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https://github.com/FFmpeg/FFmpeg.git
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608 lines
25 KiB
Python
608 lines
25 KiB
Python
# Copyright (c) 2019 Guo Yejun
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#
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# This file is part of FFmpeg.
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#
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# FFmpeg is free software; you can redistribute it and/or
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# modify it under the terms of the GNU Lesser General Public
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# License as published by the Free Software Foundation; either
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# version 2.1 of the License, or (at your option) any later version.
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#
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# FFmpeg is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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# Lesser General Public License for more details.
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#
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# You should have received a copy of the GNU Lesser General Public
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# License along with FFmpeg; if not, write to the Free Software
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# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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# ==============================================================================
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import tensorflow as tf
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import numpy as np
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import sys, struct
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import convert_header as header
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__all__ = ['convert_from_tensorflow']
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class Operand(object):
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IOTYPE_INPUT = 1
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IOTYPE_OUTPUT = 2
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IOTYPE_INTERMEDIATE = IOTYPE_INPUT | IOTYPE_OUTPUT
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DTYPE_FLOAT = 1
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DTYPE_UINT8 = 4
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index = 0
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def __init__(self, name, dtype, dims):
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self.name = name
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self.dtype = dtype
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self.dims = dims
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self.iotype = 0
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self.used_count = 0
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self.index = Operand.index
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Operand.index = Operand.index + 1
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self.iotype2str = {Operand.IOTYPE_INPUT: 'in', Operand.IOTYPE_OUTPUT: 'out', Operand.IOTYPE_INTERMEDIATE: 'inout'}
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self.dtype2str = {Operand.DTYPE_FLOAT: 'DT_FLOAT', Operand.DTYPE_UINT8: 'DT_UINT8'}
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def add_iotype(self, iotype):
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self.iotype = self.iotype | iotype
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if iotype == Operand.IOTYPE_INPUT:
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self.used_count = self.used_count + 1
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def __str__(self):
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return "{}: (name: {}, iotype: {}, dtype: {}, dims: {}, used_count: {})".format(self.index,
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self.name, self.iotype2str[self.iotype], self.dtype2str[self.dtype],
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self.dims, self.used_count)
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def __lt__(self, other):
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return self.index < other.index
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class TFConverter:
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def __init__(self, graph_def, nodes, outfile, dump4tb):
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self.graph_def = graph_def
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self.nodes = nodes
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self.outfile = outfile
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self.dump4tb = dump4tb
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self.layer_number = 0
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self.output_names = []
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self.name_node_dict = {}
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self.edges = {}
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self.conv_activations = {'Relu':0, 'Tanh':1, 'Sigmoid':2, 'None':3, 'LeakyRelu':4}
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self.conv_paddings = {'VALID':0, 'SAME':1}
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self.pool_paddings = {'VALID':0, 'SAME':1}
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self.converted_nodes = set()
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self.conv2d_scope_names = set()
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self.conv2d_scopename_inputname_dict = {}
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self.dense_scope_names = set()
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self.dense_scopename_inputname_dict = {}
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self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4,
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'MathBinary':5, 'MathUnary':6, 'AvgPool':7, 'MatMul':8}
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self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3, 'Minimum':4, 'FloorMod':5}
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self.mathun2code = {'Abs':0, 'Sin':1, 'Cos':2, 'Tan':3, 'Asin':4,
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'Acos':5, 'Atan':6, 'Sinh':7, 'Cosh':8, 'Tanh':9, 'Asinh':10,
