mirror of
https://git.ffmpeg.org/ffmpeg.git
synced 2024-12-24 08:12:44 +00:00
0b6d3f0d83
It can be tested with the model file generated with below python scripy: import tensorflow as tf import numpy as np import imageio in_img = imageio.imread('input.jpeg') 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') x1 = tf.multiply(x, 3.14) x2 = tf.sin(x1) y = tf.identity(x2, 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: Ting Fu <ting.fu@intel.com> Signed-off-by: Guo Yejun <yejun.guo@intel.com>
460 lines
19 KiB
Python
460 lines
19 KiB
Python
# 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, 'MathUnary':6}
|
|
self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3, 'Minimum':4}
|
|
self.mathun2code = {'Abs':0, 'Sin':1}
|
|
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_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_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 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[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()
|