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"""
Copyright (c) 2017-2018 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import networkx as nx
from mo.front.common.partial_infer.utils import mark_input_bins
from mo.graph.graph import Node
from mo.ops.op import Op
from mo.utils.error import Error
class LSTMCell(Op):
''' A single LSTM cell (without a loop).
3 inputs:
- [0, required] input data (2D),
- [1, required] initial hidden state (2D),
- [2, required] initial cell state (2D),
2 blobs:
- [3, required] LSTM FC weights
- [4, required] LSTM FC biases
2 outputs:
- [required] output data / resulting hidden state (2D)
- [required] resulting cell state (2D)
'''
op = 'LSTMCell'
def __init__(self, graph: nx.MultiDiGraph, attrs: dict):
mandatory_props = {
'type': __class__.op,
'op': __class__.op,
'infer': __class__.infer
}
super().__init__(graph, mandatory_props, attrs)
def supported_attrs(self):
return [
'hidden_size', # number of the elements in hidden cell size
]
@staticmethod
def infer(node: Node):
if node.has_and_set('extra_inputs'):
assert len(node.in_nodes()) == 8
else:
assert len(node.in_nodes()) == 5
assert len(node.out_nodes()) in [1, 2]
hidden_shape = node.in_node(1).shape.copy()
cell_shape = node.in_node(2).shape.copy()
mark_input_bins(node, start_port=3)
node.out_node(0).shape = hidden_shape
if len(node.out_nodes()) == 2:
node.out_node(1).shape = cell_shape
hidden_size = hidden_shape[1]
if node.has_valid('hidden_size'):
if node.hidden_size != hidden_size:
raise Error("Input shape {} for hidden size doesn't match pre-defined hidden_size in node {}".format(
node.in_node(1).shape, node.soft_get('name')))
else:
node['hidden_size'] = hidden_size
assert cell_shape[1] == hidden_size
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