diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 21bab1f7baea919e7548df5adbf4f312c7dacc75..236e319650e23972cac47ae2cad3cfc17a2fcecf 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,12 +1,8 @@ -- repo: local +- repo: https://github.com/PaddlePaddle/mirrors-yapf.git + sha: 0d79c0c469bab64f7229c9aca2b1186ef47f0e37 hooks: - id: yapf - name: yapf - entry: yapf - language: system - args: [-i, --style .style.yapf] files: \.py$ - - repo: https://github.com/pre-commit/pre-commit-hooks sha: a11d9314b22d8f8c7556443875b731ef05965464 hooks: @@ -18,6 +14,7 @@ - id: check-symlinks - id: check-added-large-files - repo: local + hooks: - id: copyright_checker name: copyright_checker diff --git a/setup.py b/setup.py index 51708204e15eac14a9bc992720bbbe63e32f2fda..490c42c95970cdd333de84e458eef910342076f1 100644 --- a/setup.py +++ b/setup.py @@ -11,8 +11,7 @@ setuptools.setup( version=x2paddle.__version__, author="dltp-sz", author_email="dltp-sz@baidu.com", - description= - "a toolkit for converting trained model to PaddlePaddle from other deep learning frameworks.", + description="a toolkit for converting trained model to PaddlePaddle from other deep learning frameworks.", long_description=long_description, long_description_content_type="text/plain", url="https://github.com/PaddlePaddle/x2paddle", @@ -23,6 +22,4 @@ setuptools.setup( "Operating System :: OS Independent", ], license='Apache 2.0', - entry_points={'console_scripts': [ - 'x2paddle=x2paddle.convert:main', - ]}) + entry_points={'console_scripts': ['x2paddle=x2paddle.convert:main', ]}) diff --git a/x2paddle/convert.py b/x2paddle/convert.py index efd665732e6b278fb862fc56df02b548eb7cdced..28a303bda9ea81899f03419dc9f538a5e5967a3d 100644 --- a/x2paddle/convert.py +++ b/x2paddle/convert.py @@ -48,8 +48,7 @@ def arg_parser(): "-f", type=_text_type, default=None, - help= - "define which deeplearning framework(tensorflow/caffe/onnx/paddle2onnx)" + help="define which deeplearning framework(tensorflow/caffe/onnx/paddle2onnx)" ) parser.add_argument( "--caffe_proto", @@ -126,7 +125,6 @@ def tf2paddle(model_path, optimizer.merge_bias() optimizer.optimize_sub_graph() - # optimizer.merge_batch_norm() # optimizer.merge_prelu() else: diff --git a/x2paddle/core/fluid_code.py b/x2paddle/core/fluid_code.py index b13d4ca55f3675bb8b8ac9b2121342c69176fcda..72ea10788e9519f8ec4256c13220969b3a0b4959 100644 --- a/x2paddle/core/fluid_code.py +++ b/x2paddle/core/fluid_code.py @@ -46,8 +46,9 @@ class Layer(object): for input in self.inputs: if isinstance(input, GraphNode): if hasattr(input, "index"): - in_list += (input.layer_name + "[{}]".format( - input.index) + ", ") + in_list += ( + input.layer_name + "[{}]".format(input.index) + ", " + ) else: in_list += (input.layer_name + ", ") elif isinstance(input, six.string_types): diff --git a/x2paddle/decoder/caffe_decoder.py b/x2paddle/decoder/caffe_decoder.py index f9b0ade43f7610dc455f1bda497d1915e6f2d817..cb9f59708fa0980b0a3bcb029319a33a3df18f92 100644 --- a/x2paddle/decoder/caffe_decoder.py +++ b/x2paddle/decoder/caffe_decoder.py @@ -34,8 +34,8 @@ class CaffeResolver(object): if not os.path.isfile(self.caffe_proto): raise Exception( "The .py file compiled by caffe.proto is not exist.") - (filepath, tempfilename) = os.path.split( - os.path.abspath(self.caffe_proto)) + (filepath, + tempfilename) = os.path.split(os.path.abspath(self.caffe_proto)) (filename, extension) = os.path.splitext(tempfilename) sys.path.append(filepath) out = __import__(filename) @@ -50,12 +50,10 @@ class CaffeGraphNode(GraphNode): def __init__(self, layer, type_str, layer_name=None): if layer_name is None: super(CaffeGraphNode, self).__init__( - layer, - layer.name.replace('/', '_').replace('-', '_')) + layer, layer.name.replace('/', '_').replace('-', '_')) else: super(CaffeGraphNode, self).__init__( - layer, - layer_name.replace('/', '_').replace('-', '_')) + layer, layer_name.replace('/', '_').replace('-', '_')) self.layer_type = type_str self.fluid_code = FluidCode() self.data = None diff --git a/x2paddle/decoder/caffe_pb2.py b/x2paddle/decoder/caffe_pb2.py index 342256b5237b20c235d96c8095b4c278110d8785..5058c4c2bff8a8b1eab838c1b01780eb3102426b 100644 --- a/x2paddle/decoder/caffe_pb2.py +++ b/x2paddle/decoder/caffe_pb2.py @@ -36,8 +36,7 @@ _PHASE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=21908, - serialized_end=21936, -) + serialized_end=21936, ) _sym_db.RegisterEnumDescriptor(_PHASE) Phase = enum_type_wrapper.EnumTypeWrapper(_PHASE) @@ -66,8 +65,7 @@ _EMITCONSTRAINT_EMITTYPE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=1146, - serialized_end=1185, -) + serialized_end=1185, ) _sym_db.RegisterEnumDescriptor(_EMITCONSTRAINT_EMITTYPE) _ANNOTATEDDATUM_ANNOTATIONTYPE = _descriptor.EnumDescriptor( @@ -82,8 +80,7 @@ _ANNOTATEDDATUM_ANNOTATIONTYPE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=1629, - serialized_end=1655, -) + serialized_end=1655, ) _sym_db.RegisterEnumDescriptor(_ANNOTATEDDATUM_ANNOTATIONTYPE) _FILLERPARAMETER_VARIANCENORM = _descriptor.EnumDescriptor( @@ -114,8 +111,7 @@ _FILLERPARAMETER_VARIANCENORM = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=1872, - serialized_end=1924, -) + serialized_end=1924, ) _sym_db.RegisterEnumDescriptor(_FILLERPARAMETER_VARIANCENORM) _SOLVERPARAMETER_SNAPSHOTFORMAT = _descriptor.EnumDescriptor( @@ -136,8 +132,7 @@ _SOLVERPARAMETER_SNAPSHOTFORMAT = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=3480, - serialized_end=3523, -) + serialized_end=3523, ) _sym_db.RegisterEnumDescriptor(_SOLVERPARAMETER_SNAPSHOTFORMAT) _SOLVERPARAMETER_SOLVERMODE = _descriptor.EnumDescriptor( @@ -154,8 +149,7 @@ _SOLVERPARAMETER_SOLVERMODE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=3525, - serialized_end=3555, -) + serialized_end=3555, ) _sym_db.RegisterEnumDescriptor(_SOLVERPARAMETER_SOLVERMODE) _SOLVERPARAMETER_SOLVERTYPE = _descriptor.EnumDescriptor( @@ -196,8 +190,7 @@ _SOLVERPARAMETER_SOLVERTYPE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=3557, - serialized_end=3642, -) + serialized_end=3642, ) _sym_db.RegisterEnumDescriptor(_SOLVERPARAMETER_SOLVERTYPE) _PARAMSPEC_DIMCHECKMODE = _descriptor.EnumDescriptor( @@ -222,8 +215,7 @@ _PARAMSPEC_DIMCHECKMODE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=4131, - serialized_end=4173, -) + serialized_end=4173, ) _sym_db.RegisterEnumDescriptor(_PARAMSPEC_DIMCHECKMODE) _RESIZEPARAMETER_RESIZE_MODE = _descriptor.EnumDescriptor( @@ -250,8 +242,7 @@ _RESIZEPARAMETER_RESIZE_MODE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=8049, - serialized_end=8120, -) + serialized_end=8120, ) _sym_db.RegisterEnumDescriptor(_RESIZEPARAMETER_RESIZE_MODE) _RESIZEPARAMETER_PAD_MODE = _descriptor.EnumDescriptor( @@ -282,8 +273,7 @@ _RESIZEPARAMETER_PAD_MODE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=8122, - serialized_end=8180, -) + serialized_end=8180, ) _sym_db.RegisterEnumDescriptor(_RESIZEPARAMETER_PAD_MODE) _RESIZEPARAMETER_INTERP_MODE = _descriptor.EnumDescriptor( @@ -319,8 +309,7 @@ _RESIZEPARAMETER_INTERP_MODE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=8182, - serialized_end=8255, -) + serialized_end=8255, ) _sym_db.RegisterEnumDescriptor(_RESIZEPARAMETER_INTERP_MODE) _LOSSPARAMETER_NORMALIZATIONMODE = _descriptor.EnumDescriptor( @@ -346,8 +335,7 @@ _LOSSPARAMETER_NORMALIZATIONMODE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=9202, - serialized_end=9268, -) + serialized_end=9268, ) _sym_db.RegisterEnumDescriptor(_LOSSPARAMETER_NORMALIZATIONMODE) _CONVOLUTIONPARAMETER_ENGINE = _descriptor.EnumDescriptor( @@ -372,8 +360,7 @@ _CONVOLUTIONPARAMETER_ENGINE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=10385, - serialized_end=10428, -) + serialized_end=10428, ) _sym_db.RegisterEnumDescriptor(_CONVOLUTIONPARAMETER_ENGINE) _DATAPARAMETER_DB = _descriptor.EnumDescriptor( @@ -394,8 +381,7 @@ _DATAPARAMETER_DB = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=10746, - serialized_end=10773, -) + serialized_end=10773, ) _sym_db.RegisterEnumDescriptor(_DATAPARAMETER_DB) _ELTWISEPARAMETER_ELTWISEOP = _descriptor.EnumDescriptor( @@ -414,8 +400,7 @@ _ELTWISEPARAMETER_ELTWISEOP = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=12106, - serialized_end=12145, -) + serialized_end=12145, ) _sym_db.RegisterEnumDescriptor(_ELTWISEPARAMETER_ELTWISEOP) _HINGELOSSPARAMETER_NORM = _descriptor.EnumDescriptor( @@ -432,8 +417,7 @@ _HINGELOSSPARAMETER_NORM = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=12680, - serialized_end=12702, -) + serialized_end=12702, ) _sym_db.RegisterEnumDescriptor(_HINGELOSSPARAMETER_NORM) _LRNPARAMETER_NORMREGION = _descriptor.EnumDescriptor( @@ -458,8 +442,7 @@ _LRNPARAMETER_NORMREGION = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=13569, - serialized_end=13622, -) + serialized_end=13622, ) _sym_db.RegisterEnumDescriptor(_LRNPARAMETER_NORMREGION) _LRNPARAMETER_ENGINE = _descriptor.EnumDescriptor( @@ -484,8 +467,7 @@ _LRNPARAMETER_ENGINE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=10385, - serialized_end=10428, -) + serialized_end=10428, ) _sym_db.RegisterEnumDescriptor(_LRNPARAMETER_ENGINE) _MULTIBOXLOSSPARAMETER_LOCLOSSTYPE = _descriptor.EnumDescriptor( @@ -506,8 +488,7 @@ _MULTIBOXLOSSPARAMETER_LOCLOSSTYPE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=14703, - serialized_end=14739, -) + serialized_end=14739, ) _sym_db.RegisterEnumDescriptor(_MULTIBOXLOSSPARAMETER_LOCLOSSTYPE) _MULTIBOXLOSSPARAMETER_CONFLOSSTYPE = _descriptor.EnumDescriptor( @@ -532,8 +513,7 @@ _MULTIBOXLOSSPARAMETER_CONFLOSSTYPE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=14741, - serialized_end=14782, -) + serialized_end=14782, ) _sym_db.RegisterEnumDescriptor(_MULTIBOXLOSSPARAMETER_CONFLOSSTYPE) _MULTIBOXLOSSPARAMETER_MATCHTYPE = _descriptor.EnumDescriptor( @@ -558,8 +538,7 @@ _MULTIBOXLOSSPARAMETER_MATCHTYPE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=14784, - serialized_end=14830, -) + serialized_end=14830, ) _sym_db.RegisterEnumDescriptor(_MULTIBOXLOSSPARAMETER_MATCHTYPE) _MULTIBOXLOSSPARAMETER_MININGTYPE = _descriptor.EnumDescriptor( @@ -586,8 +565,7 @@ _MULTIBOXLOSSPARAMETER_MININGTYPE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=14832, - serialized_end=14890, -) + serialized_end=14890, ) _sym_db.RegisterEnumDescriptor(_MULTIBOXLOSSPARAMETER_MININGTYPE) _POOLINGPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor( @@ -610,8 +588,7 @@ _POOLINGPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=15561, - serialized_end=15607, -) + serialized_end=15607, ) _sym_db.RegisterEnumDescriptor(_POOLINGPARAMETER_POOLMETHOD) _POOLINGPARAMETER_ENGINE = _descriptor.EnumDescriptor( @@ -636,8 +613,7 @@ _POOLINGPARAMETER_ENGINE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=10385, - serialized_end=10428, -) + serialized_end=10428, ) _sym_db.RegisterEnumDescriptor(_POOLINGPARAMETER_ENGINE) _PRIORBOXPARAMETER_CODETYPE = _descriptor.EnumDescriptor( @@ -668,8 +644,7 @@ _PRIORBOXPARAMETER_CODETYPE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=15980, - serialized_end=16036, -) + serialized_end=16036, ) _sym_db.RegisterEnumDescriptor(_PRIORBOXPARAMETER_CODETYPE) _REDUCTIONPARAMETER_REDUCTIONOP = _descriptor.EnumDescriptor( @@ -691,8 +666,7 @@ _REDUCTIONPARAMETER_REDUCTIONOP = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=16459, - serialized_end=16512, -) + serialized_end=16512, ) _sym_db.RegisterEnumDescriptor(_REDUCTIONPARAMETER_REDUCTIONOP) _RELUPARAMETER_ENGINE = _descriptor.EnumDescriptor( @@ -717,8 +691,7 @@ _RELUPARAMETER_ENGINE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=10385, - serialized_end=10428, -) + serialized_end=10428, ) _sym_db.RegisterEnumDescriptor(_RELUPARAMETER_ENGINE) _SIGMOIDPARAMETER_ENGINE = _descriptor.EnumDescriptor( @@ -743,8 +716,7 @@ _SIGMOIDPARAMETER_ENGINE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=10385, - serialized_end=10428, -) + serialized_end=10428, ) _sym_db.RegisterEnumDescriptor(_SIGMOIDPARAMETER_ENGINE) _SOFTMAXPARAMETER_ENGINE = _descriptor.EnumDescriptor( @@ -769,8 +741,7 @@ _SOFTMAXPARAMETER_ENGINE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=10385, - serialized_end=10428, -) + serialized_end=10428, ) _sym_db.RegisterEnumDescriptor(_SOFTMAXPARAMETER_ENGINE) _TANHPARAMETER_ENGINE = _descriptor.EnumDescriptor( @@ -795,8 +766,7 @@ _TANHPARAMETER_ENGINE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=10385, - serialized_end=10428, -) + serialized_end=10428, ) _sym_db.RegisterEnumDescriptor(_TANHPARAMETER_ENGINE) _VIDEODATAPARAMETER_VIDEOTYPE = _descriptor.EnumDescriptor( @@ -818,8 +788,7 @@ _VIDEODATAPARAMETER_VIDEOTYPE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=17621, - serialized_end=17655, -) + serialized_end=17655, ) _sym_db.RegisterEnumDescriptor(_VIDEODATAPARAMETER_VIDEOTYPE) _SPPPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor( @@ -842,8 +811,7 @@ _SPPPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=15561, - serialized_end=15607, -) + serialized_end=15607, ) _sym_db.RegisterEnumDescriptor(_SPPPARAMETER_POOLMETHOD) _SPPPARAMETER_ENGINE = _descriptor.EnumDescriptor( @@ -868,8 +836,7 @@ _SPPPARAMETER_ENGINE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=10385, - serialized_end=10428, -) + serialized_end=10428, ) _sym_db.RegisterEnumDescriptor(_SPPPARAMETER_ENGINE) _V1LAYERPARAMETER_LAYERTYPE = _descriptor.EnumDescriptor( @@ -1101,8 +1068,7 @@ _V1LAYERPARAMETER_LAYERTYPE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=20104, - serialized_end=20704, -) + serialized_end=20704, ) _sym_db.RegisterEnumDescriptor(_V1LAYERPARAMETER_LAYERTYPE) _V1LAYERPARAMETER_DIMCHECKMODE = _descriptor.EnumDescriptor( @@ -1127,8 +1093,7 @@ _V1LAYERPARAMETER_DIMCHECKMODE = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=4131, - serialized_end=4173, -) + serialized_end=4173, ) _sym_db.RegisterEnumDescriptor(_V1LAYERPARAMETER_DIMCHECKMODE) _V0LAYERPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor( @@ -1151,8 +1116,7 @@ _V0LAYERPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor( containing_type=None, serialized_options=None, serialized_start=15561, - serialized_end=15607, -) + serialized_end=15607, ) _sym_db.RegisterEnumDescriptor(_V0LAYERPARAMETER_POOLMETHOD) _BLOBSHAPE = _descriptor.Descriptor( @@ -1189,8 +1153,7 @@ _BLOBSHAPE = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=22, - serialized_end=50, -) + serialized_end=50, ) _BLOBPROTO = _descriptor.Descriptor( name='BlobProto', @@ -1362,8 +1325,7 @@ _BLOBPROTO = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=53, - serialized_end=257, -) + serialized_end=257, ) _BLOBPROTOVECTOR = _descriptor.Descriptor( name='BlobProtoVector', @@ -1399,8 +1361,7 @@ _BLOBPROTOVECTOR = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=259, - serialized_end=309, -) + serialized_end=309, ) _DATUM = _descriptor.Descriptor( name='Datum', @@ -1538,8 +1499,7 @@ _DATUM = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=312, - serialized_end=441, -) + serialized_end=441, ) _LABELMAPITEM = _descriptor.Descriptor( name='LabelMapItem', @@ -1609,8 +1569,7 @@ _LABELMAPITEM = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=443, - serialized_end=508, -) + serialized_end=508, ) _LABELMAP = _descriptor.Descriptor( name='LabelMap', @@ -1646,8 +1605,7 @@ _LABELMAP = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=510, - serialized_end=555, -) + serialized_end=555, ) _SAMPLER = _descriptor.Descriptor( name='Sampler', @@ -1734,8 +1692,7 @@ _SAMPLER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=557, - serialized_end=668, -) + serialized_end=668, ) _SAMPLECONSTRAINT = _descriptor.Descriptor( name='SampleConstraint', @@ -1856,8 +1813,7 @@ _SAMPLECONSTRAINT = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=671, - serialized_end=863, -) + serialized_end=863, ) _BATCHSAMPLER = _descriptor.Descriptor( name='BatchSampler', @@ -1961,8 +1917,7 @@ _BATCHSAMPLER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=866, - serialized_end=1044, -) + serialized_end=1044, ) _EMITCONSTRAINT = _descriptor.Descriptor( name='EmitConstraint', @@ -2008,17 +1963,14 @@ _EMITCONSTRAINT = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _EMITCONSTRAINT_EMITTYPE, - ], + enum_types=[_EMITCONSTRAINT_EMITTYPE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=1047, - serialized_end=1185, -) + serialized_end=1185, ) _NORMALIZEDBBOX = _descriptor.Descriptor( name='NormalizedBBox', @@ -2173,8 +2125,7 @@ _NORMALIZEDBBOX = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=1188, - serialized_end=1323, -) + serialized_end=1323, ) _ANNOTATION = _descriptor.Descriptor( name='Annotation', @@ -2227,8 +2178,7 @@ _ANNOTATION = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=1325, - serialized_end=1398, -) + serialized_end=1398, ) _ANNOTATIONGROUP = _descriptor.Descriptor( name='AnnotationGroup', @@ -2281,8 +2231,7 @@ _ANNOTATIONGROUP = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=1400, - serialized_end=1477, -) + serialized_end=1477, ) _ANNOTATEDDATUM = _descriptor.Descriptor( name='AnnotatedDatum', @@ -2345,17 +2294,14 @@ _ANNOTATEDDATUM = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _ANNOTATEDDATUM_ANNOTATIONTYPE, - ], + enum_types=[_ANNOTATEDDATUM_ANNOTATIONTYPE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=1480, - serialized_end=1655, -) + serialized_end=1655, ) _FILLERPARAMETER = _descriptor.Descriptor( name='FillerParameter', @@ -2503,17 +2449,14 @@ _FILLERPARAMETER = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _FILLERPARAMETER_VARIANCENORM, - ], + enum_types=[_FILLERPARAMETER_VARIANCENORM, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=1658, - serialized_end=1924, -) + serialized_end=1924, ) _NETPARAMETER = _descriptor.Descriptor( name='NetParameter', @@ -2685,8 +2628,7 @@ _NETPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=1927, - serialized_end=2197, -) + serialized_end=2197, ) _SOLVERPARAMETER = _descriptor.Descriptor( name='SolverParameter', @@ -3457,8 +3399,7 @@ _SOLVERPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=2200, - serialized_end=3642, -) + serialized_end=3642, ) _SOLVERSTATE = _descriptor.Descriptor( name='SolverState', @@ -3579,8 +3520,7 @@ _SOLVERSTATE = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=3645, - serialized_end=3810, -) + serialized_end=3810, ) _NETSTATE = _descriptor.Descriptor( name='NetState', @@ -3650,8 +3590,7 @@ _NETSTATE = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=3812, - serialized_end=3890, -) + serialized_end=3890, ) _NETSTATERULE = _descriptor.Descriptor( name='NetStateRule', @@ -3755,8 +3694,7 @@ _NETSTATERULE = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=3892, - serialized_end=4007, -) + serialized_end=4007, ) _PARAMSPEC = _descriptor.Descriptor( name='ParamSpec', @@ -3836,17 +3774,14 @@ _PARAMSPEC = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _PARAMSPEC_DIMCHECKMODE, - ], + enum_types=[_PARAMSPEC_DIMCHECKMODE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=4010, - serialized_end=4173, -) + serialized_end=4173, ) _LAYERPARAMETER = _descriptor.Descriptor( name='LayerParameter', @@ -5004,8 +4939,7 @@ _LAYERPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=4176, - serialized_end=7263, -) + serialized_end=7263, ) _TRANSFORMATIONPARAMETER = _descriptor.Descriptor( name='TransformationParameter', @@ -5262,8 +5196,7 @@ _TRANSFORMATIONPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=7266, - serialized_end=7724, -) + serialized_end=7724, ) _RESIZEPARAMETER = _descriptor.Descriptor( name='ResizeParameter', @@ -5439,8 +5372,7 @@ _RESIZEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=7727, - serialized_end=8255, -) + serialized_end=8255, ) _SALTPEPPERPARAMETER = _descriptor.Descriptor( name='SaltPepperParameter', @@ -5493,8 +5425,7 @@ _SALTPEPPERPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=8257, - serialized_end=8314, -) + serialized_end=8314, ) _NOISEPARAMETER = _descriptor.Descriptor( name='NoiseParameter', @@ -5734,8 +5665,7 @@ _NOISEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=8317, - serialized_end=8683, -) + serialized_end=8683, ) _DISTORTIONPARAMETER = _descriptor.Descriptor( name='DistortionParameter', @@ -5941,8 +5871,7 @@ _DISTORTIONPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=8686, - serialized_end=9003, -) + serialized_end=9003, ) _EXPANSIONPARAMETER = _descriptor.Descriptor( name='ExpansionParameter', @@ -5995,8 +5924,7 @@ _EXPANSIONPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=9005, - serialized_end=9071, -) + serialized_end=9071, ) _LOSSPARAMETER = _descriptor.Descriptor( name='LossParameter', @@ -6059,17 +5987,14 @@ _LOSSPARAMETER = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _LOSSPARAMETER_NORMALIZATIONMODE, - ], + enum_types=[_LOSSPARAMETER_NORMALIZATIONMODE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=9074, - serialized_end=9268, -) + serialized_end=9268, ) _ACCURACYPARAMETER = _descriptor.Descriptor( name='AccuracyParameter', @@ -6139,8 +6064,7 @@ _ACCURACYPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=9270, - serialized_end=9346, -) + serialized_end=9346, ) _ANNOTATEDDATAPARAMETER = _descriptor.Descriptor( name='AnnotatedDataParameter', @@ -6210,8 +6134,7 @@ _ANNOTATEDDATAPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=9349, - serialized_end=9498, -) + serialized_end=9498, ) _ARGMAXPARAMETER = _descriptor.Descriptor( name='ArgMaxParameter', @@ -6281,8 +6204,7 @@ _ARGMAXPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=9500, - serialized_end=9577, -) + serialized_end=9577, ) _CONCATPARAMETER = _descriptor.Descriptor( name='ConcatParameter', @@ -6335,8 +6257,7 @@ _CONCATPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=9579, - serialized_end=9636, -) + serialized_end=9636, ) _BATCHNORMPARAMETER = _descriptor.Descriptor( name='BatchNormParameter', @@ -6406,8 +6327,7 @@ _BATCHNORMPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=9638, - serialized_end=9744, -) + serialized_end=9744, ) _BIASPARAMETER = _descriptor.Descriptor( name='BiasParameter', @@ -6477,8 +6397,7 @@ _BIASPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=9746, - serialized_end=9839, -) + serialized_end=9839, ) _CONTRASTIVELOSSPARAMETER = _descriptor.Descriptor( name='ContrastiveLossParameter', @@ -6531,8 +6450,7 @@ _CONTRASTIVELOSSPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=9841, - serialized_end=9917, -) + serialized_end=9917, ) _CONVOLUTIONPARAMETER = _descriptor.Descriptor( name='ConvolutionParameter', @@ -6850,17 +6768,14 @@ _CONVOLUTIONPARAMETER = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _CONVOLUTIONPARAMETER_ENGINE, - ], + enum_types=[_CONVOLUTIONPARAMETER_ENGINE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=9920, - serialized_end=10428, -) + serialized_end=10428, ) _CROPPARAMETER = _descriptor.Descriptor( name='CropParameter', @@ -6913,8 +6828,7 @@ _CROPPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=10430, - serialized_end=10478, -) + serialized_end=10478, ) _DATAPARAMETER = _descriptor.Descriptor( name='DataParameter', @@ -7096,17 +7010,14 @@ _DATAPARAMETER = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _DATAPARAMETER_DB, - ], + enum_types=[_DATAPARAMETER_DB, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=10481, - serialized_end=10773, -) + serialized_end=10773, ) _DETECTIONEVALUATEPARAMETER = _descriptor.Descriptor( name='DetectionEvaluateParameter', @@ -7227,8 +7138,7 @@ _DETECTIONEVALUATEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=10776, - serialized_end=10996, -) + serialized_end=10996, ) _NONMAXIMUMSUPPRESSIONPARAMETER = _descriptor.Descriptor( name='NonMaximumSuppressionParameter', @@ -7298,8 +7208,7 @@ _NONMAXIMUMSUPPRESSIONPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=10998, - serialized_end=11089, -) + serialized_end=11089, ) _SAVEOUTPUTPARAMETER = _descriptor.Descriptor( name='SaveOutputParameter', @@ -7437,8 +7346,7 @@ _SAVEOUTPUTPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=11092, - serialized_end=11308, -) + serialized_end=11308, ) _DETECTIONOUTPUTPARAMETER = _descriptor.Descriptor( name='DetectionOutputParameter', @@ -7551,8 +7459,7 @@ _DETECTIONOUTPUTPARAMETER = _descriptor.Descriptor( file=DESCRIPTOR), _descriptor.FieldDescriptor( name='variance_encoded_in_target', - full_name= - 'caffe.DetectionOutputParameter.variance_encoded_in_target', + full_name='caffe.DetectionOutputParameter.variance_encoded_in_target', index=6, number=8, type=8, @@ -7662,8 +7569,7 @@ _DETECTIONOUTPUTPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=11311, - serialized_end=11766, -) + serialized_end=11766, ) _DROPOUTPARAMETER = _descriptor.Descriptor( name='DropoutParameter', @@ -7699,8 +7605,7 @@ _DROPOUTPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=11768, - serialized_end=11814, -) + serialized_end=11814, ) _DUMMYDATAPARAMETER = _descriptor.Descriptor( name='DummyDataParameter', @@ -7821,8 +7726,7 @@ _DUMMYDATAPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=11817, - serialized_end=11977, -) + serialized_end=11977, ) _ELTWISEPARAMETER = _descriptor.Descriptor( name='EltwiseParameter', @@ -7885,17 +7789,14 @@ _ELTWISEPARAMETER = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _ELTWISEPARAMETER_ELTWISEOP, - ], + enum_types=[_ELTWISEPARAMETER_ELTWISEOP, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=11980, - serialized_end=12145, -) + serialized_end=12145, ) _ELUPARAMETER = _descriptor.Descriptor( name='ELUParameter', @@ -7931,8 +7832,7 @@ _ELUPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=12147, - serialized_end=12179, -) + serialized_end=12179, ) _EMBEDPARAMETER = _descriptor.Descriptor( name='EmbedParameter', @@ -8036,8 +7936,7 @@ _EMBEDPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=12182, - serialized_end=12354, -) + serialized_end=12354, ) _EXPPARAMETER = _descriptor.Descriptor( name='ExpParameter', @@ -8107,8 +8006,7 @@ _EXPPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=12356, - serialized_end=12424, -) + serialized_end=12424, ) _FLATTENPARAMETER = _descriptor.Descriptor( name='FlattenParameter', @@ -8161,8 +8059,7 @@ _FLATTENPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=12426, - serialized_end=12483, -) + serialized_end=12483, ) _HDF5DATAPARAMETER = _descriptor.Descriptor( name='HDF5DataParameter', @@ -8232,8 +8129,7 @@ _HDF5DATAPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=12485, - serialized_end=12564, -) + serialized_end=12564, ) _HDF5OUTPUTPARAMETER = _descriptor.Descriptor( name='HDF5OutputParameter', @@ -8269,8 +8165,7 @@ _HDF5OUTPUTPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=12566, - serialized_end=12606, -) + serialized_end=12606, ) _HINGELOSSPARAMETER = _descriptor.Descriptor( name='HingeLossParameter', @@ -8299,17 +8194,14 @@ _HINGELOSSPARAMETER = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _HINGELOSSPARAMETER_NORM, - ], + enum_types=[_HINGELOSSPARAMETER_NORM, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=12608, - serialized_end=12702, -) + serialized_end=12702, ) _IMAGEDATAPARAMETER = _descriptor.Descriptor( name='ImageDataParameter', @@ -8532,8 +8424,7 @@ _IMAGEDATAPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=12705, - serialized_end=12984, -) + serialized_end=12984, ) _INFOGAINLOSSPARAMETER = _descriptor.Descriptor( name='InfogainLossParameter', @@ -8569,8 +8460,7 @@ _INFOGAINLOSSPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=12986, - serialized_end=13025, -) + serialized_end=13025, ) _INNERPRODUCTPARAMETER = _descriptor.Descriptor( name='InnerProductParameter', @@ -8691,8 +8581,7 @@ _INNERPRODUCTPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=13028, - serialized_end=13231, -) + serialized_end=13231, ) _INPUTPARAMETER = _descriptor.Descriptor( name='InputParameter', @@ -8728,8 +8617,7 @@ _INPUTPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=13233, - serialized_end=13282, -) + serialized_end=13282, ) _LOGPARAMETER = _descriptor.Descriptor( name='LogParameter', @@ -8799,8 +8687,7 @@ _LOGPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=13284, - serialized_end=13352, -) + serialized_end=13352, ) _LRNPARAMETER = _descriptor.Descriptor( name='LRNParameter', @@ -8924,8 +8811,7 @@ _LRNPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=13355, - serialized_end=13667, -) + serialized_end=13667, ) _MEMORYDATAPARAMETER = _descriptor.Descriptor( name='MemoryDataParameter', @@ -9012,8 +8898,7 @@ _MEMORYDATAPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=13669, - serialized_end=13759, -) + serialized_end=13759, ) _MULTIBOXLOSSPARAMETER = _descriptor.Descriptor( name='MultiBoxLossParameter', @@ -9411,8 +9296,7 @@ _MULTIBOXLOSSPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=13762, - serialized_end=14890, -) + serialized_end=14890, ) _MVNPARAMETER = _descriptor.Descriptor( name='MVNParameter', @@ -9482,8 +9366,7 @@ _MVNPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=14892, - serialized_end=14992, -) + serialized_end=14992, ) _NORMALIZEPARAMETER = _descriptor.Descriptor( name='NormalizeParameter', @@ -9570,8 +9453,7 @@ _NORMALIZEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=14995, - serialized_end=15141, -) + serialized_end=15141, ) _PARAMETERPARAMETER = _descriptor.Descriptor( name='ParameterParameter', @@ -9607,8 +9489,7 @@ _PARAMETERPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=15143, - serialized_end=15196, -) + serialized_end=15196, ) _PERMUTEPARAMETER = _descriptor.Descriptor( name='PermuteParameter', @@ -9644,8 +9525,7 @@ _PERMUTEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=15198, - serialized_end=15231, -) + serialized_end=15231, ) _POOLINGPARAMETER = _descriptor.Descriptor( name='PoolingParameter', @@ -9871,8 +9751,7 @@ _POOLINGPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=15234, - serialized_end=15652, -) + serialized_end=15652, ) _POWERPARAMETER = _descriptor.Descriptor( name='PowerParameter', @@ -9942,8 +9821,7 @@ _POWERPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=15654, - serialized_end=15724, -) + serialized_end=15724, ) _PRIORBOXPARAMETER = _descriptor.Descriptor( name='PriorBoxParameter', @@ -10176,17 +10054,14 @@ _PRIORBOXPARAMETER = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _PRIORBOXPARAMETER_CODETYPE, - ], + enum_types=[_PRIORBOXPARAMETER_CODETYPE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=15727, - serialized_end=16036, -) + serialized_end=16036, ) _PYTHONPARAMETER = _descriptor.Descriptor( name='PythonParameter', @@ -10273,8 +10148,7 @@ _PYTHONPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=16038, - serialized_end=16141, -) + serialized_end=16141, ) _RECURRENTPARAMETER = _descriptor.Descriptor( name='RecurrentParameter', @@ -10378,8 +10252,7 @@ _RECURRENTPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=16144, - serialized_end=16336, -) + serialized_end=16336, ) _REDUCTIONPARAMETER = _descriptor.Descriptor( name='ReductionParameter', @@ -10442,17 +10315,14 @@ _REDUCTIONPARAMETER = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _REDUCTIONPARAMETER_REDUCTIONOP, - ], + enum_types=[_REDUCTIONPARAMETER_REDUCTIONOP, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=16339, - serialized_end=16512, -) + serialized_end=16512, ) _RELUPARAMETER = _descriptor.Descriptor( name='ReLUParameter', @@ -10498,17 +10368,14 @@ _RELUPARAMETER = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _RELUPARAMETER_ENGINE, - ], + enum_types=[_RELUPARAMETER_ENGINE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=16515, - serialized_end=16656, -) + serialized_end=16656, ) _RESHAPEPARAMETER = _descriptor.Descriptor( name='ReshapeParameter', @@ -10578,8 +10445,7 @@ _RESHAPEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=16658, - serialized_end=16748, -) + serialized_end=16748, ) _SCALEPARAMETER = _descriptor.Descriptor( name='ScaleParameter', @@ -10683,8 +10549,7 @@ _SCALEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=16751, - serialized_end=16916, -) + serialized_end=16916, ) _SIGMOIDPARAMETER = _descriptor.Descriptor( name='SigmoidParameter', @@ -10713,17 +10578,14 @@ _SIGMOIDPARAMETER = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _SIGMOIDPARAMETER_ENGINE, - ], + enum_types=[_SIGMOIDPARAMETER_ENGINE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=16918, - serialized_end=17038, -) + serialized_end=17038, ) _SLICEPARAMETER = _descriptor.Descriptor( name='SliceParameter', @@ -10793,8 +10655,7 @@ _SLICEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=17040, - serialized_end=17116, -) + serialized_end=17116, ) _SOFTMAXPARAMETER = _descriptor.Descriptor( name='SoftmaxParameter', @@ -10840,17 +10701,14 @@ _SOFTMAXPARAMETER = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _SOFTMAXPARAMETER_ENGINE, - ], + enum_types=[_SOFTMAXPARAMETER_ENGINE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=17119, - serialized_end=17256, -) + serialized_end=17256, ) _TANHPARAMETER = _descriptor.Descriptor( name='TanHParameter', @@ -10879,17 +10737,14 @@ _TANHPARAMETER = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _TANHPARAMETER_ENGINE, - ], + enum_types=[_TANHPARAMETER_ENGINE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=17258, - serialized_end=17372, -) + serialized_end=17372, ) _TILEPARAMETER = _descriptor.Descriptor( name='TileParameter', @@ -10942,8 +10797,7 @@ _TILEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=17374, - serialized_end=17421, -) + serialized_end=17421, ) _THRESHOLDPARAMETER = _descriptor.Descriptor( name='ThresholdParameter', @@ -10979,8 +10833,7 @@ _THRESHOLDPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=17423, - serialized_end=17465, -) + serialized_end=17465, ) _VIDEODATAPARAMETER = _descriptor.Descriptor( name='VideoDataParameter', @@ -11060,17 +10913,14 @@ _VIDEODATAPARAMETER = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _VIDEODATAPARAMETER_VIDEOTYPE, - ], + enum_types=[_VIDEODATAPARAMETER_VIDEOTYPE, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=17468, - serialized_end=17655, -) + serialized_end=17655, ) _WINDOWDATAPARAMETER = _descriptor.Descriptor( name='WindowDataParameter', @@ -11310,8 +11160,7 @@ _WINDOWDATAPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=17658, - serialized_end=17979, -) + serialized_end=17979, ) _SPPPARAMETER = _descriptor.Descriptor( name='SPPParameter', @@ -11384,8 +11233,7 @@ _SPPPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=17982, - serialized_end=18217, -) + serialized_end=18217, ) _V1LAYERPARAMETER = _descriptor.Descriptor( name='V1LayerParameter', @@ -12138,8 +11986,7 @@ _V1LAYERPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=18220, - serialized_end=20748, -) + serialized_end=20748, ) _V0LAYERPARAMETER = _descriptor.Descriptor( name='V0LayerParameter', @@ -12797,17 +12644,14 @@ _V0LAYERPARAMETER = _descriptor.Descriptor( ], extensions=[], nested_types=[], - enum_types=[ - _V0LAYERPARAMETER_POOLMETHOD, - ], + enum_types=[_V0LAYERPARAMETER_POOLMETHOD, ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[], serialized_start=20751, - serialized_end=21772, -) + serialized_end=21772, ) _PRELUPARAMETER = _descriptor.Descriptor( name='PReLUParameter', @@ -12860,8 +12704,7 @@ _PRELUPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=21774, - serialized_end=21861, -) + serialized_end=21861, ) _SHUFFLECHANNELPARAMETER = _descriptor.Descriptor( name='ShuffleChannelParameter', @@ -12897,8 +12740,7 @@ _SHUFFLECHANNELPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[], serialized_start=21863, - serialized_end=21906, -) + serialized_end=21906, ) _BLOBPROTO.fields_by_name['shape'].message_type = _BLOBSHAPE _BLOBPROTOVECTOR.fields_by_name['blobs'].message_type = _BLOBPROTO diff --git a/x2paddle/decoder/onnx_decoder.py b/x2paddle/decoder/onnx_decoder.py index 8b309c391a822bdf79e4686d094244c0ba9d8899..922c6629582ab62843359c8f9219f9e83cb2aa13 100644 --- a/x2paddle/decoder/onnx_decoder.py +++ b/x2paddle/decoder/onnx_decoder.py @@ -492,8 +492,8 @@ class ONNXDecoder(object): sess = rt.InferenceSession(model_path) for ipt in sess.get_inputs(): datatype = datatype_map[ipt.type] - input_dict[ipt.name] = np.random.random( - ipt.shape).astype(datatype) + input_dict[ipt.name] = np.random.random(ipt.shape).astype( + datatype) res = sess.run(None, input_feed=input_dict) except: diff --git a/x2paddle/op_mapper/caffe_custom_layer/convolutiondepthwise.py b/x2paddle/op_mapper/caffe_custom_layer/convolutiondepthwise.py index 33b6cbad65fd5a01837bff0929e3885e1a5de1cd..670d5436af6af54bdc74dd1a79e4ef4e30f42205 100644 --- a/x2paddle/op_mapper/caffe_custom_layer/convolutiondepthwise.py +++ b/x2paddle/op_mapper/caffe_custom_layer/convolutiondepthwise.py @@ -120,8 +120,8 @@ def convolutiondepthwise_layer(inputs, dila_len) c_in = input_shape[0][1] c_out = num_output if num_output is not None else input_shape[0][1] - group = int(c_in / (c_in / c_out)) if c_in > c_out else int( - c_in / (c_out / c_in)) + group = int(c_in / (c_in / c_out)) if c_in > c_out else int(c_in / + (c_out / c_in)) out = fluid.layers.conv2d( input, dilation=[dila_h, dila_w], diff --git a/x2paddle/op_mapper/caffe_custom_layer/register.py b/x2paddle/op_mapper/caffe_custom_layer/register.py index 183ed4404e1e48d2e9d6c3324e1ebdfd513adc01..1d68d7fbb7f058451c4a27304943ebf85d341a21 