clean

paddle/fluid/API.spec

@@ -170,6 +170,13 @@ paddle.fluid.layers.unstack ArgSpec(args=['x', 'axis', 'num'], varargs=None, key
 paddle.fluid.layers.sequence_enumerate ArgSpec(args=['input', 'win_size', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0, None))
 paddle.fluid.layers.expand ArgSpec(args=['x', 'expand_times', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.sequence_concat ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(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)
@@ -249,13 +256,6 @@ 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=[], varargs='args', keywords='kwargs', defaults=None)
 paddle.fluid.layers.logsigmoid ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
@@ -379,7 +379,7 @@ paddle.fluid.LoDTensor.__init__ 1. __init__(self: paddle.fluid.core.LoDTensor, a
 paddle.fluid.LoDTensor.has_valid_recursive_sequence_lengths has_valid_recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor) -> bool
 paddle.fluid.LoDTensor.lod lod(self: paddle.fluid.core.LoDTensor) -> List[List[int]]
 paddle.fluid.LoDTensor.recursive_sequence_lengths recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor) -> List[List[int]]
-paddle.fluid.LoDTensor.set 1. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CPUPlace) -> None  2. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CPUPlace) -> None  3. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CPUPlace) -> None  4. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CPUPlace) -> None  5. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CPUPlace) -> None  6. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CPUPlace) -> None  7. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CPUPlace) -> None  8. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CPUPlace) -> None  9. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPlace) -> None  10. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPlace) -> None  11. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPlace) -> None  12. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPlace) -> None  13. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPlace) -> None  14. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPlace) -> None  15. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPlace) -> None  16. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CUDAPlace) -> None  17. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CUDAPinnedPlace) -> None  18. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CUDAPinnedPlace) -> None  19. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CUDAPinnedPlace) -> None  20. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CUDAPinnedPlace) -> None  21. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CUDAPinnedPlace) -> None  22. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CUDAPinnedPlace) -> None  23. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CUDAPinnedPlace) -> None  24. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CUDAPinnedPlace) -> None
+paddle.fluid.LoDTensor.set 1. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float32], arg1: paddle::platform::CPUPlace) -> None  2. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int32], arg1: paddle::platform::CPUPlace) -> None  3. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[float64], arg1: paddle::platform::CPUPlace) -> None  4. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int64], arg1: paddle::platform::CPUPlace) -> None  5. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[bool], arg1: paddle::platform::CPUPlace) -> None  6. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint16], arg1: paddle::platform::CPUPlace) -> None  7. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[uint8], arg1: paddle::platform::CPUPlace) -> None  8. set(self: paddle.fluid.core.Tensor, arg0: numpy.ndarray[int8], arg1: paddle::platform::CPUPlace) -> None
 paddle.fluid.LoDTensor.set_lod set_lod(self: paddle.fluid.core.LoDTensor, arg0: List[List[int]]) -> None
 paddle.fluid.LoDTensor.set_recursive_sequence_lengths set_recursive_sequence_lengths(self: paddle.fluid.core.LoDTensor, arg0: List[List[int]]) -> None
 paddle.fluid.LoDTensor.shape shape(self: paddle.fluid.core.Tensor) -> List[int]

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

@@ -252,9 +252,6 @@ class OpTest(unittest.TestCase):
         block = program.global_block()
         self._append_ops(block)
 
-        from paddle.fluid.transpiler.details import program_to_code
-        program_to_code(program)
-
         inputs = self._get_inputs(block)
         outputs = self._get_outputs(block)
         feed_map = self.feed_var(inputs, place)

