ConvolutionalParams *conv_params;
DepthToSpaceParams *depth_to_space_params;
LayerPadParams *pad_params;
- int32_t operand_index = 0;
model = av_malloc(sizeof(DNNModel));
if (!model){
}
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));
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);
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));
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;
}
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;
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;
}
}
+ 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){
__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.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):
# 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):
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'
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')
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):
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):
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):
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()