def find_subgraph_match_to_pattern(graph: Graph, body_pattern: dict): """ Finds sub-graph matches corresponding pattern in graph :param graph: a graph where to search for matched sub-graph :param body_pattern: a pattern :return: a list of sub-graph matches """ matches = [] for match in find_pattern_matches(graph, **body_pattern): match = inverse_dict(match) for k in match: match[k] = Node(graph, match[k]) matches.append(match) return matches
def transform_graph(self, graph: Graph, replacement_descriptions: dict): matches = find_pattern_matches(graph, self.pattern_nodes, self.pattern_edges) for match in list(matches): inverse_match = inverse_dict(match) interpolate = Node(graph, inverse_match['interpolate']) transpose_1 = Node(graph, inverse_match['transpose_1']) transpose_2 = Node(graph, inverse_match['transpose_2']) # because we remove Transpose layers the ResizeNearestNeighbor should be updated for NCHW layout interpolate.axes = int64_array([2, 3]) transpose_1.in_port(0).get_connection().set_destination( interpolate.in_port(0)) transpose_2.out_port(0).get_connection().set_source( interpolate.out_port(0)) graph.remove_nodes_from([transpose_1.id, transpose_2.id])
def replace_sub_graph(self, graph: Graph, match: dict): box_nms = match['box_nms'] top_k = box_nms.topk nms_threshold = box_nms.overlap_thresh ssd_concats = {} concat_names = ['ssd_concat1', 'ssd_concat0', 'ssd_concat2'] for i, concat_match in enumerate(self.concats_pattern): for matches in find_pattern_matches(graph, concat_match['nodes'], concat_match['edges'], None, None): for match in matches: if graph.has_node(match): n = Node(graph, match) if n.op == 'Concat': ssd_concats.update({concat_names[i]: n}) break assert concat_names[0] in ssd_concats assert concat_names[1] in ssd_concats assert concat_names[2] in ssd_concats graph.remove_nodes_from(graph.get_nodes_with_attributes(op='Result')) detection_output_node = DetectionOutput(graph, dict(name=graph.unique_id() + '/DetectionOutput_', top_k=top_k, keep_top_k=top_k, nms_threshold=nms_threshold, background_label_id=0, clip=0, decrease_label_id=1, code_type="caffe.PriorBoxParameter.CENTER_SIZE", confidence_threshold=0.01, share_location=1, variance_encoded_in_target=0, normalized=1)).create_node() reshape_node = create_op_node_with_second_input(graph, Reshape, int64_array([0, -1]), dict(name=graph.unique_id() + '/DetectionOutput_')) ssd_softmax_node = ssd_concats['ssd_concat0'].out_node().out_node() ssd_softmax_node.out_port(0).disconnect() ssd_softmax_node.out_port(0).connect(reshape_node.in_port(0)) reshape_node.out_port(0).connect(detection_output_node.in_port(1)) ssd_concats['ssd_concat2'].axis = 2 self.reshape_priorboxes(ssd_concats['ssd_concat2']) ssd_concats['ssd_concat1'].out_port(0).get_connection().set_destination(detection_output_node.in_port(0)) ssd_concats['ssd_concat2'].out_port(0).get_connection().set_destination(detection_output_node.in_port(2)) Result(graph, {'name': detection_output_node.id + '/Result'}).create_node([detection_output_node])
def transform_graph(self, graph: Graph, replacement_descriptions: dict): matches = find_pattern_matches(graph, self.pattern_nodes, self.pattern_edges) for match in list(matches): inverse_match = inverse_dict(match) interpolate = Node(graph, inverse_match['interpolate']) transpose_1 = Node(graph, inverse_match['transpose_1']) transpose_2 = Node(graph, inverse_match['transpose_2']) # Check for data layout and transposes orders if graph.graph['layout'] != 'NCHW' or np.array_equal(transpose_1.in_port(1).data.get_value(), [0, 2, 3, 1]) or \ np.array_equal(transpose_2.in_port(1).data.get_value(), [0, 3, 1, 2]): return transpose_1.in_port(0).get_connection().set_destination( interpolate.in_port(0)) transpose_2.out_port(0).get_connection().set_source( interpolate.out_port(0)) graph.remove_nodes_from([transpose_1.id, transpose_2.id])
