"add registry interface" (#6449)
* "add registry interface" * "move function to registry" * "rename with meaningful name" * "add exposed layers" * "fixed based on comments" * "remove unsed comments"del_some_in_makelist
dzhwinter
7 years ago
committed by
GitHub
parent
8ad36cdb5d
commit
697facc92f
@ -0,0 +1,186 @@
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import re
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import cStringIO
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import warnings
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import functools
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import inspect
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import proto.framework_pb2 as framework_pb2
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from framework import OpProtoHolder, Variable, Program, Operator
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from paddle.v2.fluid.layer_helper import LayerHelper, unique_name
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__all__ = ['deprecated', 'register_layer']
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def _convert_(name):
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"""
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Formatting.
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Args:
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name: The name/alias
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This function takes in a name and converts it to a standard format of
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group1_group2. Where as per the regular expression, group1 can have
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alphabets and numbers and group2 has capital alphabets.
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"""
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s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
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return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()
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def _generate_doc_string_(op_proto):
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"""
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Generate docstring by OpProto
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Args:
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op_proto (framework_pb2.OpProto): a protobuf message typed OpProto
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Returns:
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str: the document string
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"""
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def _type_to_str_(tp):
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return framework_pb2.AttrType.Name(tp)
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if not isinstance(op_proto, framework_pb2.OpProto):
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raise TypeError("OpProto should be `framework_pb2.OpProto`")
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buf = cStringIO.StringIO()
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buf.write(op_proto.comment)
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buf.write('\nArgs:\n')
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for each_input in op_proto.inputs:
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line_begin = ' {0}: '.format(_convert_(each_input.name))
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buf.write(line_begin)
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buf.write(each_input.comment)
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buf.write('\n')
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buf.write(' ' * len(line_begin))
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buf.write('Duplicable: ')
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buf.write(str(each_input.duplicable))
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buf.write(' Optional: ')
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buf.write(str(each_input.dispensable))
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buf.write('\n')
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for each_attr in op_proto.attrs:
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buf.write(' ')
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buf.write(each_attr.name)
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buf.write(' (')
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buf.write(_type_to_str_(each_attr.type))
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buf.write('): ')
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buf.write(each_attr.comment)
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buf.write('\n')
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if len(op_proto.outputs) != 0:
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buf.write('\nReturns:\n')
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buf.write(' ')
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for each_opt in op_proto.outputs:
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if not each_opt.intermediate:
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break
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buf.write(each_opt.comment)
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return buf.getvalue()
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def register_layer(op_type):
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"""
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Register an Python layer for an Operator
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Args:
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op_type: The name of the operator to be created
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This function takes in the operator type (sigmoid, mean , average etc) and
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creates the operator functionality.
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"""
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op_proto = OpProtoHolder.instance().get_op_proto(op_type)
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not_intermediate_outputs = \
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filter(lambda output: not output.intermediate, op_proto.outputs)
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intermediate_outputs = \
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filter(lambda output: output.intermediate, op_proto.outputs)
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if len(not_intermediate_outputs) != 1:
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raise ValueError("Only one non intermediate output operator can be",
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"automatically generated")
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if not_intermediate_outputs[0].duplicable:
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raise ValueError(
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"Only non duplicable op can be automatically generated")
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for output in intermediate_outputs:
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if output.duplicable:
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raise ValueError("The op can be automatically generated only when ",
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"all intermediate ops are not duplicable")
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o_name = not_intermediate_outputs[0].name
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intermediate_output_names = [output.name for output in intermediate_outputs]
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def infer_and_check_dtype(op_proto, **kwargs):
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"""
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This function performs the sanity check for dtype and
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instance type.
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"""
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dtype = None
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for ipt in op_proto.inputs:
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name = _convert_(ipt.name)
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val = kwargs.pop(name, [])
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if not isinstance(val, list) and not isinstance(val, tuple):
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val = [val]
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for each in val:
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if not isinstance(each, Variable):
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raise ValueError("input of {0} must be variable".format(
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op_type))
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if dtype is None:
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dtype = each.dtype
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elif dtype != each.dtype:
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raise ValueError(
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"operator {0} must input same dtype. {1} vs {2}".format(
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op_type, dtype, each.dtype))
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return dtype
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def func(**kwargs):
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helper = LayerHelper(op_type, **kwargs)
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dtype = infer_and_check_dtype(op_proto, **kwargs)
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inputs = dict()
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for ipt in op_proto.inputs:
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name = _convert_(ipt.name)
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val = kwargs.pop(name, [])
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if not isinstance(val, list) and not isinstance(val, tuple):
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val = [val]
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inputs[ipt.name] = val
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outputs = dict()
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out = helper.create_tmp_variable(dtype=dtype)
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outputs[o_name] = [out]
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for name in intermediate_output_names:
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outputs[name] = [helper.create_tmp_variable(dtype=dtype)]
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helper.append_op(
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type=op_type, inputs=inputs, outputs=outputs, attrs=kwargs)
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return helper.append_activation(out)
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func.__name__ = op_type
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func.__doc__ = _generate_doc_string_(op_proto)
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return func
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def deprecated(func_or_class):
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"""
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Deprecated warning decorator. It will result a warning message.
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Should be used before class or function, member function
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"""
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@functools.wraps(func)
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def func_wrapper(*args, **kwargs):
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"""
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Wrap func with deprecated warning
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"""
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warnings.simplefilter('always', DeprecationWarning) #turn off filter
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warnings.warn(
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"Call to deprecated function {}.".format(func.__name__),
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category=DeprecationWarning,
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stacklevel=2)
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warnings.simplefilter('default', DeprecationWarning) #reset filter
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return func(*args, **kwargs)
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return func_wrapper
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@ -0,0 +1,22 @@
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import unittest
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import warnings
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import paddle.v2.fluid as fluid
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import paddle.v2.fluid.framework as framework
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import paddle.v2.fluid.layers as layers
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import paddle.v2.fluid.registry as registry
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class TestRegistry(unittest.TestCase):
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def test_registry_layer(self):
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self.layer_type = "mean"
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program = framework.Program()
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x = fluid.layers.data(name='X', shape=[10, 10], dtype='float32')
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output = layers.mean(x)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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X = np.random.random((10, 10)).astype("float32")
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mean_out = exe.run(program, feed={"X": X}, fetch_list=[output])
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self.assertAlmostEqual(np.mean(X), mean_out)
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