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| author | Alexey Suhov <asuhov@users.noreply.github.com> | 2019-01-21 21:31:31 +0300 |
|---|---|---|
| committer | openvino-pushbot <44090433+openvino-pushbot@users.noreply.github.com> | 2019-01-21 21:31:31 +0300 |
| commit | 9de27f16bc8b712a5b8c99d1d4b4a66c9144942d (patch) | |
| tree | 01a383efe94d92b9870d513c2c5ea5d15b07010a /model-optimizer/mo/front | |
| parent | fbc7a4a710c24def8ab199926a7da90a0394b87d (diff) | |
| download | dldt-9de27f16bc8b712a5b8c99d1d4b4a66c9144942d.tar.gz dldt-9de27f16bc8b712a5b8c99d1d4b4a66c9144942d.tar.bz2 dldt-9de27f16bc8b712a5b8c99d1d4b4a66c9144942d.zip | |
Publishing R5 content (#72)
* Publishing R5 content
* Updated ade revision
* updated readme
* add possibility to build CPU plugin with Intel MKL package
Diffstat (limited to 'model-optimizer/mo/front')
68 files changed, 2005 insertions, 1588 deletions
diff --git a/model-optimizer/mo/front/caffe/extractor.py b/model-optimizer/mo/front/caffe/extractor.py index b90396883..72e3283f7 100644 --- a/model-optimizer/mo/front/caffe/extractor.py +++ b/model-optimizer/mo/front/caffe/extractor.py @@ -17,7 +17,6 @@ from mo.front.caffe.extractors.batchnorm import batch_norm_ext from mo.front.caffe.extractors.concat import concat_ext from mo.front.caffe.extractors.eltwise import eltwise_ext -from mo.front.caffe.extractors.flatten import flatten_ext from mo.front.caffe.extractors.inner_product import inner_product_ext from mo.front.caffe.extractors.input import global_input_ext, input_ext from mo.front.caffe.extractors.lrn import lrn_ext @@ -29,10 +28,9 @@ from mo.front.caffe.extractors.reshape import reshape_ext from mo.front.caffe.extractors.roipooling import roipooling_ext from mo.front.caffe.extractors.scale import scale_ext from mo.front.caffe.extractors.slice import slice_ext -from mo.front.caffe.extractors.softmax import softmax_ext from mo.front.common.partial_infer.elemental import copy_shape_infer from mo.front.common.register_custom_ops import extension_op_extractor -from mo.front.extractor import CaffePythonFrontExtractorOp, FrontExtractorOp +from mo.front.extractor import CaffePythonFrontExtractorOp from mo.graph.graph import Node from mo.ops.op import Op from mo.utils.error import Error @@ -69,10 +67,8 @@ caffe_type_extractors = { # Utility Layers 'concat': node_pb_arg(concat_ext), 'eltwise': node_pb_arg(eltwise_ext), - 'flatten': node_pb_arg(flatten_ext), 'reshape': node_pb_arg(reshape_ext), 'slice': node_pb_arg(slice_ext), - 'softmax': node_pb_arg(softmax_ext), # Custom, implemented in IE, SSD-specific 'permute': node_pb_arg(permute_ext), diff --git a/model-optimizer/mo/front/caffe/extractors/elu.py b/model-optimizer/mo/front/caffe/extractors/elu.py index 66f3a7b48..464a77f1e 100644 --- a/model-optimizer/mo/front/caffe/extractors/elu.py +++ b/model-optimizer/mo/front/caffe/extractors/elu.py @@ -14,6 +14,7 @@ limitations under the License. """ +from mo.front.caffe.collect_attributes import collect_attributes from mo.front.extractor import FrontExtractorOp from mo.ops.activation import Activation @@ -24,5 +25,9 @@ class ELUFrontExtractor(FrontExtractorOp): @staticmethod def extract(node): - Activation.update_node_stat(node, {'operation': 'elu'}) + param = node.pb.elu_param + attrs = collect_attributes(param) + attrs['operation'] = 'elu' + + Activation.update_node_stat(node, attrs) return ELUFrontExtractor.enabled diff --git a/model-optimizer/mo/front/caffe/extractors/flatten.py b/model-optimizer/mo/front/caffe/extractors/flatten.py deleted file mode 100644 index 2eb5f2d4a..000000000 --- a/model-optimizer/mo/front/caffe/extractors/flatten.py +++ /dev/null @@ -1,35 +0,0 @@ -""" - Copyright (c) 2018 Intel Corporation - - 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 numpy as np - -from mo.front.caffe.collect_attributes import merge_attrs -from mo.front.common.partial_infer.flatten import flatten_infer - - -def flatten_ext(pl, ml): - param = pl.flatten_param - update_attrs = { - 'axis': param.axis, - 'end_axis': param.end_axis, - 'num_axes': 0 - } - mapping_rule = merge_attrs(param, update_attrs) - mapping_rule.update({ - 'type': "Flatten", - 'infer': flatten_infer - }) - return mapping_rule diff --git a/model-optimizer/mo/front/caffe/extractors/reshape.py b/model-optimizer/mo/front/caffe/extractors/reshape.py index 734ff8c03..13deb9908 100644 --- a/model-optimizer/mo/front/caffe/extractors/reshape.py +++ b/model-optimizer/mo/front/caffe/extractors/reshape.py @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. """ +import logging as log from mo.front.common.partial_infer.elemental import single_output_infer from mo.front.common.partial_infer.reshape import tf_reshape_shape_infer @@ -29,5 +30,12 @@ def reshape_ext(pl, ml): 'dim': list(param.shape.dim), 'infer': lambda node: single_output_infer(node, tf_reshape_shape_infer) } + if attrs['axis'] != 0: + log.error('The operation "Reshape" has attribute "axis" with unsupported value "{}"'.format(attrs['axis'])) + return None + if attrs['num_axes'] != -1: + log.error('The operation "Reshape" has attribute "num_axes" with unsupported value "{}"'.format( + attrs['num_axes'])) + return None return attrs diff --git a/model-optimizer/mo/front/caffe/extractors/scale.py b/model-optimizer/mo/front/caffe/extractors/scale.py index 8ce0bd60e..196b7d56b 100644 --- a/model-optimizer/mo/front/caffe/extractors/scale.py +++ b/model-optimizer/mo/front/caffe/extractors/scale.py @@ -16,7 +16,7 @@ import numpy as np -from mo.front.caffe.extractors.utils import weights_biases +from mo.front.caffe.extractors.utils import embed_input, weights_biases from mo.front.common.partial_infer.elemental import copy_shape_infer from mo.utils.utils import NamedAttrsClass @@ -26,11 +26,22 @@ def scale_ext(pl, ml): attrs = { 'op': 'ScaleShift', 'type': 'ScaleShift', + 'axis': param.axis, 'infer': copy_shape_infer } - if ml is None: + if ml is None and len(pl.bottom) == 1: # default weights and biases for scale layer if the caffemodel file doesn't contain them ml = NamedAttrsClass({'blobs': np.array([NamedAttrsClass({'data': np.array([1])}), NamedAttrsClass({'data': np.array([0])})])}) - attrs.update(weights_biases(param.bias_term, ml)) + # scale with 1 input and 1 or 2 blobs + if ml and len(ml.blobs) != 0 and len(pl.bottom) == 1: + attrs.update(weights_biases(param.bias_term, ml)) + # 2 inputs + bias + elif len(pl.bottom) == 2 and param.bias_term: + if ml is None or len(ml.blobs) == 0: + # default bias for scale layer with 2 inputs if the caffemodel file doesn't contain them + ml = NamedAttrsClass({'blobs': np.array([NamedAttrsClass({'data': np.array([0])})])}) + + embed_input(attrs, 1, 'biases', ml.blobs[0].data) + return attrs diff --git a/model-optimizer/mo/front/caffe/extractors/slice.py b/model-optimizer/mo/front/caffe/extractors/slice.py index c8d1f379d..953f88c34 100644 --- a/model-optimizer/mo/front/caffe/extractors/slice.py +++ b/model-optimizer/mo/front/caffe/extractors/slice.py @@ -30,6 +30,8 @@ def slice_ext(proto_layer, model_layer): 'slice_point': param.slice_point, } mapping_rule = merge_attrs(param, update_attrs) + if 'slice_point' not in mapping_rule: + mapping_rule['slice_point'] = [] mapping_rule.update({ 'type': 'Slice', 'infer': caffe_slice_infer diff --git a/model-optimizer/mo/front/caffe/extractors/softmax.py b/model-optimizer/mo/front/caffe/extractors/softmax.py deleted file mode 100644 index 245fdce0c..000000000 --- a/model-optimizer/mo/front/caffe/extractors/softmax.py +++ /dev/null @@ -1,27 +0,0 @@ -""" - Copyright (c) 2018 Intel Corporation - - 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 mo.front.common.partial_infer.elemental import copy_shape_infer - - -def softmax_ext(pb_layer, pb_model): - param = pb_layer.softmax_param - - return { - 'type': 'SoftMax', - 'axis': param.axis, - 'infer': copy_shape_infer - } diff --git a/model-optimizer/mo/front/caffe/loader.py b/model-optimizer/mo/front/caffe/loader.py index 729c442ef..69f63f213 100644 --- a/model-optimizer/mo/front/caffe/loader.py +++ b/model-optimizer/mo/front/caffe/loader.py @@ -26,7 +26,7 @@ from google.protobuf.internal import api_implementation from mo.front.caffe.proto import caffe_pb2 from mo.graph.graph import Node, unique_id -from mo.utils.error import Error +from mo.utils.error import Error, FrameworkError from mo.utils.utils import refer_to_faq_msg @@ -103,18 +103,40 @@ def load_caffe_proto_model(proto_path: str, model_path: [str, None] = None): 'python -m easy_install protobuf-3.5.1-py($your_python_version)-win-amd64.egg \n' \ 'set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp' print(message + '\n\n' + refer_to_faq_msg(80)) + # Read proto layers - proto = caffe_pb2.NetParameter() - with open(proto_path, "r") as file: - text_format.Merge(str(file.read()), proto) + try: + proto = caffe_pb2.NetParameter() + with open(proto_path, "r") as file: + text_format.Merge(str(file.read()), proto) + except Exception as e: + log.error('Exception message: {}\n\n'.format(e) + + ' Possible reasons:\n' + + ' 1. {} does not exist\n'.format(proto_path) + + ' 2. {} does not have a valid structure, for example, it was downloaded as html\n'.format(proto_path) + + ' 3. {} contains custom layers or attributes that are not supported\n'.format(proto_path) + + ' in Model Optimizer by default.\n\n' + + ' After you made sure that {} has a valid structure and still see this issue, then\n'.format(proto_path) + + ' you need to generate a python parser for caffe.proto that was used when the model\n' + + ' was created.\n' + + ' Run "python3 generate_caffe_pb2.py --input_proto ${PATH_TO_CAFFE}/src/caffe/proto/caffe.proto"' + + refer_to_faq_msg(1) + '\n\n', extra={'framework_error': True}) + raise FrameworkError('Model Optimizer is not able to parse {}'.format(proto_path)) from e # Read model layer if exists model = None - if model_path: - model = caffe_pb2.NetParameter() - with open(model_path, "rb") as infile: - map = mmap.mmap(infile.fileno(), 0, access=mmap.ACCESS_READ) - model.MergeFromString(map) + try: + if model_path: + model = caffe_pb2.NetParameter() + with open(model_path, "rb") as infile: + map = mmap.mmap(infile.fileno(), 0, access=mmap.ACCESS_READ) + model.MergeFromString(map) + except Exception as e: + log.error('Exception message: {}\n\n'.format(e) + + ' Possible reasons:\n' + + ' 1. {} does not exist\n'.format(model_path) + + ' 2. {} does not have a valid structure\n'.format(model_path), extra={'framework_error': True}) + raise FrameworkError('Model Optimizer is not able to parse {}'.format(model_path)) from e return proto, model diff --git a/model-optimizer/mo/front/caffe/proto/caffe_pb2.py b/model-optimizer/mo/front/caffe/proto/caffe_pb2.py index e79d2d554..c32fa78c4 100644 --- a/model-optimizer/mo/front/caffe/proto/caffe_pb2.py +++ b/model-optimizer/mo/front/caffe/proto/caffe_pb2.py @@ -19,7 +19,7 @@ _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='mo_caffe.proto', package='mo_caffe', - 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_descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=9437, - serialized_end=9518, + serialized_start=9504, + serialized_end=9585, ) _sym_db.RegisterEnumDescriptor(_LOSSPARAMETER_NORMALIZATIONMODE) @@ -235,8 +235,8 @@ _CONVOLUTIONPARAMETER_ENGINE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=10798, - serialized_end=10841, + serialized_start=10865, + serialized_end=10908, ) _sym_db.RegisterEnumDescriptor(_CONVOLUTIONPARAMETER_ENGINE) @@ -257,8 +257,8 @@ _DATAPARAMETER_DB = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=11442, - serialized_end=11469, + serialized_start=11509, + serialized_end=11536, ) _sym_db.RegisterEnumDescriptor(_DATAPARAMETER_DB) @@ -283,8 +283,8 @@ _RESIZEPARAMETER_RESIZE_MODE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=11896, - serialized_end=11967, + serialized_start=11963, + serialized_end=12034, ) _sym_db.RegisterEnumDescriptor(_RESIZEPARAMETER_RESIZE_MODE) @@ -309,8 +309,8 @@ _RESIZEPARAMETER_PAD_MODE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=11969, - serialized_end=12027, + serialized_start=12036, + serialized_end=12094, ) _sym_db.RegisterEnumDescriptor(_RESIZEPARAMETER_PAD_MODE) @@ -343,8 +343,8 @@ _RESIZEPARAMETER_INTERP_MODE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=12029, - serialized_end=12102, + serialized_start=12096, + serialized_end=12169, ) _sym_db.RegisterEnumDescriptor(_RESIZEPARAMETER_INTERP_MODE) @@ -369,8 +369,8 @@ _ELTWISEPARAMETER_ELTWISEOP = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=13217, - serialized_end=13256, + serialized_start=13284, + serialized_end=13323, ) _sym_db.RegisterEnumDescriptor(_ELTWISEPARAMETER_ELTWISEOP) @@ -391,8 +391,8 @@ _HINGELOSSPARAMETER_NORM = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=13800, - serialized_end=13822, + serialized_start=13867, + serialized_end=13889, ) _sym_db.RegisterEnumDescriptor(_HINGELOSSPARAMETER_NORM) @@ -413,8 +413,8 @@ _LRNPARAMETER_NORMREGION = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=14704, - serialized_end=14757, + serialized_start=14771, + serialized_end=14824, ) _sym_db.RegisterEnumDescriptor(_LRNPARAMETER_NORMREGION) @@ -439,8 +439,8 @@ _LRNPARAMETER_ENGINE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=10798, - serialized_end=10841, + serialized_start=10865, + serialized_end=10908, ) _sym_db.RegisterEnumDescriptor(_LRNPARAMETER_ENGINE) @@ -465,8 +465,8 @@ _POOLINGPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=15448, - serialized_end=15494, + serialized_start=15515, + serialized_end=15561, ) _sym_db.RegisterEnumDescriptor(_POOLINGPARAMETER_POOLMETHOD) @@ -491,8 +491,8 @@ _POOLINGPARAMETER_ENGINE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=10798, - serialized_end=10841, + serialized_start=10865, + serialized_end=10908, ) _sym_db.RegisterEnumDescriptor(_POOLINGPARAMETER_ENGINE) @@ -517,8 +517,8 @@ _PRIORBOXPARAMETER_CODETYPE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=15898, - serialized_end=15954, + serialized_start=15965, + serialized_end=16021, ) _sym_db.RegisterEnumDescriptor(_PRIORBOXPARAMETER_CODETYPE) @@ -547,8 +547,8 @@ _REDUCTIONPARAMETER_REDUCTIONOP = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=16474, - serialized_end=16527, + serialized_start=16541, + serialized_end=16594, ) _sym_db.RegisterEnumDescriptor(_REDUCTIONPARAMETER_REDUCTIONOP) @@ -573,8 +573,8 @@ _RELUPARAMETER_ENGINE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=10798, - serialized_end=10841, + serialized_start=10865, + serialized_end=10908, ) _sym_db.RegisterEnumDescriptor(_RELUPARAMETER_ENGINE) @@ -599,8 +599,8 @@ _SIGMOIDPARAMETER_ENGINE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=10798, - serialized_end=10841, + serialized_start=10865, + serialized_end=10908, ) _sym_db.RegisterEnumDescriptor(_SIGMOIDPARAMETER_ENGINE) @@ -625,8 +625,8 @@ _SOFTMAXPARAMETER_ENGINE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=10798, - serialized_end=10841, + serialized_start=10865, + serialized_end=10908, ) _sym_db.RegisterEnumDescriptor(_SOFTMAXPARAMETER_ENGINE) @@ -651,8 +651,8 @@ _TANHPARAMETER_ENGINE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=10798, - serialized_end=10841, + serialized_start=10865, + serialized_end=10908, ) _sym_db.RegisterEnumDescriptor(_TANHPARAMETER_ENGINE) @@ -677,8 +677,8 @@ _SPPPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=15448, - serialized_end=15494, + serialized_start=15515, + serialized_end=15561, ) _sym_db.RegisterEnumDescriptor(_SPPPARAMETER_POOLMETHOD) @@ -703,8 +703,8 @@ _SPPPARAMETER_ENGINE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=10798, - serialized_end=10841, + serialized_start=10865, + serialized_end=10908, ) _sym_db.RegisterEnumDescriptor(_SPPPARAMETER_ENGINE) @@ -877,8 +877,8 @@ _V1LAYERPARAMETER_LAYERTYPE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=20453, - serialized_end=21053, + serialized_start=20520, + serialized_end=21120, ) _sym_db.RegisterEnumDescriptor(_V1LAYERPARAMETER_LAYERTYPE) @@ -925,8 +925,8 @@ _V0LAYERPARAMETER_POOLMETHOD = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=15448, - serialized_end=15494, + serialized_start=15515, + serialized_end=15561, ) _sym_db.RegisterEnumDescriptor(_V0LAYERPARAMETER_POOLMETHOD) @@ -947,8 +947,8 @@ _FLOWWARPPARAMETER_FILLPARAMETER = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=25927, - serialized_end=25970, + serialized_start=25994, + serialized_end=26037, ) _sym_db.RegisterEnumDescriptor(_FLOWWARPPARAMETER_FILLPARAMETER) @@ -969,8 +969,8 @@ _CORRELATIONPARAMETER_CORRELATIONTYPE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=26238, - serialized_end=26283, + serialized_start=26305, + serialized_end=26350, ) _sym_db.RegisterEnumDescriptor(_CORRELATIONPARAMETER_CORRELATIONTYPE) @@ -999,8 +999,8 @@ _RESAMPLEPARAMETER_RESAMPLETYPE = _descriptor.EnumDescriptor( ], containing_type=None, options=None, - serialized_start=26446, - serialized_end=26506, + serialized_start=26513, + serialized_end=26573, ) _sym_db.RegisterEnumDescriptor(_RESAMPLEPARAMETER_RESAMPLETYPE) @@ -2987,6 +2987,13 @@ _LAYERPARAMETER = _descriptor.Descriptor( message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, options=None), + _descriptor.FieldDescriptor( + name='shuffle_channel_param', full_name='mo_caffe.LayerParameter.shuffle_channel_param', index=89, + number=224, 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=[ ], @@ -2999,7 +3006,7 @@ _LAYERPARAMETER = _descriptor.Descriptor( oneofs=[ ], serialized_start=3844, - serialized_end=8373, + serialized_end=8440, ) @@ -3063,8 +3070,8 @@ _INTERPPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=8376, - serialized_end=8520, + serialized_start=8443, + serialized_end=8587, ) @@ -3100,8 +3107,8 @@ _RANDOMSAMPLINGSOFTMAXLOSSPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=8522, - serialized_end=8632, + serialized_start=8589, + serialized_end=8699, ) @@ -3179,8 +3186,8 @@ _PROPOSALPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=8635, - serialized_end=8835, + serialized_start=8702, + serialized_end=8902, ) @@ -3230,8 +3237,8 @@ _NORMALIZEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=8838, - serialized_end=8987, + serialized_start=8905, + serialized_end=9054, ) @@ -3260,8 +3267,8 @@ _PERMUTEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=8989, - serialized_end=9022, + serialized_start=9056, + serialized_end=9089, ) @@ -3332,8 +3339,8 @@ _TRANSFORMATIONPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=9025, - serialized_end=9207, + serialized_start=9092, + serialized_end=9274, ) @@ -3398,8 +3405,8 @@ _LOSSPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=9210, - serialized_end=9518, + serialized_start=9277, + serialized_end=9585, ) @@ -3442,8 +3449,8 @@ _ACCURACYPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=9520, - serialized_end=9596, + serialized_start=9587, + serialized_end=9663, ) @@ -3486,8 +3493,8 @@ _ARGMAXPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=9598, - serialized_end=9675, + serialized_start=9665, + serialized_end=9742, ) @@ -3530,8 +3537,8 @@ _CHANNELPERMUTATIONACTION = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=9677, - serialized_end=9745, + serialized_start=9744, + serialized_end=9812, ) @@ -3581,8 +3588,8 @@ _CHANNELPERMUTATIONPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=9748, - serialized_end=9902, + serialized_start=9815, + serialized_end=9969, ) @@ -3618,8 +3625,8 @@ _CONCATPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=9904, - serialized_end=9961, + serialized_start=9971, + serialized_end=10028, ) @@ -3662,8 +3669,8 @@ _BATCHNORMPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=9963, - serialized_end=10069, + serialized_start=10030, + serialized_end=10136, ) @@ -3699,8 +3706,8 @@ _BOXANNOTATOROHEMPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=10071, - serialized_end=10145, + serialized_start=10138, + serialized_end=10212, ) @@ -3743,8 +3750,8 @@ _BIASPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=10147, - serialized_end=10243, + serialized_start=10214, + serialized_end=10310, ) @@ -3780,8 +3787,8 @@ _CONTRASTIVELOSSPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=10245, - serialized_end=10321, + serialized_start=10312, + serialized_end=10388, ) @@ -3930,8 +3937,8 @@ _CONVOLUTIONPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=10324, - serialized_end=10841, + serialized_start=10391, + serialized_end=10908, ) @@ -3974,8 +3981,8 @@ _CROPPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=10843, - serialized_end=10908, + serialized_start=10910, + serialized_end=10975, ) @@ -4011,8 +4018,8 @@ _CTCDECODERPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=10910, - serialized_end=10990, + serialized_start=10977, + serialized_end=11057, ) @@ -4069,8 +4076,8 @@ _CTCLOSSPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=10993, - serialized_end=11171, + serialized_start=11060, + serialized_end=11238, ) @@ -4163,8 +4170,8 @@ _DATAPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=11174, - serialized_end=11469, + serialized_start=11241, + serialized_end=11536, ) @@ -4207,8 +4214,8 @@ _NONMAXIMUMSUPPRESSIONPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - 