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提交 7a96c492 编写于 作者: J jiangjiajun
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tensorflow2fluid/src/framework_pb2.py 0 → 100644
浏览文件 @ 7a96c492
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: framework.proto
import sys
_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
from google.protobuf.internal import enum_type_wrapper
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
from google.protobuf import descriptor_pb2
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name='framework.proto',
package='paddle.framework.proto',
syntax='proto2',
serialized_pb=_b('\n\x0f\x66ramework.proto\x12\x16paddle.framework.proto\"\x1d\n\x07Version\x12\x12\n\x07version\x18\x01 \x01(\x03:\x01\x30\"\xec\x03\n\x06OpDesc\x12\x0c\n\x04type\x18\x03 \x02(\t\x12\x32\n\x06inputs\x18\x01 \x03(\x0b\x32\".paddle.framework.proto.OpDesc.Var\x12\x33\n\x07outputs\x18\x02 \x03(\x0b\x32\".paddle.framework.proto.OpDesc.Var\x12\x32\n\x05\x61ttrs\x18\x04 \x03(\x0b\x32#.paddle.framework.proto.OpDesc.Attr\x12\x18\n\tis_target\x18\x05 \x01(\x08:\x05\x66\x61lse\x1a\xef\x01\n\x04\x41ttr\x12\x0c\n\x04name\x18\x01 \x02(\t\x12.\n\x04type\x18\x02 \x02(\x0e\x32 .paddle.framework.proto.AttrType\x12\t\n\x01i\x18\x03 \x01(\x05\x12\t\n\x01\x66\x18\x04 \x01(\x02\x12\t\n\x01s\x18\x05 \x01(\t\x12\x0c\n\x04ints\x18\x06 \x03(\x05\x12\x0e\n\x06\x66loats\x18\x07 \x03(\x02\x12\x0f\n\x07strings\x18\x08 \x03(\t\x12\t\n\x01\x62\x18\n \x01(\x08\x12\r\n\x05\x62ools\x18\x0b \x03(\x08\x12\x11\n\tblock_idx\x18\x0c \x01(\x05\x12\t\n\x01l\x18\r \x01(\x03\x12\x12\n\nblocks_idx\x18\x0e \x03(\x05\x12\r\n\x05longs\x18\x0f \x03(\x03\x1a+\n\x03Var\x12\x11\n\tparameter\x18\x01 \x02(\t\x12\x11\n\targuments\x18\x02 \x03(\t\"\xb3\x03\n\x07OpProto\x12\x0c\n\x04type\x18\x01 \x02(\t\x12\x33\n\x06inputs\x18\x02 \x03(\x0b\x32#.paddle.framework.proto.OpProto.Var\x12\x34\n\x07outputs\x18\x03 \x03(\x0b\x32#.paddle.framework.proto.OpProto.Var\x12\x33\n\x05\x61ttrs\x18\x04 \x03(\x0b\x32$.paddle.framework.proto.OpProto.Attr\x12\x0f\n\x07\x63omment\x18\x05 \x02(\t\x1ax\n\x03Var\x12\x0c\n\x04name\x18\x01 \x02(\t\x12\x0f\n\x07\x63omment\x18\x02 \x02(\t\x12\x19\n\nduplicable\x18\x03 \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0cintermediate\x18\x04 \x01(\x08:\x05\x66\x61lse\x12\x1a\n\x0b\x64ispensable\x18\x05 \x01(\x08:\x05\x66\x61lse\x1ao\n\x04\x41ttr\x12\x0c\n\x04name\x18\x01 \x02(\t\x12.\n\x04type\x18\x02 \x02(\x0e\x32 .paddle.framework.proto.AttrType\x12\x0f\n\x07\x63omment\x18\x03 \x02(\t\x12\x18\n\tgenerated\x18\x04 \x01(\x08:\x05\x66\x61lse\"\xda\x08\n\x07VarType\x12\x32\n\x04type\x18\x01 \x02(\x0e\x32$.paddle.framework.proto.VarType.Type\x12\x41\n\rselected_rows\x18\x02 \x01(\x0b\x32*.paddle.framework.proto.VarType.TensorDesc\x12\x41\n\nlod_tensor\x18\x03 \x01(\x0b\x32-.paddle.framework.proto.VarType.LoDTensorDesc\x12H\n\x0ctensor_array\x18\x04 \x01(\x0b\x32\x32.paddle.framework.proto.VarType.LoDTensorArrayDesc\x12:\n\x06reader\x18\x05 \x01(\x0b\x32*.paddle.framework.proto.VarType.ReaderDesc\x12\x34\n\x05tuple\x18\x07 \x01(\x0b\x32%.paddle.framework.proto.VarType.Tuple\x1aS\n\nTensorDesc\x12\x37\n\tdata_type\x18\x01 \x02(\x0e\x32$.paddle.framework.proto.VarType.Type\x12\x0c\n\x04\x64ims\x18\x02 \x03(\x03\x1a\x61\n\rLoDTensorDesc\x12:\n\x06tensor\x18\x01 \x02(\x0b\x32*.paddle.framework.proto.VarType.TensorDesc\x12\x14\n\tlod_level\x18\x02 \x01(\x05:\x01\x30\x1a\x66\n\x12LoDTensorArrayDesc\x12:\n\x06tensor\x18\x01 \x02(\x0b\x32*.paddle.framework.proto.VarType.TensorDesc\x12\x14\n\tlod_level\x18\x02 \x01(\x05:\x01\x30\x1aO\n\nReaderDesc\x12\x41\n\nlod_tensor\x18\x01 \x03(\x0b\x32-.paddle.framework.proto.VarType.LoDTensorDesc\x1a\x43\n\x05Tuple\x12:\n\x0c\x65lement_type\x18\x01 \x03(\x0e\x32$.paddle.framework.proto.VarType.Type\"\xa2\x02\n\x04Type\x12\x08\n\x04\x42OOL\x10\x00\x12\t\n\x05INT16\x10\x01\x12\t\n\x05INT32\x10\x02\x12\t\n\x05INT64\x10\x03\x12\x08\n\x04\x46P16\x10\x04\x12\x08\n\x04\x46P32\x10\x05\x12\x08\n\x04\x46P64\x10\x06\x12\n\n\x06SIZE_T\x10\x13\x12\t\n\x05UINT8\x10\x14\x12\x08\n\x04INT8\x10\x15\x12\x0e\n\nLOD_TENSOR\x10\x07\x12\x11\n\rSELECTED_ROWS\x10\x08\x12\x12\n\x0e\x46\x45\x45\x44_MINIBATCH\x10\t\x12\x0e\n\nFETCH_LIST\x10\n\x12\x0f\n\x0bSTEP_SCOPES\x10\x0b\x12\x12\n\x0eLOD_RANK_TABLE\x10\x0c\x12\x14\n\x10LOD_TENSOR_ARRAY\x10\r\x12\x0e\n\nPLACE_LIST\x10\x0e\x12\n\n\x06READER\x10\x0f\x12\x07\n\x03RAW\x10\x11\x12\t\n\x05TUPLE\x10\x12\"b\n\x07VarDesc\x12\x0c\n\x04name\x18\x01 \x02(\t\x12-\n\x04type\x18\x02 \x02(\x0b\x32\x1f.paddle.framework.proto.VarType\x12\x1a\n\x0bpersistable\x18\x03 \x01(\x08:\x05\x66\x61lse\"\xa7\x01\n\tBlockDesc\x12\x0b\n\x03idx\x18\x01 \x02(\x05\x12\x12\n\nparent_idx\x18\x02 \x02(\x05\x12-\n\x04vars\x18\x03 \x03(\x0b\x32\x1f.paddle.framework.proto.VarDesc\x12+\n\x03ops\x18\x04 \x03(\x0b\x32\x1e.paddle.framework.proto.OpDesc\x12\x1d\n\x11\x66orward_block_idx\x18\x05 \x01(\x05:\x02-1\"r\n\x0bProgramDesc\x12\x31\n\x06\x62locks\x18\x01 \x03(\x0b\x32!.paddle.framework.proto.BlockDesc\x12\x30\n\x07version\x18\x02 \x01(\x0b\x32\x1f.paddle.framework.proto.Version*\x94\x01\n\x08\x41ttrType\x12\x07\n\x03INT\x10\x00\x12\t\n\x05\x46LOAT\x10\x01\x12\n\n\x06STRING\x10\x02\x12\x08\n\x04INTS\x10\x03\x12\n\n\x06\x46LOATS\x10\x04\x12\x0b\n\x07STRINGS\x10\x05\x12\x0b\n\x07\x42OOLEAN\x10\x06\x12\x0c\n\x08\x42OOLEANS\x10\x07\x12\t\n\x05\x42LOCK\x10\x08\x12\x08\n\x04LONG\x10\t\x12\n\n\x06\x42LOCKS\x10\n\x12\t\n\x05LONGS\x10\x0b\x42\x02H\x03')
)
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
_ATTRTYPE = _descriptor.EnumDescriptor(
name='AttrType',
full_name='paddle.framework.proto.AttrType',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='INT', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='FLOAT', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='STRING', index=2, number=2,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='INTS', index=3, number=3,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='FLOATS', index=4, number=4,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='STRINGS', index=5, number=5,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='BOOLEAN', index=6, number=6,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='BOOLEANS', index=7, number=7,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='BLOCK', index=8, number=8,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='LONG', index=9, number=9,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='BLOCKS', index=10, number=10,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='LONGS', index=11, number=11,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=2511,
serialized_end=2659,
)
_sym_db.RegisterEnumDescriptor(_ATTRTYPE)
AttrType = enum_type_wrapper.EnumTypeWrapper(_ATTRTYPE)
INT = 0
FLOAT = 1
STRING = 2
INTS = 3
FLOATS = 4
STRINGS = 5
BOOLEAN = 6
BOOLEANS = 7
BLOCK = 8
LONG = 9
BLOCKS = 10
LONGS = 11
_VARTYPE_TYPE = _descriptor.EnumDescriptor(
name='Type',
full_name='paddle.framework.proto.VarType.Type',