100644 --- a/x2paddle/op_mapper/caffe_custom_layer/register.py +++ b/x2paddle/op_mapper/caffe_custom_layer/register.py @@ -23,8 +23,7 @@ def register(kind, shape, layer, weights): kind = [kind] else: assert type( - kind - ) is list, 'invalid param "kind" for register, not a list or str' + kind) is list, 'invalid param "kind" for register, not a list or str' for k in kind: assert type( diff --git a/x2paddle/op_mapper/caffe_op_mapper.py b/x2paddle/op_mapper/caffe_op_mapper.py index f1e6c4ec1e63df560d9c48e098670e44fbb738c7..f350c50ea707518e296c6e807e7d89cca686bbcf 100644 --- a/x2paddle/op_mapper/caffe_op_mapper.py +++ b/x2paddle/op_mapper/caffe_op_mapper.py @@ -144,8 +144,8 @@ class CaffeOpMapper(OpMapper): [s_h, s_w] = [params.stride] * 2 elif len(params.stride) > 0: s_h = params.stride_h if params.stride_h > 0 else params.stride[0] - s_w = params.stride_w if params.stride_w > 0 else params.stride[ - len(params.stride) - 1] + s_w = params.stride_w if params.stride_w > 0 else params.stride[len( + params.stride) - 1] elif params.stride_h > 0 or params.stride_w > 0: s_h = params.stride_h s_w = params.stride_w @@ -154,8 +154,8 @@ class CaffeOpMapper(OpMapper): [p_h, p_w] = [params.pad] * 2 elif len(params.pad) > 0: p_h = params.pad_h if params.pad_h > 0 else params.pad[0] - p_w = params.pad_w if params.pad_w > 0 else params.pad[ - len(params.pad) - 1] + p_w = params.pad_w if params.pad_w > 0 else params.pad[len( + params.pad) - 1] elif params.pad_h > 0 or params.pad_w > 0: p_h = params.pad_h p_w = params.pad_w @@ -225,11 +225,9 @@ class CaffeOpMapper(OpMapper): input_c = node.input_shape[0][1] output_c = channel data.append( - np.zeros([output_c, input_c, kernel[0], - kernel[1]]).astype('float32')) - data.append(np.zeros([ - output_c, - ])).astype('float32') + np.zeros([output_c, input_c, kernel[0], kernel[1]]).astype( + 'float32')) + data.append(np.zeros([output_c, ])).astype('float32') else: data = self.adjust_parameters(node) self.weights[node.layer_name + '_weights'] = data[0] @@ -240,24 +238,16 @@ class CaffeOpMapper(OpMapper): input = self.graph.get_bottom_node(node, idx=0, copy=True) attr = { - 'filter_size': - kernel, - 'num_filters': - channel, - 'stride': - stride, - 'padding': - pad, - 'dilation': - dilation, - 'groups': - group, - 'name': - string(node.layer_name), - 'param_attr': - string(node.layer_name + '_weights'), - 'bias_attr': - False if len(data) == 1 else string(node.layer_name + '_bias'), + 'filter_size': kernel, + 'num_filters': channel, + 'stride': stride, + 'padding': pad, + 'dilation': dilation, + 'groups': group, + 'name': string(node.layer_name), + 'param_attr': string(node.layer_name + '_weights'), + 'bias_attr': False + if len(data) == 1 else string(node.layer_name + '_bias'), } node.fluid_code.add_layer( "conv2d", inputs=input, output=node, param_attr=attr) @@ -275,11 +265,9 @@ class CaffeOpMapper(OpMapper): input_c = node.input_shape[0][1] output_c = channel data.append( - np.zeros([output_c, input_c, kernel[0], - kernel[1]]).astype('float32')) - data.append(np.zeros([ - output_c, - ]).astype('float32')) + np.zeros([output_c, input_c, kernel[0], kernel[1]]).astype( + 'float32')) + data.append(np.zeros([output_c, ]).astype('float32')) else: data = self.adjust_parameters(node) self.weights[node.layer_name + '_weights'] = data[0] @@ -289,26 +277,17 @@ class CaffeOpMapper(OpMapper): ) == 1, 'The count of Deconvolution node\'s input is not 1.' input = self.graph.get_bottom_node(node, idx=0, copy=True) attr = { - 'output_size': - None, - 'filter_size': - kernel, - 'num_filters': - channel, - 'stride': - stride, - 'padding': - pad, - 'dilation': - dilation, - 'groups': - group, - 'name': - string(node.layer_name), - 'param_attr': - string(node.layer_name + '_weights'), - 'bias_attr': - False if len(data) == 1 else string(node.layer_name + '_bias') + 'output_size': None, + 'filter_size': kernel, + 'num_filters': channel, + 'stride': stride, + 'padding': pad, + 'dilation': dilation, + 'groups': group, + 'name': string(node.layer_name), + 'param_attr': string(node.layer_name + '_weights'), + 'bias_attr': False + if len(data) == 1 else string(node.layer_name + '_bias') } node.fluid_code.add_layer( "conv2d_transpose", inputs=input, output=node, param_attr=attr) @@ -372,8 +351,8 @@ class CaffeOpMapper(OpMapper): output_c = params.num_output data = [] data.append( - np.zeros([input_c, - output_c]).astype('float32').astype('float32')) + np.zeros([input_c, output_c]).astype('float32').astype( + 'float32')) data.append( np.zeros([output_c]).astype('float32').astype('float32')) else: @@ -397,16 +376,12 @@ class CaffeOpMapper(OpMapper): assert params.bias_term == True input = self.graph.get_bottom_node(node, idx=0, copy=True) attr = { - 'size': - params.num_output, - 'name': - string(node.layer_name), - 'act': - None, - 'param_attr': - string(node.layer_name + '_weights'), - 'bias_attr': - False if len(data) == 1 else string(node.layer_name + '_bias') + 'size': params.num_output, + 'name': string(node.layer_name), + 'act': None, + 'param_attr': string(node.layer_name + '_weights'), + 'bias_attr': False + if len(data) == 1 else string(node.layer_name + '_bias') } node.fluid_code.add_layer( "fc", inputs=input, output=node, param_attr=attr) @@ -607,12 +582,8 @@ class CaffeOpMapper(OpMapper): 'The parameter of {} (type is {}) is not set. So we set the parameters as 0' .format(node.layer_name, node.layer_type)) input_c = node.input_shape[0][1] - mean = np.zeros([ - input_c, - ]).astype('float32') - variance = np.zeros([ - input_c, - ]).astype('float32') + mean = np.zeros([input_c, ]).astype('float32') + variance = np.zeros([input_c, ]).astype('float32') scale = 0 else: @@ -649,10 +620,10 @@ class CaffeOpMapper(OpMapper): input_c, ]).astype('float32') else: - self.weights[node.layer_name + '_scale'] = np.squeeze( - node.data[0]).astype('float32') - self.weights[node.layer_name + '_offset'] = np.squeeze( - node.data[1]).astype('float32') + self.weights[node.layer_name + '_scale'] = np.squeeze(node.data[ + 0]).astype('float32') + self.weights[node.layer_name + '_offset'] = np.squeeze(node.data[ + 1]).astype('float32') params = node.layer.scale_param axis = params.axis num_axes = params.num_axes @@ -750,8 +721,8 @@ class CaffeOpMapper(OpMapper): node.fluid_code.add_layer( "topk", inputs=input, - output='{}_topk_var, {}_index_var'.format( - node.layer_name, node.layer_name), + output='{}_topk_var, {}_index_var'.format(node.layer_name, + node.layer_name), param_attr=attr) attr = {'dtype': '{}_topk_var.dtype'.format(node.layer_name)} node.fluid_code.add_layer( @@ -762,8 +733,8 @@ class CaffeOpMapper(OpMapper): attr = {'axis': axis, 'name': string(node.layer_name)} node.fluid_code.add_layer( "concat", - inputs='{}_topk_var, {}_index_var'.format( - node.layer_name, node.layer_name), + inputs='{}_topk_var, {}_index_var'.format(node.layer_name, + node.layer_name), output=node, param_attr=attr) else: @@ -787,23 +758,22 @@ class CaffeOpMapper(OpMapper): offset_real = [0] * len(input_shape) if hasattr(params, "offset") and len(params.offset) > 0: offset = list(params.offset) - assert (len(input_shape) - axis) == len( - offset), "invalid offset[%s] in crop layer" % (str(offset)) + assert (len(input_shape) - axis + ) == len(offset), "invalid offset[%s] in crop layer" % ( + str(offset)) offset_real = [0] * axis + offset attr = {'offsets': list(offset_real), 'name': string(node.layer_name)} node.fluid_code.add_layer( "crop", - inputs={ - 'x': input, - 'shape': node.input_shape[1] - }, + inputs={'x': input, + 'shape': node.input_shape[1]}, output=node, param_attr=attr) def Flatten(self, node): assert len( - node.inputs - ) == 1, 'The count of DetectionOutput node\'s input is not 1.' + node. + inputs) == 1, 'The count of DetectionOutput node\'s input is not 1.' input = self.graph.get_bottom_node(node, idx=0, copy=True) shape = node.output_shape[0] attr = {'shape': shape, 'name': string(node.layer_name)} diff --git a/x2paddle/op_mapper/caffe_shape.py b/x2paddle/op_mapper/caffe_shape.py index 5cd3a5e8af84eb421f82d1443fb3f1849012bba1..c574193d45400f351da0058f5d80b152cd6505cb 100644 --- a/x2paddle/op_mapper/caffe_shape.py +++ b/x2paddle/op_mapper/caffe_shape.py @@ -33,8 +33,8 @@ def get_kernel_parameters(params): [s_h, s_w] = [params.stride] * 2 elif len(params.stride) > 0: s_h = params.stride_h if params.stride_h > 0 else params.stride[0] - s_w = params.stride_w if params.stride_w > 0 else params.stride[ - len(params.stride) - 1] + s_w = params.stride_w if params.stride_w > 0 else params.stride[len( + params.stride) - 1] elif params.stride_h > 0 or params.stride_w > 0: s_h = params.stride_h s_w = params.stride_w @@ -67,10 +67,10 @@ def get_strided_kernel_output_shape(params, input_shape, round_func): i_w = input_shape[3] dila_h, dila_w, pad_h, pad_w, kernel_h, kernel_w, stride_h, stride_w = get_kernel_parameters( params) - o_h = (i_h + 2 * pad_h - - (dila_h * (kernel_h - 1) + 1)) / float(stride_h) + 1 - o_w = (i_w + 2 * pad_w - - (dila_w * (kernel_w - 1) + 1)) / float(stride_w) + 1 + o_h = (i_h + 2 * pad_h - (dila_h * + (kernel_h - 1) + 1)) / float(stride_h) + 1 + o_w = (i_w + 2 * pad_w - (dila_w * + (kernel_w - 1) + 1)) / float(stride_w) + 1 o_h = int(round_func(o_h)) o_w = int(round_func(o_w)) has_c_o = hasattr(params, 'num_output') diff --git a/x2paddle/op_mapper/onnx_custom_layer/register.py b/x2paddle/op_mapper/onnx_custom_layer/register.py index f68bbaf03232ae65dc891353f3ae902fdf21f135..37a593648a0b8717eb22cde7f1bc8b85c4990e39 100644 --- a/x2paddle/op_mapper/onnx_custom_layer/register.py +++ b/x2paddle/op_mapper/onnx_custom_layer/register.py @@ -36,8 +36,7 @@ def register(kind, shape, layer, child_func, weights): kind = [kind] else: assert type( - kind - ) is list, 'invalid param "kind" for register, not a list or str' + kind) is list, 'invalid param "kind" for register, not a list or str' for k in kind: assert type( diff --git a/x2paddle/op_mapper/onnx_directly_map.py b/x2paddle/op_mapper/onnx_directly_map.py index ecd1a30321af5032366e3503f5e36ce95c3ed2fd..a1765f064153dca3e698857065a23b6650254f33 100644 --- a/x2paddle/op_mapper/onnx_directly_map.py +++ b/x2paddle/op_mapper/onnx_directly_map.py @@ -28,60 +28,49 @@ default_op_mapping_field_values['FILL_NAME_FIELD'] = True default_op_mapping = { 'Shape': ['shape', ['X'], ['Out']], 'Clip': [ - 'clip', ['X'], ['Out'], - dict(), - dict( - min=(_np.asarray([255, 255, 127, 255], - dtype=_np.uint8).view(_np.float32)[0]), - max=(_np.asarray([255, 255, 127, 127], - dtype=_np.uint8).view(_np.float32)[0]), - ) + 'clip', ['X'], ['Out'], dict(), dict( + min=(_np.asarray( + [255, 255, 127, 255], dtype=_np.uint8).view(_np.float32)[0]), + max=(_np.asarray( + [255, 255, 127, 127], dtype=_np.uint8).view(_np.float32)[0]), ) ], 'Erf': ['erf', ['X'], ['Out']], 'Ceil': ['ceil', ['X'], ['Out']], 'ReduceMean': [ - 'reduce_mean', ['X'], ['Out'], - dict(axes='dim', keepdims='keep_dim'), - dict(keep_dim=1) + 'reduce_mean', ['X'], ['Out'], dict( + axes='dim', keepdims='keep_dim'), dict(keep_dim=1) ], 'ReduceSum': [ - 'reduce_sum', ['X'], ['Out'], - dict(axes='dim', keepdims='keep_dim'), - dict(keep_dim=1) + 'reduce_sum', ['X'], ['Out'], dict( + axes='dim', keepdims='keep_dim'), dict(keep_dim=1) ], 'ReduceMin': [ - 'reduce_min', ['X'], ['Out'], - dict(axes='dim', keepdims='keep_dim'), - dict(keep_dim=1) + 'reduce_min', ['X'], ['Out'], dict( + axes='dim', keepdims='keep_dim'), dict(keep_dim=1) ], 'ReduceMax': [ - 'reduce_max', ['X'], ['Out'], - dict(axes='dim', keepdims='keep_dim'), - dict(keep_dim=1) + 'reduce_max', ['X'], ['Out'], dict( + axes='dim', keepdims='keep_dim'), dict(keep_dim=1) ], #active function 'Relu': ['relu', ['X'], ['Out']], - 'LeakyRelu': ['leaky_relu', ['X'], ['Out'], - dict(), dict(alpha=.01)], - 'Elu': ['elu', ['X'], ['Out'], - dict(), dict(alpha=1.)], + 'LeakyRelu': ['leaky_relu', ['X'], ['Out'], dict(), dict(alpha=.01)], + 'Elu': ['elu', ['X'], ['Out'], dict(), dict(alpha=1.)], 'ThresholdedRelu': [ - 'thresholded_relu', ['X'], ['Out'], - dict(alpha='threshold'), + 'thresholded_relu', ['X'], ['Out'], dict(alpha='threshold'), dict(alpha=1.) ], 'Tanh': ['tanh', ['X'], ['Out']], 'Sigmoid': ['sigmoid', ['X'], ['Out']], 'HardSigmoid': [ - 'hard_sigmoid', ['X'], ['Out'], - dict(alpha='slope', beta='offset'), - dict(slope=.2, offset=.5) + 'hard_sigmoid', ['X'], ['Out'], dict( + alpha='slope', beta='offset'), dict( + slope=.2, offset=.5) ], 'Softsign': ['softsign', ['X'], ['Out']], 'Softplus': ['softplus', ['X'], ['Out']], 'Exp': ['exp', ['X'], ['Out']], - 'Softmax': ['softmax', ['X'], ['Out'], - dict(), dict(axis=1)], + 'Softmax': ['softmax', ['X'], ['Out'], dict(), dict(axis=1)], 'Sqrt': ['sqrt', ['X'], ['Out']], 'Floor': ['floor', ['X'], ['Out']], 'Abs': ['abs', ['X'], ['Out']], diff --git a/x2paddle/op_mapper/onnx_op_mapper.py b/x2paddle/op_mapper/onnx_op_mapper.py index a8c656cb4487bb0af178f1b74f190fb47a4c2715..a50670b29f774a496dc0d6da450560848a47abed 100644 --- a/x2paddle/op_mapper/onnx_op_mapper.py +++ b/x2paddle/op_mapper/onnx_op_mapper.py @@ -140,8 +140,8 @@ class ONNXOpMapper(OpMapper): model.graph.ClearField('output') model.graph.output.MergeFrom(model.graph.value_info) - onnx.save(model, os.path.join(self.tmp_data_dir, - 'onnx_model_infer.onnx')) + onnx.save(model, + os.path.join(self.tmp_data_dir, 'onnx_model_infer.onnx')) sess = rt.InferenceSession( os.path.join(self.tmp_data_dir, 'onnx_model_infer.onnx')) res = sess.run(None, input_feed=inputs_dict) @@ -217,8 +217,7 @@ class ONNXOpMapper(OpMapper): default_attrs, input_perm, output_perm, - fill_name_field, - ) = info + fill_name_field, ) = info if fluid_op in default_ioa_constraint: for predicate, message in default_ioa_constraint[fluid_op]: @@ -429,10 +428,8 @@ class ONNXOpMapper(OpMapper): } node.fluid_code.add_layer( 'roi_align', - inputs={ - 'input': val_x, - 'rois': val_rois - }, + inputs={'input': val_x, + 'rois': val_rois}, output=node, param_attr=attr) @@ -449,10 +446,8 @@ class ONNXOpMapper(OpMapper): } node.fluid_code.add_layer( 'roi_pool', - inputs={ - 'input': val_x, - 'rois': val_rois - }, + inputs={'input': val_x, + 'rois': val_rois}, output=node, param_attr=attr) @@ -527,10 +522,8 @@ class ONNXOpMapper(OpMapper): val_y = self.graph.get_input_node(node, idx=1, copy=True) node.fluid_code.add_layer( 'greater_than', - inputs={ - 'x': val_x, - 'y': val_y - }, + inputs={'x': val_x, + 'y': val_y}, output=node, param_attr=None) @@ -549,11 +542,10 @@ class ONNXOpMapper(OpMapper): shape = val_output.out_shapes[0] if shape is None: shape = list(value.shape) - _logger.warning( - 'in (Constant -> %s): ' - 'attribute "shape" of %s not inferred, ' - 'using value as 1-D tensor may lead to fails', - val_output.layer_name, val_output.layer_name) + _logger.warning('in (Constant -> %s): ' + 'attribute "shape" of %s not inferred, ' + 'using value as 1-D tensor may lead to fails', + val_output.layer_name, val_output.layer_name) if len(value) == 1: value = value.tolist() @@ -616,10 +608,8 @@ class ONNXOpMapper(OpMapper): if axis == 0 and len(indices_shape) <= 1: node.fluid_code.add_layer( 'gather', - inputs={ - 'input': val_x, - 'index': indices - }, + inputs={'input': val_x, + 'index': indices}, output=node, param_attr=None) elif axis > 0 and len(indices_shape) <= 1: @@ -634,10 +624,8 @@ class ONNXOpMapper(OpMapper): param_attr=attr_trans) node.fluid_code.add_layer( 'gather', - inputs={ - 'input': name_trans, - 'index': indices - }, + inputs={'input': name_trans, + 'index': indices}, output=node, param_attr=None) node.fluid_code.add_layer( @@ -649,9 +637,7 @@ class ONNXOpMapper(OpMapper): 'reshape', inputs=indices, output=indices, - param_attr={'shape': [ - reshape_shape, - ]}) + param_attr={'shape': [reshape_shape, ]}) perm = list(range(len(val_x.out_shapes[0]))) perm = [axis] + perm[:axis] + perm[axis + 1:] @@ -664,10 +650,8 @@ class ONNXOpMapper(OpMapper): param_attr=attr_trans) node.fluid_code.add_layer( 'gather', - inputs={ - 'input': name_trans, - 'index': indices - }, + inputs={'input': name_trans, + 'index': indices}, output=node, param_attr=None) node.fluid_code.add_layer( @@ -926,8 +910,10 @@ class ONNXOpMapper(OpMapper): def Sum(self, node): val_inps = node.layer.input inputs = { - "x": self.graph.get_input_node(node, idx=0, copy=True), - "y": self.graph.get_input_node(node, idx=1, copy=True), + "x": self.graph.get_input_node( + node, idx=0, copy=True), + "y": self.graph.get_input_node( + node, idx=1, copy=True), } node.fluid_code.add_layer("elementwise_add", inputs=inputs, output=node) @@ -1022,10 +1008,8 @@ class ONNXOpMapper(OpMapper): val_y = self.graph.get_input_node(node, idx=1, copy=True) node.fluid_code.add_layer( "equal", - inputs={ - 'x': val_x, - 'y': val_y - }, + inputs={'x': val_x, + 'y': val_y}, output=node, param_attr=None) @@ -1055,29 +1039,23 @@ class ONNXOpMapper(OpMapper): mul_val_x = val_x.layer_name + '_mul' node.fluid_code.add_layer( "elementwise_mul", - inputs={ - 'x': val_x, - 'y': cast_condition - }, + inputs={'x': val_x, + 'y': cast_condition}, output=mul_val_x, param_attr=None) mul_val_y = val_y.layer_name + '_mul' node.fluid_code.add_layer( "elementwise_mul", - inputs={ - 'x': val_y, - 'y': cast_not_condition - }, + inputs={'x': val_y, + 'y': cast_not_condition}, output=mul_val_y, param_attr=None) node.fluid_code.add_layer( "elementwise_add", - inputs={ - 'x': mul_val_x, - 'y': mul_val_y - }, + inputs={'x': mul_val_x, + 'y': mul_val_y}, output=node, param_attr=None) @@ -1106,7 +1084,8 @@ class ONNXOpMapper(OpMapper): output=flatten_name, param_attr={'axis': 0}) node.fluid_code.add_layer( - "concat", inputs=flatten_names, output=node, param_attr={'axis': 0}) + "concat", inputs=flatten_names, output=node, + param_attr={'axis': 0}) def Identity(self, node): val_x = self.graph.get_input_node(node, idx=0, copy=True) @@ -1280,11 +1259,11 @@ class ONNXOpMapper(OpMapper): output_size = [0, 0] - output_size[0] = (val_x.out_shapes[0][2] - - 1) * strides[0] - 2 * paddings[0] + dilations[0] * ( + output_size[0] = (val_x.out_shapes[0][2] - 1 + ) * strides[0] - 2 * paddings[0] + dilations[0] * ( kernel_shape[0] - 1) + 1 + out_padding[0] - output_size[1] = (val_x.out_shapes[0][3] - - 1) * strides[1] - 2 * paddings[1] + dilations[1] * ( + output_size[1] = (val_x.out_shapes[0][3] - 1 + ) * strides[1] - 2 * paddings[1] + dilations[1] * ( kernel_shape[1] - 1) + 1 + out_padding[1] attr = { 'num_filters': num_out_channels, @@ -1367,29 +1346,23 @@ class ONNXOpMapper(OpMapper): 'squeeze', inputs=val_x, output=var_x0, - param_attr={ - 'axes': [1], - 