# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License" # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # TODO useless node remove from x2paddle.op_mapper.tf_op_mapper import TFOpMapper from x2paddle.core.fluid_code import Layer from x2paddle.core.util import * import six import numpy import copy as cp def exist_act(node): for layer in node.fluid_code.layers: if layer.param_attr is not None: act = layer.param_attr.get("act", None) if act is not None: return True return False class TFOptimizer(object): activation_ops = { 'Relu': 'relu', 'Sigmoid': 'sigmoid', 'Relu6': 'relu6', 'swish_f32': 'swish' } layers_with_act = [ 'Conv2D', 'BiasAdd', 'DepthwiseConv2dNative', 'Conv2DBackpropInput', 'FusedBatchNorm', 'conv2d', 'elementwise_add', 'conv2d_transpose', 'batch_norm' ] layers_with_bias = [ 'Conv2D', 'DepthwiseConv2dNative', 'Conv2DBackpropInput', 'conv2d', 'conv2d_transpose' ] def __init__(self, op_mapper): self.op_mapper = op_mapper self.graph = op_mapper.graph def delete_redundance_code(self): for node_name in self.graph.topo_sort: if node_name in self.op_mapper.omit_nodes: node = self.graph.get_node(node_name) if node is None: continue omit_freq = self.op_mapper.omit_nodes.count(node_name) if len(node.outputs) <= omit_freq: node.fluid_code.clear() # remove node from graph input_names = node.inputs output_names = node.outputs for in_name in input_names: in_node = self.graph.get_node(in_name) index = in_node.outputs.index(node_name) del in_node.outputs[index] for out_name in output_names: out_node = self.graph.get_node(out_name) index = out_node.inputs.index(node_name) del out_node.inputs[index] del self.graph.node_map[node_name] def strip_graph(self): visited_nodes = set() def visit(node_name): if node_name in visited_nodes: return visited_nodes.add(node_name) input_names = self.graph.get_node(node_name).inputs for in_name in input_names: visit(in_name) for node_name in self.graph.output_nodes: visit(node_name) for i, node_name in enumerate(self.graph.topo_sort): if node_name not in visited_nodes: node = self.graph.get_node(node_name) if node is None: continue input_names = node.inputs output_names = node.outputs for in_name in input_names: in_node = self.graph.get_node(in_name) index = in_node.outputs.index(node_name) del in_node.outputs[index] for out_name in output_names: out_node = self.graph.get_node(out_name) index = out_node.inputs.index(node_name) del out_node.inputs[index] del self.graph.node_map[node_name] def optimize_elementwise_op(self): elementwise_ops = [ 'Sub', 'Add', 'RealDiv', 'Maximum', 'Mul', 'FloorDiv', 'GreaterEqual' ] revertable_ops = ['Add', 'Mul'] for node_name in self.graph.topo_sort: node = self.graph.get_node(node_name) if node is None: continue if node.layer_type in elementwise_ops: if len(node.fluid_code.layers) != 2: continue if node.fluid_code.layers[0].op != "expand": continue expand_out = node.fluid_code.layers[0].output expand_in = node.fluid_code.layers[0].inputs expand_times = node.fluid_code.layers[0].param_attr[ "expand_times"] x = node.fluid_code.layers[1].inputs["x"] y = node.fluid_code.layers[1].inputs["y"] if isinstance( x, six.string_types) and node.layer_type in revertable_ops: node.fluid_code.layers[1].inputs["y"] = x node.fluid_code.layers[1].inputs["x"] = y x = node.fluid_code.layers[1].inputs["x"] y = expand_in elif isinstance(y, six.string_types): y = expand_in else: continue x_shape = x.out_shapes[0] y_shape = y.out_shapes[0] if len(x_shape) != len(y_shape): continue if len(x_shape) == 4: x_shape = [x_shape[i] for i in [0, 3, 1, 2]] y_shape = [y_shape[i] for i in [0, 3, 1, 