56 lines
2.1 KiB
Python
56 lines
2.1 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import math
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import numpy as np
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from mo.front.common.layout import get_batch_dim, get_features_dim, get_height_dim, get_width_dim, shape_for_layout
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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 UpsampleOp(Op):
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op = 'Upsample'
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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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'in_ports_count': 2,
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'out_ports_count': 1,
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'infer': UpsampleOp.upsample_infer
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}
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super().__init__(graph, mandatory_props, attrs)
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def supported_attrs(self):
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return [
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'height_scale',
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'width_scale',
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'mode',
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]
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@staticmethod
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def upsample_infer(node: Node):
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layout = node.graph.graph['layout']
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assert len(layout) == 4
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input_shape = node.in_node(0).shape
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if input_shape is None:
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return
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if len(node.in_nodes()) == 1:
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in_height = input_shape[get_height_dim(layout, 4)]
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in_width = input_shape[get_width_dim(layout, 4)]
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assert node.has('width_scale') is not None and node.has('height_scale') is not None
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out_height = math.floor(in_height * node.height_scale)
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out_width = math.floor(in_width * node.width_scale)
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node.out_node().shape = shape_for_layout(layout,
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batch=input_shape[get_batch_dim(layout, 4)],
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features=input_shape[get_features_dim(layout, 4)],
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height=out_height,
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width=out_width)
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else:
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assert node.in_node(1).value is not None
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eps = 1e-5 # This is to make rounding in case of very close number to round to closest instead of down
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# generic output shape calculation to support 5D input shape case
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node.out_node().shape = np.array((input_shape + eps) * node.in_node(1).value).astype(np.int64)
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