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dnn: export operand info in python script and load in c code

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Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
This commit is contained in:
Guo, Yejun 2019-08-20 16:50:34 +08:00 committed by Pedro Arthur
parent 2d5e39c13e
commit 83e0b71f66
4 changed files with 142 additions and 22 deletions
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49
libavfilter/dnn/dnn_backend_native.c
View File

@ -72,7 +72,6 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
ConvolutionalParams *conv_params;
DepthToSpaceParams *depth_to_space_params;
LayerPadParams *pad_params;
int32_t operand_index = 0;
model = av_malloc(sizeof(DNNModel));
if (!model){
@ -93,9 +92,10 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
}
model->model = (void *)network;
avio_seek(model_file_context, file_size - 4, SEEK_SET);
avio_seek(model_file_context, file_size - 8, SEEK_SET);
network->layers_num = (int32_t)avio_rl32(model_file_context);
dnn_size = 4;
network->operands_num = (int32_t)avio_rl32(model_file_context);
dnn_size = 8;
avio_seek(model_file_context, 0, SEEK_SET);
network->layers = av_mallocz(network->layers_num * sizeof(Layer));
@ -105,11 +105,6 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
return NULL;
}
/**
* Operands should be read from model file, the whole change will be huge.
* to make things step by step, we first mock the operands, instead of reading from model file.
*/
network->operands_num = network->layers_num + 1;
network->operands = av_mallocz(network->operands_num * sizeof(DnnOperand));
if (!network->operands){
avio_closep(&model_file_context);
@ -120,8 +115,6 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
for (layer = 0; layer < network->layers_num; ++layer){
layer_type = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
network->layers[layer].input_operand_indexes[0] = operand_index++;
network->layers[layer].output_operand_index = operand_index;
switch (layer_type){
case CONV:
conv_params = av_malloc(sizeof(ConvolutionalParams));
@ -162,6 +155,9 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
for (i = 0; i < conv_params->output_num; ++i){
conv_params->biases[i] = av_int2float(avio_rl32(model_file_context));
}
network->layers[layer].input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
network->layers[layer].output_operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 8;
network->layers[layer].type = CONV;
network->layers[layer].params = conv_params;
break;
@ -174,6 +170,9 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
}
depth_to_space_params->block_size = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
network->layers[layer].input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
network->layers[layer].output_operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 8;
network->layers[layer].type = DEPTH_TO_SPACE;
network->layers[layer].params = depth_to_space_params;
break;
@ -191,6 +190,9 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
pad_params->paddings[i][1] = avio_rl32(model_file_context);
dnn_size += 8;
}
network->layers[layer].input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
network->layers[layer].output_operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 8;
network->layers[layer].type = MIRROR_PAD;
network->layers[layer].params = pad_params;
break;
@ -201,6 +203,33 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
}
}
for (int32_t i = 0; i < network->operands_num; ++i){
DnnOperand *oprd;
int32_t name_len;
int32_t operand_index = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
oprd = &network->operands[operand_index];
name_len = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
avio_get_str(model_file_context, name_len, oprd->name, sizeof(oprd->name));
dnn_size += name_len;
oprd->type = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
oprd->data_type = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
for (int32_t dim = 0; dim < 4; ++dim) {
oprd->dims[dim] = (int32_t)avio_rl32(model_file_context);
dnn_size += 4;
}
oprd->isNHWC = 1;
}
avio_closep(&model_file_context);
if (dnn_size != file_size){

2
libavfilter/dnn/dnn_backend_native.h
View File

@ -36,7 +36,7 @@ typedef enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU} DNNActivationFunc;
typedef enum {VALID, SAME, SAME_CLAMP_TO_EDGE} DNNConvPaddingParam;
typedef enum {DOT_INPUT, DOT_INTERMEDIATE, DOT_OUTPUT} DNNOperandType;
typedef enum {DOT_INPUT = 1, DOT_OUTPUT = 2, DOT_INTERMEDIATE = DOT_INPUT | DOT_INPUT} DNNOperandType;
typedef struct Layer{
DNNLayerType type;

2
libavfilter/dnn_interface.h
View File

@ -32,7 +32,7 @@ typedef enum {DNN_SUCCESS, DNN_ERROR} DNNReturnType;
typedef enum {DNN_NATIVE, DNN_TF} DNNBackendType;
typedef enum {DNN_FLOAT, DNN_UINT8} DNNDataType;
typedef enum {DNN_FLOAT = 1, DNN_UINT8 = 4} DNNDataType;
typedef struct DNNInputData{
void *data;

111
tools/python/convert_from_tensorflow.py
View File

@ -23,6 +23,37 @@ import sys, struct
__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
@ -37,8 +68,28 @@ class TFConverter:
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}
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):
@ -60,11 +111,10 @@ class TFConverter:
# 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:
activation = self.edges[conv2d_scope_name + '/BiasAdd'][0]
activation = activation.op
anode = self.edges[conv2d_scope_name + '/BiasAdd'][0]
else:
activation = 'None'
return knode, bnode, dnode, activation
anode = None
return knode, bnode, dnode, anode
def dump_conv2d_to_file(self, node, f):
@ -73,16 +123,21 @@ class TFConverter:
self.converted_nodes.add(node.name)
scope_name = TFConverter.get_scope_name(node.name)
#knode for kernel, bnode for bias, dnode for dilation
knode, bnode, dnode, activation = self.get_conv2d_params(scope_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 tricky.
# 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'
@ -107,6 +162,15 @@ class TFConverter:
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_depth2space_to_file(self, node, f):
assert(node.op == 'DepthToSpace')
@ -114,6 +178,9 @@ class TFConverter:
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):
@ -127,6 +194,9 @@ class TFConverter:
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_layers_to_file(self, f):
@ -147,10 +217,21 @@ class TFConverter:
self.dump_mirrorpad_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:
self.dump_layers_to_file(f)
np.array([self.layer_number], dtype=np.uint32).tofile(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):
@ -212,19 +293,29 @@ class TFConverter:
return name[0:index]
def generate_conv2d_scope_names(self):
def generate_conv2d_scope_info(self):
# 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)
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_names()
self.generate_conv2d_scope_info()
if self.dump4tb:
self.dump_for_tensorboard()