Will remove native backend, so change the default backend in filters, and also remove the python scripts which generate native model file. Signed-off-by: Ting Fu <ting.fu@intel.com>pull/389/head
Ting Fu
2 years ago
committed by
Guo Yejun
parent
7ed6f28a7c
commit
a9fb141719
7 changed files with 7 additions and 727 deletions
@ -1,56 +0,0 @@ |
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# 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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|
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# verified with Python 3.5.2 on Ubuntu 16.04 |
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import argparse |
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import os |
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from convert_from_tensorflow import * |
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|
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def get_arguments(): |
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parser = argparse.ArgumentParser(description='generate native mode model with weights from deep learning model') |
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parser.add_argument('--outdir', type=str, default='./', help='where to put generated files') |
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parser.add_argument('--infmt', type=str, default='tensorflow', help='format of the deep learning model') |
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parser.add_argument('infile', help='path to the deep learning model with weights') |
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parser.add_argument('--dump4tb', type=str, default='no', help='dump file for visualization in tensorboard') |
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return parser.parse_args() |
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def main(): |
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args = get_arguments() |
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if not os.path.isfile(args.infile): |
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print('the specified input file %s does not exist' % args.infile) |
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exit(1) |
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if not os.path.exists(args.outdir): |
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print('create output directory %s' % args.outdir) |
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os.mkdir(args.outdir) |
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basefile = os.path.split(args.infile)[1] |
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basefile = os.path.splitext(basefile)[0] |
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outfile = os.path.join(args.outdir, basefile) + '.model' |
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dump4tb = False |
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if args.dump4tb.lower() in ('yes', 'true', 't', 'y', '1'): |
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dump4tb = True |
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if args.infmt == 'tensorflow': |
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convert_from_tensorflow(args.infile, outfile, dump4tb) |
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if __name__ == '__main__': |
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main() |
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@ -1,607 +0,0 @@ |
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# 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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|
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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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|
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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) |
||||
input_operand_index = self.add_operand(i1_node.name, Operand.IOTYPE_INPUT) |
||||
np.array([input_operand_index], dtype=np.uint32).tofile(f) |
||||
output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT) |
||||
np.array([output_operand_index], dtype=np.uint32).tofile(f) |
||||
|
||||
|
||||
def dump_mathunary_to_file(self, node, f): |
||||
self.layer_number = self.layer_number + 1 |
||||
self.converted_nodes.add(node.name) |
||||
i0_node = self.name_node_dict[node.input[0]] |
||||
np.array([self.op2code['MathUnary'], self.mathun2code[node.op]], dtype=np.uint32).tofile(f) |
||||
input_operand_index = self.add_operand(i0_node.name, Operand.IOTYPE_INPUT) |
||||
np.array([input_operand_index], dtype=np.uint32).tofile(f) |
||||
output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT) |
||||
np.array([output_operand_index],dtype=np.uint32).tofile(f) |
||||
|
||||
|
||||
def dump_avg_pool_to_file(self, node, f): |
||||
assert(node.op == 'AvgPool') |
||||
self.layer_number = self.layer_number + 1 |
||||
self.converted_nodes.add(node.name) |
||||
node0 = self.name_node_dict[node.input[0]] |
||||
strides = node.attr['strides'] |
||||
|
||||
# Tensorflow do not support pooling strides in batch dimension and |
||||
# current native NN do not support pooling strides in channel dimension, added assert() here. |
||||
assert(strides.list.i[1]==strides.list.i[2]) |
||||
assert(strides.list.i[0]==1) |
||||
assert(strides.list.i[3]==1) |
||||
strides = strides.list.i[1] |
||||
filter_node = node.attr['ksize'] |
||||
input_name = node.input[0] |
||||
|
||||
# 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() |
||||
@ -1,26 +0,0 @@ |
||||
# Copyright (c) 2019 |
||||
# |
||||
# 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 |
||||
# ============================================================================== |
||||
|
||||
str = 'FFMPEGDNNNATIVE' |
||||
|
||||
# increase major and reset minor when we have to re-convert the model file |
||||
major = 1 |
||||
|
||||
# increase minor when we don't have to re-convert the model file |
||||
minor = 23 |
||||
Loading…
Reference in new issue