69 lines
2.6 KiB
Python
69 lines
2.6 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.
|
|
"""
|
|
|
|
import math
|
|
import numpy as np
|
|
|
|
from mo.front.common.layout import get_batch_dim, get_features_dim, get_height_dim, get_width_dim, shape_for_layout
|
|
from mo.graph.graph import Node, Graph
|
|
from mo.ops.op import Op
|
|
|
|
|
|
class UpsampleOp(Op):
|
|
op = 'Upsample'
|
|
|
|
def __init__(self, graph: Graph, attrs: dict):
|
|
mandatory_props = {
|
|
'op': __class__.op,
|
|
'in_ports_count': 2,
|
|
'out_ports_count': 1,
|
|
'infer': UpsampleOp.upsample_infer
|
|
}
|
|
super().__init__(graph, mandatory_props, attrs)
|
|
|
|
def supported_attrs(self):
|
|
return [
|
|
'height_scale',
|
|
'width_scale',
|
|
'mode',
|
|
]
|
|
|
|
@staticmethod
|
|
def upsample_infer(node: Node):
|
|
layout = node.graph.graph['layout']
|
|
assert len(layout) == 4
|
|
|
|
input_shape = node.in_node(0).shape
|
|
if input_shape is None:
|
|
return
|
|
|
|
if len(node.in_nodes()) == 1:
|
|
in_height = input_shape[get_height_dim(layout, 4)]
|
|
in_width = input_shape[get_width_dim(layout, 4)]
|
|
assert node.has('width_scale') is not None and node.has('height_scale') is not None
|
|
out_height = math.floor(in_height * node.height_scale)
|
|
out_width = math.floor(in_width * node.width_scale)
|
|
node.out_node().shape = shape_for_layout(layout,
|
|
batch=input_shape[get_batch_dim(layout, 4)],
|
|
features=input_shape[get_features_dim(layout, 4)],
|
|
height=out_height,
|
|
width=out_width)
|
|
else:
|
|
assert node.in_node(1).value is not None
|
|
eps = 1e-5 # This is to make rounding in case of very close number to round to closest instead of down
|
|
# generic output shape calculation to support 5D input shape case
|
|
node.out_node().shape = np.array((input_shape + eps) * node.in_node(1).value).astype(np.int64)
|