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ef79408e97
FFmpeg/tools/python/convert_from_tensorflow.py
Guo, Yejun ef79408e97 dnn/native: add native support for 'mul' 
it can be tested with model file generated from above python script:

import tensorflow as tf
import numpy as np
import imageio

in_img = imageio.imread('input.jpg')
in_img = in_img.astype(np.float32)/255.0
in_data = in_img[np.newaxis, :]

x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')
z1 = 0.5 + 0.3 * x
z2 = z1 * 4
z3 = z2 - x - 2.0
y = tf.identity(z3, name='dnn_out')

sess=tf.Session()
sess.run(tf.global_variables_initializer())

graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out'])
tf.train.write_graph(graph_def, '.', 'image_process.pb', as_text=False)

print("image_process.pb generated, please use \
path_to_ffmpeg/tools/python/convert.py to generate image_process.model\n")

output = sess.run(y, feed_dict={x: in_data})
imageio.imsave("out.jpg", np.squeeze(output))

Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
2020-04-22 13:14:47 +08:00

450 lines
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Python
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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
import convert_header as header
__all__ = ['convert_from_tensorflow']
class Operand(object):
IOTYPE_INPUT = 1
IOTYPE_OUTPUT = 2
IOTYPE_INTERMEDIATE = IOTYPE_INPUT | IOTYPE_OUTPUT
DTYPE_FLOAT = 1
DTYPE_UINT8 = 4
index = 0
def __init__(self, name, dtype, dims):
self.name = name
self.dtype = dtype
self.dims = dims
self.iotype = 0
self.used_count = 0
self.index = Operand.index
Operand.index = Operand.index + 1
self.iotype2str = {Operand.IOTYPE_INPUT: 'in', Operand.IOTYPE_OUTPUT: 'out', Operand.IOTYPE_INTERMEDIATE: 'inout'}
self.dtype2str = {Operand.DTYPE_FLOAT: 'DT_FLOAT', Operand.DTYPE_UINT8: 'DT_UINT8'}
def add_iotype(self, iotype):
self.iotype = self.iotype | iotype
if iotype == Operand.IOTYPE_INPUT:
self.used_count = self.used_count + 1
def __str__(self):
return "{}: (name: {}, iotype: {}, dtype: {}, dims: ({},{},{},{}) used_count: {})".format(self.index,
self.name, self.iotype2str[self.iotype], self.dtype2str[self.dtype],
self.dims[0], self.dims[1], self.dims[2], self.dims[3], self.used_count)
def __lt__(self, other):
return self.index < other.index
class TFConverter:
def __init__(self, graph_def, nodes, outfile, dump4tb):
self.graph_def = graph_def
self.nodes = nodes
self.outfile = outfile
self.dump4tb = dump4tb
self.layer_number = 0
self.output_names = []
self.name_node_dict = {}
self.edges = {}
self.conv_activations = {'Relu':0, 'Tanh':1, 'Sigmoid':2, 'None':3, 'LeakyRelu':4}
self.conv_paddings = {'VALID':0, 'SAME':1}
self.converted_nodes = set()
self.conv2d_scope_names = set()
self.conv2d_scopename_inputname_dict = {}
self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4, 'MathBinary':5}
self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2}
self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}
self.name_operand_dict = {}
def add_operand(self, name, type):
node = self.name_node_dict[name]
if name not in self.name_operand_dict:
dtype = node.attr['dtype'].type
if dtype == 0:
dtype = node.attr['T'].type
dims = [-1,-1,-1,-1]
if 'shape' in node.attr:
dims[0] = node.attr['shape'].shape.dim[0].size
dims[1] = node.attr['shape'].shape.dim[1].size
dims[2] = node.attr['shape'].shape.dim[2].size
dims[3] = node.attr['shape'].shape.dim[3].size
operand = Operand(name, dtype, dims)
self.name_operand_dict[name] = operand;
self.name_operand_dict[name].add_iotype(type)
return self.name_operand_dict[name].index
def dump_for_tensorboard(self):
graph = tf.get_default_graph()
tf.import_graph_def(self.graph_def, name="")
tf.summary.FileWriter('/tmp/graph', graph)
print('graph saved, run "tensorboard --logdir=/tmp/graph" to see it')
def get_conv2d_params(self, conv2d_scope_name):
knode = self.name_node_dict[conv2d_scope_name + '/kernel']
bnode = self.name_node_dict[conv2d_scope_name + '/bias']
if conv2d_scope_name + '/dilation_rate' in self.name_node_dict:
dnode = self.name_node_dict[conv2d_scope_name + '/dilation_rate']
else:
dnode = None
# the BiasAdd name is possible be changed into the output name,
# if activation is None, and BiasAdd.next is the last op which is Identity
if conv2d_scope_name + '/BiasAdd' in self.edges:
anode = self.edges[conv2d_scope_name + '/BiasAdd'][0]
if anode.op not in self.conv_activations:
anode = None
else:
anode = None
return knode, bnode, dnode, anode
def dump_complex_conv2d_to_file(self, node, f):
assert(node.op == 'Conv2D')
self.layer_number = self.layer_number + 1
self.converted_nodes.add(node.name)
scope_name = TFConverter.get_scope_name(node.name)
#knode for kernel, bnode for bias, dnode for dilation, anode for activation
knode, bnode, dnode, anode = self.get_conv2d_params(scope_name)
