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>
52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
"""
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Copyright (C) 2018-2020 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 extensions.ops.Cast import Cast
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from extensions.ops.elementwise import Greater, Mul
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from mo.front.common.partial_infer.utils import float_array
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from mo.front.common.replacement import FrontReplacementPattern
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from mo.front.tf.graph_utils import create_op_with_const_inputs
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from mo.graph.graph import Graph, rename_nodes
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from mo.middle.passes.convert_data_type import data_type_str_to_np
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class ThresholdedReluDecomposition(FrontReplacementPattern):
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"""
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ThresholdedRelu(x, alpha) = x ? x > alpha : 0
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is replaced with
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ThresholdedRelu(x, alpha) = Mul(x, Cast(Greater(x, alpha), type=float))
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"""
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enabled = True
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def find_and_replace_pattern(self, graph: Graph):
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for node in graph.get_op_nodes(op='ThresholdedRelu'):
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name = node.soft_get('name', node.id)
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greater = create_op_with_const_inputs(graph, Greater, {1: float_array([node.alpha])})
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greater.in_port(0).connect(node.in_port(0).get_source())
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float_greater = Cast(graph,
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{'dst_type': data_type_str_to_np(graph.graph['cmd_params'].data_type)}).create_node()
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greater.out_port(0).connect(float_greater.in_port(0))
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mul = Mul(graph, {}).create_node()
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node.out_port(0).get_connection().set_source(mul.out_port(0))
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mul.in_port(0).connect(node.in_port(0).get_source())
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mul.in_port(1).connect(float_greater.out_port(0))
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rename_nodes([(node, name + '/TBR'), (mul, name)])
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