2]] continue_flag = True for i in range(len(x_shape)): if y_shape[-1 * (i + 1)] == 1 and continue_flag: expand_times[-1 * (i + 1)] = 1 else: continue_flag = False if expand_times.count(1) == len(expand_times): node.fluid_code.layers[1].inputs["y"] = expand_in del node.fluid_code.layers[0] def merge_activation(self): act_nodes = list() for node_name in self.graph.topo_sort: node = self.graph.get_node(node_name) if node is None: continue if node.layer_type in self.activation_ops: act_nodes.append(node_name) for act_node_name in act_nodes: node = self.graph.get_node(act_node_name) input = self.graph.get_node(node.inputs[0]) if input.layer_type not in self.layers_with_act: continue if len(input.fluid_code.layers) == 0: continue if 'act' in input.fluid_code.layers[ -1].param_attr and input.fluid_code.layers[-1].param_attr[ 'act'] is not None: continue if len(input.outputs) != 1: continue index = -1 for i in range(len(input.fluid_code.layers)): if input.fluid_code.layers[i].op in self.layers_with_act: index = i break input.fluid_code.layers[index].param_attr['act'] = string( self.activation_ops[node.layer_type]) input.fluid_code.layers[-1].output = node.fluid_code.layers[ 0].output self.graph.remove_node(act_node_name) def merge_bias(self): for node_name in self.graph.topo_sort: node = self.graph.get_node(node_name) if node is None: continue if node.layer_type == "BiasAdd": input = self.graph.get_node(node.inputs[0]) if input.layer_type not in self.layers_with_bias: continue if len(input.outputs) != 1: continue if len(input.fluid_code.layers) == 0: continue bias_with_act = False if 'act' in node.fluid_code.layers[-1].param_attr: bias_with_act = True layer_with_act = False index = -1 for i in range(len(input.fluid_code.layers)): if input.fluid_code.layers[i].op in self.layers_with_bias: index = i break if 'act' in input.fluid_code.layers[ index].param_attr and input.fluid_code.layers[ index].param_attr['act'] is not None: layer_with_act = True if bias_with_act and layer_with_act: continue if not input.fluid_code.layers[index].param_attr['bias_attr']: bias_name = node.inputs[1] input.fluid_code.layers[index].param_attr[ 'bias_attr'] = string(bias_name) input.fluid_code.layers[-1].output = node.fluid_code.layers[ 0].output if bias_with_act: input.fluid_code.layers[index].param_attr[ 'act'] = node.fluid_code.layers[-1].param_attr[ 'act'] node.fluid_code.clear() self.graph.remove_node(node.layer_name) self.graph.identity_map[node.layer_name] = input.layer_name def remove_transpose(self): graph_copy = cp.deepcopy(self.graph) elementwise_ops = [ 'Sub', 'Add', 'RealDiv', 'Maximum', 'Mul', 'FloorDiv', 'GreateerEqual' ] can_be_optimized_ops = [ 'Conv2D', 'MaxPool', 'FusedBatchNorm', 'DepthwiseConv2dNative', 'AvgPool', 'Pad', 'Conv2DBackpropInput', 'ResizeNearestNeighbor', 'Placeholder', 'Relu', 'Relu6', 'Abs', 'Sigmoid', 'Exp', 'Rsqrt', 'swish_f32', 'LeakyRelu', 'Cast', 'Tanh' ] # These ops may have one more Variable input can_be_optimized_special_ops = ['ResizeBilinear'] for node_name in self.graph.topo_sort: node = graph_copy.get_node(node_name) if node is None: continue if node.layer_type in can_be_optimized_ops: if node.fluid_code.layers[ -1].op != "transpose" or node.fluid_code.layers[ -1].param_attr["perm"] != [0, 2, 3, 1]: continue can_be_removed = True output_names = node.outputs for out_name in output_names: out_node = graph_copy.get_node(out_name) if hasattr(out_node, "can_be_removed"): if not out_node.can_be_removed: can_be_removed = False break elif out_node.fluid_code.layers[ 0].op != "transpose" or out_node.fluid_code.layers[ 0].param_attr["perm"] != [0, 