LGTM.
Pushed, thanks!
Em dom, 28 de jul de 2019 às 23:00, Guo, Yejun <yejun....@intel.com> escreveu:
>
> since tf.pad is enabled, the conv2d(valid) changes back to its original
> behavior.
>
> Signed-off-by: Guo, Yejun <yejun....@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(-)
>
> diff --git a/libavfilter/dnn/dnn_backend_native.c
> b/libavfilter/dnn/dnn_backend_native.c
> index 82e900b..09c583b 100644
> --- a/libavfilter/dnn/dnn_backend_native.c
> +++ b/libavfilter/dnn/dnn_backend_native.c
> @@ -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;
> }
> diff --git a/libavfilter/dnn/dnn_backend_native.h
> b/libavfilter/dnn/dnn_backend_native.h
> index 8ef1855..b6f9533 100644
> --- a/libavfilter/dnn/dnn_backend_native.h
> +++ b/libavfilter/dnn/dnn_backend_native.h
> @@ -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;
>
> diff --git a/tools/python/convert_from_tensorflow.py
> b/tools/python/convert_from_tensorflow.py
> index 37049e5..041c82c 100644
> --- a/tools/python/convert_from_tensorflow.py
> +++ b/tools/python/convert_from_tensorflow.py
> @@ -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):
> --
> 2.7.4
>
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