dnn_backend_native_layer_mathunary: add abs support

more math unary operations will be added here It can be tested with the model file generated with below python scripy: import tensorflow as tf import numpy as np import imageio in_img = imageio.imread('input.jpeg') 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') x1 = tf.subtract(x, 0.5) x2 = tf.abs(x1) y = tf.identity(x2, 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))

Ting Fu committed 6 years ago

Commit f73cc611 parent b6d65977 files · +145 −2Browse files

Unified · Split

7 files

libavfilter/dnn/Makefile +1 −0
libavfilter/dnn/dnn_backend_native.h +1 −0
libavfilter/dnn/dnn_backend_native_layer_mathunary.c +80 −0
libavfilter/dnn/dnn_backend_native_layer_mathunary.h +45 −0
libavfilter/dnn/dnn_backend_native_layers.c +2 −0
tools/python/convert_from_tensorflow.py +15 −1
tools/python/convert_header.py +1 −1

libavfilter/dnn/Makefile+1−0

@@ -6,6 +6,7 @@ OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_con · 5 unmodified lines
6	6	OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_depth2space.o
7	7	OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_maximum.o
8	8	OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_mathbinary.o
	9	+OBJS-$(CONFIG_DNN) += dnn/dnn_backend_native_layer_mathunary.o
9	10
10	11	DNN-OBJS-$(CONFIG_LIBTENSORFLOW) += dnn/dnn_backend_tf.o
11	12

libavfilter/dnn/dnn_backend_native.h+1−0

@@ -42,6 +42,7 @@ typedef enum { · 41 unmodified lines
42	42	DLT_MIRROR_PAD = 3,
43	43	DLT_MAXIMUM = 4,
44	44	DLT_MATH_BINARY = 5,
	45	+ DLT_MATH_UNARY = 6,
45	46	DLT_COUNT
46	47	} DNNLayerType;
47	48

libavfilter/dnn/dnn_backend_native_layer_mathunary.c+80−0

@@ -0,0 +1,80 @@
	1	+/*
	2	+ * Copyright (c) 2020
	3	+ *
	4	+ * This file is part of FFmpeg.
	5	+ *
	6	+ * FFmpeg is free software; you can redistribute it and/or
	7	+ * modify it under the terms of the GNU Lesser General Public
	8	+ * License as published by the Free Software Foundation; either
	9	+ * version 2.1 of the License, or (at your option) any later version.
	10	+ *
	11	+ * FFmpeg is distributed in the hope that it will be useful,
	12	+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
	13	+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
	14	+ * Lesser General Public License for more details.
	15	+ *
	16	+ * You should have received a copy of the GNU Lesser General Public
	17	+ * License along with FFmpeg; if not, write to the Free Software
	18	+ * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
	19	+ */
	20	+
	21	+/**
	22	+ * @file
	23	+ * DNN native backend implementation.
	24	+ */
	25	+
	26	+#include "dnn_backend_native.h"
	27	+#include "libavutil/avassert.h"
	28	+#include "dnn_backend_native_layer_mathunary.h"
	29	+
	30	+int dnn_load_layer_math_unary(Layer layer, AVIOContext model_file_context, int file_size)
	31	+{
	32	+ DnnLayerMathUnaryParams *params;
	33	+ int dnn_size = 0;
	34	+ params = av_malloc(sizeof(*params));
	35	+ if(!params)
	36	+ return 0;
	37	+
	38	+ params->un_op = (int32_t)avio_rl32(model_file_context);
	39	+ dnn_size += 4;
	40	+ layer->params = params;
	41	+ layer->input_operand_indexes[0] = (int32_t)avio_rl32(model_file_context);
	42	+ layer->output_operand_index = (int32_t)avio_rl32(model_file_context);
	43	+ dnn_size += 8;
	44	+
	45	+ return dnn_size;
	46	+
	47	+}
	48	+
	49	+int dnn_execute_layer_math_unary(DnnOperand operands, const int32_t input_operand_indexes,
	50	+ int32_t output_operand_index, const void *parameters)
	51	+{
	52	+ const DnnOperand *input = &operands[input_operand_indexes[0]];
	53	+ DnnOperand *output = &operands[output_operand_index];
	54	+ const DnnLayerMathUnaryParams params = (const DnnLayerMathUnaryParams )parameters;
	55	+ int dims_count;
	56	+ const float *src;
	57	+ float *dst;
	58	+
	59	+ for (int i = 0; i < 4; ++i)
	60	+ output->dims[i] = input->dims[i];
	61	+
	62	+ output->data_type = input->data_type;
	63	+ output->length = calculate_operand_data_length(output);
	64	+ output->data = av_realloc(output->data, output->length);
	65	+ if (!output->data)
	66	+ return DNN_ERROR;
	67	+
	68	+ dims_count = calculate_operand_dims_count(output);
	69	+ src = input->data;
	70	+ dst = output->data;
	71	+
	72	+ switch (params->un_op) {
	73	+ case DMUO_ABS:
	74	+ for (int i = 0; i < dims_count; ++i)
	75	+ dst[i] = FFABS(src[i]);
	76	+ return 0;
	77	+ default:
	78	+ return -1;
	79	+ }
	80	+}

