Vladimir Gavrilov
3aed19a46a
* Started to write equality comparator for StridedSlice layers. * Now considered only unique StridedSlice consumers of non-constant data nodes. * Fixed test for the transformation ConvertGroupedStridedSlice. * Deleted commented code. * Small fixes. * Moved functions unique_by and group_by_with_binary_predicate to mo/utils/utils.py. * Deleted function collect_sequences. * Added asserts into the constructor of StridedSlice. * Written test for the case when 1) there are 4 StridedSlice operations; 2) 2 of StridedSlice have the same data; 3) 2 others StridedSlice have the same data; 4) all StridedSlice operations outputs are consumed by different operations. * Added some comments.
273 lines
11 KiB
Python
273 lines
11 KiB
Python
"""
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Copyright (c) 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 typing import List
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from extensions.ops.interpolate import Interpolate
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from mo.front.common.partial_infer.utils import int64_array
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from mo.front.tf.graph_utils import create_op_with_const_inputs
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from mo.graph.graph import Graph, Node, rename_nodes
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from mo.middle.replacement import MiddleReplacementPattern
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from mo.utils.error import Error
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from mo.utils.utils import group_by_with_binary_predicate
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def node_has_one_consumer(node: Node) -> bool:
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return len(node.out_port(0).get_destinations()) == 1
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def is_next(first: Node, second: Node) -> bool:
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"""
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This function checks if 'first' is predecessor of 'second'. The node 'first' is called to be
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a predecessor of the node 'second', if an output of 'first' is an input of 'second', and
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number of destinations of 'first' is equal to 1.
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:param first: an Interpolate layer
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:param second: another Interpolate layer
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:return: True, if 'first' is an predecessor of 'second', and False otherwise.
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"""
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if not node_has_one_consumer(first):
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return False
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dests = first.out_port(0).get_destinations()
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if len(dests) != 1:
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return False
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return second.id == dests[0].node.id
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class CanBeFused:
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def __init__(self):
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# We need to accumulate set of axes of compared nodes, because there can be a sequence of a set of axes
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# {i}{j}{i}
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self.accumulated_axes = set()
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self.default_values_for_opset4 = {
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'mode': None,
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'shape_calculation_mode': None,
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'coordinate_transformation_mode': 'half_pixel',
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'nearest_mode': 'round_prefer_floor',
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'antialias': 0,
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'cube_coeff': -0.75
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}
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self.default_pads = int64_array([0])
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def _compare_attributes_of_interpolate1(self, first: Node, second: Node) -> bool:
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"""
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This function checks whether attributes of Interpolate-1 nodes first and second are identical
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(except attribute 'axes').
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:param first: the first of compared nodes
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:param second: the second of compared nodes
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:return: True, if attributes of nodes are identical and False otherwise
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"""
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# If some of attributes 'mode', 'align_corners', 'antialias', 'pads_begin', 'pads_end' are different,
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# then attributes of nodes are not identical.
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op = Interpolate(graph=first.graph, attrs={})
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for attr in ['mode', 'align_corners', 'antialias', 'pads_begin', 'pads_end']:
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if first.soft_get(attr, default=op.attrs[attr]) != second.soft_get(attr, default=op.attrs[attr]):
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return False
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return True
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def _compare_attributes_of_interpolate4(self, first: Node, second: Node) -> bool:
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"""
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This function checks whether attributes of Interpolate-4 nodes first and second are identical.
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:param first: the first of compared nodes
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:param second: the second of compared nodes
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:return: True, if attributes of nodes are identical and False otherwise
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"""
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# If some of attributes 'mode', 'coordinate_transformation_mode', 'nearest_mode', 'antialias', 'cube_coeff'
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# are different, then attributes of first and second are not identical.
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for attr in self.default_values_for_opset4.keys():
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default_value = self.default_values_for_opset4[attr]
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if first.soft_get(attr, default=default_value) != second.soft_get(attr, default=default_value):
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return False
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# If attributes 'pads_begin' or 'pads_end' of nodes first and second are different, then attributes
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# of first and second are not identical.
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for attr in ['pads_begin', 'pads_end']:
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if not np.array_equal(first.soft_get(attr, default=self.default_pads),
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second.soft_get(attr, default=self.default_pads)):
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return False
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return True
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def _compare_attributes(self, first: Node, second: Node) -> bool:
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"""
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This function checks whether attributes of nodes first and second are identical (except attribute 'axes').
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:param first: the first of compared nodes
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:param second: the second of compared nodes
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:return: True, if attributes of nodes are identical and False otherwise
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"""
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# If opsets of nodes are different, then nodes have different attributes.
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fst_opset = first.get_opset()
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snd_opset = second.get_opset()
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if fst_opset != snd_opset:
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return False
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if fst_opset not in ['opset1', 'opset4']:
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fst_name = first.soft_get('name', first.id)
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snd_name = second.soft_get('name', second.id)
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raise Error('Unsupported opset {} for nodes with names {} and {}'.format(fst_opset, fst_name, snd_name))
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if fst_opset == 'opset1':
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return self._compare_attributes_of_interpolate1(first, second)
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else:
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return self._compare_attributes_of_interpolate4(first, second)
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def __call__(self, first: Node, second: Node) -> bool:
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"""
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This function checks whether Interpolate nodes 'first' and 'second' can be fused.
