def replace_pattern(graph: Graph, match: dict):
node_ss = match['strided_slice']
# slices = [elem for elem in node_ss.slices if elem is not None]
# node_ss.slices = np.array(slices)
if node_ss.out_port(0).data.get_value() is not None:
# StridedSlices(SS) in shape-calculating sub-graphs that should not be deleted that easily
# Example:
# In RetinaNetFilteredDetectionsReplacement we have SS that slices first batch
# We delete such SS for batch 1, but it should be performed while reshaping the model
return
output_data_node = node_ss.out_node(0)
input_data_node = node_ss.in_node(0)
out_shape = output_data_node.shape
if not np.all(node_ss.shrink_axis_mask == 0):
out_shape = list(out_shape)
for i in range(len(node_ss.shrink_axis_mask)):
if node_ss.shrink_axis_mask[i] == 1:
out_shape.insert(i, 1)
out_shape = int64_array(out_shape)
if not np.all(node_ss.new_axis_mask == 0):
out_shape = list(out_shape)
for i in reversed(range(len(node_ss.new_axis_mask))):
if node_ss.new_axis_mask[i] == 1:
out_shape.pop(i)
out_shape = int64_array(out_shape)
if np.array_equal(input_data_node.shape, out_shape) and \
all(elem.step == 1 for elem in match['strided_slice'].slices):
if not np.all(node_ss.shrink_axis_mask == 0):
ConvertGroupedStridedSlice.add_squeeze_for_shrink(
graph, node_ss)
if not np.all(node_ss.new_axis_mask == 0):
ConvertGroupedStridedSlice.add_unsqueeze_for_new(
graph, node_ss)
log.info("Useless StridedSlice op '{}' has been detected".format(
match['strided_slice'].id))
# remove inputs to Strided Slice so it has just one input with data so we can use 'remove_op_node' function
graph.remove_edge(match['strided_slice'].in_node(1).id,
match['strided_slice'].id)
graph.remove_edge(match['strided_slice'].in_node(2).id,
match['strided_slice'].id)
if len(match['strided_slice'].in_nodes()) > 3:
graph.remove_edge(match['strided_slice'].in_node(3).id,
match['strided_slice'].id)
remove_op_node_with_data_node(graph, match['strided_slice'])
def replace_pattern(graph: Graph, match: dict):
node_ss = match['strided_slice']
output_data_node = node_ss.out_node(0)
input_data_node = node_ss.in_node(0)
out_shape = output_data_node.shape
if not np.all(node_ss.shrink_axis_mask == 0):
out_shape = list(out_shape)
for i in range(len(node_ss.shrink_axis_mask)):
if node_ss.shrink_axis_mask[i] == 1:
out_shape.insert(i, 1)
out_shape = int64_array(out_shape)
if not np.all(node_ss.new_axis_mask == 0):
out_shape = list(out_shape)
for i in reversed(range(len(node_ss.new_axis_mask))):
if node_ss.new_axis_mask[i] == 1:
out_shape.pop(i)
out_shape = int64_array(out_shape)
if np.array_equal(input_data_node.shape, out_shape) and \
all(elem.step == 1 for elem in match['strided_slice'].slices):
if not np.all(node_ss.shrink_axis_mask == 0):
ConvertGroupedStridedSlice.add_squeeze_for_shrink(
graph, node_ss)
if not np.all(node_ss.new_axis_mask == 0):
ConvertGroupedStridedSlice.add_unsqueeze_for_new(
graph, node_ss)
log.info("Useless StridedSlice op '{}' has been detected".format(
match['strided_slice'].id))
# remove inputs to Strided Slice so it has just one input with data so we can use 'remove_op_node' function
graph.remove_edge(match['strided_slice'].in_node(1).id,
match['strided_slice'].id)
graph.remove_edge(match['strided_slice'].in_node(2).id,
match['strided_slice'].id)
if len(match['strided_slice'].in_nodes()) > 3:
graph.remove_edge(match['strided_slice'].in_node(3).id,
match['strided_slice'].id)
remove_op_node_with_data_node(graph, match['strided_slice'])