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# Copyright (c) 2019 Guo Yejun
#
# This file is part of FFmpeg.
#
# FFmpeg is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2.1 of the License, or (at your option) any later version.
#
# FFmpeg is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with FFmpeg; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
# ==============================================================================
import tensorflow as tf
import numpy as np
import sys, struct
__all__ = ['convert_from_tensorflow']
class TFConverter:
def __init__(self, graph_def, nodes, outfile):
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':0, 'SAME':1}
self.converted_nodes = set()
self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3}
self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}
def dump_for_tensorboard(self):
graph = tf.get_default_graph()
tf.import_graph_def(self.graph_def, name="")
# 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 dump_mirrorpad_to_file(self, node, f):
assert(node.op == 'MirrorPad')
self.layer_number = self.layer_number + 1
mode = node.attr['mode'].s
mode = self.mirrorpad_mode[mode.decode("utf-8")]
np.array([self.op2code[node.op], mode], dtype=np.uint32).tofile(f)
pnode = self.name_node_dict[node.input[1]]
self.converted_nodes.add(pnode.name)
paddings = pnode.attr['value'].tensor.tensor_content
f.write(paddings)
self.converted_nodes.add(node.name)
def generate_layer_number(self):
# in current hard code implementation, the layer number is the first data written to the native model file
# it is not easy to know it at the beginning time in the general converter, so first do a dry run for compatibility
# will be refined later.
with open('/tmp/tmp.model', 'wb') as f:
self.dump_layers_to_file(f)
self.converted_nodes.clear()
def dump_layers_to_file(self, f):
for node in self.nodes:
if node.name in self.converted_nodes:
continue
if node.op == 'Conv2D':
self.dump_conv2d_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)
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()
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