if dnode is not None:
dilation = struct.unpack('i', dnode.attr['value'].tensor.tensor_content[0:4])[0]
else:
dilation = 1
if anode is not None:
activation = anode.op
else:
activation = 'None'
padding = node.attr['padding'].s.decode("utf-8")
# conv2d with dilation > 1 generates tens of nodes, not easy to parse them, so use this tricky method.
if dilation > 1 and scope_name + '/stack' in self.name_node_dict:
if self.name_node_dict[scope_name + '/stack'].op == "Const":
padding = 'SAME'
padding = self.conv_paddings[padding]
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])
has_bias = 1
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)
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)
input_name = self.conv2d_scopename_inputname_dict[scope_name]
input_operand_index = self.add_operand(input_name, Operand.IOTYPE_INPUT)
if anode is not None:
output_operand_index = self.add_operand(anode.name, Operand.IOTYPE_OUTPUT)
else:
output_operand_index = self.add_operand(self.edges[bnode.name][0].name, Operand.IOTYPE_OUTPUT)
np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
def dump_simple_conv2d_to_file(self, node, f):
assert(node.op == 'Conv2D')
self.layer_number = self.layer_number + 1
self.converted_nodes.add(node.name)
node0 = self.name_node_dict[node.input[0]]
node1 = self.name_node_dict[node.input[1]]
if node0.op == 'Const':
knode = node0
input_name = node.input[1]
else:
knode = node1
input_name = node.input[0]
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
if filter_height * filter_width * in_channels * out_channels == 1:
kernel = np.float32(ktensor.float_val[0])
else:
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])
has_bias = 0
dilation = 1
padding = node.attr['padding'].s.decode("utf-8")
np.array([self.op2code[node.op], dilation, self.conv_paddings[padding], self.conv_activations['None'],
in_channels, out_channels, filter_height, has_bias], dtype=np.uint32).tofile(f)
kernel.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_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)
input_operand_index = self.add_operand(node.input[0], 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_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)
input_operand_index = self.add_operand(node.input[0], 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_maximum_to_file(self, node, f):
assert(node.op == 'Maximum')
self.layer_number = self.layer_number + 1
ynode = self.name_node_dict[node.input[1]]
y = ynode.attr['value'].tensor.float_val[0]
np.array([self.op2code[node.op]], dtype=np.uint32).tofile(f)
np.array([y], dtype=np.float32).tofile(f)
self.converted_nodes.add(node.name)
input_operand_index = self.add_operand(node.input[0], 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_mathbinary_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]]
i1_node = self.name_node_dict[node.input[1]]
np.array([self.op2code['MathBinary'], self.mathbin2code[node.op]], dtype=np.uint32).tofile(f)
if i0_node.op == 'Const':
scalar = i0_node.attr['value'].tensor.float_val[0]
np.array([1], dtype=np.uint32).tofile(f) # broadcast: 1
np.array([scalar], dtype=np.float32).tofile(f)
np.array([0], dtype=np.uint32).tofile(f) # broadcast: 0
input_operand_index = self.add_operand(i1_node.name, Operand.IOTYPE_INPUT)
np.array([input_operand_index], dtype=np.uint32).tofile(f)
elif i1_node.op == 'Const':
scalar = i1_node.attr['value'].tensor.float_val[0]
np.array([0], 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)
np.array([1], dtype=np.uint32).tofile(f)
np.array([scalar], dtype=np.float32).tofile(f)
else:
np.array([0], 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)
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_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 node.op == 'Conv2D':
self.dump_simple_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)
elif node.op == 'Maximum':
self.dump_maximum_to_file(node, f)
elif node.op == 'Sub':
self.dump_mathbinary_to_file(node, f)
elif node.op == 'Add':
self.dump_mathbinary_to_file(node, f)
elif node.op == 'Mul':
self.dump_mathbinary_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[0], operand.dims[1], operand.dims[2], operand.dims[3]], 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):
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]
@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 generate_conv2d_scope_info(self):
# mostly, conv2d 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)
# get the input name to the conv2d 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
def run(self):
self.generate_name_node_dict()
self.generate_output_names()
self.remove_identity()
self.generate_edges()
self.generate_conv2d_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()
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