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'Acosh':11, 'Atanh':12, 'Ceil':13, 'Floor':14, 'Round':15,
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'Exp':16}
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self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}
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self.name_operand_dict = {}
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def add_operand(self, name, type):
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node = self.name_node_dict[name]
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if name not in self.name_operand_dict:
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dtype = node.attr['dtype'].type
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if dtype == 0:
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dtype = node.attr['T'].type
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dims = [-1,-1,-1,-1]
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if 'shape' in node.attr:
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dims[0] = node.attr['shape'].shape.dim[0].size
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dims[1] = node.attr['shape'].shape.dim[1].size
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dims[2] = node.attr['shape'].shape.dim[2].size
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dims[3] = node.attr['shape'].shape.dim[3].size
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operand = Operand(name, dtype, dims)
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self.name_operand_dict[name] = operand;
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self.name_operand_dict[name].add_iotype(type)
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return self.name_operand_dict[name].index
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def dump_for_tensorboard(self):
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graph = tf.get_default_graph()
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tf.import_graph_def(self.graph_def, name="")
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tf.summary.FileWriter('/tmp/graph', graph)
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print('graph saved, run "tensorboard --logdir=/tmp/graph" to see it')
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def get_conv2d_params(self, conv2d_scope_name):
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knode = self.name_node_dict[conv2d_scope_name + '/kernel']
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bnode = self.name_node_dict[conv2d_scope_name + '/bias']
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if conv2d_scope_name + '/dilation_rate' in self.name_node_dict:
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dnode = self.name_node_dict[conv2d_scope_name + '/dilation_rate']
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else:
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dnode = None
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# the BiasAdd name is possible be changed into the output name,
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# if activation is None, and BiasAdd.next is the last op which is Identity
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if conv2d_scope_name + '/BiasAdd' in self.edges:
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anode = self.edges[conv2d_scope_name + '/BiasAdd'][0]
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if anode.op not in self.conv_activations:
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anode = None
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else:
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anode = None
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return knode, bnode, dnode, anode
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def get_dense_params(self, dense_scope_name):
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knode = self.name_node_dict[dense_scope_name + '/kernel']
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bnode = self.name_node_dict.get(dense_scope_name + '/bias')
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# the BiasAdd name is possible be changed into the output name,
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# if activation is None, and BiasAdd.next is the last op which is Identity
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anode = None
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if bnode:
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if dense_scope_name + '/BiasAdd' in self.edges:
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anode = self.edges[dense_scope_name + '/BiasAdd'][0]
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if anode.op not in self.conv_activations:
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anode = None
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else:
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anode = None
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return knode, bnode, anode
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def dump_complex_conv2d_to_file(self, node, f):
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assert(node.op == 'Conv2D')
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self.layer_number = self.layer_number + 1
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self.converted_nodes.add(node.name)
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scope_name = TFConverter.get_scope_name(node.name)
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#knode for kernel, bnode for bias, dnode for dilation, anode for activation
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knode, bnode, dnode, anode = self.get_conv2d_params(scope_name)
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if dnode is not None:
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dilation = struct.unpack('i', dnode.attr['value'].tensor.tensor_content[0:4])[0]
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else:
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dilation = 1
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if anode is not None:
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activation = anode.op
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else:
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activation = 'None'
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padding = node.attr['padding'].s.decode("utf-8")
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# conv2d with dilation > 1 generates tens of nodes, not easy to parse them, so use this tricky method.
