67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
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"""
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Copyright (c) 2018 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 networkx as nx
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import numpy as np
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from mo.graph.graph import Node
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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 TensorArrayGather(Op):
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op = "TensorArrayGatherV3"
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def __init__(self, graph: nx.MultiDiGraph, attrs: dict):
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mandatory_props = {
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'type': __class__.op,
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'op': __class__.op,
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'infer': TensorArrayGather.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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assert len(node.in_nodes()) == 3
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handle = node.in_node(0)
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indices = node.in_node(1)
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flow_in = node.in_node(2)
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ta_node = Node(node.graph, str(handle.value))
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if ta_node.has_valid('element_shape'):
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assert match_shapes(ta_node['element_shape'], node.element_shape)
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ta_node['element_shape'] = node.element_shape
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data_shape = ta_node['element_shape']
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if -1 in data_shape:
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assert data_shape.size == 2 and data_shape[0] == -1 and data_shape[1] != -1
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# This is a workaround for a bug that we cannot deduce element_shape
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# when it is required for inference, so we putting 1 here instead of -1.
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# This makes impossible to have batch size > 1 for LSTM-like loops
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data_shape[0] = 1
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ta_node.element_shape = data_shape
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assert ta_node.has_valid('size')
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size = ta_node['size']
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output_shape = [size] + [data_shape[i] for i in range(len(data_shape))]
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output_value = None
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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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