3, 1, 2]: can_be_removed = False break elif out_node.layer_type in elementwise_ops or out_node.layer_type in can_be_optimized_special_ops: can_be_removed = False break if can_be_removed and len(node.fluid_code.layers) > 1: true_node = self.graph.get_node(node_name) if true_node.layer_type == "Placeholder": index = self.graph.input_nodes.index( true_node.fluid_code.layers[-2].output) if isinstance(true_node.fluid_code.layers[-1].output, str): self.graph.input_nodes[ index] = true_node.fluid_code.layers[-1].output else: self.graph.input_nodes[ index] = true_node.fluid_code.layers[ -1].output.layer_name true_node.fluid_code.layers[ -2].output = true_node.fluid_code.layers[-1].output node.removed = True del true_node.fluid_code.layers[-1] for out_name in output_names: out_node = self.graph.get_node(out_name) out_node.fluid_code.layers[ 1].inputs = out_node.fluid_code.layers[0].inputs del out_node.fluid_code.layers[0] for node_name in self.graph.topo_sort: node = graph_copy.get_node(node_name) if node is None: continue if node.layer_type in elementwise_ops: can_be_removed = True if node.fluid_code.layers[ -1].op != "transpose" or node.fluid_code.layers[ -1].param_attr["perm"] != [0, 2, 3, 1]: continue can_be_removed = True output_names = node.outputs for out_name in output_names: out_node = graph_copy.get_node(out_name) if len(out_node.fluid_code.layers) < 3: can_be_removed = False break if hasattr(out_node, "can_be_removed"): if not out_node.can_be_removed: can_be_removed = False break if out_node.layer_type in can_be_optimized_ops: if out_node.fluid_code.layers[ 0].op != "transpose" or out_node.fluid_code.layers[ 0].param_attr["perm"] != [0, 3, 1, 2]: can_be_removed = False break elif out_node.layer_type in elementwise_ops: if out_node.fluid_code.layers[ 0].op != "transpose" and out_node.fluid_code.layers[ 1].op != "transpose": can_be_removed = False break if out_node.fluid_code.layers[0].op == "transpose": if out_node.fluid_code.layers[0].param_attr[ "perm"] != [0, 3, 1, 2]: can_be_removed = False break if out_node.fluid_code.layers[1].op == "transpose": if out_node.fluid_code.layers[1].param_attr[ "perm"] != [0, 3, 1, 2]: can_be_removed = False break if can_be_removed and len(node.fluid_code.layers) > 1: true_node = self.graph.get_node(node_name) true_node.fluid_code.layers[ -2].output = true_node.fluid_code.layers[-1].output del true_node.fluid_code.layers[-1] for out_name in output_names: out_node = self.graph.get_node(out_name) if out_node.layer_type in can_be_optimized_ops: out_node.fluid_code.layers[ 1].inputs = out_node.fluid_code.layers[0].inputs del out_node.fluid_code.layers[0] elif out_node.layer_type in elementwise_ops: if out_node.inputs[0] in node.layer_name: if out_node.fluid_code.layers[ 1].op == 'transpose': out_node.fluid_code.layers[2].inputs[ 'x'] = out_node.fluid_code.layers[ 0].inputs del out_node.fluid_code.layers[0] else: out_node.fluid_code.layers[1].inputs[ 'x'] = out_node.fluid_code.layers[ 0].inputs del out_node.fluid_code.layers[0] elif out_node.inputs[1] in node.layer_name: if out_node.fluid_code.layers[ 1].op == 'transpose': out_node.fluid_code.layers[2].inputs[ 'y'] = out_node.fluid_code.layers[ 1].inputs del out_node.fluid_code.layers[1] else: out_node.fluid_code.layers[1].inputs[ 'y'] = out_node.fluid_code.layers[ 0].inputs del out_node.fluid_code.layers[0] graph_copy = cp.deepcopy(self.graph) for node_name in self.graph.topo_sort: node = graph_copy.get_node(node_name) if node is None or len(node.fluid_code.layers) < 2: continue if node.layer_type in can_be_optimized_ops and node.layer_type != "Placeholder": if