def replace_sub_graph(self, graph: Graph, match: dict):
log.debug('Matched NearestNeighborUpsampling pattern: {}'.format([node.id for node in match.values()]))
try:
input_height = match['pack_1'].in_node(1).value.item()
input_width = match['pack_1'].in_node(3).value.item()
height_scale = match['mul_const'].shape[-4]
width_scale = match['mul_const'].shape[-2]
except Exception as ex:
log.warning('Failed to determine scaling parameters from the topology. Do not apply pattern.')
return
axes = int64_array([2, 3]) if graph.graph['layout'] == 'NCHW' else int64_array([1, 2])
resample_op = Interpolate(graph, {'name': 'Resample_', 'antialias': 0, 'mode': 'nearest', 'axes': axes})
resample_node = resample_op.create_node([match['op']])
const = Const(graph, {'value': np.array([input_height * height_scale, input_width * width_scale]),
'name': resample_node.name + '/target_shape'}).create_node()
match['reshape_2'].replace_node(resample_node)
resample_node.add_input_port(1, skip_if_exist=True)
assert resample_node.in_port(1).disconnected()
const.out_port(0).connect(resample_node.in_port(1))
graph.remove_nodes_from([node.id for node in match.values() if node.id != match['op'].id])
def replace_sub_graph(self, graph: Graph, match: dict):
log.debug('Matched NearestNeighborUpsampling pattern: {}'.format(
[node.id for node in match.values()]))
try:
input_height = match['pack_1'].in_node(1).value.item()
input_width = match['pack_1'].in_node(3).value.item()
height_scale = match['mul_const'].shape[-4]
width_scale = match['mul_const'].shape[-2]
except Exception as ex:
log.warning(
'Failed to determine scaling parameters from the topology. Do not apply pattern.'
)
return
reshape2_name = match['reshape_2'].name
resample_op = Interpolate(
graph, {
'mode': 'nearest',
'antialias': 0,
'pads_begin': int64_array([0]),
'pads_end': int64_array([0]),
'coordinate_transformation_mode': 'half_pixel',
'nearest_mode': 'round_prefer_floor',
'cube_coeff': -0.75,
'version': 'opset4',
'name': reshape2_name + '/Resample',
'shape_calculation_mode': 'scales',
'in_ports_count': 4
})
resample_node = resample_op.create_node([match['op']])
axes_node = Const(
graph, {
'name':
resample_node.name + '/axes',
'value':
int64_array([2, 3])
if graph.graph['layout'] == 'NCHW' else int64_array([1, 2])
}).create_node()
sizes_node = Const(
graph, {
'value':
np.array(
[input_height * height_scale, input_width * width_scale]),
'name':
resample_node.name + '/target_shape'
}).create_node()
scales_node = Const(
graph, {
'value': np.array([height_scale, width_scale],
dtype=np.float32),
'name': resample_node.name + '/scales'
}).create_node()
match['reshape_2'].replace_node(resample_node)
resample_node.add_input_port(1, skip_if_exist=True)
assert resample_node.in_port(1).disconnected()
sizes_node.out_port(0).connect(resample_node.in_port(1))
scales_node.out_port(0).connect(resample_node.in_port(2))
axes_node.out_port(0).connect(resample_node.in_port(3))
graph.remove_nodes_from(
[node.id for node in match.values() if node.id != match['op'].id])