Unify fluid submodules to fluid module (#5924)

Change books just use `import fluid`, not submodules

Author: reyoung

python/paddle/v2/fluid/__init__.py

@@ -1,11 +1,41 @@
-import sys
-import core
-__all__ = ['proto']
-argv = []
-if core.is_compile_gpu():
-    argv = list(sys.argv) + [
-        "--tryfromenv=fraction_of_gpu_memory_to_use,use_pinned_memory"
-    ]
-else:
-    argv = list(sys.argv) + ["--tryfromenv=use_pinned_memory"]
-core.init_gflags(argv)
+# import all class inside framework into fluid module
+import framework
+from framework import *
+# import all class inside executor into fluid module
+import executor
+from executor import *
+
+import io
+import evaluator
+import initializer
+import layers
+import nets
+import optimizer
+import backward
+import regularizer
+
+from core import LoDTensor, CPUPlace, GPUPlace
+
+Tensor = LoDTensor
+__all__ = framework.__all__ + executor.__all__ + [
+    'io', 'initializer', 'layers', 'nets', 'optimizer', 'backward',
+    'regularizer', 'LoDTensor', 'CPUPlace', 'GPUPlace', 'Tensor'
+]
+
+
+def __read_gflags_from_env__():
+    """
+    Enable reading gflags from environment variables.
+    
+    Returns:
+        None
+    """
+    import sys
+    import core
+    read_env_flags = ['use_pinned_memory']
+    if core.is_compile_gpu():
+        read_env_flags.append('fraction_of_gpu_memory_to_use')
+    core.init_gflags(sys.argv + ["--tryfromenv=" + ",".join(read_env_flags)])
+
+
+__read_gflags_from_env__()

python/paddle/v2/fluid/evaluator.py

@@ -1,9 +1,8 @@
 import numpy as np
 
-import paddle.v2.fluid.layers as layers
-from paddle.v2.fluid.framework import Program, unique_name, \
-    Variable
-from paddle.v2.fluid.layer_helper import LayerHelper
+import layers
+from framework import Program, unique_name, Variable
+from layer_helper import LayerHelper
 
 __all__ = ['Accuracy']
 

python/paddle/v2/fluid/executor.py

@@ -1,6 +1,8 @@
 import numpy as np
-import paddle.v2.fluid.core as core
-from paddle.v2.fluid.framework import Block, Program, g_main_program
+from . import core
+from framework import Program, g_main_program
+
+__all__ = ['Executor', 'g_scope']
 
 g_scope = core.Scope()
 

python/paddle/v2/fluid/framework.py

@@ -1,12 +1,12 @@
-import paddle.v2.fluid.core as core
-import paddle.v2.fluid.proto.framework_pb2 as framework_pb2
 import collections
+
 import numpy as np
-import copy
+from . import core
+import proto.framework_pb2 as framework_pb2
 
 __all__ = [
     'Block', 'Variable', 'Program', 'Operator', 'default_startup_program',
-    'default_main_program'
+    'default_main_program', 'g_startup_program', 'g_main_program'
 ]
 
 

python/paddle/v2/fluid/initializer.py

@@ -1,10 +1,7 @@
-import paddle.v2.fluid.framework as framework
+import framework
 import numpy as np
 
-__all__ = [
-    'ConstantInitializer', 'UniformInitializer', 'NormalInitializer',
-    'XavierInitializer'
-]
+__all__ = ['Constant', 'Uniform', 'Normal', 'Xavier']
 
 
 class Initializer(object):
@@ -368,3 +365,19 @@ def __call__(self, var, block):
                 })
         var.op = op
         return op
+
+
+# We short the class name, since users will use the initializer with the package
+# name. The sample code:
+#
+# import paddle.fluid as fluid
+#
+# hidden = fluid.layers.fc(...,
+#                          param_attr=ParamAttr(fluid.initializer.Xavier()))
+#
+# It is no need to add an `Initializer` as the class suffix
+Constant = ConstantInitializer
+Uniform = UniformInitializer
+Normal = NormalInitializer
+Xavier = XavierInitializer
+MSRA = MSRAInitializer

python/paddle/v2/fluid/layer_helper.py

@@ -1,10 +1,9 @@
 import copy
 import itertools
 
-from paddle.v2.fluid.framework import Variable, g_main_program, \
-    g_startup_program, unique_name, Program, dtype_is_floating
-from paddle.v2.fluid.initializer import ConstantInitializer, \
-    UniformInitializer, XavierInitializer
+from framework import Variable, g_main_program, \
+    g_startup_program, unique_name, dtype_is_floating
+from paddle.v2.fluid.initializer import Constant, Xavier
 
