[FFmpeg-cvslog] avfilter/vf_nnedi: rewrite and cleanup code
Paul B Mahol
git at videolan.org
Mon Jan 18 15:14:10 EET 2021
ffmpeg | branch: master | Paul B Mahol <onemda at gmail.com> | Sun Jan 17 17:39:28 2021 +0100| [117bf7394f7d5c47104bd30d141466decd01dda1] | committer: Paul B Mahol
avfilter/vf_nnedi: rewrite and cleanup code
Also add slice threading support.
Also add support for >8 depth formats.
Also add support for commands.
> http://git.videolan.org/gitweb.cgi/ffmpeg.git/?a=commit;h=117bf7394f7d5c47104bd30d141466decd01dda1
---
doc/filters.texi | 12 +-
libavfilter/vf_nnedi.c | 1535 ++++++++++++++++++++++++------------------------
2 files changed, 775 insertions(+), 772 deletions(-)
diff --git a/doc/filters.texi b/doc/filters.texi
index aa1389e407..3ce6699d7c 100644
--- a/doc/filters.texi
+++ b/doc/filters.texi
@@ -14649,9 +14649,9 @@ Set which set of weights to use in the predictor.
Can be one of the following:
@table @samp
- at item a
+ at item a, abs
weights trained to minimize absolute error
- at item s
+ at item s, mse
weights trained to minimize squared error
@end table
@@ -14673,14 +14673,16 @@ Can be one of the following:
@item none
@item original
@item new
+ at item new2
+ at item new3
@end table
Default is @code{new}.
-
- at item fapprox
-Set various debugging flags.
@end table
+ at subsection Commands
+This filter supports same @ref{commands} as options, excluding @var{weights} option.
+
@section noformat
Force libavfilter not to use any of the specified pixel formats for the
diff --git a/libavfilter/vf_nnedi.c b/libavfilter/vf_nnedi.c
index 33ff503d92..7f209cb68c 100644
--- a/libavfilter/vf_nnedi.c
+++ b/libavfilter/vf_nnedi.c
@@ -24,6 +24,7 @@
#include "libavutil/common.h"
#include "libavutil/float_dsp.h"
#include "libavutil/imgutils.h"
+#include "libavutil/mem_internal.h"
#include "libavutil/opt.h"
#include "libavutil/pixdesc.h"
#include "avfilter.h"
@@ -31,21 +32,45 @@
#include "internal.h"
#include "video.h"
-typedef struct FrameData {
- uint8_t *paddedp[3];
- int padded_stride[3];
- int padded_width[3];
- int padded_height[3];
-
- uint8_t *dstp[3];
- int dst_stride[3];
-
- int field[3];
-
- int32_t *lcount[3];
- float *input;
- float *temp;
-} FrameData;
+static const size_t NNEDI_WEIGHTS_SIZE = 13574928;
+static const uint8_t NNEDI_XDIM[] = { 8, 16, 32, 48, 8, 16, 32 };
+static const uint8_t NNEDI_YDIM[] = { 6, 6, 6, 6, 4, 4, 4 };
+static const uint16_t NNEDI_NNS[] = { 16, 32, 64, 128, 256 };
+
+static const unsigned NNEDI_DIMS0 = 49 * 4 + 5 * 4 + 9 * 4;
+static const unsigned NNEDI_DIMS0_NEW = 4 * 65 + 4 * 5;
+
+typedef struct PrescreenerOldCoefficients {
+ DECLARE_ALIGNED(32, float, kernel_l0)[4][14 * 4];
+ float bias_l0[4];
+
+ DECLARE_ALIGNED(32, float, kernel_l1)[4][4];
+ float bias_l1[4];
+
+ DECLARE_ALIGNED(32, float, kernel_l2)[4][8];
+ float bias_l2[4];
+} PrescreenerOldCoefficients;
+
+typedef struct PrescreenerNewCoefficients {
+ DECLARE_ALIGNED(32, float, kernel_l0)[4][16 * 4];
+ float bias_l0[4];
+
+ DECLARE_ALIGNED(32, float, kernel_l1)[4][4];
+ float bias_l1[4];
+} PrescreenerNewCoefficients;
+
+typedef struct PredictorCoefficients {
+ int xdim, ydim, nns;
+ float *data;
+ float *softmax_q1;
+ float *elliott_q1;
+ float *softmax_bias_q1;
+ float *elliott_bias_q1;
+ float *softmax_q2;
+ float *elliott_q2;
+ float *softmax_bias_q2;
+ float *elliott_bias_q2;
+} PredictorCoefficients;
typedef struct NNEDIContext {
const AVClass *class;
@@ -59,16 +84,21 @@ typedef struct NNEDIContext {
int64_t cur_pts;
AVFloatDSPContext *fdsp;
+ int depth;
int nb_planes;
+ int nb_threads;
int linesize[4];
+ int planewidth[4];
int planeheight[4];
+ int field_n;
+
+ PrescreenerOldCoefficients prescreener_old;
+ PrescreenerNewCoefficients prescreener_new[3];
+ PredictorCoefficients coeffs[2][5][7];
- float *weights0;
- float *weights1[2];
- int asize;
- int nns;
- int xdia;
- int ydia;
+ float half;
+ float in_scale;
+ float out_scale;
// Parameters
int deint;
@@ -79,104 +109,84 @@ typedef struct NNEDIContext {
int qual;
int etype;
int pscrn;
- int fapprox;
-
- int max_value;
-
- void (*copy_pad)(const AVFrame *, FrameData *, struct NNEDIContext *, int);
- void (*evalfunc_0)(struct NNEDIContext *, FrameData *);
- void (*evalfunc_1)(struct NNEDIContext *, FrameData *);
-
- // Functions used in evalfunc_0
- void (*readpixels)(const uint8_t *, const int, float *);
- void (*compute_network0)(struct NNEDIContext *s, const float *, const float *, uint8_t *);
- int32_t (*process_line0)(const uint8_t *, int, uint8_t *, const uint8_t *, const int, const int, const int);
-
- // Functions used in evalfunc_1
- void (*extract)(const uint8_t *, const int, const int, const int, float *, float *);
- void (*dot_prod)(struct NNEDIContext *, const float *, const float *, float *, const int, const int, const float *);
- void (*expfunc)(float *, const int);
- void (*wae5)(const float *, const int, float *);
- FrameData frame_data;
+ int input_size;
+ uint8_t *prescreen_buf;
+ float *input_buf;
+ float *output_buf;
+
+ void (*read)(const uint8_t *src, float *dst,
+ int src_stride, int dst_stride,
+ int width, int height, float scale);
+ void (*write)(const float *src, uint8_t *dst,
+ int src_stride, int dst_stride,
+ int width, int height, int depth, float scale);
+ void (*prescreen[2])(AVFilterContext *ctx,
+ const void *src, ptrdiff_t src_stride,
+ uint8_t *prescreen, int N, void *data);
} NNEDIContext;
#define OFFSET(x) offsetof(NNEDIContext, x)
+#define RFLAGS AV_OPT_FLAG_VIDEO_PARAM|AV_OPT_FLAG_FILTERING_PARAM|AV_OPT_FLAG_RUNTIME_PARAM
#define FLAGS AV_OPT_FLAG_VIDEO_PARAM|AV_OPT_FLAG_FILTERING_PARAM
static const AVOption nnedi_options[] = {
{"weights", "set weights file", OFFSET(weights_file), AV_OPT_TYPE_STRING, {.str="nnedi3_weights.bin"}, 0, 0, FLAGS },
- {"deint", "set which frames to deinterlace", OFFSET(deint), AV_OPT_TYPE_INT, {.i64=0}, 0, 1, FLAGS, "deint" },
- {"all", "deinterlace all frames", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "deint" },
- {"interlaced", "only deinterlace frames marked as interlaced", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "deint" },
- {"field", "set mode of operation", OFFSET(field), AV_OPT_TYPE_INT, {.i64=-1}, -2, 3, FLAGS, "field" },