tools/test_generator.py

-# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import print_function
-import re
-import functools
-import warnings
-import string
-
-from six.moves import cStringIO
-from paddle.fluid.proto import framework_pb2
-from paddle.fluid.framework import OpProtoHolder, Variable
-from paddle.fluid.layer_helper import LayerHelper
-
-g_filer_attrs = ['op_role', 'op_role_var', 'op_namescope']
-
-
-def _convert_(name):
-    """
-    Formatting.
-
-    Args:
-       name: The name/alias
-
-    This function takes in a name and converts it to a standard format of
-    group1_group2. Where as per the regular expression, group1 can have
-    alphabets and numbers and group2 has capital alphabets.
-
-    """
-    s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
-    return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()
-
-
-def _get_inputs(op_type):
-    op_proto = OpProtoHolder.instance().get_op_proto(op_type)
-    inputs = dict()
-    for ipt in op_proto.inputs:
-        inputs[ipt.name] = ipt.comment
-
-    return inputs
-
-
-def _get_outputs(op_type):
-    op_proto = OpProtoHolder.instance().get_op_proto(op_type)
-    outputs = {}
-    for ipt in op_proto.outputs:
-        outputs[ipt.name] = ""
-
-    return outputs
-
-
-_two_dollar_pattern_ = re.compile(r"\$\$([^\$]+)\$\$")
-_single_dollar_pattern_ = re.compile(r"\$([^\$]+)\$")
-_two_bang_pattern_ = re.compile(r"!!([^!]+)!!")
-
-
-def escape_math(text):
-    return _two_bang_pattern_.sub(
-        r'$$\1$$',
-        _single_dollar_pattern_.sub(r':math:`\1`',
-                                    _two_dollar_pattern_.sub(r"!!\1!!", text)))
-
-
-def get_comment(op_type):
-    op_proto = OpProtoHolder.instance().get_op_proto(op_type)
-
-    comment_lines = op_proto.comment.split("\n")
-    comment = ""
-    for line in comment_lines:
-        line = line.strip()
-        if len(line) != 0:
-            comment += escape_math(line)
-            comment += " "
-        elif len(comment) != 0:
-            comment += "\n    "
-
-    return comment
-
-
-def _get_attrs(op_type):
-    op_proto = OpProtoHolder.instance().get_op_proto(op_type)
-    return op_proto.attrs
-
-
-def get_indent_space(indent, space_num=4):
-    ret = ""
-    for i in range(0, indent * space_num):
-        ret += " "
-
-    return ret
-
-
-def get_input_comments(op_type, indent=2):
-    ret = ""
-    inputs = _get_inputs(op_type)
-    for t in inputs:
-        ret += get_indent_space(2) + "%s (Type): %s\n" % (_convert_(t),
-                                                          inputs[t])
-
-    for t in _get_attrs(op_type):
-        if t.name in g_filer_attrs:
-            continue
-        ret += get_indent_space(2) + "%s (%s): %s\n" % (
-            _convert_(t.name), t.type, _convert_(t.comment))
-
-    return ret
-
-
-def get_output_comments(op_type, indent=2):
-    ret = ""
-    for t in _get_outputs(op_type):
-        ret += get_indent_space(2) + "output(${%s_type}): ${%s_comment}\n" % (
-            _convert_(t), _convert_(t))
-    return ret
-
-
-def get_func_args(op_type):
-    ret = ""
-    inputs = _get_inputs(op_type)
-    for t in inputs:
-        ret += "%s," % _convert_(t)
-
-    for t in _get_attrs(op_type):
-        if t.name in g_filer_attrs:
-            continue
-
-        default = re.findall("\(.+\, default (.+)\(?\)", t.comment)
-        if len(default) > 0:
-            #print(default[0])
-            ret += "{}={},".format(_convert_(t.name), default[0])
-            continue
-
-        ret += "%s=," % _convert_(t.name)
-
-    return ret.strip(',')
-
-
-def get_inputs(op_type):
-    ret = "inputs={"
-    inputs = _get_inputs(op_type)
-    for t in inputs:
-        ret += "'{}': {},".format(t, _convert_(t))
-    ret = ret.strip(",")
-    ret += "}"
-
-    if ret == "inputs={}":
-        return ""
-
-    return ret
-
-
-def get_outputs(op_type):
-    ret = "outputs={"
-    inputs = _get_outputs(op_type)
-    for t in inputs:
-        ret += "'{}': {},".format(t, _convert_(t))
-    ret = ret.strip(",")
-    ret += "}"
-
-    if ret == "inputs={}":
-        return ""
-
-    return ret
-
-
-def get_attrs(op_type):
-    ret = "attrs={"
-    for t in _get_attrs(op_type):
-        if t.name in g_filer_attrs:
-            continue
-
-        ret += "'%s': %s," % (t.name, _convert_(t.name))
-
-    ret = ret.strip(",")
-    ret += "}"
-
-    return ret
-
-
-def get_outvars(op_type, indent=1):
-    inputs = _get_inputs(op_type)
-    if len(inputs) == 0:
-        return ""
-    ret = ""
-    for t in _get_outputs(op_type):
-        ret += get_indent_space(
-            indent
-        ) + "%s = helper.create_tmp_variable(dtype=helper.input_dtype('%s'))\n" % (
-            (_convert_(t), list(inputs)[0]))
-    ret = ret.strip('\n')
-    return ret
-
-
-def get_op_py(op_type):
-    input_comments = get_input_comments(op_type)
-    output_comments = get_output_comments(op_type)
-    args = get_func_args(op_type)
-    inputs = get_inputs(op_type)
-    outputs = get_outputs(op_type)
-    attrs = get_attrs(op_type)
-    out_vars = get_outvars(op_type)
-    comment = get_comment(op_type)
-
-    code = """
-def {op_type}({args}):
-    \"\"\"
-    {comment}
-    Args:
-{input_comments}
-
-    Returns:
-{output_comments}
-    \"\"\"
-    
-    helper = LayerHelper('{op_type}', **locals())
-{generated_outvar}
-    helper.append_op(
-        type='{op_type}',
-        {inputs},
-        {outputs},
-        {attrs})    
-    
-    return out
-""".format(
-        comment=comment,
-        input_comments=input_comments.strip('\n'),
-        output_comments=output_comments,
-        args=args,
-        generated_outvar=out_vars,
-        op_type=op_type,
-        inputs=inputs,
-        outputs=outputs,
-        attrs=attrs)
-
-    return code
-
-
-#print(get_op_py("uniform_random_batch_size_like"))
-#print(get_op_py("gaussian_random"))
-print(get_op_py("sampling_id"))
-print(get_op_py("gaussian_random_batch_size_like"))
-print(get_op_py("sum"))
-print(get_op_py("slice"))
-print(get_op_py("shape"))