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_descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=14106, - serialized_end=14145, + serialized_start=14173, + serialized_end=14212, ) @@ -5112,8 +5119,8 @@ _INNERPRODUCTPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=14148, - serialized_end=14357, + serialized_start=14215, + serialized_end=14424, ) @@ -5142,8 +5149,8 @@ _INPUTPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=14359, - serialized_end=14411, + serialized_start=14426, + serialized_end=14478, ) @@ -5186,8 +5193,8 @@ _LOGPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=14413, - serialized_end=14481, + serialized_start=14480, + serialized_end=14548, ) @@ -5253,8 +5260,8 @@ _LRNPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=14484, - serialized_end=14802, + serialized_start=14551, + serialized_end=14869, ) @@ -5283,8 +5290,8 @@ _GRNPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=14804, - serialized_end=14835, + serialized_start=14871, + serialized_end=14902, ) @@ -5334,8 +5341,8 @@ _MEMORYDATAPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=14837, - serialized_end=14927, + serialized_start=14904, + serialized_end=14994, ) @@ -5378,8 +5385,8 @@ _MVNPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=14929, - serialized_end=15029, + serialized_start=14996, + serialized_end=15096, ) @@ -5408,8 +5415,8 @@ _PARAMETERPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=15031, - serialized_end=15087, + serialized_start=15098, + serialized_end=15154, ) @@ -5524,8 +5531,8 @@ _POOLINGPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=15090, - serialized_end=15539, + serialized_start=15157, + serialized_end=15606, ) @@ -5568,8 +5575,8 @@ _POWERPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=15541, - serialized_end=15611, + serialized_start=15608, + serialized_end=15678, ) @@ -5697,8 +5704,8 @@ _PRIORBOXPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=15614, - serialized_end=15954, + serialized_start=15681, + serialized_end=16021, ) @@ -5741,8 +5748,8 @@ _PSROIPOOLINGPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=15956, - serialized_end=16042, + serialized_start=16023, + serialized_end=16109, ) @@ -5792,8 +5799,8 @@ _PYTHONPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=16044, - serialized_end=16147, + serialized_start=16111, + serialized_end=16214, ) @@ -5850,8 +5857,8 @@ _RECURRENTPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=16150, - serialized_end=16348, + serialized_start=16217, + serialized_end=16415, ) @@ -5895,8 +5902,8 @@ _REDUCTIONPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=16351, - serialized_end=16527, + serialized_start=16418, + serialized_end=16594, ) @@ -5933,8 +5940,8 @@ _RELUPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=16530, - serialized_end=16674, + serialized_start=16597, + serialized_end=16741, ) @@ -5963,8 +5970,8 @@ _RELU6PARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=16676, - serialized_end=16706, + serialized_start=16743, + serialized_end=16773, ) @@ -6007,8 +6014,8 @@ _RESHAPEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=16708, - serialized_end=16801, + serialized_start=16775, + serialized_end=16868, ) @@ -6037,8 +6044,8 @@ _REVERSEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=16803, - serialized_end=16838, + serialized_start=16870, + serialized_end=16905, ) @@ -6081,8 +6088,8 @@ _ROIPOOLINGPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=16840, - serialized_end=16929, + serialized_start=16907, + serialized_end=16996, ) @@ -6125,8 +6132,8 @@ _ROIWARPINGTESTPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=16931, - serialized_end=17024, + serialized_start=16998, + serialized_end=17091, ) @@ -6169,8 +6176,8 @@ _ROIWARPINGPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=17026, - serialized_end=17115, + serialized_start=17093, + serialized_end=17182, ) @@ -6227,8 +6234,8 @@ _SCALEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=17118, - serialized_end=17289, + serialized_start=17185, + serialized_end=17356, ) @@ -6258,8 +6265,8 @@ _SIGMOIDPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=17291, - serialized_end=17414, + serialized_start=17358, + serialized_end=17481, ) @@ -6302,8 +6309,8 @@ _SLICEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=17416, - serialized_end=17492, + serialized_start=17483, + serialized_end=17559, ) @@ -6332,8 +6339,8 @@ _SMOOTHL1LOSSPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=17494, - serialized_end=17535, + serialized_start=17561, + serialized_end=17602, ) @@ -6370,8 +6377,8 @@ _SOFTMAXPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=17538, - serialized_end=17678, + serialized_start=17605, + serialized_end=17745, ) @@ -6401,8 +6408,8 @@ _TANHPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=17680, - serialized_end=17797, + serialized_start=17747, + serialized_end=17864, ) @@ -6438,8 +6445,8 @@ _TILEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=17799, - serialized_end=17846, + serialized_start=17866, + serialized_end=17913, ) @@ -6468,8 +6475,8 @@ _THRESHOLDPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=17848, - serialized_end=17890, + serialized_start=17915, + serialized_end=17957, ) @@ -6582,8 +6589,8 @@ _WINDOWDATAPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=17893, - serialized_end=18214, + serialized_start=17960, + serialized_end=18281, ) @@ -6628,8 +6635,8 @@ _SPPPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=18217, - serialized_end=18458, + serialized_start=18284, + serialized_end=18525, ) @@ -6954,8 +6961,8 @@ _V1LAYERPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=18461, - serialized_end=21097, + serialized_start=18528, + serialized_end=21164, ) @@ -7244,8 +7251,8 @@ _V0LAYERPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=21100, - serialized_end=22136, + serialized_start=21167, + serialized_end=22203, ) @@ -7281,8 +7288,8 @@ _PRELUPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=22138, - serialized_end=22228, + serialized_start=22205, + serialized_end=22295, ) @@ -7346,8 +7353,8 @@ _REGIONYOLOPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=22231, - serialized_end=22365, + serialized_start=22298, + serialized_end=22432, ) @@ -7376,8 +7383,8 @@ _REORGYOLOPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=22367, - serialized_end=22406, + serialized_start=22434, + serialized_end=22473, ) @@ -7455,8 +7462,8 @@ _RANDOMGENERATORPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=22409, - serialized_end=22616, + serialized_start=22476, + serialized_end=22683, ) @@ -7499,8 +7506,8 @@ _COEFFSCHEDULEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=22618, - serialized_end=22714, + serialized_start=22685, + serialized_end=22781, ) @@ -7816,8 +7823,8 @@ _AUGMENTATIONCOEFF = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=22717, - serialized_end=23707, + serialized_start=22784, + serialized_end=23774, ) @@ -8161,8 +8168,8 @@ _AUGMENTATIONPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=23710, - serialized_end=25834, + serialized_start=23777, + serialized_end=25901, ) @@ -8192,8 +8199,8 @@ _FLOWWARPPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=25837, - serialized_end=25970, + serialized_start=25904, + serialized_end=26037, ) @@ -8272,8 +8279,8 @@ _CORRELATIONPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=25973, - serialized_end=26283, + serialized_start=26040, + serialized_end=26350, ) @@ -8331,8 +8338,8 @@ _RESAMPLEPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=26286, - serialized_end=26506, + serialized_start=26353, + serialized_end=26573, ) @@ -8382,8 +8389,38 @@ _ACCUMPARAMETER = _descriptor.Descriptor( extension_ranges=[], oneofs=[ ], - serialized_start=26508, - serialized_end=26630, + serialized_start=26575, + serialized_end=26697, +) + + +_SHUFFLECHANNELPARAMETER = _descriptor.Descriptor( + name='ShuffleChannelParameter', + full_name='mo_caffe.ShuffleChannelParameter', + filename=None, + file=DESCRIPTOR, + containing_type=None, + fields=[ + _descriptor.FieldDescriptor( + name='group', full_name='mo_caffe.ShuffleChannelParameter.group', index=0, + number=1, type=13, cpp_type=3, 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), + ], + extensions=[ + ], + nested_types=[], + enum_types=[ + ], + options=None, + is_extendable=False, + extension_ranges=[], + oneofs=[ + ], + serialized_start=26699, + serialized_end=26739, ) _BLOBPROTO.fields_by_name['shape'].message_type = _BLOBSHAPE @@ -8494,6 +8531,7 @@ _LAYERPARAMETER.fields_by_name['resample_param'].message_type = _RESAMPLEPARAMET _LAYERPARAMETER.fields_by_name['flow_warp_param'].message_type = _FLOWWARPPARAMETER _LAYERPARAMETER.fields_by_name['accum_param'].message_type = _ACCUMPARAMETER _LAYERPARAMETER.fields_by_name['coeff_schedule_param'].message_type = _COEFFSCHEDULEPARAMETER +_LAYERPARAMETER.fields_by_name['shuffle_channel_param'].message_type = _SHUFFLECHANNELPARAMETER _NORMALIZEPARAMETER.fields_by_name['scale_filler'].message_type = _FILLERPARAMETER _LOSSPARAMETER.fields_by_name['normalization'].enum_type = _LOSSPARAMETER_NORMALIZATIONMODE _LOSSPARAMETER_NORMALIZATIONMODE.containing_type = _LOSSPARAMETER @@ -8740,6 +8778,7 @@ DESCRIPTOR.message_types_by_name['FlowWarpParameter'] = _FLOWWARPPARAMETER DESCRIPTOR.message_types_by_name['CorrelationParameter'] = _CORRELATIONPARAMETER DESCRIPTOR.message_types_by_name['ResampleParameter'] = _RESAMPLEPARAMETER DESCRIPTOR.message_types_by_name['AccumParameter'] = _ACCUMPARAMETER +DESCRIPTOR.message_types_by_name['ShuffleChannelParameter'] = _SHUFFLECHANNELPARAMETER DESCRIPTOR.enum_types_by_name['Phase'] = _PHASE BlobShape = _reflection.GeneratedProtocolMessageType('BlobShape', (_message.Message,), dict( @@ -9456,6 +9495,13 @@ AccumParameter = _reflection.GeneratedProtocolMessageType('AccumParameter', (_me )) _sym_db.RegisterMessage(AccumParameter) +ShuffleChannelParameter = _reflection.GeneratedProtocolMessageType('ShuffleChannelParameter', (_message.Message,), dict( + DESCRIPTOR = _SHUFFLECHANNELPARAMETER, + __module__ = 'mo_caffe_pb2' + # @@protoc_insertion_point(class_scope:mo_caffe.ShuffleChannelParameter) + )) +_sym_db.RegisterMessage(ShuffleChannelParameter) + _BLOBSHAPE.fields_by_name['dim'].has_options = True _BLOBSHAPE.fields_by_name['dim']._options = _descriptor._ParseOptions(descriptor_pb2.FieldOptions(), _b('\020\001')) diff --git a/model-optimizer/mo/front/caffe/proto/generate_caffe_pb2.py b/model-optimizer/mo/front/caffe/proto/generate_caffe_pb2.py index 941f65c1f..2a86db4d1 100644 --- a/model-optimizer/mo/front/caffe/proto/generate_caffe_pb2.py +++ b/model-optimizer/mo/front/caffe/proto/generate_caffe_pb2.py @@ -22,12 +22,8 @@ import sys def shell(cmd, env=None, cwd=None): - kwargs = dict(cwd=cwd, env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - if sys.platform.startswith('linux') or sys.platform == 'darwin': - cmd = ['/bin/bash', '-c', "".join(cmd)] - else: - kwargs.update({'shell': True}) - print('Running: "{}"'.format(''.join(cmd))) + kwargs = dict(cwd=cwd, env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=False) + print('Running: "{}"'.format(' '.join(cmd))) p = subprocess.Popen(cmd, **kwargs) (stdout, stderr) = p.communicate() return p.returncode, stdout, stderr @@ -42,21 +38,19 @@ def get_cli_parser(): def build_proto(proto_file_path, python_path): - retcode, out, err = shell('protoc -h') + retcode, out, err = shell(['protoc', '-h']) if retcode: print(err) return 1 if not (os.path.exists(proto_file_path) and os.path.isfile(proto_file_path)): print('File {} does not exist'.format(proto_file_path)) return 1 - if not os.path.exists(proto_file_path): - os.makedirs(python_path) proto_path = os.path.split(proto_file_path)[0] if not proto_path: proto_path = os.getcwd() proto_file = os.path.split(proto_file_path)[1] - command = 'protoc {} --python_out={}'.format(proto_file, python_path) + command = ['protoc', proto_file, '--python_out={}'.format(python_path)] retcode, out, err = shell(command, cwd=proto_path) @@ -78,5 +72,8 @@ if __name__ == "__main__": argv = get_cli_parser().parse_args() proto_file_path = argv.input_proto python_path = argv.output + if not os.path.exists(python_path): + print("Output directory {} does not exist".format(python_path)) + sys.exit(1) status = build_proto(proto_file_path, python_path) exit(status) diff --git a/model-optimizer/mo/front/caffe/proto/mo_caffe.proto b/model-optimizer/mo/front/caffe/proto/mo_caffe.proto index 852fccfca..82f83a5d2 100644 --- a/model-optimizer/mo/front/caffe/proto/mo_caffe.proto +++ b/model-optimizer/mo/front/caffe/proto/mo_caffe.proto @@ -534,6 +534,9 @@ message LayerParameter { optional FlowWarpParameter flow_warp_param = 221; optional AccumParameter accum_param = 222; optional CoeffScheduleParameter coeff_schedule_param = 223; + + // for Shufflenet v2 + optional ShuffleChannelParameter shuffle_channel_param= 224; } message InterpParameter { @@ -2078,3 +2081,7 @@ message AccumParameter { optional uint32 size_divisible_by = 3 [default = 0]; // Upscales to the minimal size divisible by the given number optional bool have_reference = 4 [ default = false ]; } + +message ShuffleChannelParameter { + required uint32 group = 1; +} diff --git a/model-optimizer/mo/front/common/layout.py b/model-optimizer/mo/front/common/layout.py index 2f368dc26..6da786138 100644 --- a/model-optimizer/mo/front/common/layout.py +++ b/model-optimizer/mo/front/common/layout.py @@ -14,9 +14,10 @@ limitations under the License. """ +import logging as log + import numpy as np -from mo.graph.graph import Node from mo.utils.error import Error nchw_to_nhwc_permute = np.array([0, 2, 3, 1], dtype=np.int64) diff --git a/model-optimizer/mo/front/common/partial_infer/elemental.py b/model-optimizer/mo/front/common/partial_infer/elemental.py index adbf6ec03..99adb1ff9 100644 --- a/model-optimizer/mo/front/common/partial_infer/elemental.py +++ b/model-optimizer/mo/front/common/partial_infer/elemental.py @@ -17,9 +17,11 @@ def single_output_infer(node, shape_infer, value_infer=None): node.out_node(0).shape = shape_infer(node) - if value_infer is not None: - node.out_node(0).value = value_infer(node) + if value_infer is not None and \ + 'value' in node.in_node() and \ + node.in_node().value is not None: + node.out_node(0).value = value_infer(node) def copy_shape_infer(node): """ diff --git a/model-optimizer/mo/front/common/partial_infer/expand_dims.py b/model-optimizer/mo/front/common/partial_infer/expand_dims.py index bf0803bd6..50ac4f06f 100644 --- a/model-optimizer/mo/front/common/partial_infer/expand_dims.py +++ b/model-optimizer/mo/front/common/partial_infer/expand_dims.py @@ -50,9 +50,7 @@ def tf_expand_dims_infer(node): if input_node.value is not None: output_node.value = np.array(np.reshape(input_node.value, output_node.shape)) - node['axis'] = 0 - node['num_axes'] = -1 node['dim'] = output_node.shape - PermuteAttrs.create_permute_attrs(node, attrs=[('axis','output:0'), ('dim','output:0')]) + PermuteAttrs.create_permute_attrs(node, attrs=[('dim', 'output:0')]) diff --git a/model-optimizer/mo/front/common/partial_infer/flatten.py b/model-optimizer/mo/front/common/partial_infer/flatten.py deleted file mode 100644 index 223f53d86..000000000 --- a/model-optimizer/mo/front/common/partial_infer/flatten.py +++ /dev/null @@ -1,43 +0,0 @@ -""" - Copyright (c) 2018 Intel Corporation - - 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 logging as log - -import numpy as np - -from mo.front.caffe.extractors.utils import get_canonical_axis_index - - -def flatten_infer(node): - """ - Infers shape of flatten node as it is done in Caffe. - Output shape: [Batch is the same, Production of other dims] - Args: - node: graph flatten node - - """ - input_shape = node.in_node(0).shape - if input_shape is None: - return - - # TODO: Should check that input_shape[1:] part doesn't contain -1 elements - axis = get_canonical_axis_index(input_shape, node.axis) - end_axis = node.end_axis if node.has('end_axis') else -1 - end_axis = get_canonical_axis_index(input_shape, end_axis) - prod_axes = np.prod(input_shape[axis: end_axis + 1]) - node.out_node(0).shape = np.array([*input_shape[0: axis], prod_axes, *input_shape[end_axis + 1:]], dtype=np.int64) - log.debug('input_shape: {}, output_shape: {}'.format(input_shape, node.out_node().shape)) - diff --git a/model-optimizer/mo/front/common/partial_infer/inner_product.py b/model-optimizer/mo/front/common/partial_infer/inner_product.py index b1d7189b5..765363b2f 100644 --- a/model-optimizer/mo/front/common/partial_infer/inner_product.py +++ b/model-optimizer/mo/front/common/partial_infer/inner_product.py @@ -52,20 +52,13 @@ def onnx_matmul_infer(node): if len(node.in_nodes()) != 2: raise Error("Wrong number of input nodes for {} node. Should be 2 instead of {}".format(node.name, len(node.in_nodes()))) + input_0_shape = node.in_node(0).shape + input_1_shape = node.in_node(1).shape - input_node = node.in_node(0) - weights_node = node.in_node(1) + input_shapes = [node.in_node(port).shape for port in node.in_nodes()] + max_len = max([len(shape) for shape in input_shapes]) + new_input_shapes = [np.concatenate([np.ones(max_len - len(input_shapes[i])), input_shapes[i]], axis=0) + for i in range(len(input_shapes))] - input_shape = input_node.shape - weights_shape = weights_node.shape - - if len(weights_shape) > 2: - raise Error("MatMul {} with weights shape != 2 is not supported".format(node.name)) - - mark_input_bins(node) - assign_dims_to_weights(weights_node, None, 0, 1, 2) - PermuteAttrs.set_permutation(weights_node, node, PermuteAttrs.Permutation(perm=int64_array([1, 0]), - inv=int64_array([0, 1]))) - - node['out-size'] = weights_shape[1] - node.out_node().shape = np.array([*input_shape[0:-1], weights_shape[1]]) + node.out_node().shape = np.concatenate([np.maximum(*[shape[0:-2] for shape in new_input_shapes]), + [input_0_shape[-2], input_1_shape[-1]]], axis=0) diff --git a/model-optimizer/mo/front/common/partial_infer/matmul.py b/model-optimizer/mo/front/common/partial_infer/matmul.py index f776a1274..157402c9c 100644 --- a/model-optimizer/mo/front/common/partial_infer/matmul.py +++ b/model-optimizer/mo/front/common/partial_infer/matmul.py @@ -18,16 +18,44 @@ import logging as log import numpy as np +from mo.front.common.partial_infer.utils import int64_array +from mo.ops.op import PermuteAttrs from mo.utils.error import Error def tf_matmul_infer(node): assert (len(node.in_nodes()) == 2) - shapes = [node.in_node(i).shape for i in range(2)] + + shapes = [node.in_node(i).shape.copy() for i in range(2)] log.debug('matmul shapes: {}'.format(shapes)) - if node.transpose_a or node.transpose_b or any(s is None or len(s) < 2 for s in shapes): + if any(s is None or len(s) < 2 for s in shapes): log.error("MatMul wasn't able to infer shape") return + + if node.transpose_a: + if not node.in_node(0).has_valid('value'): + log.error("MatMul wasn't able to infer shape") + return + else: + perm = np.array(range(len(node.in_node(0).shape)), dtype=np.int64) + perm[-1], perm[-2] = perm[-2], perm[-1] + inv = PermuteAttrs.get_inverse_permutation(perm) + permutation = PermuteAttrs.Permutation(perm=perm, inv=int64_array(inv)) + PermuteAttrs.set_permutation(node.in_node(0), node, permutation) + shapes[0] = shapes[0][perm] + + if node.transpose_b: + if not node.in_node(1).has_valid('value'): + log.error("MatMul wasn't able to infer shape") + return + else: + perm = np.array(range(len(node.in_node(1).shape)), dtype=np.int64) + perm[-1], perm[-2] = perm[-2], perm[-1] + inv = PermuteAttrs.get_inverse_permutation(perm) + permutation = PermuteAttrs.Permutation(perm=perm, inv=int64_array(inv)) + PermuteAttrs.set_permutation(node.in_node(1), node, permutation) + shapes[1] = shapes[1][perm] + if any(shapes[0][:-2] != shapes[1][:-2]) or shapes[0][-1] != shapes[1][-2]: log.error("MatMul wasn't able to infer shape because input dimensions are not compatible") return @@ -49,7 +77,6 @@ def tf_matmul_infer(node): log.debug('matmul shape: {}'.format(node.out_node().shape)) - def onnx_gemm_infer(node): assert (len(node.in_nodes()) == 3) shapeA = node.in_node(0).shape @@ -60,16 +87,15 @@ def onnx_gemm_infer(node): if shapeA.size > 2 and node.transpose_a: raise Error( - 'ONNX Gemm operation do not support {}dimensional input with set transA key'.format(shapeA.size)) + 'ONNX Gemm operation do not support {} dimensional input with set transA key'.format(shapeA.size)) # apply transposes and broadcasts if node.transpose_a: - shapeA = shapeA[[1,0]] + shapeA = shapeA[[1, 0]] if node.transpose_b: - shapeB = shapeB[[1,0]] + shapeB = shapeB[[1, 0]] if node.broadcast_c and shapeC.size == 1: shapeC = np.array([shapeA[0], shapeC[0]]) node.out_node().shape = shapeC return - diff --git a/model-optimizer/mo/front/common/partial_infer/reduce.py b/model-optimizer/mo/front/common/partial_infer/reduce.py index 74fdd40e9..627badced 100644 --- a/model-optimizer/mo/front/common/partial_infer/reduce.py +++ b/model-optimizer/mo/front/common/partial_infer/reduce.py @@ -26,6 +26,9 @@ def tf_reduce_infer(node, op=None): if input_shape is None or axis is None or input_shape.ndim != 1 or axis.ndim > 1: return output_shape = np.array(input_shape) + if len(axis.shape) == 0: # fix since np.delete deprecate negative idxs + axis = axis.reshape([1]) + axis[axis < 0] += output_shape.shape[0] if node.keep_dims: output_shape[axis] = 1 else: @@ -34,4 +37,4 @@ def tf_reduce_infer(node, op=None): if op is not None and node.in_node(0).value is not None: node.out_node(0).value = np.array([op(node.in_node(0).value, (*axis,))], dtype=node.in_node(0).value.dtype) # TODO extend to multi-dimensional axis - log.debug("value: {}".format(node.out_node(0).value)) + log.debug("value: {}".format(node.out_node(0).value))