- {"af", "use frame flags, both fields", 0, AV_OPT_TYPE_CONST, {.i64=-2}, 0, 0, FLAGS, "field" },
- {"a", "use frame flags, single field", 0, AV_OPT_TYPE_CONST, {.i64=-1}, 0, 0, FLAGS, "field" },
- {"t", "use top field only", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "field" },
- {"b", "use bottom field only", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "field" },
- {"tf", "use both fields, top first", 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "field" },
- {"bf", "use both fields, bottom first", 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, FLAGS, "field" },
- {"planes", "set which planes to process", OFFSET(process_plane), AV_OPT_TYPE_INT, {.i64=7}, 0, 7, FLAGS },
- {"nsize", "set size of local neighborhood around each pixel, used by the predictor neural network", OFFSET(nsize), AV_OPT_TYPE_INT, {.i64=6}, 0, 6, FLAGS, "nsize" },
- {"s8x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "nsize" },
- {"s16x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "nsize" },
- {"s32x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "nsize" },
- {"s48x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, FLAGS, "nsize" },
- {"s8x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, FLAGS, "nsize" },
- {"s16x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=5}, 0, 0, FLAGS, "nsize" },
- {"s32x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=6}, 0, 0, FLAGS, "nsize" },
- {"nns", "set number of neurons in predictor neural network", OFFSET(nnsparam), AV_OPT_TYPE_INT, {.i64=1}, 0, 4, FLAGS, "nns" },
- {"n16", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "nns" },
- {"n32", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "nns" },
- {"n64", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "nns" },
- {"n128", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, FLAGS, "nns" },
- {"n256", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, FLAGS, "nns" },
- {"qual", "set quality", OFFSET(qual), AV_OPT_TYPE_INT, {.i64=1}, 1, 2, FLAGS, "qual" },
- {"fast", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "qual" },
- {"slow", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "qual" },
- {"etype", "set which set of weights to use in the predictor", OFFSET(etype), AV_OPT_TYPE_INT, {.i64=0}, 0, 1, FLAGS, "etype" },
- {"a", "weights trained to minimize absolute error", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "etype" },
- {"s", "weights trained to minimize squared error", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "etype" },
- {"pscrn", "set prescreening", OFFSET(pscrn), AV_OPT_TYPE_INT, {.i64=2}, 0, 2, FLAGS, "pscrn" },
- {"none", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, FLAGS, "pscrn" },
- {"original", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, FLAGS, "pscrn" },
- {"new", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, FLAGS, "pscrn" },
- {"fapprox", NULL, OFFSET(fapprox), AV_OPT_TYPE_INT, {.i64=0}, 0, 3, FLAGS },
+ {"deint", "set which frames to deinterlace", OFFSET(deint), AV_OPT_TYPE_INT, {.i64=0}, 0, 1, RFLAGS, "deint" },
+ {"all", "deinterlace all frames", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, "deint" },
+ {"interlaced", "only deinterlace frames marked as interlaced", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "deint" },
+ {"field", "set mode of operation", OFFSET(field), AV_OPT_TYPE_INT, {.i64=-1}, -2, 3, RFLAGS, "field" },
+ {"af", "use frame flags, both fields", 0, AV_OPT_TYPE_CONST, {.i64=-2}, 0, 0, RFLAGS, "field" },
+ {"a", "use frame flags, single field", 0, AV_OPT_TYPE_CONST, {.i64=-1}, 0, 0, RFLAGS, "field" },
+ {"t", "use top field only", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, "field" },
+ {"b", "use bottom field only", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "field" },
+ {"tf", "use both fields, top first", 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, RFLAGS, "field" },
+ {"bf", "use both fields, bottom first", 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, RFLAGS, "field" },
+ {"planes", "set which planes to process", OFFSET(process_plane), AV_OPT_TYPE_INT, {.i64=7}, 0, 15, RFLAGS },
+ {"nsize", "set size of local neighborhood around each pixel, used by the predictor neural network", OFFSET(nsize), AV_OPT_TYPE_INT, {.i64=6}, 0, 6, RFLAGS, "nsize" },
+ {"s8x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, "nsize" },
+ {"s16x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "nsize" },
+ {"s32x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, RFLAGS, "nsize" },
+ {"s48x6", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, RFLAGS, "nsize" },
+ {"s8x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, RFLAGS, "nsize" },
+ {"s16x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=5}, 0, 0, RFLAGS, "nsize" },
+ {"s32x4", NULL, 0, AV_OPT_TYPE_CONST, {.i64=6}, 0, 0, RFLAGS, "nsize" },
+ {"nns", "set number of neurons in predictor neural network", OFFSET(nnsparam), AV_OPT_TYPE_INT, {.i64=1}, 0, 4, RFLAGS, "nns" },
+ {"n16", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, "nns" },
+ {"n32", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "nns" },
+ {"n64", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, RFLAGS, "nns" },
+ {"n128", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, RFLAGS, "nns" },
+ {"n256", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, RFLAGS, "nns" },
+ {"qual", "set quality", OFFSET(qual), AV_OPT_TYPE_INT, {.i64=1}, 1, 2, RFLAGS, "qual" },
+ {"fast", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "qual" },
+ {"slow", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, RFLAGS, "qual" },
+ {"etype", "set which set of weights to use in the predictor", OFFSET(etype), AV_OPT_TYPE_INT, {.i64=0}, 0, 1, RFLAGS, "etype" },
+ {"a", "weights trained to minimize absolute error", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, "etype" },
+ {"abs","weights trained to minimize absolute error", 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, "etype" },
+ {"s", "weights trained to minimize squared error", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "etype" },
+ {"mse","weights trained to minimize squared error", 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "etype" },
