Polish PyLayers

test=develop

python/paddle/fluid/imperative/layers.py

@@ -24,19 +24,7 @@ __all__ = ['PyLayer']
 
 
 class PyLayer(core.Layer):
-    def __init__(self,
-                 dtype=core.VarDesc.VarType.FP32,
-                 param_attr=None,
-                 bias_attr=None,
-                 name=None):
-        from ..layer_helper import LayerHelper
-        self._helper = LayerHelper(
-            type(self).__name__,
-            param_attr=param_attr,
-            bias_attr=bias_attr,
-            dtype=dtype,
-            name=name)
-
+    def __init__(self, dtype=core.VarDesc.VarType.FP32, name=None):
         self._once_built = False
         self._dtype = dtype
 

python/paddle/fluid/imperative/nn.py

@@ -46,8 +46,15 @@ class Conv2D(layers.PyLayer):
                  name=None,
                  dtype=core.VarDesc.VarType.FP32):
         assert param_attr is not False, "param_attr should not be False here."
-        super(Conv2D, self).__init__(
-            param_attr=param_attr, bias_attr=bias_attr, name=name, dtype=dtype)
+        super(Conv2D, self).__init__(name=name, dtype=dtype)
+
+        from ..layer_helper import LayerHelper
+        self._helper = LayerHelper(
+            type(self).__name__,
+            param_attr=param_attr,
+            bias_attr=bias_attr,
+            dtype=dtype,
+            name=name)
 
         self._groups = groups
         self._stride = utils.convert_to_list(stride, 2, 'stride')
@@ -163,6 +170,9 @@ class Pool2D(layers.PyLayer):
 
         super(Pool2D, self).__init__(name=name, dtype=dtype)
 
+        from ..layer_helper import LayerHelper
+        self._helper = LayerHelper(type(self).__name__, dtype=dtype, name=name)
+
         self._pool_type = pool_type
         self._pool_size = utils.convert_to_list(pool_size, 2, 'pool_size')
         self._pool_padding = utils.convert_to_list(pool_padding, 2,
@@ -197,32 +207,22 @@ class Pool2D(layers.PyLayer):
 
 class FC(layers.PyLayer):
     def __init__(self,
-                 size_in,
-                 size_out,
-                 num_flatten_dims=1,
+                 size,
                  param_attr=None,
+                 num_flatten_dims=1,
                  dtype=core.VarDesc.VarType.FP32):
-        super(FC, self).__init__(param_attr=param_attr, dtype=dtype)
-
-        self._size_in = size_in
-        self._size_out = size_out
+        super(FC, self).__init__()
+        self._size = size
         self._num_flatten_dims = num_flatten_dims
         self._dtype = dtype
-        if self._size_in != -1:
-            self._w = self._helper.create_parameter(
-                attr=self._helper.param_attr,
-                shape=[size_in, size_out],
-                dtype=self._dtype,
-                is_bias=False)
+        from ..layer_helper import LayerHelper
+        self._helper = LayerHelper('FC', param_attr=param_attr)
 
     def _build_once(self, input):
-        if self._size_in != -1:
-            return
-
         input_shape = input.shape
         param_shape = [
             reduce(lambda a, b: a * b, input_shape[self._num_flatten_dims:], 1)
-        ] + [self._size_out]
+        ] + [self._size]
         self._w = self._helper.create_parameter(
             attr=self._helper.param_attr,
             shape=param_shape,

python/paddle/fluid/layers/nn.py

@@ -9713,47 +9713,3 @@ def huber_loss(input, label, delta):
                  'Residual': residual},
         attrs={'delta': delta})
     return out
-
-
-class FC(layers.PyLayer):
-    def __init__(self,
-                 size,
-                 param_attr=None,
-                 num_flatten_dims=1,
-                 dtype=core.VarDesc.VarType.FP32):
-        super(FC, self).__init__(param_attr=param_attr)
-        self._size = size
-        self._num_flatten_dims = num_flatten_dims
-        self._dtype = dtype
-        self._tmp = self._helper.create_variable_for_type_inference(self._dtype)
-        self._out = self._helper.create_variable_for_type_inference(self._dtype)
-
-    def _build_once(self, inputs):
-        input_shape = inputs.shape
-        param_shape = [
-            reduce(lambda a, b: a * b, input_shape[self._num_flatten_dims:], 1)
-        ] + [self._size]
-        self._w = self._helper.create_parameter(
-            attr=self._helper.param_attr,
-            shape=param_shape,
-            dtype=self._dtype,
-            is_bias=False)
-
-    def forward(self, inputs):
-        self._helper.append_op(
-            type="mul",
-            inputs={"X": inputs,
-                    "Y": self._w},
-            outputs={"Out": self._tmp},
-            attrs={
-                "x_num_col_dims": self._num_flatten_dims,
-                "y_num_col_dims": 1
-            })
-
-        self._helper.append_op(
-            type="sum",
-            inputs={"X": [self._tmp]},
-            outputs={"Out": self._out},
-            attrs={"use_mkldnn": False})
-
-        return self._out

python/paddle/fluid/tests/unittests/test_imperative.py

@@ -18,7 +18,7 @@ import numpy as np
 
 import paddle.fluid as fluid
 from paddle.fluid import core
-from paddle.fluid.layers.nn import FC
+from paddle.fluid.imperative.nn import FC
 from test_imperative_base import new_program_scope
 
 

python/paddle/fluid/tests/unittests/test_imperative_optimizer.py

@@ -74,7 +74,7 @@ class SimpleImgConvPool(fluid.imperative.PyLayer):
 
 class MNIST(fluid.imperative.PyLayer):
     def __init__(self, param_attr=None, bias_attr=None):
-        super(MNIST, self).__init__(param_attr=param_attr, bias_attr=bias_attr)
+        super(MNIST, self).__init__()
 
         self._simple_img_conv_pool_1 = SimpleImgConvPool(
             1, 20, 5, 2, 2, act="relu")
@@ -85,8 +85,7 @@ class MNIST(fluid.imperative.PyLayer):
         pool_2_shape = 50 * 8 * 8
         SIZE = 10
         scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
-        self._fc = FC(-1,
-                      10,
+        self._fc = FC(10,
                       param_attr=fluid.param_attr.ParamAttr(
                           initializer=fluid.initializer.NormalInitializer(
                               loc=0.0, scale=scale)))