'name': string(var_x0) - }) + param_attr={'axes': [1], + 'name': string(var_x0)}) var_w0 = node.layer_name + '_w0' node.fluid_code.add_layer( 'squeeze', inputs=val_w, output=var_w0, - param_attr={ - 'axes': [0], - 'name': string(var_w0) - }) + param_attr={'axes': [0], + 'name': string(var_w0)}) var_fc = node.layer_name + '_fc' var_mm = (node.layer_name + '_mm') if val_b else var_fc node.fluid_code.add_layer( 'matmul', - inputs={ - 'x': var_x0, - 'y': var_w0 - }, + inputs={'x': var_x0, + 'y': var_w0}, output=var_mm, param_attr={ 'transpose_x': 0, @@ -1402,10 +1375,8 @@ class ONNXOpMapper(OpMapper): 'squeeze', inputs=val_r, output=var_r0, - param_attr={ - 'axes': [0], - 'name': string(var_r0) - }) + param_attr={'axes': [0], + 'name': string(var_r0)}) var_r0t = node.layer_name + '_r0t' @@ -1413,10 +1384,8 @@ class ONNXOpMapper(OpMapper): 'transpose', inputs=var_r0, output=var_r0t, - param_attr={ - 'perm': [1, 0], - 'name': string(var_r0t) - }) + param_attr={'perm': [1, 0], + 'name': string(var_r0t)}) if val_b: var_bi = node.layer_name + '_bi' var_bh = node.layer_name + '_bh' @@ -1434,10 +1403,8 @@ class ONNXOpMapper(OpMapper): 'squeeze', inputs=var_bi, output=var_bi0, - param_attr={ - 'axes': [0], - 'name': string(var_bi0) - }) + param_attr={'axes': [0], + 'name': string(var_bi0)}) node.fluid_code.add_layer( 'elmentwise_add', @@ -1454,10 +1421,8 @@ class ONNXOpMapper(OpMapper): 'squeeze', inputs=val_xh, output=var_xh0, - param_attr={ - 'axes': [1], - 'name': string(var_xh0) - }) + param_attr={'axes': [1], + 'name': string(var_xh0)}) var_y00 = node.layer_name + '_y00' attr = { diff --git a/x2paddle/op_mapper/paddle_custom_layer/im2sequence.py b/x2paddle/op_mapper/paddle_custom_layer/im2sequence.py index 91fa2ca30a2a472d6190a424a522103f46b0f9d1..aeb4a9ceddb280295aafb2ebcafa3a25f8767d75 100644 --- a/x2paddle/op_mapper/paddle_custom_layer/im2sequence.py +++ b/x2paddle/op_mapper/paddle_custom_layer/im2sequence.py @@ -30,8 +30,8 @@ def im2sequence(op, block): slice_blocks = list() for i in range(out_h): for j in range(out_w): - starts_name = "im2sequence.starts.{}.{}.{}".format( - im2seq_counter, i, j) + starts_name = "im2sequence.starts.{}.{}.{}".format(im2seq_counter, + i, j) starts_tensor = helper.make_tensor( name=starts_name, data_type=onnx_pb.TensorProto.INT64, diff --git a/x2paddle/op_mapper/paddle_custom_layer/multiclass_nms.py b/x2paddle/op_mapper/paddle_custom_layer/multiclass_nms.py index 743c20f3fe001c2105dbc268bbfc7a8597a833b2..5d30f651bbe2e51a2d328a81ceb8c52391374d60 100644 --- a/x2paddle/op_mapper/paddle_custom_layer/multiclass_nms.py +++ b/x2paddle/op_mapper/paddle_custom_layer/multiclass_nms.py @@ -44,8 +44,7 @@ def multiclass_nms(op, block): if normalized == False: warnings.warn( 'The parameter normalized of multiclass_nms OP of Paddle is False, which has diff with ONNX. \ - Please set normalized=True in multiclass_nms of Paddle' - ) + Please set normalized=True in multiclass_nms of Paddle') #convert the paddle attribute to onnx tensor name_score_threshold = [outputs['Out'][0] + "@score_threshold"] @@ -353,7 +352,8 @@ def multiclass_nms(op, block): outputs_gather_topk_class = [result_name + "@gather_topk_class"] node_gather_topk_class = onnx.helper.make_node( 'Gather', - inputs=outputs_gather_1_nonzero + [outputs_topk_select_topk_indices[1]], + inputs=outputs_gather_1_nonzero + + [outputs_topk_select_topk_indices[1]], outputs=outputs_gather_topk_class, axis=1) node_list.append(node_gather_topk_class) @@ -362,7 +362,8 @@ def multiclass_nms(op, block): outputs_gather_topk_boxes_id = [result_name + "@gather_topk_boxes_id"] node_gather_topk_boxes_id = onnx.helper.make_node( 'Gather', - inputs=outputs_gather_2_nonzero + [outputs_topk_select_topk_indices[1]], + inputs=outputs_gather_2_nonzero + + [outputs_topk_select_topk_indices[1]], outputs=outputs_gather_topk_boxes_id, axis=1) node_list.append(node_gather_topk_boxes_id) diff --git a/x2paddle/op_mapper/paddle_custom_layer/yolo_box.py b/x2paddle/op_mapper/paddle_custom_layer/yolo_box.py index 1b2711f0b62e3b15e07b1a12e2cfa018669ce7bd..a1e49e77b32ce1d64c11ca35ac69ed6cb20ee51c 100644 --- a/x2paddle/op_mapper/paddle_custom_layer/yolo_box.py +++ b/x2paddle/op_mapper/paddle_custom_layer/yolo_box.py @@ -38,8 +38,8 @@ def yolo_box(op, block): downsample_ratio = attrs['downsample_ratio'] input_size = input_height * downsample_ratio conf_thresh = attrs['conf_thresh'] - conf_thresh_mat = np.ones([num_anchors * input_height * input_width - ]) * conf_thresh + conf_thresh_mat = np.ones([num_anchors * input_height * + input_width]) * conf_thresh node_list = [] im_outputs = [] diff --git a/x2paddle/op_mapper/paddle_op_mapper.py b/x2paddle/op_mapper/paddle_op_mapper.py index 953937631d63734c49634836590e4d110ee47a11..0ba7ad682528b4062dea381964835271f0177432 100644 --- a/x2paddle/op_mapper/paddle_op_mapper.py +++ b/x2paddle/op_mapper/paddle_op_mapper.py @@ -250,8 +250,7 @@ class PaddleOpMapper(object): node = helper.make_node( pool_type[op.attr('pooling_type')][1], inputs=op.input('X'), - outputs=op.output('Out'), - ) + outputs=op.output('Out'), ) else: input_shape = block.var(op.input('X')[0]).shape k_size = op.attr('ksize') @@ -407,8 +406,7 @@ class PaddleOpMapper(object): node = helper.make_node( 'Clip', inputs=[op.input('X')[0], min_name, max_name], - outputs=op.output('Out'), - ) + outputs=op.output('Out'), ) return [min_node, max_node, node] def shape(self, op, block): @@ -450,8 +448,7 @@ class PaddleOpMapper(object): node = helper.make_node( "Slice", inputs=[op.input('Input')[0], starts_name, ends_name, axes_name], - outputs=op.output('Out'), - ) + outputs=op.output('Out'), ) return [starts_node, ends_node, axes_node, node] def fill_constant(self, op, block): @@ -551,8 +548,8 @@ class PaddleOpMapper(object): if op.attr('align_corners'): coordinate_transformation_mode = 'align_corners' if ('OutSize' in input_names and len(op.input('OutSize')) > 0) or ( - 'SizeTensor' in input_names - and len(op.input('SizeTensor')) > 0): + 'SizeTensor' in input_names and + len(op.input('SizeTensor')) > 0): node_list = list() roi_node = self.make_constant_node( self.get_name(op.type, 'roi'), onnx_pb.TensorProto.FLOAT, @@ -631,8 +628,7 @@ class PaddleOpMapper(object): elif 'Scale' in input_names and len(op.input('Scale')) > 0: node = helper.make_node( 'Resize', - inputs=[op.input('X')[0], - op.input('Scale')[0]], + inputs=[op.input('X')[0], op.input('Scale')[0]], outputs=op.output('Out'), mode='linear', coordinate_transformation_mode=coordinate_transformation_mode) @@ -641,8 +637,9 @@ class PaddleOpMapper(object): scale = op.attr('scale') if out_shape.count(-1) > 0: scale_name = self.get_name(op.type, 'scale') - scale_node = self.make_constant_node( - scale_name, onnx_pb.TensorProto.FLOAT, [1, 1, scale, scale]) + scale_node = self.make_constant_node(scale_name, + onnx_pb.TensorProto.FLOAT, + [1, 1, scale, scale]) roi_name = self.get_name(op.type, 'roi') roi_node = self.make_constant_node(roi_name, onnx_pb.TensorProto.FLOAT, @@ -667,16 +664,14 @@ class PaddleOpMapper(object): if 'OutSize' in input_names and len(op.input('OutSize')) > 0: node = helper.make_node( 'Resize', - inputs=[op.input('X')[0], '', - op.input('OutSize')[0]], + inputs=[op.input('X')[0], '', op.input('OutSize')[0]], outputs=op.output('Out'), mode='nearest', coordinate_transformation_mode=coordinate_transformation_mode) elif 'Scale' in input_names and len(op.input('Scale')) > 0: node = helper.make_node( 'Resize', - inputs=[op.input('X')[0], - op.input('Scale')[0]], + inputs=[op.input('X')[0], op.input('Scale')[0]], outputs=op.output('Out'), mode='nearest', coordinate_transformation_mode=coordinate_transformation_mode) @@ -685,8 +680,9 @@ class PaddleOpMapper(object): scale = op.attr('scale') if out_shape.count(-1) > 0: scale_name = self.get_name(op.type, 'scale') - scale_node = self.make_constant_node( - scale_name, onnx_pb.TensorProto.FLOAT, [1, 1, scale, scale]) + scale_node = self.make_constant_node(scale_name, + onnx_pb.TensorProto.FLOAT, + [1, 1, scale, scale]) roi_name = self.get_name(op.type, 'roi') roi_node = self.make_constant_node(roi_name, onnx_pb.TensorProto.FLOAT, @@ -737,8 +733,7 @@ class PaddleOpMapper(object): node1 = helper.make_node( 'Clip', inputs=[name0, min_name, max_name], - outputs=[name1], - ) + outputs=[name1], ) name2 = self.get_name(op.type, 'mul') node2 = helper.make_node( 'Mul', inputs=[op.input('X')[0], name1], outputs=[name2]) diff --git a/x2paddle/op_mapper/tf_op_mapper.py b/x2paddle/op_mapper/tf_op_mapper.py index 2be5a73cbc9bfd8b1c720d0b30817086b07c2b62..3a88f47330a7d63cff02358aa476d5216c183e9f 100644 --- a/x2paddle/op_mapper/tf_op_mapper.py +++ b/x2paddle/op_mapper/tf_op_mapper.py @@ -114,9 +114,8 @@ class TFOpMapper(OpMapper): else: unsupported_ops.add(op) if len(unsupported_ops) > 0: - sys.stderr.write( - "=========={} Ops are not supported yet======\n".format( - len(unsupported_ops))) + sys.stderr.write("=========={} Ops are not supported yet======\n". + format(len(unsupported_ops))) for op in unsupported_ops: sys.stderr.write("========== {} ==========\n".format(op)) sys.exit(-1) @@ -296,8 +295,8 @@ class TFOpMapper(OpMapper): shape = [shape[i] for i in [0, 3, 1, 2]] if len(shape) == 3: shape = [shape[i] for i in [2, 0, 1]] - self.weights[node.layer_name] = numpy.transpose( - node.value, (2, 