+ {"pscrn", "set prescreening", OFFSET(pscrn), AV_OPT_TYPE_INT, {.i64=2}, 0, 4, RFLAGS, "pscrn" },
+ {"none", NULL, 0, AV_OPT_TYPE_CONST, {.i64=0}, 0, 0, RFLAGS, "pscrn" },
+ {"original", NULL, 0, AV_OPT_TYPE_CONST, {.i64=1}, 0, 0, RFLAGS, "pscrn" },
+ {"new", NULL, 0, AV_OPT_TYPE_CONST, {.i64=2}, 0, 0, RFLAGS, "pscrn" },
+ {"new2", NULL, 0, AV_OPT_TYPE_CONST, {.i64=3}, 0, 0, RFLAGS, "pscrn" },
+ {"new3", NULL, 0, AV_OPT_TYPE_CONST, {.i64=4}, 0, 0, RFLAGS, "pscrn" },
{ NULL }
};
AVFILTER_DEFINE_CLASS(nnedi);
-static int config_input(AVFilterLink *inlink)
-{
- AVFilterContext *ctx = inlink->dst;
- NNEDIContext *s = ctx->priv;
- const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(inlink->format);
- int ret;
-
- s->nb_planes = av_pix_fmt_count_planes(inlink->format);
- if ((ret = av_image_fill_linesizes(s->linesize, inlink->format, inlink->w)) < 0)
- return ret;
-
- s->planeheight[1] = s->planeheight[2] = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h);
- s->planeheight[0] = s->planeheight[3] = inlink->h;
-
- return 0;
-}
-
static int config_output(AVFilterLink *outlink)
{
AVFilterContext *ctx = outlink->src;
- NNEDIContext *s = ctx->priv;
outlink->time_base.num = ctx->inputs[0]->time_base.num;
outlink->time_base.den = ctx->inputs[0]->time_base.den * 2;
outlink->w = ctx->inputs[0]->w;
outlink->h = ctx->inputs[0]->h;
- if (s->field > 1 || s->field == -2)
- outlink->frame_rate = av_mul_q(ctx->inputs[0]->frame_rate,
- (AVRational){2, 1});
+ outlink->frame_rate = av_mul_q(ctx->inputs[0]->frame_rate,
+ (AVRational){2, 1});
return 0;
}
@@ -184,14 +194,28 @@ static int config_output(AVFilterLink *outlink)
static int query_formats(AVFilterContext *ctx)
{
static const enum AVPixelFormat pix_fmts[] = {
+ AV_PIX_FMT_GRAY8,
+ AV_PIX_FMT_GRAY9, AV_PIX_FMT_GRAY10, AV_PIX_FMT_GRAY12, AV_PIX_FMT_GRAY14, AV_PIX_FMT_GRAY16,
AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P,
AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P,
AV_PIX_FMT_YUV440P, AV_PIX_FMT_YUV444P,
AV_PIX_FMT_YUVJ444P, AV_PIX_FMT_YUVJ440P,
AV_PIX_FMT_YUVJ422P, AV_PIX_FMT_YUVJ420P,
AV_PIX_FMT_YUVJ411P,
- AV_PIX_FMT_GBRP,
- AV_PIX_FMT_GRAY8,
+ AV_PIX_FMT_YUVA420P, AV_PIX_FMT_YUVA422P, AV_PIX_FMT_YUVA444P,
+ AV_PIX_FMT_GBRP, AV_PIX_FMT_GBRAP,
+ AV_PIX_FMT_YUV420P9, AV_PIX_FMT_YUV422P9, AV_PIX_FMT_YUV444P9,
+ AV_PIX_FMT_YUV420P10, AV_PIX_FMT_YUV422P10, AV_PIX_FMT_YUV444P10,
+ AV_PIX_FMT_YUV440P10,
+ AV_PIX_FMT_YUV420P12, AV_PIX_FMT_YUV422P12, AV_PIX_FMT_YUV444P12,
+ AV_PIX_FMT_YUV440P12,
+ AV_PIX_FMT_YUV420P14, AV_PIX_FMT_YUV422P14, AV_PIX_FMT_YUV444P14,
+ AV_PIX_FMT_YUV420P16, AV_PIX_FMT_YUV422P16, AV_PIX_FMT_YUV444P16,
+ AV_PIX_FMT_GBRP9, AV_PIX_FMT_GBRP10, AV_PIX_FMT_GBRP12, AV_PIX_FMT_GBRP14, AV_PIX_FMT_GBRP16,
+ AV_PIX_FMT_YUVA444P9, AV_PIX_FMT_YUVA444P10, AV_PIX_FMT_YUVA444P12, AV_PIX_FMT_YUVA444P16,
+ AV_PIX_FMT_YUVA422P9, AV_PIX_FMT_YUVA422P10, AV_PIX_FMT_YUVA422P12, AV_PIX_FMT_YUVA422P16,
+ AV_PIX_FMT_YUVA420P9, AV_PIX_FMT_YUVA420P10, AV_PIX_FMT_YUVA420P16,
+ AV_PIX_FMT_GBRAP10, AV_PIX_FMT_GBRAP12, AV_PIX_FMT_GBRAP16,
AV_PIX_FMT_NONE
};
@@ -201,592 +225,480 @@ static int query_formats(AVFilterContext *ctx)
return ff_set_common_formats(ctx, fmts_list);
}
-static void copy_pad(const AVFrame *src, FrameData *frame_data, NNEDIContext *s, int fn)
+static float dot_dsp(NNEDIContext *s, const float *kernel, const float *input,
+ unsigned n, float scale, float bias)
{
- const int off = 1 - fn;
- int plane, y, x;
+ float sum;
- for (plane = 0; plane < s->nb_planes; plane++) {
- const uint8_t *srcp = (const uint8_t *)src->data[plane];
- uint8_t *dstp = (uint8_t *)frame_data->paddedp[plane];
+ sum = s->fdsp->scalarproduct_float(kernel, input, n);
- const int src_stride = src->linesize[plane];
- const int dst_stride = frame_data->padded_stride[plane];
+ return sum * scale + bias;
+}
- const int src_height = s->planeheight[plane];
- const int dst_height = frame_data->padded_height[plane];
+static float dot_product(const float *kernel, const float *input,
+ unsigned n, float scale, float bias)
+{
+ float sum = 0.0f;
- const int src_width = s->linesize[plane];
- const int dst_width = frame_data->padded_width[plane];
+ for (int i = 0; i < n; i++)
+ sum += kernel[i] * input[i];
- int c = 4;
+ return sum * scale + bias;
+}
- if (!(s->process_plane & (1 << plane)))
- continue;
+static float elliott(float x)
+{
+ return x / (1.0f + fabsf(x));
+}
- // Copy.
- for (y = off; y < src_height; y += 2)
- memcpy(dstp + 32 + (6 + y) * dst_stride,
- srcp + y * src_stride,
- src_width * sizeof(uint8_t));
+static void transform_elliott(float *input, int size)
+{
+ for (int i = 0; i < size; i++)
+ input[i] = elliott(input[i]);
+}
- // And pad.
- dstp += (6 + off) * dst_stride;
- for (y = 6 + off; y < dst_height - 6; y += 2) {
- int c = 2;
+static void process_old(AVFilterContext *ctx,
+ const void *src, ptrdiff_t src_stride,
+ uint8_t *prescreen, int N,
+ void *data)
+{
+ NNEDIContext *s = ctx->priv;
+ PrescreenerOldCoefficients *m_data = data;
+ const float *src_p = src;
- for (x = 0; x < 32; x++)
- dstp[x] = dstp[64 - x];
+ // Adjust source pointer to point to top-left of filter window.
+ const float *window = src_p - 2 * src_stride - 5;
- for (x = dst_width - 32; x < dst_width; x++, c += 2)
- dstp[x] = dstp[x - c];
+ for (int j = 0; j < N; j++) {
+ LOCAL_ALIGNED_32(float, input, [48]);
+ float state[12];
- dstp += dst_stride * 2;
- }
+ for (int i = 0; i < 4; i++)
+ memcpy(input + i * 12, window + i * src_stride + j, 12 * sizeof(float));
- dstp = (uint8_t *)frame_data->paddedp[plane];
- for (y = off; y < 6; y += 2)
- memcpy(dstp + y * dst_stride,
- dstp + (12 + 2 * off - y) * dst_stride,
- dst_width * sizeof(uint8_t));
+ // Layer 0.
+ for (int n = 0; n < 4; n++)
+ state[n] = dot_dsp(s, m_data->kernel_l0[n], input, 48, 1.0f, m_data->bias_l0[n]);
+ transform_elliott(state + 1, 3);
- for (y = dst_height - 6 + off; y < dst_height; y += 2, c += 4)
- memcpy(dstp + y * dst_stride,
- dstp + (y - c) * dst_stride,
- dst_width * sizeof(uint8_t));
- }
-}
+ // Layer 1.
+ for (int n = 0; n < 4; n++)
+ state[n + 4] = dot_product(m_data->kernel_l1[n], state, 4, 1.0f, m_data->bias_l1[n]);
+ transform_elliott(state + 4, 3);
-static void elliott(float *data, const int n)
-{
- int i;
+ // Layer 2.
