# 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. import os import sys from google.protobuf import text_format import numpy as np from x2paddle.core.graph import GraphNode, Graph from x2paddle.core.fluid_code import FluidCode from x2paddle.op_mapper import caffe_shape class CaffeResolver(object): def __init__(self, caffe_proto): self.caffe_proto = caffe_proto self.import_caffe() def import_caffepb(self): if self.caffe_proto is None: from x2paddle.decoder import caffe_pb2 out = caffe_pb2 else: if not os.path.isfile(self.caffe_proto): raise Exception( "The .py file compiled by caffe.proto is not exist.") (filepath, tempfilename) = os.path.split(os.path.abspath(self.caffe_proto)) (filename, extension) = os.path.splitext(tempfilename) sys.path.append(filepath) out = __import__(filename) return out def import_caffe(self): self.caffepb = self.import_caffepb() self.NetParameter = self.caffepb.NetParameter class CaffeGraphNode(GraphNode): def __init__(self, layer, type_str, layer_name=None): if layer_name is None: super(CaffeGraphNode, self).__init__(layer, layer.name.replace('/', '_').replace('-', '_')) else: super(CaffeGraphNode, self).__init__(layer, layer_name.replace('/', '_').replace('-', '_')) self.layer_type = type_str self.fluid_code = FluidCode() self.data = None def set_params(self, params): self.data = params class CaffeGraph(Graph): def __init__(self, model, params, caffe_pb): self.params = params self.caffe_pb = caffe_pb super(CaffeGraph, self).__init__(model) def filter_layers(self, layers): '''Filter out layers based on the current phase.''' phase_map = {0: 'train', 1: 'test'} filtered_layer_names = set() filtered_layers = [] for layer in layers: if hasattr(layer, 'input'): continue type_str = self.get_layer_type(layer) phase = 'test' if len(layer.include): phase = phase_map[layer.include[0].phase] if len(layer.exclude): phase = phase_map[1 - layer.include[0].phase] exclude = (phase != 'test') # Dropout layers appear in a fair number of Caffe # test-time networks. These are just ignored. We'll # filter them out here. if (not exclude) and (phase == 'test'): exclude = (type_str == 'Dropout') if not exclude: filtered_layers.append(layer) # Guard against dupes. assert layer.name not in filtered_layer_names filtered_layer_names.add(layer.name) else: print('The filter layer:' + layer.name) return filtered_layers def generate_input_layer(self, dims, index): dim_str = '' for dim in dims: dim_str += 'dim: {}\n'.format(str(dim)) input_str = 'layer {\n' input_str += 'name: \"{}\"\n '.format(str(self.model.input[index])) input_str += 'type: "Input"\n' input_str += 'top: \"{}\"\n'.format(str(self.model.input[index])) input_str += 'input_param {\n' input_str += 'shape {\n' input_str += dim_str input_str += '}}}' input_str = str.encode(input_str) net = self.caffe_pb.NetParameter() text_format.Merge(input_str, net) return net.layers or net.layer def input2layers(self, input_layers=[]): inputs_num = len(self.model.input) if inputs_num != 0: input_dims_num = len(self.model.input_dim) if input_dims_num != 0: if input_dims_num > 0 and input_dims_num != inputs_num * 4: raise Error('invalid input_dim[%d] param in prototxt' % (input_dims_num)) for i in range(inputs_num): dims = self.model.input_dim[i * 4:(i + 1) * 4] l = self.generate_input_layer(dims, i) input_layers.append(l[0]) else: for i in range(inputs_num): dims = self.model.input_shape[i].dim[0:4] l = self.generate_input_layer(dims, i) input_layers.append(l[0]) def transform_input_layers(self, layers, input_layers=[]): for layer in layers: if hasattr(layer, 'input'): input_dims_num = len(layers.input_dim) if input_dims_num > 0 and input_dims_num != 4: raise Error('invalid input_dim[%d] param in prototxt' % (input_dims_num)) dims = self.model.input_dim[0:4] l = self.generate_input_layer(dims, i) input_layers.append(l[0]) def get_layer_type(self, layer): if isinstance(layer.type, int): enum_values = self.caffe_pb._V1LAYERPARAMETER_LAYERTYPE.values vals = [val for val in enum_values if val.number == layer.type] part = vals[0].name.split('_') part = [s.capitalize() for s in part] type_str = '' type_str = type_str.join(part) if 'relu' in type_str.lower(): type_str = type_str.replace('elu', 'eLU') elif type_str.lower() == 'lrn': type_str = 'LRN' return type_str else: return layer.type def build(self): layers = self.model.layers or self.model.layer layers = self.filter_layers(layers) input_layers = [] self.input2layers(input_layers) self.transform_input_layers(layers, input_layers) layers = input_layers + layers top_layer = {} for layer in layers: if hasattr(layer, 'input'): continue type_str = self.get_layer_type(layer) self.node_map[layer.name] = CaffeGraphNode(layer, type_str) for in_name in layer.bottom: if in_name in top_layer: self.connect(top_layer[in_name][-1], layer.name) else: raise Exception( 'input[{}] of node[{}] does not exist in node_map'. format(in_name, layer.name)) for out_name in layer.top: if out_name not in top_layer: top_layer[out_name] = [layer.name] else: top_layer[out_name].append(layer.name) for layer_name, data in self.params: if layer_name in self.node_map: node = self.node_map[layer_name] node.set_params(data) else: print('Ignoring parameters for non-existent layer: %s' % \ layer_name) super(CaffeGraph, self).build() def get_bottom_node(self, node, idx=0, copy=False): input_node_name = node.inputs[idx] assert input_node_name in self.node_map, 'The {} isn\'t a valid node'.format( name) input_node = self.node_map[input_node_name] if len(input_node.layer.top) > 1: need_idx = list(input_node.layer.top).index(node.layer.bottom[idx]) name = input_node_name + ':' + str(need_idx) else: name = input_node_name return self.get_node(name, copy=copy) class CaffeDecoder(object): def __init__(self, proto_path, model_path, caffe_proto): self.proto_path = proto_path self.model_path = model_path self.resolver = CaffeResolver(caffe_proto=caffe_proto) self.net = self.resolver.NetParameter() with open(proto_path, 'rb') as proto_file: proto_str = proto_file.read() text_format.Merge(proto_str, self.net) self.load_using_pb() self.caffe_graph = CaffeGraph(self.net, self.params, self.resolver.caffepb) self.caffe_graph.build() def load_using_pb(self): data = self.resolver.NetParameter() data.MergeFromString(open(self.model_path, 'rb').read()) pair = lambda layer: (layer.name, self.normalize_pb_data(layer)) layers = data.layers or data.layer self.params = [pair(layer) for layer in layers if layer.blobs] def normalize_pb_data(self, layer): transformed = [] for blob in layer.blobs: if len(blob.shape.dim): dims = blob.shape.dim if layer.type == 'PReLU': c_o, c_i, h, w = map(int, [1] + \ list(dims) + [1]* (3 - len(dims))) else: c_o, c_i, h, w = map(int, [1] * (4 - len(dims)) \ + list(dims)) else: c_o = blob.num c_i = blob.channels h = blob.height w = blob.width data = np.asarray(list(blob.data), dtype=np.float32).reshape(c_o, c_i, h, w) transformed.append(data) return transformed