\ No newline at end of file diff --git a/model-optimizer/mo/front/common/partial_infer/reshape.py b/model-optimizer/mo/front/common/partial_infer/reshape.py index db7cc98c9..ae616022f 100644 --- a/model-optimizer/mo/front/common/partial_infer/reshape.py +++ b/model-optimizer/mo/front/common/partial_infer/reshape.py @@ -14,11 +14,9 @@ limitations under the License. """ -import logging as log - -import numpy as np - +from mo.front.common.partial_infer.utils import int64_array from mo.ops.op import PermuteAttrs +from mo.utils.error import Error def tf_reshape_shape_infer(node): @@ -32,12 +30,9 @@ def tf_reshape_shape_infer(node): input_shape = node.in_node(0).shape reshape_output = node.in_node(1).value if len(node.in_nodes()) > 1 else node.dim - # In case if Reshape operation was created with two inputs and dim attr wasn't set, we set in automatically - if not node.has_valid('dim'): - node['dim'] = np.array(reshape_output, dtype=np.int64) - if node.in_node(0).shape is None: return None + total = 1 for index, i in enumerate(input_shape): total *= i @@ -65,11 +60,16 @@ def tf_reshape_shape_infer(node): out_shape_total *= i if total != out_shape_total: - log.error( + raise Error( "Number of elements in input {} and output {} of reshape node {} mismatch".format(input_shape, output_shape, node.name)) - return None PermuteAttrs.create_permute_attrs(node, attrs=[('dim', 'output:0')]) - return np.array(output_shape, dtype=np.int64) + output_shape = int64_array(output_shape) + + # In case if Reshape operation was created with two inputs and dim attr wasn't set, we set in automatically + if not node.has_valid('dim'): + node['dim'] = output_shape + + return output_shape diff --git a/model-optimizer/mo/front/common/partial_infer/slice.py b/model-optimizer/mo/front/common/partial_infer/slice.py index cac47d45c..bf23763fe 100644 --- a/model-optimizer/mo/front/common/partial_infer/slice.py +++ b/model-optimizer/mo/front/common/partial_infer/slice.py @@ -71,7 +71,6 @@ def tf_strided_slice_infer(node): new_axis_mask.append(False) value = node.in_node(0).value if node.in_node(0).value is not None else np.zeros(shape) - # fix for the warning: "FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated use # `arr[tuple(seq)]` instead of `arr[seq]`" value = value[tuple(slice_idx)] diff --git a/model-optimizer/mo/front/common/partial_infer/split.py b/model-optimizer/mo/front/common/partial_infer/split.py index 24bf678b4..ff8abb8eb 100644 --- a/model-optimizer/mo/front/common/partial_infer/split.py +++ b/model-optimizer/mo/front/common/partial_infer/split.py @@ -19,7 +19,7 @@ import logging as log import numpy as np from mo.ops.op import PermuteAttrs -from mo.utils.error import Error +from mo.graph.graph import Node def part_sizes_to_indices(part_sizes: list): @@ -39,13 +39,13 @@ def part_sizes_to_indices(part_sizes: list): return np.array(indices) -def split(input, node, outputs, axis, part_sizes): +def split(input_data_node: Node, node: Node, axis: int, part_sizes: list): """ Partial inference of generic split node. Args: @input: input tensor node, subject to split - @outputs: output tensor nodes where we put inferred output shapes + @node: node of one of the Split types @axis: split dimension index @part_sizes: a NumPy array with sizes of all pieces that we split to @@ -54,18 +54,14 @@ def split(input, node, outputs, axis, part_sizes): """ - if input.shape is None: - return - - if len(outputs) != len(part_sizes): - log.error('Number of outputs do not match the number of parts with sizes.') + if input_data_node.shape is None: return # normalize axis if axis < 0: - axis = input.shape.size + axis + axis = input_data_node.shape.size + axis - if axis < 0 or axis >= input.shape.size: + if axis < 0 or axis >= input_data_node.shape.size: log.error('Model is incorrect: axis for split node is out of range') return @@ -77,64 +73,67 @@ def split(input, node, outputs, axis, part_sizes): if undef_indices.size == 1: undef_index = undef_indices[0] part_sizes[undef_index] = 0 - deduced_dim = input.shape[axis] - np.add.reduce(part_sizes) + deduced_dim = input_data_node.shape[axis] - np.add.reduce(part_sizes) if deduced_dim < 0: - log.error( - 'Just deduced dimension for the split has negative value that means that split input shape and desired parts are not compatible') + log.error('Just deduced dimension for the split has negative value that means that split input shape and ' + 'desired parts are not compatible') return all_parts_size = np.add.reduce(part_sizes) - if all_parts_size != input.shape[axis]: - log.error("input.shape[{}] = {} != {} = sum of all parts in part_sizes".format(axis, input.shape[axis], + if all_parts_size != input_data_node.shape[axis]: + log.error("input.shape[{}] = {} != {} = sum of all parts in part_sizes".format(axis, + input_data_node.shape[axis], all_parts_size)) return - for i, part_size in enumerate(part_sizes): - shape = input.shape.copy() - shape[axis] = part_size - outputs[i].shape = shape + splitted = None + if input_data_node.value is not None: + splitted = np.split(input_data_node.value, part_sizes_to_indices(part_sizes), axis) - if input.value is not None: - splitted = np.split(input.value, part_sizes_to_indices(part_sizes), axis) - # log.debug("splitted = {}".format(splitted)) - for i, part in enumerate(splitted): - outputs[i].value = part - # log.debug('outputs[i].value.shape = {}, outputs[i].shape = {}'.format(outputs[i].value.shape, outputs[i].shape)) - assert all(outputs[i].value.shape == outputs[i].shape) + # not all outputs from the split could be used so it is necessary to iterate over output edges and infer shape for + # necessary nodes only + for _, dst, edge_attrs in node.graph.out_edges(node.id, data=True): + out_port = edge_attrs['out'] + out_node = node.out_node(out_port) + + new_out_shape = input_data_node.shape.copy() + new_out_shape[axis] = part_sizes[out_port] + node.out_node(out_port).shape = new_out_shape + if splitted is not None: + out_node.value = splitted[out_port] + assert all(out_node.value.shape == out_node.shape) assert not node.has_valid('axis') or node.axis == axis node.axis = axis - # WARNING: != 4 is supposed to work for NHWC to NCHW translation only; if other global permutations happen this will fail + # WARNING: != 4 is supposed to work for NHWC to NCHW translation only. + # if other global permutations happen this will fail # TODO: redesign it to have this logic built in NHWC to NCHW translation pass; it requires # additional attributes with layout to be propagated through the network - if len(input.shape) != 4 and node.has_valid('dim_attrs') and 'axis' in node.dim_attrs: - log.warning( - 'Removed "axis" attribute from the scope of the model relayout pass because len(input.shape) == {} != 4 for node {}'.format( - len(input.shape), - node.name if node.has_valid('name') else '<UNKNOWN>')) + if len(input_data_node.shape) != 4 and node.has_valid('dim_attrs') and 'axis' in node.dim_attrs: + log.warning('Removed "axis" attribute from the scope of the model relayout pass because len(input.shape) == {} ' + '!= 4 for node {}'.format(len(input_data_node.shape), node.soft_get('name'))) node.dim_attrs.remove('axis') assert 'axis' not in node.dim_attrs + log.debug('output shapes after split: {}'.format([v.shape for k, v in node.out_nodes().items()])) def tf_split_infer(node): """ Partial infer of split node similar to Split op of TF. """ - - if len(node.in_nodes()) == 1: - return True - - # Two inputs: [split_dim, input) - assert (len(node.in_nodes()) == 2) + # Two inputs: [split_dim, input] + assert len(node.in_nodes()) == 2, 'Node "{}" must have exactly two inputs'.format(node.soft_get('name')) split_dim = node.in_node(0).value if split_dim is None: log.error('split_dim value for node {} is None. Cannot do shape inference.') return - assert split_dim.ndim == 0 + + assert split_dim.ndim == 0, 'The split dimension for node "{}" must be a scalar.'.format(node.soft_get('name')) split_dim = split_dim.item() input = node.in_node(1) - if split_dim is None or input.shape is None: + if input.shape is None: + log.error('Input shape for node {} is not defined'.format(node.soft_get('name'))) return log.debug('input shape for split: {}, should be split along {} dim'.format(input.shape, split_dim)) @@ -145,42 +144,36 @@ def tf_split_infer(node): log.error("split_dim cannot be evenly divided by a given number of parts") return - outputs = node.out_nodes() # split_dim is a numpy array, axis is split_dim[0] - log.debug( - 'split_dim_size = {}, node.num_split = {}, div = {}, typeof div = {}'.format(split_dim_size, node.num_split, - split_dim_size / node.num_split, - type( - split_dim_size / node.num_split))) - split(input, node, [outputs[i] for i in range(len(outputs))], split_dim, - [int(split_dim_size / node.num_split)] * node.num_split) - log.debug('output shapes after split: {}'.format([v.shape for k, v in outputs.items()])) + log.debug('split_dim_size = {}, node.num_split = {}, div = {}, typeof div = {}'.format( + split_dim_size, node.num_split, split_dim_size / node.num_split, type(split_dim_size / node.num_split))) + split(input, node, split_dim, [int(split_dim_size / node.num_split)] * node.num_split) node.graph.remove_edge(node.in_node(0).id, node.id) node['input_port'] = 1 PermuteAttrs.create_permute_attrs(node, attrs=[('axis', 'input:1')]) -def tf_split_v_infer(node): +def tf_split_v_infer(node: Node): """ Partial infer of split node similar to SplitV op of TF. """ if len(node.in_nodes()) == 1 and not (node.has_valid('axis') and node.has_valid('size_splits')): - return True + return if len(node.in_nodes()) == 3 and (node.has_valid('axis') or node.has_valid('size_splits')): - return True + return # Three inputs: [input, size_splits, split_dim) - if len(node.in_nodes())==3 : + if len(node.in_nodes()) == 3: split_dim = node.in_node(2).value assert split_dim.ndim == 0 split_dim = split_dim.item() size_splits = node.in_node(1).value node.graph.remove_edge(node.in_node(1).id, node.id) node.graph.remove_edge(node.in_node(2).id, node.id) - else : + else: split_dim = node.axis size_splits = node.size_splits @@ -189,21 +182,19 @@ def tf_split_v_infer(node): return input = node.in_node(0) - - log.debug( - 'split_dim = {}, input.shape = {}, size_splits.value = {}'.format(split_dim, input.shape, size_splits)) - - if split_dim is None or input.shape is None or size_splits is None: + if input.shape is None or size_splits is None: + log.error('input shape or size of splits are not defined for node {}'.format(node.soft_get('name'))) return - outputs = node.out_nodes() + log.debug('split_dim = {}, input.shape = {}, size_splits.value = {}'.format(split_dim, input.shape, size_splits)) + # split_dim is a numpy array, axis is split_dim - split(input, node, [outputs[i] for i in range(len(outputs))], split_dim, size_splits) - log.debug('output shapes after split: {}'.format([v.shape for k, v in outputs.items()])) - - PermuteAttrs.create_permute_attrs(node, attrs=[('axis','input:0')]) + split(input, node, split_dim, size_splits) + + PermuteAttrs.create_permute_attrs(node, attrs=[('axis', 'input:0')]) -def tf_unpack_infer(node): + +def tf_unpack_infer(node: Node): if len(node.in_nodes()) != 1: log.debug('Unpack node "{}" must have one input.'.format(node.name)) return @@ -229,9 +220,5 @@ def tf_unpack_infer(node): log.error("split_dim cannot be evenly divided by a given number of parts") return - outputs = node.out_nodes() - split(node.in_node(), node, [outputs[i] for i in range(len(outputs))], split_dim, - [int(split_dim_size / node.num_split)] * node.num_split) - + split(node.in_node(), node, split_dim, [int(split_dim_size / node.num_split)] * node.num_split) # node shapes will be squeezed in the separate pass - log.debug('output shapes after split: {}'.format([v.shape for k, v in outputs.items()])) diff --git a/model-optimizer/mo/front/common/partial_infer/squeeze.py b/model-optimizer/mo/front/common/partial_infer/squeeze.py index 351a391be..574ba85c5 100644 --- a/model-optimizer/mo/front/common/partial_infer/squeeze.py +++ b/model-optimizer/mo/front/common/partial_infer/squeeze.py @@ -13,30 +13,68 @@ See the License for the specific language governing permissions and limitations under the License. """ +import logging as log import numpy as np -from mo.front.caffe.extractors.utils import get_canonical_axis_index +from mo.front.caffe.extractors.utils import get_canonical_axis_index +from mo.front.common.layout import get_height_dim, get_width_dim, get_depth_dim +from mo.front.common.partial_infer.utils import int64_array from mo.ops.op import PermuteAttrs +from mo.utils.error import Error + + +def is_spatial_squeeze(layout: str, input_shape: np.ndarray, squeeze_dims: np.ndarray): + """ + Checks that the squeeze operation removes all spatial dimensions. + :param layout: graph layout. + :param input_shape: numpy array with input shape. + :param squeeze_dims: numpy array with dims to squeeze. + :return: result of the check. + """ + if len(input_shape) < 4 or len(input_shape) > 5: + return False + spatial_dims = [get_height_dim(layout, len(input_shape)), get_width_dim(layout, len(input_shape))] + if len(input_shape) == 5: + spatial_dims.append(get_depth_dim(layout, len(input_shape))) + for dim in spatial_dims: + if input_shape[dim] != 1: + log.debug('The reshape from "{}" with squeezed dims "{}" is not a spatial squeeze'.format(input_shape, + squeeze_dims)) + return False + if len(squeeze_dims) != len(spatial_dims): + log.debug('The reshape from "{}" with squeezed dims "{}" is not a spatial squeeze'.format(input_shape, + squeeze_dims)) + return False + log.debug('The reshape from "{}" with squeezed dims "{}" is not a spatial squeeze'.format(input_shape, + squeeze_dims)) + return True def tf_squeeze_infer(node): if node.squeeze_dims is None: # TODO: implement; there is no implementation now because no test return + real_squeeze_dims = [] - shape = node.in_node().shape - if shape is None: + input_shape = node.in_node().shape + if input_shape is None: return # UGLY - shape = shape.copy() + output_shape = input_shape.copy() for n in node.squeeze_dims: - if shape[n] == 1: - real_squeeze_dims.append(get_canonical_axis_index(shape, n)) - shape = np.delete(shape, real_squeeze_dims) - node.out_node().shape = shape - node['dim'] = shape + if output_shape[n] == 1: + real_squeeze_dims.append(get_canonical_axis_index(output_shape, n)) + else: + raise Error('Trying to squeeze dimension not equal to 1 for node "{}"'.format(node.soft_get('name'))) + + output_shape = np.delete(output_shape, real_squeeze_dims) + node.out_node().shape = output_shape + + if is_spatial_squeeze(node.graph.graph['layout'], input_shape, output_shape): + output_shape = int64_array([0, -1]) + node['dim'] = output_shape if node.in_node().value is not None: - node.out_node().value = np.array(np.reshape(node.in_node().value, shape)) + node.out_node().value = np.array(np.reshape(node.in_node().value, output_shape)) - PermuteAttrs.create_permute_attrs(node, attrs =[('dim','output:0')])
\ No newline at end of file + PermuteAttrs.create_permute_attrs(node, attrs=[('dim', 'output:0')]) diff --git a/model-optimizer/mo/front/common/partial_infer/up_sampling.py b/model-optimizer/mo/front/common/partial_infer/up_sampling.py deleted file mode 100644 index 8a2dc6a76..000000000 --- a/model-optimizer/mo/front/common/partial_infer/up_sampling.py +++ /dev/null @@ -1,28 +0,0 @@ -""" - Copyright (c) 2018 Intel Corporation - - 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 numpy as np - - -def up_sampling_infer(node): - if node.scale is None: - return - input_shape = node.in_node(0).shape - batch = input_shape[0] - channel = input_shape[1] - y = input_shape[2] * node.scale - x = input_shape[3] * node.scale - node.out_node(0).shape = np.array([batch, channel, y, x]) diff --git a/model-optimizer/mo/front/common/partial_infer/utils.py b/model-optimizer/mo/front/common/partial_infer/utils.py index dbb76a5cd..0056a0a57 100644 --- a/model-optimizer/mo/front/common/partial_infer/utils.py +++ b/model-optimizer/mo/front/common/partial_infer/utils.py @@ -24,21 +24,17 @@ def int64_array(l: list): def float_array(l: list): - return np.array(l, dtype=np.int64) + return np.array(l, dtype=np.float64) def mark_input_bins(node, names=('weights', 'biases'), start_port: int = 1): """ Preparing necessary attributes for edges at input ports starting from start_port. - It is applicable for convolution and other operations that has constant inputs which + It is applicable for convolution and other operations that have constant inputs which are intended to be dumped as IE IR bin file. """ - nports = len(node.in_nodes()) - for i, name in enumerate(names): - port = i + start_port - if port >= nports: - break - if node.in_node(port).value is not None: + for port, name in enumerate(names, start=start_port): + if port in node.in_nodes() and node.in_node(port).has_valid('value'): node.in_edge(port)['bin'] = name diff --git a/model-optimizer/mo/front/common/register_custom_ops.py b/model-optimizer/mo/front/common/register_custom_ops.py index 89607f8e3..1172bf303 100644 --- a/model-optimizer/mo/front/common/register_custom_ops.py +++ b/model-optimizer/mo/front/common/register_custom_ops.py @@ -53,7 +53,6 @@ def check_for_duplicates(extractors_collection: dict): Check if extractors_collection has case-insensitive duplicates, if it does, raise exception with information about duplicates """ - assert extractors_collection, 'Extractors collection can not be empty.' # Check if extractors_collection is a normal form, that is it doesn't have case-insensitive duplicates duplicates, keys = find_case_insensitive_duplicates(extractors_collection) if len(duplicates) > 0: diff --git a/model-optimizer/mo/front/extractor.py b/model-optimizer/mo/front/extractor.py index bf555187f..6ba1ea4c4 100644 --- a/model-optimizer/mo/front/extractor.py +++ b/model-optimizer/mo/front/extractor.py @@ -15,6 +15,8 @@ """ import ast import logging as log +from collections import defaultdict +from copy import copy import networkx as nx import numpy as np @@ -27,8 +29,6 @@ from mo.utils.error import Error from mo.utils.graph import dfs from mo.utils.unsupported_ops import UnsupportedOps from mo.utils.utils import refer_to_faq_msg -from collections import defaultdict -from copy import copy def restore_edges(graph: nx.DiGraph, get_edges: callable): @@ -473,6 +473,7 @@ def update_ie_fields(attrs: dict, ir_version = None): ir_version_mapping = { # Default behaviour is IR V3 attributes None: ir_v3_attrs, + 4: ir_v3_attrs, 3: ir_v3_attrs, 2: ir_v2_attrs } @@ -884,15 +885,9 @@ def add_output_ops(graph: nx.MultiDiGraph, user_defined_outputs: dict, inputs: d return sinks -def set_is_input_true(graph: nx.MultiDiGraph, placeholders: list): +def set_is_input(graph: nx.MultiDiGraph, placeholders: list, is_input: bool): for placeholder in placeholders: - graph.node[placeholder]['is_input'] = True - - -def set_is_input_false(graph: nx.MultiDiGraph): - for node, data in list(graph.nodes(data=True)): - if 'op' in data and data['op'] == 'Placeholder': - graph.node[node]['is_input'] = False + graph.node[placeholder]['is_input'] = is_input def check_input(graph: nx.MultiDiGraph, node_name: str): @@ -912,120 +907,240 @@ def split_node_in_port(node_id: str): node_name = separator.join(parts[1:]) try: port = int(parts[0]) - return (node_name, port) + return node_name, port except ValueError as err: log.warning('Didn\'t recognize port:node format for "{}" because port is not an integer.'