提交 f9d93bfd 编写于 作者: Y yuyang18

Add document to random crop operator

上级 1af0b28c
......@@ -36,11 +36,11 @@ class RandomCropOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Seed", "The random seed.");
AddOutput("Out", "The cropped instance batch.");
AddOutput("SeedOut", "The random seed after random cropping.")
.AsDispensable();
.AsIntermediate();
AddAttr<std::vector<int>>("shape", "The shape of a cropped instance.");
AddComment(R"DOC(
This operator takes a batch of instance, and do random cropping on each instance.
It means that cropping positions differs on each instance, which is determined
This operator takes a batch of instance, and do random cropping on each instance.
It means that cropping positions differs on each instance, which is determined
by an uniform random generator. All cropped instances have the same shape, which
is determined by the operator's attribute 'shape'.
)DOC");
......
......@@ -15,16 +15,13 @@ import re
import cStringIO
import functools
import warnings
import string
from ..proto import framework_pb2
from ..framework import OpProtoHolder, Variable
from ..layer_helper import LayerHelper
__all__ = [
'deprecated',
'generate_layer_fn',
'autodoc',
]
__all__ = ['deprecated', 'generate_layer_fn', 'autodoc', 'templatedoc']
def _convert_(name):
......@@ -43,6 +40,10 @@ def _convert_(name):
return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()
def _type_to_str_(tp):
return framework_pb2.AttrType.Name(tp)
def _generate_doc_string_(op_proto):
"""
Generate docstring by OpProto
......@@ -54,9 +55,6 @@ def _generate_doc_string_(op_proto):
str: the document string
"""
def _type_to_str_(tp):
return framework_pb2.AttrType.Name(tp)
if not isinstance(op_proto, framework_pb2.OpProto):
raise TypeError("OpProto should be `framework_pb2.OpProto`")
......@@ -220,3 +218,42 @@ def autodoc(comment=""):
return func
return __impl__
def templatedoc():
"""
Decorator of layer function. It will use the docstring from the layer
function as the template. The template arguments are:
* ${comment}: The operator comment written in CPP.
* ${{name}_comment}: The comment of ${name} written with AddAttr, AddOutput,
and AddInput. The ${name} is Python snake style. i.e., xxx_xxx.
* ${{name}_type}: The type of ${name}.
Returns:
Decorated funciton.
"""
def __impl__(func):
op_proto = OpProtoHolder.instance().get_op_proto(func.__name__)
tmpl = string.Template(func.__doc__)
args = {"comment": " ".join(op_proto.comment.split())}
for each_input in op_proto.inputs:
input_name = _convert_(each_input.name)
args["{0}_comment".format(input_name)] = each_input.comment
args["{0}_type".format(input_name)] = "Variable"
for each_attr in op_proto.attrs:
input_name = _convert_(each_attr.name)
args["{0}_comment".format(input_name)] = each_attr.comment
args["{0}_type".format(input_name)] = _type_to_str_(each_attr.type)
for each_opt in op_proto.outputs:
output_name = _convert_(each_opt.name)
args["{0}_comment".format(output_name)] = each_opt.comment
args["{0}_type".format(output_name)] = "Variable"
func.__doc__ = tmpl.substitute(args)
return func
return __impl__
......@@ -19,9 +19,10 @@ from ..layer_helper import LayerHelper
from ..initializer import Normal, Constant
from ..framework import Variable
from ..param_attr import ParamAttr
from layer_function_generator import autodoc
from layer_function_generator import autodoc, templatedoc
from tensor import concat
import utils
import random
__all__ = [
'fc',
......@@ -3992,10 +3993,34 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
return out
def random_crop(input, shape, seed=1):
@templatedoc()
def random_crop(x, shape, seed=None):
"""
**Random crop operator**
${comment}
Examples:
>>> img = fluid.layers.data("img", [3, 256, 256])
>>> cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224])
Args:
x(${x_type}): ${x_comment}
shape(${shape_type}): ${shape_comment}
seed(int|${seed_type}|None): ${seed_comment} By default, the seed will
get from `random.randint(-65536, 65535)`.
Returns:
${out_comment}
"""
helper = LayerHelper("random_crop", **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
if seed is None:
seed = random.randint(-65536, 65535)
if isinstance(seed, int):
seed_value = seed
seed = helper.create_tmp_variable(dtype="int64")
......
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