libavfilter/dnn/dnn_backend_native_layer_mathunary.h+45−0

@@ -0,0 +1,45 @@
	1	+/*
	2	+ * Copyright (c) 2020
	3	+ *
	4	+ * This file is part of FFmpeg.
	5	+ *
	6	+ * FFmpeg is free software; you can redistribute it and/or
	7	+ * modify it under the terms of the GNU Lesser General Public
	8	+ * License as published by the Free Software Foundation; either
	9	+ * version 2.1 of the License, or (at your option) any later version.
	10	+ *
	11	+ * FFmpeg is distributed in the hope that it will be useful,
	12	+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
	13	+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
	14	+ * Lesser General Public License for more details.
	15	+ *
	16	+ * You should have received a copy of the GNU Lesser General Public
	17	+ * License along with FFmpeg; if not, write to the Free Software
	18	+ * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
	19	+ */
	20	+
	21	+/**
	22	+ * @file
	23	+ * DNN inference functions interface for native backend.
	24	+ */
	25	+
	26	+#ifndef AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_MATHUNARY_H
	27	+#define AVFILTER_DNN_DNN_BACKEND_NATIVE_LAYER_MATHUNARY_H
	28	+
	29	+#include "libavformat/avio.h"
	30	+#include "dnn_backend_native.h"
	31	+
	32	+typedef enum {
	33	+ DMUO_ABS = 0,
	34	+ DMUO_COUNT
	35	+} DNNMathUnaryOperation;
	36	+
	37	+typedef struct DnnLayerMathUnaryParams{
	38	+ DNNMathUnaryOperation un_op;
	39	+} DnnLayerMathUnaryParams;
	40	+
	41	+int dnn_load_layer_math_unary(Layer layer, AVIOContext model_file_context, int file_size);
	42	+int dnn_execute_layer_math_unary(DnnOperand operands, const int32_t input_operand_indexes,
	43	+ int32_t output_operand_index, const void *parameters);
	44	+
	45	+#endif

libavfilter/dnn/dnn_backend_native_layers.c+2−0

@@ -25,6 +25,7 @@ · 24 unmodified lines
25	25	#include "dnn_backend_native_layer_depth2space.h"
26	26	#include "dnn_backend_native_layer_maximum.h"
27	27	#include "dnn_backend_native_layer_mathbinary.h"
	28	+#include "dnn_backend_native_layer_mathunary.h"
28	29
29	30	LayerFunc layer_funcs[DLT_COUNT] = {
30	31	{NULL, NULL},
@@ -33,4 +34,5 @@ LayerFunc layer_funcs[DLT_COUNT] = { · 2 unmodified lines
33	34	{dnn_execute_layer_pad, dnn_load_layer_pad},
34	35	{dnn_execute_layer_maximum, dnn_load_layer_maximum},
35	36	{dnn_execute_layer_math_binary, dnn_load_layer_math_binary},
	37	+ {dnn_execute_layer_math_unary, dnn_load_layer_math_unary},
36	38	};

tools/python/convert_from_tensorflow.py+15−1

@@ -70,8 +70,9 @@ class TFConverter: · 69 unmodified lines
70	70	self.converted_nodes = set()
71	71	self.conv2d_scope_names = set()
72	72	self.conv2d_scopename_inputname_dict = {}
73		− self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4, 'MathBinary':5}
	73	+ self.op2code = {'Conv2D':1, 'DepthToSpace':2, 'MirrorPad':3, 'Maximum':4, 'MathBinary':5, 'MathUnary':6}
74	74	self.mathbin2code = {'Sub':0, 'Add':1, 'Mul':2, 'RealDiv':3, 'Minimum':4}
	75	+ self.mathun2code = {'Abs':0}
75	76	self.mirrorpad_mode = {'CONSTANT':0, 'REFLECT':1, 'SYMMETRIC':2}
76	77	self.name_operand_dict = {}
77	78
@@ -286,6 +287,17 @@ class TFConverter: · 208 unmodified lines
286	287	np.array([output_operand_index], dtype=np.uint32).tofile(f)
287	288
288	289
	290	+ def dump_mathunary_to_file(self, node, f):
	291	+ self.layer_number = self.layer_number + 1
	292	+ self.converted_nodes.add(node.name)
	293	+ i0_node = self.name_node_dict[node.input[0]]
	294	+ np.array([self.op2code['MathUnary'], self.mathun2code[node.op]], dtype=np.uint32).tofile(f)
	295	+ input_operand_index = self.add_operand(i0_node.name, Operand.IOTYPE_INPUT)
	296	+ np.array([input_operand_index], dtype=np.uint32).tofile(f)
	297	+ output_operand_index = self.add_operand(node.name, Operand.IOTYPE_OUTPUT)
	298	+ np.array([output_operand_index],dtype=np.uint32).tofile(f)
	299	+
	300	+
289	301	def dump_layers_to_file(self, f):
290	302	for node in self.nodes:
291	303	if node.name in self.converted_nodes:
@@ -307,6 +319,8 @@ class TFConverter: · 15 unmodified lines
307	319	self.dump_maximum_to_file(node, f)
308	320	elif node.op in self.mathbin2code:
309	321	self.dump_mathbinary_to_file(node, f)
	322	+ elif node.op in self.mathun2code:
	323	+ self.dump_mathunary_to_file(node, f)
310	324
311	325
312	326	def dump_operands_to_file(self, f):

tools/python/convert_header.py+1−1

@@ -23,4 +23,4 @@ str = 'FFMPEGDNNNATIVE' · 22 unmodified lines
23	23	major = 1
24	24
25	25	# increase minor when we don't have to re-convert the model file
26		−minor = 5
	26	+minor = 6

dnn_backend_native_layer_mathunary: add abs support

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