.format( node_id)) - return (node_id, None) - - -def add_input_op(graph: nx.MultiDiGraph, node_id: str, port: int = 0, data: bool = False, shape=None): - # we import it here because Op imports add_attrs_props and update_ie_fields from this file + return node_id, None + + +def add_input_op_input_port_without_data(graph: nx.MultiDiGraph, node_id: str, input_op, edge_attrs: dict): + input_node = input_op.create_node() + graph.add_edge(input_node.id, node_id, **edge_attrs) + log.debug('Input: {} for node {}'.format(input_node.id, node_id)) + log.debug("Add edge from {} to {}".format(input_node.id, node_id)) + return input_node.id + + +def add_input_op_input_port_with_data(graph: nx.MultiDiGraph, node_id: str, input_op, edge_attrs: dict): + input_data_node = input_op.create_node_with_data() + input_node = input_data_node.in_node() + graph.add_edge(input_data_node.id, node_id, **edge_attrs) + update_ie_fields(graph.node[input_node.id]) + log.debug('Input: {} for node {}'.format(input_node.id, node_id)) + log.debug("Add edge from {} to {}".format(input_node.id, input_data_node.id)) + log.debug("Add edge from {} to {}".format(input_data_node.id, node_id)) + return input_node.id + + +def add_input_op_output_port_without_data(graph: nx.MultiDiGraph, node_id: str, input_op, port: int): + input_node = input_op.create_node() + # In this case it can be more than one out edge from one port and we should iterate over all output edges + for _, out_node, attrs in graph.out_edges(node_id, data=True): + if attrs['out'] == port: + # new out port = 0 + attrs = attrs.copy() + attrs['out'] = 0 + graph.add_edge(input_node.id, out_node, **attrs) + log.debug('Input: {} for node {} output port {}'.format(input_node.id, node_id, port)) + log.debug("Add edge from {} to {}".format(input_node.id, out_node)) + return input_node.id + + +def add_input_op_output_port_with_data(graph: nx.MultiDiGraph, node_id: str, input_op, port: int): + # we assume that after op always data node + data_node = Node(graph, node_id).out_node(port) + assert data_node.has_valid('kind') and data_node.kind == 'data' + input_op.create_node_with_data(data_nodes=data_node) + input_node = data_node.in_node() + update_ie_fields(graph.node[input_node.id]) + log.debug('Input: {} for node {}'.format(input_node.id, node_id)) + log.debug("Add edge from {} to {}".format(input_node.id, node_id)) + return input_node.id + + +def add_input_op(graph: nx.MultiDiGraph, node_id: str, port: int = 0, data: bool = False, shape=None, + is_out_port: bool = False): + """ + This function adds Input node to node with id==node_id to specified port (in or out defined with is_out_port). + :param graph: graph to operate on. + :param node_id: node_id for node to which we should add new input. + :param port: number of port of node_id node for adding input node. + :param data: flag that define whether data nodes is needed or not. + :param shape: shape for new input node. + :param is_out_port: flag that define whether port is output port or not. + :return: id of new Input operation + """ + # We import it here because Op imports add_attrs_props and update_ie_fields from this file from mo.ops.input import Input - input = Input(graph, dict(shape=shape, initial_node_name=node_id, name='{}/placeholder_port_{}'.format(node_id, port))) + port_type = '_out' if is_out_port else '' + input_op = Input(graph, dict(shape=shape, initial_node_name=node_id, + name='{}/placeholder{}_port_{}'.format(node_id, port_type, port))) edge_attrs = {'in': port, 'out': 0, 'in_attrs': ['in'], 'out_attrs': ['out'], 'fw_tensor_debug_info': [(Node(graph, node_id).soft_get('name'), port)], 'data_attrs': ['fw_tensor_debug_info']} if not data: - input_node = input.create_node() - graph.add_edge(input_node.id, node_id, **edge_attrs) - log.debug('Input: {} for node {}'.format(input_node.id, node_id)) - log.debug("Add edge from {} to {}".format(node_id, input_node.id)) - return input_node.id + if is_out_port: + new_input_id = add_input_op_output_port_without_data(graph=graph, node_id=node_id, input_op=input_op, + port=port) + else: + new_input_id = add_input_op_input_port_without_data(graph=graph, node_id=node_id, input_op=input_op, + edge_attrs=edge_attrs) else: - input_data_node = input.create_node_with_data() - input = input_data_node.in_node() - graph.add_edge(input_data_node.id, node_id, **edge_attrs) - update_ie_fields(graph.node[input.id]) - log.debug('Input: {} for node {}'.format(input.id, node_id)) - log.debug("Add edge from {} to {}".format(input.id, input_data_node.id)) - log.debug("Add edge from {} to {}".format(input_data_node.id, node_id)) - return input.id + if is_out_port: + new_input_id = add_input_op_output_port_with_data(graph=graph, node_id=node_id, input_op=input_op, + port=port) + else: + new_input_id = add_input_op_input_port_with_data(graph=graph, node_id=node_id, input_op=input_op, + edge_attrs=edge_attrs) + return new_input_id + + +def add_input_ops_helper_before_infer_input_port(graph: nx.MultiDiGraph, smart_node: Node, port: int, node_id: str, + shape: np.array, inputs: list, edges_to_remove: list): + n_inputs = len(smart_node.in_nodes()) + if n_inputs > 1 and port is None: + raise Error( + 'Node {} has more than 1 input and input shapes were provided. Try not to provide input' + ' shapes or specify input port with port:node notation, where port is an integer. ' + '{}'.format(smart_node.soft_get('name'), refer_to_faq_msg(30))) + port = port if port is not None else 0 + edges_to_remove.append((smart_node.in_node(port).id, smart_node.id)) + inputs.append(add_input_op(graph=graph, node_id=node_id, port=port, data=False, + shape=shape)) + + +def add_input_ops_helper_after_infer_input_port(graph: nx.MultiDiGraph, smart_node: Node, port:int, node_id: str, + inputs: list, edges_to_remove: list): + n_inputs = len(smart_node.in_nodes()) + if n_inputs > 1 and port is not None and port != 0: + raise Error( + 'Input port > 0 in --input is not supported if --input_shape is not provided. Node:' + ' "{}". Omit port index and all input ports will be replaced by placeholders. ' + 'Or provide --input_shape. ' + refer_to_faq_msg(31), node_id) + port = port if port is not None else 0 + in_node = smart_node.in_node(port) + shape = in_node['shape'] if 'shape' in in_node else None + if shape is None: + raise Error('Shape for tensor "{}" is not defined. Can not proceed.' + refer_to_faq_msg(41), + in_node.soft_get('name')) + inputs.append(add_input_op(graph=graph, node_id=node_id, port=port, data=True, + shape=shape.copy())) + edges_to_remove.append((in_node.id, node_id)) + + +def add_input_ops_helper_before_infer_output_port(graph: nx.MultiDiGraph, port:int, node_id: str, + shape: np.array, inputs: list, edges_to_remove: list): + for u, v, edge_attrs in graph.out_edges(node_id, data=True): + if edge_attrs['out'] == port: + edges_to_remove.append((u, v)) # we need to remove all edges from this port + inputs.append(add_input_op(graph=graph, node_id=node_id, port=port, data=False, + shape=shape, is_out_port=True)) + +def add_input_ops_helper_after_infer_output_port(graph: nx.MultiDiGraph, smart_node: Node, port:int, node_id: str, + inputs: list, edges_to_remove: list): + out_node = smart_node.out_node(port) + shape = out_node['shape'] if 'shape' in out_node else None + if shape is None: + raise Error('Shape for tensor "{}" is not defined. Can not proceed.' + refer_to_faq_msg(41), + out_node.soft_get('name')) + inputs.append(add_input_op(graph=graph, node_id=node_id, port=port, data=True, + shape=shape.copy(), is_out_port=True)) + edges_to_remove.append((node_id, out_node.id)) def add_input_ops(graph: nx.MultiDiGraph, user_defined_inputs: dict, before_infer: bool): + """ + This function add user defined input operations. + For cutting without port: + Op_1 -> Op_2 -> output, user_defined_inputs = {'Op_2': {'shape':[1, 2]}} => + Op_1, New_input (op=Placeholder, shape=[1, 2]) -> Op_2 -> output + + For cutting with input port: + Op_1 -> Op_2 -> output, user_defined_inputs = {'Op_2': {'shape':[1, 2], 'in': 0}} => + Op_1, New_input (op=Placeholder, shape=[1, 2]) -> Op_2 -> output + + For cutting with output port: + Op_1 -> Op_2 -> output, user_defined_inputs = {'Op_2': {'shape':[1, 2], 'out': 0}} => + Op_1 -> Op_2, New_input (op=Placeholder, shape=[1, 2]) -> output + + For case with before_infer=False data nodes are added to this schemes. + """ inputs = [] - set_is_input_false(graph) + set_is_input(graph, get_nodes_with_attributes(graph, op='Placeholder'), False) if user_defined_inputs is None: inputs = get_nodes_with_attributes(graph, op='Placeholder') else: # cutting the net by inputs assert isinstance(user_defined_inputs, dict) - for key, values in user_defined_inputs.items(): - for value in values: - if 'out' in value: - raise Error( - 'Cutting the net by output ports of nodes is forbidden. Can not cut the edge from output port ' - '{} of node {}'.format(value['out'], key)) - edges_to_remove = [] for node_id in user_defined_inputs: for port_and_shape_info in user_defined_inputs[node_id]: if 'added' in port_and_shape_info and port_and_shape_info['added']: continue + + is_out_port = 'out' in port_and_shape_info # by default we assume input port or input node without port shape = port_and_shape_info['shape'] if 'shape' in port_and_shape_info else None - port = port_and_shape_info['in'] if 'in' in port_and_shape_info else None smart_node = Node(graph, node_id) - n_inputs = len(smart_node.in_nodes()) - # specific Placeholder cases + + # Common port index check + if is_out_port: + port = port_and_shape_info['out'] # we check that 'out' in port_and_shape_info earlier + if port is None: + raise Error('Output port for input node {} should be specified, it cannot be None!'.format( + node_id + )) + if port is not None and port not in smart_node.out_nodes(): + raise Error('Output port index {} is out of number of available output ports for node "{}". ' + + refer_to_faq_msg(29), port, node_id) + else: + port = port_and_shape_info['in'] if 'in' in port_and_shape_info else None + if port is not None and port not in smart_node.in_nodes(): + raise Error('Input port index {} is out of number of available input ports for node "{}". ' + + refer_to_faq_msg(29), port, node_id) + + # specific Placeholder case if smart_node.op == 'Placeholder': if port is not None: - raise Error('Placeholder node "{}" doesn\'t have input port, but input port {} was provided. ' + - refer_to_faq_msg(28), node_id, port) + raise Error( + 'Placeholder node "{}" doesn\'t have input port, but input port {} was provided. ' + + refer_to_faq_msg(28), node_id, port) if shape is not None: graph.node[node_id]['shape'] = shape inputs.append(node_id) port_and_shape_info['added'] = True continue - # common port index check - if port is not None and port >= n_inputs: - raise Error('Port index {} is out of number of available input ports for node "{}". ' + - refer_to_faq_msg(29), port, n_inputs) + if before_infer: if shape is None: continue - # we cut with shapes provided by user and there is no need to wait till infer - if n_inputs > 1 and port is None: - raise Error('Node {} has more than 1 input and input shapes were provided. Try not to provide input' - ' shapes or specify input port with port:node notation, where port is an integer. ' - '{}'.format(smart_node.soft_get('name'), refer_to_faq_msg(30))) - if port is None: - assert n_inputs == 1 - port = 0 - edges_to_remove = [(smart_node.in_node(port).id, smart_node.id)] - inputs.append(add_input_op(graph=graph, node_id=node_id, port=port, data=False, shape=shape)) - port_and_shape_info['added'] = True + # We cut with shapes provided by user and there is no need to wait till infer + if is_out_port: + add_input_ops_helper_before_infer_output_port(graph, port, node_id, shape, inputs, + edges_to_remove) + else: + add_input_ops_helper_before_infer_input_port(graph, smart_node, port, node_id, shape, inputs, + edges_to_remove) else: - - # we cut after infer and - if n_inputs > 1 and port is not None and port != 0: - raise Error('Input port > 0 in --input is not supported if --input_shape is not provided. Node:' - ' "{}". Omit port index and all input ports will be replaced by placeholders. ' - 'Or provide --input_shape. ' + refer_to_faq_msg(31), node_id) - for first, second, edge_attrs in list(graph.in_edges(node_id, data=True)): - if graph.node[first]['value'] is not None: - continue - if port is not None and edge_attrs['in'] != port: - continue - shape = graph.node[first]['shape'].copy() - if shape is None: - raise Error('Shape for tensor "{}" is not defined. Can not proceed. ' + refer_to_faq_msg(41), - first) - port = port if port is not None else edge_attrs['in'] - inputs.append(add_input_op(graph=graph, node_id=node_id, port=port, data=True, shape=shape)) - port_and_shape_info['added'] = True - edges_to_remove.append((first, second)) - graph.remove_edges_from(edges_to_remove) - edges_to_remove = [] + # We cut after infer and we need inferred shapes in nodes + if is_out_port: + add_input_ops_helper_after_infer_output_port(graph, smart_node, port, node_id, inputs, + edges_to_remove) + else: + add_input_ops_helper_after_infer_input_port(graph, smart_node, port, node_id, inputs, + edges_to_remove) + port_and_shape_info['added'] = True + graph.remove_edges_from(edges_to_remove) # if len(inputs) == 0, shapes were not provided for all nodes in input-cut request, # we didn't cut inputs before infer, so this check is useless and invalid if len(inputs): - set_is_input_true(graph, inputs) + set_is_input(graph, inputs, True) # Check if there are inputs that are not listed in user_defined_inputs and are needed to calculate outputs outputs = get_nodes_with_attributes(graph, is_output=True) + visited = set() for output_name in outputs: - reverse_dfs(graph, output_name, check_input) + reverse_dfs(graph, output_name, check_input, visited) return inputs diff --git a/model-optimizer/mo/front/kaldi/extractor.py b/model-optimizer/mo/front/kaldi/extractor.py index 2d4e9e1cd..f0e3b3b11 100644 --- a/model-optimizer/mo/front/kaldi/extractor.py +++ b/model-optimizer/mo/front/kaldi/extractor.py @@ -21,39 +21,27 @@ from mo.utils.utils import refer_to_faq_msg def node_pb_arg(pb_extractor): - return lambda node: pb_extractor(node.pb) + return lambda node: pb_extractor(node.parameters) -kaldi_type_extractors = { - # Data Layers - 'globalinput': node_pb_arg(lambda x: dict(op='Placeholder', type='Input', - infer=lambda node: single_output_infer(node, lambda n: n.shape))), - - # Utility Layers - 'softmax': node_pb_arg(lambda _: dict(op='SoftMax', type='SoftMax', infer=copy_shape_infer)), -} +kaldi_type_extractors = {} def common_kaldi_fields(node: Node) -> dict: - pb = node.pb if node.pb else node - layer_type = pb.type + layer_type = node.op return { 'kind': 'op', - 'name': pb.name, - 'type': layer_type, + 'name': node.id, 'op': layer_type, # generic code relies on op; it should be overridden by specific op extractor 'infer': None, - 'precision': 'FP32' # TODO use real precision derived from the model + 'precision': 'FP32' } def kaldi_extractor(node: Node) -> (bool, dict): - if node.has_valid('op') and node.op == 'Identity': - return True, {} result = common_kaldi_fields(node) - - layer_type = result['type'].lower() + layer_type = result['op'] if layer_type not in kaldi_type_extractors: raise Error('Found unsupported layer {}. '.format(node.id) + 'Model Optimizer does not support this layer type: {}. '.format(layer_type) + diff --git a/model-optimizer/mo/front/kaldi/extractors/add_shift_ext.py b/model-optimizer/mo/front/kaldi/extractors/add_shift_ext.py new file mode 100644 index 000000000..ff5dff90b --- /dev/null +++ b/model-optimizer/mo/front/kaldi/extractors/add_shift_ext.py @@ -0,0 +1,38 @@ +""" + Copyright (c) 2018 Intel Corporation + + 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 numpy as np + +from mo.front.caffe.extractors.utils import embed_input +from mo.front.extractor import FrontExtractorOp +from mo.front.kaldi.utils import read_binary_vector, read_learning_info +from mo.ops.scale_shift import ScaleShiftOp + + +class AddShiftFrontExtractor(FrontExtractorOp): + op = 'addshift' + enabled = True + + @staticmethod + def extract(node): + pb = node.parameters + read_learning_info(pb) + biases = read_binary_vector(pb) + bias_term = True + mapping_rule = {'bias_term': bias_term} + embed_input(mapping_rule, 1, 'weights', np.ones(biases.shape)) + embed_input(mapping_rule, 2, 'biases', biases) + ScaleShiftOp.update_node_stat(node, mapping_rule) + return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/memory_ext.py b/model-optimizer/mo/front/kaldi/extractors/affine_component_ext.py index 58bcf43cd..7900639b2 100644 --- a/model-optimizer/mo/front/kaldi/extractors/memory_ext.py +++ b/model-optimizer/mo/front/kaldi/extractors/affine_component_ext.py @@ -13,21 +13,17 @@ See the License for the specific language governing permissions and limitations under the License. """ - from mo.front.extractor import FrontExtractorOp -from mo.ops.op import Op +from mo.front.kaldi.extractors.fixed_affine_component_ext import FixedAffineComponentFrontExtractor +from mo.front.kaldi.utils import read_learning_info +from mo.graph.graph import Node -class MemoryFrontExtractor(FrontExtractorOp): - op = 'memory' +class AffineComponentFrontExtractor(FrontExtractorOp): + op = 'affinecomponent' enabled = True @staticmethod - def extract(node): - mapping_rule = { - 'id': node.pb.id, - 'index': node.pb.index, - 'size': node.pb.size - } - Op.get_op_class_by_name('Memory').update_node_stat(node, mapping_rule) - return __class__.enabled + def extract(node: Node): + read_learning_info(node.parameters) + return FixedAffineComponentFrontExtractor.extract(node) diff --git a/model-optimizer/mo/front/kaldi/extractors/affine_component_preconditioned_online_ext.py b/model-optimizer/mo/front/kaldi/extractors/affine_component_preconditioned_online_ext.py new file mode 100644 index 000000000..70a8c41f9 --- /dev/null +++ b/model-optimizer/mo/front/kaldi/extractors/affine_component_preconditioned_online_ext.py @@ -0,0 +1,29 @@ +""" + Copyright (c) 2018 Intel Corporation + + 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 mo.front.extractor import FrontExtractorOp +from mo.front.kaldi.extractors.fixed_affine_component_ext import FixedAffineComponentFrontExtractor +from mo.front.kaldi.utils import read_learning_info +from mo.graph.graph import Node + + +class AffineComponentFrontExtractor(FrontExtractorOp): + op = 'affinecomponentpreconditionedonline' + enabled = True + + @staticmethod + def extract(node: Node): + read_learning_info(node.parameters) + return FixedAffineComponentFrontExtractor.extract(node) diff --git a/model-optimizer/mo/front/kaldi/extractors/affine_transform_ext.py b/model-optimizer/mo/front/kaldi/extractors/affine_transform_ext.py index c335d92ac..8175fb108 100644 --- a/model-optimizer/mo/front/kaldi/extractors/affine_transform_ext.py +++ b/model-optimizer/mo/front/kaldi/extractors/affine_transform_ext.py @@ -13,10 +13,10 @@ See the License for the specific language governing permissions and limitations under the License. """ - -from mo.front.caffe.extractors.utils import weights_biases +from mo.front.caffe.extractors.utils import embed_input from mo.front.extractor import FrontExtractorOp -from mo.ops.op import Op +from mo.front.kaldi.utils import read_binary_matrix, read_binary_vector, read_learning_info +from mo.ops.inner_product import InnerProduct class AffineTransformFrontExtractor(FrontExtractorOp): @@ -25,11 +25,17 @@ class AffineTransformFrontExtractor(FrontExtractorOp): @staticmethod def extract(node): + pb = node.parameters + read_learning_info(pb) + weights, weights_shape = read_binary_matrix(pb) + biases = read_binary_vector(pb) + mapping_rule = { - 'out-size': node.pb.num_output, + 'out-size': weights_shape[0], 'layout': 'NCHW' } - mapping_rule.update(weights_biases(node.pb.bias_term, node.pb)) + embed_input(mapping_rule, 1, 'weights', weights) + embed_input(mapping_rule, 2, 'biases', biases) - Op.get_op_class_by_name('FullyConnected').update_node_stat(node, mapping_rule) + InnerProduct.update_node_stat(node, mapping_rule) return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/concat_ext.py b/model-optimizer/mo/front/kaldi/extractors/concat_ext.py index b0cd82214..9299c7c2d 100644 --- a/model-optimizer/mo/front/kaldi/extractors/concat_ext.py +++ b/model-optimizer/mo/front/kaldi/extractors/concat_ext.py @@ -15,7 +15,7 @@ """ from mo.front.extractor import FrontExtractorOp -from mo.ops.op import Op +from mo.ops.concat import Concat class ConcatFrontExtractor(FrontExtractorOp): @@ -25,8 +25,7 @@ class ConcatFrontExtractor(FrontExtractorOp): @staticmethod def extract(node): mapping_rule = { - 'axis': node.pb.axis + 'axis': 1 } - - Op.get_op_class_by_name('Concat').update_node_stat(node, mapping_rule) + Concat.update_node_stat(node, mapping_rule) return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/convolution_ext.py b/model-optimizer/mo/front/kaldi/extractors/convolution_ext.py deleted file mode 100644 index 16998d83e..000000000 --- a/model-optimizer/mo/front/kaldi/extractors/convolution_ext.py +++ /dev/null @@ -1,61 +0,0 @@ -""" - Copyright (c) 2018 Intel Corporation - - 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 copy - -import numpy as np - -from mo.front.caffe.extractors.utils import weights_biases -from mo.front.common.extractors.utils import layout_attrs -from mo.front.common.partial_infer.utils import int64_array -from mo.front.extractor import FrontExtractorOp -from mo.graph.graph import Node -from mo.ops.convolution import Convolution -from mo.ops.op import Op - - -class Convolution1DFrontExtractor(FrontExtractorOp): - op = 'convolution' - enabled = True - - @staticmethod - def extract(node: Node) -> bool: - params = node.pb - mapping_rule = { - 'output': params.output, - 'patch_stride': params.patch_stride, - 'bias_term': None, - 'pad': int64_array([[0, 0], [0, 0], [0, 0], [0, 0]]), - 'pad_spatial_shape': int64_array([[0, 0], [0, 0]]), - 'dilation': int64_array([1, 1, 1, 1]), - 'kernel': int64_array([1, 1, 1, params.kernel]), - 'stride': int64_array([1, 1, 1, params.stride]), - 'kernel_spatial': int64_array([1, params.kernel]), - 'input_feature_channel': 1, - 'output_feature_channel': 0, - 'kernel_spatial_idx': [2,3], - 