> 在 2019年5月28日，下午4:01，Guo, Yejun <yejun....@intel.com> 写道：
>
> 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_and_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....@intel.com>
> ---
> libavfilter/dnn/python/convert.py | 52 ++++++
> libavfilter/dnn/python/convert_from_tensorflow.py | 200 ++++++++++++++++++++++
What about move them into ./tools/ ?
> 2 files changed, 252 insertions(+)
> create mode 100644 libavfilter/dnn/python/convert.py
> create mode 100644 libavfilter/dnn/python/convert_from_tensorflow.py
>
> diff --git a/libavfilter/dnn/python/convert.py
> b/libavfilter/dnn/python/convert.py
> new file mode 100644
> index 0000000..662b429
> --- /dev/null
> +++ b/libavfilter/dnn/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/libavfilter/dnn/python/convert_from_tensorflow.py
> b/libavfilter/dnn/python/convert_from_tensorflow.py
> new file mode 100644
> index 0000000..436ec0e
> --- /dev/null
> +++ b/libavfilter/dnn/python/convert_from_tensorflow.py
> @@ -0,0 +1,200 @@
> +# 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()
> +
> +
> + 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([1, 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([2, 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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