49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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from mo.front.common.partial_infer.utils import mark_input_bins
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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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class BlockLSTM(Op):
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op = 'BlockLSTM'
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'op': __class__.op,
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'infer': __class__.infer,
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'type': None,
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}
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super().__init__(graph, mandatory_props, attrs)
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@staticmethod
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def infer(node: Node):
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"""
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MO input edges: | Description:
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-------------------------------------------------
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0 | x: The sequence input to the LSTM, shape (timelen, batch_size, num_inputs)
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1 | w: The weight matrix
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2 | b: The bias vector
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3 | h_prev: Previous/initial hidden state
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4 | cs_prev: Value of the initial cell state
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"""
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assert len(node.in_nodes()) == 5
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"""
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MO output edges: | Description:
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0 | cs: Output data / output hidden states concatenated over the whole time sequence
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1 | h: Output cell states concatenated over the whole time sequence
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"""
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assert len(node.out_nodes()) in [1, 2]
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mark_input_bins(node)
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input_shape = node.in_node(0).shape
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assert len(input_shape) == 3
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out_shape = input_shape
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node.out_node(0).shape = out_shape
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if len(node.out_nodes()) > 1:
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node.out_node(1).shape = out_shape
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