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if dilation > 1 and scope_name + '/stack' in self.name_node_dict:
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if self.name_node_dict[scope_name + '/stack'].op == "Const":
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padding = 'SAME'
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padding = self.conv_paddings[padding]
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ktensor = knode.attr['value'].tensor
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filter_height = ktensor.tensor_shape.dim[0].size
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filter_width = ktensor.tensor_shape.dim[1].size
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in_channels = ktensor.tensor_shape.dim[2].size
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out_channels = ktensor.tensor_shape.dim[3].size
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kernel = np.frombuffer(ktensor.tensor_content, dtype=np.float32)
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kernel = kernel.reshape(filter_height, filter_width, in_channels, out_channels)
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kernel = np.transpose(kernel, [3, 0, 1, 2])
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has_bias = 1
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np.array([self.op2code[node.op], dilation, padding, self.conv_activations[activation], in_channels, out_channels, filter_height, has_bias], dtype=np.uint32).tofile(f)
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kernel.tofile(f)
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btensor = bnode.attr['value'].tensor
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if btensor.tensor_shape.dim[0].size == 1:
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bias = struct.pack("f", btensor.float_val[0])
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else:
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bias = btensor.tensor_content
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f.write(bias)
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input_name = self.conv2d_scopename_inputname_dict[scope_name]
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input_operand_index = self.add_operand(input_name, Operand.IOTYPE_INPUT)
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if anode is not None:
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output_operand_index = self.add_operand(anode.name, Operand.IOTYPE_OUTPUT)
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else:
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output_operand_index = self.add_operand(self.edges[bnode.name][0].name, Operand.IOTYPE_OUTPUT)
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np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
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def dump_dense_to_file(self, node, f):
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assert(node.op == 'MatMul')
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self.layer_number = self.layer_number + 1
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self.converted_nodes.add(node.name)
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scope_name = TFConverter.get_scope_name(node.name)
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#knode for kernel, bnode for bias, anode for activation
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knode, bnode, anode = self.get_dense_params(scope_name.split('/')[0])
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if bnode is not None:
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has_bias = 1
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btensor = bnode.attr['value'].tensor
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if btensor.tensor_shape.dim[0].size == 1:
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bias = struct.pack("f", btensor.float_val[0])
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else:
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bias = btensor.tensor_content
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else:
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has_bias = 0
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if anode is not None:
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activation = anode.op
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else:
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activation = 'None'
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ktensor = knode.attr['value'].tensor
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in_channels = ktensor.tensor_shape.dim[0].size
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out_channels = ktensor.tensor_shape.dim[1].size
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if in_channels * out_channels == 1:
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kernel = np.float32(ktensor.float_val[0])
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else:
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kernel = np.frombuffer(ktensor.tensor_content, dtype=np.float32)
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kernel = kernel.reshape(in_channels, out_channels)
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kernel = np.transpose(kernel, [1, 0])
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np.array([self.op2code[node.op], self.conv_activations[activation], in_channels, out_channels, has_bias], dtype=np.uint32).tofile(f)
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kernel.tofile(f)
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if has_bias:
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f.write(bias)
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input_name = self.dense_scopename_inputname_dict[scope_name.split('/')[0]]
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input_operand_index = self.add_operand(input_name, Operand.IOTYPE_INPUT)
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if anode is not None:
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output_operand_index = self.add_operand(anode.name, Operand.IOTYPE_OUTPUT)
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else:
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if bnode is not None:
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output_operand_index = self.add_operand(self.edges[bnode.name][0].name, Operand.IOTYPE_OUTPUT)
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else:
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output_operand_index = self.add_operand(self.edges[scope_name+'/concat_1'][0].name, Operand.IOTYPE_OUTPUT)
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np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
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def dump_simple_conv2d_to_file(self, node, f):
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assert(node.op == 'Conv2D')
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self.layer_number = self.layer_number + 1
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self.converted_nodes.add(node.name)
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node0 = self.name_node_dict[node.input[0]]
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node1 = self.name_node_dict[node.input[1]]
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if node0.op == 'Const':
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knode = node0
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input_name = node.input[1]
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else:
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knode = node1
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input_name = node.input[0]
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ktensor = knode.attr['value'].tensor
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filter_height = ktensor.tensor_shape.dim[0].size
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filter_width = ktensor.tensor_shape.dim[1].size
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in_channels = ktensor.tensor_shape.dim[2].size
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out_channels = ktensor.tensor_shape.dim[3].size
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if filter_height * filter_width * in_channels * out_channels == 1:
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kernel = np.float32(ktensor.float_val[0])
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else:
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kernel = np.frombuffer(ktensor.tensor_content, dtype=np.float32)
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kernel = kernel.reshape(filter_height, filter_width, in_channels, out_channels)
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kernel = np.transpose(kernel, [3, 0, 1, 2])
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has_bias = 0
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dilation = 1