 
 class LayerHelper(object):
@@ -209,7 +208,7 @@ def append_activation(self, input_var):
 
     def _get_default_initializer(self, dtype):
         if dtype is None or dtype_is_floating(dtype) is True:
-            return XavierInitializer()
+            return Xavier()
         else:
             # For integer and boolean types, initialize with all zeros
-            return ConstantInitializer()
+            return Constant()

python/paddle/v2/fluid/layers.py

@@ -1,9 +1,7 @@
-import paddle.v2.fluid.core as core
-import paddle.v2.fluid.proto.framework_pb2 as framework_pb2
-from paddle.v2.fluid.framework import OpProtoHolder, Variable, Program, \
-    Operator
-from paddle.v2.fluid.initializer import ConstantInitializer, \
-    NormalInitializer, XavierInitializer
+from . import core
+import proto.framework_pb2 as framework_pb2
+from framework import OpProtoHolder, Variable, Program, Operator
+from initializer import Constant, Normal, Xavier
 from paddle.v2.fluid.layer_helper import LayerHelper, unique_name
 import re
 import cStringIO
@@ -58,10 +56,10 @@ def fc(input,
     """
 
     def _get_default_param_initializer():
-        return XavierInitializer()
+        return Xavier()
 
     def _get_default_bias_initializer():
-        return ConstantInitializer()
+        return Constant()
 
     helper = LayerHelper('fc', **locals())
 
@@ -139,7 +137,7 @@ def embedding(input,
     """
 
     def _get_default_param_initializer():
-        return XavierInitializer()
+        return Xavier()
 
     helper = LayerHelper('embedding', **locals())
     w = helper.create_parameter(
@@ -477,7 +475,7 @@ def linear_chain_crf(input,
                      main_program=None,
                      startup_program=None):
     def _get_default_param_initializer():
-        return XavierInitializer()
+        return Xavier()
 
     helper = LayerHelper('linear_chain_crf', **locals())
     size = input.shape[1]
@@ -661,10 +659,10 @@ def sequence_conv(input,
     """
 
     def _get_default_bias_initializer():
-        return ConstantInitializer()
+        return Constant()
 
     def _get_default_param_initializer():
-        return XavierInitializer()
+        return Xavier()
 
     # FIXME(dzh) : want to unify the argument of python layer
     # function. So we ignore some unecessary attributes.
@@ -725,11 +723,11 @@ def conv2d(input,
     """
 
     def _get_default_bias_initializer():
-        return ConstantInitializer()
+        return Constant()
 
     def _get_default_param_initializer(filter_size, num_channels):
         std = (2.0 / (filter_size[0]**2 * num_channels))**0.5
-        return NormalInitializer(0.0, std, 0)
+        return Normal(0.0, std, 0)
 
     helper = LayerHelper('conv2d', **locals())
     dtype = helper.input_dtype()
@@ -878,22 +876,20 @@ def batch_norm(input,
         attr=helper.param_attr,
         shape=param_shape,
         dtype=dtype,
-        initializer=ConstantInitializer(1.0))
+        initializer=Constant(1.0))
     bias = helper.create_parameter(
         attr=helper.param_attr,
         shape=param_shape,
         dtype=dtype,
-        initializer=ConstantInitializer(0.0))
+        initializer=Constant(0.0))
 
     mean = helper.create_global_variable(
         dtype=input.dtype, shape=param_shape, persistable=True)
-    helper.set_variable_initializer(
-        var=mean, initializer=ConstantInitializer(0.0))
+    helper.set_variable_initializer(var=mean, initializer=Constant(0.0))
 
     variance = helper.create_global_variable(
         dtype=input.dtype, shape=param_shape, persistable=True)
-    helper.set_variable_initializer(
-        var=variance, initializer=ConstantInitializer(1.0))
+    helper.set_variable_initializer(var=variance, initializer=Constant(1.0))
 