+ for (int n = 0; n < 4; n++)
+ state[n + 8] = dot_product(m_data->kernel_l2[n], state, 8, 1.0f, m_data->bias_l2[n]);
- for (i = 0; i < n; i++)
- data[i] = data[i] / (1.0f + FFABS(data[i]));
+ prescreen[j] = FFMAX(state[10], state[11]) <= FFMAX(state[8], state[9]) ? 255 : 0;
+ }
}
-static void dot_prod(NNEDIContext *s, const float *data, const float *weights, float *vals, const int n, const int len, const float *scale)
+static void process_new(AVFilterContext *ctx,
+ const void *src, ptrdiff_t src_stride,
+ uint8_t *prescreen, int N,
+ void *data)
{
- int i;
+ NNEDIContext *s = ctx->priv;
+ PrescreenerNewCoefficients *m_data = data;
+ const float *src_p = src;
- for (i = 0; i < n; i++) {
- float sum;
+ // Adjust source pointer to point to top-left of filter window.
+ const float *window = src_p - 2 * src_stride - 6;
- sum = s->fdsp->scalarproduct_float(data, &weights[i * len], len);
+ for (int j = 0; j < N; j += 4) {
+ LOCAL_ALIGNED_32(float, input, [64]);
+ float state[8];
- vals[i] = sum * scale[0] + weights[n * len + i];
- }
-}
+ for (int i = 0; i < 4; i++)
+ memcpy(input + i * 16, window + i * src_stride + j, 16 * sizeof(float));
-static void dot_prods(NNEDIContext *s, const float *dataf, const float *weightsf, float *vals, const int n, const int len, const float *scale)
-{
- const int16_t *data = (int16_t *)dataf;
- const int16_t *weights = (int16_t *)weightsf;
- const float *wf = (float *)&weights[n * len];
- int i, j;
+ for (int n = 0; n < 4; n++)
+ state[n] = dot_dsp(s, m_data->kernel_l0[n], input, 64, 1.0f, m_data->bias_l0[n]);
+ transform_elliott(state, 4);
- for (i = 0; i < n; i++) {
- int sum = 0, off = ((i >> 2) << 3) + (i & 3);
- for (j = 0; j < len; j++)
- sum += data[j] * weights[i * len + j];
+ for (int n = 0; n < 4; n++)
+ state[n + 4] = dot_product(m_data->kernel_l1[n], state, 4, 1.0f, m_data->bias_l1[n]);
- vals[i] = sum * wf[off] * scale[0] + wf[off + 4];
+ for (int n = 0; n < 4; n++)
+ prescreen[j + n] = state[n + 4] > 0.f;
}
}
-static void compute_network0(NNEDIContext *s, const float *input, const float *weights, uint8_t *d)
+static size_t filter_offset(unsigned nn, PredictorCoefficients *model)
{
- float t, temp[12], scale = 1.0f;
-
- dot_prod(s, input, weights, temp, 4, 48, &scale);
- t = temp[0];
- elliott(temp, 4);
- temp[0] = t;
- dot_prod(s, temp, weights + 4 * 49, temp + 4, 4, 4, &scale);
- elliott(temp + 4, 4);
- dot_prod(s, temp, weights + 4 * 49 + 4 * 5, temp + 8, 4, 8, &scale);
- if (FFMAX(temp[10], temp[11]) <= FFMAX(temp[8], temp[9]))
- d[0] = 1;
- else
- d[0] = 0;
+ return nn * model->xdim * model->ydim;
}
-static void compute_network0_i16(NNEDIContext *s, const float *inputf, const float *weightsf, uint8_t *d)
+static const float *softmax_q1_filter(unsigned nn, PredictorCoefficients *model)
{
- const float *wf = weightsf + 2 * 48;
- float t, temp[12], scale = 1.0f;
-
- dot_prods(s, inputf, weightsf, temp, 4, 48, &scale);
- t = temp[0];
- elliott(temp, 4);
- temp[0] = t;
- dot_prod(s, temp, wf + 8, temp + 4, 4, 4, &scale);
- elliott(temp + 4, 4);
- dot_prod(s, temp, wf + 8 + 4 * 5, temp + 8, 4, 8, &scale);
- if (FFMAX(temp[10], temp[11]) <= FFMAX(temp[8], temp[9]))
- d[0] = 1;
- else
- d[0] = 0;
+ return model->softmax_q1 + filter_offset(nn, model);
}
-static void pixel2float48(const uint8_t *t8, const int pitch, float *p)
+static const float *elliott_q1_filter(unsigned nn, PredictorCoefficients *model)
{
- const uint8_t *t = (const uint8_t *)t8;
- int y, x;
-
- for (y = 0; y < 4; y++)
- for (x = 0; x < 12; x++)
- p[y * 12 + x] = t[y * pitch * 2 + x];
+ return model->elliott_q1 + filter_offset(nn, model);
}
-static void byte2word48(const uint8_t *t, const int pitch, float *pf)
+static const float *softmax_q2_filter(unsigned nn, PredictorCoefficients *model)
{
- int16_t *p = (int16_t *)pf;
- int y, x;
-
- for (y = 0; y < 4; y++)
- for (x = 0; x < 12; x++)
- p[y * 12 + x] = t[y * pitch * 2 + x];
+ return model->softmax_q2 + filter_offset(nn, model);
}
-static int32_t process_line0(const uint8_t *tempu, int width, uint8_t *dstp8, const uint8_t *src3p8, const int src_pitch, const int max_value, const int chroma)
+static const float *elliott_q2_filter(unsigned nn, PredictorCoefficients *model)
{
- uint8_t *dstp = (uint8_t *)dstp8;
- const uint8_t *src3p = (const uint8_t *)src3p8;
- int minimum = 0;
- int maximum = max_value - 1; // Technically the -1 is only needed for 8 and 16 bit input.
- int count = 0, x;
- for (x = 0; x < width; x++) {
- if (tempu[x]) {
- int tmp = 19 * (src3p[x + src_pitch * 2] + src3p[x + src_pitch * 4]) - 3 * (src3p[x] + src3p[x + src_pitch * 6]);
- tmp /= 32;
- dstp[x] = FFMAX(FFMIN(tmp, maximum), minimum);
- } else {
- dstp[x] = 255;
- count++;
- }
- }
- return count;
-}
-
-// new prescreener functions
-static void byte2word64(const uint8_t *t, const int pitch, float *p)
-{
- int16_t *ps = (int16_t *)p;
- int y, x;
-
- for (y = 0; y < 4; y++)
- for (x = 0; x < 16; x++)
- ps[y * 16 + x] = t[y * pitch * 2 + x];
+ return model->elliott_q2 + filter_offset(nn, model);
}
-static void compute_network0new(NNEDIContext *s, const float *datai, const float *weights, uint8_t *d)
+static void gather_input(const float *src, ptrdiff_t src_stride,
+ float *buf, float mstd[4],
+ PredictorCoefficients *model)
{
- int16_t *data = (int16_t *)datai;
- int16_t *ws = (int16_t *)weights;
- float *wf = (float *)&ws[4 * 64];
- float vals[8];
- int mask, i, j;
-
- for (i = 0; i < 4; i++) {
- int sum = 0;
- float t;
-
- for (j = 0; j < 64; j++)
- sum += data[j] * ws[(i << 3) + ((j >> 3) << 5) + (j & 7)];
- t = sum * wf[i] + wf[4 + i];
- vals[i] = t / (1.0f + FFABS(t));
- }
-
- for (i = 0; i < 4; i++) {
- float sum = 0.0f;
-
- for (j = 0; j < 4; j++)
- sum += vals[j] * wf[8 + i + (j << 2)];
- vals[4 + i] = sum + wf[8 + 16 + i];
- }
-
- mask = 0;
- for (i = 0; i < 4; i++) {
- if (vals[4 + i] > 0.0f)
- mask |= (0x1 << (i << 3));
- }
-
- ((int *)d)[0] = mask;
-}
-
-static void evalfunc_0(NNEDIContext *s, FrameData *frame_data)
-{
- float *input = frame_data->input;
- const float *weights0 = s->weights0;
- float *temp = frame_data->temp;
- uint8_t *tempu = (uint8_t *)temp;
- int plane, x, y;
-
- // And now the actual work.
