217 lines
12 KiB
Python
217 lines
12 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import inspect
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import logging as log
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import numpy as np
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from extensions.ops.elementwise import Mul, Add
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from mo.front.common.replacement import FrontReplacementOp
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from mo.graph.graph import Graph
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from mo.ops.const import Const
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from mo.ops.shape import Shape
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from mo.ops.strided_slice import StridedSlice
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from mo.utils.utils import refer_to_faq_msg
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class InterpolateNormalizer(FrontReplacementOp):
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op = 'Interpolate'
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enabled = True
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def replace_sub_graph(self, graph: Graph, match: dict):
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node = match['op']
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if 1 not in node.in_ports() or node.in_port(1).disconnected():
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if node.has_valid('factor') and not node.has_valid('width') and not node.has_valid('height'):
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factor = Const(graph, {'value': np.array(node.factor)}).create_node()
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shape = Shape(graph, {'name': node.name + '/shape'}).create_node()
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begin = Const(graph, {'value': np.array([2])}).create_node()
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end = Const(graph, {'value': np.array([4])}).create_node()
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stride = Const(graph, {'value': np.array([1])}).create_node()
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ss = StridedSlice(graph, {'name': node.name + '/ss_0_port', 'begin_mask': np.array([1]),
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'end_mask': np.array([0]), 'new_axis_mask': np.array([0]),
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'shrink_axis_mask': np.array([0]),
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'ellipsis_mask': np.array([0])}).create_node()
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mul = Mul(graph, {'name': node.name + '/factor_mul_'}).create_node()
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source = node.in_port(0).get_connection().get_source()
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source.connect(shape.in_port(0))
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shape.out_port(0).connect(ss.in_port(0))
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begin.out_port(0).connect(ss.in_port(1))
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end.out_port(0).connect(ss.in_port(2))
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stride.out_port(0).connect(ss.in_port(3))
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ss.out_port(0).connect(mul.in_port(0))
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factor.out_port(0).connect(mul.in_port(1))
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node.add_input_port(1, skip_if_exist=True)
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assert node.in_port(1).disconnected()
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mul.out_port(0).connect(node.in_port(1))
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else:
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shape = Shape(graph, {'name': node.name + '/shape'}).create_node()
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begin = Const(graph, {'value': np.array([2])}).create_node()
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end = Const(graph, {'value': np.array([4])}).create_node()
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stride = Const(graph, {'value': np.array([1])}).create_node()
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ss = StridedSlice(graph, {'name': node.name + '/ss_0_port', 'begin_mask': np.array([1]),
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'end_mask': np.array([0]), 'new_axis_mask': np.array([0]),
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'shrink_axis_mask': np.array([0]),
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'ellipsis_mask': np.array([0])}).create_node()
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source = node.in_port(0).get_connection().get_source()
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source.connect(shape.in_port(0))
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shape.out_port(0).connect(ss.in_port(0))
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begin.out_port(0).connect(ss.in_port(1))
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end.out_port(0).connect(ss.in_port(2))
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stride.out_port(0).connect(ss.in_port(3))
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pads_value = node.pads_begin + node.pads_end
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pads_const = Const(graph, {'value': np.array(pads_value)}).create_node()
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add = Add(graph, {'name': node.name + '/pad_add'}).create_node()
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ss.out_port(0).connect(add.in_port(0))
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add.in_port(1).connect(pads_const.out_port(0))
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if node.soft_get('shrink_factor') != 1 and node.soft_get('zoom_factor') == 1:
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shrink_factor = node.shrink_factor
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if shrink_factor < 1:
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log.error('Shrink factor should be positive in node {}'.format(node.id))
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return None
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const = Const(graph, {'name': node.name + '/pre_shrink_sub_const',
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'value': np.array(-1)}).create_node()
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sub = Add(graph, {'name': node.name + '/pre_shrink_sub'}).create_node()
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add.out_port(0).connect(sub.in_port(0))
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sub.in_port(1).connect(const.out_port(0))
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const = Const(graph, {'value': np.array(1 / shrink_factor),
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'name': node.name + 'shrink_factor_div_const'}).create_node()
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div = Mul(graph, {'name': node.name + 'shrink_factor_div'}).create_node()
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sub.out_port(0).connect(div.in_port(0))
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div.in_port(1).connect(const.out_port(0))
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const = Const(graph, {'name': node.name + '/shrink_factor_add_one_const', 'value': np.array(1)
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}).create_node()
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add = Add(graph, {'name': node.name + '/shrink_factor_add_one'}).create_node()
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div.out_port(0).connect(add.in_port(0))
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const.out_port(0).connect(add.in_port(1))
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node.add_input_port(1, skip_if_exist=True)
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assert node.in_port(1).disconnected()
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add.out_port(0).connect(node.in_port(1))
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elif node.soft_get('shrink_factor') == 1 and node.soft_get('zoom_factor') != 1:
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zoom_factor = node.zoom_factor
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if zoom_factor < 1:
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log.error('Zoom factor should be positive in node {}'.format(node.id))
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return None
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node['debug_message'] = 'Interpolate layer replacer may be wrong, please, try to update it in the' \
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' file (extensions/front/InterpolateNormalizer.py at the line {}).' \
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''.format(inspect.currentframe().f_lineno) + refer_to_faq_msg(100)