def replace_sub_graph(self, graph: Graph, match: dict): slice_like = match['slice_like'] const = slice_like.in_nodes()[0] crop_shape = slice_like.in_nodes()[1] variants_dict = { 'mul_scalar1x': 0.1, 'mul_scalar2x': 0.2, 'mul_scalar1y': 0.1, 'mul_scalar2y': 0.2 } for matches in find_pattern_matches(graph, self.variants_pattern['nodes'], self.variants_pattern['edges'], None, None): for k, v in matches.items(): if v in variants_dict.keys(): variants_dict[v] = Node(graph, k).in_nodes()[1].value[0] variants = np.array([ variants_dict['mul_scalar1x'], variants_dict['mul_scalar1y'], variants_dict['mul_scalar2x'], variants_dict['mul_scalar2y'] ] * int(const.value.size / 4)).reshape(const.value.shape) priorbox_variants = Const( graph, dict(value=variants, symbol_dict={'name': const.id + '/priorbox_variants'})).create_node() variants_slice_like = Crop(graph, dict(axis=slice_like.axis, offset=slice_like.offset, dim=slice_like.dim, axes=slice_like.axes, symbol_dict={'name': slice_like.id + '/variants_slice_like'})) \ .create_node() variants_slice_like.in_port(0).connect(priorbox_variants.out_port(0)) variants_slice_like.in_port(1).connect(crop_shape.out_port(0)) concat = match['reshape3'].out_port(0).get_destination().node assert concat.op == 'Concat' concat_nodes_count = len(concat.in_nodes()) concat.add_input_port(concat_nodes_count) concat.in_port(concat_nodes_count).get_connection().set_source( variants_slice_like.out_port(0))
def replace_pattern(graph: Graph, match: dict): node = match['op'] if node.name == 'iteration_number_out': return # calculate length of context when state of inference becomes meaningful inputs = [] for n in graph.get_op_nodes(**{'op': 'Parameter'}): inputs.append(n) in_nodes = [] for inp in inputs: for ins in inp.out_port(0).get_destinations(): in_nodes.append(ins.node.name) context_len = 1 try: subgraph = invert_sub_graph_between_nodes( graph, [node.in_port(0).get_source().node.name], in_nodes) except Error: return for n in subgraph: n_node = Node(graph, n) if n_node.kind == 'op' and n_node.op == 'Splice': context_len += len(n_node.context) - 1 if context_len == 1: return in_node_port = node.in_port(0).get_source() in_node_shape = node.in_port(0).data.get_shape() node.in_port(0).disconnect() # add Select before saving state to avoid saving garbage select_node = Select(graph, { 'name': 'select_' + node.name }).create_node() zero_else = Const(graph, { 'name': 'zero_else', 'value': np.zeros(in_node_shape) }).create_node() select_node.in_port(1).connect(in_node_port) select_node.in_port(2).connect(zero_else.out_port(0)) # check if we have already appropriate iteration counter existing_counters = find_pattern_matches( graph, nodes=[('mem_in', dict(op='ReadValue')), ('mem_in_data', dict(shape=int64_array([context_len]))), ('crop_mem_in', dict(op='Crop', axis=int64_array([1]), offset=int64_array([1]), dim=int64_array([context_len - 1]))), ('crop_mem_in_data', dict()), ('concat', dict(op='Concat', axis=1)), ('concat_data', dict()), ('const_1', dict(op='Const')), ('const_1_data', dict()), ('mem_out', dict(op='Assign')), ('crop_out', dict(op='Crop', axis=int64_array([1]), offset=int64_array([0]), dim=int64_array([1]))), ('crop_out_data', dict()), ('select', dict(op='Select'))], edges=[('mem_in', 'mem_in_data'), ('mem_in_data', 'crop_mem_in'), ('crop_mem_in', 'crop_mem_in_data'), ('crop_mem_in_data', 'concat', { 'in': 0 }), ('const_1', 'const_1_data'), ('const_1_data', 'concat', { 'in': 1 }), ('concat', 'concat_data'), ('concat_data', 'mem_out'), ('concat_data', 'crop_out'), ('crop_out', 'crop_out_data'), ('crop_out_data', 'select')]) counter_match = next(existing_counters, None) if counter_match is not None: ones = Node(graph, inverse_dict(counter_match)['const_1']) input_port = Node( graph, inverse_dict(counter_match)['crop_out']).out_port(0) else: init_value_mem_out = create_zero_value_with_batch_from_input( in_node_port, context_len, np.int32) mem_out = ReadValue( graph, { 'name': 'iteration_number', 'variable_id': 'iteration_' + node.name }).create_node() mem_out.in_port(0).connect(init_value_mem_out.out_port(0)) cut_first = Crop( graph, { 'name': 'cut_first', 'axis': int64_array([1]), 'offset': int64_array([1]), 'dim': int64_array([context_len - 1]) }).create_node() cut_first.in_port(0).connect(mem_out.out_port(0)) ones = Const(graph, { 'name': 'ones', 'value': np.ones([1, 1], dtype=np.int32) }).create_node() concat = Concat(graph, { 'name': 'concat_ones', 'in_ports_count': 2, 'axis': 1 }).create_node() concat.in_port(0).connect(cut_first.out_port(0)) concat.in_port(1).connect(ones.out_port(0)) mem_in = Assign( graph, { 'name': 'iteration_number_out', 'variable_id': 'iteration_' + node.name }).create_node() mem_in.in_port(0).connect(concat.out_port(0)) res = Result(graph, {}).create_node() mem_in.out_port(0).connect(res.in_port(0)) cut_last = Crop( graph, { 'name': 'cut_last', 'axis': int64_array([1]), 'offset': int64_array([0]), 'dim': int64_array([1]) }).create_node() cut_last.in_port(0).connect(concat.out_port(0)) input_port = cut_last.out_port(0) # Check if data from memory is 1 # if it is True, we have correct data and should proceed with saving it to memory # else we have not gathered context and have garbage here, shouldn't change initial state of memory cast_in = Equal(graph, { 'name': input_port.node.name + '/cast_to_bool' }).create_node() cast_in.in_port(0).connect(ones.out_port(0)) cast_in.in_port(1).connect(input_port) select_node.in_port(0).connect(cast_in.out_port(0)) select_node.out_port(0).connect(node.in_port(0)) select_node.out_port(0).data.set_shape(in_node_shape)
def replace_pattern(graph: Graph, match: dict): node = match['op'] if node.name == 'iteration_number_out': return # calculate length of context when state of inference becomes meaningful inputs = [] for n in graph.get_op_nodes(**{'op': 'Parameter'}): inputs.append(n) in_nodes = [] for inp in inputs: for ins in inp.out_port(0).get_destinations(): in_nodes.append(ins.node.name) context_len = 1 try: subgraph = invert_sub_graph_between_nodes( graph, [node.in_port(0).get_source().node.name], in_nodes) except Error: return for n in subgraph: n_node = Node(graph, n) if n_node.kind == 'op' and n_node.op == 'Splice': context_len += len(n_node.context) - 1 if context_len == 1: return in_node_port = node.in_port(0).get_source() in_node_shape = node.in_port(0).data.get_shape() node.in_port(0).disconnect() # add Select before saving state to avoid saving garbage select_node = Select(graph, { 'name': 'select_' + node.name }).create_node() zero_else = Const(graph, { 'name': 'zero_else', 'value': np.zeros(in_node_shape) }).create_node() select_node.in_port(1).connect(in_node_port) select_node.in_port(2).connect(zero_else.out_port(0)) # check if we have already appropriate iteration counter existing_counters = find_pattern_matches( graph, nodes=[('mem_in', dict(op='Memory', index=1, shape=int64_array([context_len]))), ('mem_in_data', dict()), ('crop_mem_in', dict(op='Crop', axis=int64_array([1]), offset=int64_array([1]), dim=int64_array([context_len - 1]))), ('crop_mem_in_data', dict()), ('concat', dict(op='Concat', axis=1)), ('concat_data', dict()), ('const_1', dict(op='Const')), ('const_1_data', dict()), ('mem_out', dict(op='Memory', index=0, shape=int64_array([context_len]))), ('crop_out', dict(op='Crop', axis=int64_array([1]), offset=int64_array([0]), dim=int64_array([1]))), ('crop_out_data', dict()), ('select', dict(op='Select'))], edges=[('mem_in', 'mem_in_data'), ('mem_in_data', 'crop_mem_in'), ('crop_mem_in', 'crop_mem_in_data'), ('crop_mem_in_data', 'concat', { 'in': 0 }), ('const_1', 'const_1_data'), ('const_1_data', 'concat', { 'in': 1 }), ('concat', 'concat_data'), ('concat_data', 'mem_out'), ('concat_data', 'crop_out'), ('crop_out', 'crop_out_data'), ('crop_out_data', 'select')]) counter_match = next(existing_counters, None) if counter_match is not None: input_port = Node( graph, inverse_dict(counter_match)['crop_out']).out_port(0) else: mem_out = Memory( graph, { 'name': 'iteration_number', 'size': 2, 'index': 1, 'id': 'iteration_' + node.name, 'shape': int64_array([context_len]), 'dst_type': np.int32 }).create_node() cut_first = Crop( graph, { 'name': 'cut_first', 'axis': int64_array([1]), 'offset': int64_array([1]), 'dim': int64_array([context_len - 1]) }).create_node() cut_first.in_port(0).connect(mem_out.out_port(0)) ones = Const(graph, { 'name': 'ones', 'value': np.ones([1, 1], dtype=np.int32) }).create_node() concat = Concat(graph, { 'name': 'concat_ones', 'in_ports_count': 2, 'axis': 1 }).create_node() concat.in_port(0).connect(cut_first.out_port(0)) concat.in_port(1).connect(ones.out_port(0)) mem_in = Memory( graph, { 'name': 'iteration_number_out', 'size': 2, 'index': 0, 'id': 'iteration_' + node.name, 'shape': int64_array([context_len]) }).create_node() mem_in.in_port(0).connect(concat.out_port(0)) res = Result(graph, {}).create_node() mem_in.out_port(0).connect(res.in_port(0)) cut_last = Crop( graph, { 'name': 'cut_last', 'axis': int64_array([1]), 'offset': int64_array([0]), 'dim': int64_array([1]) }).create_node() cut_last.in_port(0).connect(concat.out_port(0)) input_port = cut_last.out_port(0) select_node.in_port(0).connect(input_port) select_node.out_port(0).connect(node.in_port(0)) select_node.out_port(0).data.set_shape(in_node_shape)
def insert_select(graph: Graph, node: Node): context_len = node.frame_time + 1 if context_len == 1: return in_node_port = node.in_port(0).get_source() in_node_shape = node.in_port(0).data.get_shape() node.in_port(0).disconnect() # add Select before saving state to avoid saving garbage select_node = Select(graph, {'name': 'select_' + node.name}).create_node() zero_else = create_const_with_batch_from_input(in_node_port, in_node_shape[1]) select_node.in_port(1).connect(in_node_port) select_node.in_port(2).connect(zero_else.out_port(0)) # check if we have already appropriate iteration counter existing_counters = find_pattern_matches(graph, nodes=[('mem_in', dict(op='ReadValue')), ('mem_in_data', dict(shape=int64_array([context_len]))), ('crop_mem_in', dict(op='Crop', axis=int64_array([1]), offset=int64_array([1]), dim=int64_array([context_len - 1]))), ('crop_mem_in_data', dict()), ('concat', dict(op='Concat', axis=1)), ('concat_data', dict()), ('const_1', dict(op='Const')), ('const_1_data', dict()), ('mem_out', dict(op='Assign')), ('crop_out', dict(op='Crop', axis=int64_array([1]), offset=int64_array([0]), dim=int64_array([1]))), ('crop_out_data', dict()), ('select', dict(op='Select')) ], edges=[('mem_in', 'mem_in_data'), ('mem_in_data', 'crop_mem_in'), ('crop_mem_in', 'crop_mem_in_data'), ('crop_mem_in_data', 'concat', {'in': 0}), ('const_1', 'const_1_data'), ('const_1_data', 'concat', {'in': 1}), ('concat', 'concat_data'), ('concat_data', 'mem_out'), ('concat_data', 'crop_out'), ('crop_out', 'crop_out_data'), ('crop_out_data', 'select')]) counter_match = next(existing_counters, None) if counter_match is not None: ones = Node(graph, inverse_dict(counter_match)['const_1']) input_port = Node(graph, inverse_dict(counter_match)['crop_out']).out_port(0) else: init_value_mem_out = create_const_with_batch_from_input(in_node_port, context_len, precision=np.int32) mem_out = ReadValue(graph, {'name': 'iteration_number', 'variable_id': 'iteration_' + node.name}).create_node() mem_out.in_port(0).connect(init_value_mem_out.out_port(0)) cut_first = Crop(graph, {'name': 'cut_first', 'axis': int64_array([1]), 'offset': int64_array([1]), 'dim': int64_array([context_len - 1])}).create_node() cut_first.in_port(0).connect(mem_out.out_port(0)) ones = create_const_with_batch_from_input(in_node_port, 1, 1, np.int32) concat = Concat(graph, {'name': 'concat_ones', 'in_ports_count': 2, 'axis': 1}).create_node() concat.in_port(0).connect(cut_first.out_port(0)) concat.in_port(1).connect(ones.out_port(0)) mem_in = Assign(graph, {'name': 'iteration_number_out', 'variable_id': 'iteration_' + node.name}).create_node() mem_in.in_port(0).connect(concat.out_port(0)) res = Result(graph, {}).create_node() mem_in.out_port(0).connect(res.in_port(0)) cut_last = Crop(graph, {'name': 'cut_last', 'axis': int64_array([1]), 'offset': int64_array([0]), 'dim': int64_array([1])}).create_node() cut_last.in_port(0).connect(concat.out_port(0)) input_port = cut_last.out_port(0) # Check if data from memory is 1 # if it is True, we have correct data and should proceed with saving it to memory # else we have not gathered context and have garbage here, shouldn't change initial state of memory cast_in = Equal(graph, {'name': input_port.node.name + '/cast_to_bool'}).create_node() cast_in.in_port(0).connect(ones.out_port(0)) cast_in.in_port(1).connect(input_port) select_node.in_port(0).connect(cast_in.out_port(0)) select_node.out_port(0).connect(node.in_port(0)) select_node.out_port(0).data.set_shape(in_node_shape)