From 674327a4b179286bbe7db2e2b96f8e59c0120562 Mon Sep 17 00:00:00 2001
From: yuyang18 <reyoung@126.com>
Date: Wed, 13 Jun 2018 18:06:59 +0800
Subject: [PATCH] Polish several API

---
 paddle/fluid/operators/activation_op.cc |  3 +-
 python/paddle/fluid/layers/detection.py | 38 ++++++++++++-------------
 python/paddle/fluid/layers/ops.py       | 28 ++++++++++++++++--
 3 files changed, 46 insertions(+), 23 deletions(-)

diff --git a/paddle/fluid/operators/activation_op.cc b/paddle/fluid/operators/activation_op.cc
index 5e2fa5667..89bb1e2d4 100644
--- a/paddle/fluid/operators/activation_op.cc
+++ b/paddle/fluid/operators/activation_op.cc
@@ -271,7 +271,8 @@ class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput("X", "Input of HardShrink operator");
     AddOutput("Out", "Output of HardShrink operator");
-    AddAttr<float>("threshold", "The value of threshold for HardShrink")
+    AddAttr<float>("threshold",
+                   "The value of threshold for HardShrink. [default: 0.5]")
         .SetDefault(0.5f);
     AddComment(R"DOC(
 HardShrink Activation Operator.
diff --git a/python/paddle/fluid/layers/detection.py b/python/paddle/fluid/layers/detection.py
index 1e8dfbe52..d5f7e420d 100644
--- a/python/paddle/fluid/layers/detection.py
+++ b/python/paddle/fluid/layers/detection.py
@@ -403,25 +403,6 @@ def ssd_loss(location,
 
       5.3 Compute the overall weighted loss.
 
-    >>> import paddle.fluid.layers as layers
-    >>> pb = layers.data(
-    >>>            name='prior_box',
-    >>>            shape=[10, 4],
-    >>>            append_batch_size=False,
-    >>>            dtype='float32')
-    >>> pbv = layers.data(
-    >>>            name='prior_box_var',
-    >>>            shape=[10, 4],
-    >>>            append_batch_size=False,
-    >>>            dtype='float32')
-    >>> loc = layers.data(name='target_box', shape=[10, 4], dtype='float32')
-    >>> scores = layers.data(name='scores', shape=[10, 21], dtype='float32')
-    >>> gt_box = layers.data(
-    >>>         name='gt_box', shape=[4], lod_level=1, dtype='float32')
-    >>> gt_label = layers.data(
-    >>>         name='gt_label', shape=[1], lod_level=1, dtype='float32')
-    >>>     loss = layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
-
     Args:
         location (Variable): The location predictions are a 3D Tensor with
             shape [N, Np, 4], N is the batch size, Np is total number of
@@ -465,6 +446,25 @@ def ssd_loss(location,
     Raises:
         ValueError: If mining_type is 'hard_example', now only support mining \
         type of `max_negative`.
+
+    Examples:
+        >>> pb = fluid.layers.data(
+        >>>                   name='prior_box',
+        >>>                   shape=[10, 4],
+        >>>                   append_batch_size=False,
+        >>>                   dtype='float32')
+        >>> pbv = fluid.layers.data(
+        >>>                   name='prior_box_var',
+        >>>                   shape=[10, 4],
+        >>>                   append_batch_size=False,
+        >>>                   dtype='float32')
+        >>> loc = fluid.layers.data(name='target_box', shape=[10, 4], dtype='float32')
+        >>> scores = fluid.layers.data(name='scores', shape=[10, 21], dtype='float32')
+        >>> gt_box = fluid.layers.data(
+        >>>         name='gt_box', shape=[4], lod_level=1, dtype='float32')
+        >>> gt_label = fluid.layers.data(
+        >>>         name='gt_label', shape=[1], lod_level=1, dtype='float32')
+        >>> loss = fluid.layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
     """
 
     helper = LayerHelper('ssd_loss', **locals())
diff --git a/python/paddle/fluid/layers/ops.py b/python/paddle/fluid/layers/ops.py
index 46c6fd686..f0abd3089 100644
--- a/python/paddle/fluid/layers/ops.py
+++ b/python/paddle/fluid/layers/ops.py
@@ -40,7 +40,6 @@ __activations__ = [
     'relu6',
     'pow',
     'stanh',
-    'hard_shrink',
     'thresholded_relu',
     'hard_sigmoid',
     'swish',
@@ -92,9 +91,32 @@ def uniform_random(shape, dtype=None, min=None, max=None, seed=None):
             kwargs[name] = val
     return _uniform_random_(**kwargs)
 
-uniform_random.__doc__ = _uniform_random_.__doc__  + "\n"\
-+"""
+
+uniform_random.__doc__ = _uniform_random_.__doc__ + "\n" \
+                         + """
 Examples:
 
     >>> result = fluid.layers.uniform_random(shape=[32, 784])
 """
+
+__all__ += ['hard_shrink']
+
+_hard_shrink_ = generate_layer_fn('hard_shrink')
+
+
+def hard_shrink(x, threshold=None):
+    kwargs = dict()
+    for name in locals():
+        val = locals()[name]
+        if val is not None:
+            kwargs[name] = val
+    return _hard_shrink_(**kwargs)
+
+
+hard_shrink.__doc__ = _hard_shrink_.__doc__ + "\n" \
+                      + """
+Examples:
+
+    >>> data = fluid.layers.data(name="input", shape=[784])
+    >>> result = fluid.layers.hard_shrink(x=data, threshold=0.3)
+"""
-- 
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