node.fluid_code.layers[ -1].op != "transpose" or node.fluid_code.layers[ -1].param_attr["perm"] != [0, 2, 3, 1]: continue can_be_removed = True output_names = node.outputs for out_name in output_names: out_node = graph_copy.get_node(out_name) if hasattr(out_node, "can_be_removed"): if not out_node.can_be_removed: can_be_removed = False break if len(out_node.fluid_code.layers) < 2: can_be_removed = False break if out_node.layer_type in can_be_optimized_ops: if out_node.fluid_code.layers[ 0].op != "transpose" or out_node.fluid_code.layers[ 0].param_attr["perm"] != [0, 3, 1, 2]: can_be_removed = False break elif out_node.layer_type in elementwise_ops: if out_node.fluid_code.layers[ 0].op != "transpose" and out_node.fluid_code.layers[ 1].op != "transpose": can_be_removed = False break if out_node.fluid_code.layers[ 0].op == "expand" or out_node.fluid_code.layers[ 1].op == "expand": can_be_removed = False break if out_node.fluid_code.layers[0].op == "transpose": if out_node.fluid_code.layers[0].param_attr[ "perm"] != [0, 3, 1, 2]: can_be_removed = False break if out_node.fluid_code.layers[1].op == "transpose": if out_node.fluid_code.layers[1].param_attr[ "perm"] != [0, 3, 1, 2]: can_be_removed = False break elif out_node.layer_type not in elementwise_ops and out_node.layer_type not in can_be_optimized_ops: can_be_removed = False break if can_be_removed: true_node = self.graph.get_node(node_name) if len(true_node.fluid_code.layers) < 2: continue true_node.fluid_code.layers[ -2].output = true_node.fluid_code.layers[-1].output del true_node.fluid_code.layers[-1] for out_name in output_names: out_node = self.graph.get_node(out_name) if out_node.layer_type in can_be_optimized_ops: out_node.fluid_code.layers[ 1].inputs = out_node.fluid_code.layers[0].inputs del out_node.fluid_code.layers[0] elif out_node.layer_type in elementwise_ops: if out_node.inputs[0] in node.layer_name: if out_node.fluid_code.layers[ 1].op == 'transpose': if out_node.fluid_code.layers[ 2].op == 'transpose': out_node.fluid_code.layers[3].inputs[ 'x'] = out_node.fluid_code.layers[ 0].inputs else: out_node.fluid_code.layers[2].inputs[ 'x'] = out_node.fluid_code.layers[ 0].inputs del out_node.fluid_code.layers[0] else: out_node.fluid_code.layers[1].inputs[ 'x'] = out_node.fluid_code.layers[ 0].inputs del out_node.fluid_code.layers[0] elif out_node.inputs[1] in node.layer_name: if out_node.fluid_code.layers[ 1].op == 'transpose': out_node.fluid_code.layers[2].inputs[ 'y'] = out_node.fluid_code.layers[ 1].inputs del out_node.fluid_code.layers[1] else: out_node.fluid_code.layers[1].inputs[ 'y'] = out_node.fluid_code.layers[ 0].inputs del out_node.fluid_code.layers[0] graph_copy = cp.deepcopy(self.graph) for node_name in self.graph.topo_sort: node = graph_copy.get_node(node_name) if node is None: continue if node.layer_type in elementwise_ops: can_be_removed = True if len(node.fluid_code.layers) < 3: continue numTranspose = 0 numNotTranspose = 0 for i in range(len(node.fluid_code.layers)): if node.fluid_code.layers[i].op == 'transpose': numTranspose += 1 elif node.fluid_code.layers[i].op != 'expand': numNotTranspose += 1 if numTranspose > numNotTranspose: if node.fluid_code.layers[0].op == 'expand': if node.fluid_code.layers[ 1].op != 'transpose' or node.fluid_code.layers[ 2].op != 'transpose': continue else: true_node = self.graph.get_node(node_name) true_node.fluid_code.layers[3].inputs[ 'x'] = true_node.fluid_code.layers[1].inputs true_node.fluid_code.layers[3].inputs[ 'y'] = true_node.fluid_code.layers[2].inputs l = Layer() l.op = 'transpose' l.inputs = true_node.fluid_code.layers[3].output