@@ -2,6 +2,7 @@ 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-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_dense.o
OBJS-$(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
@@ -45,11 +45,13 @@ typedef enum {
DLT_MATH_BINARY = 5,
DLT_MATH_UNARY = 6,
DLT_AVG_POOL = 7,
+ DLT_DENSE = 8,
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 enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU} DNNActivationFunc;
typedef struct Layer{
DNNLayerType type;
@@ -23,7 +23,6 @@
#include "dnn_backend_native.h"
-typedef enum {RELU, TANH, SIGMOID, NONE, LEAKY_RELU} DNNActivationFunc;
typedef struct ConvolutionalParams{
int32_t input_num, output_num, kernel_size;
new file mode 100644
@@ -0,0 +1,151 @@
+/*
+ * 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
+ */
+
+#include "libavutil/avassert.h"
+#include "dnn_backend_native_layer_dense.h"
+
+int dnn_load_layer_dense(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num)
+{
+ DenseParams *dense_params;
+ int kernel_size;
+ int dnn_size = 0;
+ dense_params = av_malloc(sizeof(*dense_params));
+ if (!dense_params)
+ return 0;
+
+ dense_params->activation = (int32_t)avio_rl32(model_file_context);
+ dense_params->input_num = (int32_t)avio_rl32(model_file_context);
+ dense_params->output_num = (int32_t)avio_rl32(model_file_context);
+ dense_params->has_bias = (int32_t)avio_rl32(model_file_context);
+ dnn_size += 16;
+
+ kernel_size = dense_params->input_num * dense_params->output_num;
+ dnn_size += kernel_size * 4;
+ if (dense_params->has_bias)
+ dnn_size += dense_params->output_num * 4;
+
+ if (dnn_size > file_size || dense_params->input_num <= 0 ||
+ dense_params->output_num <= 0){
+ av_freep(&dense_params);
+ return 0;
+ }
+
+ dense_params->kernel = av_malloc(kernel_size * sizeof(float));
+ if (!dense_params->kernel) {
+ av_freep(&dense_params);
+ return 0;
+ }
+ for (int i = 0; i < kernel_size; ++i) {
+ dense_params->kernel[i] = av_int2float(avio_rl32(model_file_context));
+ }
+
+ dense_params->biases = NULL;
+ if (dense_params->has_bias) {
+ dense_params->biases = av_malloc(dense_params->output_num * sizeof(float));
+ if (!dense_params->biases){
+ av_freep(&dense_params->kernel);
+ av_freep(&dense_params);
+ return 0;
+ }
+ for (int i = 0; i < dense_params->output_num; ++i){
+ dense_params->biases[i] = av_int2float(avio_rl32(model_file_context));
+ }
+ }
+
+ layer->params = dense_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_dense(DnnOperand *operands, const int32_t *input_operand_indexes,
+ int32_t output_operand_index, const void *parameters, NativeContext *ctx)
+{
+ float *output;
+ 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 DenseParams *dense_params = (const DenseParams *)parameters;
+
+ int src_linesize = width * channel;
+ DnnOperand *output_operand = &operands[output_operand_index];
+ output_operand->dims[0] = number;
+ output_operand->dims[1] = height;
+ output_operand->dims[2] = width;
+ output_operand->dims[3] = dense_params->output_num;
+ output_operand->data_type = operands[input_operand_index].data_type;
+ output_operand->length = calculate_operand_data_length(output_operand);
+ if (output_operand->length <= 0) {
+ av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
+ return DNN_ERROR;
+ }
+ output_operand->data = av_realloc(output_operand->data, output_operand->length);
+ if (!output_operand->data) {
+ av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
+ return DNN_ERROR;
+ }
+ output = output_operand->data;
+
+ av_assert0(channel == dense_params->input_num);
+
+ for (int y = 0; y < height; ++y) {
+ for (int x = 0; x < width; ++x) {
+ for (int n_filter = 0; n_filter < dense_params->output_num; ++n_filter) {
+ if (dense_params->has_bias)