0, 1)) + self.weights[node.layer_name] = numpy.transpose(node.value, + (2, 0, 1)) elif node.tf_data_format == "NCHW": if len(shape) == 4: self.graph.data_format_propagation(node) @@ -534,8 +533,8 @@ class TFOpMapper(OpMapper): attr = {"shape": shape} self.add_omit_nodes(param.layer_name, node.layer_name) else: - assert len(param.out_shapes[0] - ) == 1, "Unexpected situation of shape parameter" + assert len(param.out_shapes[ + 0]) == 1, "Unexpected situation of shape parameter" attr = {"shape": [-1]} node.fluid_code.add_layer( "reshape", @@ -647,15 +646,15 @@ class TFOpMapper(OpMapper): def ConcatV2(self, node): inputs = [ - self.graph.get_node(name, copy=True) - for name in node.layer.input[:-1] + self.graph.get_node( + name, copy=True) for name in node.layer.input[:-1] ] axis = self.graph.get_node(node.layer.input[-1], copy=True) assert axis.layer_type == "Const" self.add_omit_nodes(axis.layer_name, node.layer_name) axis = axis.value - if inputs[0].tf_data_format == "NHWC" and len( - inputs[0].out_shapes[0]) == 4: + if inputs[0].tf_data_format == "NHWC" and len(inputs[0].out_shapes[ + 0]) == 4: axis = nhwc_dim_to_nchw(inputs[0], axis) attr = {"axis": axis} node.fluid_code.add_layer( @@ -684,11 +683,12 @@ class TFOpMapper(OpMapper): def Pack(self, node): inputs = [ - self.graph.get_node(name, copy=True) for name in node.layer.input + self.graph.get_node( + name, copy=True) for name in node.layer.input ] axis = node.get_attr("axis") - if inputs[0].tf_data_format == "NHWC" and len( - inputs[0].out_shapes[0]) == 4: + if inputs[0].tf_data_format == "NHWC" and len(inputs[0].out_shapes[ + 0]) == 4: tf_data_format = list(inputs[0].tf_data_format) tf_data_format.insert(axis, str(len(tf_data_format))) axis = nhwc_dim_to_nchw(inputs[0], axis) @@ -1010,8 +1010,8 @@ class TFOpMapper(OpMapper): if resize_shape.layer_type == "Const": resize_shape = resize_shape.value.tolist() else: - resize_shape = self.decoder.infer_shape_tensor( - resize_shape, node.out_shapes[0]) + resize_shape = self.decoder.infer_shape_tensor(resize_shape, + node.out_shapes[0]) align_corners = node.get_attr("align_corners") attr = {"align_corners": align_corners, "out_shape": resize_shape} node.fluid_code.add_layer( @@ -1024,8 +1024,8 @@ class TFOpMapper(OpMapper): if resize_shape.layer_type == "Const": resize_shape = resize_shape.value.tolist() else: - resize_shape = self.decoder.infer_shape_tensor( - resize_shape, node.out_shapes[0]) + resize_shape = self.decoder.infer_shape_tensor(resize_shape, + node.out_shapes[0]) align_corners = node.get_attr("align_corners") attr = { "align_corners": align_corners, diff --git a/x2paddle/op_mapper/tf_op_mapper_nhwc.py b/x2paddle/op_mapper/tf_op_mapper_nhwc.py index 05a06ac91247b11111200c1406a5466a616f4847..6f36c8dbaaccdc03ea3c482a1a011bd3a6b275ee 100644 --- a/x2paddle/op_mapper/tf_op_mapper_nhwc.py +++ b/x2paddle/op_mapper/tf_op_mapper_nhwc.py @@ -486,8 +486,8 @@ class TFOpMapperNHWC(OpMapper): attr = {"shape": shape} self.add_omit_nodes(param.layer_name, node.layer_name) else: - assert len(param.out_shapes[0] - ) == 1, "Unexpected situation of shape parameter" + assert len(param.out_shapes[ + 0]) == 1, "Unexpected situation of shape parameter" attr = {"shape": [-1]} node.fluid_code.add_layer( "reshape", @@ -577,8 +577,8 @@ class TFOpMapperNHWC(OpMapper): def ConcatV2(self, node): inputs = [ - self.graph.get_node(name, copy=True) - for name in node.layer.input[:-1] + self.graph.get_node( + name, copy=True) for name in node.layer.input[:-1] ] axis = self.graph.get_node(node.layer.input[-1], copy=True) assert axis.layer_type == "Const" @@ -608,7 +608,8 @@ class TFOpMapperNHWC(OpMapper): def Pack(self, node): inputs = [ - self.graph.get_node(name, copy=True) for name in node.layer.input + self.graph.get_node( + name, copy=True) for name in node.layer.input ] axis = node.get_attr("axis") attr = {"axis": axis} @@ -949,8 +950,8 @@ class TFOpMapperNHWC(OpMapper): if resize_shape.layer_type == "Const": resize_shape = resize_shape.value.tolist() else: - resize_shape = self.decoder.infer_shape_tensor( - resize_shape, node.out_shapes[0]) + resize_shape = self.decoder.infer_shape_tensor(resize_shape, + node.out_shapes[0]) align_corners = node.get_attr("align_corners") attr = {"perm": [0, 3, 1, 2]} node.fluid_code.add_layer( @@ -969,8 +970,8 @@ class TFOpMapperNHWC(OpMapper): if resize_shape.layer_type == "Const": resize_shape = resize_shape.value.tolist() else: - resize_shape = self.decoder.infer_shape_tensor( - resize_shape, node.out_shapes[0]) + resize_shape = self.decoder.infer_shape_tensor(resize_shape, + node.out_shapes[0]) align_corners = node.get_attr("align_corners") attr = {"perm": [0, 3, 1, 2]} node.fluid_code.add_layer( diff --git a/x2paddle/optimizer/tf_optimizer.py b/x2paddle/optimizer/tf_optimizer.py index 98e1a5d6ce7768bf37aeeb5f10fca98ca09ef794..8b63df18017295aefb25155d85e0c1595edec88a 100644 --- a/x2paddle/optimizer/tf_optimizer.py +++ b/x2paddle/optimizer/tf_optimizer.py @@ -768,8 +768,8 @@ class TFOptimizer(object): is_prelu = False continue - if len(in_nodes0[0].outputs) != 1 or len( - in_nodes0[1].outputs) != 1: + if len(in_nodes0[0].outputs) != 1 or len(in_nodes0[1] + .outputs) != 1: is_prelu = False continue @@ -778,8 +778,8 @@ class TFOptimizer(object): self.graph.get_node(in_name) for in_name in in_nodes0[1].inputs ] - if in_nodes2[1].layer_type != "Const" or numpy.fabs( - in_nodes2[1].value - 0.5) > 1e-06: + if in_nodes2[1].layer_type != "Const" or numpy.fabs(in_nodes2[ + 1].value - 0.5) > 1e-06: is_prelu = False continue if in_nodes2[0].layer_type != "Mul": @@ -788,8 +788,8 @@ class TFOptimizer(object): if exist_act(in_nodes2[0]): is_prelu = False continue - if len(in_nodes2[1].outputs) != 1 or len( - in_nodes2[0].outputs) != 1: + if len(in_nodes2[1].outputs) != 1 or len(in_nodes2[0] + .outputs) != 1: is_prelu = False continue @@ -804,8 +804,8 @@ class TFOptimizer(object): if exist_act(in_nodes3[1]): is_prelu = False continue - if len(in_nodes3[0].outputs) != 1 or len( - in_nodes3[1].outputs) != 1: + if len(in_nodes3[0].outputs) != 1 or len(in_nodes3[1] + .outputs) != 1: is_prelu = False continue @@ -857,12 +857,12 @@ class TFOptimizer(object): mode = "element" elif len(in_nodes3[0].value.shape) == 0: mode = "all" - elif len(in_nodes3[0].value.shape - ) == 1 and in_nodes3[0].value.shape[0] == 1: + elif len(in_nodes3[0].value.shape) == 1 and in_nodes3[ + 0].value.shape[0] == 1: mode = "all" - elif len(in_shape) == 4 and len( - in_nodes3[0].value.shape - ) == 1 and in_nodes3[0].value.shape[0] == in_shape[-1]: + elif len(in_shape) == 4 and len(in_nodes3[ + 0].value.shape) == 1 and in_nodes3[0].value.shape[ + 0] == in_shape[-1]: mode = "channel" weight = self.op_mapper.weights[in_nodes3[0].layer_name] weight = numpy.expand_dims(weight, 0) @@ -917,14 +917,15 @@ class TFOptimizer(object): self.graph.get_node(in_name) for in_name in node.inputs ] if in_nodes0[0].layer_type != "Mul" or in_nodes0[ - 1].layer_type != "Const" or in_nodes0[1].value.size != 1: + 1].layer_type != "Const" or in_nodes0[ + 1].value.size != 1: is_scale = False continue if exist_act(in_nodes0[0]): is_scale = False continue - if len(in_nodes0[0].outputs) != 1 or len( - in_nodes0[1].outputs) != 1: + if len(in_nodes0[0].outputs) != 1 or len(in_nodes0[1] + .outputs) != 1: is_scale = False continue @@ -940,8 +941,8 @@ class TFOptimizer(object): if exist_act(in_nodes1[1]): is_scale = False continue - if len(in_nodes1[0].outputs) != 1 or len( - in_nodes1[1].outputs) != 1: + if len(in_nodes1[0].outputs) != 1 or len(in_nodes1[1] + .outputs) != 1: is_scale = False continue @@ -963,8 +964,8 @@ class TFOptimizer(object): scale = 1.0 / in_nodes2[1].value * in_nodes1[0].value act = None if node.fluid_code.layers[0].param_attr is not None: - act = node.fluid_code.layers[0].param_attr.get( - "act", None) + act = node.fluid_code.layers[0].param_attr.get("act", + None) node.fluid_code.clear() attr = { @@ -1003,17 +1004,17 @@ class TFOptimizer(object): if exist_act(in_nodes0[0]): is_affine_channel = False continue - if len(in_nodes0[0].outputs) != 1 or len( - in_nodes0[1].outputs) != 1: + if len(in_nodes0[0].outputs) != 1 or len(in_nodes0[1] + .outputs) != 1: is_affine_channel = False continue in_nodes1 = [ self.graph.get_node(in_name) for in_name in in_nodes0[0].inputs ] - if len(in_nodes1[0].out_shapes[0] - ) != 4 or in_nodes1[1].layer_type != "Const" or len( - in_nodes1[1].value.shape) != 3: + if len(in_nodes1[0].out_shapes[0]) != 4 or in_nodes1[ + 1].layer_type != "Const" or len(in_nodes1[1] + .value.shape) != 3: is_affine_channel = False continue if len(in_nodes1[1].outputs) != 1: @@ -1036,8 +1037,8 @@ class TFOptimizer(object): node.layer_type = "AffineChannel" node.inputs = [in_node.layer_name] scale = 1.0 / in_nodes0[1].value.flatten() - bias = in_nodes1[1].value.flatten( - ) / in_nodes0[1].value.flatten() + bias = in_nodes1[1].value.flatten() / in_nodes0[ + 1].value.flatten() if not bias_add: bias *= -1.0 self.op_mapper.weights[node.layer_name + "_scale"] = scale @@ -1045,8 +1046,8 @@ class TFOptimizer(object): act = None if node.fluid_code.layers[0].param_attr is not None: - act = node.fluid_code.layers[0].param_attr.get( - "act", None) + act = node.fluid_code.layers[0].param_attr.get("act", + None) node.fluid_code.clear() attr = {