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padding = node.attr['padding'].s.decode("utf-8")
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np.array([self.op2code[node.op], dilation, self.conv_paddings[padding], self.conv_activations['None'],
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in_channels, out_channels, filter_height, has_bias], dtype=np.uint32).tofile(f)
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kernel.tofile(f)
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input_operand_index = self.add_operand(input_name, Operand.IOTYPE_INPUT)
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output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT)
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np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
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def dump_depth2space_to_file(self, node, f):
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assert(node.op == 'DepthToSpace')
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self.layer_number = self.layer_number + 1
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block_size = node.attr['block_size'].i
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np.array([self.op2code[node.op], block_size], dtype=np.uint32).tofile(f)
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self.converted_nodes.add(node.name)
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input_operand_index = self.add_operand(node.input[0], Operand.IOTYPE_INPUT)
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output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT)
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np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
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def dump_mirrorpad_to_file(self, node, f):
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assert(node.op == 'MirrorPad')
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self.layer_number = self.layer_number + 1
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mode = node.attr['mode'].s
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mode = self.mirrorpad_mode[mode.decode("utf-8")]
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np.array([self.op2code[node.op], mode], dtype=np.uint32).tofile(f)
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pnode = self.name_node_dict[node.input[1]]
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self.converted_nodes.add(pnode.name)
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paddings = pnode.attr['value'].tensor.tensor_content
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f.write(paddings)
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self.converted_nodes.add(node.name)
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input_operand_index = self.add_operand(node.input[0], Operand.IOTYPE_INPUT)
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output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT)
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np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
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def dump_maximum_to_file(self, node, f):
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assert(node.op == 'Maximum')
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self.layer_number = self.layer_number + 1
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ynode = self.name_node_dict[node.input[1]]
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y = ynode.attr['value'].tensor.float_val[0]
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np.array([self.op2code[node.op]], dtype=np.uint32).tofile(f)
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np.array([y], dtype=np.float32).tofile(f)
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self.converted_nodes.add(node.name)
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input_operand_index = self.add_operand(node.input[0], Operand.IOTYPE_INPUT)
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output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT)
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np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
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def dump_mathbinary_to_file(self, node, f):
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self.layer_number = self.layer_number + 1
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self.converted_nodes.add(node.name)
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i0_node = self.name_node_dict[node.input[0]]
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i1_node = self.name_node_dict[node.input[1]]
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np.array([self.op2code['MathBinary'], self.mathbin2code[node.op]], dtype=np.uint32).tofile(f)
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if i0_node.op == 'Const':
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scalar = i0_node.attr['value'].tensor.float_val[0]
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np.array([1], dtype=np.uint32).tofile(f) # broadcast: 1
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np.array([scalar], dtype=np.float32).tofile(f)
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np.array([0], dtype=np.uint32).tofile(f) # broadcast: 0
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input_operand_index = self.add_operand(i1_node.name, Operand.IOTYPE_INPUT)
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np.array([input_operand_index], dtype=np.uint32).tofile(f)
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elif i1_node.op == 'Const':
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scalar = i1_node.attr['value'].tensor.float_val[0]
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np.array([0], dtype=np.uint32).tofile(f)
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input_operand_index = self.add_operand(i0_node.name, Operand.IOTYPE_INPUT)
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np.array([input_operand_index], dtype=np.uint32).tofile(f)
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np.array([1], dtype=np.uint32).tofile(f)
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np.array([scalar], dtype=np.float32).tofile(f)
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else:
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np.array([0], dtype=np.uint32).tofile(f)
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input_operand_index = self.add_operand(i0_node.name, Operand.IOTYPE_INPUT)
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np.array([input_operand_index], dtype=np.uint32).tofile(f)
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np.array([0], dtype=np.uint32).tofile(f)
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input_operand_index = self.add_operand(i1_node.name, Operand.IOTYPE_INPUT)
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np.array([input_operand_index], dtype=np.uint32).tofile(f)
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output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT)
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np.array([output_operand_index], dtype=np.uint32).tofile(f)
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def dump_mathunary_to_file(self, node, f):
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self.layer_number = self.layer_number + 1
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self.converted_nodes.add(node.name)
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i0_node = self.name_node_dict[node.input[0]]
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np.array([self.op2code['MathUnary'], self.mathun2code[node.op]], dtype=np.uint32).tofile(f)
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input_operand_index = self.add_operand(i0_node.name, Operand.IOTYPE_INPUT)
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np.array([input_operand_index], dtype=np.uint32).tofile(f)
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output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT)
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np.array([output_operand_index],dtype=np.uint32).tofile(f)
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def dump_avg_pool_to_file(self, node, f):
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assert(node.op == 'AvgPool')
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self.layer_number = self.layer_number + 1
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self.converted_nodes.add(node.name)
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node0 = self.name_node_dict[node.input[0]]
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strides = node.attr['strides']
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# Tensorflow do not support pooling strides in batch dimension and
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# current native NN do not support pooling strides in channel dimension, added assert() here.
