85 lines
3.3 KiB
Python
85 lines
3.3 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 logging as log
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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 SparseFillEmptyRows(Op):
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''' The operation fills empty rows in the input 2-D sparse tensor with a default value.
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For more details see https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/sparse-fill-empty-rows
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4 inputs:
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- [0, required] input indices of the sparse tensor (2D),
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- [1, required] input values of the sparse tensor (1D),
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- [2, required] shape of the sparse tensor. Value of this input is required for the Model Optimizer (1D),
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- [3, required] default value to insert at rows missing from the input sparse tensor (0D),
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3 outputs:
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- [0, optional] indices of the filled sparse tensor (2D)
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- [1, optional] values of the filled sparse tensor (1D)
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- [2, optional] indicator of whether the dense row was missing in the input sparse tensor (1D)
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'''
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op = 'SparseFillEmptyRows'
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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': 4,
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'out_ports_count': 3
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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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assert len(node.in_nodes()) == 4
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# check that shape value is defined that is needed for shape inference
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shape = node.in_node(2)
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assert shape.value is not None and shape.value.size == 2, \
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"SparseFillEmptyRows is supported only with constant shape value"
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shape_value = np.array(shape.value, dtype=np.int64)
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# check that default value is scalar
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default_value = node.in_node(3)
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assert default_value.shape is not None and len(default_value.shape) == 0, \
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"Default value for SparseFillEmptyRows must be scalar"
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for out_node_ind in node.out_nodes():
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if out_node_ind == 0: # set a shape for output indices
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node.out_node(0).shape = np.array([np.prod(shape_value), 2], dtype=np.int64)
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continue
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elif out_node_ind == 1: # set a shape for output values
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node.out_node(1).shape = np.array([np.prod(shape_value)], dtype=np.int64)
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continue
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elif out_node_ind == 2: # set a shape for empty row indicator
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node.out_node(2).shape = np.array([shape_value[0]], dtype=np.int64)
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continue
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else:
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log.error("SparseFillEmptyRows has only three outputs")
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return
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