diff options
author | Sergey Lavrushkin <dualfal@gmail.com> | 2018-04-10 19:41:40 +0300 |
---|---|---|
committer | Pedro Arthur <bygrandao@gmail.com> | 2018-05-04 14:24:56 +0000 |
commit | 9479955c626529550d337067af85760e256eabb3 (patch) | |
tree | 481a91fb8167f56b40e2b3231ca4209f7465662b | |
parent | 4bad76b6e9e44b54bf08fd190c22edeb8222d83d (diff) | |
download | ffmpeg-9479955c626529550d337067af85760e256eabb3.tar.gz |
Adds SRCNN filter.
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
-rw-r--r-- | Changelog | 1 | ||||
-rw-r--r-- | libavfilter/Makefile | 1 | ||||
-rw-r--r-- | libavfilter/allfilters.c | 1 | ||||
-rw-r--r-- | libavfilter/vf_srcnn.c | 420 | ||||
-rw-r--r-- | libavfilter/vf_srcnn.h | 855 |
5 files changed, 1278 insertions, 0 deletions
@@ -60,6 +60,7 @@ version 4.0: - Haivision SRT protocol via libsrt - segafilm muxer - vfrdet filter +- SRCNN filter version 3.4: diff --git a/libavfilter/Makefile b/libavfilter/Makefile index 3454f25e7d..a416fa1fae 100644 --- a/libavfilter/Makefile +++ b/libavfilter/Makefile @@ -331,6 +331,7 @@ OBJS-$(CONFIG_SMARTBLUR_FILTER) += vf_smartblur.o OBJS-$(CONFIG_SOBEL_FILTER) += vf_convolution.o OBJS-$(CONFIG_SPLIT_FILTER) += split.o OBJS-$(CONFIG_SPP_FILTER) += vf_spp.o +OBJS-$(CONFIG_SRCNN_FILTER) += vf_srcnn.o OBJS-$(CONFIG_SSIM_FILTER) += vf_ssim.o framesync.o OBJS-$(CONFIG_STEREO3D_FILTER) += vf_stereo3d.o OBJS-$(CONFIG_STREAMSELECT_FILTER) += f_streamselect.o framesync.o diff --git a/libavfilter/allfilters.c b/libavfilter/allfilters.c index d958f9b2da..42f956b3bf 100644 --- a/libavfilter/allfilters.c +++ b/libavfilter/allfilters.c @@ -322,6 +322,7 @@ extern AVFilter ff_vf_smartblur; extern AVFilter ff_vf_sobel; extern AVFilter ff_vf_split; extern AVFilter ff_vf_spp; +extern AVFilter ff_vf_srcnn; extern AVFilter ff_vf_ssim; extern AVFilter ff_vf_stereo3d; extern AVFilter ff_vf_streamselect; diff --git a/libavfilter/vf_srcnn.c b/libavfilter/vf_srcnn.c new file mode 100644 index 0000000000..d9b4891f7f --- /dev/null +++ b/libavfilter/vf_srcnn.c @@ -0,0 +1,420 @@ +/* + * Copyright (c) 2018 Sergey Lavrushkin + * + * 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 + * Filter implementing image super-resolution using deep convolutional networks. + * https://arxiv.org/abs/1501.00092 + */ + +#include "avfilter.h" +#include "formats.h" +#include "internal.h" +#include "libavutil/opt.h" +#include "unistd.h" +#include "vf_srcnn.h" +#include "libavformat/avio.h" + +typedef struct Convolution +{ + double* kernel; + double* biases; + int32_t size, input_channels, output_channels; +} Convolution; + +typedef struct SRCNNContext { + const AVClass *class; + + /// SRCNN convolutions + struct Convolution conv1, conv2, conv3; + /// Path to binary file with kernels specifications + char* config_file_path; + /// Buffers for network input/output and feature maps + double* input_output_buf; + double* conv1_buf; + double* conv2_buf; +} SRCNNContext; + + +#define OFFSET(x) offsetof(SRCNNContext, x) +#define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM +static const AVOption srcnn_options[] = { + { "config_file", "path to configuration file with network parameters", OFFSET(config_file_path), AV_OPT_TYPE_STRING, {.str=NULL}, 0, 0, FLAGS }, + { NULL } +}; + +AVFILTER_DEFINE_CLASS(srcnn); + +#define CHECK_FILE_SIZE(file_size, srcnn_size, avio_context) if (srcnn_size > file_size){ \ + av_log(context, AV_LOG_ERROR, "error reading configuration file\n");\ + avio_closep(&avio_context); \ + return AVERROR(EIO); \ + } + +#define CHECK_ALLOCATION(call, end_call) if (call){ \ + av_log(context, AV_LOG_ERROR, "could not allocate memory for convolutions\n"); \ + end_call; \ + return AVERROR(ENOMEM); \ + } + +static int allocate_read_conv_data(Convolution* conv, AVIOContext* config_file_context) +{ + int32_t kernel_size = conv->output_channels * conv->size * conv->size * conv->input_channels; + int32_t i; + + conv->kernel = av_malloc(kernel_size * sizeof(double)); + if (!conv->kernel){ + return AVERROR(ENOMEM); + } + for (i = 0; i < kernel_size; ++i){ + conv->kernel[i] = av_int2double(avio_rl64(config_file_context)); + } + + conv->biases = av_malloc(conv->output_channels * sizeof(double)); + if (!conv->biases){ + return AVERROR(ENOMEM); + } + for (i = 0; i < conv->output_channels; ++i){ + conv->biases[i] = av_int2double(avio_rl64(config_file_context)); + } + + return 0; +} + +static int allocate_copy_conv_data(Convolution* conv, const double* kernel, const double* biases) +{ + int32_t kernel_size = conv->output_channels * conv->size * conv->size * conv->input_channels; + + conv->kernel = av_malloc(kernel_size * sizeof(double)); + if (!conv->kernel){ + return AVERROR(ENOMEM); + } + memcpy(conv->kernel, kernel, kernel_size * sizeof(double)); + + conv->biases = av_malloc(conv->output_channels * sizeof(double)); + if (!conv->kernel){ + return AVERROR(ENOMEM); + } + memcpy(conv->biases, biases, conv->output_channels * sizeof(double)); + + return 0; +} + +static av_cold int init(AVFilterContext* context) +{ + SRCNNContext *srcnn_context = context->priv; + AVIOContext* config_file_context; + int64_t file_size, srcnn_size; + + /// Check specified confguration file name and read network weights from it + if (!srcnn_context->config_file_path){ + av_log(context, AV_LOG_INFO, "configuration file for network was not specified, using default weights for x2 upsampling\n"); + + /// Create convolution kernels and copy default weights + srcnn_context->conv1.input_channels = 1; + srcnn_context->conv1.output_channels = 64; + srcnn_context->conv1.size = 9; + CHECK_ALLOCATION(allocate_copy_conv_data(&srcnn_context->conv1, conv1_kernel, conv1_biases), ) + + srcnn_context->conv2.input_channels = 64; + srcnn_context->conv2.output_channels = 32; + srcnn_context->conv2.size = 1; + CHECK_ALLOCATION(allocate_copy_conv_data(&srcnn_context->conv2, conv2_kernel, conv2_biases), ) + + srcnn_context->conv3.input_channels = 32; + srcnn_context->conv3.output_channels = 1; + srcnn_context->conv3.size = 5; + CHECK_ALLOCATION(allocate_copy_conv_data(&srcnn_context->conv3, conv3_kernel, conv3_biases), ) + } + else if (access(srcnn_context->config_file_path, R_OK) != -1){ + if (avio_open(&config_file_context, srcnn_context->config_file_path, AVIO_FLAG_READ) < 0){ + av_log(context, AV_LOG_ERROR, "failed to open configuration file\n"); + return AVERROR(EIO); + } + + file_size = avio_size(config_file_context); + + /// Create convolution kernels and read weights from file + srcnn_context->conv1.input_channels = 1; + srcnn_context->conv1.size = (int32_t)avio_rl32(config_file_context); + srcnn_context->conv1.output_channels = (int32_t)avio_rl32(config_file_context); + srcnn_size = 8 + (srcnn_context->conv1.output_channels * srcnn_context->conv1.size * + srcnn_context->conv1.size * srcnn_context->conv1.input_channels + + srcnn_context->conv1.output_channels << 3); + CHECK_FILE_SIZE(file_size, srcnn_size, config_file_context) + CHECK_ALLOCATION(allocate_read_conv_data(&srcnn_context->conv1, config_file_context), avio_closep(&config_file_context)) + + srcnn_context->conv2.input_channels = (int32_t)avio_rl32(config_file_context); + srcnn_context->conv2.size = (int32_t)avio_rl32(config_file_context); + srcnn_context->conv2.output_channels = (int32_t)avio_rl32(config_file_context); + srcnn_size += 12 + (srcnn_context->conv2.output_channels * srcnn_context->conv2.size * + srcnn_context->conv2.size * srcnn_context->conv2.input_channels + + srcnn_context->conv2.output_channels << 3); + CHECK_FILE_SIZE(file_size, srcnn_size, config_file_context) + CHECK_ALLOCATION(allocate_read_conv_data(&srcnn_context->conv2, config_file_context), avio_closep(&config_file_context)) + + srcnn_context->conv3.input_channels = (int32_t)avio_rl32(config_file_context); + srcnn_context->conv3.size = (int32_t)avio_rl32(config_file_context); + srcnn_context->conv3.output_channels = 1; + srcnn_size += 8 + (srcnn_context->conv3.output_channels * srcnn_context->conv3.size * + srcnn_context->conv3.size * srcnn_context->conv3.input_channels + + srcnn_context->conv3.output_channels << 3); + if (file_size != srcnn_size){ + av_log(context, AV_LOG_ERROR, "error reading configuration file\n"); + avio_closep(&config_file_context); + return AVERROR(EIO); + } + CHECK_ALLOCATION(allocate_read_conv_data(&srcnn_context->conv3, config_file_context), avio_closep(&config_file_context)) + + avio_closep(&config_file_context); + } + else{ + av_log(context, AV_LOG_ERROR, "specified configuration file does not exist or not readable\n"); + return AVERROR(EIO); + } + + return 0; +} + +static int query_formats(AVFilterContext* context) +{ + const enum AVPixelFormat pixel_formats[] = {AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P, AV_PIX_FMT_YUV444P, + AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P, AV_PIX_FMT_GRAY8, + AV_PIX_FMT_NONE}; + AVFilterFormats *formats_list; + + formats_list = ff_make_format_list(pixel_formats); + if (!formats_list){ + av_log(context, AV_LOG_ERROR, "could not create formats list\n"); + return AVERROR(ENOMEM); + } + return ff_set_common_formats(context, formats_list); +} + +static int config_props(AVFilterLink* inlink) +{ + AVFilterContext *context = inlink->dst; + SRCNNContext *srcnn_context = context->priv; + int min_dim; + + /// Check if input data width or height is too low + min_dim = FFMIN(inlink->w, inlink->h); + if (min_dim <= srcnn_context->conv1.size >> 1 || min_dim <= srcnn_context->conv2.size >> 1 || min_dim <= srcnn_context->conv3.size >> 1){ + av_log(context, AV_LOG_ERROR, "input width or height is too low\n"); + return AVERROR(EIO); + } + + /// Allocate network buffers + srcnn_context->input_output_buf = av_malloc(inlink->h * inlink->w * sizeof(double)); + srcnn_context->conv1_buf = av_malloc(inlink->h * inlink->w * srcnn_context->conv1.output_channels * sizeof(double)); + srcnn_context->conv2_buf = av_malloc(inlink->h * inlink->w * srcnn_context->conv2.output_channels * sizeof(double)); + + if (!srcnn_context->input_output_buf || !srcnn_context->conv1_buf || !srcnn_context->conv2_buf){ + av_log(context, AV_LOG_ERROR, "could not allocate memory for srcnn buffers\n"); + return AVERROR(ENOMEM); + } + + return 0; +} + +typedef struct ThreadData{ + uint8_t* out; + int out_linesize, height, width; +} ThreadData; + +typedef struct ConvThreadData +{ + const Convolution* conv; + const double* input; + double* output; + int height, width; +} ConvThreadData; + +/// Convert uint8 data to double and scale it to use in network +static int uint8_to_double(AVFilterContext* context, void* arg, int jobnr, int nb_jobs) +{ + SRCNNContext* srcnn_context = context->priv; + const ThreadData* td = arg; + const int slice_start = (td->height * jobnr ) / nb_jobs; + const int slice_end = (td->height * (jobnr + 1)) / nb_jobs; + const uint8_t* src = td->out + slice_start * td->out_linesize; + double* dst = srcnn_context->input_output_buf + slice_start * td->width; + int y, x; + + for (y = slice_start; y < slice_end; ++y){ + for (x = 0; x < td->width; ++x){ + dst[x] = (double)src[x] / 255.0; + } + src += td->out_linesize; + dst += td->width; + } + + return 0; +} + +/// Convert double data from network to uint8 and scale it to output as filter result +static int double_to_uint8(AVFilterContext* context, void* arg, int jobnr, int nb_jobs) +{ + SRCNNContext* srcnn_context = context->priv; + const ThreadData* td = arg; + const int slice_start = (td->height * jobnr ) / nb_jobs; + const int slice_end = (td->height * (jobnr + 1)) / nb_jobs; + const double* src = srcnn_context->input_output_buf + slice_start * td->width; + uint8_t* dst = td->out + slice_start * td->out_linesize; + int y, x; + + for (y = slice_start; y < slice_end; ++y){ + for (x = 0; x < td->width; ++x){ + dst[x] = (uint8_t)(255.0 * FFMIN(src[x], 1.0)); + } + src += td->width; + dst += td->out_linesize; + } + + return 0; +} + +#define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x))) + +static int convolve(AVFilterContext* context, void* arg, int jobnr, int nb_jobs) +{ + const ConvThreadData* td = arg; + const int slice_start = (td->height * jobnr ) / nb_jobs; + const int slice_end = (td->height * (jobnr + 1)) / nb_jobs; + const double* src = td->input; + double* dst = td->output + slice_start * td->width * td->conv->output_channels; + int y, x; + int32_t n_filter, ch, kernel_y, kernel_x; + int32_t radius = td->conv->size >> 1; + int src_linesize = td->width * td->conv->input_channels; + int filter_linesize = td->conv->size * td->conv->input_channels; + int filter_size = td->conv->size * filter_linesize; + + for (y = slice_start; y < slice_end; ++y){ + for (x = 0; x < td->width; ++x){ + for (n_filter = 0; n_filter < td->conv->output_channels; ++n_filter){ + dst[n_filter] = td->conv->biases[n_filter]; + for (ch = 0; ch < td->conv->input_channels; ++ch){ + for (kernel_y = 0; kernel_y < td->conv->size; ++kernel_y){ + for (kernel_x = 0; kernel_x < td->conv->size; ++kernel_x){ + dst[n_filter] += src[CLAMP_TO_EDGE(y + kernel_y - radius, td->height) * src_linesize + + CLAMP_TO_EDGE(x + kernel_x - radius, td->width) * td->conv->input_channels + ch] * + td->conv->kernel[n_filter * filter_size + kernel_y * filter_linesize + + kernel_x * td->conv->input_channels + ch]; + } + } + } + dst[n_filter] = FFMAX(dst[n_filter], 0.0); + } + dst += td->conv->output_channels; + } + } + + return 0; +} + +static int filter_frame(AVFilterLink* inlink, AVFrame* in) +{ + AVFilterContext* context = inlink->dst; + SRCNNContext* srcnn_context = context->priv; + AVFilterLink* outlink = context->outputs[0]; + AVFrame* out = ff_get_video_buffer(outlink, outlink->w, outlink->h); + ThreadData td; + ConvThreadData ctd; + + if (!out){ + av_log(context, AV_LOG_ERROR, "could not allocate memory for output frame\n"); + av_frame_free(&in); + return AVERROR(ENOMEM); + } + av_frame_copy_props(out, in); + av_frame_copy(out, in); + av_frame_free(&in); + td.out = out->data[0]; + td.out_linesize = out->linesize[0]; + td.height = ctd.height = out->height; + td.width = ctd.width = out->width; + + context->internal->execute(context, uint8_to_double, &td, NULL, FFMIN(td.height, context->graph->nb_threads)); + ctd.conv = &srcnn_context->conv1; + ctd.input = srcnn_context->input_output_buf; + ctd.output = srcnn_context->conv1_buf; + context->internal->execute(context, convolve, &ctd, NULL, FFMIN(ctd.height, context->graph->nb_threads)); + ctd.conv = &srcnn_context->conv2; + ctd.input = srcnn_context->conv1_buf; + ctd.output = srcnn_context->conv2_buf; + context->internal->execute(context, convolve, &ctd, NULL, FFMIN(ctd.height, context->graph->nb_threads)); + ctd.conv = &srcnn_context->conv3; + ctd.input = srcnn_context->conv2_buf; + ctd.output = srcnn_context->input_output_buf; + context->internal->execute(context, convolve, &ctd, NULL, FFMIN(ctd.height, context->graph->nb_threads)); + context->internal->execute(context, double_to_uint8, &td, NULL, FFMIN(td.height, context->graph->nb_threads)); + + return ff_filter_frame(outlink, out); +} + +static av_cold void uninit(AVFilterContext* context) +{ + SRCNNContext* srcnn_context = context->priv; + + /// Free convolution data + av_freep(&srcnn_context->conv1.kernel); + av_freep(&srcnn_context->conv1.biases); + av_freep(&srcnn_context->conv2.kernel); + av_freep(&srcnn_context->conv2.biases); + av_freep(&srcnn_context->conv3.kernel); + av_freep(&srcnn_context->conv3.kernel); + + /// Free network buffers + av_freep(&srcnn_context->input_output_buf); + av_freep(&srcnn_context->conv1_buf); + av_freep(&srcnn_context->conv2_buf); +} + +static const AVFilterPad srcnn_inputs[] = { + { + .name = "default", + .type = AVMEDIA_TYPE_VIDEO, + .config_props = config_props, + .filter_frame = filter_frame, + }, + { NULL } +}; + +static const AVFilterPad srcnn_outputs[] = { + { + .name = "default", + .type = AVMEDIA_TYPE_VIDEO, + }, + { NULL } +}; + +AVFilter ff_vf_srcnn = { + .name = "srcnn", + .description = NULL_IF_CONFIG_SMALL("Apply super resolution convolutional neural network to the input. Use bicubic upsamping with corresponding scaling factor before."), + .priv_size = sizeof(SRCNNContext), + .init = init, + .uninit = uninit, + .query_formats = query_formats, + .inputs = srcnn_inputs, + .outputs = srcnn_outputs, + .priv_class = &srcnn_class, + .flags = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC | AVFILTER_FLAG_SLICE_THREADS, +}; diff --git a/libavfilter/vf_srcnn.h b/libavfilter/vf_srcnn.h new file mode 100644 index 0000000000..1b57bcdcd4 --- /dev/null +++ b/libavfilter/vf_srcnn.h @@ -0,0 +1,855 @@ +/* + * Copyright (c) 2018 Sergey Lavrushkin + * + * 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 + * Default cnn weights for x2 upsampling with srcnn filter. + */ +#ifndef AVFILTER_SRCNN_H +#define