111 lines
4.7 KiB
Python
111 lines
4.7 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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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 SparseSegmentSqrtN(Op):
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''' The operation computes the sum along sparse segments of a tensor and divides it by the square root of N, where N is a number of rows in a segment.
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For more details, see https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/sparse-segment-sqrt-n.
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Three inputs:
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- [0, required] Data tensor from which rows are selected for the sum divided by sqrt of N (ND),
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- [1, required] Tensor of indices of selected rows from the first input tensor along 0 dimension (1D),
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- [2, required] Tensor of segment IDs to which selected rows belong.
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Selected rows belonging to the same segment are summed up. The tensor has the same size as the second input.
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Values must be sorted and can be repeated. (1D).
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One output:
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- [0, required] The output has the same shape as the data tensor, except for dimension 0, which has a size equal to a number of segments (ND).
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'''
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op = 'SparseSegmentSqrtN'
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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': 'experimental',
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'infer': __class__.infer,
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'in_ports_count': 3,
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'out_ports_count': 1,
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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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@staticmethod
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def infer(node: Node):
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# check a number of input/output edges
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assert len(node.in_nodes()) == 3
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assert len(node.out_nodes()) == 1
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data_shape = node.in_port(0).data.get_shape()
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indices_shape = node.in_port(1).data.get_shape()
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segment_ids_shape = node.in_port(2).data.get_shape()
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data_value = node.in_port(0).data.get_value()
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indices_value = node.in_port(1).data.get_value()
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segment_ids_value = node.in_port(2).data.get_value()
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# check input shapes
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assert data_shape is not None, \
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"Shape for input data tensor to SparseSegmentSqrtN must be defined"
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assert indices_shape is not None and indices_shape.size == 1, \
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"SparseSegmentSqrtN supports only 1D indices tensor"
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assert segment_ids_shape is not None and segment_ids_shape.size == 1, \
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"SparseSegmentSqrtN supports only 1D segment IDs tensor"
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assert segment_ids_shape == indices_shape, \
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"Indices and segment IDs tensors must have the same shape"
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# computes output shape
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output_shape = data_shape
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output_shape[0] = segment_ids_shape[0]
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node.out_port(0).data.set_shape(output_shape)
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# infer if all input is constant
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if data_value is None or indices_value is None or segment_ids_value is None:
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return
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# check that values in segment_ids are sorted
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for i in range(1, len(segment_ids_value)):
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assert segment_ids_value[i-1] <= segment_ids_value[i], \
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"Values in segment IDs are not sorted"
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num_segments = int(segment_ids_value[-1]) + 1
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# check that indices are in a range [0, data_shape[0])
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assert np.all(indices_value >= 0) and np.all(indices_value < data_shape[0]), \
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"Some value in indices tensor is out of range"
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# infer
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num_adds = np.zeros(num_segments, dtype=np.int)
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output_value = np.zeros([num_segments] + data_shape[1:].tolist(), dtype=np.float)
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output_shape = output_value.shape
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for i in range(len(segment_ids_value)):
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segment_id = int(segment_ids_value[i])
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indice = int(indices_value[i])
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output_value[segment_id, :] += data_value[indice, :]
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num_adds[segment_id] += 1
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num_adds = np.sqrt(num_adds)
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for segment_id in range(num_segments):
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if num_adds[segment_id] != 0:
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output_value[segment_id, :] /= num_adds[segment_id]
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node.out_port(0).data.set_shape(output_shape)
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node.out_port(0).data.set_value(output_value)
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