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:param first: the first of fused nodes
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:param second: the second of fused nodes
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:return: True, if nodes can be fused, and False otherwise
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"""
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if not self._compare_attributes(first, second):
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return False
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fst_axes = set([a for a in Interpolate.get_axes(first)])
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snd_axes = set([a for a in Interpolate.get_axes(second)])
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self.accumulated_axes = self.accumulated_axes | fst_axes
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# If the set of accumulated axes and the set of axes of 'second' do not intersect then nodes can be fused,
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# because interpolations with respect to various axes do not affect each other.
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if not(self.accumulated_axes & snd_axes):
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return True
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# Otherwise, nodes cannot be fused.
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self.accumulated_axes = set()
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return False
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def get_interpolate_attributes(node: Node) -> dict:
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opset_to_default_values = {
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'opset1': {
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'mode': None,
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'align_corners': 0,
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'antialias': 0,
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'pads_begin': 0,
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'pads_end': 0,
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'version': 'opset1'
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},
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'opset4': {
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'mode': None,
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'shape_calculation_mode': None,
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'antialias': 0,
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'pads_begin': int64_array([0]),
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'pads_end': int64_array([0]),
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'coordinate_transformation_mode': 'half_pixel',
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'nearest_mode': 'round_prefer_floor',
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'cube_coeff': -0.75,
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'version': 'opset4'
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},
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}
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opset = node.get_opset()
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result = {}
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if opset in opset_to_default_values:
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default_values = opset_to_default_values[opset]
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for attr in default_values.keys():
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value = node.soft_get(attr, default=default_values[attr])
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result[attr] = value
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return result
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else:
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raise Error('Unsupported opset {} for node with name {}.'.format(opset, node.soft_get('name', node.id)))
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def replace_sequence(seq: List[Node], graph: Graph):
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"""
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This function replaces a sequence of consecutive Interpolate layers with one Interpolate layer,
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if modes of all nodes of a sequence are the same.
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:param seq: sequence of Interpolate layers
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:param graph: graph to which nodes of seq belong
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:return: Nothing
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"""
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if not seq:
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return
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if len(seq) == 1:
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return
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modes = set([n.mode for n in seq])
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if len(modes) != 1:
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return
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dims_and_scales_ = []
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# Each element of the list dims_and_scales_ is a pair
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# (axis, output size for this axis) (opset1)
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# or
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# (axis, output size for this axis, output scales for this axis) (opset4)
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if seq[0].get_opset() == 'opset1':
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for interp in seq:
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dims_and_scales_.extend(zip(Interpolate.get_axes(interp),
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interp.in_port(1).get_connection().get_source().data.get_value()))
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axis_to_size = sorted(list(dict(dims_and_scales_).items()), key=lambda x: x[0])
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axes_of_node = int64_array([z[0] for z in axis_to_size])
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sizes = int64_array([z[1] for z in axis_to_size])
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scales = np.ones(len(axis_to_size))
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else:
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for interp in seq:
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dims_and_scales_.extend(zip(Interpolate.get_axes(interp),
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interp.in_port(1).get_connection().get_source().data.get_value(),
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interp.in_port(2).get_connection().get_source().data.get_value()))
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axis_to_size = sorted(dims_and_scales_, key=lambda x: x[0])
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axes_of_node = int64_array([z[0] for z in axis_to_size])
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sizes = int64_array([z[1] for z in axis_to_size])
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scales = np.array([z[2] for z in axis_to_size])
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fst_interp_node = seq[0]
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last_interp_node = seq[-1]
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last_interp_node_name = last_interp_node.soft_get('name', last_interp_node.id)
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attributes = get_interpolate_attributes(fst_interp_node)
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opset = fst_interp_node.get_opset()
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if opset == 'opset1':
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attributes['axes'] = axes_of_node
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interp_node = create_op_with_const_inputs(graph, Interpolate, {1: sizes}, attributes)
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fst_interp_connection = fst_interp_node.in_port(0).get_connection()
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fst_interp_connection.set_destination(interp_node.in_port(0))
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last_interp_node.out_port(0).get_connection().set_source(interp_node.out_port(0))
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else:
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attributes['in_ports_count'] = 4
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interp_node = create_op_with_const_inputs(graph, Interpolate,
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{1: sizes, 2: scales, 3: axes_of_node},
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attributes)
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fst_interp_connection = fst_interp_node.in_port(0).get_connection()
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fst_interp_connection.set_destination(interp_node.in_port(0))
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last_interp_node.out_port(0).get_connection().set_source(interp_node.out_port(0))
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rename_nodes([(last_interp_node, last_interp_node_name + '/delete_'), (interp_node, last_interp_node_name)])
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class InterpolateSequenceToInterpolate(MiddleReplacementPattern):
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"""
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This transformation replaces a sequence of Interpolate layers by one Interpolate layer.
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"""
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enabled = True
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def run_before(self):
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from extensions.middle.UpsampleToResample import UpsampleToResample
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return [UpsampleToResample]
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def find_and_replace_pattern(self, graph: Graph):
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log.debug('Enabled replacement of a sequence of Interpolate layers with one Interpolate layer.')
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interps = [n for n in graph.pseudo_topological_sort() if n.kind == 'op' and n.op == 'Interpolate']
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fuser = CanBeFused()
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sequences = group_by_with_binary_predicate(interps, lambda prev, x: is_next(prev, x) and fuser(prev, x))
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for seq in sequences:
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replace_sequence(seq, graph)
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