'group': 1, - 'reshape_kernel': True, - } - mapping_rule.update(layout_attrs()) - mapping_rule.update(weights_biases(params.bias_term, params)) - if len(params.blobs) > 1 and len(params.blobs[1]) > 0: - mapping_rule['bias_addable'] = True - else: - mapping_rule['bias_addable'] = False - - Op.get_op_class_by_name('Convolution').update_node_stat(node, mapping_rule) - return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/convolutional_1d_component_ext.py b/model-optimizer/mo/front/kaldi/extractors/convolutional_1d_component_ext.py new file mode 100644 index 000000000..d77eeb33e --- /dev/null +++ b/model-optimizer/mo/front/kaldi/extractors/convolutional_1d_component_ext.py @@ -0,0 +1,96 @@ +""" + Copyright (c) 2018 Intel Corporation + + 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 numpy as np + +from mo.front.caffe.extractors.utils import embed_input +from mo.front.common.extractors.utils import layout_attrs +from mo.front.extractor import FrontExtractorOp +from mo.front.kaldi.loader.utils import read_token_value, collect_until_whitespace, find_next_tag +from mo.front.kaldi.utils import read_learning_info, read_binary_matrix, read_binary_vector +from mo.graph.graph import Node +from mo.ops.convolution import Convolution +from mo.utils.error import Error +from mo.utils.utils import refer_to_faq_msg + + +class ConvolutionalComponentFrontExtractor(FrontExtractorOp): + op = 'convolutional1dcomponent' # Naming like in Kaldi + enabled = True + + @staticmethod + def extract(node: Node) -> bool: + """ + Extract conv parameters from node.parameters. + node.parameters like file descriptor object. + :param node: Convolution node + :return: + """ + pb = node.parameters + read_learning_info(pb) + + kernel = read_token_value(pb, b'<PatchDim>') + stride = read_token_value(pb, b'<PatchStep>') + patch_stride = read_token_value(pb, b'<PatchStride>') + + token = find_next_tag(pb) + if token == '<AppendedConv>': + appended_conv = True + token = find_next_tag(pb) + if token != '<FilterParams>': + raise Error('Can not load token {} from Kaldi model'.format(token) + + refer_to_faq_msg(94)) + collect_until_whitespace(pb) + weights, weights_shape = read_binary_matrix(pb) + + collect_until_whitespace(pb) + biases = read_binary_vector(pb) + + if (patch_stride - kernel) % stride != 0: + raise Error( + 'Kernel size and stride does not correspond to `patch_stride` attribute of Convolution layer. ' + + refer_to_faq_msg(93)) + + output = biases.shape[0] + if weights_shape[0] != output: + raise Error('Weights shape does not correspond to the `output` attribute of Convolution layer. ' + + refer_to_faq_msg(93)) + + mapping_rule = { + 'output': output, + 'patch_stride': patch_stride, + 'bias_term': None, + 'pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]], dtype=np.int64), + 'pad_spatial_shape': np.array([[0, 0], [0, 0]], dtype=np.int64), + 'dilation': np.array([1, 1, 1, 1], dtype=np.int64), + 'kernel': np.array([1, 1, 1, kernel], dtype=np.int64), + 'stride': np.array([1, 1, 1, stride], dtype=np.int64), + 'kernel_spatial': np.array([1, kernel], dtype=np.int64), + 'input_feature_channel': 1, + 'output_feature_channel': 0, + 'kernel_spatial_idx': [2, 3], + 'group': 1, + 'reshape_kernel': True, + 'appended_conv': appended_conv + } + + mapping_rule.update(layout_attrs()) + embed_input(mapping_rule, 1, 'weights', weights) + embed_input(mapping_rule, 2, 'biases', biases) + + mapping_rule['bias_addable'] = len(biases) > 0 + + Convolution.update_node_stat(node, mapping_rule) + return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/convolutional_component_ext.py b/model-optimizer/mo/front/kaldi/extractors/convolutional_component_ext.py new file mode 100644 index 000000000..21a1e3350 --- /dev/null +++ b/model-optimizer/mo/front/kaldi/extractors/convolutional_component_ext.py @@ -0,0 +1,88 @@ +""" + Copyright (c) 2018 Intel Corporation + + 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 numpy as np + +from mo.front.caffe.extractors.utils import embed_input +from mo.front.common.extractors.utils import layout_attrs +from mo.front.extractor import FrontExtractorOp +from mo.front.kaldi.loader.utils import read_token_value, collect_until_whitespace +from mo.front.kaldi.utils import read_learning_info, read_binary_matrix, read_binary_vector +from mo.graph.graph import Node +from mo.ops.convolution import Convolution +from mo.utils.error import Error +from mo.utils.utils import refer_to_faq_msg + + +class ConvolutionalComponentFrontExtractor(FrontExtractorOp): + op = 'convolutionalcomponent' # Naming like in Kaldi + enabled = True + + @staticmethod + def extract(node: Node) -> bool: + """ + Extract conv parameters from node.parameters. + node.parameters like file descriptor object. + :param node: Convolution node + :return: + """ + pb = node.parameters + kernel = read_token_value(pb, b'<PatchDim>') + stride = read_token_value(pb, b'<PatchStep>') + patch_stride = read_token_value(pb, b'<PatchStride>') + + read_learning_info(pb) + + collect_until_whitespace(pb) + weights, weights_shape = read_binary_matrix(pb) + + collect_until_whitespace(pb) + biases = read_binary_vector(pb) + + if (patch_stride - kernel) % stride != 0: + raise Error( + 'Kernel size and stride does not correspond to `patch_stride` attribute of Convolution layer. ' + + refer_to_faq_msg(93)) + + output = biases.shape[0] + if weights_shape[0] != output: + raise Error('Weights shape does not correspond to the `output` attribute of Convolution layer. ' + + refer_to_faq_msg(93)) + + mapping_rule = { + 'output': output, + 'patch_stride': patch_stride, + 'bias_term': None, + 'pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]], dtype=np.int64), + 'pad_spatial_shape': np.array([[0, 0], [0, 0]], dtype=np.int64), + 'dilation': np.array([1, 1, 1, 1], dtype=np.int64), + 'kernel': np.array([1, 1, 1, kernel], dtype=np.int64), + 'stride': np.array([1, 1, 1, stride], dtype=np.int64), + 'kernel_spatial': np.array([1, kernel], dtype=np.int64), + 'input_feature_channel': 1, + 'output_feature_channel': 0, + 'kernel_spatial_idx': [2, 3], + 'group': 1, + 'reshape_kernel': True, + } + + mapping_rule.update(layout_attrs()) + embed_input(mapping_rule, 1, 'weights', weights) + embed_input(mapping_rule, 2, 'biases', biases) + + mapping_rule['bias_addable'] = len(biases) > 0 + + Convolution.update_node_stat(node, mapping_rule) + return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/inner_product_ext.py b/model-optimizer/mo/front/kaldi/extractors/copy_ext.py index 0ec3f5726..3348ef14c 100644 --- a/model-optimizer/mo/front/kaldi/extractors/inner_product_ext.py +++ b/model-optimizer/mo/front/kaldi/extractors/copy_ext.py @@ -14,22 +14,27 @@ limitations under the License. """ -from mo.front.caffe.extractors.utils import weights_biases -from mo.front.common.extractors.utils import layout_attrs +import numpy as np + +from mo.front.caffe.extractors.utils import embed_input +from mo.front.common.partial_infer.elemental import copy_shape_infer from mo.front.extractor import FrontExtractorOp -from mo.ops.op import Op +from mo.front.kaldi.loader.utils import read_binary_integer32_token, read_blob +from mo.ops.permute import Permute -class InnerProductFrontExtractor(FrontExtractorOp): - op = 'fullyconnected' +class CopyFrontExtractor(FrontExtractorOp): + op = 'copy' enabled = True @staticmethod def extract(node): - mapping_rule = { - 'out-size': node.pb.num_output + pb = node.parameters + weights_size = read_binary_integer32_token(pb) + weights = read_blob(pb, weights_size, dtype=np.int32) - 1 + attrs = { + 'infer': copy_shape_infer } - mapping_rule.update(layout_attrs()) - mapping_rule.update(weights_biases(node.pb.bias_term, node.pb)) - Op.get_op_class_by_name('FullyConnected').update_node_stat(node, mapping_rule) + embed_input(attrs, 1, 'indexes', weights) + Permute.update_node_stat(node, attrs) return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/fixed_affine_component_ext.py b/model-optimizer/mo/front/kaldi/extractors/fixed_affine_component_ext.py new file mode 100644 index 000000000..eee267f16 --- /dev/null +++ b/model-optimizer/mo/front/kaldi/extractors/fixed_affine_component_ext.py @@ -0,0 +1,51 @@ +""" + Copyright (c) 2018 Intel Corporation + + 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 mo.front.caffe.extractors.utils import embed_input +from mo.front.extractor import FrontExtractorOp +from mo.front.kaldi.loader.utils import find_next_tag, read_placeholder +from mo.front.kaldi.utils import read_binary_matrix, read_binary_vector +from mo.ops.inner_product import InnerProduct +from mo.utils.error import Error + + +class FixedAffineComponentFrontExtractor(FrontExtractorOp): + op = 'fixedaffinecomponent' + enabled = True + + @staticmethod + def extract(node): + pb = node.parameters + tag = find_next_tag(pb) + if tag != '<LinearParams>': + raise Error('FixedAffineComponent must contain LinearParams') + read_placeholder(pb, 1) + weights, weights_shape = read_binary_matrix(pb) + tag = find_next_tag(pb) + read_placeholder(pb, 1) + if tag != '<BiasParams>': + raise Error('FixedAffineComponent must contain BiasParams') + biases = read_binary_vector(pb) + + mapping_rule = { + 'out-size': weights_shape[0], + 'layout': 'NCHW' + } + embed_input(mapping_rule, 1, 'weights', weights) + embed_input(mapping_rule, 2, 'biases', biases) + + InnerProduct.update_node_stat(node, mapping_rule) + return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/lstm_projected_streams_ext.py b/model-optimizer/mo/front/kaldi/extractors/lstm_projected_streams_ext.py new file mode 100644 index 000000000..09e8061d3 --- /dev/null +++ b/model-optimizer/mo/front/kaldi/extractors/lstm_projected_streams_ext.py @@ -0,0 +1,68 @@ +""" + Copyright (c) 2018 Intel Corporation + + 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 extensions.ops.lstm_cell import LSTMCell +from mo.front.caffe.extractors.utils import embed_input +from mo.front.extractor import FrontExtractorOp +from mo.front.kaldi.loader.utils import collect_until_token, collect_until_whitespace, get_uint32 +from mo.front.kaldi.utils import read_binary_matrix, read_binary_vector + + +class LSTMProjectedStreamsFrontExtractor(FrontExtractorOp): + op = 'lstmprojectedstreams' + enabled = True + + @staticmethod + def extract(node): + clip_value = 50 + pb = node.parameters + res = collect_until_whitespace(pb) + if res == b'<CellClip>': + clip_value = get_uint32(pb.read(4)) + collect_until_token(pb, b'FM') + gifo_x_weights, gifo_x_weights_shape = read_binary_matrix(pb, False) + gifo_r_weights, gifo_r_weights_shape = read_binary_matrix(pb) + gifo_biases = read_binary_vector(pb) + input_gate_weights = read_binary_vector(pb) + forget_gate_weights = read_binary_vector(pb) + output_gate_weights = read_binary_vector(pb) + + projection_weights, projection_weights_shape = read_binary_matrix(pb) + + mapping_rule = {'gifo_x_weights_shape': gifo_x_weights_shape, + 'gifo_r_weights_shape': gifo_r_weights_shape, + 'projection_weights_shape': projection_weights_shape, + 'clip_value': clip_value + } + + embed_input(mapping_rule, 1, 'gifo_x_weights', gifo_x_weights) + embed_input(mapping_rule, 2, 'gifo_r_weights', gifo_r_weights) + embed_input(mapping_rule, 3, 'gifo_biases', gifo_biases) + embed_input(mapping_rule, 4, 'input_gate_weights', input_gate_weights) + embed_input(mapping_rule, 5, 'forget_gate_weights', forget_gate_weights) + embed_input(mapping_rule, 6, 'output_gate_weights', output_gate_weights) + embed_input(mapping_rule, 7, 'projection_weights', projection_weights) + + LSTMCell.update_node_stat(node, mapping_rule) + return __class__.enabled + + +class LSTMProjectedFrontExtractor(FrontExtractorOp): + op = 'lstmprojected' + enabled = True + + @staticmethod + def extract(node): + return LSTMProjectedStreamsFrontExtractor.extract(node) diff --git a/model-optimizer/mo/front/kaldi/extractors/max_pooling_ext.py b/model-optimizer/mo/front/kaldi/extractors/max_pooling_ext.py new file mode 100644 index 000000000..0e38dd33e --- /dev/null +++ b/model-optimizer/mo/front/kaldi/extractors/max_pooling_ext.py @@ -0,0 +1,60 @@ +""" + Copyright (c) 2018 Intel Corporation + + 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 numpy as np + +from mo.front.common.extractors.utils import layout_attrs +from mo.front.extractor import FrontExtractorOp +from mo.front.kaldi.loader.utils import read_token_value, collect_until_whitespace, collect_until_token, \ + read_binary_integer32_token, find_next_tag, read_placeholder +from mo.ops.pooling import Pooling +from mo.utils.error import Error + + +class MaxPoolingComponentFrontExtractor(FrontExtractorOp): + op = 'maxpoolingcomponent' + enabled = True + + @staticmethod + def extract(node): + pb = node.parameters + collect_until_token(pb, b'<PoolSize>') + kernel = read_binary_integer32_token(pb) + tag = find_next_tag(pb) + if tag == '<PoolStep>': + read_placeholder(pb, 1) + stride = read_binary_integer32_token(pb) + pool_step = stride + pool_stride = read_token_value(pb, b'<PoolStride>') + elif tag == '<PoolStride>': + stride = 1 + pool_step = None + read_placeholder(pb, 1) + pool_stride = read_binary_integer32_token(pb) + else: + raise Error('Can not extract parameters for {}'.format(node)) + + mapping_rule = { + 'window': np.array([1, 1, 1, kernel], dtype=np.int64), + 'stride': np.array([1, 1, stride, stride], dtype=np.int64), + 'pool_stride': pool_stride, + 'pool_step': pool_step, + 'pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]], dtype=np.int64), + 'pad_spatial_shape': np.array([[0, 0], [0, 0]], dtype=np.int64), + 'pool_method': 'max', + } + mapping_rule.update(layout_attrs()) + Pooling.update_node_stat(node, mapping_rule) + return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/normalize_component_ext.py b/model-optimizer/mo/front/kaldi/extractors/normalize_component_ext.py new file mode 100644 index 000000000..4d1e9e930 --- /dev/null +++ b/model-optimizer/mo/front/kaldi/extractors/normalize_component_ext.py @@ -0,0 +1,43 @@ +""" + Copyright (c) 2018 Intel Corporation + + 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 numpy as np + +from mo.front.caffe.extractors.utils import embed_input +from mo.front.extractor import FrontExtractorOp +from mo.front.kaldi.loader.utils import read_binary_integer32_token, collect_until_token +from mo.ops.scale_shift import ScaleShiftOp + + +class NormalizeComponentFrontExtractor(FrontExtractorOp): + op = 'normalizecomponent' + enabled = True + + @staticmethod + def extract(node): + pb = node.parameters + collect_until_token(pb, b'<Dim>') + dim = read_binary_integer32_token(pb) + target_rms = 1 + d_scaled = dim * target_rms ** 2 + in_norm = np.zeros([dim], np.float64) + in_norm += 1.0 / d_scaled + in_norm = np.maximum(in_norm, 2. ** (-66)) + in_norm = np.power(in_norm, -0.5) + attrs = {} + embed_input(attrs, 1, 'weights', in_norm) + ScaleShiftOp.update_node_stat(node, attrs) + return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/pooling_ext.py b/model-optimizer/mo/front/kaldi/extractors/pooling_ext.py deleted file mode 100644 index 44c64a32d..000000000 --- a/model-optimizer/mo/front/kaldi/extractors/pooling_ext.py +++ /dev/null @@ -1,38 +0,0 @@ -""" - Copyright (c) 2018 Intel Corporation - - 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 mo.front.common.extractors.utils import layout_attrs -from mo.front.common.partial_infer.utils import int64_array -from mo.front.extractor import FrontExtractorOp -from mo.ops.op import Op - - -class PoolingFrontExtractor(FrontExtractorOp): - op = 'pooling' - enabled = True - - @staticmethod - def extract(node): - mapping_rule = { - 'window': int64_array([1, 1, 1, node.pb.kernel]), - 'stride': int64_array([1, 1, node.pb.stride, node.pb.stride]), - 'pool_stride': node.pb.pool_stride, - 'pad': int64_array([[0, 0], [0, 0], [0, 0], [0, 0]]), - 'pad_spatial_shape': int64_array([[0, 0], [0, 0]]), - } - mapping_rule.update(layout_attrs()) - Op.get_op_class_by_name('Pooling').update_node_stat(node, mapping_rule) - return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/clamp_ext.py b/model-optimizer/mo/front/kaldi/extractors/rectified_linear_component_ext.py index 80157db80..713db4beb 100644 --- a/model-optimizer/mo/front/kaldi/extractors/clamp_ext.py +++ b/model-optimizer/mo/front/kaldi/extractors/rectified_linear_component_ext.py @@ -15,19 +15,14 @@ """ from mo.front.extractor import FrontExtractorOp -from mo.ops.op import Op +from mo.ops.relu import ReLU -class ClampFrontExtractor(FrontExtractorOp): - op = 'clamp' +class RectifiedLinearComponentFrontExtractor(FrontExtractorOp): + op = 'rectifiedlinearcomponent' enabled = True @staticmethod def extract(node): - mapping_rule = { - 'min': node.pb.min, - 'max': node.pb.max, - } - - Op.get_op_class_by_name('Clamp').update_node_stat(node, mapping_rule) + ReLU.update_node_stat(node, {}) return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/scale_shift.py b/model-optimizer/mo/front/kaldi/extractors/rescale_ext.py index 5ade29505..459e5582e 100644 --- a/model-optimizer/mo/front/kaldi/extractors/scale_shift.py +++ b/model-optimizer/mo/front/kaldi/extractors/rescale_ext.py @@ -14,19 +14,23 @@ limitations under the License. """ -from mo.front.caffe.extractors.utils import weights_biases +from mo.front.caffe.extractors.utils import embed_input from mo.front.extractor import FrontExtractorOp -from mo.ops.op import Op +from mo.front.kaldi.loader.utils import collect_until_whitespace, read_binary_integer32_token +from mo.front.kaldi.utils import read_binary_vector, read_learning_info +from mo.ops.scale_shift import ScaleShiftOp -class ScaleShiftFrontExtractor(FrontExtractorOp): - op = 'scaleshift' +class RescaleFrontExtractor(FrontExtractorOp): + op = 'rescale' enabled = True @staticmethod def extract(node): + pb = node.parameters + read_learning_info(pb) + weights = read_binary_vector(pb) mapping_rule = {} - mapping_rule.update(weights_biases(node.pb.bias_term, node.pb)) - # update the attributes of the node - Op.get_op_class_by_name('ScaleShift').update_node_stat(node, mapping_rule) + embed_input(mapping_rule, 1, 'weights', weights) + ScaleShiftOp.update_node_stat(node, mapping_rule) return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/reshape.py b/model-optimizer/mo/front/kaldi/extractors/reshape.py deleted file mode 100644 index cf47f67ab..000000000 --- a/model-optimizer/mo/front/kaldi/extractors/reshape.py +++ /dev/null @@ -1,68 +0,0 @@ -""" - Copyright (c) 2018 Intel Corporation - - 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 numpy as np - -from mo.front.common.partial_infer.reshape import tf_reshape_shape_infer -from mo.front.common.partial_infer.utils import int64_array -from mo.front.extractor import FrontExtractorOp -from mo.graph.graph import Node -from mo.ops.op import Op -from mo.ops.reshape import Reshape - - -class ReshapeFrontExtractor(FrontExtractorOp): - op = 'reshape' - enabled = True - - @staticmethod - def extract(node): - mapping_rule = { - 'dim': node.pb.dim if hasattr(node.pb, 'dim') else None, - 'axis': node.pb.axis, - 'num_axes': node.pb.num_axes, - 'infer': ReshapeFrontExtractor.infer - } - Op.get_op_class_by_name('Reshape').update_node_stat(node, mapping_rule) - return __class__.enabled - - @staticmethod - def infer(node: Node): - in_node = node.in_node().in_node() # prev_layer_node -> data -> this_node - input_shape = node.in_node().shape - # Kaldi Reshape hugely depends on the layers that precedes or succeeds - # Convolution/Pooling layers. Therefore there are 4 cases with different - # partial inference. - batch = input_shape[0] - if in_node.type == 'Convolution' or in_node.type == 'Pooling': - output_spatial = int64_array([batch, np.prod(input_shape[1:])]) - return ReshapeFrontExtractor.set_shape_and_dim(node, output_spatial) - # Supports ONLY NCHW and NH layouts - spatial_shape = input_shape[1] - if input_shape.shape == (4,): - spatial_shape = input_shape[2:3] - out_node = node.out_node().out_node() - if out_node.type == 'Convolution': - output_spatial = int64_array([batch, int(np.ceil(spatial_shape / out_node.patch_stride)), 1, out_node.patch_stride]) - return ReshapeFrontExtractor.set_shape_and_dim(node, output_spatial) - elif out_node.type == 'Pooling': - output_spatial = int64_array([batch, out_node.pool_stride, 1, int(np.ceil(spatial_shape / out_node.pool_stride))]) - return ReshapeFrontExtractor.set_shape_and_dim(node, output_spatial) - - @staticmethod - def set_shape_and_dim(node: Node, reshape_dim): - Reshape.update_node_stat(node, {'dim': reshape_dim}) - node.out_node().shape = reshape_dim - diff --git a/model-optimizer/mo/front/kaldi/extractors/sigmoid_ext.py b/model-optimizer/mo/front/kaldi/extractors/sigmoid_ext.py index 31f284574..a68ad4f3f 100644 --- a/model-optimizer/mo/front/kaldi/extractors/sigmoid_ext.py +++ b/model-optimizer/mo/front/kaldi/extractors/sigmoid_ext.py @@ -18,7 +18,7 @@ from mo.front.extractor import FrontExtractorOp from mo.ops.activation import Activation -class Sigmoid(FrontExtractorOp): +class SigmoidFrontExtractor(FrontExtractorOp): op = 'sigmoid' enabled = True diff --git a/model-optimizer/mo/front/kaldi/extractors/slice_ext.py b/model-optimizer/mo/front/kaldi/extractors/slice_ext.py