- for (plane = 0; plane < s->nb_planes; plane++) {
- const uint8_t *srcp = (const uint8_t *)frame_data->paddedp[plane];
- const int src_stride = frame_data->padded_stride[plane] / sizeof(uint8_t);
-
- const int width = frame_data->padded_width[plane];
- const int height = frame_data->padded_height[plane];
-
- uint8_t *dstp = (uint8_t *)frame_data->dstp[plane];
- const int dst_stride = frame_data->dst_stride[plane] / sizeof(uint8_t);
- const uint8_t *src3p;
- int ystart, ystop;
- int32_t *lcount;
-
- if (!(s->process_plane & (1 << plane)))
- continue;
-
- for (y = 1 - frame_data->field[plane]; y < height - 12; y += 2) {
- memcpy(dstp + y * dst_stride,
- srcp + 32 + (6 + y) * src_stride,
- (width - 64) * sizeof(uint8_t));
+ float sum = 0;
+ float sum_sq = 0;
+ float tmp;
- }
+ for (int i = 0; i < model->ydim; i++) {
+ for (int j = 0; j < model->xdim; j++) {
+ float val = src[i * src_stride + j];
- ystart = 6 + frame_data->field[plane];
- ystop = height - 6;
- srcp += ystart * src_stride;
- dstp += (ystart - 6) * dst_stride - 32;
- src3p = srcp - src_stride * 3;
- lcount = frame_data->lcount[plane] - 6;
-
- if (s->pscrn == 1) { // original
- for (y = ystart; y < ystop; y += 2) {
- for (x = 32; x < width - 32; x++) {
- s->readpixels((const uint8_t *)(src3p + x - 5), src_stride, input);
- s->compute_network0(s, input, weights0, tempu+x);
- }
- lcount[y] += s->process_line0(tempu + 32, width - 64, (uint8_t *)(dstp + 32), (const uint8_t *)(src3p + 32), src_stride, s->max_value, plane);
- src3p += src_stride * 2;
- dstp += dst_stride * 2;
- }
- } else if (s->pscrn > 1) { // new
- for (y = ystart; y < ystop; y += 2) {
- for (x = 32; x < width - 32; x += 4) {
- s->readpixels((const uint8_t *)(src3p + x - 6), src_stride, input);
- s->compute_network0(s, input, weights0, tempu + x);
- }
- lcount[y] += s->process_line0(tempu + 32, width - 64, (uint8_t *)(dstp + 32), (const uint8_t *)(src3p + 32), src_stride, s->max_value, plane);
- src3p += src_stride * 2;
- dstp += dst_stride * 2;
- }
- } else { // no prescreening
- for (y = ystart; y < ystop; y += 2) {
- memset(dstp + 32, 255, (width - 64) * sizeof(uint8_t));
- lcount[y] += width - 64;
- dstp += dst_stride * 2;
- }
+ buf[i * model->xdim + j] = val;
+ sum += val;
+ sum_sq += val * val;
}
}
-}
-static void extract_m8(const uint8_t *srcp8, const int stride, const int xdia, const int ydia, float *mstd, float *input)
-{
- // uint8_t or uint16_t or float
- const uint8_t *srcp = (const uint8_t *)srcp8;
- float scale;
- double tmp;
-
- // int32_t or int64_t or double
- int64_t sum = 0, sumsq = 0;
- int y, x;
-
- for (y = 0; y < ydia; y++) {
- const uint8_t *srcpT = srcp + y * stride * 2;
-
- for (x = 0; x < xdia; x++) {
- sum += srcpT[x];
- sumsq += (uint32_t)srcpT[x] * (uint32_t)srcpT[x];
- input[x] = srcpT[x];
- }
- input += xdia;
- }
- scale = 1.0f / (xdia * ydia);
- mstd[0] = sum * scale;
- tmp = (double)sumsq * scale - (double)mstd[0] * mstd[0];
- mstd[3] = 0.0f;
- if (tmp <= FLT_EPSILON)
- mstd[1] = mstd[2] = 0.0f;
- else {
- mstd[1] = sqrt(tmp);
+ mstd[0] = sum / (model->xdim * model->ydim);
+ mstd[3] = 0.f;
+
+ tmp = sum_sq / (model->xdim * model->ydim) - mstd[0] * mstd[0];
+ if (tmp < FLT_EPSILON) {
+ mstd[1] = 0.0f;
+ mstd[2] = 0.0f;
+ } else {
+ mstd[1] = sqrtf(tmp);
mstd[2] = 1.0f / mstd[1];
}
}
-static void extract_m8_i16(const uint8_t *srcp, const int stride, const int xdia, const int ydia, float *mstd, float *inputf)
+static float softmax_exp(float x)
{
- int16_t *input = (int16_t *)inputf;
- float scale;
- int sum = 0, sumsq = 0;
- int y, x;
-
- for (y = 0; y < ydia; y++) {
- const uint8_t *srcpT = srcp + y * stride * 2;
- for (x = 0; x < xdia; x++) {
- sum += srcpT[x];
- sumsq += srcpT[x] * srcpT[x];
- input[x] = srcpT[x];
- }
- input += xdia;
- }
- scale = 1.0f / (float)(xdia * ydia);
- mstd[0] = sum * scale;
- mstd[1] = sumsq * scale - mstd[0] * mstd[0];
- mstd[3] = 0.0f;
- if (mstd[1] <= FLT_EPSILON)
- mstd[1] = mstd[2] = 0.0f;
- else {
- mstd[1] = sqrt(mstd[1]);
- mstd[2] = 1.0f / mstd[1];
- }
+ return expf(av_clipf(x, -80.f, 80.f));
}
-
-static const float exp_lo = -80.0f;
-static const float exp_hi = +80.0f;
-
-static void e2_m16(float *s, const int n)
+static void transform_softmax_exp(float *input, int size)
{
- int i;
-
- for (i = 0; i < n; i++)
- s[i] = exp(av_clipf(s[i], exp_lo, exp_hi));
+ for (int i = 0; i < size; i++)
+ input[i] = softmax_exp(input[i]);
}
-const float min_weight_sum = 1e-10f;
-
-static void weighted_avg_elliott_mul5_m16(const float *w, const int n, float *mstd)
+static void wae5(const float *softmax, const float *el,
+ unsigned n, float mstd[4])
{
float vsum = 0.0f, wsum = 0.0f;
- int i;
- for (i = 0; i < n; i++) {
- vsum += w[i] * (w[n + i] / (1.0f + FFABS(w[n + i])));
- wsum += w[i];
+ for (int i = 0; i < n; i++) {
+ vsum += softmax[i] * elliott(el[i]);
+ wsum += softmax[i];
}
- if (wsum > min_weight_sum)
- mstd[3] += ((5.0f * vsum) / wsum) * mstd[1] + mstd[0];
+
+ if (wsum > 1e-10f)
+ mstd[3] += (5.0f * vsum) / wsum * mstd[1] + mstd[0];
else
mstd[3] += mstd[0];
}
-
-static void evalfunc_1(NNEDIContext *s, FrameData *frame_data)
+static void predictor(AVFilterContext *ctx,
+ const void *src, ptrdiff_t src_stride, void *dst,
+ const uint8_t *prescreen, int N,
+ void *data, int use_q2)
{
- float *input = frame_data->input;
- float *temp = frame_data->temp;
- float **weights1 = s->weights1;
- const int qual = s->qual;
- const int asize = s->asize;
- const int nns = s->nns;
- const int xdia = s->xdia;
- const int xdiad2m1 = (xdia / 2) - 1;
- const int ydia = s->ydia;
- const float scale = 1.0f / (float)qual;
- int plane, y, x, i;
-
- for (plane = 0; plane < s->nb_planes; plane++) {
- const uint8_t *srcp = (const uint8_t *)frame_data->paddedp[plane];
- const int src_stride = frame_data->padded_stride[plane] / sizeof(uint8_t);
-
- const int width = frame_data->padded_width[plane];
- const int height = frame_data->padded_height[plane];
-
- uint8_t *dstp = (uint8_t *)frame_data->dstp[plane];
- const int dst_stride = frame_data->dst_stride[plane] / sizeof(uint8_t);
-
- const int ystart = frame_data->field[plane];
- const int ystop = height - 12;
- const uint8_t *srcpp;
-
- if (!(s->process_plane & (1 << plane)))
+ NNEDIContext *s = ctx->priv;
+ PredictorCoefficients *model = data;
+ const float *src_p = src;
+ float *dst_p = dst;
+
+ // Adjust source pointer to point to top-left of filter window.
