@@ -25,6 +25,7 @@
#include "dnn_backend_native.h"
#include "libavutil/avassert.h"
+#include "dnn_backend_native_layer_pad.h"
static DNNReturnType set_input_output_native(void *model, DNNInputData *input, const char *input_name, const char **output_names, uint32_t nb_output)
{
@@ -32,6 +33,7 @@ static DNNReturnType set_input_output_native(void *model, DNNInputData *input, c
InputParams *input_params;
ConvolutionalParams *conv_params;
DepthToSpaceParams *depth_to_space_params;
+ LayerPadParams *pad_params;
int cur_width, cur_height, cur_channels;
int32_t layer;
@@ -77,6 +79,12 @@ static DNNReturnType set_input_output_native(void *model, DNNInputData *input, c
cur_height *= depth_to_space_params->block_size;
cur_width *= depth_to_space_params->block_size;
break;
+ case MIRROR_PAD:
+ pad_params = (LayerPadParams *)network->layers[layer].params;
+ cur_height = cur_height + pad_params->paddings[1][0] + pad_params->paddings[1][1];
+ cur_width = cur_width + pad_params->paddings[2][0] + pad_params->paddings[2][1];
+ cur_channels = cur_channels + pad_params->paddings[3][0] + pad_params->paddings[3][1];
+ break;
default:
return DNN_ERROR;
}
@@ -110,6 +118,7 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
DNNLayerType layer_type;
ConvolutionalParams *conv_params;
DepthToSpaceParams *depth_to_space_params;
+ LayerPadParams *pad_params;
model = av_malloc(sizeof(DNNModel));
if (!model){
@@ -207,6 +216,23 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename)
network->layers[layer].type = DEPTH_TO_SPACE;
network->layers[layer].params = depth_to_space_params;
break;
+ case MIRROR_PAD:
+ pad_params = av_malloc(sizeof(LayerPadParams));
+ if (!pad_params){
+ avio_closep(&model_file_context);
+ ff_dnn_free_model_native(&model);
+ return NULL;
+ }
+ pad_params->mode = (int32_t)avio_rl32(model_file_context);
+ dnn_size += 4;
+ for (i = 0; i < 4; ++i) {
+ pad_params->paddings[i][0] = avio_rl32(model_file_context);
+ pad_params->paddings[i][1] = avio_rl32(model_file_context);
+ dnn_size += 8;
+ }
+ network->layers[layer].type = MIRROR_PAD;
+ network->layers[layer].params = pad_params;
+ break;
default:
avio_closep(&model_file_context);
ff_dnn_free_model_native(&model);
@@ -314,6 +340,7 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output
InputParams *input_params;
ConvolutionalParams *conv_params;
DepthToSpaceParams *depth_to_space_params;
+ LayerPadParams *pad_params;
if (network->layers_num <= 0 || network->layers[0].type != INPUT || !network->layers[0].output){
return DNN_ERROR;
@@ -348,6 +375,14 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNData *output
cur_width *= depth_to_space_params->block_size;
cur_channels /= depth_to_space_params->block_size * depth_to_space_params->block_size;
break;
+ case MIRROR_PAD:
+ pad_params = (LayerPadParams *)network->layers[layer].params;
+ dnn_execute_layer_pad(network->layers[layer - 1].output, network->layers[layer].output,
+ pad_params, 1, cur_height, cur_width, cur_channels);
+ cur_height = cur_height + pad_params->paddings[1][0] + pad_params->paddings[1][1];
+ cur_width = cur_width + pad_params->paddings[2][0] + pad_params->paddings[2][1];
+ cur_channels = cur_channels + pad_params->paddings[3][0] + pad_params->paddings[3][1];
+ break;
case INPUT:
return DNN_ERROR;
}
@@ -30,7 +30,7 @@
#include "../dnn_interface.h"
#include "libavformat/avio.h"
-typedef enum {INPUT, CONV, DEPTH_TO_SPACE} DNNLayerType;
+typedef enum {INPUT, CONV, DEPTH_TO_SPACE, MIRROR_PAD} DNNLayerType;
typedef enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU} DNNActivationFunc;
@@ -23,9 +23,6 @@ import sys, struct
__all__ = ['convert_from_tensorflow']
-# as the first step to be compatible with vf_sr, it is not general.
-# it will be refined step by step.
-
class TFConverter:
def __init__(self, graph_def, nodes, outfile):
self.graph_def = graph_def
@@ -36,9 +33,10 @@ class TFConverter:
self.name_node_dict = {}
self.edges = {}
self.conv_activations = {'Relu':0, 'Tanh':1, 'Sigmoid':2, 'LeakyRelu':4}
- self.conv_paddings = {'VALID':2, 'SAME':1}
+ self.conv_paddings = {'VALID':0, 'SAME':1}
self.converted_nodes = set()
- self.op2code = {'Conv2D':1, 'DepthToSpace':2}
+ self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3}
+ self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}
def dump_for_tensorboard(self):
@@ -101,6 +99,19 @@ class TFConverter:
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
@@ -118,6 +129,8 @@ class TFConverter:
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):
since tf.pad is enabled, the conv2d(valid) changes back to its original behavior. Signed-off-by: Guo, Yejun <yejun.guo@intel.com> --- libavfilter/dnn/dnn_backend_native.c | 35 +++++++++++++++++++++++++++++++++ libavfilter/dnn/dnn_backend_native.h | 2 +- tools/python/convert_from_tensorflow.py | 23 +++++++++++++++++----- 3 files changed, 54 insertions(+), 6 deletions(-)