Roman Kazantsev
753150642e
* Extend MO for operation CTCLoss * Change sequence length format to a mask format * Add fixes after first-round review * Add fixes after the second-round review * Fixing CTCLossPlusCTCGreedyDecoder transformation
68 lines
3.2 KiB
Python
68 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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import numpy as np
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from extensions.ops.Cast import Cast
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from extensions.ops.scatternd import ScatterNDUpdate
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from mo.front.common.replacement import FrontReplacementOp
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from mo.graph.graph import Node, Graph, rename_nodes
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from mo.ops.broadcast import Broadcast
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from mo.ops.const import Const
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class SparseToDenseReplacer(FrontReplacementOp):
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"""
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This replacer substitutes TensorFlow SparseToDense operation with Broadcast -> ScatterND chain.
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The Broadcast operation creates a tensor filled with default value and of required shape.
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The ScatterND operation updates the created tensor with required values at required locations.
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"""
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op = "SparseToDense"
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enabled = True
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def run_after(self):
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from extensions.front.tf.CTCGreedyDecoderReplacement import CTCGreedyDecoderReplacement
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from extensions.front.tf.CTCLossReplacement import CTCLossReplacement
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return [CTCGreedyDecoderReplacement, CTCLossReplacement]
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def replace_op(self, graph: Graph, node: Node):
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node_name = node.soft_get('name', node.id)
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# broadcast default value to required shape
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broadcast_node = Broadcast(graph, {'name': node_name + '/Broadcast_'}).create_node()
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node.in_port(1).get_connection().set_destination(broadcast_node.in_port(1))
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if not node.in_port(3).disconnected():
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# TODO: remove casting once we start to support I64 model input
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# cast default value to I32 due limitation about I64 input support
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# so that input parameter and default value will be of the same I32 type as required ScatterNDUpdate
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cast_default_value = Cast(graph, {'name': node_name + '/CastDefaultValue', 'dst_type': np.int32}).create_node()
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node.in_port(3).get_connection().set_destination(cast_default_value.in_port(0))
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broadcast_node.in_port(0).connect(cast_default_value.out_port(0))
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else:
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broadcast_node.in_port(0).connect(Const(graph, {'name': broadcast_node.name + '/FillValue_',
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'value': np.float32(0)}
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).create_node().out_port(0))
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# update broadcasted tensor with required values at required locations
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scatternd_node = ScatterNDUpdate(graph, {'name': node_name + '/ScatterNDUpdate_'}).create_node()
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scatternd_node.in_port(0).connect(broadcast_node.out_port(0))
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node.in_port(0).get_connection().set_destination(scatternd_node.in_port(1))
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node.in_port(2).get_connection().set_destination(scatternd_node.in_port(2))
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rename_nodes([(node, node_name + "/AbandonedName"), (scatternd_node, node_name)])
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return [scatternd_node.id]
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