Merge pull request #13531 from gongweibao/generator2

Hide kwargs

Author: panyx0718

paddle/fluid/API.spec

@@ -153,6 +153,13 @@ paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'out', 'axis', 'use_
 paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
 paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
 paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None))
+paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0))
+paddle.fluid.layers.gaussian_random ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype', 'use_mkldnn'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32', False))
+paddle.fluid.layers.sampling_id ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32'))
+paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32'))
+paddle.fluid.layers.sum ArgSpec(args=['x', 'use_mkldnn'], varargs=None, keywords=None, defaults=(False,))
+paddle.fluid.layers.slice ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.layers.shape ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
 paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
@@ -224,13 +231,6 @@ paddle.fluid.layers.logical_and ArgSpec(args=[], varargs='args', keywords='kwarg
 paddle.fluid.layers.logical_or ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
 paddle.fluid.layers.logical_xor ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
 paddle.fluid.layers.logical_not ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.uniform_random_batch_size_like ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.gaussian_random ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.sampling_id ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.gaussian_random_batch_size_like ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.sum ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.slice ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
-paddle.fluid.layers.shape ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
 paddle.fluid.layers.maxout ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
 paddle.fluid.layers.sigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.logsigmoid ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))

paddle/fluid/operators/sampling_id_op.cc

@@ -53,15 +53,16 @@ class SamplingIdOpMaker : public framework::OpProtoAndCheckerMaker {
 SamplingId Operator.
 A layer for sampling id from multinomial distribution from the
  input. Sampling one id for one sample.)DOC");
-    AddAttr<float>("min", "Minimum value of random. [default 0.0].")
+    AddAttr<float>("min", "Minimum value of random. (float, default 0.0).")
         .SetDefault(0.0f);
-    AddAttr<float>("max", "Maximun value of random. [default 1.0].")
+    AddAttr<float>("max", "Maximun value of random. (float, default 1.0).")
         .SetDefault(1.0f);
-    AddAttr<int>("seed",
-                 "Random seed used for the random number engine. "
-                 "0 means use a seed generated by the system."
-                 "Note that if seed is not 0, this operator will always "
-                 "generate the same random numbers every time. [default 0].")
+    AddAttr<int>(
+        "seed",
+        "Random seed used for the random number engine. "
+        "0 means use a seed generated by the system."
+        "Note that if seed is not 0, this operator will always "
+        "generate the same random numbers every time. (int, default 0).")
         .SetDefault(0);
   }
 };

python/paddle/fluid/layers/detection.py

@@ -284,7 +284,7 @@ class number, M is number of bounding boxes. For each category
         target_box=loc,
         code_type='decode_center_size')
     compile_shape = scores.shape
-    run_shape = ops.shape(scores)
+    run_shape = nn.shape(scores)
     scores = nn.flatten(x=scores, axis=2)
     scores = nn.softmax(input=scores)
     scores = nn.reshape(x=scores, shape=compile_shape, actual_shape=run_shape)
@@ -697,7 +697,7 @@ def ssd_loss(location,
         raise ValueError("Only support mining_type == max_negative now.")
 
     num, num_prior, num_class = confidence.shape
-    conf_shape = ops.shape(confidence)
+    conf_shape = nn.shape(confidence)
 
     def __reshape_to_2d(var):
         return nn.flatten(x=var, axis=2)
@@ -724,7 +724,7 @@ def __reshape_to_2d(var):
     target_label.stop_gradient = True
     conf_loss = nn.softmax_with_cross_entropy(confidence, target_label)
     # 3. Mining hard examples
-    actual_shape = ops.slice(conf_shape, axes=[0], starts=[0], ends=[2])
+    actual_shape = nn.slice(conf_shape, axes=[0], starts=[0], ends=[2])
     actual_shape.stop_gradient = True
     conf_loss = nn.reshape(
         x=conf_loss, shape=(num, num_prior), actual_shape=actual_shape)