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assert(strides.list.i[1]==strides.list.i[2])
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assert(strides.list.i[0]==1)
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assert(strides.list.i[3]==1)
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strides = strides.list.i[1]
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filter_node = node.attr['ksize']
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input_name = node.input[0]
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# Tensorflow do not support pooling ksize in batch dimension and channel dimension.
|
|
assert(filter_node.list.i[0]==1)
|
|
assert(filter_node.list.i[3]==1)
|
|
filter_height = filter_node.list.i[1]
|
|
filter_width = filter_node.list.i[2]
|
|
|
|
padding = node.attr['padding'].s.decode("utf-8")
|
|
np.array([self.op2code[node.op], strides, self.pool_paddings[padding], filter_height],
|
|
dtype=np.uint32).tofile(f)
|
|
|
|
input_operand_index = self.add_operand(input_name, Operand.IOTYPE_INPUT)
|
|
output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT)
|
|
np.array([input_operand_index, output_operand_index],dtype=np.uint32).tofile(f)
|
|
|
|
|
|
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
|
|
if self.in_conv2d_scope(node.name):
|
|
if node.op == 'Conv2D':
|
|
self.dump_complex_conv2d_to_file(node, f)
|
|
continue
|
|
if self.in_dense_scope(node.name):
|
|
if node.op == 'MatMul':
|
|
self.dump_dense_to_file(node, f)
|
|
continue
|
|
|
|
|
|
if node.op == 'Conv2D':
|
|
self.dump_simple_conv2d_to_file(node, f)
|
|
continue
|
|
if node.name in self.output_names:
|
|
input_name = self.id_different_scope_dict[node.name]
|
|
if TFConverter.get_scope_name(input_name)!=TFConverter.get_scope_name(node.name):
|
|
continue
|
|
if node.op == 'AvgPool':
|
|
self.dump_avg_pool_to_file(node, f)
|
|
elif node.op == 'DepthToSpace':
|
|
self.dump_depth2space_to_file(node, f)
|
|
elif node.op == 'MirrorPad':
|
|
self.dump_mirrorpad_to_file(node, f)
|
|
elif node.op == 'Maximum':
|
|
self.dump_maximum_to_file(node, f)
|
|
elif node.op in self.mathbin2code:
|
|
self.dump_mathbinary_to_file(node, f)
|
|
elif node.op in self.mathun2code:
|
|
self.dump_mathunary_to_file(node, f)
|
|
|
|
|
|
def dump_operands_to_file(self, f):
|
|
operands = sorted(self.name_operand_dict.values())
|
|
for operand in operands:
|
|
#print('{}'.format(operand))
|
|
np.array([operand.index, len(operand.name)], dtype=np.uint32).tofile(f)
|
|
f.write(operand.name.encode('utf-8'))
|
|
np.array([operand.iotype, operand.dtype], dtype=np.uint32).tofile(f)
|
|
np.array(operand.dims, dtype=np.uint32).tofile(f)
|
|
|
|
|
|
def dump_to_file(self):
|
|
with open(self.outfile, 'wb') as f:
|
|
f.write(header.str.encode('utf-8'))
|
|
np.array([header.major, header.minor], dtype=np.uint32).tofile(f)
|
|
self.dump_layers_to_file(f)
|
|
self.dump_operands_to_file(f)
