86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
"""
|
|
Copyright (C) 2018-2020 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.
|
|
"""
|
|
from mo.front.common.partial_infer.utils import mark_input_bins
|
|
from mo.graph.graph import Graph, Node
|
|
from mo.ops.op import Op
|
|
from mo.utils.error import Error
|
|
|
|
|
|
class RNNCell(Op):
|
|
""" A single RNN cell (without a loop).
|
|
|
|
2 inputs:
|
|
- [0, required] input data (2D),
|
|
- [1, required] initial hidden state (2D),
|
|
|
|
2 blobs:
|
|
- [2, required] cell FC weights
|
|
- [3, required] cell FC biases
|
|
|
|
1 outputs:
|
|
- [required] output data / resulting hidden state (2D)
|
|
"""
|
|
op = 'RNNCell'
|
|
|
|
def __init__(self, graph: Graph, attrs: dict):
|
|
mandatory_props = {
|
|
'type': __class__.op,
|
|
'op': __class__.op,
|
|
'version': 'experimental',
|
|
'infer': __class__.infer,
|
|
'in_ports_count': 4,
|
|
'out_ports_count': 1,
|
|
'version': 'opset3',
|
|
'wr_input_id': 2,
|
|
'gates_count': 1
|
|
}
|
|
super().__init__(graph, mandatory_props, attrs)
|
|
|
|
def supported_attrs(self):
|
|
return [
|
|
'hidden_size', # number of the elements in hidden cell size
|
|
'activations',
|
|
'activation_alpha',
|
|
'activation_beta',
|
|
'clip',
|
|
]
|
|
|
|
def backend_attrs(self):
|
|
return [
|
|
'hidden_size', # number of the elements in hidden cell size
|
|
('activations', lambda node: ','.join(node.activations) if node.activations is not None else None),
|
|
'activation_alpha',
|
|
'activation_beta',
|
|
'clip',
|
|
]
|
|
|
|
@staticmethod
|
|
def infer(node: Node):
|
|
assert len(node.out_nodes()) in [1, 2]
|
|
|
|
hidden_shape = node.in_node(1).shape.copy()
|
|
|
|
mark_input_bins(node, start_port=2)
|
|
node.out_node(0).shape = hidden_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
|