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name='BOOL', index=0, number=0,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='INT16', index=1, number=1,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='INT32', index=2, number=2,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='INT64', index=3, number=3,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='FP16', index=4, number=4,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='FP32', index=5, number=5,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='FP64', index=6, number=6,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SIZE_T', index=7, number=19,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='UINT8', index=8, number=20,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='INT8', index=9, number=21,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='LOD_TENSOR', index=10, number=7,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='SELECTED_ROWS', index=11, number=8,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='FEED_MINIBATCH', index=12, number=9,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='FETCH_LIST', index=13, number=10,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='STEP_SCOPES', index=14, number=11,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='LOD_RANK_TABLE', index=15, number=12,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='LOD_TENSOR_ARRAY', index=16, number=13,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='PLACE_LIST', index=17, number=14,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='READER', index=18, number=15,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='RAW', index=19, number=17,
options=None,
type=None),
_descriptor.EnumValueDescriptor(
name='TUPLE', index=20, number=18,
options=None,
type=None),
],
containing_type=None,
options=None,
serialized_start=1832,
serialized_end=2122,
)
_sym_db.RegisterEnumDescriptor(_VARTYPE_TYPE)
_VERSION = _descriptor.Descriptor(
name='Version',
full_name='paddle.framework.proto.Version',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='version', full_name='paddle.framework.proto.Version.version', index=0,
number=1, type=3, cpp_type=2, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=43,
serialized_end=72,
)
_OPDESC_ATTR = _descriptor.Descriptor(
name='Attr',
full_name='paddle.framework.proto.OpDesc.Attr',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='paddle.framework.proto.OpDesc.Attr.name', index=0,
number=1, type=9, cpp_type=9, label=2,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='type', full_name='paddle.framework.proto.OpDesc.Attr.type', index=1,
number=2, type=14, cpp_type=8, label=2,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='i', full_name='paddle.framework.proto.OpDesc.Attr.i', index=2,
number=3, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='f', full_name='paddle.framework.proto.OpDesc.Attr.f', index=3,
number=4, type=2, cpp_type=6, label=1,
has_default_value=False, default_value=float(0),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='s', full_name='paddle.framework.proto.OpDesc.Attr.s', index=4,
number=5, type=9, cpp_type=9, label=1,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='ints', full_name='paddle.framework.proto.OpDesc.Attr.ints', index=5,
number=6, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='floats', full_name='paddle.framework.proto.OpDesc.Attr.floats', index=6,
number=7, type=2, cpp_type=6, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='strings', full_name='paddle.framework.proto.OpDesc.Attr.strings', index=7,
number=8, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='b', full_name='paddle.framework.proto.OpDesc.Attr.b', index=8,
number=10, type=8, cpp_type=7, label=1,
has_default_value=False, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='bools', full_name='paddle.framework.proto.OpDesc.Attr.bools', index=9,
number=11, type=8, cpp_type=7, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='block_idx', full_name='paddle.framework.proto.OpDesc.Attr.block_idx', index=10,
number=12, type=5, cpp_type=1, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='l', full_name='paddle.framework.proto.OpDesc.Attr.l', index=11,
number=13, type=3, cpp_type=2, label=1,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='blocks_idx', full_name='paddle.framework.proto.OpDesc.Attr.blocks_idx', index=12,
number=14, type=5, cpp_type=1, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='longs', full_name='paddle.framework.proto.OpDesc.Attr.longs', index=13,
number=15, type=3, cpp_type=2, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=283,
serialized_end=522,
)
_OPDESC_VAR = _descriptor.Descriptor(
name='Var',
full_name='paddle.framework.proto.OpDesc.Var',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='parameter', full_name='paddle.framework.proto.OpDesc.Var.parameter', index=0,
number=1, type=9, cpp_type=9, label=2,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='arguments', full_name='paddle.framework.proto.OpDesc.Var.arguments', index=1,
number=2, type=9, cpp_type=9, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=524,
serialized_end=567,
)
_OPDESC = _descriptor.Descriptor(
name='OpDesc',
full_name='paddle.framework.proto.OpDesc',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='type', full_name='paddle.framework.proto.OpDesc.type', index=0,
number=3, type=9, cpp_type=9, label=2,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='inputs', full_name='paddle.framework.proto.OpDesc.inputs', index=1,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='outputs', full_name='paddle.framework.proto.OpDesc.outputs', index=2,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='attrs', full_name='paddle.framework.proto.OpDesc.attrs', index=3,
number=4, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='is_target', full_name='paddle.framework.proto.OpDesc.is_target', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[_OPDESC_ATTR, _OPDESC_VAR, ],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=75,
serialized_end=567,
)
_OPPROTO_VAR = _descriptor.Descriptor(
name='Var',
full_name='paddle.framework.proto.OpProto.Var',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='paddle.framework.proto.OpProto.Var.name', index=0,
number=1, type=9, cpp_type=9, label=2,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='comment', full_name='paddle.framework.proto.OpProto.Var.comment', index=1,
number=2, type=9, cpp_type=9, label=2,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='duplicable', full_name='paddle.framework.proto.OpProto.Var.duplicable', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='intermediate', full_name='paddle.framework.proto.OpProto.Var.intermediate', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='dispensable', full_name='paddle.framework.proto.OpProto.Var.dispensable', index=4,
number=5, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=772,
serialized_end=892,
)
_OPPROTO_ATTR = _descriptor.Descriptor(
name='Attr',
full_name='paddle.framework.proto.OpProto.Attr',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='paddle.framework.proto.OpProto.Attr.name', index=0,
number=1, type=9, cpp_type=9, label=2,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='type', full_name='paddle.framework.proto.OpProto.Attr.type', index=1,
number=2, type=14, cpp_type=8, label=2,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='comment', full_name='paddle.framework.proto.OpProto.Attr.comment', index=2,
number=3, type=9, cpp_type=9, label=2,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='generated', full_name='paddle.framework.proto.OpProto.Attr.generated', index=3,
number=4, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=894,
serialized_end=1005,
)
_OPPROTO = _descriptor.Descriptor(
name='OpProto',
full_name='paddle.framework.proto.OpProto',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='type', full_name='paddle.framework.proto.OpProto.type', index=0,
number=1, type=9, cpp_type=9, label=2,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='inputs', full_name='paddle.framework.proto.OpProto.inputs', index=1,
number=2, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='outputs', full_name='paddle.framework.proto.OpProto.outputs', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='attrs', full_name='paddle.framework.proto.OpProto.attrs', index=3,