AVFILTER_SRCNN_H + +/// First convolution kernel +static const double conv1_kernel[] = { +-0.08866337686777115, 0.055409565567970276, 0.037196505814790726, -0.11961404234170914, -0.12341991066932678, 0.2996342182159424, -0.09118170291185379, -0.00013613555347546935, -0.049023594707250595, +0.038421183824539185, -0.07726779580116272, 0.027273094281554222, 0.45762088894844055, 0.023581644520163536, -0.20363852381706238, -0.07738441973924637, 0.07288403809070587, 0.06679897010326385, +-0.07315085083246231, 0.2665306031703949, 0.07836370915174484, -0.2898528277873993, -0.506912887096405, 0.0446651317179203, -0.06246858835220337, -0.024424826726317406, -0.029072929173707962, +-0.16884787380695343, 0.20686236023902893, -0.2536700665950775, -0.38499701023101807, -0.12601901590824127, 0.7257882356643677, 0.09588377922773361, -0.04487278312444687, -0.011251595802605152, +-0.02955852635204792, 0.2211706042289734, -0.3845316469669342, -0.053509537130594254, 0.10955915600061417, 0.3725738823413849, -0.28968045115470886, -0.10883618891239166, 0.19628790020942688, +0.09746792912483215, -0.005647096782922745, -0.13035884499549866, 0.6254252195358276, 0.19104234874248505, -0.011692332103848457, -0.27067890763282776, -0.030754156410694122, 0.029875505715608597, +0.016808750107884407, -0.1831790804862976, -0.16016070544719696, 0.3598291277885437, -0.1889011114835739, -0.22343340516090393, -0.11111768335103989, 0.3221142590045929, -0.18514074385166168, +-0.06647012382745743, 0.1800554096698761, -0.0356597937643528, -0.25352174043655396, -0.18370479345321655, 0.2839542329311371, 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0.012340170331299305, 0.11861597746610641, 0.09244049340486526, 0.034744489938020706, -0.024895809590816498, 0.035870663821697235, +-0.040758680552244186, 0.013552304357290268, -0.009587780572474003, 0.01752828061580658, 0.07450903207063675, 0.0692441463470459, 0.0011354148155078292, -0.06966304779052734, 0.008092116564512253, +0.02915072999894619, 0.04350604489445686, -0.03975748270750046, 0.014310178346931934, 0.0008435412310063839, -0.013759381137788296, 0.02635800465941429, 0.08823874592781067, 0.0821344256401062 +}; + +/// First convolution biases +static const double conv1_biases[] = { +-0.01660689152777195, +-0.011107334867119789, +-0.00483096856623888, +-0.048673778772354126, +-0.030040957033634186, +-0.07297247648239136, +-0.01945866458117962, +-0.009738028049468994, +0.6951230764389038, +-0.07369442284107208, +-0.01354204025119543, +0.010336088016629219, +-0.02956176921725273, +0.018691286444664, +-0.02167515829205513, +0.004088825546205044, +-0.08033090084791183, +-0.02080247923731804, +-0.00025044166250154376, +0.015665635466575623, +-0.024439847096800804, +-0.00014609392383135855, +-5.004818376619369e-05, +-0.0011854040203616023, +-8.353819430340081e-05, +0.0, +-0.00037626884295605123, +0.02971580997109413, +-9.940328891389072e-05, +-0.020136557519435883, +-0.00029498152434825897, +0.019285818561911583, +0.005577716510742903, +-0.0700315311551094, +-0.024398552253842354, +-1.7726930309436284e-05, +0.0050997124053537846, +-0.011211924254894257, +0.004536128137260675, +-0.024612177163362503, +-0.0225357823073864, +-0.00016232267080340534, +-0.00017362962535116822, +-1.552602043375373e-05, +-0.06559696048498154, +-0.0065806107595562935, +6.683926585537847e-06, +0.0252289529889822, +-0.0013730665668845177, +0.030351819470524788, +0.0, +0.003285621991381049, +0.0055278814397752285, +0.020766519010066986, +-0.00849062204360962, +0.026332268491387367, +-0.04678987339138985, +0.010329566895961761, +-0.023649320006370544, +0.027851572260260582, +0.05440746247768402, +-0.08068252354860306, +-0.009446502663195133, +-0.04663233831524849 +}; + +/// Second convolution kernel +static const double conv2_kernel[] = { +-0.24004751443862915, 0.10371380299329758, 0.11173403263092041, 0.04352091997861862, -0.2372848093509674, 0.1215374693274498, -0.23676058650016785, -0.28548064827919006, -0.6127380132675171, -0.12218937277793884, -0.06005159020423889, 0.18506519496440887, 0.16014674305915833, 0.13922938704490662, 0.2135942578315735, 0.10787945240736008, -0.26999104022979736, -0.14241325855255127, -0.0013871012488380075, -0.004951587878167629, 0.3402169644832611, 0.0005581695586442947, 4.7351106331916526e-05, 0.0037513396237045527, 0.0011569701600819826, 0.001654176157899201, -0.0011876021744683385, 0.049887992441654205, -7.201619882835075e-05, -0.17735491693019867, 0.0011244657216593623, -0.22381487488746643, 0.0035836962051689625, -0.3210694193840027, -0.16167263686656952, -0.0007922835648059845, 0.05056063085794449, 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-0.0007333348621614277, -0.2809791564941406, -0.001019181334413588, 0.019602011889219284, 0.09314858913421631, -0.5306268334388733, 0.17580009996891022, -1.5525150956818834e-05, -0.2091640830039978, -0.6634456515312195, -0.14848995208740234, -0.06595541536808014, -0.034880805760622025, -1.1966582178501994e-06, 0.00036120228469371796, -0.0012568037491291761, -0.10609765350818634, -0.5816946625709534, -0.0006648825947195292, 0.4456590414047241, -0.512170672416687, 0.24637466669082642, 0.0010917421896010637, -0.23795755207538605, -0.2328575998544693, -0.07656370103359222, 0.33030301332473755, 0.2731899917125702, 0.09424741566181183, -0.1698189079761505, 0.01702292636036873, -0.10659436136484146, 0.11089695990085602, -0.08941251039505005, -0.35293179750442505, 0.06545879691839218 +}; + +/// Second convolution biases +static const double conv2_biases[] = { +0.12326373159885406, +0.13270756602287292, +0.07082673907279968, +0.051456157118082047, +0.05844561755657196, +0.1315319687128067, +0.08097290247678757, +0.10153213143348694, +0.055915363132953644, +0.052281659096479416, +-0.11212895810604095, +0.07462140917778015, +-0.10300768911838531, +0.108175128698349, +0.15686865150928497, +0.18225829303264618, +0.06780990958213806, +-0.027867194265127182, +0.10957542806863785, +0.03425288572907448, +0.05988423153758049, +-0.04818560183048248, +0.07478125393390656, +-0.07338137924671173, +-0.022100606933236122, +-0.04947621747851372, +0.00016185229469556361, +0.16787314414978027, +-0.08640440553426743, +0.060469429939985275, +-0.17337509989738464, +0.26549988985061646 +}; + +/// Third convolution kernel +static const double conv3_kernel[] = { +-0.01733648031949997, 0.014926089905202389, 0.019393086433410645, -0.00444532185792923, 0.02693970873951912, 0.0003883102326653898, 0.004221527837216854, 0.0050745452754199505, 0.012986100278794765, 0.008007168769836426, 0.008950762450695038, 0.005279690958559513, 0.01519874669611454, -0.010862989351153374, 0.011825689114630222, 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