save_inference_model,
MODEL_DIR + "/", [x, y], [avg_cost], exe)
self.assertRaises(ValueError, paddle.static.save_inference_model,
MODEL_DIR, ['x', 'y'], [avg_cost], exe)
self.assertRaises(ValueError, paddle.static.save_inference_model,
MODEL_DIR, 'x', [avg_cost], exe)
self.assertRaises(ValueError, paddle.static.save_inference_model,
MODEL_DIR, [x, y], ['avg_cost'], exe)
self.assertRaises(ValueError, paddle.static.save_inference_model,
MODEL_DIR, [x, y], 'avg_cost', exe)
model_path = MODEL_DIR + "_isdir.pdmodel"
os.makedirs(model_path)
self.assertRaises(ValueError, paddle.static.save_inference_model,
MODEL_DIR + "_isdir", [x, y], [avg_cost], exe)
os.rmdir(model_path)
params_path = MODEL_DIR + "_isdir.pdmodel"
os.makedirs(params_path)
self.assertRaises(ValueError, paddle.static.save_inference_model,
MODEL_DIR + "_isdir", [x, y], [avg_cost], exe)
os.rmdir(params_path)
paddle.static.io.save_inference_model(MODEL_DIR, [x, y], [avg_cost], exe)
self.assertTrue(os.path.exists(MODEL_DIR + ".pdmodel"))
self.assertTrue(os.path.exists(MODEL_DIR + ".pdiparams"))
expected = exe.run(program,
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost])[0]
six.moves.reload_module(executor) # reload to build a new scope
self.assertRaises(ValueError, paddle.static.load_inference_model,
None, exe)
self.assertRaises(ValueError, paddle.static.load_inference_model,
MODEL_DIR + "/", exe)
self.assertRaises(ValueError, paddle.static.load_inference_model,
[MODEL_DIR], exe)
self.assertRaises(ValueError, paddle.static.load_inference_model,
MODEL_DIR, exe, pserver_endpoints=None)
self.assertRaises(ValueError, paddle.static.load_inference_model,
MODEL_DIR, exe, unsupported_param=None)
self.assertRaises((TypeError, ValueError), paddle.static.load_inference_model,
None, exe, model_filename="illegal", params_filename="illegal")
model = InferModel(paddle.static.io.load_inference_model(MODEL_DIR, exe))
outs = exe.run(model.program,
feed={
model.feed_var_names[0]: tensor_x,
model.feed_var_names[1]: tensor_y
},
fetch_list=model.fetch_vars)
actual = outs[0]
self.assertEqual(model.feed_var_names, ["x", "y"])
self.assertEqual(len(model.fetch_vars), 1)
self.assertEqual(expected, actual)
class TestLoadInferenceModelError(unittest.TestCase):
def test_load_model_not_exist(self):
place = core.CPUPlace()
......
python/paddle/static/__init__.py
浏览文件 @ db2e6cee
......@@ -23,6 +23,8 @@ __all__ = [
]
from . import nn
from .io import save_inference_model
from .io import load_inference_model
from ..fluid import Scope #DEFINE_ALIAS
from .input import data #DEFINE_ALIAS
from .input import InputSpec #DEFINE_ALIAS
......@@ -48,8 +50,6 @@ from ..fluid.parallel_executor import ParallelExecutor #DEFINE_ALIAS
from ..fluid.param_attr import WeightNormParamAttr #DEFINE_ALIAS
from ..fluid.io import save #DEFINE_ALIAS
from ..fluid.io import load #DEFINE_ALIAS
from ..fluid.io import save_inference_model #DEFINE_ALIAS
from ..fluid.io import load_inference_model #DEFINE_ALIAS
from ..fluid.io import load_program_state #DEFINE_ALIAS
from ..fluid.io import set_program_state #DEFINE_ALIAS
from ..fluid.layers import create_parameter #DEFINE_ALIAS
......
python/paddle/static/io.py 0 → 100644
浏览文件 @ db2e6cee
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import errno
import inspect
import logging
import os
import six
import paddle
from paddle.fluid import core, Variable, CompiledProgram, program_guard, default_main_program, Program
from paddle.fluid.framework import static_only
from paddle.fluid import layers
from paddle.fluid.io import _get_valid_program, save_vars, _save_distributed_persistables
from paddle.fluid.io import prepend_feed_ops, append_fetch_ops, save_persistables
from paddle.fluid.io import load_persistables, _endpoints_replacement
from paddle.fluid.log_helper import get_logger
__all__ = [
'save_inference_model',
'load_inference_model',
]
_logger = get_logger(
__name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s')
def _check_args(caller, args, supported_args=[], deprecated_args=[]):
for arg in args:
if arg in deprecated_args:
raise ValueError("argument '{}' in function '{}' is deprecated, only {} are supported.".format(arg, caller, supported_args))
elif arg not in supported_args:
raise ValueError(
"function '{}' doesn't support argument '{}',\n only {} are supported.".format(caller, arg, supported_args))
@static_only
def save_inference_model(path_prefix, feed_vars, fetch_vars, executor):
"""
:api_attr: Static Graph
Save current model and its parameters to given path. i.e.