index 1acfb9bba..4235c0dbc 100644 --- a/model-optimizer/mo/front/kaldi/extractors/slice_ext.py +++ b/model-optimizer/mo/front/kaldi/extractors/slice_ext.py @@ -13,9 +13,12 @@ See the License for the specific language governing permissions and limitations under the License. """ +import numpy as np + from mo.front.common.partial_infer.slice import caffe_slice_infer from mo.front.extractor import FrontExtractorOp -from mo.ops.op import Op +from mo.front.kaldi.loader.utils import read_binary_integer32_token, read_blob +from mo.ops.slice import Slice class SliceFrontExtractor(FrontExtractorOp): @@ -24,12 +27,15 @@ class SliceFrontExtractor(FrontExtractorOp): @staticmethod def extract(node): + pb = node.parameters + num_slice_points = read_binary_integer32_token(pb) mapping_rule = { - 'axis': node.pb.axis if hasattr(node.pb, 'axis') else 1, - 'slice_point': node.pb.slice_point, + 'axis': 1, + 'slice_point': read_blob(pb, num_slice_points, np.int32), 'batch_dims': 0, 'spatial_dims': 1, 'infer': caffe_slice_infer } - Op.get_op_class_by_name('Slice').update_node_stat(node, mapping_rule) + node.parameters.close() + Slice.update_node_stat(node, mapping_rule) return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/eltwise_ext.py b/model-optimizer/mo/front/kaldi/extractors/softmax_ext.py index b44fe70e3..da9f0a1eb 100644 --- a/model-optimizer/mo/front/kaldi/extractors/eltwise_ext.py +++ b/model-optimizer/mo/front/kaldi/extractors/softmax_ext.py @@ -13,21 +13,25 @@ See the License for the specific language governing permissions and limitations under the License. """ - +from mo.front.common.partial_infer.elemental import copy_shape_infer from mo.front.extractor import FrontExtractorOp -from mo.ops.op import Op +from mo.ops.softmax import Softmax -class EltwiseFrontExtractor(FrontExtractorOp): - op = 'eltwise' +class SoftmaxComponentFrontExtractor(FrontExtractorOp): + op = 'softmaxcomponent' enabled = True @staticmethod def extract(node): - mapping_rule = { - 'operation': node.pb.operation, - } - # update the attributes of the node - Op.get_op_class_by_name('Eltwise').update_node_stat(node, mapping_rule) - return __class__.enabled + return SoftmaxFrontExtractor.extract(node) + + +class SoftmaxFrontExtractor(FrontExtractorOp): + op = 'softmax' + enabled = True + @staticmethod + def extract(node): + Softmax.update_node_stat(node, {'infer': copy_shape_infer}) + return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/splice_component_ext.py b/model-optimizer/mo/front/kaldi/extractors/splice_component_ext.py new file mode 100644 index 000000000..47cbc23f6 --- /dev/null +++ b/model-optimizer/mo/front/kaldi/extractors/splice_component_ext.py @@ -0,0 +1,53 @@ +""" + Copyright (c) 2018 Intel Corporation + + 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 numpy as np + +from extensions.ops.splice import Splice +from mo.front.extractor import FrontExtractorOp +from mo.front.kaldi.loader.utils import find_next_tag, read_placeholder, read_binary_integer32_token, \ + collect_until_whitespace +from mo.front.kaldi.utils import read_binary_vector +from mo.utils.error import Error + + +class SpliceFrontExtractor(FrontExtractorOp): + op = 'splicecomponent' + enabled = True + + @staticmethod + def extract(node): + pb = node.parameters + mapping_rule = { + 'context': list() + } + tag = find_next_tag(pb) + if tag == '<LeftContext>': + read_placeholder(pb, 1) + l_context = read_binary_integer32_token(pb) + tag = find_next_tag(pb) + if tag != '<RightContext>': + raise Error('Unknown token {} in SpliceComponent node {}'.format(tag, node.id)) + read_placeholder(pb, 1) + r_context = read_binary_integer32_token(pb) + for i in range(-l_context, r_context + 1): + mapping_rule['context'].append(i) + elif tag == '<Context>': + collect_until_whitespace(pb) + mapping_rule['context'] = read_binary_vector(pb, False, dtype=np.int32) + else: + raise Error('Unknown token {} in SpliceComponent node {}'.format(tag, node.id)) + Splice.update_node_stat(node, mapping_rule) + return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/split.py b/model-optimizer/mo/front/kaldi/extractors/split.py deleted file mode 100644 index 6e85cc889..000000000 --- a/model-optimizer/mo/front/kaldi/extractors/split.py +++ /dev/null @@ -1,32 +0,0 @@ -""" - Copyright (c) 2018 Intel Corporation - - 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 mo.front.common.partial_infer.slice import caffe_slice_infer -from mo.front.extractor import FrontExtractorOp -from mo.ops.op import Op - - -class SplitFrontExtractor(FrontExtractorOp): - op = 'split' - enabled = True - - @staticmethod - def extract(node): - mapping_rule = { - 'axis': node.pb.axis if node.pb.axis else 1, - 'num_split': node.pb.num_split, - } - Op.get_op_class_by_name('Split').update_node_stat(node, mapping_rule) - return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/extractors/activation_ext.py b/model-optimizer/mo/front/kaldi/extractors/tanh_component_ext.py index 3125387b0..e67f9c410 100644 --- a/model-optimizer/mo/front/kaldi/extractors/activation_ext.py +++ b/model-optimizer/mo/front/kaldi/extractors/tanh_component_ext.py @@ -15,18 +15,14 @@ """ from mo.front.extractor import FrontExtractorOp -from mo.ops.op import Op +from mo.ops.activation import Activation -class ActivationFrontExtractor(FrontExtractorOp): - op = 'activation' +class TanhFrontExtractor(FrontExtractorOp): + op = 'tanhcomponent' enabled = True @staticmethod def extract(node): - mapping_rule = { - 'operation': node.pb.operation - } - - Op.get_op_class_by_name('Activation').update_node_stat(node, mapping_rule) + Activation.update_node_stat(node, {'operation': 'tanh'}) return __class__.enabled diff --git a/model-optimizer/mo/front/kaldi/loader.py b/model-optimizer/mo/front/kaldi/loader.py deleted file mode 100644 index 7638a6c5a..000000000 --- a/model-optimizer/mo/front/kaldi/loader.py +++ /dev/null @@ -1,544 +0,0 @@ -""" - Copyright (c) 2018 Intel Corporation - - 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 os -from io import IOBase - -from mo.front.kaldi.extractor import common_kaldi_fields -from mo.front.kaldi.utils import get_uint32, get_uint16, KaldiNode -from mo.graph.graph import unique_id, Node -from mo.utils.error import Error - -import networkx as nx -import numpy as np - -from mo.utils.utils import refer_to_faq_msg - - -def read_placeholder(file_desc): - """ - Placeholder is like: |FW | or |FV | - they take 3 spaces and appear before a matrix or a vector respectively - :param file_path: - :return: - """ - file_desc.read(3) - - -def read_binary_matrix(file_desc, skip: bool = False): - if not skip: - read_placeholder(file_desc) - rows_number = read_binary_integer_token(file_desc) - cols_number = read_binary_integer_token(file_desc) - # to compare: ((float *)a->buffer())[10] - return read_blob(file_desc, rows_number * cols_number), (rows_number, cols_number) - - -def read_binary_vector(file_desc): - read_placeholder(file_desc) - elements_number = read_binary_integer_token(file_desc) - return read_blob(file_desc, elements_number) - - -def collect_until_token(f, token): - while True: - # usually there is the following structure <CellDim> DIM<ClipGradient> VALUEFM - res = collect_until_whitespace(f) - if res[-2:] == token: - return - - -class KaldiLayer: - def __init__(self, f, graph, layer_i, layer_o, layer_name, prev_layer_name, is_switch_board_style=False, - type='General'): - self.f = f - self.graph = graph - self.type = type - self.layer_i = layer_i - self.layer_o = layer_o - self.layer_name = layer_name - self.prev_layer_name = prev_layer_name - self.is_switch_board_style = is_switch_board_style - self.attrs = dict(type=type) - self.weights = None - self.biases = None - - def construct_sub_graph(self): - return add_single_node(self.graph, self.layer_name, self.prev_layer_name, self.attrs, self.weights, self.biases) - - def load_build(self): - return self.construct_sub_graph() - - -class SigmoidKaldiLayer(KaldiLayer): - def load_build(self): - self.attrs.update({ - 'operation': 'sigmoid' - }) - return self.construct_sub_graph() - - -class AffineTransformKaldiLayer(KaldiLayer): - def load_weights_biases_attrs(self): - collect_until_token(self.f, b'FM') - self.weights, weights_shape = read_binary_matrix(self.f, skip=True) - self.biases = read_binary_vector(self.f) - self.attrs = { - 'num_output': self.layer_o, - 'bias_term': True, - 'weights_shape': weights_shape, - 'type': 'AffineTransform' - } - - def load_build(self): - self.load_weights_biases_attrs() - return self.construct_sub_graph() - - -class LSTMProjectedStreamsKaldiLayer(KaldiLayer): - def __init__(self, f, graph, layer_i, layer_o, layer_name, prev_layer_name, is_switch_board_style=False, - type='General'): - super().__init__(f, graph, layer_i, layer_o, layer_name, prev_layer_name, is_switch_board_style, type) - self.clip_value = None - self.gifo_x_weights = None - self.gifo_r_weights = None - self.gifo_biases = None - self.input_gate_weights = None - self.forget_gate_weights = None - self.output_gate_weights = None - self.projection_weights = None - self.gifo_x_weights_shape = None - self.gifo_r_weights_shape = None - self.projection_weights_shape = None - - def load_weights_biases_attrs(self): - self.clip_value = 1 if self.is_switch_board_style else 50 - - if not self.is_switch_board_style: - res = collect_until_whitespace(self.f) # <CellClip> - if res == b'<CellClip>': - self.clip_value = get_uint32(self.f.read(4)) - - collect_until_token(self.f, b'FM') - - self.gifo_x_weights, self.gifo_x_weights_shape = read_binary_matrix(self.f, skip=True) - self.gifo_r_weights, self.gifo_r_weights_shape = read_binary_matrix(self.f) - self.gifo_biases = read_binary_vector(self.f) - self.input_gate_weights = read_binary_vector(self.f) - self.forget_gate_weights = read_binary_vector(self.f) - self.output_gate_weights = read_binary_vector(self.f) - - if not self.is_switch_board_style: - self.projection_weights, self.projection_weights_shape = read_binary_matrix(self.f) - - def load_build(self): - self.load_weights_biases_attrs() - return self.construct_sub_graph() - - def construct_sub_graph(self): - self.attrs.update(dict(gifo_x_weights=self.gifo_x_weights, gifo_r_weights=self.gifo_r_weights, - gifo_biases=self.gifo_biases, input_gate_weights=self.input_gate_weights, - forget_gate_weights=self.forget_gate_weights, - clip_value=self.clip_value, - output_gate_weights=self.output_gate_weights, - projection_weights=self.projection_weights, - gifo_x_weights_shape=self.gifo_x_weights_shape, - gifo_r_weights_shape=self.gifo_r_weights_shape, - projection_weights_shape=self.projection_weights_shape, - type='LSTMProjectedStreams')) - return add_single_node(self.graph, self.layer_name, self.prev_layer_name, self.attrs, self.weights, self.biases) - - -class ConvolutionKaldiLayer(KaldiLayer): - def __init__(self, f, graph, layer_i, layer_o, layer_name, prev_layer_name, is_switch_board_style=False): - super().__init__(f, graph, layer_i, layer_o, layer_name, prev_layer_name, is_switch_board_style, 'Convolution') - self.kernel = None - self.stride = None - self.output = None - self.weights_shape = None - self.shape = None - self.patch_stride = None - - def load_build(self): - ''' - /* Prepare feature patches, the layout is: - 274 |----------|----------|----------|---------| (in = spliced frames) - 275 xxx xxx xxx xxx (x = selected elements) - 276 - 277 xxx : patch dim - 278 xxx - 279 ^---: patch step - 280 |----------| : patch stride - 281 - 282 xxx-xxx-xxx-xxx : filter dim - 283 - ''' - self.kernel = read_token_value(self.f, b'<PatchDim>') - self.stride = read_token_value(self.f, b'<PatchStep>') - self.patch_stride = read_token_value(self.f, b'<PatchStride>') - - if (self.patch_stride - self.kernel) % self.stride != 0: - raise Error( - 'Kernel size and stride does not correspond to `patch_stride` attribute of Convolution layer. ' + - refer_to_faq_msg(93)) - - do_loop = True - while do_loop: - self.f.read(1) - first_char = self.f.read(1) - self.f.seek(-2, os.SEEK_CUR) - if first_char == b'L': - read_token_value(self.f, b'<LearnRateCoef>') - elif first_char == b'B': - read_token_value(self.f, b'<BiasLearnRateCoef>') - elif first_char == b'M': - read_token_value(self.f, b'<MaxNorm>') - elif first_char == b'!': - read_token_value(self.f, b'<EndOfComponent>') - do_loop = False - else: - do_loop = False - self.load_weights_biases_attrs() - - self.output = self.biases.shape[0] - if self.weights_shape[0] != self.output: - raise Error('Weights shape does not correspond to the `output` attribute of Convolution layer. ' + - refer_to_faq_msg(93)) - self.attrs.update({ - 'kernel': self.kernel, - 'stride': self.stride, - 'output': self.output, - 'bias_term': True, - 'patch_stride': self.patch_stride - }) - return self.construct_sub_graph() - - def load_weights_biases_attrs(self): - collect_until_whitespace(self.f) - self.weights, self.weights_shape = read_binary_matrix(self.f) - collect_until_whitespace(self.f) - self.biases = read_binary_vector(self.f) - - -class PoolingKaldiLayer(KaldiLayer): - def __init__(self, f, graph, layer_i, layer_o, layer_name, prev_layer_name, is_switch_board_style=False, - pool_method='Max'): - super().__init__(f, graph, layer_i, layer_o, layer_name, prev_layer_name, is_switch_board_style, 'Pooling') - self.pad = 0 - self.window = None - self.pool_method = pool_method - self.stride = None - - def load_build(self): - self.window = read_token_value(self.f, b'<PoolSize>') - self.stride = read_token_value(self.f, b'<PoolStep>') - pool_stride = read_token_value(self.f, b'<PoolStride>') - - self.attrs.update({ - 'kernel': self.window, - 'stride': self.stride, - 'pool_stride': pool_stride, - 'pool_method': self.pool_method - }) - return self.construct_sub_graph() - - -class ScaleShiftKaldiLayer(KaldiLayer): - def __init__(self, f, graph, layer_i, layer_o, layer_name, prev_layer_name, is_switch_board_style=False, - weights=None): - super().__init__(f, graph, layer_i, layer_o, layer_name, prev_layer_name, is_switch_board_style, 'ScaleShift') - self.weights = weights - self.bias_term = False - - def load_build(self): - if collect_until_whitespace(self.f) == b'<AddShift>': - self.layer_o = read_binary_integer_token(self.f) - self.layer_o = read_binary_integer_token(self.f) - self.biases = read_binary_vector(self.f) - self.bias_term = True - self.attrs.update({'bias_term': self.bias_term}) - return self.construct_sub_graph() - - -class RescaleKaldiLayer(KaldiLayer): - def __init__(self, f, graph, layer_i, layer_o, layer_name, prev_layer_name, is_switch_board_style=False): - super().__init__(f, graph, layer_i, layer_o, layer_name, prev_layer_name, is_switch_board_style, 'ScaleShift') - self.weights = None - self.bias_term = False - - def load_build(self): - if self.f.read(1) == b'<': - self.f.seek(-1, os.SEEK_CUR) - read_token_value(self.f, b'<LearnRateCoef>') - else: - self.f.seek(-1, os.SEEK_CUR) - self.weights = read_binary_vector(self.f) - next_token = collect_until_whitespace(self.f) - if next_token == b'<!EndOfComponent>': - next_token = collect_until_whitespace(self.f) - if next_token == b'<AddShift>': - read_binary_integer_token(self.f) # input layer - self.layer_o = read_binary_integer_token(self.f) - if self.f.read(1) == b'<': - self.f.seek(-1, os.SEEK_CUR) - read_token_value(self.f, b'<LearnRateCoef>') - else: - self.f.seek(-1, os.SEEK_CUR) - self.biases = read_binary_vector(self.f) - self.bias_term = True - self.attrs.update({'bias_term': self.bias_term}) - else: - self.f.seek(-len(next_token), os.SEEK_CUR) - return self.construct_sub_graph() - - -class ParallelKaldiLayer(KaldiLayer): - def __init__(self, f, graph, layer_i, layer_o, layer_name, prev_layer_name, is_switch_board_style=False): - super().__init__(f, graph, layer_i, layer_o, layer_name, prev_layer_name, is_switch_board_style, 'Parallel') - self.output_nodes = [] - self.edge_attrs = { - 'out': None, - 'in': 0, - 'name': None, - 'fw_tensor_name': None, - 'fw_tensor_port': None, - 'in_attrs': ['in', 'name'], - 'out_attrs': ['out', 'name'], - 'data_attrs': ['fw_tensor_name', 'fw_tensor_port'] - } - - def load_build(self): - nnet_count = read_token_value(self.f, b'<NestedNnetCount>') - slice_id = add_single_node(self.graph, 'Slice', self.prev_layer_name, - {'type': 'Slice', 'axis': 1, 'slice_point': []}, None, None) - for i in range(nnet_count): - read_token_value(self.f, b'<NestedNnet>') - graph, shape = load_kaldi_nnet_model(self.f, None) - input_nodes = [n for n in graph.nodes(data=True) if n[1]['type'] == 'GlobalInput'] - for input_node in input_nodes: - shape_subgraph = input_node[1]['shape'] - if i != nnet_count - 1: - self.graph.node[slice_id]['pb'].slice_point.append(shape_subgraph[1]) - graph.remove_node(input_node[0]) - mapping = {node: unique_id(self.graph, node) for node in graph.nodes(data=False) if node in self.graph} - g = nx.relabel_nodes(graph, mapping) - for val in mapping.values(): - g.node[val]['name'] = val - self.graph.add_nodes_from(g.nodes(data=True)) - self.graph.add_edges_from(g.edges(data=True)) - sorted_nodes = tuple(nx.topological_sort(g)) - self.edge_attrs['out'] = i - self.edge_attrs['name'] = sorted_nodes[0] - self.edge_attrs['fw_tensor_name'] = slice_id - self.edge_attrs['fw_tensor_port'] = sorted_nodes[0] - self.graph.add_edge(slice_id, sorted_nodes[0], **self.edge_attrs) - self.output_nodes.append(sorted_nodes[-1]) - end_token = collect_until_whitespace(self.f) - if end_token != b'</ParallelComponent>': - raise Error('Expected token `</ParallelComponent>`, has {}'.format(end_token) + refer_to_faq_msg(99)) - return self.construct_sub_graph() - - def construct_sub_graph(self): - new_id = unique_id(self.graph, '{}_'.format('Concat')) - layer = KaldiNode(new_id) - layer.set_attrs(dict(axis=1)) - layer.type = 'Concat' - self.graph.add_node(new_id, pb=layer, kind='op') - self.graph.node[layer.name].update(common_kaldi_fields(Node(self.graph, layer.name))) - self.edge_attrs['out'] = 0 - self.edge_attrs['name'] = layer.name - self.edge_attrs['fw_tensor_port'] = layer.name - for i, output_node in enumerate(self.output_nodes): - self.edge_attrs['fw_tensor_name'] = output_node - self.edge_attrs['in'] = i - self.graph.add_edge(output_node, layer.name, **self.edge_attrs) - return new_id - - -def read_token_value(file, token: bytes = b'', value_type: type = np.uint32): - getters = { - np.uint32: read_binary_integer_token - } - current_token = collect_until_whitespace(file) - if token != b'' and token != current_token: - raise Error('Can not load token {} from Kaldi model'.format(token) + - refer_to_faq_msg(94)) - return getters[value_type](file) - - -def read_binary_integer_token(file_path): - buffer_size = file_path.read(1) - return get_uint32(file_path.read(buffer_size[0])) - - -def collect_until_whitespace(file_path): - res = b'' - while True: - new_sym = file_path.read(1) - if new_sym == b' ': - break - res += new_sym - return res - - -def read_blob(file_path, size): - float_size = 4 - data = file_path.read(size * float_size) - return np.fromstring(data, dtype='<f4') - - -layer_weights_biases_attrs_getter = { - 'affinetransform': AffineTransformKaldiLayer, - 'sigmoid': lambda f, g, i, o, name, prev, style: KaldiLayer(f, g, i, o, name, prev, style, type='Sigmoid'), - 'softmax': lambda f, g, i, o, name, prev, style: KaldiLayer(f, g, i, o, name, prev, style, type='SoftMax'), - 'lstmprojectedstreams': LSTMProjectedStreamsKaldiLayer, - 'lstmprojected': LSTMProjectedStreamsKaldiLayer, - 'maxpoolingcomponent': PoolingKaldiLayer, - 'convolutionalcomponent': ConvolutionKaldiLayer, - 'rescale': RescaleKaldiLayer, - 'parallelcomponent': ParallelKaldiLayer, -} - - -def add_single_node(graph, layer_name, prev_layer_name, attrs, weights, biases): - new_id = unique_id(graph, '{}_'.format(layer_name)) - - layer = KaldiNode(new_id) - layer.set_weight(weights) - layer.set_bias(biases) - if attrs: - layer.set_attrs(attrs) - - graph.add_node(layer.name, pb=layer, kind='op') - graph.node[layer.name].update(common_kaldi_fields(Node(graph, layer.name))) - - edge_attrs = { - 'out': 0, - 'in': 0, - 'name': layer.name, - 'fw_tensor_debug_info': [(prev_layer_name, layer.name)], # debug anchor for a framework tensor name and port - 'in_attrs': ['in', 'name'], - 'out_attrs': ['out', 'name'], - 'data_attrs': ['fw_tensor_debug_info'] - } - - graph.add_edge(prev_layer_name, layer.name, **edge_attrs) - - return new_id - - -def find_first_tag(file): - tag = b'' - while True: - symbol = file.read(1) - if tag == b'' and symbol != b'<': - continue - tag += symbol - if symbol != b'>': - continue - return tag - - -def find_first_component(file): - while True: - tag = find_first_tag(file) - component_name = tag.decode('ascii').lower() - if component_name[1:-1] in layer_weights_biases_attrs_getter.keys() or tag == b'</Nnet>' or tag == b'<EndOfComponent>': - file.read(1) # read ' ' - return component_name - - -def load_kaldi_nnet_model(nnet_path, check_sum: int = 16896): - """ - Structure of the file is the following: - magic-number(16896)<Nnet> <Next Layer Name> weights etc. - :param nnet_path: - :param check_sum: - :return: - """ - if isinstance(nnet_path, str): - file = open(nnet_path, "rb") - elif