[FFmpeg-devel] [PATCH V3 1/3] tools/python: add script to convert TensorFlow model (.pb) to native model (.model)

Guo, Yejun yejun.guo at intel.com
Thu Jun 20 03:29:11 EEST 2019



> -----Original Message-----
> From: Guo, Yejun
> Sent: Thursday, June 13, 2019 1:31 PM
> To: ffmpeg-devel at ffmpeg.org
> Cc: Guo, Yejun <yejun.guo at intel.com>
> Subject: [PATCH V3 1/3] tools/python: add script to convert TensorFlow model
> (.pb) to native model (.model)
> 
> For example, given TensorFlow model file espcn.pb,
> to generate native model file espcn.model, just run:
> python convert.py espcn.pb
> 
> In current implementation, the native model file is generated for
> specific dnn network with hard-code python scripts maintained out of ffmpeg.
> For example, srcnn network used by vf_sr is generated with
> https://github.com/HighVoltageRocknRoll/sr/blob/master/generate_header_a
> nd_model.py#L85
> 
> In this patch, the script is designed as a general solution which
> converts general TensorFlow model .pb file into .model file. The script
> now has some tricky to be compatible with current implemention, will
> be refined step by step.
> 
> The script is also added into ffmpeg source tree. It is expected there
> will be many more patches and community needs the ownership of it.
> 
> Another technical direction is to do the conversion in c/c++ code within
> ffmpeg source tree. While .pb file is organized with protocol buffers,
> it is not easy to do such work with tiny c/c++ code, see more discussion
> at http://ffmpeg.org/pipermail/ffmpeg-devel/2019-May/244496.html. So,
> choose the python script.
> 
> Signed-off-by: Guo, Yejun <yejun.guo at intel.com>
> ---
>  .gitignore                              |   1 +
>  tools/python/convert.py                 |  52 +++++++++
>  tools/python/convert_from_tensorflow.py | 201
> ++++++++++++++++++++++++++++++++
>  3 files changed, 254 insertions(+)
>  create mode 100644 tools/python/convert.py
>  create mode 100644 tools/python/convert_from_tensorflow.py

this patch set ping for review, thanks.

> 
> diff --git a/.gitignore b/.gitignore
> index 0e57cb0..2450ee8 100644
> --- a/.gitignore
> +++ b/.gitignore
> @@ -36,3 +36,4 @@
>  /lcov/
>  /src
>  /mapfile
> +/tools/python/__pycache__/
> diff --git a/tools/python/convert.py b/tools/python/convert.py
> new file mode 100644
> index 0000000..662b429
> --- /dev/null
> +++ b/tools/python/convert.py
> @@ -0,0 +1,52 @@
> +# 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
> +#
> ================================================================
> ==============
> +
> +# verified with Python 3.5.2 on Ubuntu 16.04
> +import argparse
> +import os
> +from convert_from_tensorflow import *
> +
> +def get_arguments():
> +    parser = argparse.ArgumentParser(description='generate native mode
> model with weights from deep learning model')
> +    parser.add_argument('--outdir', type=str, default='./', help='where to put
> generated files')
> +    parser.add_argument('--infmt', type=str, default='tensorflow',
> help='format of the deep learning model')
> +    parser.add_argument('infile', help='path to the deep learning model with
> weights')
> +
> +    return parser.parse_args()
> +
> +def main():
> +    args = get_arguments()
> +
> +    if not os.path.isfile(args.infile):
> +        print('the specified input file %s does not exist' % args.infile)
> +        exit(1)
> +
> +    if not os.path.exists(args.outdir):
> +        print('create output directory %s' % args.outdir)
> +        os.mkdir(args.outdir)
> +
> +    basefile = os.path.split(args.infile)[1]
> +    basefile = os.path.splitext(basefile)[0]
> +    outfile = os.path.join(args.outdir, basefile) + '.model'
> +
> +    if args.infmt == 'tensorflow':
> +        convert_from_tensorflow(args.infile, outfile)
> +
> +if __name__ == '__main__':
> +    main()
> diff --git a/tools/python/convert_from_tensorflow.py
> b/tools/python/convert_from_tensorflow.py
> new file mode 100644
> index 0000000..37049e5
> --- /dev/null
> +++ b/tools/python/convert_from_tensorflow.py
> @@ -0,0 +1,201 @@
> +# 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']
> +
> +# as the first step to be compatible with vf_sr, it is not general.
> +# it will be refined step by step.
> +
> +class TFConverter:
> +    def __init__(self, graph_def, nodes, outfile):
> +        self.graph_def = graph_def
> +        self.nodes = nodes
> +        self.outfile = outfile
> +        self.layer_number = 0
> +        self.output_names = []
> +        self.name_node_dict = {}
> +        self.edges = {}
> +        self.conv_activations = {'Relu':0, 'Tanh':1, 'Sigmoid':2, 'LeakyRelu':4}
> +        self.conv_paddings = {'VALID':2, 'SAME':1}
> +        self.converted_nodes = set()
> +        self.op2code = {'Conv2D':1, 'DepthToSpace':2}
> +
> +
> +    def dump_for_tensorboard(self):
> +        graph = tf.get_default_graph()
> +        tf.import_graph_def(self.graph_def, name="")
> +        # tensorboard --logdir=/tmp/graph
> +        tf.summary.FileWriter('/tmp/graph', graph)
> +
> +
> +    def get_conv2d_params(self, node):
> +        knode = self.name_node_dict[node.input[1]]
> +        bnode = None
> +        activation = 'None'
> +        next = self.edges[node.name][0]
> +        if next.op == 'BiasAdd':
> +            self.converted_nodes.add(next.name)
> +            bnode = self.name_node_dict[next.input[1]]
> +            next = self.edges[next.name][0]
> +        if next.op in self.conv_activations:
> +            self.converted_nodes.add(next.name)
> +            activation = next.op
> +        return knode, bnode, 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)
> +        knode, bnode, activation = self.get_conv2d_params(node)
> +
> +        dilation = node.attr['dilations'].list.i[0]
> +        padding = node.attr['padding'].s
> +        padding = self.conv_paddings[padding.decode("utf-8")]
> +
> +        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 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
> +            if node.op == 'Conv2D':
> +                self.dump_conv2d_to_file(node, f)
> +            elif node.op == 'DepthToSpace':
> +                self.dump_depth2space_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]
> +
> +
> +    def run(self):
> +        self.generate_name_node_dict()
> +        self.generate_output_names()
> +        self.remove_identity()
> +        self.generate_edges()
> +
> +        #check the graph with tensorboard with human eyes
> +        #self.dump_for_tensorboard()
> +
> +        self.dump_to_file()
> +
> +
> +def convert_from_tensorflow(infile, outfile):
> +    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)
> +    converter.run()
> --
> 2.7.4



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