# 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. from x2paddle.core.graph import GraphNode, Graph from x2paddle.core.fluid_code import FluidCode from tensorflow.python.framework import tensor_util from tensorflow.python.platform import gfile from tensorflow.core.framework import attr_value_pb2 import tensorflow as tf import copy as cp import numpy import sys class TFGraphNode(GraphNode): def __init__(self, layer, layer_name=None): if layer_name is None: super(TFGraphNode, self).__init__(layer, layer.name.replace('/', '_').replace('-', '_')) else: super(TFGraphNode, self).__init__(layer, layer_name.replace('/', '_').replace('-', '_')) self.layer_type = layer.op self.fluid_code = FluidCode() self.dtype_map = {1: "float32", 3: "int32", 4: "int8", 9: "int64"} @property def out_shapes(self): values = self.layer.attr["_output_shapes"].list.shape out_shapes = list() for value in values: shape = [dim.size for dim in value.dim] out_shapes.append(shape) return out_shapes @property def dtype(self): dtype = self.layer.attr["dtype"].type if dtype not in self.dtype_map: raise Exception("Dtype[{}] not in dtype_map".format(dtype)) return self.dtype_map[dtype] @property def value(self): assert self.layer_type == "Const", "Only Const node has value." attr = self.layer.attr['value'] field = getattr(attr, attr.WhichOneof('value')) return tensor_util.MakeNdarray(field) def get_attr(self, name): if name not in self.layer.attr: return None attr = self.layer.attr[name] field = attr.WhichOneof('value') value = getattr(attr, field) if field else None if isinstance(value, attr_value_pb2.AttrValue.ListValue): result = list(value.ListFields()[0][1]) for i in range(len(result)): if isinstance(result[i], int): result[i] = int(result[i]) try: if isinstance(result[i], long): result[i] = int(result[i]) except: pass return result else: return value class TFGraph(Graph): def __init__(self, model): super(TFGraph, self).__init__(model) self.identity_map = dict() self.multi_out_ops = ['Split', 'SplitV'] def build(self): for layer in self.model.node: self.node_map[layer.name.replace('/', '_').replace( '-', '_')] = TFGraphNode(layer) for layer_name, node in self.node_map.items(): for in_node in node.layer.input: in_node = in_node.replace('/', '_').replace('-', '_') if in_node not in self.node_map: if in_node.strip().split(':')[0] in self.node_map: self.connect(in_node.strip().split(':')[0], layer_name) else: raise Exception( 'input[{}] of node[{}] does not exist in node_map'. format(in_node, layer_name)) else: self.connect(in_node, layer_name) super(TFGraph, self).build() # tensorflow graph optimize self._remove_isolated_node() self._remove_identity_node() def get_node(self, node_name, copy=False): items = node_name.strip().split(':') items[0] = items[0].replace('/', '_').replace('-', '_') if items[0] in self.identity_map: items[0] = self.identity_map[items[0]] new_node_name = ":".join(items) node = super(TFGraph, self).get_node(new_node_name, copy) if len(items) == 1 and node.layer_type in self.multi_out_ops: node.index = 0 return node def _remove_isolated_node(self): # delete isolated nodes isolated_nodes = list() for node_name in self.node_map.keys(): if len(self.get_node(node_name).inputs) == 0 and len( self.get_node(node_name).outputs) == 0: isolated_nodes.append(node_name) for node_name in isolated_nodes: self.remove_node(node_name) def _remove_identity_node(self): identity_node = list() for node_name, node in self.node_map.items(): if node.layer_type == "Identity": identity_node.append(node_name) for node_name in identity_node: node = self.get_node(node_name) # Remind: Only 1 input for Identity node input_node = self.get_node(node.inputs[0]) # remove identity node from graph self.identity_map[node_name] = input_node.layer_name idx = input_node.outputs.index(node_name) del input_node.outputs[idx] output_names = node.outputs for output_name in output_names: output_node = self.get_node(output_name) idx = output_node.inputs.index(node_name) output_node.inputs[idx] = input_node.layer_name idx = self.topo_sort.index(node_name) del self.topo_sort[idx] if node_name in self.output_nodes: idx = self.output_nodes.index(node_name) self.output_nodes[idx] = input_node.layer_name class TFDecoder(object): def __init__(self, pb_model): self.sess = tf.Session() self.input_info = dict() with gfile.FastGFile(pb_model, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) input_map = self._check_input_shape(graph_def) self._fix_output_shape(graph_def) self.sess.graph.as_default() tf.import_graph_def(graph_def, name='', input_map=input_map) # for node in graph_def.node: # print(node.name, node.op, node.input) self.sess.run(tf.global_variables_initializer()) self.tf_graph = TFGraph( self.sess.graph._as_graph_def(add_shapes=True)[0]) self.tf_graph.build() def _fix_output_shape(self, graph): for i in range(len(graph.node)): node = graph.node[i] if node.op == "swish_f32": graph.node[i].attr['_disable_call_shape_inference'].b = False def _check_input_shape(self, graph_def): numpy.random.seed(13) graph_def = cp.deepcopy(graph_def) input_map = dict() for layer in graph_def.node: if layer.op != "Placeholder": continue graph_node = TFGraphNode(layer) dtype = graph_node.dtype need_define_shape = 0 if not graph_node.get_attr("shape"): need_define_shape = 1 else: value = graph_node.layer.attr["shape"].shape shape = [dim.size for dim in value.dim] if shape.count(-1) > 1: need_define_shape = 2 if need_define_shape > 0: if need_define_shape == 1: print( "\nUnknown shape for input tensor[tensor name: \"{}\"]". format(layer.name)) else: print( "\nShape[now is {}] for input tensor[tensor name: \"{}\"] not support yet" .format(shape, layer.name)) print( "Use your keyboard type the shape of input tensor below :)") right_shape_been_input = False while not right_shape_been_input: shape = input("Shape of Input(e.g. None,224,224,3): ") if shape.count("None") > 1: print("Only 1 dimension can be None, type again:)") else: right_shape_been_input = True shape = [ None if dim == "None" else int(dim) for dim in shape.strip().split(',') ] assert shape.count(None) <= 1, "Only one dimension can be None" x2paddle_input = tf.placeholder(dtype=dtype, shape=shape, name="x2paddle_{}".format( layer.name)) input_map["{}:0".format(layer.name)] = x2paddle_input shape[shape.index(None)] = -1 # self.input_example_data["x2paddle_{}".format(layer.name)] = numpy.random.random_sample(shape).astype(dtype) self.input_info["x2paddle_{}".format(layer.name)] = (shape, dtype) else: value = graph_node.layer.attr["shape"].shape shape = [dim.size for dim in value.dim] # self.input_example_data[graph_node.layer_name] = numpy.random.random_sample(shape).astype(dtype) self.input_info[graph_node.layer_name] = (shape, dtype) return input_map # trick method # should be removed after PaddlePaddle V1.6 been released def infer_tensor(self, graph_node): print("========== Use infer_tensor for tensor: ", graph_node.layer.name) if hasattr(graph_node, "index"): tensor_name = graph_node.layer.name + ":{}".format(graph_node.index) else: tensor_name = graph_node.layer.name + ":0" feed = dict() for input_name, info in self.input_info.items(): (shape, dtype) = cp.deepcopy(info) input_tensor = self.sess.graph.get_tensor_by_name(input_name + ":0") if shape.count(-1) > 0: shape[shape.index(-1)] = 2 feed[input_tensor] = numpy.random.random_sample(shape) output_tensor = self.sess.graph.get_tensor_by_name(tensor_name) return self.sess.run([output_tensor], feed)[0] def infer_shape_tensor(self, graph_node, out_shape=None): print("========== Use infer_shape_tensor for tensor: ", graph_node.layer.name) if hasattr(graph_node, "index"): tensor_name = graph_node.layer.name + ":{}".format(graph_node.index) else: tensor_name = graph_node.layer.name + ":0" feed = dict() batch_size = [2, 3, 5] results = list() for b in batch_size: for input_name, info in self.input_info.items(): (shape, dtype) = cp.deepcopy(info) input_tensor = self.sess.graph.get_tensor_by_name(input_name + ":0") if shape.count(-1) > 0: shape[shape.index(-1)] = b feed[input_tensor] = numpy.random.random_sample(shape) output_tensor = self.sess.graph.get_tensor_by_name(tensor_name) results.append(self.sess.run([output_tensor], feed)[0].flatten()) compare01 = (results[0] == results[1]) compare12 = (results[1] == results[2]) if compare01.all() and compare12.all(): return results[0].tolist() if (compare01 == compare12).all(): index = numpy.argwhere(compare01 == False).flatten() if index.shape[0] != 1: raise Exception("There's not only one unstable dimension") results[0][index[0]] = -1 index = numpy.argwhere(results[0] < 0).flatten() if index.shape[0] > 2: print("Warning: More than two dimension less than zero") if index.shape[0] == 2 and out_shape is not None: if out_shape[index[1]] > 0: results[0][index[1]] = out_shape[index[1]] else: results[0][index[0]] = out_shape[index[0]] return results[0].tolist() else: raise Exception("Couldn't infer a stable shape shape tensor value")