# Copyright (c) 2019 Guo Yejun # # This file is part of FFmpeg. # # FFmpeg is free software; you can redistribute it and/or # modify it under the terms of the GNU Lesser General Public # License as published by the Free Software Foundation; either # version 2.1 of the License, or (at your option) any later version. # # FFmpeg is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with FFmpeg; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA # ============================================================================== import tensorflow as tf import numpy as np import sys, struct __all__ = ['convert_from_tensorflow'] class TFConverter: def __init__(self, graph_def, nodes, outfile, dump4tb): self.graph_def = graph_def self.nodes = nodes self.outfile = outfile self.dump4tb = dump4tb self.layer_number = 0 self.output_names = [] self.name_node_dict = {} self.edges = {} self.conv_activations = {'Relu':0, 'Tanh':1, 'Sigmoid':2, 'None':3, 'LeakyRelu':4} self.conv_paddings = {'VALID':0, 'SAME':1} self.converted_nodes = set() self.conv2d_scope_names = set() self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3} self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2} def dump_for_tensorboard(self): graph = tf.get_default_graph() tf.import_graph_def(self.graph_def, name="") tf.summary.FileWriter('/tmp/graph', graph) print('graph saved, run "tensorboard --logdir=/tmp/graph" to see it') def get_conv2d_params(self, conv2d_scope_name): knode = self.name_node_dict[conv2d_scope_name + '/kernel'] bnode = self.name_node_dict[conv2d_scope_name + '/bias'] if conv2d_scope_name + '/dilation_rate' in self.name_node_dict: dnode = self.name_node_dict[conv2d_scope_name + '/dilation_rate'] else: dnode = None # the BiasAdd name is possible be changed into the output name, # if activation is None, and BiasAdd.next is the last op which is Identity if conv2d_scope_name + '/BiasAdd' in self.edges: activation = self.edges[conv2d_scope_name + '/BiasAdd'][0] activation = activation.op else: activation = 'None' return knode, bnode, dnode, activation def dump_conv2d_to_file(self, node, f): assert(node.op == 'Conv2D') self.layer_number = self.layer_number + 1 self.converted_nodes.add(node.name) scope_name = TFConverter.get_scope_name(node.name) #knode for kernel, bnode for bias, dnode for dilation knode, bnode, dnode, activation = self.get_conv2d_params(scope_name) if dnode is not None: dilation = struct.unpack('i', dnode.attr['value'].tensor.tensor_content[0:4])[0] else: dilation = 1 padding = node.attr['padding'].s.decode("utf-8") # conv2d with dilation > 1 generates tens of nodes, not easy to parse them, so use tricky. if dilation > 1 and scope_name + '/stack' in self.name_node_dict: if self.name_node_dict[scope_name + '/stack'].op == "Const": padding = 'SAME' padding = self.conv_paddings[padding] ktensor = knode.attr['value'].tensor filter_height = ktensor.tensor_shape.dim[0].size filter_width = ktensor.tensor_shape.dim[1].size in_channels = ktensor.tensor_shape.dim[2].size out_channels = ktensor.tensor_shape.dim[3].size kernel = np.frombuffer(ktensor.tensor_content, dtype=np.float32) kernel = kernel.reshape(filter_height, filter_width, in_channels, out_channels) kernel = np.transpose(kernel, [3, 0, 1, 2]) np.array([self.op2code[node.op], dilation, padding, self.conv_activations[activation], in_channels, out_channels, filter_height], dtype=np.uint32).tofile(f) kernel.tofile(f) btensor = bnode.attr['value'].tensor if btensor.tensor_shape.dim[0].size == 1: bias = struct.pack("f", btensor.float_val[0]) else: bias = btensor.tensor_content f.write(bias) def dump_depth2space_to_file(self, node, f): assert(node.op == 'DepthToSpace') self.layer_number = self.layer_number + 1 block_size = node.attr['block_size'].i np.array([self.op2code[node.op], block_size], dtype=np.uint32).tofile(f) self.converted_nodes.add(node.name) def dump_mirrorpad_to_file(self, node, f): assert(node.op == 'MirrorPad') self.layer_number = self.layer_number + 1 mode = node.attr['mode'].s mode = self.mirrorpad_mode[mode.decode("utf-8")] np.array([self.op2code[node.op], mode], dtype=np.uint32).tofile(f) pnode = self.name_node_dict[node.input[1]] self.converted_nodes.add(pnode.name) paddings = pnode.attr['value'].tensor.tensor_content f.write(paddings) self.converted_nodes.add(node.name) def generate_layer_number(self): # in current hard code implementation, the layer number is the first data written to the native model file # it is not easy to know it at the beginning time in the general converter, so first do a dry run for compatibility # will be refined later. with open('/tmp/tmp.model', 'wb') as f: self.dump_layers_to_file(f) self.converted_nodes.clear() def dump_layers_to_file(self, f): for node in self.nodes: if node.name in self.converted_nodes: continue # conv2d with dilation generates very complex nodes, so handle it in special scope_name = TFConverter.get_scope_name(node.name) if scope_name in self.conv2d_scope_names: if node.op == 'Conv2D': self.dump_conv2d_to_file(node, f) continue if node.op == 'DepthToSpace': self.dump_depth2space_to_file(node, f) elif node.op == 'MirrorPad': self.dump_mirrorpad_to_file(node, f) def dump_to_file(self): self.generate_layer_number() with open(self.outfile, 'wb') as f: np.array([self.layer_number], dtype=np.uint32).tofile(f) self.dump_layers_to_file(f) def generate_name_node_dict(self): for node in self.nodes: self.name_node_dict[node.name] = node def generate_output_names(self): used_names = [] for node in self.nodes: for input in node.input: used_names.append(input) for node in self.nodes: if node.name not in used_names: self.output_names.append(node.name) def remove_identity(self): id_nodes = [] id_dict = {} for node in self.nodes: if node.op == 'Identity': name = node.name input = node.input[0] id_nodes.append(node) # do not change the output name if name in self.output_names: self.name_node_dict[input].name = name self.name_node_dict[name] = self.name_node_dict[input] del self.name_node_dict[input] else: id_dict[name] = input for idnode in id_nodes: self.nodes.remove(idnode) for node in self.nodes: for i in range(len(node.input)): input = node.input[i] if input in id_dict: node.input[i] = id_dict[input] def generate_edges(self): for node in self.nodes: for input in node.input: if input in self.edges: self.edges[input].append(node) else: self.edges[input] = [node] @staticmethod def get_scope_name(name): index = name.rfind('/') if index == -1: return "" return name[0:index] def generate_conv2d_scope_names(self): for node in self.nodes: if node.op == 'Conv2D': scope = TFConverter.get_scope_name(node.name) self.conv2d_scope_names.add(scope) def run(self): self.generate_name_node_dict() self.generate_output_names() self.remove_identity() self.generate_edges() self.generate_conv2d_scope_names() if self.dump4tb: self.dump_for_tensorboard() self.dump_to_file() def convert_from_tensorflow(infile, outfile, dump4tb): with open(infile, 'rb') as f: # read the file in .proto format graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) nodes = graph_def.node converter = TFConverter(graph_def, nodes, outfile, dump4tb) converter.run()