[FFmpeg-devel] [PATCH V6 1/2] dnn/native: add native support for avg_pool
Ting Fu
ting.fu at intel.com
Wed Aug 5 09:53:45 EEST 2020
Not support pooling strides in channel dimension now.
It can be tested with the model generated with below python script:
import tensorflow as tf
import numpy as np
import imageio
in_img = imageio.imread('input_odd.jpg')
in_img = in_img.astype(np.float32)/255.0
in_data = in_img[np.newaxis, :]
x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')
x_pool = tf.nn.avg_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME') #please alter the params as needed
y = tf.identity(x_pool, name='dnn_out')
sess=tf.Session()
sess.run(tf.global_variables_initializer())
graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out'])
tf.train.write_graph(graph_def, '.', 'image_process.pb', as_text=False)
print("image_process.pb generated, please use \
path_to_ffmpeg/tools/python/convert.py to generate image_process.model\n")
output = sess.run(y, feed_dict={x: in_data})
imageio.imsave("out.jpg", np.squeeze(output))
Signed-off-by: Ting Fu <ting.fu at intel.com>
---
libavfilter/dnn/Makefile | 1 +
libavfilter/dnn/dnn_backend_native.h | 2 +
.../dnn/dnn_backend_native_layer_avgpool.c | 141 ++++++++++++++++++
.../dnn/dnn_backend_native_layer_avgpool.h | 40 +++++
.../dnn/dnn_backend_native_layer_conv2d.h | 3 +-
libavfilter/dnn/dnn_backend_native_layers.c | 2 +
tools/python/convert_from_tensorflow.py | 37 ++++-
7 files changed, 223 insertions(+), 3 deletions(-)
create mode 100644 libavfilter/dnn/dnn_backend_native_layer_avgpool.c
create mode 100644 libavfilter/dnn/dnn_backend_native_layer_avgpool.h
diff --git a/libavfilter/dnn/Makefile b/libavfilter/dnn/Makefile
index d90137ec42..e0957073ee 100644
--- a/libavfilter/dnn/Makefile
+++ b/libavfilter/dnn/Makefile
@@ -1,6 +1,7 @@
OBJS-$(CONFIG_DNN) += dnn/dnn_interface.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layers.o
+OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_avgpool.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_pad.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_conv2d.o
OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_depth2space.o
diff --git a/libavfilter/dnn/dnn_backend_native.h b/libavfilter/dnn/dnn_backend_native.h
index 62191ffe88..26e9a33387 100644
--- a/libavfilter/dnn/dnn_backend_native.h
+++ b/libavfilter/dnn/dnn_backend_native.h
@@ -43,10 +43,12 @@ typedef enum {
DLT_MAXIMUM = 4,
DLT_MATH_BINARY = 5,
DLT_MATH_UNARY = 6,
+ DLT_AVG_POOL = 7,
DLT_COUNT
} DNNLayerType;
typedef enum {DOT_INPUT = 1, DOT_OUTPUT = 2, DOT_INTERMEDIATE = DOT_INPUT | DOT_OUTPUT} DNNOperandType;
+typedef enum {VALID, SAME, SAME_CLAMP_TO_EDGE} DNNPaddingParam;
typedef struct Layer{
DNNLayerType type;
diff --git a/libavfilter/dnn/dnn_backend_native_layer_avgpool.c b/libavfilter/dnn/dnn_backend_native_layer_avgpool.c
new file mode 100644
index 0000000000..d745c35b4a
--- /dev/null
+++ b/libavfilter/dnn/dnn_backend_native_layer_avgpool.c
@@ -0,0 +1,141 @@
+/*
+ * Copyright (c) 2020
+ *
+ * This file is part of FFmpeg.
+ *
+ * FFmpeg is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Lesser General Public
+ * License as published by the Free Software Foundation; either
+ * version 2.1 of the License, or (at your option) any later version.
+ *
+ * FFmpeg is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+ * Lesser General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public
+ * License along with FFmpeg; if not, write to the Free Software
+ * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
+ */
+
+/**
+ * @file
+ * DNN native backend implementation.
+ */
+
+#include "libavutil/avassert.h"
+#include "dnn_backend_native_layer_avgpool.h"
+
+int dnn_load_layer_avg_pool(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
+{
+ AvgPoolParams *avgpool_params;
+ int dnn_size = 0;
+ avgpool_params = av_malloc(sizeof(*avgpool_params));
+ if(!avgpool_params)
+ return 0;
+
+ avgpool_params->strides = (int32_t)avio_rl32(model_file_context);
+ avgpool_params->padding_method = (int32_t)avio_rl32(model_file_context);
+ avgpool_params->kernel_size = (int32_t)avio_rl32(model_file_context);
+ dnn_size += 12;
+
+ if (dnn_size > file_size || avgpool_params->kernel_size <= 0 || avgpool_params->strides <=0){
+ av_freep(&avgpool_params);
+ return 0;
+ }
+
+ layer->params = avgpool_params;
+ layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
+ layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
+ dnn_size += 8;
+
+ if (layer->input_operand_indexes[0] >= operands_num || layer->output_operand_index >= operands_num) {
+ return 0;
+ }
+ return dnn_size;
+}
+
+int dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t *input_operand_indexes,
+ int32_t output_operand_index, const void *parameters)
+{
+ float *output;
+ int height_end, width_end, height_radius, width_radius, output_height, output_width, kernel_area;
+ int32_t input_operand_index = input_operand_indexes[0];
+ int number = operands[input_operand_index].dims[0];
+ int height = operands[input_operand_index].dims[1];
+ int width = operands[input_operand_index].dims[2];
+ int channel = operands[input_operand_index].dims[3];
+ const float *input = operands[input_operand_index].data;
+ const AvgPoolParams *avgpool_params = (const AvgPoolParams *)parameters;
+
+ int kernel_strides = avgpool_params->strides;
+ int src_linesize = width * channel;
+ DnnOperand *output_operand = &operands[output_operand_index];
+
+ /**
+ * When padding_method = SAME, the tensorflow will only padding the hald number of 0 pxiels
+ * except the remainders.