+ output[n_filter] = dense_params->biases[n_filter];
+ else
+ output[n_filter] = 0.f;
+
+ for (int ch = 0; ch < dense_params->input_num; ++ch) {
+ float input_pel;
+ input_pel = input[y * src_linesize + x * dense_params->input_num + ch];
+ output[n_filter] += input_pel * dense_params->kernel[n_filter*dense_params->input_num + ch];
+ }
+ switch (dense_params->activation){
+ case RELU:
+ output[n_filter] = FFMAX(output[n_filter], 0.0);
+ break;
+ case TANH:
+ output[n_filter] = 2.0f / (1.0f + exp(-2.0f * output[n_filter])) - 1.0f;
+ break;
+ case SIGMOID:
+ output[n_filter] = 1.0f / (1.0f + exp(-output[n_filter]));
+ break;
+ case NONE:
+ break;
+ case LEAKY_RELU:
+ output[n_filter] = FFMAX(output[n_filter], 0.0) + 0.2 * FFMIN(output[n_filter], 0.0);
+ }
+ }
+ output += dense_params->output_num;
+ }
+ }
+ return 0;
+}
new file mode 100644
@@ -0,0 +1,37 @@
+/*
+ * 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
+ */
+
+#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_DENSE_H
+#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_DENSE_H
+
+#include "dnn_backend_native.h"
+
+typedef struct DenseParams{
+ int32_t input_num, output_num;
+ DNNActivationFunc activation;
+ int32_t has_bias;
+ float *kernel;
+ float *biases;
+} DenseParams;
+
+int dnn_load_layer_dense(Layer *layer, AVIOContext *model_file_context, int file_size, int operands_num);
+int dnn_execute_layer_dense(DnnOperand *operands, const int32_t *input_operand_indexes,
+ int32_t output_operand_index, const void *parameters, NativeContext *ctx);
+#endif
@@ -27,6 +27,7 @@
#include "dnn_backend_native_layer_mathbinary.h"
#include "dnn_backend_native_layer_mathunary.h"
#include "dnn_backend_native_layer_avgpool.h"
+#include "dnn_backend_native_layer_dense.h"
LayerFunc layer_funcs[DLT_COUNT] = {
{NULL, NULL},
@@ -37,4 +38,5 @@ LayerFunc layer_funcs[DLT_COUNT] = {
{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},
+ {dnn_execute_layer_dense, dnn_load_layer_dense},
};
new file mode 100644
@@ -0,0 +1,131 @@
+/*
+ * 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
+ */
+
+#include <stdio.h>
+#include <string.h>
+#include <math.h>
+#include "libavfilter/dnn/dnn_backend_native_layer_dense.h"
+
+#define EPSON 0.00001
+
+static int test(void)
+{
+ // the input data and expected data are generated with below python code.
+ /*
+ x = tf.placeholder(tf.float32, shape=[1, None, None, 3])
+ y = tf.layers.dense(input_x, 3, activation=tf.nn.sigmoid, bias_initializer=tf.keras.initializers.he_normal())
+ data = np.random.rand(1, 5, 6, 3);
+
+ sess=tf.Session()
+ sess.run(tf.global_variables_initializer())
+
+ weights = dict([(var.name, sess.run(var)) for var in tf.trainable_variables()])
+ kernel = weights['dense/kernel:0']
+ kernel = np.transpose(kernel, [1, 0])
+ print("kernel:")
+ print(kernel.shape)
+ print(list(kernel.flatten()))
+
+ bias = weights['dense/bias:0']
+ print("bias:")
+ print(bias.shape)
+ print(list(bias.flatten()))
+
+ output = sess.run(y, feed_dict={x: data})
+
+ print("input:")
+ print(data.shape)
+ print(list(data.flatten()))
+
+ print("output:")
+ print(output.shape)
+ print(list(output.flatten()))
+ */
+
+ ConvolutionalParams params;
+ DnnOperand operands[2];
+ int32_t input_indexes[1];
+ float input[1*5*6*3] = {
+ 0.5552418686576308, 0.20653189262022464, 0.31115120939398877, 0.5897014433221428, 0.37340078861060655, 0.6470921693941893, 0.8039950367872679, 0.8762700891949274,
+ 0.6556655583829558, 0.5911096107039339, 0.18640250865290997, 0.2803248779238966, 0.31586613136402053, 0.9447300740056483, 0.9443980824873418, 0.8158851991115941,
+ 0.5631010340387631, 0.9407402251929046, 0.6485434876551682, 0.5631376966470001, 0.17581924875609634, 0.7033802439103178, 0.04802402495561675, 0.9183681450194972,