number=4, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='comment', full_name='paddle.framework.proto.OpProto.comment', index=4,
number=5, type=9, cpp_type=9, label=2,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[_OPPROTO_VAR, _OPPROTO_ATTR, ],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=570,
serialized_end=1005,
)
_VARTYPE_TENSORDESC = _descriptor.Descriptor(
name='TensorDesc',
full_name='paddle.framework.proto.VarType.TensorDesc',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='data_type', full_name='paddle.framework.proto.VarType.TensorDesc.data_type', index=0,
number=1, type=14, cpp_type=8, label=2,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='dims', full_name='paddle.framework.proto.VarType.TensorDesc.dims', index=1,
number=2, type=3, cpp_type=2, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=1393,
serialized_end=1476,
)
_VARTYPE_LODTENSORDESC = _descriptor.Descriptor(
name='LoDTensorDesc',
full_name='paddle.framework.proto.VarType.LoDTensorDesc',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='tensor', full_name='paddle.framework.proto.VarType.LoDTensorDesc.tensor', index=0,
number=1, type=11, cpp_type=10, label=2,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='lod_level', full_name='paddle.framework.proto.VarType.LoDTensorDesc.lod_level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=1478,
serialized_end=1575,
)
_VARTYPE_LODTENSORARRAYDESC = _descriptor.Descriptor(
name='LoDTensorArrayDesc',
full_name='paddle.framework.proto.VarType.LoDTensorArrayDesc',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='tensor', full_name='paddle.framework.proto.VarType.LoDTensorArrayDesc.tensor', index=0,
number=1, type=11, cpp_type=10, label=2,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='lod_level', full_name='paddle.framework.proto.VarType.LoDTensorArrayDesc.lod_level', index=1,
number=2, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=1577,
serialized_end=1679,
)
_VARTYPE_READERDESC = _descriptor.Descriptor(
name='ReaderDesc',
full_name='paddle.framework.proto.VarType.ReaderDesc',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='lod_tensor', full_name='paddle.framework.proto.VarType.ReaderDesc.lod_tensor', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=1681,
serialized_end=1760,
)
_VARTYPE_TUPLE = _descriptor.Descriptor(
name='Tuple',
full_name='paddle.framework.proto.VarType.Tuple',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='element_type', full_name='paddle.framework.proto.VarType.Tuple.element_type', index=0,
number=1, type=14, cpp_type=8, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=1762,
serialized_end=1829,
)
_VARTYPE = _descriptor.Descriptor(
name='VarType',
full_name='paddle.framework.proto.VarType',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='type', full_name='paddle.framework.proto.VarType.type', index=0,
number=1, type=14, cpp_type=8, label=2,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='selected_rows', full_name='paddle.framework.proto.VarType.selected_rows', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='lod_tensor', full_name='paddle.framework.proto.VarType.lod_tensor', index=2,
number=3, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='tensor_array', full_name='paddle.framework.proto.VarType.tensor_array', index=3,
number=4, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='reader', full_name='paddle.framework.proto.VarType.reader', index=4,
number=5, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='tuple', full_name='paddle.framework.proto.VarType.tuple', index=5,
number=7, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[_VARTYPE_TENSORDESC, _VARTYPE_LODTENSORDESC, _VARTYPE_LODTENSORARRAYDESC, _VARTYPE_READERDESC, _VARTYPE_TUPLE, ],
enum_types=[
_VARTYPE_TYPE,
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=1008,
serialized_end=2122,
)
_VARDESC = _descriptor.Descriptor(
name='VarDesc',
full_name='paddle.framework.proto.VarDesc',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='name', full_name='paddle.framework.proto.VarDesc.name', index=0,
number=1, type=9, cpp_type=9, label=2,
has_default_value=False, default_value=_b("").decode('utf-8'),
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='type', full_name='paddle.framework.proto.VarDesc.type', index=1,
number=2, type=11, cpp_type=10, label=2,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='persistable', full_name='paddle.framework.proto.VarDesc.persistable', index=2,
number=3, type=8, cpp_type=7, label=1,
has_default_value=True, default_value=False,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=2124,
serialized_end=2222,
)
_BLOCKDESC = _descriptor.Descriptor(
name='BlockDesc',
full_name='paddle.framework.proto.BlockDesc',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='idx', full_name='paddle.framework.proto.BlockDesc.idx', index=0,
number=1, type=5, cpp_type=1, label=2,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='parent_idx', full_name='paddle.framework.proto.BlockDesc.parent_idx', index=1,
number=2, type=5, cpp_type=1, label=2,
has_default_value=False, default_value=0,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='vars', full_name='paddle.framework.proto.BlockDesc.vars', index=2,
number=3, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='ops', full_name='paddle.framework.proto.BlockDesc.ops', index=3,
number=4, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='forward_block_idx', full_name='paddle.framework.proto.BlockDesc.forward_block_idx', index=4,
number=5, type=5, cpp_type=1, label=1,
has_default_value=True, default_value=-1,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=2225,
serialized_end=2392,
)
_PROGRAMDESC = _descriptor.Descriptor(
name='ProgramDesc',
full_name='paddle.framework.proto.ProgramDesc',
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name='blocks', full_name='paddle.framework.proto.ProgramDesc.blocks', index=0,
number=1, type=11, cpp_type=10, label=3,
has_default_value=False, default_value=[],
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
_descriptor.FieldDescriptor(
name='version', full_name='paddle.framework.proto.ProgramDesc.version', index=1,
number=2, type=11, cpp_type=10, label=1,
has_default_value=False, default_value=None,
message_type=None, enum_type=None, containing_type=None,
is_extension=False, extension_scope=None,
options=None),
],
extensions=[
],
nested_types=[],
enum_types=[
],
options=None,
is_extendable=False,
syntax='proto2',
extension_ranges=[],
oneofs=[
],
serialized_start=2394,
serialized_end=2508,
)
_OPDESC_ATTR.fields_by_name['type'].enum_type = _ATTRTYPE
_OPDESC_ATTR.containing_type = _OPDESC
_OPDESC_VAR.containing_type = _OPDESC
_OPDESC.fields_by_name['inputs'].message_type = _OPDESC_VAR
_OPDESC.fields_by_name['outputs'].message_type = _OPDESC_VAR
_OPDESC.fields_by_name['attrs'].message_type = _OPDESC_ATTR
_OPPROTO_VAR.containing_type = _OPPROTO
_OPPROTO_ATTR.fields_by_name['type'].enum_type = _ATTRTYPE
_OPPROTO_ATTR.containing_type = _OPPROTO
_OPPROTO.fields_by_name['inputs'].message_type = _OPPROTO_VAR
_OPPROTO.fields_by_name['outputs'].message_type = _OPPROTO_VAR
_OPPROTO.fields_by_name['attrs'].message_type = _OPPROTO_ATTR
_VARTYPE_TENSORDESC.fields_by_name['data_type'].enum_type = _VARTYPE_TYPE
_VARTYPE_TENSORDESC.containing_type = _VARTYPE
_VARTYPE_LODTENSORDESC.fields_by_name['tensor'].message_type = _VARTYPE_TENSORDESC
_VARTYPE_LODTENSORDESC.containing_type = _VARTYPE
_VARTYPE_LODTENSORARRAYDESC.fields_by_name['tensor'].message_type = _VARTYPE_TENSORDESC
_VARTYPE_LODTENSORARRAYDESC.containing_type = _VARTYPE
_VARTYPE_READERDESC.fields_by_name['lod_tensor'].message_type = _VARTYPE_LODTENSORDESC
