62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
from mo.front.common.partial_infer.elemental import copy_shape_infer
|
|
from mo.front.common.partial_infer.utils import int64_array
|
|
from mo.graph.graph import Node, Graph
|
|
from mo.middle.passes.convert_data_type import data_type_str_to_np
|
|
from mo.ops.op import Op
|
|
from mo.utils.error import Error
|
|
from mo.utils.utils import refer_to_faq_msg
|
|
|
|
|
|
class Memory(Op):
|
|
op = 'Memory'
|
|
enabled = True
|
|
|
|
def __init__(self, graph: Graph, attrs: dict):
|
|
super().__init__(graph, {
|
|
'type': 'Memory',
|
|
'op': 'Memory',
|
|
'id': None,
|
|
'size': None,
|
|
'index': None,
|
|
'infer': Memory.infer,
|
|
'in_ports_count': 1,
|
|
'out_ports_count': 1,
|
|
'type_infer': __class__.type_infer,
|
|
}, attrs)
|
|
|
|
def supported_attrs(self):
|
|
return ['id', 'size', 'index']
|
|
|
|
@staticmethod
|
|
def infer(node: Node):
|
|
if len(node.in_nodes()) > 0:
|
|
# In case this is a memory node with input,
|
|
# It should not have output
|
|
# However in order not to break MO pipeline,
|
|
# we just set the same shape to the output
|
|
# node that will be removed later in pipeline
|
|
copy_shape_infer(node)
|
|
return
|
|
elif node.has_valid('shape'):
|
|
# For Memories, that has not input infer shapes is very difficult
|
|
# But often we can know shape in extracting attributes
|
|
# And we can set the attribute 'shape' in extracting
|
|
batch = 1
|
|
for out_node in node.out_nodes().values():
|
|
out_node.shape = int64_array([batch, *node.shape[:]])
|
|
return
|
|
else:
|
|
raise Error('Model Optimizer is unable to calculate output shape of Memory node {}. ' +
|
|
refer_to_faq_msg(88),
|
|
node.id)
|
|
|
|
@staticmethod
|
|
def type_infer(node: Node):
|
|
if node.has_valid('dst_type'):
|
|
node.out_port(0).set_data_type(node.dst_type)
|
|
else:
|
|
node.out_port(0).set_data_type(data_type_str_to_np(node.graph.graph['cmd_params'].data_type))
|