     # create output
     # mean and mean_out share the same memory

python/paddle/v2/fluid/nets.py

@@ -1,4 +1,4 @@
-import paddle.v2.fluid.layers as layers
+import layers
 
 __all__ = ["simple_img_conv_pool", "sequence_conv_pool"]
 

python/paddle/v2/fluid/optimizer.py

@@ -1,16 +1,13 @@
 from collections import defaultdict
 
-import paddle.v2.fluid.framework as framework
-from paddle.v2.fluid.framework import unique_name, Program
-from paddle.v2.fluid.backward import append_backward_ops
-from paddle.v2.fluid.initializer import ConstantInitializer
-from paddle.v2.fluid.regularizer import append_regularization_ops
-from paddle.v2.fluid.layer_helper import LayerHelper
+import framework
+from backward import append_backward_ops
+from framework import unique_name
+from initializer import Constant
+from layer_helper import LayerHelper
+from regularizer import append_regularization_ops
 
-__all__ = [
-    'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer', 'AdamOptimizer',
-    'AdamaxOptimizer', 'DecayedAdagradOptimizer'
-]
+__all__ = ['SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad']
 
 
 class Optimizer(object):
@@ -48,7 +45,7 @@ def _create_param_lr(self, param_and_grad):
             persistable=True)
         param_lr = param_lr * self._learning_rate
         self.helper.set_variable_initializer(
-            var=param_lr_var, initializer=ConstantInitializer(param_lr))
+            var=param_lr_var, initializer=Constant(param_lr))
         return param_lr_var
 
     def _create_accumulators(self, block, parameters):
@@ -96,7 +93,7 @@ def _add_accumulator(self, name, param, dtype=None, fill_value=0.0):
             type=param.type,
             shape=param.shape)
         self.helper.set_variable_initializer(
-            var, initializer=ConstantInitializer(value=float(fill_value)))
+            var, initializer=Constant(value=float(fill_value)))
         self._accumulators[name][param.name] = var
 
     def _get_accumulator(self, name, param):
@@ -360,7 +357,7 @@ def _create_accumulators(self, block, parameters):
             lod_level=0,
             persistable=True)
         self.helper.set_variable_initializer(
-            self._beta1_pow_acc, initializer=ConstantInitializer(self._beta1))
+            self._beta1_pow_acc, initializer=Constant(self._beta1))
 
         self._beta2_pow_acc = self.helper.create_global_variable(
             name=unique_name('beta2_pow_acc'),
@@ -370,7 +367,7 @@ def _create_accumulators(self, block, parameters):
             persistable=True)
 
         self.helper.set_variable_initializer(
-            self._beta2_pow_acc, initializer=ConstantInitializer(self._beta2))
+            self._beta2_pow_acc, initializer=Constant(self._beta2))
 
         # Create accumulator tensors for first and second moments
         for p in parameters:
@@ -462,7 +459,7 @@ def _create_accumulators(self, block, parameters):
             lod_level=0,
             persistable=True)
         self.helper.set_variable_initializer(
-            self._beta1_pow_acc, initializer=ConstantInitializer(self._beta1))
+            self._beta1_pow_acc, initializer=Constant(self._beta1))
 
         # Create accumulator tensors for first moment and infinity norm
         for p in parameters:
@@ -559,3 +556,19 @@ def _append_optimize_op(self, block, param_and_grad):
             attrs={"epsilon": self._epsilon})
 
         return decayed_adagrad_op
+
+
+# We short the class name, since users will use the optimizer with the package
+# name. The sample code:
+#
+# import paddle.fluid as fluid
+#
+# sgd = fluid.optimizer.SGD(...)
+#
+# It is no need to add an `Optimizer` as the class suffix
+SGD = SGDOptimizer
+Momentum = MomentumOptimizer
+Adagrad = AdagradOptimizer
+Adam = AdamOptimizer
+Adamax = AdamaxOptimizer
+DecayedAdagrad = DecayedAdagradOptimizer

python/paddle/v2/fluid/regularizer.py

@@ -1,8 +1,6 @@
-import paddle.v2.fluid.framework as framework
+import framework
 
-__all__ = [
-    'append_regularization_ops', 'L2DecayRegularizer', 'L1DecayRegularizer'
-]
+__all__ = ['append_regularization_ops', 'L1Decay', 'L2Decay']
 
 
 def append_regularization_ops(parameters_and_grads):
@@ -139,3 +137,16 @@ def __call__(self, param, block):
             attrs={"scale": self._regularization_coeff})
 
         return decay
+
+
+# We short the class name, since users will use the regulaizer with the package
+# name. The sample code:
+#
+# import paddle.fluid as fluid
+#
+# hidden = fluid.layers.fc(...,
+#                          param_attr=ParamAttr(fluid.regularizer.Xavier()))
+#
+# It is no need to add a `Regularizer` as the class suffix
+L1Decay = L1DecayRegularizer
+L2Decay = L2DecayRegularizer