_VARTYPE_READERDESC.containing_type = _VARTYPE
_VARTYPE_TUPLE.fields_by_name['element_type'].enum_type = _VARTYPE_TYPE
_VARTYPE_TUPLE.containing_type = _VARTYPE
_VARTYPE.fields_by_name['type'].enum_type = _VARTYPE_TYPE
_VARTYPE.fields_by_name['selected_rows'].message_type = _VARTYPE_TENSORDESC
_VARTYPE.fields_by_name['lod_tensor'].message_type = _VARTYPE_LODTENSORDESC
_VARTYPE.fields_by_name['tensor_array'].message_type = _VARTYPE_LODTENSORARRAYDESC
_VARTYPE.fields_by_name['reader'].message_type = _VARTYPE_READERDESC
_VARTYPE.fields_by_name['tuple'].message_type = _VARTYPE_TUPLE
_VARTYPE_TYPE.containing_type = _VARTYPE
_VARDESC.fields_by_name['type'].message_type = _VARTYPE
_BLOCKDESC.fields_by_name['vars'].message_type = _VARDESC
_BLOCKDESC.fields_by_name['ops'].message_type = _OPDESC
_PROGRAMDESC.fields_by_name['blocks'].message_type = _BLOCKDESC
_PROGRAMDESC.fields_by_name['version'].message_type = _VERSION
DESCRIPTOR.message_types_by_name['Version'] = _VERSION
DESCRIPTOR.message_types_by_name['OpDesc'] = _OPDESC
DESCRIPTOR.message_types_by_name['OpProto'] = _OPPROTO
DESCRIPTOR.message_types_by_name['VarType'] = _VARTYPE
DESCRIPTOR.message_types_by_name['VarDesc'] = _VARDESC
DESCRIPTOR.message_types_by_name['BlockDesc'] = _BLOCKDESC
DESCRIPTOR.message_types_by_name['ProgramDesc'] = _PROGRAMDESC
DESCRIPTOR.enum_types_by_name['AttrType'] = _ATTRTYPE
Version = _reflection.GeneratedProtocolMessageType('Version', (_message.Message,), dict(
DESCRIPTOR = _VERSION,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.Version)
))
_sym_db.RegisterMessage(Version)
OpDesc = _reflection.GeneratedProtocolMessageType('OpDesc', (_message.Message,), dict(
Attr = _reflection.GeneratedProtocolMessageType('Attr', (_message.Message,), dict(
DESCRIPTOR = _OPDESC_ATTR,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.OpDesc.Attr)
))
,
Var = _reflection.GeneratedProtocolMessageType('Var', (_message.Message,), dict(
DESCRIPTOR = _OPDESC_VAR,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.OpDesc.Var)
))
,
DESCRIPTOR = _OPDESC,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.OpDesc)
))
_sym_db.RegisterMessage(OpDesc)
_sym_db.RegisterMessage(OpDesc.Attr)
_sym_db.RegisterMessage(OpDesc.Var)
OpProto = _reflection.GeneratedProtocolMessageType('OpProto', (_message.Message,), dict(
Var = _reflection.GeneratedProtocolMessageType('Var', (_message.Message,), dict(
DESCRIPTOR = _OPPROTO_VAR,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.OpProto.Var)
))
,
Attr = _reflection.GeneratedProtocolMessageType('Attr', (_message.Message,), dict(
DESCRIPTOR = _OPPROTO_ATTR,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.OpProto.Attr)
))
,
DESCRIPTOR = _OPPROTO,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.OpProto)
))
_sym_db.RegisterMessage(OpProto)
_sym_db.RegisterMessage(OpProto.Var)
_sym_db.RegisterMessage(OpProto.Attr)
VarType = _reflection.GeneratedProtocolMessageType('VarType', (_message.Message,), dict(
TensorDesc = _reflection.GeneratedProtocolMessageType('TensorDesc', (_message.Message,), dict(
DESCRIPTOR = _VARTYPE_TENSORDESC,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.VarType.TensorDesc)
))
,
LoDTensorDesc = _reflection.GeneratedProtocolMessageType('LoDTensorDesc', (_message.Message,), dict(
DESCRIPTOR = _VARTYPE_LODTENSORDESC,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.VarType.LoDTensorDesc)
))
,
LoDTensorArrayDesc = _reflection.GeneratedProtocolMessageType('LoDTensorArrayDesc', (_message.Message,), dict(
DESCRIPTOR = _VARTYPE_LODTENSORARRAYDESC,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.VarType.LoDTensorArrayDesc)
))
,
ReaderDesc = _reflection.GeneratedProtocolMessageType('ReaderDesc', (_message.Message,), dict(
DESCRIPTOR = _VARTYPE_READERDESC,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.VarType.ReaderDesc)
))
,
Tuple = _reflection.GeneratedProtocolMessageType('Tuple', (_message.Message,), dict(
DESCRIPTOR = _VARTYPE_TUPLE,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.VarType.Tuple)
))
,
DESCRIPTOR = _VARTYPE,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.VarType)
))
_sym_db.RegisterMessage(VarType)
_sym_db.RegisterMessage(VarType.TensorDesc)
_sym_db.RegisterMessage(VarType.LoDTensorDesc)
_sym_db.RegisterMessage(VarType.LoDTensorArrayDesc)
_sym_db.RegisterMessage(VarType.ReaderDesc)
_sym_db.RegisterMessage(VarType.Tuple)
VarDesc = _reflection.GeneratedProtocolMessageType('VarDesc', (_message.Message,), dict(
DESCRIPTOR = _VARDESC,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.VarDesc)
))
_sym_db.RegisterMessage(VarDesc)
BlockDesc = _reflection.GeneratedProtocolMessageType('BlockDesc', (_message.Message,), dict(
DESCRIPTOR = _BLOCKDESC,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.BlockDesc)
))
_sym_db.RegisterMessage(BlockDesc)
ProgramDesc = _reflection.GeneratedProtocolMessageType('ProgramDesc', (_message.Message,), dict(
DESCRIPTOR = _PROGRAMDESC,
__module__ = 'framework_pb2'
# @@protoc_insertion_point(class_scope:paddle.framework.proto.ProgramDesc)
))
_sym_db.RegisterMessage(ProgramDesc)
DESCRIPTOR.has_options = True
DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('H\003'))
# @@protoc_insertion_point(module_scope)
tensorflow2fluid/src/graph.py 0 → 100644
浏览文件 @ 7a96c492
# Copyright (c) 2019 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 name_generator import NameGenerator
class GraphNode(object):
def __init__(self, layer):
self.inputs = list()
self.outputs = list()
self.layer = layer
self.ref_name = None
self.output_name = None
def __hash__(self):
return hash(self.layer.name)
def __eq__(self, other):
if self.layer.name == other.layer.name:
return True
return False
class Graph(object):
def __init__(self, model):
self.node_map = dict()
self.input_nodes = list()
self.output_nodes = list()
self.topological_sort = list()
self.model = model
self.name_generator = NameGenerator()
def build(self):
self._make_input_nodes()
self._make_output_nodes()
self._get_topological_sort()
self._gen_newname_for_nodes()
def _make_input_nodes(self):
for name, node in self.node_map.items():
if len(node.outputs) == 0 and len(node.inputs) == 0:
continue
node.left_inputs = len(node.inputs)
if len(node.inputs) == 0:
self.input_nodes.append(name)
def _make_output_nodes(self):
for name, node in self.node_map.items():
if len(node.outputs) == 0 and len(node.inputs) == 0:
continue
if len(node.outputs) == 0:
self.output_nodes.append(name)
def _get_topological_sort(self):
self.topological_sort = self.input_nodes[:]
idx = 0
while idx < len(self.topological_sort):
current_node = self.node_map[self.topological_sort[idx]]
for next_node in current_node.outputs:
next_node_info = self.node_map[next_node.layer_name]
next_node_info.left_inputs -= 1
if next_node_info.left_inputs == 0:
self.topological_sort.append(next_node.layer_name)
idx += 1
def _gen_newname_for_nodes(self):
for node_name in self.topological_sort:
node = self.node_map[node_name]
ref_name = self.name_generator.get_name(node)
self.node_map[node.layer.name].ref_name = ref_name
self.node_map[node.layer.name].output_name = ref_name.split('[')[0]
def get_node(self, name):
if name not in self.node_map:
raise Exception("Graph doesn't have node [%s]." % name)
else:
return self.node_map[name]
def _make_connection(self, src, dst):
if src.layer_name == dst.layer_name or src.layer_name not in self.node_map or dst.layer_name not in self.node_map:
raise Exception('Warning: Node not exist or there is a self-loop')
if src not in self.node_map[dst.layer_name].inputs:
self.node_map[dst.layer_name].inputs.append(src)
if dst not in self.node_map[src.layer_name].outputs:
self.node_map[src.layer_name].outputs.append(dst)