+ const float *window = src_p - (model->ydim / 2) * src_stride - (model->xdim / 2 - 1);
+ unsigned filter_size = model->xdim * model->ydim;
+ unsigned nns = model->nns;
+
+ for (int i = 0; i < N; i++) {
+ LOCAL_ALIGNED_32(float, input, [48 * 6]);
+ float activation[256 * 2];
+ float mstd[4];
+ float scale;
+
+ if (prescreen[i])
continue;
- srcp += (ystart + 6) * src_stride;
- dstp += ystart * dst_stride - 32;
- srcpp = srcp - (ydia - 1) * src_stride - xdiad2m1;
+ gather_input(window + i, src_stride, input, mstd, model);
+ scale = mstd[2];
- for (y = ystart; y < ystop; y += 2) {
- for (x = 32; x < width - 32; x++) {
- float mstd[4];
+ for (int nn = 0; nn < nns; nn++)
+ activation[nn] = dot_dsp(s, softmax_q1_filter(nn, model), input, filter_size, scale, model->softmax_bias_q1[nn]);
- if (dstp[x] != 255)
- continue;
+ for (int nn = 0; nn < nns; nn++)
+ activation[model->nns + nn] = dot_dsp(s, elliott_q1_filter(nn, model), input, filter_size, scale, model->elliott_bias_q1[nn]);
- s->extract((const uint8_t *)(srcpp + x), src_stride, xdia, ydia, mstd, input);
- for (i = 0; i < qual; i++) {
- s->dot_prod(s, input, weights1[i], temp, nns * 2, asize, mstd + 2);
- s->expfunc(temp, nns);
- s->wae5(temp, nns, mstd);
- }
+ transform_softmax_exp(activation, nns);
+ wae5(activation, activation + nns, nns, mstd);
- dstp[x] = FFMIN(FFMAX((int)(mstd[3] * scale + 0.5f), 0), s->max_value);
- }
- srcpp += src_stride * 2;
- dstp += dst_stride * 2;
+ if (use_q2) {
+ for (int nn = 0; nn < nns; nn++)
+ activation[nn] = dot_dsp(s, softmax_q2_filter(nn, model), input, filter_size, scale, model->softmax_bias_q2[nn]);
+
+ for (int nn = 0; nn < nns; nn++)
+ activation[nns + nn] = dot_dsp(s, elliott_q2_filter(nn, model), input, filter_size, scale, model->elliott_bias_q2[nn]);
+
+ transform_softmax_exp(activation, nns);
+ wae5(activation, activation + nns, nns, mstd);
}
+
+ dst_p[i] = mstd[3] / (use_q2 ? 2 : 1);
}
}
-#define NUM_NSIZE 7
-#define NUM_NNS 5
-
-static int roundds(const double f)
+static void read_bytes(const uint8_t *src, float *dst,
+ int src_stride, int dst_stride,
+ int width, int height, float scale)
{
- if (f - floor(f) >= 0.5)
- return FFMIN((int)ceil(f), 32767);
- return FFMAX((int)floor(f), -32768);
+ for (int y = 0; y < height; y++) {
+ for (int x = 0; x < 32; x++)
+ dst[-x - 1] = src[x];
+
+ for (int x = 0; x < width; x++)
+ dst[x] = src[x];
+
+ for (int x = 0; x < 32; x++)
+ dst[width + x] = src[width - x - 1];
+
+ dst += dst_stride;
+ src += src_stride;
+ }
}
-static void select_functions(NNEDIContext *s)
+static void read_words(const uint8_t *srcp, float *dst,
+ int src_stride, int dst_stride,
+ int width, int height, float scale)
{
- s->copy_pad = copy_pad;
- s->evalfunc_0 = evalfunc_0;
- s->evalfunc_1 = evalfunc_1;
+ const uint16_t *src = (const uint16_t *)srcp;
- // evalfunc_0
- s->process_line0 = process_line0;
+ src_stride /= 2;
- if (s->pscrn < 2) { // original prescreener
- if (s->fapprox & 1) { // int16 dot products
- s->readpixels = byte2word48;
- s->compute_network0 = compute_network0_i16;
- } else {
- s->readpixels = pixel2float48;
- s->compute_network0 = compute_network0;
- }
- } else { // new prescreener
- // only int16 dot products
- s->readpixels = byte2word64;
- s->compute_network0 = compute_network0new;
- }
+ for (int y = 0; y < height; y++) {
+ for (int x = 0; x < 32; x++)
+ dst[-x - 1] = src[x] * scale;
- // evalfunc_1
- s->wae5 = weighted_avg_elliott_mul5_m16;
+ for (int x = 0; x < width; x++)
+ dst[x] = src[x] * scale;
- if (s->fapprox & 2) { // use int16 dot products
- s->extract = extract_m8_i16;
- s->dot_prod = dot_prods;
- } else { // use float dot products
- s->extract = extract_m8;
- s->dot_prod = dot_prod;
- }
+ for (int x = 0; x < 32; x++)
+ dst[width + x] = src[width - x - 1] * scale;
- s->expfunc = e2_m16;
+ dst += dst_stride;
+ src += src_stride;
+ }
}
-static int modnpf(const int m, const int n)
+static void write_bytes(const float *src, uint8_t *dst,
+ int src_stride, int dst_stride,
+ int width, int height, int depth,
+ float scale)
{
- if ((m % n) == 0)
- return m;
- return m + n - (m % n);
+ for (int y = 0; y < height; y++) {
+ for (int x = 0; x < width; x++)
+ dst[x] = av_clip_uint8(src[x]);
+
+ dst += dst_stride;
+ src += src_stride;
+ }
}
-static int get_frame(AVFilterContext *ctx, int is_second)
+static void write_words(const float *src, uint8_t *dstp,
+ int src_stride, int dst_stride,
+ int width, int height, int depth,
+ float scale)
{
- NNEDIContext *s = ctx->priv;
- AVFilterLink *outlink = ctx->outputs[0];
- AVFrame *src = s->src;
- FrameData *frame_data;
- int effective_field = s->field;
- size_t temp_size;
- int field_n;
- int plane;
+ uint16_t *dst = (uint16_t *)dstp;
- if (effective_field > 1)
- effective_field -= 2;
- else if (effective_field < 0)
- effective_field += 2;
+ dst_stride /= 2;
- if (s->field < 0 && src->interlaced_frame && src->top_field_first == 0)
- effective_field = 0;
- else if (s->field < 0 && src->interlaced_frame && src->top_field_first == 1)
- effective_field = 1;
- else
- effective_field = !effective_field;
+ for (int y = 0; y < height; y++) {
+ for (int x = 0; x < width; x++)
+ dst[x] = av_clip_uintp2_c(src[x] * scale, depth);
- if (s->field > 1 || s->field == -2) {
- if (is_second) {
- field_n = (effective_field == 0);
- } else {
- field_n = (effective_field == 1);
- }
- } else {
- field_n = effective_field;
+ dst += dst_stride;
+ src += src_stride;
}
+}
- s->dst = ff_get_video_buffer(outlink, outlink->w, outlink->h);
- if (!s->dst)
- return AVERROR(ENOMEM);
- av_frame_copy_props(s->dst, src);
- s->dst->interlaced_frame = 0;
+static void interpolation(const void *src, ptrdiff_t src_stride,
+ void *dst, const uint8_t *prescreen, unsigned n)
+{
+ const float *src_p = src;
+ float *dst_p = dst;
+ const float *window = src_p - 2 * src_stride;
- frame_data = &s->frame_data;
+ for (int i = 0; i < n; i++) {
