Evgeny LazarevandEvgeny Lazarev
cabf8d8534
* Generate TensorIterator without back edges from TensorFlow models * Added a check in the MarkSubgraphsWithCorrectLayout to not fail when port is not connected * Updated the 'protobuf2nx' to consume the graph protobuf message * Cleanup TI from the IRv7 specific code * Do not run some front transformations recursively * Draft support for the ONNX Loop operation when 'cond' = True * LoopToTI transformation changes * Added draft of Loop operation and parser for ONNX Loop operation body * Updated Loop body parser + added shape and type infer for the Loop operation * Fixes for ONNX Loop operation parser * Moved Loop parsing to Loop op extractor. Added generation of external edges for the Loop body ops * Added support for ThresholdedRelu using decomposition * Added support for Min ONNX operation * Draft fixes for port_map generation for the Loop * Rename transformation file and fix BOM * Fixed shape inference for Loop scan outputs (axis is not None) * Fixed shape inference for ONNX Loop operation * Refactor checks in the TensorIteratorMerge transformation * Code refactoring. Enabled commented transformations * Documentation update for ONNX Loop, ThresholdedRelu and Min * Fixed typo in the Loop front transformation where execution condition input is connected. Other refactorings * Fixed in the Loop extractor * Added printing 'internal_layer_id' attribute in the graph dumper * Updated calculation of iterations number for the Loop * Added missing code * Fixed output port shapes and types generation for Loop operation * Update function names and variable names in the Loop operation * Fixed type inference for iteration count input * Added removal of input/output ports of the Loop if they are not used * Fixed renumbering Loop operations input/output ports to keep mandatory * Fixed ThresholdedReluDecomposition transformation * Updated MO IR Reader to know about Loop operation. But it is still not supported by the MO IR Reader * Added unit test for Slice op shape infer (reverse the sequence of elements) * Reverted changes in the ONNX loader function call to protobuf2nx * Enable Reshape0DToSqueeze transformation recursively * Refactored Loop operation support implementation * Changed ThresholdedReluDecomposition to generate Const with shape [1] instead of scalar * Code style and wording fixes * Restored accidentally removed 'return' statement in the TI shape infer function * Fixed comments * Fixed comment Co-authored-by: Evgeny Lazarev <elazarev.nnov@gmail.com>
74 lines
3.2 KiB
Python
74 lines
3.2 KiB
Python
"""
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Copyright (C) 2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import logging as log
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from extensions.load.loader import Loader
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from mo.front.common.register_custom_ops import update_extractors_with_extensions, check_for_duplicates
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from mo.front.extractor import extract_node_attrs
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from mo.front.onnx.extractor import onnx_op_extractor, onnx_op_extractors
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from mo.front.onnx.loader import load_onnx_model, protobuf2nx
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from mo.graph.graph import Graph
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from mo.utils.error import Error
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from mo.utils.utils import refer_to_faq_msg
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class ONNXLoader(Loader):
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enabled = True
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run_not_recursively = True
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def load(self, graph: Graph):
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argv = graph.graph['cmd_params']
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model_proto = load_onnx_model(argv.input_model)
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model_graph = model_proto.graph # pylint: disable=no-member
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# print(model_graph)
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# assert len(model_graph) == 1, "An ONNX model contains more than 1 graph: unsupported"
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log.debug("Number of nodes in graph_def: {}".format(len(model_graph.node)))
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log.debug("Number of all input ports (not true inputs) in graph_def: {}".format(len(model_graph.input)))
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log.debug("Number of initializers in graph_def: {}".format(len(model_graph.initializer)))
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log.debug(
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"Number of real inputs in graph_def: {}".format(len(model_graph.input) - len(model_graph.initializer)))
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update_extractors_with_extensions(onnx_op_extractors)
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try:
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protobuf2nx(graph, model_proto)
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except Exception as e:
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raise Error(
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'Cannot pre-process ONNX graph after reading from model file "{}". ' \
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'File is corrupt or has unsupported format. Details: {}. ' +
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refer_to_faq_msg(44),
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argv.input_model,
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str(e)
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) from e
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log.debug("Number of nodes in NX graph: {}".format(graph.number_of_nodes()))
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graph.__setattr__('name',
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argv.model_name if argv.model_name else model_proto.graph.name) # pylint: disable=no-member
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graph.graph['layout'] = 'NCHW'
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graph.graph['fw'] = 'onnx'
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graph.graph['feature_dim'] = 1
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if hasattr(model_proto, 'opset_import'):
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graph.graph['fw_opset_version'] = model_proto.opset_import[0].version # pylint: disable=no-member
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else:
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graph.graph['fw_opset_version'] = None
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graph.check_empty_graph('protobuf2nx. It may happen due to problems with loaded model')
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extract_node_attrs(graph, lambda node: onnx_op_extractor(node, check_for_duplicates(onnx_op_extractors)))
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