tensorflow2fluid/src/name_generator.py 0 → 100644
浏览文件 @ 7a96c492
# Copyright (c) 2019 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.
class NameGenerator(object):
def __init__(self):
self.param_index = 0
self.input_index = 0
self.net_index = 0
self.const_index = 0
self.names = dict()
def get_name(self, node):
ref_name = None
op_name = node.layer_type
if node.layer.name in self.names:
return self.names[node.layer.name]
if op_name == "variablev2":
ref_name = "param_" + str(self.param_index)
self.param_index += 1
elif op_name == "placeholder":
ref_name = "input_" + str(self.input_index)
self.input_index += 1
elif op_name == "const":
ref_name = "const_" + str(self.const_index)
self.const_index += 1
elif op_name.lower() == "identity":
ref_name = self.names[node.layer.input[0]]
else:
ref_name = "net_" + str(self.net_index)
self.net_index += 1
self.names[node.layer.name] = ref_name
return ref_name
tensorflow2fluid/src/paddle_emitter.py 0 → 100644
浏览文件 @ 7a96c492
# Copyright (c) 2019 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 tensorflow.core.framework import attr_value_pb2
from tensorflow.python.framework import tensor_util
from six import string_types as _string_types
import framework_pb2 as framework
import math
import struct
import numpy
import logging
import sys
class PaddleEmitter(object):
skip_op = set(['variablev2', 'identity'])
skip_op = set()
dtype_map = {1: "float32", 3: "int32", 9: "int64"}
def __init__(self, parser, save_dir):
self.graph = parser.tf_graph
self.weights = parser.weights
self.save_dir = save_dir
self.body_code = ""
self.tab = " " * 4
@staticmethod
def tensor_shape_to_list(shapes):
if isinstance(shapes, attr_value_pb2.AttrValue):
return [dim.size for dim in shapes.shape.dim]
else:
ret = []
for shape in shapes:
this_one = [dim.size for dim in shape.dim]
ret.append(this_one)
return ret
@staticmethod
def compute_padding_size(in_size, filter_size, stride):
new_size = int(math.ceil(in_size * 1.0 / stride))
pad_size = (new_size - 1) * stride + filter_size - in_size
return pad_size
def get_axis(self, node1, node2):
shape1 = self.tensor_shape_to_list(node1.get_attr('_output_shapes'))[0]
shape2 = self.tensor_shape_to_list(node2.get_attr('_output_shapes'))[0]
if len(shape1) == 4 and len(
shape2) == 1 and node1.data_format == "NHWC":
axis = 1
elif len(shape2) == 4 and len(
shape1) == 1 and node2.data_format == "NHWC":
axis = 1
else:
axis = -1
return axis
def export_weights(self, weight, paddle_var_name, dir):
numpy_dtype_map = {
"int16": framework.VarType.INT16,
"int32": framework.VarType.INT32,
"int64": framework.VarType.INT64,
"float16": framework.VarType.FP16,
"float32": framework.VarType.FP32,
"float64": framework.VarType.FP64
}
struct_write_format = {
"int16": "h",
"int32": "i",
"int64": "q",
"float16": "e",
"float32": "f",
"float64": "d"
}
shape = weight.shape
filew = open(dir + "/" + paddle_var_name, "wb")
filew.write(struct.pack('i', 0))
filew.write(struct.pack('L', 0))
filew.write(struct.pack('i', 0))
tensor_desc = framework.VarType.TensorDesc()
if str(weight.dtype) in numpy_dtype_map:
tensor_desc.data_type = numpy_dtype_map[str(weight.dtype)]
else:
raise Exception("Unexpected array dtype [{}]".format(weight.dtype))
tensor_desc.dims.extend(shape)
desc_size = tensor_desc.ByteSize()
filew.write(struct.pack('i', desc_size))
filew.write(tensor_desc.SerializeToString())
tensor_size = reduce(lambda x, y: x * y, shape)
weight = weight.flatten()
for i in range(0, tensor_size):
filew.write(
struct.pack(struct_write_format[str(weight.dtype)], weight[i]))
filew.close()
@property
def header_code(self):
code = list()
code.append("import paddle.fluid.layers as layers")
code.append("import paddle.fluid as fluid")
code.append("")
code.append("def KitModel():")
return code
def add_codes(self, indent, codes):
if isinstance(codes, _string_types):
codes = codes.strip().split("\n")
for code in codes:
self.body_code += (self.tab * indent) + code + "\n"
def run(self):
node = self.graph.tf_graph.node[0]
self.add_codes(0, self.header_code)
ref_name_recorder = open(self.save_dir + "/ref_name.txt", 'w')
total_nodes_num = len(self.graph.topological_sort)
translated_nodes_count = 0
sys.stderr.write("Start to translate all the nodes(Total_num:{})\n".
format(total_nodes_num))
for node in self.graph.topological_sort:
sys.stderr.write(
"\rTranslated nodes num: {}, Current node: {}".format(
translated_nodes_count, node))
sys.stderr.flush()
translated_nodes_count += 1
current_node = self.graph.get_node(node)
ref_name_recorder.write("{}\t{}\n".format(
current_node.layer_name, current_node.output_name))
# skip isolated nodes
if len(current_node.inputs) == 0 and len(
current_node.outputs) == 0:
continue
op = current_node.layer_type
if op in self.skip_op:
continue
if hasattr(self, "emit_" + op):
func = getattr(self, "emit_" + op)
codes = func(current_node)
if not isinstance(codes, list):
codes = [codes]
self.graph.get_node(node).codes = codes
else:
raise Exception("Unknow node op: {}".format(op))
ref_name_recorder.close()
for node in self.graph.topological_sort:
codes = self.graph.get_node(node).codes
self.add_codes(1, codes)
outs = []
for node in self.graph.output_nodes:
outs.append(self.graph.get_node(node).output_name)
self.add_codes(1, "return {}".format(", ".join(outs)))
filew = open(self.save_dir + "/mymodel.py", 'w')
filew.write(self.body_code)
filew.close()
return self.body_code
def emit_placeholder(self, node):
shape = self.tensor_shape_to_list(node.get_attr('_output_shapes'))[0]
if node.data_format == "NHWC" and len(shape) == 4:
shape = [shape[0], shape[3], shape[1], shape[2]]
dtype = node.get_attr("dtype")
if dtype in self.dtype_map:
dtype = self.dtype_map[dtype]
else:
raise Exception("Unknow dtype: {}".format(dtype))
code = list()
code.append("# placehoder[{}]:\t{}".format(node.output_name,
node.layer_name))
code.append(
"{} = layers.data(name=\'{}\', shape={}, dtype=\'{}\')".format(
node.output_name, node.ref_name, shape, dtype))
return code
def emit_const(self, node):
try:
float_val = node.layer.attr['value'].tensor.float_val
if len(float_val) == 1:
code = list()
code.append("# {} {}".format(node.layer_name, node.ref_name))
code.append(
"{} = layers.fill_constant(shape=[1], value={}, dtype=\'float32\')"
.format(node.output_name, float_val[0]))
return code
int_val = node.layer.attr['value'].tensor.int_val
if len(int_val) == 1:
code = list()
code.append("# {} {}".format(node.layer_name, node.ref_name))
code.append(
"{} = layers.fill_constant(shape=[1], value={}, dtype=\'int32\')"
.format(node.output_name, int_val[0]))
return code
node.layer.attr['value'].tensor.tensor_content
weight = tensor_util.MakeNdarray(node.layer.attr['value'].tensor)
if len(weight) == 0:
return []
shape = list(weight.shape)
dtype = node.get_attr('dtype')
if dtype in self.dtype_map:
dtype = self.dtype_map[dtype]
else:
raise Exception("Unknow dtype[{}] of node[{}]".format(
dtype, node.layer_name))
code = list()
code.append("# {} {}".format(node.layer_name, node.ref_name))
if dtype.startswith('int'):
code.append(
"{} = layers.create_parameter({}, \'{}\', \'{}\', default_initializer=fluid.initializer.Constant(0))"
.format(node.output_name, shape, dtype, node.ref_name))
else:
code.append(
"{} = layers.create_parameter({}, \'{}\', \'{}\')".format(
node.output_name, shape, dtype, node.ref_name))
self.export_weights(weight, node.ref_name, self.save_dir)
return code
except:
return []
def emit_conv2d(self, node):
data = node.inputs[0]
kernel = node.inputs[1]
if len(kernel.outputs) == 1:
kernel.codes = []
padding_mode = node.get_attr("padding")
strides = node.get_attr("strides")[1:3]
k_shape = self.tensor_shape_to_list(
kernel.get_attr("_output_shapes"))[0]
