119 lines
5.4 KiB
Python
119 lines
5.4 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 extensions.ops.upsample import UpsampleOp
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from mo.front.common.partial_infer.utils import int64_array
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from mo.front.common.replacement import FrontReplacementSubgraph
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from mo.graph.graph import Graph, Node
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class BatchToSpaceToUpsample(FrontReplacementSubgraph):
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"""
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The transformation looks for pattern that performs NX upscale of the input image specified in the NHWC layout.
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"""
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enabled = True
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def run_before(self):
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from extensions.front.tf.space_to_batch import BatchToSpaceNormalizer
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return [BatchToSpaceNormalizer]
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@staticmethod
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def pattern(**kwargs):
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return dict(
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nodes=[
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('transpose', dict(op='Transpose')),
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('expand_dims', dict(op='Unsqueeze')),
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('tile', dict(op='Tile')),
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('batch_to_space_nd', dict(op='BatchToSpace')),
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('strided_slice', dict(op='StridedSlice')),
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('transpose_back', dict(op='Transpose')),
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],
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edges=[
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('transpose', 'expand_dims', {'out': 0}),
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('expand_dims', 'tile', {'out': 0}),
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('tile', 'batch_to_space_nd', {'out': 0}),
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('batch_to_space_nd', 'strided_slice', {'out': 0}),
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('strided_slice', 'transpose_back', {'out': 0})
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]
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)
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@staticmethod
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def replace_sub_graph(graph: Graph, match: dict, **kwargs):
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def _input_node_value(node: Node, port_ind: int):
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input_node = node.in_port(port_ind).get_source().node
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return input_node.value if input_node.op == 'Const' else None
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transpose = match['transpose']
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transpose_order = _input_node_value(transpose, 1)
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if transpose_order is None or not np.all(np.equal(transpose_order, int64_array([1, 2, 3, 0]))):
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log.debug('The transpose order {} for node {} is not equal to [1, 2, 3, 0]. Cannot apply '
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'BatchToSpaceToUpsample transformation.'.format(transpose_order, transpose.name))
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return
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expand_axis = match['expand_dims']
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expand_axis_value = _input_node_value(expand_axis, 1)
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if expand_axis_value != 0:
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log.debug('The expand axis {} for node {} is not equal to 0. Cannot apply BatchToSpaceToUpsample '
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'transformation.'.format(expand_axis_value, expand_axis.name))
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return
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tile = match['tile']
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tile_value = _input_node_value(tile, 1)
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if tile_value is None:
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log.debug('The tile value is not defined for node {}. Cannot apply BatchToSpaceToUpsample '
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'transformation.'.format(tile.name))
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return
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if len(np.where(tile_value != 1)) != 1:
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log.debug('The number of tiles not equal to 1 not equal to 1. Cannot apply BatchToSpaceToUpsample '
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'transformation.')
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return
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tile_batch = tile_value[0]
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batch_to_space_nd = match['batch_to_space_nd']
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block_shape = _input_node_value(batch_to_space_nd, 1)
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if block_shape is None or tile_batch != np.prod(block_shape):
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log.debug('The block shape {} for node {} is not defined or inconsistent with the tile size. Cannot apply '
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'BatchToSpaceToUpsample transformation.'.format(block_shape, batch_to_space_nd.name))
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return
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if len(block_shape) != 2:
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log.debug('The block shape len is not equal to 2 for node {}. Cannot apply BatchToSpaceToUpsample '
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'transformation.'.format(batch_to_space_nd.name))
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return
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crops = _input_node_value(batch_to_space_nd, 2)
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if crops is None or np.count_nonzero(crops) != 0:
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log.debug('Crops for node {} are non zero. Cannot apply BatchToSpaceToUpsample '
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'transformation.'.format(batch_to_space_nd.name))
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return
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transpose_back = match['transpose_back']
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transpose_back_order = _input_node_value(transpose_back, 1)
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if transpose_back_order is None or not np.all(np.equal(transpose_back_order, int64_array([3, 0, 1, 2]))):
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log.debug('The transpose order {} for node {} is not equal to [3, 0, 1, 2]. Cannot apply '
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'BatchToSpaceToUpsample transformation.'.format(transpose_back_order, transpose_back.name))
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return
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upsample_node = UpsampleOp(graph, {'height_scale': block_shape[0], 'width_scale': block_shape[1],
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'mode': 'nearest',
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'name': transpose.name + '/upsample'}).create_node()
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match['transpose'].in_port(0).get_connection().set_destination(upsample_node.in_port(0))
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match['transpose_back'].out_port(0).get_connection().set_source(upsample_node.out_port(0))
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