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# Reshape methods can be different in some cases
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# Commented out section represents reshape that used in deeplab-caffe
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# Uncomment the following lines, if your model was trained with deeplab-caffe
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# or have the same reshape method
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# const = Const(graph, {'value': np.array(-1),
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# 'name': node.name + 'zoom_factor_deeplab-caffe_sub_const'}).create_node()
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# sub = Add(graph, {'name': node.name + 'zoom_factor_deeplab-caffe_sub'}).create_node()
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# add.out_port(0).connect(sub.in_port(0))
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# const.out_port(0).connect(sub.in_port(1))
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#
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# const = Const(graph, {'value': np.array(zoom_factor - 1),
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# 'name': node.name + 'zoom_factor_deeplab-caffe_mul_const'}).create_node()
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# mul = Mul(graph, {'name': node.name + 'zoom_factor_deeplab-caffe_mul'}).create_node()
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# sub.out_port(0).connect(mul.in_port(0))
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# const.out_port(0).connect(mul.in_port(1))
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#
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# sum = Add(graph, {'name': node.name + 'zoom_factor_deeplab-caffe_sum'}).create_node()
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# add.out_port(0).connect(sum.in_port(0))
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# mul.out_port(0).connect(sum.in_port(1))
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#
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# node.add_input_port(1, skip_if_exist=True)
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# assert node.in_port(1).disconnected()
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# sum.out_port(0).connect(node.in_port(1))
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# Comment out the following lines if you use the reshape method from previous section
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const = Const(graph, {'value': np.array(zoom_factor),
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'name': node.name + '/zoom_factor_mul_const'}).create_node()
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mul = Mul(graph, {'name': node.name + '/zoom_factor_mul'}).create_node()
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add.out_port(0).connect(mul.in_port(0))
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const.out_port(0).connect(mul.in_port(1))
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node.add_input_port(1, skip_if_exist=True)
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assert node.in_port(1).disconnected()
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mul.out_port(0).connect(node.in_port(1))
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elif node.soft_get('width') != 0 and node.soft_get('height') != 0:
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const = Const(graph, {'value': np.array([node.height, node.width])}).create_node()
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node.add_input_port(1, skip_if_exist=True)
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assert node.in_port(1).disconnected()
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const.out_port(0).connect(node.in_port(1))
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elif node.soft_get('shrink_factor') != 1 and node.soft_get('zoom_factor') != 1:
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shrink_factor = node.shrink_factor
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zoom_factor = node.zoom_factor
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if shrink_factor < 1:
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log.error('Shrink factor should be positive in node {}'.format(node.id))
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return None
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if zoom_factor < 1:
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log.error('Zoom factor should be positive in node {}'.format(node.id))
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return None
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const = Const(graph, {'value': np.array(-1)}).create_node()
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sub = Add(graph, {'name': node.name + '/shrink_zoom_factor_sub'}).create_node()
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add.out_port(0).connect(sub.in_port(0))
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const.out_port(0).connect(sub.in_port(1))
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const = Const(graph, {'value': np.array(1 / (shrink_factor + 1))}).create_node()
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div = Mul(graph, {'name': node.name + '/shrink_factor_div'}).create_node()
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sub.out_port(0).connect(div.in_port(0))
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const.out_port(0).connect(div.in_port(1))
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const = Const(graph, {'value': np.array(-1),
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'name': node.name + 'shrink_zoom_factor_sum_const'}).create_node()
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sum = Add(graph, {'name': node.name + '/shrink_zoom_factor_sum'}).create_node()
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div.out_port(0).connect(sum.in_port(0))
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const.out_port(0).connect(sum.in_port(1))
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const = Const(graph, {'value': np.array(zoom_factor - 1)}).create_node()
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mul = Mul(graph, {'name': node.name + '/zoom_factor_mul'}).create_node()
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sum.out_port(0).connect(mul.in_port(0))
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const.out_port(0).connect(mul.in_port(1))
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sum = Add(graph, {'name': node.name + '/final_shrink_zoom_factor_sum'}).create_node()
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div.out_port(0).connect(sum.in_port(0))
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mul.out_port(0).connect(sum.in_port(1))
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node.add_input_port(1, skip_if_exist=True)
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assert node.in_port(1).disconnected()
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sum.out_port(0).connect(node.in_port(1))
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else:
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if node.soft_get('fw') == 'caffe':
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shape = Shape(graph, {'name': node.name + '/shape'}).create_node()
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begin = Const(graph, {'value': np.array([2])}).create_node()
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end = Const(graph, {'value': np.array([4])}).create_node()
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stride = Const(graph, {'value': np.array([1])}).create_node()
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ss = StridedSlice(graph, {'name': node.name + '/ss_0_port', 'begin_mask': np.array([1]),
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'end_mask': np.array([0]), 'new_axis_mask': np.array([0]),
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'shrink_axis_mask': np.array([0]),
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'ellipsis_mask': np.array([0])}).create_node()
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source = node.in_port(1).get_connection().get_source()
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node.in_port(1).disconnect()
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source.connect(shape.in_port(0))
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shape.out_port(0).connect(ss.in_port(0))
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begin.out_port(0).connect(ss.in_port(1))
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end.out_port(0).connect(ss.in_port(2))
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stride.out_port(0).connect(ss.in_port(3))
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ss.out_port(0).connect(node.in_port(1))
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