+ * Eg: assuming the input height = 1080, the strides = 11, so the remainders = 1080 % 11 = 2
+ * and if ksize = 5: it will fill (5 - 2) >> 1 = 1 line before the first line of input image,
+ * and 5 - 2 - 1 = 2 lines after the last line of input image.
+ * and if ksize = 7: it will fill (7 - 2) >> 1 = 2 lines before the first line of input image,
+ * and 7 - 2 - 2 = 3 lines after the last line of input image.
+ */
+ if (avgpool_params->padding_method == SAME) {
+ height_end = height;
+ width_end = width;
+ height_radius = avgpool_params->kernel_size - ((height - 1) % kernel_strides + 1);
+ width_radius = avgpool_params->kernel_size - ((width - 1) % kernel_strides + 1);
+ height_radius = height_radius < 0 ? 0 : height_radius >> 1;
+ width_radius = width_radius < 0 ? 0 : width_radius >> 1;
+ output_height = ceil(height / (kernel_strides * 1.0));
+ output_width = ceil(width / (kernel_strides * 1.0));
+ } else {
+ assert(avgpool_params->padding_method = VALID);
+ height_end = height - avgpool_params->kernel_size + 1;
+ width_end = width - avgpool_params->kernel_size + 1;
+ height_radius = 0;
+ width_radius = 0;
+ output_height = ceil((height - avgpool_params->kernel_size + 1) / (kernel_strides * 1.0));
+ output_width = ceil((width - avgpool_params->kernel_size + 1) / (kernel_strides * 1.0));
+ }
+
+ output_operand->dims[0] = number;
+ output_operand->dims[1] = output_height;
+ output_operand->dims[2] = output_width;
+ // not support pooling in channel dimension now
+ output_operand->dims[3] = channel;
+ output_operand->data_type = operands[input_operand_index].data_type;
+ output_operand->length = calculate_operand_data_length(output_operand);
+ output_operand->data = av_realloc(output_operand->data, output_operand->length);
+ if (!output_operand->data)
+ return -1;
+ output = output_operand->data;
+
+ for (int y = 0; y < height_end; y += kernel_strides) {
+ for (int x = 0; x < width_end; x += kernel_strides) {
+ for (int n_channel = 0; n_channel < channel; ++n_channel) {
+ output[n_channel] = 0.0;
+ kernel_area = 0;
+ for (int kernel_y = 0; kernel_y < avgpool_params->kernel_size; ++kernel_y) {
+ for (int kernel_x = 0; kernel_x < avgpool_params->kernel_size; ++kernel_x) {
+ float input_pel;
+ int y_pos = y + (kernel_y - height_radius);
+ int x_pos = x + (kernel_x - width_radius);
+ if (x_pos < 0 || x_pos >= width || y_pos < 0 || y_pos >= height) {
+ input_pel = 0.0;
+ } else {
+ kernel_area++;
+ input_pel = input[y_pos * src_linesize + x_pos * channel + n_channel];
+ }
+ output[n_channel] += input_pel;
+ }
+ }
+ output[n_channel] /= kernel_area;
+ }
+ output += channel;
+ }
+ }
+
+ return 0;
+}
diff --git a/libavfilter/dnn/dnn_backend_native_layer_avgpool.h b/libavfilter/dnn/dnn_backend_native_layer_avgpool.h
new file mode 100644
index 0000000000..8e31ddb7c8
--- /dev/null
+++ b/libavfilter/dnn/dnn_backend_native_layer_avgpool.h
@@ -0,0 +1,40 @@
+/*
+ * Copyright (c) 2020
+ *
+ * This file is part of FFmpeg.
+ *
+ * FFmpeg is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Lesser General Public
+ * License as published by the Free Software Foundation; either
+ * version 2.1 of the License, or (at your option) any later version.
+ *
+ * FFmpeg is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+ * Lesser General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public
+ * License along with FFmpeg; if not, write to the Free Software
+ * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
+ */
+
+/**
+ * @file
+ * DNN inference functions interface for native backend.
