45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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from mo.graph.graph import Node, Graph
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from mo.ops.op import Op
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from mo.utils.utils import match_shapes
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class TensorArrayScatter(Op):
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op = "TensorArrayScatterV3"
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'type': None,
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'op': __class__.op,
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'infer': TensorArrayScatter.array_infer,
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}
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super().__init__(graph, mandatory_props, attrs)
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@staticmethod
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def array_infer(node: Node):
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handle = node.in_node(0)
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indices = node.in_node(1)
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value = node.in_node(2)
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flow_in = node.in_node(3)
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ta_node = Node(node.graph, str(handle.value))
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if ta_node.has_valid('element_shape') and len(ta_node.element_shape) > 0:
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assert match_shapes(ta_node['element_shape'], value.shape[1:]), \
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'Shapes are not compatible: {} and {}'.format(ta_node['element_shape'], value.shape[1:])
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else:
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ta_node['element_shape'] = value.shape[1:]
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# Assign element_shape anyway, because the original element_shape can contain -1
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ta_node['element_shape'] = value.shape[1:]
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output_shape = flow_in.shape
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output_value = flow_in.value
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#flow_out
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for _, out_node in node.graph.out_edges(node.id):
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node.graph.node[out_node]['shape'] = np.array(output_shape)
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node.graph.node[out_node]['value'] = None if output_value is None else np.array(output_value)
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