Given path_prefix = "/path/to/modelname", after invoking
save_inference_model(path_prefix, feed_vars, fetch_vars, executor),
you will find two files named modelname.pdmodel and modelname.pdiparams
under "/path/to", which represent your model and parameters respectively.
Args:
path_prefix(str): Directory path to save model + model name without suffix.
feed_vars(Variable | list[Variable]): Variables needed by inference.
fetch_vars(Variable | list[Variable]): Variables returned by inference.
executor(Executor): The executor that saves the inference model. You can refer
to :ref:`api_guide_executor_en` for more details.
Returns:
None
Raises:
ValueError: If `feed_vars` is not a Variable or a list of Variable, an exception is thrown.
ValueError: If `fetch_vars` is not a Variable or a list of Variable, an exception is thrown.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
paddle.enable_static()
path_prefix = "./infer_model"
# User defined network, here a softmax regession example
image = fluid.data(name='img', shape=[None, 28, 28], dtype='float32')
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
feeder = fluid.DataFeeder(feed_list=[image, label], place=fluid.CPUPlace())
predict = fluid.layers.fc(input=image, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=predict, label=label)
avg_loss = fluid.layers.mean(loss)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
# Feed data and train process
# Save inference model. Note we don't save label and loss in this example
paddle.static.io.save_inference_model(path_prefix, [image], [predict], exe)
# In this example, the save_inference_mode inference will prune the default
# main program according to the network's input node (img) and output node(predict).
# The pruned inference program is going to be saved in file "./infer_model.pdmodel"
# and parameters are going to be saved in file "./infer_model.pdiparams".
"""
# check path_prefix, set model_path and params_path
if not isinstance(path_prefix, six.string_types):
raise ValueError("'path_prefix' should be a string.")
if path_prefix.endswith("/"):
raise ValueError("'path_prefix' should not be a directory")
path_prefix = os.path.normpath(path_prefix)
path_prefix = os.path.abspath(path_prefix)
try:
# mkdir may conflict if pserver and trainer are running on the same machine
dirname = os.path.dirname(path_prefix)
os.makedirs(dirname)
except OSError as e:
if e.errno != errno.EEXIST:
raise
model_path = path_prefix + ".pdmodel"
params_path = path_prefix + ".pdiparams"
if os.path.isdir(model_path):
raise ValueError("'{}' is an existing directory.".format(model_path))
if os.path.isdir(params_path):
raise ValueError("'{}' is an existing directory.".format(params_path))
# verify feed_vars
if not isinstance(feed_vars, list):
feed_vars = [feed_vars]
if not feed_vars or not all([isinstance(var, Variable) for var in feed_vars]):
raise ValueError("'feed_vars' should be a Variable or a list of Variable.")
# verify fetch_vars
if not isinstance(fetch_vars, list):
fetch_vars = [fetch_vars]
if not fetch_vars or not all([isinstance(var, Variable) for var in fetch_vars]):
raise ValueError("'fetch_vars' should be a Variable or a list of Variable.")
main_program = _get_valid_program()
# remind users to set auc_states to 0 if auc op were found.
for op in main_program.global_block().ops:
# clear device of Op
device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
op._set_attr(device_attr_name, "")
if op.type == 'auc':
warnings.warn("Be sure that you have set auc states to 0 before saving inference model.")
break
# fix the bug that the activation op's output as target will be pruned.
# will affect the inference performance.
# TODO(Superjomn) add an IR pass to remove 1-scale op.
with program_guard(main_program):
uniq_fetch_vars = []
for i, var in enumerate(fetch_vars):
var = layers.scale(var, 1., name="save_infer_model/scale_{}".format(i))
uniq_fetch_vars.append(var)
fetch_vars = uniq_fetch_vars
# save model
origin_program = main_program.clone()
main_program = main_program.clone()
global_block = main_program.global_block()
remove_op_idx = []
for i, op in enumerate(global_block.ops):
op.desc.set_is_target(False)
if op.type == "feed" or op.type == "fetch":
remove_op_idx.append(i)
for idx in remove_op_idx[::-1]:
global_block._remove_op(idx)
main_program.desc.flush()
feed_var_names = [var.name for var in feed_vars]
main_program = main_program._prune_with_input(
feeded_var_names=feed_var_names, targets=fetch_vars)
main_program = main_program._inference_optimize(prune_read_op=True)
fetch_var_names = [var.name for var in fetch_vars]
prepend_feed_ops(main_program, feed_var_names)
append_fetch_ops(main_program, fetch_var_names)
main_program.desc._set_version()
paddle.fluid.core.save_op_version_info(main_program.desc)
with open(model_path, "wb") as f:
f.write(main_program.desc.serialize_to_string())
main_program._copy_dist_param_info_from(origin_program)
# save params
dirname = os.path.dirname(params_path)
basename = os.path.basename(params_path)
save_persistables(executor, dirname, main_program, basename)
@static_only
def load_inference_model(path_prefix, executor, **configs):
"""
:api_attr: Static Graph
Load inference model from a given path. By this API, you can get the model
structure(Inference Program) and model parameters.
