147 lines
6.6 KiB
Python
147 lines
6.6 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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from extensions.ops.resize_factor_utils import factor_update
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from mo.front.common.layout import get_batch_dim, get_features_dim, get_height_dim, get_width_dim, shape_for_layout
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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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from mo.utils.utils import refer_to_faq_msg
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class InterpOp(Op):
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op = 'Interp'
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enabled = False
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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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'factor': None,
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'align_corners': 1,
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'parse_2nd_input': 'value',
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'in_ports_count': 2,
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'out_ports_count': 1,
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'infer': None
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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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'height',
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'width',
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'zoom_factor',
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'shrink_factor',
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'factor', # float factor required by IE shape inference
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'pad_beg',
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'pad_end',
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'align_corners'
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]
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@staticmethod
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def interp_infer(node: Node):
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layout = node.graph.graph['layout']
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assert len(layout) == 4
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if len(node.in_nodes()) == 2:
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src_shape = node.in_node(0).shape
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dst_shape = node.in_node(1).shape
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# in Caffe can be 2 inputs too, but shape should be got from shape of the second input
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if node.parse_2nd_input == 'shape':
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dst_shape = [dst_shape[get_height_dim(layout, 4)], dst_shape[get_width_dim(layout, 4)]]
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else:
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# it is TF case
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dst_shape = node.in_node(1).value
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if src_shape is None or dst_shape is None or len(src_shape) != 4 or len(dst_shape) != 2:
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log.error(
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'Node {} with op {} cannot be converted to Resample layer because there is no enough info about '
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'src/dst shapes: src_shape = {}, dst_shape = {}'.format(node.name, node.op, src_shape, dst_shape))
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node.type = None # prevent translation to a valid IE layer
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return
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in_height = src_shape[get_height_dim(layout, 4)]
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in_width = src_shape[get_width_dim(layout, 4)]
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out_height = dst_shape[0]
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out_width = dst_shape[1]
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node.factor = factor_update(
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node.factor,
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[float(out_height) / in_height, float(out_width) / in_width],
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[in_height, in_width],
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[out_height, out_width],
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node.soft_get('name')
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)
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if node.factor is None:
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node['width'] = out_width
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node['height'] = out_height
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node.out_node().shape = shape_for_layout(layout,
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batch=src_shape[get_batch_dim(layout, 4)],
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features=src_shape[get_features_dim(layout, 4)],
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height=out_height,
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width=out_width)
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node.graph.remove_edge(node.in_node(1).id, node.id)
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else:
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outn = node.out_node(0)
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in_shape = node.in_node(0)
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num_ = in_shape.shape[get_batch_dim(layout, 4)]
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channels_ = in_shape.shape[get_features_dim(layout, 4)]
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height_in_ = in_shape.shape[get_height_dim(layout, 4)]
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width_in_ = in_shape.shape[get_width_dim(layout, 4)]
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height_out_ = height_in_ + node.pad_beg + node.pad_end
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width_out_ = width_in_ + node.pad_beg + node.pad_end
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if node.shrink_factor != 1 and node.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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height_out_ = (height_out_ - 1) / shrink_factor + 1
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width_out_ = (width_out_ - 1) / shrink_factor + 1
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elif node.shrink_factor == 1 and node.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'] = 'Interp layer shape inference function may be wrong, please, try to update ' \
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'layer shape inference function in the file (extensions/ops/interp.op at the ' \
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'line {}).'.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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# height_out_ = height_out_ + (height_out_ - 1) * (zoom_factor - 1)
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# width_out_ = width_out_ + (width_out_ - 1) * (zoom_factor - 1)
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# Comment out the following lines if you use the reshape method from previous section
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height_out_ = height_out_ * zoom_factor
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width_out_ = width_out_ * zoom_factor
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elif node.width != 0 and node.height != 0:
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height_out_ = node.height
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width_out_ = node.width
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elif node.shrink_factor != 1 and node.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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height_out_ = (height_out_ - 1) / shrink_factor + 1
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width_out_ = (width_out_ - 1) / shrink_factor + 1
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height_out_ = height_out_ + (height_out_ - 1) * (zoom_factor - 1)
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width_out_ = width_out_ + (width_out_ - 1) * (zoom_factor - 1)
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outn.shape = shape_for_layout(layout,
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batch=num_,
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features=channels_,
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height=height_out_,
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width=width_out_)
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