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Code Issues Releases Activity

dnn: convert tf.pad to native model in python script, and load/execute it in the c code.

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since tf.pad is enabled, the conv2d(valid) changes back to its original behavior.

Signed-off-by: Guo, Yejun <yejun.guo@intel.com>
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
This commit is contained in:
Guo, Yejun 2019-07-29 09:56:54 +08:00 committed by Pedro Arthur
parent 3805aae479
commit ccbab41039
3 changed files with 54 additions and 6 deletions
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35
libavfilter/dnn/dnn_backend_native.c
View File

@ -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;
}

2
libavfilter/dnn/dnn_backend_native.h
View File

@ -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
tools/python/convert_from_tensorflow.py
View File

@ -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):