l.param_attr = {'perm': [0, 3, 1, 2]} if isinstance(l.inputs, six.string_types): l.output = l.inputs else: l.output = l.inputs.layer_name true_node.fluid_code.layers.append(l) del true_node.fluid_code.layers[1] del true_node.fluid_code.layers[1] else: if node.fluid_code.layers[ 0].op != 'transpose' or node.fluid_code.layers[ 1].op != 'transpose': continue else: true_node = self.graph.get_node(node_name) true_node.fluid_code.layers[2].inputs[ 'x'] = true_node.fluid_code.layers[0].inputs true_node.fluid_code.layers[2].inputs[ 'y'] = true_node.fluid_code.layers[1].inputs l = Layer() l.op = 'transpose' l.inputs = true_node.fluid_code.layers[2].output l.param_attr = {'perm': [0, 3, 1, 2]} l.output = l.inputs.layer_name true_node.fluid_code.layers.append(l) del true_node.fluid_code.layers[0] del true_node.fluid_code.layers[0] def make_nchw_input_output(self): for i, name in enumerate(self.graph.input_nodes): node = self.graph.get_node(name) if len(node.out_shapes[0]) == 4 and node.tf_data_format == "NHWC": shape = node.fluid_code.layers[0].param_attr["shape"] shape = [shape[j] for j in [0, 3, 1, 2]] node.fluid_code.layers[0].param_attr["shape"] = shape node.fluid_code.layers[0].output = "nhwc_" + name attr = {"perm": [0, 2, 3, 1]} node.fluid_code.add_layer( "transpose", inputs="nhwc_" + name, output=node, param_attr=attr) self.graph.input_nodes[i] = "nhwc_" + name for i, name in enumerate(self.graph.output_nodes): node = self.graph.get_node(name) if node.layer_type != "transpose": if node.fluid_code.layers[-1].op == "transpose": node.fluid_code.layers[-2].output = name del node.fluid_code.layers[-1] def optimize_sub_graph(self): self.merge_batch_norm() self.merge_prelu() self.merge_scale() self.merge_affine_channel() def merge_batch_norm(self): for i, name in enumerate(self.graph.topo_sort): node = self.graph.get_node(name) if node is None: continue is_batch_norm = True if node.layer_type == "Add": in_nodes0 = [ self.graph.get_node(in_name) for in_name in node.inputs ] if in_nodes0[0].layer_type != "Mul" or in_nodes0[ 1].layer_type != "Sub": is_batch_norm = False continue if exist_act(in_nodes0[0]) or exist_act(in_nodes0[1]): is_batch_norm = False continue in_nodes1 = [ self.graph.get_node(in_name) for in_name in in_nodes0[0].inputs ] in_nodes2 = [ self.graph.get_node(in_name) for in_name in in_nodes0[1].inputs ] if len(in_nodes1[0].out_shapes[0]) != 4: is_batch_norm = False continue if in_nodes1[1].layer_type != "Mul": is_batch_norm = False continue if exist_act(in_nodes1[1]): is_batch_norm = False continue if in_nodes2[0].layer_type != "Const" or in_nodes2[ 1].layer_type != "Mul": is_batch_norm = False continue if exist_act(in_nodes2[1]): is_batch_norm = False continue in_nodes3 = [ self.graph.get_node(in_name) for in_name in in_nodes1[1].inputs ] if in_nodes3[0].layer_type != "Rsqrt" or in_nodes3[ 1].layer_type != "Const": is_batch_norm = False continue in_nodes4 = [ self.graph.get_node(in_name) for in_name in in_nodes2[1].inputs ] if in_nodes4[0].layer_type != "Const" or in_nodes4[ 1].layer_name != in_nodes1[1].layer_name: is_batch_norm = False continue in_nodes5 = self.graph.get_node(in_nodes3[0].inputs[0]) if in_nodes5.layer_type != "Add": is_batch_norm = False continue if exist_act(in_nodes5): is_batch_norm = False continue in_nodes6 = [ self.graph.get_node(in_name) for in_name in in_nodes5.inputs ] if in_nodes6[0].layer_type != "Const" or in_nodes6[ 1].layer_type != "Const": is_batch_norm = False continue if len(in_nodes0[0].outputs) != 1: is_batch_norm = False continue if len(in_nodes0[1].outputs) != 1: is_batch_norm = False continue if len(in_nodes1[1].outputs) != 2: is_batch_norm = False continue if len(in_nodes2[0].outputs) != 1: is_batch_norm = False continue if len(in_nodes2[1].outputs) != 1: is_batch_norm = False continue if len(in_nodes3[0].outputs) != 1: is_batch_norm = False continue if len(in_nodes3[1].outputs) != 1: is_batch_norm = False continue if len(in_nodes4[0].outputs) != 1: is_batch_norm = False continue if len(in_nodes5.outputs) != 1: is_batch_norm = False continue if len(in_nodes6[0].outputs) != 1: is_batch_norm = False continue if len(in_nodes6[1].outputs) != 1: is_batch_norm = False continue conv_shape = in_nodes1[0].out_shapes[0] if conv_shape[3] < 0: is_batch_norm = False continue # moving_variance if in_nodes6[0].value.size != conv_shape[3]: is_batch_norm = False continue # epsilon if in_nodes6[1].value.size != 1: is_batch_norm = False continue # gamma if in_nodes3[1].value.size != conv_shape[3]: is_batch_norm = False continue # moving_mean if in_nodes4[0].value.size != conv_shape[3]: is_batch_norm = False continue # beta if in_nodes2[0].value.size != conv_shape[3]: is_batch_norm = False continue if is_batch_norm: index = in_nodes1[0].outputs.index(in_nodes0[0].layer_name) in_nodes1[0].outputs[index] = node.layer_name node.layer_type = "FusedBatchNorm" node.inputs = [in_nodes1[0].layer_name] act = node.fluid_code.layers[-1].param_attr.get("act", None) node.fluid_code.clear() attr = { "epsilon": in_nodes6[1].value, "param_attr": string(in_nodes3[1].layer_name), "bias_attr": string(in_nodes2[0].layer_name), "moving_mean_name": string(in_nodes4[0].layer_name), "moving_variance_name": string(in_nodes6[0].layer_name), "is_test": True, "act": act } node.fluid_code.add_layer( "batch_norm", inputs=in_nodes1[0].fluid_code.layers[-1].output, output=node, param_attr=attr) del self.graph.node_map[in_nodes0[0].layer_name] del self.graph.node_map[in_nodes0[1].layer_name] del self.graph.node_map[in_nodes1[1].layer_name] del self.graph.node_map[in_nodes2[1].layer_name] del self.graph.node_map[in_nodes3[0].layer_name] del self.graph.node_map[in_nodes4[0].layer_name] del self.graph.node_map[in_nodes5.layer_name] def merge_prelu(self): for i, name in enumerate(self.graph.topo_sort): node = self.graph.get_node(name) if node is None: continue is_prelu = True if node.layer_type == "Add": if exist_act(node): is_prelu = False continue in_nodes0 = [ self.graph.get_node(in_name) for in_name in node.inputs ] if in_nodes0[0].layer_type != "Relu" or in_nodes0[ 1].layer_type != "Mul": is_prelu = False continue if exist_act(in_nodes0[1]): is_prelu = False continue if len(in_nodes0[0].outputs) != 1 or len(in_nodes0[1] .outputs) != 1: is_prelu = False continue in_nodes1 = self.graph.get_node(in_nodes0[0].inputs[0]) in_nodes2 = [ self.graph.get_node(in_name) for in_name in in_nodes0[1].inputs ] if in_nodes2[1].layer_type != "Const" or numpy.fabs(in_nodes2[ 1].value - 0.5) > 1e-06: is_prelu = False continue if in_nodes2[0].layer_type != "Mul": is_prelu = False continue if exist_act(in_nodes2[0]): is_prelu = False continue if len(in_nodes2[1].outputs) != 1 or len(in_nodes2[0] .outputs) != 1: is_prelu = False continue in_nodes3 = [ self.graph.get_node(in_name) for in_name in in_nodes2[0].inputs ] if in_nodes3[0].layer_type != "Const" or in_nodes3[ 1].layer_type != "Sub": is_prelu = False continue if exist_act(in_nodes3[1]): is_prelu = False continue if len(in_nodes3[0].outputs) != 1 or len(in_nodes3[1] .outputs) != 1: is_prelu = False continue in_nodes4 = [ self.graph.get_node(in_name) for in_name in in_nodes3[1].inputs ] if in_nodes4[0].layer_name != in_nodes1.layer_name