+ float accum = 0.0f;
- for (plane = 0; plane < s->nb_planes; plane++) {
- int dst_height = s->planeheight[plane];
- int dst_width = s->linesize[plane];
+ if (!prescreen[i])
+ continue;
- const int min_alignment = 16;
- const int min_pad = 10;
+ accum += (-3.0f / 32.0f) * window[0 * src_stride + i];
+ accum += (19.0f / 32.0f) * window[1 * src_stride + i];
+ accum += (19.0f / 32.0f) * window[2 * src_stride + i];
+ accum += (-3.0f / 32.0f) * window[3 * src_stride + i];
- if (!(s->process_plane & (1 << plane))) {
- av_image_copy_plane(s->dst->data[plane], s->dst->linesize[plane],
- src->data[plane], src->linesize[plane],
- s->linesize[plane],
- s->planeheight[plane]);
+ dst_p[i] = accum;
+ }
+}
+
+static int filter_slice(AVFilterContext *ctx, void *arg, int jobnr, int nb_jobs)
+{
+ NNEDIContext *s = ctx->priv;
+ AVFrame *out = s->dst;
+ AVFrame *in = s->src;
+ const float in_scale = s->in_scale;
+ const float out_scale = s->out_scale;
+ const int depth = s->depth;
+ const int interlaced = in->interlaced_frame;
+ const int tff = s->field_n == (s->field < 0 ? interlaced ? in->top_field_first : 1 :
+ (s->field & 1) ^ 1);
+
+
+ for (int p = 0; p < s->nb_planes; p++) {
+ const int height = s->planeheight[p];
+ const int width = s->planewidth[p];
+ const int slice_start = 2 * ((height / 2 * jobnr) / nb_jobs);
+ const int slice_end = 2 * ((height / 2 * (jobnr+1)) / nb_jobs);
+ const uint8_t *src_data = in->data[p];
+ uint8_t *dst_data = out->data[p];
+ uint8_t *dst = out->data[p] + slice_start * out->linesize[p];
+ const int src_linesize = in->linesize[p];
+ const int dst_linesize = out->linesize[p];
+ uint8_t *prescreen_buf = s->prescreen_buf + s->planewidth[0] * jobnr;
+ float *srcbuf = s->input_buf + s->input_size * jobnr;
+ const int srcbuf_stride = width + 64;
+ float *dstbuf = s->output_buf + s->input_size * jobnr;
+ const int dstbuf_stride = width;
+ const int slice_height = (slice_end - slice_start) / 2;
+ const int last_slice = slice_end == height;
+ const uint8_t *in_line;
+ uint8_t *out_line;
+ int y_out;
+
+ if (!(s->process_plane & (1 << p))) {
+ av_image_copy_plane(dst, out->linesize[p],
+ in->data[p] + slice_start * in->linesize[p],
+ in->linesize[p],
+ s->linesize[p], slice_end - slice_start);
continue;
}
- frame_data->padded_width[plane] = dst_width + 64;
- frame_data->padded_height[plane] = dst_height + 12;
- frame_data->padded_stride[plane] = modnpf(frame_data->padded_width[plane] + min_pad, min_alignment); // TODO: maybe min_pad is in pixels too?
- if (!frame_data->paddedp[plane]) {
- frame_data->paddedp[plane] = av_malloc_array(frame_data->padded_stride[plane], frame_data->padded_height[plane]);
- if (!frame_data->paddedp[plane])
- return AVERROR(ENOMEM);
+ y_out = slice_start + (tff ^ (slice_start & 1));
+ in_line = src_data + (y_out * src_linesize);
+ out_line = dst_data + (y_out * dst_linesize);
+
+ while (y_out < slice_end) {
+ memcpy(out_line, in_line, s->linesize[p]);
+ y_out += 2;
+ in_line += src_linesize * 2;
+ out_line += dst_linesize * 2;
}
- frame_data->dstp[plane] = s->dst->data[plane];
- frame_data->dst_stride[plane] = s->dst->linesize[plane];
+ y_out = slice_start + ((!tff) ^ (slice_start & 1));
+
+ s->read(src_data + FFMAX(y_out - 5, tff) * src_linesize,
+ srcbuf + 32,
+ src_linesize * 2, srcbuf_stride,
+ width, 1, in_scale);
+ srcbuf += srcbuf_stride;
+
+ s->read(src_data + FFMAX(y_out - 3, tff) * src_linesize,
+ srcbuf + 32,
+ src_linesize * 2, srcbuf_stride,
+ width, 1, in_scale);
+ srcbuf += srcbuf_stride;
+
+ s->read(src_data + FFMAX(y_out - 1, tff) * src_linesize,
+ srcbuf + 32,
+ src_linesize * 2, srcbuf_stride,
+ width, 1, in_scale);
+ srcbuf += srcbuf_stride;
+
+ in_line = src_data + FFMIN(y_out + 1, height - 1 - !tff) * src_linesize;
+ out_line = dst_data + (y_out * dst_linesize);
+
+ s->read(in_line, srcbuf + 32, src_linesize * 2, srcbuf_stride,
+ width, slice_height - last_slice, in_scale);
+
+ y_out += (slice_height - last_slice) * 2;
+
+ s->read(src_data + FFMIN(y_out + 1, height - 1 - !tff) * src_linesize,
+ srcbuf + 32 + srcbuf_stride * (slice_height - last_slice),
+ src_linesize * 2, srcbuf_stride,
+ width, 1, in_scale);
+
+ s->read(src_data + FFMIN(y_out + 3, height - 1 - !tff) * src_linesize,
+ srcbuf + 32 + srcbuf_stride * (slice_height + 1 - last_slice),
+ src_linesize * 2, srcbuf_stride,
+ width, 1, in_scale);
+
+ s->read(src_data + FFMIN(y_out + 5, height - 1 - !tff) * src_linesize,
+ srcbuf + 32 + srcbuf_stride * (slice_height + 2 - last_slice),
+ src_linesize * 2, srcbuf_stride,
+ width, 1, in_scale);
+
+ for (int y = 0; y < slice_end - slice_start; y += 2) {
+ if (s->pscrn > 1) {
+ s->prescreen[1](ctx, srcbuf + (y / 2) * srcbuf_stride + 32,
+ srcbuf_stride, prescreen_buf, width,
+ &s->prescreener_new[s->pscrn - 2]);
+ } else if (s->pscrn == 1) {
+ s->prescreen[0](ctx, srcbuf + (y / 2) * srcbuf_stride + 32,
+ srcbuf_stride, prescreen_buf, width,
+ &s->prescreener_old);
+ }
- if (!frame_data->lcount[plane]) {
- frame_data->lcount[plane] = av_calloc(dst_height, sizeof(int32_t) * 16);
- if (!frame_data->lcount[plane])
- return AVERROR(ENOMEM);
- } else {
- memset(frame_data->lcount[plane], 0, dst_height * sizeof(int32_t) * 16);
+ predictor(ctx,
+ srcbuf + (y / 2) * srcbuf_stride + 32,
+ srcbuf_stride,
+ dstbuf + (y / 2) * dstbuf_stride,
+ prescreen_buf, width,
+ &s->coeffs[s->etype][s->nnsparam][s->nsize], s->qual == 2);
+
+ if (s->prescreen > 0)
+ interpolation(srcbuf + (y / 2) * srcbuf_stride + 32,
+ srcbuf_stride,
+ dstbuf + (y / 2) * dstbuf_stride,
+ prescreen_buf, width);
}
- frame_data->field[plane] = field_n;
+ s->write(dstbuf, out_line, dstbuf_stride, dst_linesize * 2,
+ width, slice_height, depth, out_scale);
}
- if (!frame_data->input) {
- frame_data->input = av_malloc(512 * sizeof(float));
- if (!frame_data->input)
- return AVERROR(ENOMEM);
- }
- // evalfunc_0 requires at least padded_width[0] bytes.