Args:
path_prefix(str | None): One of the following:
- Directory path to save model + model name without suffix.
- Set to None when reading the model from memory.
executor(Executor): The executor to run for loading inference model.
See :ref:`api_guide_executor_en` for more details about it.
Returns:
list: The return of this API is a list with three elements:
(program, feed_target_names, fetch_targets). The `program` is a
``Program`` (refer to :ref:`api_guide_Program_en`), which is used for inference.
The `feed_target_names` is a list of ``str``, which contains names of variables
that need to feed data in the inference program. The `fetch_targets` is a list of
``Variable`` (refer to :ref:`api_guide_Program_en`). It contains variables from which
we can get inference results.
Raises:
ValueError: If `path_prefix.pdmodel` or `path_prefix.pdiparams` doesn't exist.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
paddle.enable_static()
# Build the model
startup_prog = fluid.default_startup_program()
main_prog = fluid.default_main_program()
with fluid.program_guard(main_prog, startup_prog):
image = fluid.layers.data(name="img", shape=[64, 784], append_batch_size=False)
w = fluid.layers.create_parameter(shape=[784, 200], dtype='float32')
b = fluid.layers.create_parameter(shape=[200], dtype='float32')
hidden_w = fluid.layers.matmul(x=image, y=w)
hidden_b = fluid.layers.elementwise_add(hidden_w, b)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
# Save the inference model
path_prefix = "./infer_model"
paddle.static.io.save_inference_model(path_prefix, [image], [hidden_b], exe)
[inference_program, feed_target_names, fetch_targets] = (
paddle.static.io.load_inference_model(path_prefix, exe))
tensor_img = np.array(np.random.random((1, 64, 784)), dtype=np.float32)
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
# In this example, the inference program was saved in file
# "./infer_model.pdmodel" and parameters were saved in file
# " ./infer_model.pdiparams".
# By the inference program, feed_target_names and
# fetch_targets, we can use an executor to run the inference
# program to get the inference result.
"""
# check configs
supported_args = ('model_filename', 'params_filename')
deprecated_args = ('pserver_endpoints',)
caller = inspect.currentframe().f_code.co_name
_check_args(caller, configs, supported_args, deprecated_args)
# load from memory
if path_prefix is None:
_logger.warning("Load inference model from memory is deprecated.")
model_filename = configs.get('model_filename', None)
params_filename = configs.get('params_filename', None)
if params_filename is None:
raise ValueError(
"params_filename cannot be None when path_prefix is None."
)
load_dirname = path_prefix
program_desc_str = model_filename
params_filename = params_filename
# load from file
else:
# check and norm path_prefix
if not isinstance(path_prefix, six.string_types):
raise ValueError("'path_prefix' should be a string.")
if path_prefix.endswith("/"):
raise ValueError("'path_prefix' should not be a directory")
path_prefix = os.path.normpath(path_prefix)
path_prefix = os.path.abspath(path_prefix)
# set model_path and params_path in new way,
# path_prefix represents a file path without suffix in this case.
if not configs:
model_path = path_prefix + ".pdmodel"
params_path = path_prefix + ".pdiparams"
# set model_path and params_path in old way for compatible,
# path_prefix represents a directory path.
else:
model_filename = configs.get('model_filename', None)
params_filename = configs.get('params_filename', None)
# set model_path
if model_filename is None:
model_path = os.path.join(path_prefix, "__model__")
else:
model_path = os.path.join(path_prefix, model_filename + ".pdmodel")
if not os.path.exists(model_path):
model_path = os.path.join(path_prefix, model_filename)
# set params_path
if params_filename is None:
params_path = os.path.join(path_prefix, "")
else:
params_path = os.path.join(path_prefix, params_filename + ".pdiparams")
if not os.path.exists(params_path):
params_path = os.path.join(path_prefix, params_filename)
_logger.warning("The old way to load inference model is deprecated."
" model path: {}, params path: {}".format(model_path, params_path))
with open(model_path, "rb") as f:
program_desc_str = f.read()
load_dirname = os.path.dirname(params_path)
params_filename = os.path.basename(params_path)
program = Program.parse_from_string(program_desc_str)
if not core._is_program_version_supported(program._version()):
raise ValueError("Unsupported program version: %d\n" %
program._version())
# Binary data also need versioning.
load_persistables(executor, load_dirname, program, params_filename)
feed_target_names = program.desc.get_feed_target_names()
fetch_target_names = program.desc.get_fetch_target_names()
fetch_targets = [
program.global_block().var(name) for name in fetch_target_names
]
return [program, feed_target_names, fetch_targets]
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