or in_nodes4[ 1].layer_type != "Abs": is_prelu = False continue if len(in_nodes4[1].outputs) != 1: is_prelu = False continue in_nodes5 = self.graph.get_node(in_nodes4[1].inputs[0]) if in_nodes5.layer_name != in_nodes1.layer_name: is_prelu = False continue if len(in_nodes0[0].outputs) != 1: is_prelu = false continue if len(in_nodes0[1].outputs) != 1: is_prelu = False continue if len(in_nodes1.outputs) < 3: is_prelu = False continue if len(in_nodes2[0].outputs) != 1: is_prelu = false continue if len(in_nodes2[1].outputs) != 1: is_prelu = False continue if len(in_nodes3[0].outputs) != 1: is_prelu = False continue if len(in_nodes3[1].outputs) != 1: is_prelu = false continue if len(in_nodes4[1].outputs) != 1: is_prelu = False continue mode = None in_shape = in_nodes1.out_shapes[0] if in_shape == list(in_nodes3[0].value.shape): mode = "element" elif len(in_nodes3[0].value.shape) == 0: mode = "all" elif len(in_nodes3[0].value.shape) == 1 and in_nodes3[ 0].value.shape[0] == 1: mode = "all" elif len(in_shape) == 4 and len(in_nodes3[ 0].value.shape) == 1 and in_nodes3[0].value.shape[ 0] == in_shape[-1]: mode = "channel" weight = self.op_mapper.weights[in_nodes3[0].layer_name] weight = numpy.expand_dims(weight, 0) weight = numpy.expand_dims(weight, 2) weight = numpy.expand_dims(weight, 3) self.op_mapper.weights[in_nodes3[0].layer_name] = weight # fix bug in Paddle1.8.3 and may change in next version. # self.op_mapper.weights[in_nodes3[0].layer_name + # '_1'] = weight.reshape(1, -1) in_nodes3[0].fluid_code.layers[0].param_attr["shape"] = [ 1, in_shape[-1], 1, 1 ] else: is_prelu = False continue if is_prelu: index = in_nodes1.outputs.index(in_nodes0[0].layer_name) del in_nodes1.outputs[index] index = in_nodes1.outputs.index(in_nodes3[1].layer_name) del in_nodes1.outputs[index] index = in_nodes1.outputs.index(in_nodes4[1].layer_name) del in_nodes1.outputs[index] in_nodes1.outputs.append(node.layer_name) node.layer_type = "Prelu" node.inputs = [in_nodes1.layer_name] act = node.fluid_code.layers[-1].param_attr.get("act", None) node.fluid_code.clear() attr = { "mode": string(mode), "param_attr": string(in_nodes3[0].layer_name) } node.fluid_code.add_layer( "prelu", inputs=in_nodes1.fluid_code.layers[-1].output, output=node, param_attr=attr) del self.graph.node_map[in_nodes0[0].layer_name] del self.graph.node_map[in_nodes0[1].layer_name] del self.graph.node_map[in_nodes2[0].layer_name] del self.graph.node_map[in_nodes2[1].layer_name] del self.graph.node_map[in_nodes3[1].layer_name] del self.graph.node_map[in_nodes4[1].layer_name] def merge_scale(self): for i, name in enumerate(self.graph.topo_sort): node = self.graph.get_node(name) if node is None: continue is_scale = True if node.layer_type == "Sub": in_nodes0 = [ self.graph.get_node(in_name) for in_name in node.inputs ] if in_nodes0[0].layer_type != "Mul" or in_nodes0[ 1].layer_type != "Const" or in_nodes0[ 1].value.size != 1: is_scale = False continue if exist_act(in_nodes0[0]): is_scale = False continue if len(in_nodes0[0].outputs) != 1 or len(in_nodes0[1] .outputs) != 1: is_scale = False continue in_nodes1 = [ self.graph.get_node(in_name) for in_name in in_nodes0[0].inputs ] if in_nodes1[0].layer_type != "Const" or in_nodes1[ 1].layer_type != "RealDiv" or in_nodes1[ 0].value.size != 1: is_scale = False continue if exist_act(in_nodes1[1]): is_scale = False continue if len(in_nodes1[0].outputs) != 1 or len(in_nodes1[1] .outputs) != 1: is_scale = False continue in_nodes2 = [ self.graph.get_node(in_name) for in_name in in_nodes1[1].inputs ] if in_nodes2[1].layer_type != "Const" or in_nodes2[ 1].value.size != 1: is_scale = False continue if is_scale: in_node = self.graph.get_node(in_nodes1[1].inputs[0]) index = in_node.outputs.index(in_nodes1[1].layer_name) in_node.outputs[index] = node.layer_name node.layer_type = "Scale" node.inputs = [in_node.layer_name] scale = 1.0 / in_nodes2[1].value * in_nodes1[0].value act = None if node.fluid_code.layers[0].param_attr is not None: act = node.fluid_code.layers[0].param_attr.get("act", None) node.fluid_code.clear() attr = { "scale": scale, "bias": in_nodes0[1].value, "bias_after_scale": True, "act": act } node.fluid_code.add_layer( "scale", inputs=in_node, output=node, param_attr=attr) del self.graph.node_map[in_nodes0[0].layer_name] del self.graph.node_map[in_nodes0[1].layer_name] del self.graph.node_map[in_nodes1[0].layer_name] del self.graph.node_map[in_nodes1[1].layer_name] del self.graph.node_map[in_nodes2[1].layer_name] def merge_affine_channel(self): for i, name in enumerate(self.graph.topo_sort): node = self.graph.get_node(name) if node is None: continue is_affine_channel = True if node.layer_type == "RealDiv": in_nodes0 = [ self.graph.get_node(in_name) for in_name in node.inputs ] bias_add = True if (in_nodes0[0].layer_type != "Sub" and in_nodes0[0].layer_type != "Add") or in_nodes0[1].layer_type != "Const" or len( in_nodes0[1].value.shape) != 3: is_affine_channel = False continue if in_nodes0[0].layer_type == "Sub": bias_add = False if exist_act(in_nodes0[0]): is_affine_channel = False continue if len(in_nodes0[0].outputs) != 1 or len(in_nodes0[1] .outputs) != 1: is_affine_channel = False continue in_nodes1 = [ self.graph.get_node(in_name) for in_name in in_nodes0[0].inputs ] if len(in_nodes1[0].out_shapes[0]) != 4 or in_nodes1[ 1].layer_type != "Const" or len(in_nodes1[1] .value.shape) != 3: is_affine_channel = False continue if len(in_nodes1[1].outputs) != 1: is_affine_channel = False continue channel = in_nodes1[0].out_shapes[0][-1] if channel < 0 or channel != in_nodes0[ 1].value.size or channel != in_nodes1[1].value.size: is_affine_channel = False continue if in_nodes0[1].out_shapes[0][-1] != in_nodes0[ 1].value.size or in_nodes1[1].out_shapes[0][ -1] != in_nodes1[1].value.size: is_affine_channel = False continue if is_affine_channel: in_node = in_nodes1[0] index = in_node.outputs.index(in_nodes0[0].layer_name) in_node.outputs[index] = node.layer_name node.layer_type = "AffineChannel" node.inputs = [in_node.layer_name] scale = 1.0 / in_nodes0[1].value.flatten() bias = in_nodes1[1].value.flatten() / in_nodes0[ 1].value.flatten() if not bias_add: bias *= -1.0 self.op_mapper.weights[node.layer_name + "_scale"] = scale self.op_mapper.weights[node.layer_name + "_bias"] = bias act = None if node.fluid_code.layers[0].param_attr is not None: act = node.fluid_code.layers[0].param_attr.get("act", None) node.fluid_code.clear() attr = { "dtype": string(scale.dtype), "shape": [channel], "name": string(node.layer_name + "_scale") } node.fluid_code.add_layer( "create_parameter", inputs=None, output=node.layer_name + "_scale", param_attr=attr) attr = { "dtype": string(scale.dtype), "shape": [channel], "name": string(node.layer_name + "_bias") } node.fluid_code.add_layer( "create_parameter", inputs=None, output=node.layer_name + "_bias", param_attr=attr) inputs = { "x": in_node, "scale": node.layer_name + "_scale", "bias": node.layer_name + "_bias" } attr = {"act": act} node.fluid_code.add_layer( "affine_channel", inputs=inputs, output=node, param_attr=attr) del self.graph.node_map[in_nodes0[0].layer_name] del self.graph.node_map[in_nodes0[1].layer_name] del self.graph.node_map[in_nodes1[1].layer_name]