+ 0.46059317944364, 0.07964160481596883, 0.871787076270302, 0.973743142324361, 0.15923146943258415, 0.8212946080584571, 0.5415954459227064, 0.9552813822803975,
+ 0.4908552668172057, 0.33723691635292274, 0.46588057864910026, 0.8994239961321776, 0.09845220457674186, 0.1713400292123486, 0.39570294912818826, 0.08018956486392803,
+ 0.5290478278169032, 0.7141906125920976, 0.0320878067840098, 0.6412406575332606, 0.0075712007102423096, 0.7150828462386156, 0.1311989216968138, 0.4706847944253756,
+ 0.5447610794883336, 0.3430923933318001, 0.536082357943209, 0.4371629342483694, 0.40227962985019927, 0.3553806249465469, 0.031806622424259245, 0.7053916426174,
+ 0.3261570237309813, 0.419500213292063, 0.3155691223480851, 0.05664028113178088, 0.3636491555914486, 0.8502419746667123, 0.9836596530684955, 0.1628681802975801,
+ 0.09410832912479894, 0.28407218939480294, 0.7983417928813697, 0.24132158596506748, 0.8154729498062224, 0.29173768373895637, 0.13407102008052096, 0.18705786678800385,
+ 0.7167943621295573, 0.09222004247174376, 0.2319220738766018, 0.17708964382285064, 0.1391440370249517, 0.3254088083499256, 0.4013916894718289, 0.4819742663322323,
+ 0.15080103744648077, 0.9302407847555013, 0.9397597961319524, 0.5719200825550793, 0.9538938024682824, 0.9583882089203861, 0.5168861091262276, 0.1926396841842669,
+ 0.6781176744337578, 0.719366447288566
+ };
+ float expected_output[1*5*6*3] = {
+ -0.3921688, -0.9243112, -0.29659146, -0.64000785, -0.9466343, -0.62125254, -0.71759033, -0.9171336, -0.735589, -0.34365994,
+ -0.92100817, -0.23903961, -0.8962277, -0.9521279, -0.90962386, -0.7488303, -0.9563761, -0.7701762, -0.40800542, -0.87684774,
+ -0.3339763, -0.6354543, -0.97068924, -0.6246325, -0.6992075, -0.9706726, -0.6818918, -0.51864433, -0.9592881, -0.51187396,
+ -0.7423632, -0.89911884, -0.7457824, -0.82009757, -0.96402895, -0.8235518, -0.61980766, -0.94494647, -0.5410502, -0.8281218,
+ -0.95508635, -0.8201453, -0.5937325, -0.8679507, -0.500767, -0.39430764, -0.93967676, -0.32183182, -0.58913624, -0.939717,
+ -0.55179894, -0.55004454, -0.9214453, -0.4889004, -0.75294703, -0.9118363, -0.7200309, -0.3248641, -0.8878874, -0.18977344,
+ -0.8873837, -0.9571257, -0.90145934, -0.50521654, -0.93739635, -0.39051685, -0.61143184, -0.9591179, -0.605999, -0.40008977,
+ -0.92219675, -0.26732883, -0.19607787, -0.9172511, -0.07068595, -0.5409857, -0.9387041, -0.44181606, -0.4705004, -0.8899935,
+ -0.37997037, -0.66105115, -0.89754754, -0.68141997, -0.6324047, -0.886776, -0.65066385, -0.8334821, -0.94801456, -0.83297
+ };
+ float *output;
+ float kernel[3*3] = {
+ 0.56611896, -0.5144603, -0.82600045, 0.19219112, 0.3835776, -0.7475352, 0.5209291, -0.6301091, -0.99442935};
+ float bias[3] = {-0.3654299, -1.5711838, -0.15546428};
+
+ params.activation = TANH;
+ params.has_bias = 1;
+ params.biases = bias;
+ params.input_num = 3;
+ params.kernel = kernel;
+ params.output_num = 3;
+
+ operands[0].data = input;
+ operands[0].dims[0] = 1;
+ operands[0].dims[1] = 5;
+ operands[0].dims[2] = 6;
+ operands[0].dims[3] = 3;
+ operands[1].data = NULL;
+
+ input_indexes[0] = 0;
+ dnn_execute_layer_dense(operands, input_indexes, 1, ¶ms, NULL);
+
+ output = operands[1].data;
+ for (int i = 0; i < sizeof(expected_output) / sizeof(float); i++) {
+ if (fabs(output[i] - expected_output[i]) > EPSON) {
+ printf("at index %d, output: %f, expected_output: %f\n", i, output[i], expected_output[i]);
+ av_freep(&output);
+ return 1;
+ }
+ }
+
+ av_freep(&output);
+ return 0;
+}
+
+int main(int argc, char **argv)
+{
+ if (test())
+ return 1;
+
+ return 0;
+}
@@ -48,9 +48,9 @@ class Operand(object):
self.used_count = self.used_count + 1