input_shape = self.tensor_shape_to_list(
data.get_attr("_output_shapes"))[0]
input_h, input_w = input_shape[2:4]
kernel_num, channel, k_h, k_w = k_shape
if node.data_format == "NHWC":
k_h, k_w, channel, kernel_num = k_shape
input_h, input_w = input_shape[1:3]
if k_h < strides[0] or k_w < strides[1]:
raise Exception(
"Unexpected situation with kernel's height/width less than the corresponding stride"
)
if kernel.layer_name in self.weights:
if node.data_format == "NHWC":
weight = self.weights[kernel.layer_name]
self.weights[kernel.layer_name] = numpy.transpose(
weight, (3, 2, 0, 1))
self.export_weights(self.weights[kernel.layer_name],
kernel.ref_name, self.save_dir)
code = list()
padding = [0, 0]
if padding_mode == "SAME":
total_pad_h = self.compute_padding_size(input_h, k_h, strides[0])
total_pad_w = self.compute_padding_size(input_w, k_w, strides[1])
if total_pad_h % 2 == 0 and total_pad_w % 2 == 0:
padding = map(int, [total_pad_h / 2, total_pad_w / 2])
code.append(
"{} = layers.conv2d({}, {}, {}, padding={}, stride={}, param_attr=\'{}\', bias_attr=False)"
.format(node.output_name, data.ref_name, kernel_num,
[k_h, k_w], padding, strides, kernel.ref_name))
else:
padding = [0] * 4
padding[0] = total_pad_h / 2
padding[1] = total_pad_h - padding[0]
padding[2] = total_pad_w / 2
padding[3] = total_pad_w - padding[2]
code.append("{} = layers.pad2d({}, {})".format(
node.output_name, data.ref_name, padding))
code.append(
"{} = layers.conv2d({}, {}, {}, stride={}, param_attr=\'{}\', bias_attr=False)"
.format(node.output_name, node.ref_name, kernel_num,
[k_h, k_w], strides, kernel.ref_name))
else:
code.append(
"{} = layers.conv2d({}, {}, {}, stride={}, param_attr=\'{}\', bias_attr=False)"
.format(node.output_name, data.ref_name, kernel_num,
[k_h, k_w], strides, kernel.ref_name))
return code
def emit_variablev2(self, node):
shape = self.tensor_shape_to_list(node.get_attr("_output_shapes"))[0]
dtype = node.get_attr("dtype")
if dtype in self.dtype_map:
dtype = self.dtype_map[dtype]
else:
raise Exception("Unknow dtype[{}] of node[{}]".format(
dtype, node.layer_name))
code = list()
code.append("# variable[{}]:\t{}".format(node.output_name,
node.layer_name))
if dtype.startswith('int'):
code.append(
"{} = layers.create_parameter(name=\'{}\', shape={}, dtype=\'{}\', default_initializer=fluid.initializer.Constant(0))"
.format(node.output_name, node.ref_name, shape, dtype))
else:
code.append(
"{} = layers.create_parameter(name=\'{}\', shape={}, dtype=\'{}\')"
.format(node.output_name, node.ref_name, shape, dtype))
return code
def emit_biasadd(self, node):
data = node.inputs[0]
bias = node.inputs[1]
axis = self.get_axis(data, bias)
if bias.layer_name in self.weights:
self.export_weights(self.weights[bias.layer_name], bias.ref_name,
self.save_dir)
code = list()
code = code + self.emit_variablev2(bias)
code.append("{} = layers.elementwise_add({}, {}, axis={})".format(
node.output_name, data.ref_name, bias.ref_name, axis))
return code
def emit_relu(self, node):
data = node.inputs[0]
code = "{} = layers.relu({})".format(node.output_name, data.ref_name)
return code
def emit_maxpool(self, node):
data = node.inputs[0]
padding_mode = node.get_attr("padding")
strides = node.get_attr("strides")[1:3]
pool_size = node.get_attr("ksize")[1:3]
if padding_mode == "SAME":
pad_right = (pool_size[0] - 1) / 2
pad_bottom = (pool_size[1] - 1) / 2
padding = [0, pad_right * 2, 0, pad_bottom * 2]
code = [
"pad_net = layers.pad2d({}, paddings={})".format(
data.ref_name, padding)
]
code.append("{} = layers.pool2d(pad_net, {}, \'max\', {})".format(
node.output_name, pool_size, strides))
return code
else:
code = "{} = layers.pool2d({}, {}, \'max\', {})".format(
node.output_name, data.ref_name, pool_size, strides)
return code
def emit_squeeze(self, node):
data = node.inputs[0]
axis = node.get_attr("squeeze_dims")
input_shape = self.tensor_shape_to_list(
data.get_attr("_output_shapes"))[0]
if node.data_format == "NHWC" and len(input_shape) == 4:
for i in range(0, len(axis)):
if axis[i] == 1:
axis[i] = 2
elif axis[i] == 2:
axis[i] = 3
elif axis[i] == 3:
axis[i] = 1
code = "{} = layers.squeeze({}, {})".format(node.output_name,
data.ref_name, axis)
return code
def emit_add(self, node):
data1 = node.inputs[0]
data2 = node.inputs[1]
axis = self.get_axis(data1, data2)
code = list()
code.append("# {}, {}, {}".format(node.layer_name, data1.layer_name,
data2.layer_name))
code.append("{} = layers.elementwise_add({}, {}, axis={})".format(
node.output_name, data1.ref_name, data2.ref_name, axis))
return code
def emit_mean(self, node):
data = node.inputs[0]
reduce_idx = node.inputs[1]
reduce_idx.codes = []
idxs = tensor_util.MakeNdarray(reduce_idx.layer.attr['value'].tensor)
shape = idxs.shape
assert len(shape) == 1
data_shape = self.tensor_shape_to_list(
data.get_attr('_output_shapes'))[0]
keep_dims = node.layer.attr['keep_dims'].b
if node.data_format == "NHWC" and len(data_shape) == 4:
for i in range(0, shape[0]):
if idxs[i] == 1:
idxs[i] = 2
elif idxs[i] == 2:
idxs[i] = 3
elif idxs[i] == 3:
idxs[i] = 1
code = "{} = layers.reduce_mean({}, {}, keep_dim={})".format(
node.output_name, data.ref_name, list(idxs), keep_dims)
return code
def emit_fusedbatchnorm(self, node):
data = node.inputs[0]
gamma = node.inputs[1]
beta = node.inputs[2]
moving_mean = node.inputs[3]
moving_variance = node.inputs[4]
if len(gamma.outputs) == 1:
gamma.codes = []
if len(beta.outputs) == 1:
beta.codes = []
if len(moving_mean.outputs) == 1:
moving_mean.codes = []
if len(moving_variance.outputs) == 1:
moving_variance.codes = []
epsilon = round(node.get_attr('epsilon'), 6)
is_training = node.get_attr('is_training')
if gamma.layer_name in self.weights:
self.export_weights(self.weights[gamma.layer_name], gamma.ref_name,
self.save_dir)
if beta.layer_name in self.weights:
self.export_weights(self.weights[beta.layer_name], beta.ref_name,
self.save_dir)
if moving_mean.layer_name in self.weights:
self.export_weights(self.weights[moving_mean.layer_name],
moving_mean.ref_name, self.save_dir)
if moving_variance.layer_name in self.weights:
self.export_weights(self.weights[moving_variance.layer_name],
moving_variance.ref_name, self.save_dir)
code = "{} = layers.batch_norm({}, epsilon={}, param_attr=\'{}\', bias_attr=\'{}\', moving_mean_name=\'{}\', moving_variance_name=\'{}\', is_test={})".format(
node.output_name, data.ref_name, epsilon, gamma.ref_name,
beta.ref_name, moving_mean.ref_name, moving_variance.ref_name,
not is_training)
return code
def emit_concatv2(self, node):
input_shape = self.tensor_shape_to_list(
node.inputs[0].get_attr('_output_shapes'))[0]
axis = node.inputs[-1]
axis.codes = []
axis = axis.layer.attr['value'].tensor.int_val[0]
if node.data_format == "NHWC" and len(input_shape) == 4:
if axis == 1:
axis = 2
elif axis == 2:
axis = 3
elif axis == 3:
axis = 1
num_tensor = len(node.inputs) - 1
code = "{} = layers.concat([{}], {})".format(
node.output_name, ", ".join(
[input.ref_name for input in node.inputs[:num_tensor]]), axis)
return code
def emit_avgpool(self, node):
data = node.inputs[0]
padding_mode = node.get_attr("padding")
strides = node.get_attr("strides")[1:3]
pool_size = node.get_attr("ksize")[1:3]
padding = [0, 0]
if padding_mode == "SAME":
pad_h = (pool_size[0] - 1) / 2
pad_w = (pool_size[1] - 1) / 2
padding = [pad_h, pad_w]
code = "{} = layers.pool2d({}, {}, \'avg\', {}, {})".format(
node.output_name, data.ref_name, pool_size, strides, padding)
return code
def emit_rsqrt(self, node):
data = node.inputs[0]
code = list()
code.append("sqrt_res = layers.sqrt({})".format(data.ref_name))
code.append("{} = layers.pow(sqrt_res, -1.0)".format(node.output_name))
return code
def emit_mul(self, node):