isinstance(nnet_path, IOBase): - file = nnet_path - - # 1. check the file - # first element is 16896<Nnet> - if check_sum and get_uint16(file.read(2)) != check_sum: - raise Error('File {} does not appear to be a Kaldi file (magic number does not match). ', nnet_path, - refer_to_faq_msg(89) - ) - - while True: - name = find_first_tag(file) - if name == b'<Nnet>': - file.read(1) - break - elif len(name) == 6: - raise Error('Kaldi model should start with <Nnet> tag. ', - refer_to_faq_msg(89)) - graph = nx.MultiDiGraph() - input_name = 'Input' - graph.add_node(input_name, pb=None, type='GlobalInput', name=input_name, shape=None, kind='op') - - prev_layer_name = input_name - input_shapes = {} - - while True: - """ - Typical structure of the layer - <Layer> |Size of output value in bits|Actual value of output|Size of input value in bits|Actual value of input|\ - FM Matrix|FV Vector| </Layer> - """ - layer_name = find_first_component(file) - if layer_name == '</nnet>': - break - elif layer_name == '<!endofcomponent>': - continue - extracted_name = layer_name[1:-1] - - layer_o = read_binary_integer_token(file) - layer_i = read_binary_integer_token(file) - - if prev_layer_name == 'Input': - graph.node['Input']['shape'] = np.array([1, layer_i], dtype=np.int64) - - cls = layer_weights_biases_attrs_getter[extracted_name] - cls_instance = cls(file, graph, layer_i, layer_o, extracted_name, prev_layer_name, False) - - prev_layer_name = cls_instance.load_build() - return graph, input_shapes - - -def read_counts_file(file_path): - with open(file_path, 'r') as f: - file_content = f.readlines() - if len(file_content) > 1: - raise Error('Expect counts file to be one-line file. ' + - refer_to_faq_msg(90)) - - counts_line = file_content[0].strip().replace('[', '').replace(']', '') - try: - counts = np.fromstring(counts_line, dtype=int, sep=' ') - except TypeError: - raise Error('Expect counts file to contain list of integers.' + - refer_to_faq_msg(90)) - cutoff = 1.00000001e-10 - counts = [cutoff if count < cutoff else count for count in counts] - scale = 1.0 / np.sum(counts) - for idx, count in enumerate(counts): - val = np.log(scale * count) - if count == cutoff: - val += np.iinfo(np.float32).max / 2 - counts[idx] = val - return counts diff --git a/model-optimizer/mo/front/kaldi/loader/__init__.py b/model-optimizer/mo/front/kaldi/loader/__init__.py new file mode 100644 index 000000000..e69de29bb --- /dev/null +++ b/model-optimizer/mo/front/kaldi/loader/__init__.py diff --git a/model-optimizer/mo/front/kaldi/loader/loader.py b/model-optimizer/mo/front/kaldi/loader/loader.py new file mode 100644 index 000000000..8bf9085a1 --- /dev/null +++ b/model-optimizer/mo/front/kaldi/loader/loader.py @@ -0,0 +1,215 @@ +""" + Copyright (c) 2018 Intel Corporation + + 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 io + +import numpy as np +import struct +from io import IOBase + +import networkx as nx +import logging as log + +from mo.front.kaldi.loader.utils import find_next_tag, read_placeholder, find_next_component, get_name_from_path, \ + find_end_of_component, end_of_nnet_tag, read_binary_integer32_token, get_parameters, read_token_value, collect_until_token, \ + create_edge_attrs +from mo.graph.graph import unique_id, Node +from mo.utils.error import Error +from mo.utils.utils import refer_to_faq_msg + + +def read_counts_file(file_path): + with open(file_path, 'r') as f: + file_content = f.readlines() + if len(file_content) > 1: + raise Error('Expect counts file to be one-line file. ' + + refer_to_faq_msg(90)) + + counts_line = file_content[0].strip().replace('[', '').replace(']', '') + try: + counts = np.fromstring(counts_line, dtype=int, sep=' ') + except TypeError: + raise Error('Expect counts file to contain list of integers.' + + refer_to_faq_msg(90)) + cutoff = 1.00000001e-10 + cutoff_idxs = np.where(counts < cutoff) + counts[cutoff_idxs] = cutoff + scale = 1.0 / np.sum(counts) + counts = np.log(counts * scale) + counts[cutoff_idxs] += np.finfo(np.float32).max / 2 + return counts + + +def load_parallel_component(file_descr, graph: nx.MultiDiGraph, prev_layer_id): + """ + Load ParallelComponent of the Kaldi model. + ParallelComponent contains parallel nested networks. + Slice is inserted before nested networks. + Outputs of nested networks concatenate with layer Concat. + + :param file_descr: descriptor of the model file + :param graph: graph with the topology. + :param prev_layer_id: id of the input layers for parallel component layer + :return: id of the concat layer - last layer of the parallel component layers + """ + nnet_count = read_token_value(file_descr, b'<NestedNnetCount>') + log.debug('Model contains parallel component with {} nested networks'.format(nnet_count)) + + slice_id = unique_id(graph, prefix='Slice') + graph.add_node(slice_id, parameters=None, op='slice', kind='op') + + slice_node = Node(graph, slice_id) + graph.add_edge(prev_layer_id, slice_id, **create_edge_attrs(prev_layer_id, slice_id)) + slices_points = [] + + outputs = [] + + for i in range(nnet_count): + read_token_value(file_descr, b'<NestedNnet>') + collect_until_token(file_descr, b'<Nnet>') + g, shape = load_kalid_nnet1_model(file_descr, 'Nested_net_{}'.format(i)) + input_nodes = [n for n in graph.nodes(data=True) if n[1]['op'] == 'Input'] + if i != nnet_count - 1: + slices_points.append(shape[1]) + g.remove_node(input_nodes[0][0]) + mapping = {node: unique_id(graph, node) for node in g.nodes(data=False) if node in graph} + g = nx.relabel_nodes(g, mapping) + for val in mapping.values(): + g.node[val]['name'] = val + graph.add_nodes_from(g.nodes(data=True)) + graph.add_edges_from(g.edges(data=True)) + sorted_nodes = tuple(nx.topological_sort(g)) + edge_attrs = create_edge_attrs(slice_id, sorted_nodes[0]) + edge_attrs['out'] = i + graph.add_edge(slice_id, sorted_nodes[0], **edge_attrs) + outputs.append(sorted_nodes[-1]) + packed_sp = struct.pack("B", 4) + struct.pack("I", len(slices_points)) + for i in slices_points: + packed_sp += struct.pack("I", i) + slice_node.parameters = io.BytesIO(packed_sp) + concat_id = unique_id(graph, prefix='Concat') + graph.add_node(concat_id, parameters=None, op='concat', kind='op') + for i, output in enumerate(outputs): + edge_attrs = create_edge_attrs(output, concat_id) + edge_attrs['in'] = i + graph.add_edge(output, concat_id, **edge_attrs) + return concat_id + + +def load_kaldi_model(nnet_path): + """ + Structure of the file is the following: + magic-number(16896)<Nnet> <Next Layer Name> weights etc. + :param nnet_path: + :param check_sum: + :return: + """ + nnet_name = None + if isinstance(nnet_path, str): + file_desc = open(nnet_path, "rb") + nnet_name = get_name_from_path(nnet_path) + elif isinstance(nnet_path, IOBase): + file_desc = nnet_path + else: + raise Error('Unsupported type of Kaldi model') + + name = find_next_tag(file_desc) + # start new model / submodel + if name == '<Nnet>': + load_function = load_kalid_nnet1_model + elif name == '<TransitionModel>': + load_function = load_kalid_nnet2_model + else: + raise Error('Kaldi model should start with <Nnet> or <TransitionModel> tag. ', + refer_to_faq_msg(89)) + read_placeholder(file_desc, 1) + + return load_function(file_desc, nnet_name) + + +def load_kalid_nnet1_model(file_descr, name): + graph = nx.MultiDiGraph(name=name) + + prev_layer_id = 'Input' + graph.add_node(prev_layer_id, name=prev_layer_id, kind='op', op='Input', parameters=None) + input_shape = [] + + while True: + component_type = find_next_component(file_descr) + if component_type == end_of_nnet_tag.lower()[1:-1]: + break + + layer_o = read_binary_integer32_token(file_descr) + layer_i = read_binary_integer32_token(file_descr) + + if component_type == 'parallelcomponent': + prev_layer_id = load_parallel_component(file_descr, graph, prev_layer_id) + continue + + start_index = file_descr.tell() + end_tag, end_index = find_end_of_component(file_descr, component_type) + end_index -= len(end_tag) + layer_id = unique_id(graph, prefix=component_type) + graph.add_node(layer_id, + parameters=get_parameters(file_descr, start_index, end_index), + op=component_type, + kind='op', + layer_i=layer_i, + layer_o=layer_o) + + prev_node = Node(graph, prev_layer_id) + if prev_node.op == 'Input': + prev_node['shape'] = np.array([1, layer_i], dtype=np.int64) + input_shape = np.array([1, layer_i], dtype=np.int64) + graph.add_edge(prev_layer_id, layer_id, **create_edge_attrs(prev_layer_id, layer_id)) + prev_layer_id = layer_id + log.debug('{} (type is {}) was loaded'.format(prev_layer_id, component_type)) + return graph, input_shape + + +def load_kalid_nnet2_model(file_descr, nnet_name): + graph = nx.MultiDiGraph(name=nnet_name) + input_name = 'Input' + graph.add_node(input_name, name=input_name, kind='op', op='Input', parameters=None, shape=None) + + prev_layer_id = input_name + + collect_until_token(file_descr, b'<Nnet>') + num_components = read_token_value(file_descr, b'<NumComponents>') + log.debug('Network contains {} components'.format(num_components)) + collect_until_token(file_descr, b'<Components>') + for _ in range(num_components): + component_type = find_next_component(file_descr) + + if component_type == end_of_nnet_tag.lower()[1:-1]: + break + start_index = file_descr.tell() + end_tag, end_index = find_end_of_component(file_descr, component_type) + layer_id = unique_id(graph, prefix=component_type) + graph.add_node(layer_id, + parameters=get_parameters(file_descr, start_index, end_index), + op=component_type, + kind='op') + + prev_node = Node(graph, prev_layer_id) + if prev_node.op == 'Input': + parameters = Node(graph, layer_id).parameters + input_dim = read_token_value(parameters, b'<InputDim>') + prev_node['shape'] = np.array([1, input_dim], dtype=np.int64) + input_shape = np.array([1, input_dim], dtype=np.int64) + graph.add_edge(prev_layer_id, layer_id, **create_edge_attrs(prev_layer_id, layer_id)) + prev_layer_id = layer_id + log.debug('{} (type is {}) was loaded'.format(prev_layer_id, component_type)) + return graph, input_shape diff --git a/model-optimizer/mo/front/kaldi/loader/utils.py b/model-optimizer/mo/front/kaldi/loader/utils.py new file mode 100644 index 000000000..4dbba940d --- /dev/null +++ b/model-optimizer/mo/front/kaldi/loader/utils.py @@ -0,0 +1,299 @@ +""" + Copyright (c) 2018 Intel Corporation + + 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 io + +import numpy as np +import os +import struct + +from mo.utils.error import Error +from mo.utils.utils import refer_to_faq_msg + +end_of_nnet_tag = '</Nnet>' +end_of_component_tag = '<!EndOfComponent>' + +supported_components = [ + 'addshift', + 'affinecomponent', + 'affinetransform', + 'convolutional1dcomponent', + 'convolutionalcomponent', + 'copy', + 'fixedaffinecomponent', + 'lstmprojected', + 'lstmprojectedstreams', + 'maxpoolingcomponent', + 'parallelcomponent', + 'rescale', + 'sigmoid', + 'softmax', + 'softmaxcomponent', + 'splicecomponent', + 'tanhcomponent', + 'normalizecomponent', + 'affinecomponentpreconditionedonline', + 'rectifiedlinearcomponent' +] + + +def get_bool(s: bytes) -> bool: + """ + Get bool value from bytes + :param s: bytes array contains bool value + :return: bool value from bytes array + """ + return struct.unpack('?', s)[0] + + +def get_uint16(s: bytes) -> int: + """ + Get unsigned int16 value from bytes + :param s: bytes array contains unsigned int16 value + :return: unsigned int16 value from bytes array + """ + return struct.unpack('H', s)[0] + + +def get_uint32(s: bytes) -> int: + """ + Get unsigned int32 value from bytes + :param s: bytes array contains unsigned int32 value + :return: unsigned int32 value from bytes array + """ + return struct.unpack('I', s)[0] + + +def get_uint64(s: bytes) -> int: + """ + Get unsigned int64 value from bytes + :param s: bytes array contains unsigned int64 value + :return: unsigned int64 value from bytes array + """ + return struct.unpack('q', s)[0] + + +def read_binary_bool_token(file_desc: io.BufferedReader) -> bool: + """ + Get next bool value from file + The carriage moves forward to 1 position. + :param file_desc: file descriptor + :return: next boolean value in file + """ + return get_bool(file_desc.read(1)) + + +def read_binary_integer32_token(file_desc: io.BufferedReader) -> int: + """ + Get next int32 value from file + The carriage moves forward to 5 position. + :param file_desc: file descriptor + :return: next uint32 value in file + """ + buffer_size = file_desc.read(1) + return get_uint32(file_desc.read(buffer_size[0])) + + +def read_binary_integer64_token(file_desc: io.BufferedReader) -> int: + """ + Get next int64 value from file + The carriage moves forward to 9 position. + :param file_desc: file descriptor + :return: next uint64 value in file + """ + buffer_size = file_desc.read(1) + return get_uint64(file_desc.read(buffer_size[0])) + + +def find_next_tag(file_desc: io.BufferedReader) -> str: + """ + Get next tag in the file + :param file_desc:file descriptor + :return: string like '<sometag>' + """ + tag = b'' + while True: + symbol = file_desc.read(1) + if symbol == b'': + raise Error('Unexpected end of Kaldi model') + if tag == b'' and symbol != b'<': + continue + elif symbol == b'<': + tag = b'' + tag += symbol + if symbol != b'>': + continue + try: + return tag.decode('ascii') + except UnicodeDecodeError: + # Tag in Kaldi model always in ascii encoding + tag = b'' + + +def read_placeholder(file_desc: io.BufferedReader, size=3) -> bytes: + """ + Read size bytes from file + :param file_desc:file descriptor + :param size:number of reading bytes + :return: bytes + """ + return file_desc.read(size) + + +def find_next_component(file_desc: io.BufferedReader) -> str: + """ + Read next component in the file. + All components are contained in supported_components + :param file_desc:file descriptor + :return: string like '<component>' + """ + while True: + tag = find_next_tag(file_desc) + # Tag is <NameOfTheLayer>. But we want get without '<' and '>' + component_name = tag[1:-1].lower() + if component_name in supported_components or tag == end_of_nnet_tag: + # There is whitespace after component's name + read_placeholder(file_desc, 1) + return component_name + + +def get_name_from_path(path: str) -> str: + """ + Get name from path to the file + :param path: path to the file + :return: name of the file + """ + return os.path.splitext(os.path.basename(path))[0] + + +def find_end_of_component(file_desc: io.BufferedReader, component: str, end_tags: tuple = ()): + """ + Find an index and a tag of the ent of the component + :param file_desc: file descriptor + :param component: component from supported_components + :param end_tags: specific end tags + :return: the index and the tag of the end of the component + """ + end_tags_of_component = ['</{}>'.format(component), + end_of_component_tag.lower(), + end_of_nnet_tag.lower(), + *end_tags, + *['<{}>'.format(component) for component in supported_components]] + next_tag = find_next_tag(file_desc) + while next_tag.lower() not in end_tags_of_component: + next_tag = find_next_tag(file_desc) + return next_tag, file_desc.tell() + + +def get_parameters(file_desc: io.BufferedReader, start_index: int, end_index: int): + """ + Get part of file + :param file_desc: file descriptor + :param start_index: Index of the start reading + :param end_index: Index of the end reading + :return: part of the file + """ + file_desc.seek(start_index) + buffer = file_desc.read(end_index - start_index) + return io.BytesIO(buffer) + + +def read_token_value(file_desc: io.BufferedReader, token: bytes = b'', value_type: type = np.uint32): + """ + Get value of the token. + Read next token (until whitespace) and check if next teg equals token + :param file_desc: file descriptor + :param token: token + :param value_type: type of the reading value + :return: value of the token + """ + getters = { + np.uint32: read_binary_integer32_token, + np.uint64: read_binary_integer64_token, + bool: read_binary_bool_token + } + current_token = collect_until_whitespace(file_desc) + if token != b'' and token != current_token: + raise Error('Can not load token {} from Kaldi model'.format(token) + + refer_to_faq_msg(94)) + return getters[value_type](file_desc) + + +def collect_until_whitespace(file_desc: io.BufferedReader): + """ + Read from file until whitespace + :param file_desc: file descriptor + :return: + """ + res = b'' + while True: + new_sym = file_desc.read(1) + if new_sym == b' ' or new_sym == b'': + break + res += new_sym + return res + + +def collect_until_token(file_desc: io.BufferedReader, token): + """ + Read from file until the token + :param file_desc: file descriptor + :return: + """ + while True: + # usually there is the following structure <CellDim> DIM<ClipGradient> VALUEFM + res = collect_until_whitespace(file_desc) + if res == token or res[-len(token):] == token: + return + if isinstance(file_desc, io.BytesIO): + size = len(file_desc.getbuffer()) + elif isinstance(file_desc, io.BufferedReader): + size = os.fstat(file_desc.fileno()).st_size + if file_desc.tell() == size: + raise Error('End of the file. Token {} not found. {}'.format(token, file_desc.tell())) + + +def create_edge_attrs(prev_layer_id: str, next_layer_id: str) -> dict: + """ + Create common edge's attributes + :param prev_layer_id: id of previous layer + :param next_layer_id: id of next layer + :return: dictionary contains common attributes for edge + """ + return { + 'out': 0, + 'in': 0, + 'name': next_layer_id, + 'fw_tensor_debug_info': [(prev_layer_id, next_layer_id)], + 'in_attrs': ['in', 'name'], + 'out_attrs': ['out', 'name'], + 'data_attrs': ['fw_tensor_debug_info'] + } + + +def read_blob(file_desc: io.BufferedReader, size: int, dtype=np.float32): + """ + Read blob from the file + :param file_desc: file descriptor + :param size: size of the blob + :param dtype: type of values of the blob + :return: np array contains blob + """ + dsizes = { + np.float32: 4, + np.int32: 4 + } + data = file_desc.read(size * dsizes[dtype]) + return np.fromstring(data, dtype=dtype) diff --git a/model-optimizer/mo/front/kaldi/register_custom_ops.py b/model-optimizer/mo/front/kaldi/register_custom_ops.py index 1dfb5455e..237ee9121 100644 --- a/model-optimizer/mo/front/kaldi/register_custom_ops.py +++ b/model-optimizer/mo/front/kaldi/register_custom_ops.py @@ -1,5 +1,5 @@ """ - Copyright (c) 2017-2018 Intel Corporation + Copyright (c) 2018 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. diff --git a/model-optimizer/mo/front/kaldi/utils.py b/model-optimizer/mo/front/kaldi/utils.py index ef251feff..f29a643fd 100644 --- a/model-optimizer/mo/front/kaldi/utils.py +++ b/model-optimizer/mo/front/kaldi/utils.py @@ -13,29 +13,57 @@ See the License for the specific language governing permissions and limitations under the License. """ +import io +import numpy as np +import os +import logging as log -import struct +from mo.front.kaldi.loader.utils import read_placeholder, read_binary_integer32_token, read_blob, read_token_value, find_next_tag +from mo.utils.error import Error -def get_uint16(s): - return struct.unpack('H', s)[0] +def read_binary_matrix(file_desc: io.BufferedReader, read_token: bool = True): + if read_token: + read_placeholder(file_desc) + rows_number = read_binary_integer32_token(file_desc) + cols_number = read_binary_integer32_token(file_desc) + # to compare: ((float *)a->buffer())[10] + return read_blob(file_desc, rows_number * cols_number), (rows_number, cols_number) -def get_uint32(s): - return struct.unpack('I', s)[0] +def read_binary_vector(file_desc: io.BufferedReader, read_token: bool = True, dtype=np.float32): + if read_token: + read_placeholder(file_desc) + elements_number = read_binary_integer32_token(file_desc) + return read_blob(file_desc, elements_number, dtype) -class KaldiNode: - def __init__(self, name): - self.name = name - self.blobs = [None, None] +def read_learning_info(pb: io.BufferedReader): + while True: + read_placeholder(pb, 1) + first_char = pb.read(1) + pb.seek(-2, os.SEEK_CUR) + position = pb.tell() + if first_char == b'L': + cur_pos = pb.tell() + token = find_next_tag(pb) + pb.seek(cur_pos) + if token in ['<LearnRateCoef>', '<LearningRate>']: + token = bytes(token, 'ascii') + else: + log.debug('Unexpected tag: {}'.format(token)) + break + elif first_char == b'B': + token = b'<BiasLearnRateCoef>' + elif first_char == b'M': + token = b'<MaxNorm>' + elif first_char == b'!': # token = b'<EndOfComponent>' + break + else: + break + try: + read_token_value(pb, token) + except Error: + pb.seek(position) + break - def set_weight(self, w): - self.blobs[0] = w - - def set_bias(self, b): - self.blobs[1] = b - - def set_attrs(self, attrs: dict): - for k, v in attrs.items(): - setattr(self, k, v) diff --git a/model-optimizer/mo/front/mxnet/extractor.py b/model-optimizer/mo/front/mxnet/extractor.py index e9783de4e..ad613f8b9 100644 --- a/model-optimizer/mo/front/mxnet/extractor.py +++ b/model-optimizer/mo/front/mxnet/extractor.py @@ -13,28 +13,26 @@ See the License for the specific language governing permissions and limitations under the License. """ -from mo.front.common.partial_infer.elemental import copy_shape_infer + from