def __str__(self):
- return "{}: (name: {}, iotype: {}, dtype: {}, dims: ({},{},{},{}) used_count: {})".format(self.index,
+ 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)
+ self.dims, self.used_count)
def __lt__(self, other):
return self.index < other.index
@@ -71,8 +71,10 @@ class TFConverter:
self.converted_nodes = set()
self.conv2d_scope_names = set()
self.conv2d_scopename_inputname_dict = {}
+ self.dense_scope_names = set()
+ self.dense_scopename_inputname_dict = {}
self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4,
- 'MathBinary':5, 'MathUnary':6, 'AvgPool':7}
+ 'MathBinary':5, 'MathUnary':6, 'AvgPool':7, 'MatMul':8}
self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3, 'Minimum':4, 'FloorMod':5}
self.mathun2code = {'Abs':0, 'Sin':1, 'Cos':2, 'Tan':3, 'Asin':4,
'Acos':5, 'Atan':6, 'Sinh':7, 'Cosh':8, 'Tanh':9, 'Asinh':10,
@@ -126,6 +128,22 @@ class TFConverter:
return knode, bnode, dnode, anode
+ def get_dense_params(self, dense_scope_name):
+ knode = self.name_node_dict[dense_scope_name + '/kernel']
+ bnode = self.name_node_dict.get(dense_scope_name + '/bias')
+ # 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
+ anode = None
+ if bnode:
+ if dense_scope_name + '/BiasAdd' in self.edges:
+ anode = self.edges[dense_scope_name + '/BiasAdd'][0]
+ if anode.op not in self.conv_activations:
+ anode = None
+ else:
+ anode = None
+ return knode, bnode, anode
+
+
def dump_complex_conv2d_to_file(self, node, f):
assert(node.op == 'Conv2D')
self.layer_number = self.layer_number + 1
@@ -181,6 +199,57 @@ class TFConverter:
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_dense_to_file(self, node, f):
+ assert(node.op == 'MatMul')
+ self.layer_number = self.layer_number + 1
+ self.converted_nodes.add(node.name)
+
+ scope_name = TFConverter.get_scope_name(node.name)
+ #knode for kernel, bnode for bias, anode for activation
+ knode, bnode, anode = self.get_dense_params(scope_name.split('/')[0])
+
+ if bnode is not None:
+ has_bias = 1
+ btensor = bnode.attr['value'].tensor
+ if btensor.tensor_shape.dim[0].size == 1:
+ bias = struct.pack("f", btensor.float_val[0])
+ else:
+ bias = btensor.tensor_content
+ else:
+ has_bias = 0
+
+ if anode is not None:
+ activation = anode.op
+ else:
+ activation = 'None'
+
+ ktensor = knode.attr['value'].tensor
+ in_channels = ktensor.tensor_shape.dim[0].size
+ out_channels = ktensor.tensor_shape.dim[1].size
+ if in_channels * out_channels == 1:
+ kernel = np.float32(ktensor.float_val[0])
+ else:
+ kernel = np.frombuffer(ktensor.tensor_content, dtype=np.float32)
+ kernel = kernel.reshape(in_channels, out_channels)
+ kernel = np.transpose(kernel, [1, 0])
+
+ np.array([self.op2code[node.op], self.conv_activations[activation], in_channels, out_channels, has_bias], dtype=np.uint32).tofile(f)
+ kernel.tofile(f)
+ if has_bias:
+ f.write(bias)
+
+ input_name = self.dense_scopename_inputname_dict[scope_name.split('/')[0]]
+ 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:
+ if bnode is not None:
+ output_operand_index = self.add_operand(self.edges[bnode.name][0].name, Operand.IOTYPE_OUTPUT)
+ else:
+ output_operand_index = self.add_operand(self.edges[scope_name+'/concat_1'][0].name, Operand.IOTYPE_OUTPUT)
+ np.array([input_operand_index, output_operand_index], dtype=np.uint32).tofile(f)
+
def dump_simple_conv2d_to_file(self, node, f):
assert(node.op == 'Conv2D')
@@ -343,9 +412,19 @@ class TFConverter:
if node.op == 'Conv2D':
self.dump_complex_conv2d_to_file(node, f)
continue
+ if self.in_dense_scope(node.name):
+ if node.op == 'MatMul':
+ self.dump_dense_to_file(node, f)
+ continue
+
if node.op == 'Conv2D':
self.dump_simple_conv2d_to_file(node, f)
+ continue
+ if node.name in self.output_names:
+ input_name = self.id_different_scope_dict[node.name]
+ if TFConverter.get_scope_name(input_name)!=TFConverter.get_scope_name(node.name):
+ continue
if node.op == 'AvgPool':
self.dump_avg_pool_to_file(node, f)
elif node.op == 'DepthToSpace':
@@ -367,7 +446,7 @@ class TFConverter:
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)
+ np.array(operand.dims, dtype=np.uint32).tofile(f)
def dump_to_file(self):
@@ -396,6 +475,7 @@ class TFConverter:
def remove_identity(self):
+ self.id_different_scope_dict = {}
id_nodes = []
id_dict = {}
for node in self.nodes:
@@ -408,6 +488,7 @@ class TFConverter:
self.name_node_dict[input].name = name
self.name_node_dict[name] = self.name_node_dict[input]
del self.name_node_dict[input]
+ self.id_different_scope_dict[name] = input
else:
id_dict[name] = input
@@ -449,8 +530,18 @@ class TFConverter:
return False
- def generate_conv2d_scope_info(self):
- # mostly, conv2d is a sub block in graph, get the scope name
+ def in_dense_scope(self, name):
+ inner_scope = TFConverter.get_scope_name(name)
+ if inner_scope == "":
+ return False;
+ for scope in self.dense_scope_names:
+ index = inner_scope.find(scope)
+ if index == 0:
+ return True
+ return False
+
+ def generate_sub_block_op_scope_info(self):
+ # mostly, conv2d/dense 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)
@@ -461,8 +552,17 @@ class TFConverter:
if scope + '/kernel' not in self.name_node_dict:
continue
self.conv2d_scope_names.add(scope)
+ elif node.op == 'MatMul':
+ scope = TFConverter.get_scope_name(node.name)
+ # for the case tf.nn.dense is called directly
+ if scope == '':
+ continue
+ # for the case tf.nn.dense is called within a scope
+ if scope + '/kernel' not in self.name_node_dict and scope.split('/Tensordot')[0] + '/kernel' not in self.name_node_dict:
+ continue
+ self.dense_scope_names.add(scope.split('/Tensordot')[0])
- # get the input name to the conv2d sub block
+ # get the input name to the conv2d/dense sub block
for node in self.nodes:
scope = TFConverter.get_scope_name(node.name)
if scope in self.conv2d_scope_names:
@@ -470,6 +570,16 @@ class TFConverter:
for inp in node.input:
if TFConverter.get_scope_name(inp) != scope:
self.conv2d_scopename_inputname_dict[scope] = inp
+ elif scope in self.dense_scope_names:
+ if node.op == 'MatMul' or node.op == 'Shape':
+ for inp in node.input:
+ if TFConverter.get_scope_name(inp) != scope:
+ self.dense_scopename_inputname_dict[scope] = inp
+ elif scope.split('/Tensordot')[0] in self.dense_scope_names:
+ if node.op == 'Transpose':
+ for inp in node.input:
+ if TFConverter.get_scope_name(inp).find(scope)<0 and TFConverter.get_scope_name(inp).find(scope.split('/')[0])<0:
+ self.dense_scopename_inputname_dict[scope.split('/Tensordot')[0]] = inp
def run(self):
@@ -477,7 +587,7 @@ class TFConverter:
self.generate_output_names()
self.remove_identity()
self.generate_edges()
- self.generate_conv2d_scope_info()
+ self.generate_sub_block_op_scope_info()
if self.dump4tb:
self.dump_for_tensorboard()
Signed-off-by: Mingyu Yin <mingyu.yin@intel.com> --- libavfilter/dnn/Makefile | 1 + libavfilter/dnn/dnn_backend_native.h | 2 + .../dnn/dnn_backend_native_layer_conv2d.h | 1 - .../dnn/dnn_backend_native_layer_dense.c | 151 ++++++++++++++++++ .../dnn/dnn_backend_native_layer_dense.h | 37 +++++ libavfilter/dnn/dnn_backend_native_layers.c | 2 + tests/dnn/dnn-layer-dense-test.c | 131 +++++++++++++++ tools/python/convert_from_tensorflow.py | 126 ++++++++++++++- 8 files changed, 442 insertions(+), 9 deletions(-) create mode 100644 libavfilter/dnn/dnn_backend_native_layer_dense.c create mode 100644 libavfilter/dnn/dnn_backend_native_layer_dense.h create mode 100644 tests/dnn/dnn-layer-dense-test.c