def add_convolution_to_swap_xy_coordinates(graph: Graph, input_node: Node,
coordinates_size: int):
"""
The function add convolution node after the node 'input_node' to swap xy coordinates of the boxes produced
by the node 'input_node'. It is expected that box coordinates are located in the fastest changing dimension of the
'input_node' output, i.e. the input tensor could be reshaped to [num_boxes, 4] or [num_boxes, 5]. If the size is 5,
then the 0-th element for each of num_boxes blocks is not changed and element 1 is swapped with element 2, element 3
is swapped with element 4. This is the case when boxes coordinates are produced by the layer "Proposal". The exact
amount of elements in each block is equal to the 'coordinates_size' parameter.
:param graph: graph to operate on.
:param input_node: node producing boxes coordinates.
:param coordinates_size: integer value equal to 4 or 5.
:return convolution node that swaps coordinates.
"""
# swap of input tensor with 4 or 5 numbers describing boxes are supported
assert (coordinates_size in [4, 5])
input_reshape_4d_node = create_op_node_with_second_input(
graph, Reshape, int64_array([-1, 1, 1, coordinates_size]),
dict(name=input_node.name + '/reshape_4d'), input_node)
mark_input_as_in_correct_layout(input_reshape_4d_node, 0)
# do not mark second input because the reshape works in initial model layout and needs to be transformed to NCHW
mark_output_as_in_correct_layout(input_reshape_4d_node, 0)
if coordinates_size == 5:
# zero indexed element is not box coordinate ("batch id" in case of Proposal)
conv_filter_data = mo_array(
mo_array([[[[1, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 1, 0, 0, 0],
[0, 0, 0, 0, 1], [0, 0, 0, 1, 0]]]],
dtype=np.float32))
else:
conv_filter_data = mo_array(
mo_array(
[[[[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0]]]],
dtype=np.float32))
conv_filter_data = np.transpose(conv_filter_data, [2, 3, 0, 1])
conv_filter_const_op = Const(graph, dict(value=conv_filter_data))
conv_filter_const_node = conv_filter_const_op.create_node(
[], dict(name=input_node.name + '/weights'))
conv_op = Convolution(
graph, {
'bias_addable': True,
'channel_dims': mo_array([3]),
'batch_dims': mo_array([0]),
'input_feature_channel': 0,
'output_feature_channel': 1,
'group': 1,
'layout': 'NHWC',
})
return conv_op.create_node([input_reshape_4d_node, conv_filter_const_node],
dict(name=input_node.name + "/conv"))
def replace_sub_graph(self, graph: Graph, match: dict):
node = match['softmax']
if 'temperature' in node and node['temperature'] != 1.0:
in_node = node.in_node()
out_nodes = [node for node in node.out_nodes().values()]
graph.remove_edge(node.in_node().id, node.id)
temperature = mo_array([1.0 / node.temperature])
scalar_value_op = Const(graph, dict(value=temperature, shape=temperature.shape,
symbol_dict={'name': node.id + '/const'}))
mul_op = Mul(graph, dict(name=node.id + '/mul_', symbol_dict={'name': node.id + '/mul_'}))
mul_node = mul_op.create_node(inputs=[in_node, scalar_value_op.create_node()])
edge_attrs = graph.get_edge_data(node.id, out_nodes[0].id)[0]
graph.add_edges_from([(mul_node.id, node.id, edge_attrs)])