mo.front.mxnet.extractors.batchnorm import batch_norm_ext from mo.front.mxnet.extractors.concat import concat_ext from mo.front.mxnet.extractors.crop import crop_ext from mo.front.mxnet.extractors.eltwise import eltwise_ext -from mo.front.mxnet.extractors.flatten import flatten_ext from mo.front.mxnet.extractors.fully_connected import fully_connected_ext from mo.front.mxnet.extractors.l2_normalization import l2_normalization_ext from mo.front.mxnet.extractors.lrn import lrn_ext from mo.front.mxnet.extractors.multibox_detection import multi_box_detection_ext from mo.front.mxnet.extractors.multibox_prior import multi_box_prior_ext from mo.front.mxnet.extractors.null import null_ext -from mo.front.mxnet.extractors.reshape import reshape_ext from mo.front.mxnet.extractors.scaleshift import scale_shift_ext +from mo.front.mxnet.extractors.slice_axis import slice_axis_ext from mo.front.mxnet.extractors.transpose import transpose_ext -from mo.front.mxnet.extractors.up_sampling import up_sampling_ext from mo.front.mxnet.extractors.utils import get_mxnet_layer_attrs -from mo.front.mxnet.extractors.slice_axis import slice_axis_ext -from mo.utils.error import Error from mo.graph.graph import Node +from mo.utils.error import Error from mo.utils.utils import refer_to_faq_msg + def extractor_wrapper(mxnet_extractor): return lambda node: mxnet_extractor(get_mxnet_layer_attrs(node.symbol_dict)) @@ -49,15 +47,10 @@ mxnet_op_extractors = { 'elemwise_add': extractor_wrapper(lambda attrs: eltwise_ext(attrs, infer=lambda a, b: a + b, op_type="sum")), 'elemwise_mul': extractor_wrapper(lambda attrs: eltwise_ext(attrs, infer=lambda a, b: a * b, op_type="mul")), '_Plus': extractor_wrapper(lambda attrs: eltwise_ext(attrs, infer=lambda a, b: a + b, op_type="sum")), - 'Flatten': extractor_wrapper(flatten_ext), 'FullyConnected': extractor_wrapper(fully_connected_ext), - 'Reshape': extractor_wrapper(reshape_ext), - 'UpSampling': extractor_wrapper(up_sampling_ext), 'transpose': extractor_wrapper(transpose_ext), 'LRN': extractor_wrapper(lrn_ext), 'L2Normalization': extractor_wrapper(l2_normalization_ext), - 'Dropout': extractor_wrapper(lambda _: dict(infer=copy_shape_infer)), - '_copy': extractor_wrapper(lambda _: dict(infer=copy_shape_infer)), '_contrib_MultiBoxPrior': extractor_wrapper(multi_box_prior_ext), '_contrib_MultiBoxDetection': extractor_wrapper(multi_box_detection_ext), 'broadcast_add': extractor_wrapper(lambda attrs: eltwise_ext(attrs, infer=lambda a, b: a + b, op_type="sum")), @@ -84,6 +77,11 @@ def mxnet_op_extractor(node: Node): refer_to_faq_msg(86), op) result_attr = mxnet_op_extractors[op](node) + + if result_attr is None: + raise Error('Model Optimizer does not support layer "{}". Please, implement extension. '.format(node.name) + + refer_to_faq_msg(45)) + result.update(result_attr) supported = bool(result_attr) return supported, result diff --git a/model-optimizer/mo/front/mxnet/extractors/flatten.py b/model-optimizer/mo/front/mxnet/extractors/flatten.py deleted file mode 100644 index 1f6f22e28..000000000 --- a/model-optimizer/mo/front/mxnet/extractors/flatten.py +++ /dev/null @@ -1,29 +0,0 @@ -""" - Copyright (c) 2018 Intel Corporation - - 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 numpy as np - -from mo.front.common.partial_infer.flatten import flatten_infer - - -def flatten_ext(attrs): - node_attrs = { - 'type': 'Flatten', - 'axis': 1, - 'num_axes': 0, - 'infer': flatten_infer - } - return node_attrs diff --git a/model-optimizer/mo/front/mxnet/extractors/reshape.py b/model-optimizer/mo/front/mxnet/extractors/reshape.py deleted file mode 100644 index 6c5b65b7d..000000000 --- a/model-optimizer/mo/front/mxnet/extractors/reshape.py +++ /dev/null @@ -1,32 +0,0 @@ -""" - Copyright (c) 2018 Intel Corporation - - 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 numpy as np - -from mo.front.common.partial_infer.elemental import single_output_infer -from mo.front.common.partial_infer.reshape import tf_reshape_shape_infer - - -def reshape_ext(attr): - dim = attr.tuple("shape", int, None) - node_attrs = { - 'type': 'Reshape', - 'axis': 0, - 'num_axes': -1, - 'dim': np.array(dim), - 'infer': lambda node: single_output_infer(node, tf_reshape_shape_infer) - } - return node_attrs diff --git a/model-optimizer/mo/front/mxnet/extractors/up_sampling.py b/model-optimizer/mo/front/mxnet/extractors/up_sampling.py deleted file mode 100644 index 5804e1977..000000000 --- a/model-optimizer/mo/front/mxnet/extractors/up_sampling.py +++ /dev/null @@ -1,28 +0,0 @@ -""" - Copyright (c) 2018 Intel Corporation - - 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 mo.front.common.partial_infer.up_sampling import up_sampling_infer - - -def up_sampling_ext(attrs): - node_attrs = { - 'type': 'Resample', - 'scale': attrs.int("scale", 1), - 'sample_type': 'caffe.ResampleParameter.NEAREST', - 'antialias': 0, - 'infer': up_sampling_infer - } - return node_attrs diff --git a/model-optimizer/mo/front/mxnet/extractors/utils.py b/model-optimizer/mo/front/mxnet/extractors/utils.py index e77306b71..8c8d23df9 100644 --- a/model-optimizer/mo/front/mxnet/extractors/utils.py +++ b/model-optimizer/mo/front/mxnet/extractors/utils.py @@ -69,6 +69,8 @@ class AttrDictionary(object): if attr is None: return default if isinstance(attr, str): + if (not '(' in attr and not ')' in attr) and (not '[' in attr and not ']' in attr): + return (valtype(attr),) if (not attr) or (not attr[1:-1].split(',')[0]): return tuple([valtype(x) for x in default]) return StrTo.tuple(valtype, attr) @@ -104,9 +106,10 @@ def get_mxnet_node_edges(node: dict, node_id: [int, str], nodes_list: list, inde edge_list = [] for in_port, src_node_id in enumerate(node['inputs']): src_node = src_node_id[0] + dest_port = src_node_id[1] edge_attrs = { 'in': in_port, - 'out': 0, # TODO Check if src_node_id[1] should be here (already used as fw_tensor_debug_info) + 'out': dest_port, # debug anchor for name of tensor consumed at this input port 'fw_tensor_debug_info': [(nodes_list[src_node]['name'], src_node_id[1])], 'in_attrs': ['in'], @@ -152,6 +155,9 @@ def load_params(input_model, data_names = ('data',)): elif len(keys)>1 and 'arg' == keys[0]: arg_keys.append(keys[1]) arg_params[keys[1]] = loaded_weight[key] + else: + arg_keys.append(key) + arg_params[key] = loaded_weight[key] elif file_format == 'nd': for key in loaded_weight: if 'auxs' in input_model: diff --git a/model-optimizer/mo/front/onnx/extractor.py b/model-optimizer/mo/front/onnx/extractor.py index e4f631858..76a666c92 100644 --- a/model-optimizer/mo/front/onnx/extractor.py +++ b/model-optimizer/mo/front/onnx/extractor.py @@ -14,20 +14,17 @@ limitations under the License. """ -import logging as log - import numpy as np +from mo.front.onnx.extractors.concat import concat_ext from mo.front.onnx.extractors.const import onnx_const_ext from mo.front.onnx.extractors.constant import onnx_constant_ext +from mo.front.onnx.extractors.dropout import dropout_ext from mo.front.onnx.extractors.eltwise import make_tf_eltwise from mo.front.onnx.extractors.fused_bn import tf_fused_bn_extractor from mo.front.onnx.extractors.matmul import onnx_gemm_ext from mo.front.onnx.extractors.placeholder import onnx_placeholder_ext -from mo.front.onnx.extractors.concat import concat_ext -from mo.front.onnx.extractors.dropout import dropout_ext from mo.front.onnx.extractors.reshape import onnx_reshape_ext -from mo.front.tf.extractors.softmax import tf_softmax_ext from mo.graph.graph import Node @@ -48,14 +45,14 @@ onnx_op_extractors = { make_tf_eltwise(lambda a, b: a + b, attrs={'type': 'Eltwise', 'operation': 'sum', 'can_be_bias': True})), 'Relu': node_pb_arg(make_tf_eltwise(lambda v: np.maximum(0, v), attrs={'type': 'ReLU'})), # 0 is an integer 'Reshape': onnx_reshape_ext, - 'Softmax': node_pb_arg(tf_softmax_ext), } def common_onnx_fields(node: Node): return { 'kind': 'op', - 'name': node.id, # no reliable name for an onnx node, name can be empty, so we use that surrogate built as ID in the loaader + 'name': node.id, + # no reliable name for an onnx node, name can be empty, so we use that surrogate built as ID in the loaader 'op': node.op if node.has_valid('op') else node.pb.op_type, 'precision': 'FP32' # TODO use real precision derived from the model } diff --git a/model-optimizer/mo/front/tf/change_placeholder_type.py b/model-optimizer/mo/front/tf/change_placeholder_type.py index f798f1569..8c35bc3a5 100644 --- a/model-optimizer/mo/front/tf/change_placeholder_type.py +++ b/model-optimizer/mo/front/tf/change_placeholder_type.py @@ -36,6 +36,8 @@ def change_placeholders_types_to_FP32(graph: nx.MultiDiGraph): if all([is_node_casts_to_float(op) and len(op.in_nodes()) == 1 for op in next_ops]): change_node_type(node, tf_types.DT_FLOAT) remove_node_preserving_edges(node, next_ops) # remove 'Cast' nodes + elif all([is_node_gather(op) for op in next_ops] for op in next_ops): + change_node_type(node, tf_types.DT_FLOAT) else: raise Error( ('Cannot convert type of placeholder "{}" because not all of its outputs are "Cast" to float ' @@ -52,6 +54,11 @@ def is_node_casts_to_float(node: Node): return 'pb' in attrs and attrs['pb'].op == 'Cast' and attrs['pb'].attr['DstT'].type == tf_types.DT_FLOAT +def is_node_gather(node: Node): + attrs = node.graph.node[node.id] + return 'pb' in attrs and attrs['pb'].op == 'GatherV2' and attrs['precision'] == 'FP32' + + def change_node_type(node: Node, new_type: type): node.graph.node[node.id]['pb'].attr['dtype'].type = new_type diff --git a/model-optimizer/mo/front/tf/extractor.py b/model-optimizer/mo/front/tf/extractor.py index a653a25f1..d7af0d506 100644 --- a/model-optimizer/mo/front/tf/extractor.py +++ b/model-optimizer/mo/front/tf/extractor.py @@ -34,7 +34,6 @@ from mo.front.tf.extractors.random_uniform import tf_random_uniform_ext from mo.front.tf.extractors.range import tf_range_ext from mo.front.tf.extractors.reshape import tf_reshape_ext from mo.front.tf.extractors.shape import tf_shape_ext -from mo.front.tf.extractors.softmax import tf_softmax_ext from mo.front.tf.extractors.space_to_batch import tf_space_to_batch_ext, tf_batch_to_space_ext from mo.front.tf.extractors.split import tf_split_ext from mo.front.tf.extractors.squeeze import tf_squeeze_ext @@ -95,7 +94,7 @@ tf_op_extractors = { 'Prod': node_pb_arg(tf_reduce_prod_ext), 'Const': node_pb_arg(tf_const_ext), 'Placeholder': node_pb_arg(tf_placeholder_ext), - 'Identity': node_pb_arg(make_tf_eltwise(lambda v: v)), + 'Identity': node_pb_arg(make_tf_eltwise(lambda v: v, attrs={'identity': True})), 'Add': node_pb_arg( make_tf_eltwise(lambda a, b: a + b, attrs={'type': 'Eltwise', 'operation': 'sum', 'can_be_bias': True})), 'Mul': node_pb_arg(make_tf_eltwise(lambda a, b: a * b, attrs={'type': 'Eltwise', 'operation': 'mul'})), @@ -111,18 +110,16 @@ tf_op_extractors = { 'Reshape': node_pb_arg(tf_reshape_ext), 'Squeeze': node_pb_arg(tf_squeeze_ext), 'Shape': node_pb_arg(tf_shape_ext), - 'Softmax': node_pb_arg(tf_softmax_ext), 'SpaceToBatchND': node_pb_arg(tf_space_to_batch_ext), 'BatchToSpaceND': node_pb_arg(tf_batch_to_space_ext), - 'StopGradient': node_pb_arg(make_tf_eltwise(lambda v: v)), 'Square': node_pb_arg(make_tf_eltwise(lambda a: a * a)), 'Minimum': node_pb_arg(make_tf_eltwise(lambda a, b: np.minimum(a, b))), # can use clamp if one argument is const 'Maximum': node_pb_arg(make_tf_eltwise(lambda a, b: np.maximum(a, b), attrs={'type': 'Eltwise', 'operation': 'max'})), 'Sum': node_pb_arg(tf_sum_ext), 'Range': node_pb_arg(tf_range_ext), - 'ReadVariableOp': node_pb_arg(make_tf_eltwise(lambda v: v, attrs={'op': 'Identity'})), - 'PlaceholderWithDefault': node_pb_arg(make_tf_eltwise(lambda v: v, attrs={'op': 'Identity'})) + 'ReadVariableOp': node_pb_arg(make_tf_eltwise(lambda v: v, attrs={'identity': True})), + 'PlaceholderWithDefault': node_pb_arg(make_tf_eltwise(lambda v: v, attrs={'identity': True})) } diff --git a/model-optimizer/mo/front/tf/extractors/softmax.py b/model-optimizer/mo/front/tf/extractors/softmax.py deleted file mode 100644 index bc2eb7c8f..000000000 --- a/model-optimizer/mo/front/tf/extractors/softmax.py +++ /dev/null @@ -1,24 +0,0 @@ -""" - Copyright (c) 2018 Intel Corporation - - 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 mo.front.common.partial_infer.elemental import copy_shape_infer - - -def tf_softmax_ext(pb): - return { - 'type': 'SoftMax', - 'infer': copy_shape_infer - } diff --git a/model-optimizer/mo/front/tf/extractors/utils.py b/model-optimizer/mo/front/tf/extractors/utils.py index a60d3e82a..5b736df0c 100644 --- a/model-optimizer/mo/front/tf/extractors/utils.py +++ b/model-optimizer/mo/front/tf/extractors/utils.py @@ -80,7 +80,7 @@ def tf_tensor_content(tf_dtype, shape, pb_tensor): # return np.array(type_helper[1](pb_tensor), dtype=type_helper[0]) else: if pb_tensor.tensor_content: - flat = np.frombuffer(pb_tensor.tensor_content, type_helper[0]) + flat = np.array(np.frombuffer(pb_tensor.tensor_content, type_helper[0])) if len(flat) == shape.prod(): return flat.reshape(shape) else: diff --git a/model-optimizer/mo/front/tf/graph_utils.py b/model-optimizer/mo/front/tf/graph_utils.py index 67335a245..2a8454c67 100644 --- a/model-optimizer/mo/front/tf/graph_utils.py +++ b/model-optimizer/mo/front/tf/graph_utils.py @@ -133,7 +133,7 @@ def add_activation_function_after_node(graph: nx.MultiDiGraph, node: Node, activ """ if activation_function == 'SOFTMAX': # softmax to be applied to the confidence - softmax_conf_op = Softmax(graph, dict(axis=1, nchw_layout=True)) + softmax_conf_op = Softmax(graph, dict(axis=-1, nchw_layout=True)) activation_node = softmax_conf_op.create_node([node], dict(name=node.name + '/softmax')) elif activation_function == 'SIGMOID': # sigmoid activation function to be applied to the confidence diff --git a/model-optimizer/mo/front/tf/loader.py b/model-optimizer/mo/front/tf/loader.py index b8d8ca192..8310e0acb 100644 --- a/model-optimizer/mo/front/tf/loader.py +++ b/model-optimizer/mo/front/tf/loader.py @@ -14,9 +14,12 @@ limitations under the License. """ +import logging as log import os import re +import networkx as nx + from mo.utils.error import Error, FrameworkError from mo.utils.utils import refer_to_faq_msg @@ -31,6 +34,55 @@ from mo.graph.graph import create_graph_with_nodes from mo.utils.summarize_graph import summarize_graph +def freeze_checkpoints(graph_def: tf.GraphDef, checkpoint_dir: str, output_node_names: list): + """ + Loads all the variables in a graph and stores them in a separate dictionary. Freezes output nodes in the graph + :param graph_def: GraphDef object holding the network. + :param checkpoint_dir: path to directory with checkpoint files with values of graph variables. + :param output_node_names: list of output node names. + :return: GraphDef containing a simplified version of the original. + """ + log.debug("Loading checkpoint files from directory: {}".format(checkpoint_dir)) + checkpoint_files = [] + for checkpoint_name in sorted(os.listdir(checkpoint_dir)): + checkpoint_path = os.path.join(checkpoint_dir, checkpoint_name) + if os.path.isfile(checkpoint_path): + checkpoint_files.append(checkpoint_path) + log.debug("File {} will be loaded".format(checkpoint_path)) + else: + log.debug("Path {} is not a file. Skipping") + + if len(checkpoint_files) == 0: + raise Error("There are no checkpoint files in directory: {}".format(checkpoint_dir)) + + tf.import_graph_def(graph_def, name='') + + with tf.Session() as sess: + uninitialized_variables = [str(v, 'utf-8') for v in set(sess.run(tf.report_uninitialized_variables()))] + all_variables = [n.name for n in sess.graph.as_graph_def().node if n.op in ['Variable', 'VariableV2']] + white_list = [v for v in all_variables if v not in uninitialized_variables] + black_list = [v for v in all_variables if v in uninitialized_variables] + output_graph_def = tf.graph_util.convert_variables_to_constants(sess, graph_def, output_node_names, + variable_names_whitelist=white_list, + variable_names_blacklist=black_list) + variable_values = {} + for checkpoint_file in checkpoint_files: + log.debug("Loading {}".format(checkpoint_file)) + with tf.Session() as sess: + var_list = {} + var_to_shape_map = tf.pywrap_tensorflow.NewCheckpointReader(checkpoint_file).get_variable_to_shape_map() + for key in var_to_shape_map: + try: + tensor = sess.graph.get_operation_by_name(key).outputs[0] + except KeyError: + continue + var_list[key] = tensor + tf.train.Saver(var_list=var_list).restore(sess, checkpoint_file) + for name, tensor in var_list.items(): + variable_values[name] = sess.run(tensor) + return output_graph_def, variable_values + + def freeze_checkpoint(graph_def, checkpoint, output_node_names): """ Replaces all the variables in a graph with constants of the same values. @@ -40,6 +92,7 @@ def freeze_checkpoint(graph_def, checkpoint, output_node_names): :return: GraphDef containing a simplified version of the original. """ tf.import_graph_def(graph_def, name="") + with tf.Session() as sess: var_list = {} var_to_shape_map = tf.pywrap_tensorflow.NewCheckpointReader(checkpoint).get_variable_to_shape_map() @@ -54,7 +107,8 @@ def freeze_checkpoint(graph_def, checkpoint, output_node_names): return output_graph_def -def read_file_to_graph_def(graph_def: [tf.GraphDef, tf.MetaGraphDef], graph_file_name: str = "", is_binary: bool = True): +def read_file_to_graph_def(graph_def: [tf.GraphDef, tf.MetaGraphDef], graph_file_name: str = "", + is_binary: bool = True): """ Reads file to protobuf :param graph_def: GraphDef orr MetaGraphDef object to store the network @@ -141,16 +195,22 @@ def load_tf_graph_def(graph_file_name: str = "", is_binary: bool = True, checkpo '--input_checkpoint "path/to/*.ckpt"' '\n\n2. For "*.meta" file:' '\npython3 mo_tf.py --input_meta_graph "path/to/*.meta"') - + variables_values = {} try: if graph_file_name and not meta_graph_file and not checkpoint: # frozen graph - return read_file_to_graph_def(graph_def, graph_file_name, is_binary) + return read_file_to_graph_def(graph_def, graph_file_name, is_binary), variables_values if graph_file_name and not meta_graph_file and checkpoint: # inference graph and checkpoint graph_def = read_file_to_graph_def(graph_def, graph_file_name, is_binary) outputs = get_output_node_names_list(graph_def, user_output_node_names_list) - return freeze_checkpoint(graph_def=graph_def, checkpoint=checkpoint, output_node_names=outputs) + if os.path.isfile(checkpoint): + graph_def = freeze_checkpoint(graph_def=graph_def, checkpoint=checkpoint, output_node_names=outputs) + elif os.path.isdir(checkpoint): + graph_def, variables_values = freeze_checkpoints(graph_def=graph_def, checkpoint_dir=checkpoint, + output_node_names=outputs) + # we are sure that checkpoint is existing file or directory due to cli_parser configuration + return graph_def, variables_values if not graph_file_name and meta_graph_file: meta_graph_file = deducing_metagraph_path(meta_graph_file) input_meta_graph_def = read_file_to_graph_def(tf.MetaGraphDef(), meta_graph_file, is_binary) @@ -159,14 +219,16 @@ def load_tf_graph_def(graph_file_name: str = "", is_binary: bool = True, checkpo restorer = tf.train.import_meta_graph(input_meta_graph_def) restorer.restore(sess, re.sub('\.meta$', '', meta_graph_file)) outputs = get_output_node_names_list(input_meta_graph_def.graph_def, user_output_node_names_list) - return tf.graph_util.convert_variables_to_constants(sess, input_meta_graph_def.graph_def, outputs) + graph_def = tf.graph_util.convert_variables_to_constants(sess, input_meta_graph_def.graph_def, outputs) + return graph_def, variables_values if model_dir: # saved model directory tags = saved_model_tags if saved_model_tags is not None else [tf.saved_model.tag_constants.SERVING] with tf.Session() as sess: meta_graph_def = tf.saved_model.loader.load(sess, tags, model_dir) outputs = get_output_node_names_list(meta_graph_def.graph_def, user_output_node_names_list) - return tf.graph_util.convert_variables_to_constants(sess, meta_graph_def.graph_def, outputs) + graph_def = tf.graph_util.convert_variables_to_constants(sess, meta_graph_def.graph_def, outputs) + return graph_def, variables_values except Exception as e: raise FrameworkError('Cannot load input model: {}', e) from e raise Error("Unknown configuration of input model parameters") @@ -194,3 +256,24 @@ def protobuf2nx(pb: tf.GraphDef): index = index + 1 return graph + + +def variables_to_constants(graph: nx.MultiDiGraph, variables_values: dict): + """ + Converts `Variable<V2>` operations to FakeConst operations with `value` from `variables_values` dictionary + :param graph: graph to operate on + :param variables_values: dictionary with variable names as keys and np.array data as values + """ + variable_operations = ['Variable', 'VariableV2'] + for node_name in graph.nodes(): + node_attr_dict = graph.node[node_name] + if 'op' not in node_attr_dict: + continue + op_name = node_attr_dict['op'] + if op_name not in variable_operations: + continue + if node_name not in variables_values: + log.debug("There is no value for '{}': {} in checkpoint variable values".format(op_name, node_name)) + continue + graph.node[node_name]['op'] = 'FakeConst' + graph.node[node_name]['value'] = variables_values[node_name] |