67 lines
1.9 KiB
Python
67 lines
1.9 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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from math import ceil
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# Concat infer : N - number of inputs to concat
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# axis - dimension number for tensors concatenation
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import numpy as np
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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 CorrelationOp(Op):
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op = 'Correlation'
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'type': __class__.op,
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'op': __class__.op,
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'version': 'extension',
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'in_ports_count': 1,
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'out_ports_count': 1,
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'infer': CorrelationOp.corr_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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'pad',
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'kernel_size',
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'max_displacement',
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'stride_1',
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'stride_2',
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'single_direction',
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'do_abs',
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'correlation_type'
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]
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@staticmethod
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def corr_infer(node: Node):
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outn = node.out_node(0)
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inn = node.in_node(0)
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outn.shape = np.zeros(4, dtype=int)
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outn.shape[0] = inn.shape[0]
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bottomchannels = inn.shape[1]
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paddedbottomheight = inn.shape[2]
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paddedbottomwidth = inn.shape[3] + 2 * node.pad
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kernel_radius_ = (node.kernel_size - 1) / 2;
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border_size_ = node.max_displacement + kernel_radius_
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outn.shape[3] = ceil((float)(paddedbottomwidth - border_size_ * 2) / node.stride_1)
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outn.shape[2] = ceil((float)(paddedbottomheight - kernel_radius_ * 2) / node.stride_1)
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neighborhood_grid_radius_ = node.max_displacement / node.stride_2
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if node.single_direction != 0:
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neighborhood_grid_width_ = neighborhood_grid_radius_ + 1
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else:
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neighborhood_grid_width_ = neighborhood_grid_radius_ * 2 + 1
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outn.shape[1] = neighborhood_grid_width_ * neighborhood_grid_width_
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