|
|
np.array([self.layer_number, len(self.name_operand_dict)], dtype=np.uint32).tofile(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):
|
|
self.id_different_scope_dict = {}
|
|
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]
|
|
self.id_different_scope_dict[name] = 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 in_conv2d_scope(self, name):
|
|
inner_scope = TFConverter.get_scope_name(name)
|
|
if inner_scope == "":
|
|
return False;
|
|
for scope in self.conv2d_scope_names:
|
|
index = inner_scope.find(scope)
|
|
if index == 0:
|
|
return True
|
|
return False
|
|
|
|
|
|
def in_dense_scope(self, name):
|
|
inner_scope = TFConverter.get_scope_name(name)
|
|
if inner_scope == "":
|
|
return False;
|
|
for scope in self.dense_scope_names:
|
|
index = inner_scope.find(scope)
|
|
if index == 0:
|
|
return True
|
|
return False
|
|
|
|
def generate_sub_block_op_scope_info(self):
|
|
# mostly, conv2d/dense is a sub block in graph, get the scope name
|
|
for node in self.nodes:
|
|
if node.op == 'Conv2D':
|
|
scope = TFConverter.get_scope_name(node.name)
|
|
# for the case tf.nn.conv2d is called directly
|
|
if scope == '':
|
|
continue
|
|
# for the case tf.nn.conv2d is called within a scope
|
|
if scope + '/kernel' not in self.name_node_dict:
|
|
continue
|
|
self.conv2d_scope_names.add(scope)
|
|
elif node.op == 'MatMul':
|
|
scope = TFConverter.get_scope_name(node.name)
|
|
# for the case tf.nn.dense is called directly
|
|
if scope == '':
|
|
continue
|
|
# for the case tf.nn.dense is called within a scope
|
|
if scope + '/kernel' not in self.name_node_dict and scope.split('/Tensordot')[0] + '/kernel' not in self.name_node_dict:
|
|
continue
|
|
self.dense_scope_names.add(scope.split('/Tensordot')[0])
|
|
|
|
# get the input name to the conv2d/dense sub block
|
|
for node in self.nodes:
|
|
scope = TFConverter.get_scope_name(node.name)
|
|
if scope in self.conv2d_scope_names:
|
|
if node.op == 'Conv2D' or node.op == 'Shape':
|
|
for inp in node.input:
|
|
if TFConverter.get_scope_name(inp) != scope:
|
|
self.conv2d_scopename_inputname_dict[scope] = inp
|
|
elif scope in self.dense_scope_names:
|
|
if node.op == 'MatMul' or node.op == 'Shape':
|
|
for inp in node.input:
|
|
if TFConverter.get_scope_name(inp) != scope:
|
|
self.dense_scopename_inputname_dict[scope] = inp
|
|
elif scope.split('/Tensordot')[0] in self.dense_scope_names:
|
|
if node.op == 'Transpose':
|
|
for inp in node.input:
|
|
if TFConverter.get_scope_name(inp).find(scope)<0 and TFConverter.get_scope_name(inp).find(scope.split('/')[0])<0:
|
|
self.dense_scopename_inputname_dict[scope.split('/Tensordot')[0]] = inp
|
|
|
|
|
|
def run(self):
|
|
self.generate_name_node_dict()
|
|
self.generate_output_names()
|
|
self.remove_identity()
|
|
self.generate_edges()
|
|
self.generate_sub_block_op_scope_info()
|
|
|
|
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()
|