Move trainer to contrib

Author: reyoung

python/paddle/fluid/__init__.py

@@ -19,17 +19,8 @@
 # import all class inside executor into fluid module
 from . import executor
 from .executor import *
-
 from . import trainer
-from .trainer import Trainer
-from .trainer import BeginEpochEvent
-from .trainer import EndEpochEvent
-from .trainer import BeginStepEvent
-from .trainer import EndStepEvent
-from .trainer import CheckpointConfig
-
 from . import inferencer
-from .inferencer import Inferencer
 
 from . import io
 from . import evaluator

python/paddle/fluid/contrib/inferencer.py

@@ -0,0 +1,112 @@
+#   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 contextlib
+
+from .. import core
+
+from .. import executor
+from .. import framework
+from .. import io
+from .. import parallel_executor
+from .. import unique_name
+from .trainer import check_and_get_place
+
+__all__ = ['Inferencer', ]
+
+
+class Inferencer(object):
+    """
+    Inferencer High Level API.
+
+    Args:
+        infer_func (Python func): Infer function that will return predict Variable
+        param_path (str): The path where the inference model is saved by fluid.io.save_params
+        place (Place): place to do the inference
+        parallel (bool): use parallel_executor to run the inference, it will use multi CPU/GPU.
+
+    Examples:
+        .. code-block:: python
+
+            def inference_program():
+                x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+                y_predict = fluid.layers.fc(input=x, size=1, act=None)
+                return y_predict
+
+            place = fluid.CPUPlace()
+            inferencer = fluid.Inferencer(
+                infer_func=inference_program, param_path="/tmp/model", place=place)
+
+    """
+
+    def __init__(self, infer_func, param_path, place=None, parallel=False):
+        self.param_path = param_path
+        self.scope = core.Scope()
+        self.parallel = parallel
+        self.place = check_and_get_place(place)
+
+        self.inference_program = framework.Program()
+        with framework.program_guard(self.inference_program):
+            with unique_name.guard():
+                self.predict_var = infer_func()
+
+        with self._prog_and_scope_guard():
+            # load params from param_path into scope
+            io.load_params(executor.Executor(self.place), param_path)
+
+        if parallel:
+            with self._prog_and_scope_guard():
+                self.exe = parallel_executor.ParallelExecutor(
+                    use_cuda=isinstance(self.place, core.CUDAPlace),
+                    loss_name=self.predict_var.name)
+        else:
+            self.exe = executor.Executor(self.place)
+
+        self.inference_program = self.inference_program.clone(for_test=True)
+
+    def infer(self, inputs, return_numpy=True):
+        """
+        Do Inference for Inputs
+
+        Args:
+            inputs (map): a map of {"input_name": input_var} that will be feed into the inference program
+            return_numpy (bool): transform return value into numpy or not
+
+        Returns:
+            Tensor or Numpy: the predict value of the inference model for the inputs
+
+        Examples:
+            .. code-block:: python
+
+                tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
+                results = inferencer.infer({'x': tensor_x})
+        """
+        if not isinstance(inputs, dict):
+            raise ValueError(
+                "inputs should be a map of {'input_name': input_var}")
+
+        with self._prog_and_scope_guard():
+            results = self.exe.run(feed=inputs,
+                                   fetch_list=[self.predict_var.name],
+                                   return_numpy=return_numpy)
+
+        return results
+
+    @contextlib.contextmanager
+    def _prog_and_scope_guard(self):
+        with framework.program_guard(main_program=self.inference_program):
+            with executor.scope_guard(self.scope):
+                yield