data1 = node.inputs[0]
data2 = node.inputs[1]
axis = self.get_axis(data1, data2)
code = "{} = layers.elementwise_mul({}, {}, axis={})".format(
node.output_name, data1.ref_name, data2.ref_name, axis)
shape1 = self.tensor_shape_to_list(data1.get_attr('_output_shapes'))[0]
shape2 = self.tensor_shape_to_list(data2.get_attr('_output_shapes'))[0]
if len(shape2) > len(shape1):
code = "{} = layers.elementwise_mul({}, {}, axis={})".format(
node.output_name, data2.ref_name, data1.ref_name, axis)
return code
def emit_sub(self, node):
data1 = node.inputs[0]
data2 = node.inputs[1]
axis = self.get_axis(data1, data2)
code = "{} = layers.elementwise_sub({}, {}, axis={})".format(
node.output_name, data1.ref_name, data2.ref_name, axis)
return code
def emit_shape(self, node):
data = node.inputs[0]
code = "{} = layers.shape({})".format(node.output_name, data.ref_name)
return code
def emit_pad(self, node):
data = node.inputs[0]
padding = node.inputs[1]
padding.codes = []
padding = padding.layer.attr['value'].tensor
padding = tensor_util.MakeNdarray(padding)
if node.data_format == "NHWC" and padding.shape[0] == 4:
padding = padding[[0, 3, 1, 2]]
code = "{} = layers.pad({}, {})".format(node.output_name,
data.ref_name,
list(padding.flatten()))
return code
def emit_stridedslice(self, node):
data = node.inputs[0]
begin = node.inputs[1]
end = node.inputs[2]
strides = node.inputs[3]
begin.codes = []
end.codes = []
strides.codes = []
begin = list(tensor_util.MakeNdarray(begin.layer.attr['value'].tensor))
end = list(tensor_util.MakeNdarray(end.layer.attr['value'].tensor))
strides = list(
tensor_util.MakeNdarray(strides.layer.attr['value'].tensor))
for i in range(len(strides)):
assert strides[i] == 1
code = "{} = layers.slice({}, axes={}, starts={}, ends={})".format(
node.output_name, data.ref_name, [i for i in range(len(begin))],
begin, end)
return code
def emit_resizenearestneighbor(self, node):
data = node.inputs[0]
output_shape = node.inputs[1]
align_corners = node.get_attr('align_corners')
if output_shape.layer_type == "const":
output_shape.codes = []
output_shape = tensor_util.MakeNdarray(
output_shape.layer.attr['value'].tensor)
code = "{} = layers.resize_nearest({}, {}, align_corners={}, align_mode=1)".format(
node.output_name, data.ref_name, list(output_shape),
align_corners)
else:
code = "{} = layers.resize_nearest({}, {}, align_corners={}, align_mode=1)".format(
node.output_name, data.ref_name, output_shape.ref_name,
align_corners)
logging.warn(
"\tNotice there's RESIZE_NEAREST in translated code, and the code list below:"
)
logging.warn("\t\t{}".format(code))
logging.warn(
"\tPaddle doesn't support tensor type for output_shape now")
logging.warn(
"\tYou need to change \'{}\'(in tf model: \'{}\') to a list with constant value, e.g. [28, 28]. IMPORTANT!!!\n"
.format(output_shape.ref_name, output_shape.layer_name))
return code
def emit_maximum(self, node):
data1 = node.inputs[0]
data2 = node.inputs[1]
axis = self.get_axis(data1, data2)
code = "{} = layers.elementwise_max({}, {}, axis={})".format(
node.output_name, data1.ref_name, data2.ref_name, axis)
return code
def emit_minimum(self, node):
data1 = node.inputs[0]
data2 = node.inputs[1]
axis = self.get_axis(data1, data2)
code = "{} = layers.elementwise_min({}, {}, axis={})".format(
node.output_name, data1.ref_name, data2.ref_name, axis)
return code
def emit_sigmoid(self, node):
data = node.inputs[0]
code = "{} = layers.sigmoid({})".format(node.output_name,
data.ref_name)
return code
def emit_pack(self, node):
ref_name = [input.ref_name for input in node.inputs]
code = "{} = layers.stack([{}])".format(node.output_name,
", ".join(ref_name))
return code
def emit_reshape(self, node):
data = node.inputs[0]
shape = node.inputs[1]
if shape.layer_type == "const":
shape = shape.layer.attr['value'].tensor
shape = list(tensor_util.MakeNdarray(shape))
code = "{} = layers.reshape({}, {})".format(
node.output_name, data.ref_name, shape)
else:
code = "{} = layers.reshape({}, {})".format(
node.output_name, data.ref_name, shape.ref_name)
return code
def emit_conv2dbackpropinput(self, node):
output_shape = node.inputs[0]
kernel = node.inputs[1]
data = node.inputs[2]
if len(kernel.outputs) == 1:
kernel.codes = []
padding_mode = node.get_attr("padding")
strides = node.get_attr("strides")[1:3]
k_shape = self.tensor_shape_to_list(
kernel.get_attr("_output_shapes"))[0]
k_num, channel, k_h, k_w = k_shape
if node.data_format == "NHWC":
k_h, k_w, channel, k_num = k_shape
if strides[0] > k_h or strides[1] > k_w:
raise Exception(
"Paddle cannot process the situation now[kernel's height/width less than the corresponding stride]"
)
padding = [0, 0]
if padding_mode == "SAME":
padding = map(int, [(k_h - strides[0]) / 2,
(k_w - strides[1]) / 2])
if kernel.layer_name in self.weights:
if node.data_format == "NHWC":
weight = self.weights[kernel.layer_name]
self.weights[kernel.layer_name] = numpy.transpose(
weight, (3, 2, 0, 1))
self.export_weights(self.weights[kernel.layer_name],
kernel.ref_name, self.save_dir)
code = []
if output_shape.layer_type == "const":
output_shape.codes = []
output_shape = tensor_util.MakeNdarray(
output_shape.layer.attr['value'].tensor)
if node.data_format == "NHWC" and output_shape[0] == 4:
output_shape = output_shape[[0, 3, 1, 2]]
code.append(
"{} = layers.conv2d_transpose({}, {}, None, {}, {}, {}, param_attr=\'{}\', bias_attr=False)"
.format(node.output_name, data.ref_name, k_num, [k_h, k_w],
padding, strides, kernel.ref_name))
if padding_mode == "SAME":
code.append("{} = layers.crop({}, shape={})".format(
node.output_name, node.output_name, list(output_shape)))
else:
code.append(
"{} = layers.conv2d_transpose({}, {}, None, {}, 0, {}, param_attr=\'{}\', bias_attr=False)"
.format(node.output_name, data.ref_name, k_num, [k_h, k_w],
strides, kernel.ref_name))
if padding_mode == "SAME":
code.append("{} = layers.crop({}, shape={})".format(
node.output_name, node.output_name, output_shape.ref_name))
return code
def emit_depthwiseconv2dnative(self, node):
data = node.inputs[0]
kernel = node.inputs[1]
if len(kernel.outputs) == 1:
kernel.codes = []
padding_mode = node.get_attr("padding")
strides = node.get_attr("strides")[1:3]
k_shape = self.tensor_shape_to_list(
kernel.get_attr("_output_shapes"))[0]
input_shape = self.tensor_shape_to_list(
data.get_attr("_output_shapes"))[0]
input_h, input_w = input_shape[2:4]
in_channels, channel_multiplier, k_h, k_w = k_shape
if node.data_format == "NHWC":
k_h, k_w, in_channels, channel_multiplier = k_shape
input_h, input_w = input_shape[1:3]
if k_h < strides[0] or k_w < strides[1]:
raise Exception(
"Unexpected situation with kernel's height/width less than the corresponding stride"
)
groups = channel_multiplier * in_channels
if kernel.layer_name in self.weights:
if node.data_format == "NHWC":
weight = self.weights[kernel.layer_name]
self.weights[kernel.layer_name] = numpy.transpose(
weight, (2, 3, 0, 1))
self.export_weights(self.weights[kernel.layer_name],
kernel.ref_name, self.save_dir)
code = list()
padding = [0, 0]
if padding_mode == "SAME":
total_pad_h = self.compute_padding_size(input_h, k_h, strides[0])
total_pad_w = self.compute_padding_size(input_w, k_w, strides[1])
if total_pad_h % 2 == 0 and total_pad_w % 2 == 0:
padding = map(int, [total_pad_h / 2, total_pad_w / 2])
code.append(
"{} = layers.conv2d({}, {}, {}, padding={}, stride={}, param_attr=\'{}\', bias_attr=False, groups={})"
.format(node.output_name, data.ref_name, in_channels,
[k_h, k_w], padding, strides, kernel.ref_name,
groups))
else:
padding = [0] * 4
padding[0] = total_pad_h / 2
padding[1] = total_pad_h - padding[0]
padding[2] = total_pad_w / 2
padding[3] = total_pad_w - padding[2]
