def replace_op(self, graph: nx.MultiDiGraph, node: Node):
# reshape tensor with batch indices to 2d
unsqueeze_op = Unsqueeze(
graph, {'unsqueeze_dims': np.array([1], dtype=np.int64)})
unsqueeze_node = unsqueeze_op.create_node([node.in_node(2)])
concat_op = Concat(
graph, {
'axis': 1,
'name': node.name + '/concat_batch_indices_and_boxes'
})
concat_node = concat_op.create_node([unsqueeze_node, node.in_node(1)])
# do not remove edge with crop_size because it is needed in the partial infer
graph.remove_edge(node.in_node(1).id, node.id)
# input to the CropAndResize contains boxes coordinates in YXYX layout. But IE layer ROIPooling expects
# coordinates in the XYXY layout, so convolution is added here to swap coordinates
swapped_box_coordinates_node = add_convolution_to_swap_xy_coordinates(
graph, concat_node, 5)
# reshape locations tensor to 2D so it could be passed to Eltwise which will be converted to ScaleShift
reshape_2d_op = Reshape(graph, dict(dim=np.array([-1, 5])))
reshape_2d_node = reshape_2d_op.create_node(
[swapped_box_coordinates_node], dict(name='reshape_2d_'))
create_edge(reshape_2d_node, node, 0, 1)
# do not replace any output edge
return []
def replace_op(self, graph: Graph, node: Node):
if node.has_and_set('inputs_preprocessed'):
log.debug('Node "{}" has already been preprocessed'.format(
node.soft_get('name')))
return []
# reshape tensor with batch indices to 2d
unsqueeze_op = Unsqueeze(
graph, {'unsqueeze_dims': np.array([1], dtype=np.int64)})
unsqueeze_node = unsqueeze_op.create_node([node.in_node(2)])
concat_op = Concat(
graph, {
'axis': 1,
'name': node.name + '/concat_batch_indices_and_boxes',
'in_ports_count': 2
})
concat_node = concat_op.create_node([unsqueeze_node, node.in_node(1)])
# do not remove edge with crop_size because it is needed in the partial infer
graph.remove_edge(node.in_node(1).id, node.id)
# input to the CropAndResize contains boxes coordinates in YXYX layout. But IE layer ROIPooling expects
# coordinates in the XYXY layout, so convolution is added here to swap coordinates
swapped_box_coordinates_node = add_convolution_to_swap_xy_coordinates(
graph, concat_node, 5)
# reshape locations tensor to 2D so it could be passed to Eltwise which will be converted to ScaleShift
reshape_2d_op = Reshape(graph, dict(dim=np.array([-1, 5])))
reshape_2d_node = reshape_2d_op.create_node(
[swapped_box_coordinates_node],
dict(name=swapped_box_coordinates_node.id + '/reshape_2d_',
nchw_layout=True))
graph.create_edge(reshape_2d_node, node, 0, 1)
# do not replace any output edge
return []
