Merge pull request #7421 from emailweixu/fetch_var

helper functions fetch_var and get_var

Author: emailweixu

python/paddle/v2/fluid/executor.py

@@ -17,7 +17,9 @@
 from framework import Program, default_main_program
 from . import core
 
-__all__ = ['Executor', 'global_scope', 'scope_guard', 'switch_scope']
+__all__ = [
+    'Executor', 'global_scope', 'scope_guard', 'switch_scope', 'fetch_var'
+]
 
 g_scope = core.Scope()
 
@@ -80,12 +82,12 @@ def has_feed_operators(block, feed_targets, feed_holder_name):
     Args:
         block: a block instance (typically global block of a program)
         feed_targets: a dictionary of {feed_target_name: feed_target_data}
-        feed_holder_name: the name of the variable that holds the data of 
-            all feed targets. The type of this feed_holder variable is 
+        feed_holder_name: the name of the variable that holds the data of
+            all feed targets. The type of this feed_holder variable is
             FEED_MINIBATCH, which is essentially vector<LoDTensor>.
 
     Returns:
-        A boolean value that indicates whether a block has feed operators 
+        A boolean value that indicates whether a block has feed operators
         that match the info contained in feed_targets and feed_holder_name.
     """
 
@@ -108,7 +110,7 @@ def has_feed_operators(block, feed_targets, feed_holder_name):
 
 def has_fetch_operators(block, fetch_targets, fetch_holder_name):
     """ Check whether the block already has fetch operators.
-    
+
     Return false if the block does not have any fetch operators.
     If some fetch operators have been appended to the block, check that
     the info contained in these fetch operators matches the fetch_targets
@@ -118,13 +120,13 @@ def has_fetch_operators(block, fetch_targets, fetch_holder_name):
     Args:
         block: a block instance (typically global block of a program)
         fetch_targets: a dictionary of {fetch_target_name: fetch_target_data}
-        fetch_holder_name: the name of the variable that holds the data of 
-            all fetch targets. The type of this fetch_holder variable is 
-            FETCH_LIST, which is essentially vector<LoDTensor>.    
+        fetch_holder_name: the name of the variable that holds the data of
+            all fetch targets. The type of this fetch_holder variable is
+            FETCH_LIST, which is essentially vector<LoDTensor>.
 
-    Return:    
-        A boolean value that indicates whether a block has fetch operators 
-        that match the info contained in fetch_targets and fetch_holder_name.     
+    Return:
+        A boolean value that indicates whether a block has fetch operators
+        that match the info contained in fetch_targets and fetch_holder_name.
     """
 
     fetch_count = 0
@@ -146,6 +148,35 @@ def has_fetch_operators(block, fetch_targets, fetch_holder_name):
     return fetch_count > 0
 
 
+def fetch_var(name, scope=None, return_numpy=True):
+    """
+    Fetch the value of the variable with the given name from the given scope
+    Args:
+        name(str): name of the variable. Typically, only persistable variables
+            can be found in the scope used for running the program.
+        scope(core.Scope|None): scope object. It should be the scope where
+            you pass to Executor.run() when running your program.
+            If None, global_scope() will be used.
+        return_numpy(bool): whether convert the tensor to numpy.ndarray
+    Returns:
+       LodTensor|numpy.ndarray
+    """
+    assert isinstance(name, str)
+    if scope is None:
+        scope = global_scope()
+    assert isinstance(scope, core.Scope)
+
+    var = global_scope().find_var(name)
+    assert var is not None, (
+        "Cannot find " + name + " in scope. Perhaps you need to make the"
+        " variable persistable by using var.persistable = True in your"
+        " program.")
+    tensor = var.get_tensor()
+    if return_numpy:
+        tensor = as_numpy(tensor)
+    return tensor
+
+
 class Executor(object):
     def __init__(self, places):
         if not isinstance(places, list) and not isinstance(places, tuple):

python/paddle/v2/fluid/framework.py

@@ -31,6 +31,7 @@
     'program_guard',
     'switch_startup_program',
     'switch_main_program',
+    'get_var',
 ]
 
 EMPTY_VAR_NAME = core.kEmptyVarName()
@@ -1123,3 +1124,22 @@ def program_guard(main_program, startup_program=None):
     switch_main_program(main_program)
     if startup_program is not None:
         switch_startup_program(startup_program)
+
+
+def get_var(name, program=None):
+    """
+    Get a variable by name from the global block of a program
+    Args:
+        name(str): name of the variable
+        program(Program|None): program object.
+             If None, default_global_program() will be used.
+
+    Returns:
+        Variable
+    """
+    if program is None:
+        program = default_main_program()
+    assert isinstance(name, str)
+    assert isinstance(name, Program)
+
+    return program.global_block().var(name)

python/paddle/v2/fluid/layers/tensor.py

@@ -35,13 +35,15 @@
 ]
 
 
-def create_tensor(dtype, name=None):
+def create_tensor(dtype, name=None, persistable=False):
     helper = LayerHelper("create_tensor", **locals())
-    return helper.create_variable(name=helper.name, dtype=dtype)
+    return helper.create_variable(
+        name=helper.name, dtype=dtype, persistable=persistable)
 
 
 def create_parameter(shape,
                      dtype,
+                     name=None,
                      attr=None,
                      is_bias=False,
                      default_initializer=None):
@@ -62,7 +64,7 @@ def create_parameter(shape,
     """
     helper = LayerHelper("create_parameter", **locals())
     if attr is None:
-        attr = ParamAttr()
+        attr = ParamAttr(name=name)
     return helper.create_parameter(attr, shape, dtype, is_bias,
                                    default_initializer)
 

python/paddle/v2/fluid/tests/test_fetch_var.py

@@ -0,0 +1,37 @@
+#   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.
+
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.layers as layers
+import op_test
+import numpy
+import unittest
+
+
+class TestFetchVar(op_test.OpTest):
+    def test_fetch_var(self):
+        val = numpy.array([1, 3, 5]).astype(numpy.int32)
+        x = layers.create_tensor(dtype="int32", persistable=True, name="x")
+        layers.assign(input=val, output=x)
+        exe = fluid.Executor(fluid.CPUPlace())
+        exe.run(fluid.default_main_program(), feed={}, fetch_list=[])
+        fetched_x = fluid.fetch_var("x")
+        self.assertTrue(
+            numpy.array_equal(fetched_x, val),
+            "fetch_x=%s val=%s" % (fetched_x, val))
+        self.assertEqual(fetched_x.dtype, val.dtype)
+
+
+if __name__ == '__main__':
+    unittest.main()