+ */
+
+#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_AVGPOOL_H
+#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_AVGPOOL_H
+
+#include "dnn_backend_native.h"
+
+typedef struct AvgPoolParams{
+ int32_t strides, kernel_size;
+ DNNPaddingParam padding_method;
+} AvgPoolParams;
+
+int dnn_load_layer_avg_pool(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
+int dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t *input_operand_indexes,
+ int32_t output_operand_index, const void *parameters);
+
+#endif
diff --git a/libavfilter/dnn/dnn_backend_native_layer_conv2d.h b/libavfilter/dnn/dnn_backend_native_layer_conv2d.h
index eeb15fdf01..b240b7ef6b 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_conv2d.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_conv2d.h
@@ -24,12 +24,11 @@
#include "dnn_backend_native.h"
typedef enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU} DNNActivationFunc;
-typedef enum {VALID, SAME, SAME_CLAMP_TO_EDGE} DNNConvPaddingParam;
typedef struct ConvolutionalParams{
int32_t input_num, output_num, kernel_size;
DNNActivationFunc activation;
- DNNConvPaddingParam padding_method;
+ DNNPaddingParam padding_method;
int32_t dilation;
int32_t has_bias;
float *kernel;
diff --git a/libavfilter/dnn/dnn_backend_native_layers.c b/libavfilter/dnn/dnn_backend_native_layers.c
index 70f9a5f958..4f42f62abb 100644
--- a/libavfilter/dnn/dnn_backend_native_layers.c
+++ b/libavfilter/dnn/dnn_backend_native_layers.c
@@ -26,6 +26,7 @@
#include "dnn_backend_native_layer_maximum.h"
#include "dnn_backend_native_layer_mathbinary.h"
#include "dnn_backend_native_layer_mathunary.h"
+#include "dnn_backend_native_layer_avgpool.h"
LayerFunc layer_funcs[DLT_COUNT] = {
{NULL, NULL},
@@ -35,4 +36,5 @@ LayerFunc layer_funcs[DLT_COUNT] = {
{dnn_execute_layer_maximum, dnn_load_layer_maximum},
{dnn_execute_layer_math_binary, dnn_load_layer_math_binary},
{dnn_execute_layer_math_unary, dnn_load_layer_math_unary},
+ {dnn_execute_layer_avg_pool, dnn_load_layer_avg_pool},
};
diff --git a/tools/python/convert_from_tensorflow.py b/tools/python/convert_from_tensorflow.py
index 85db7bf710..baff602cf2 100644
--- a/tools/python/convert_from_tensorflow.py
+++ b/tools/python/convert_from_tensorflow.py
@@ -67,10 +67,12 @@ class TFConverter:
self.edges = {}
self.conv_activations = {'Relu':0, 'Tanh':1, 'Sigmoid':2, 'None':3, 'LeakyRelu':4}
self.conv_paddings = {'VALID':0, 'SAME':1}
+ self.pool_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, 'Maximum':4, 'MathBinary':5, 'MathUnary':6}
+ self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4,
+ 'MathBinary':5, 'MathUnary':6, 'AvgPool':7}
self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3, 'Minimum':4}
self.mathun2code = {'Abs':0, 'Sin':1, 'Cos':2, 'Tan':3, 'Asin':4, 'Acos':5, 'Atan':6, 'Sinh':7, 'Cosh':8, 'Tanh':9, 'Asinh':10, 'Acosh':11, 'Atanh':12}
self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}
@@ -298,6 +300,37 @@ class TFConverter:
np.array([output_operand_index],dtype=np.uint32).tofile(f)
+ def dump_avg_pool_to_file(self, node, f):
+ assert(node.op == 'AvgPool')
+ self.layer_number = self.layer_number + 1
+ self.converted_nodes.add(node.name)
+ node0 = self.name_node_dict[node.input[0]]
+ strides = node.attr['strides']
+
+ # Tensorflow do not support pooling strides in batch dimension and
+ # current native NN do not support pooling strides in channel dimension, added assert() here.
+ assert(strides.list.i[1]==strides.list.i[2])
+ assert(strides.list.i[0]==1)
+ assert(strides.list.i[3]==1)
+ strides = strides.list.i[1]
+ filter_node = node.attr['ksize']
+ input_name = node.input[0]
+
+ # Tensorflow do not support pooling ksize in batch dimension and channel dimension.
+ assert(filter_node.list.i[0]==1)
+ assert(filter_node.list.i[3]==1)
+ filter_height = filter_node.list.i[1]
+ filter_width = filter_node.list.i[2]
+
+ padding = node.attr['padding'].s.decode("utf-8")
+ np.array([self.op2code[node.op], strides, self.pool_paddings[padding], filter_height],
+ dtype=np.uint32).tofile(f)
+
+ input_operand_index = self.add_operand(input_name, 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):
for node in self.nodes:
if node.name in self.converted_nodes:
@@ -311,6 +344,8 @@ class TFConverter:
if node.op == 'Conv2D':
self.dump_simple_conv2d_to_file(node, f)
+ if node.op == 'AvgPool':
+ self.dump_avg_pool_to_file(node, f)
elif node.op == 'DepthToSpace':
self.dump_depth2space_to_file(node, f)
elif node.op == 'MirrorPad':
--
2.17.1
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