code.append("{} = layers.pad2d({}, {})".format(
node.output_name, data.ref_name, padding))
code.append(
"{} = layers.conv2d({}, {}, {}, stride={}, param_attr=\'{}\', bias_attr=False, groups={})"
.format(node.output_name, node.ref_name, in_channels,
[k_h, k_w], strides, kernel.ref_name, groups))
else:
code.append(
"{} = layers.conv2d({}, {}, {}, stride={}, param_attr=\'{}\', bias_attr=False, groups={})"
.format(node.output_name, data.ref_name, in_channels,
[k_h, k_w], strides, kernel.ref_name, groups))
return code
tensorflow2fluid/src/tensorflow_graph.py 0 → 100644
浏览文件 @ 7a96c492
# Copyright (c) 2019 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 graph import GraphNode, Graph
from tensorflow.core.framework import attr_value_pb2
class TensorflowGraphNode(GraphNode):
def __init__(self, layer):
super(TensorflowGraphNode, self).__init__(layer)
self.codes = list()
self.data_format = 'NCHW'
@property
def layer_type(self):
return self.layer.op.lower()
@property
def layer_name(self):
return self.layer.name
def get_attr(self, name, default_value=None):
if name in self.layer.attr:
attr = self.layer.attr[name]
field = attr.WhichOneof('value')
val = getattr(attr, field) if field else default_value
if isinstance(val, attr_value_pb2.AttrValue.ListValue):
return list(val.ListFields()[0][1])
else:
return val.decode('utf-8') if isinstance(val, bytes) else val
else:
return default_value
class TensorflowGraph(Graph):
def __init__(self, tf_graph):
super(TensorflowGraph, self).__init__(tf_graph)
self.tf_graph = tf_graph
def build(self):
skip_node = set(['const'])
for i, layer in enumerate(self.tf_graph.node):
self.node_map[layer.name] = TensorflowGraphNode(layer)
for i, layer in enumerate(self.tf_graph.node):
if layer.op.lower() in skip_node:
continue
for pred in layer.input:
if pred not in self.node_map and pred.split(
':')[0] in self.node_map:
node = self.node_map[pred.split(':')[0]]
if node.layer_type == "switch":
self._make_connection(node, self.node_map[layer.name])
else:
raise Exception("Need to fix here")
elif pred in self.node_map:
self._make_connection(self.node_map[pred],
self.node_map[layer.name])
else:
raise Exception("input: {} not in node_map".format(pred))
super(TensorflowGraph, self).build()
self._remove_useless_nodes()
self._check_dataformat()
def _check_dataformat(self):
ss = list()
for i in range(0, len(self.topological_sort)):
current_node = self.node_map[self.topological_sort[i]]
if 'data_format' in current_node.layer.attr:
s = current_node.layer.attr['data_format'].s
if s != 'NHWC' and s != 'NCHW':
raise Exception('Unkown dataformat {}'.format(s))
ss.append(s)
if len(set(ss)) > 1:
raise Exception("Two type of dataformat exist in this model")
if len(set(ss)) == 0:
return
for k, v in self.node_map.items():
self.node_map[k].data_format = ss[0]
def _remove_useless_nodes(self):
useless_type = set(
['identity', 'placeholderwithdefault', 'switch', 'merge'])
remove_index = list()
for i in range(0, len(self.topological_sort)):
name = self.topological_sort[i]
current_node = self.node_map[name]
if current_node.layer_type in useless_type:
input = current_node.inputs[0]
for node in current_node.outputs:
for k in range(0, len(node.inputs)):
if node.inputs[k] == current_node:
node.inputs[k] = input
if node not in input.outputs:
input.outputs.append(node)
input.outputs.remove(current_node)
del self.node_map[name]
if name in self.output_nodes:
self.output_nodes.remove(name)
if name in self.input_nodes:
self.input_nodes.remove(name)
remove_index.append(i)
remove_index.sort(reverse=True)
for i in range(0, len(remove_index)):
del self.topological_sort[remove_index[i]]
tensorflow2fluid/src/tensorflow_parser.py 0 → 100644
浏览文件 @ 7a96c492
# Copyright (c) 2019 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.
import tensorflow
from tensorflow_graph import TensorflowGraph
from tensorflow.python.framework import tensor_util
from tensorflow.python.tools import strip_unused_lib
from tensorflow.python.framework import dtypes
class TensorflowCkptParser(object):
def __init__(self,
meta_file,
checkpoint_file,
dest_nodes,
input_shape=None,
in_nodes=None):
graph_def = None
self.weights = None
with tensorflow.Session() as sess:
if meta_file is None:
raise Exception("meta_file must be provided")
new_saver = tensorflow.train.import_meta_graph(meta_file)
if checkpoint_file is not None:
self.weights = dict()
new_saver.restore(
sess, tensorflow.train.latest_checkpoint(checkpoint_file))
for var in tensorflow.global_variables():
value = var.eval(sess)
self.weights[var.name.split(':')[0]] = value
graph_def, ver = tensorflow.get_default_graph()._as_graph_def(
add_shapes=True)
if in_nodes is not None and input_shape is not None:
graph_def = strip_unused_lib.strip_unused(
input_graph_def=graph_def,
input_node_names=in_nodes,
output_node_names=dest_nodes,
placeholder_type_enum=dtypes.float32.as_datatype_enum)
self.tf_graph = TensorflowGraph(graph_def)
else:
raise Exception('in_nodes and output_nodes need be provided')
self.tf_graph.build()
class TensorflowPbParser(object):
def __init__(self, pb_file, dest_nodes, input_shape=None, in_nodes=None):
with open(pb_file) as f:
serialized = f.read()
tensorflow.reset_default_graph()
original_graph_def = tensorflow.GraphDef()
original_graph_def.ParseFromString(serialized)
original_graph_def = strip_unused_lib.strip_unused(
input_graph_def=original_graph_def,
input_node_names=in_nodes,
output_node_names=dest_nodes,
placeholder_type_enum=dtypes.float32.as_datatype_enum)
graph_def = tensorflow.GraphDef()
graph_def.ParseFromString(original_graph_def.SerializeToString())
in_type_list = dict()
for node in graph_def.node:
if node.name in in_nodes:
in_type_list[node.name] = node.attr['dtype'].type
input_shape = list(input_shape)
if not isinstance(input_shape[0], list):
input_shape = [input_shape]
input_map = dict()
for i in range(len(input_shape)):
if in_type_list[in_nodes[i]] == 1 or in_type_list[
in_nodes[i]] == 0:
dtype = tensorflow.float32
x = tensorflow.placeholder(dtype, shape=input_shape[i])
elif in_type_list[in_nodes[i]] == 3:
dtype = tensorflow.int32
x = tensorflow.placehoder(dtype, shape=input_shape[i])
else:
raise Exception(
"Unexpected dtype for input, only support float32 and int32 now"
)
input_map[in_nodes[i] + ":0"] = x
tensorflow.import_graph_def(graph_def, name="", input_map=input_map)
graph_def = tensorflow.get_default_graph()._as_graph_def(
add_shapes=True)[0]
node = graph_def.node[0]
self.tf_graph = TensorflowGraph(graph_def)
self.tf_graph.build()
self.weights = dict()
for node in graph_def.node:
if node.op.lower() == "const":
try:
node.attr['value'].tensor.tensor_content
weight = tensor_util.MakeNdarray(node.attr['value'].tensor)
self.weights[node.name] = weight
except:
continue
tensorflow2fluid/src/transformer.py 0 → 100644
浏览文件 @ 7a96c492
from paddle_emitter import PaddleEmitter
from tensorflow_parser import TensorflowCkptParser
from tensorflow_parser import TensorflowPbParser
class Transformer(object):
def __init__(self, meta_file, ckpt_file, out_nodes, in_shape, in_nodes, save_dir):
self.parser = TensorflowCkptParser(meta_file, ckpt_file, out_nodes,
in_shape, in_nodes)
self.emitter = PaddleEmitter(self.parser, save_dir)
def transform_code(self):
codes = self.emitter.run()
def run(self):
self.transform_code()
class PbTransformer(object):
def __init__(self, pb_file, out_nodes, in_shape, in_nodes, save_dir):
self.parser = TensorflowPbParser(pb_file, out_nodes, in_shape, in_nodes)
self.emitter = PaddleEmitter(self.parser, save_dir)
node = self.parser.tf_graph.tf_graph.node[0]
def transform_code(self):
codes = self.emitter.run()
def run(self):
self.transform_code()
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