- // evalfunc_1 requires at least 512 floats.
- if (!frame_data->temp) {
- temp_size = FFMAX(frame_data->padded_width[0], 512 * sizeof(float));
- frame_data->temp = av_malloc(temp_size);
- if (!frame_data->temp)
- return AVERROR(ENOMEM);
- }
+ return 0;
+}
+
+static int get_frame(AVFilterContext *ctx, int is_second)
+{
+ NNEDIContext *s = ctx->priv;
+ AVFilterLink *outlink = ctx->outputs[0];
+ AVFrame *src = s->src;
- // Copy src to a padded "frame" in frame_data and mirror the edges.
- s->copy_pad(src, frame_data, s, field_n);
+ s->dst = ff_get_video_buffer(outlink, outlink->w, outlink->h);
+ if (!s->dst)
+ return AVERROR(ENOMEM);
+ av_frame_copy_props(s->dst, src);
+ s->dst->interlaced_frame = 0;
- // Handles prescreening and the cubic interpolation.
- s->evalfunc_0(s, frame_data);
+ ctx->internal->execute(ctx, filter_slice, NULL, NULL, FFMIN(s->planeheight[1] / 2, s->nb_threads));
- // The rest.
- s->evalfunc_1(s, frame_data);
+ if (s->field == -2 || s->field > 1)
+ s->field_n = !s->field_n;
return 0;
}
@@ -904,23 +816,221 @@ static int request_frame(AVFilterLink *link)
return 0;
}
+static void read(float *dst, size_t n, const float **data)
+{
+ memcpy(dst, *data, n * sizeof(float));
+ *data += n;
+}
+
+static float *allocate(float **ptr, size_t size)
+{
+ float *ret = *ptr;
+
+ *ptr += size;
+
+ return ret;
+}
+
+static int allocate_model(PredictorCoefficients *coeffs, int xdim, int ydim, int nns)
+{
+ size_t filter_size = nns * xdim * ydim;
+ size_t bias_size = nns;
+ float *data;
+
+ data = av_malloc_array(filter_size + bias_size, 4 * sizeof(float));
+ if (!data)
+ return AVERROR(ENOMEM);
+
+ coeffs->data = data;
+ coeffs->xdim = xdim;
+ coeffs->ydim = ydim;
+ coeffs->nns = nns;
+
+ coeffs->softmax_q1 = allocate(&data, filter_size);
+ coeffs->elliott_q1 = allocate(&data, filter_size);
+ coeffs->softmax_bias_q1 = allocate(&data, bias_size);
+ coeffs->elliott_bias_q1 = allocate(&data, bias_size);
+
+ coeffs->softmax_q2 = allocate(&data, filter_size);
+ coeffs->elliott_q2 = allocate(&data, filter_size);
+ coeffs->softmax_bias_q2 = allocate(&data, bias_size);
+ coeffs->elliott_bias_q2 = allocate(&data, bias_size);
+
+ return 0;
+}
+
+static int read_weights(AVFilterContext *ctx, const float *bdata)
+{
+ NNEDIContext *s = ctx->priv;
+ int ret;
+
+ read(&s->prescreener_old.kernel_l0[0][0], 4 * 48, &bdata);
+ read(s->prescreener_old.bias_l0, 4, &bdata);
+
+ read(&s->prescreener_old.kernel_l1[0][0], 4 * 4, &bdata);
+ read(s->prescreener_old.bias_l1, 4, &bdata);
+
+ read(&s->prescreener_old.kernel_l2[0][0], 4 * 8, &bdata);
+ read(s->prescreener_old.bias_l2, 4, &bdata);
+
+ for (int i = 0; i < 3; i++) {
+ PrescreenerNewCoefficients *data = &s->prescreener_new[i];
+ float kernel_l0_shuffled[4 * 64];
+ float kernel_l1_shuffled[4 * 4];
+
+ read(kernel_l0_shuffled, 4 * 64, &bdata);
+ read(data->bias_l0, 4, &bdata);
+
+ read(kernel_l1_shuffled, 4 * 4, &bdata);
+ read(data->bias_l1, 4, &bdata);
+
+ for (int n = 0; n < 4; n++) {
+ for (int k = 0; k < 64; k++)
+ data->kernel_l0[n][k] = kernel_l0_shuffled[(k / 8) * 32 + n * 8 + k % 8];
+ for (int k = 0; k < 4; k++)
+ data->kernel_l1[n][k] = kernel_l1_shuffled[k * 4 + n];
+ }
+ }
+
+ for (int m = 0; m < 2; m++) {
+ // Grouping by neuron count.
+ for (int i = 0; i < 5; i++) {
+ int nns = NNEDI_NNS[i];
+
+ // Grouping by window size.
+ for (int j = 0; j < 7; j++) {
+ PredictorCoefficients *model = &s->coeffs[m][i][j];
+ int xdim = NNEDI_XDIM[j];
+ int ydim = NNEDI_YDIM[j];
+ size_t filter_size = xdim * ydim;
+
+ ret = allocate_model(model, xdim, ydim, nns);
+ if (ret < 0)
+ return ret;
+
+ // Quality 1 model. NNS[i] * (XDIM[j] * YDIM[j]) * 2 coefficients.
+ read(model->softmax_q1, nns * filter_size, &bdata);
+ read(model->elliott_q1, nns * filter_size, &bdata);
+
+ // Quality 1 model bias. NNS[i] * 2 coefficients.
+ read(model->softmax_bias_q1, nns, &bdata);
+ read(model->elliott_bias_q1, nns, &bdata);
+
+ // Quality 2 model. NNS[i] * (XDIM[j] * YDIM[j]) * 2 coefficients.
+ read(model->softmax_q2, nns * filter_size, &bdata);
+ read(model->elliott_q2, nns * filter_size, &bdata);
+
+ // Quality 2 model bias. NNS[i] * 2 coefficients.
+ read(model->softmax_bias_q2, nns, &bdata);
+ read(model->elliott_bias_q2, nns, &bdata);
+ }
+ }
+ }
+
+ return 0;
+}
+
+static float mean(const float *input, int size)
+{
+ float sum = 0.;
+
+ for (int i = 0; i < size; i++)
+ sum += input[i];
+
+ return sum / size;
+}
+
+static void transform(float *input, int size, float mean, float half)
+{
+ for (int i = 0; i < size; i++)
+ input[i] = (input[i] - mean) / half;
+}
+
+static void subtract_mean_old(PrescreenerOldCoefficients *coeffs, float half)
+{
+ for (int n = 0; n < 4; n++) {
+ float m = mean(coeffs->kernel_l0[n], 48);
+
+ transform(coeffs->kernel_l0[n], 48, m, half);
+ }
+}
+
+static void subtract_mean_new(PrescreenerNewCoefficients *coeffs, float half)
+{
+ for (int n = 0; n < 4; n++) {
+ float m = mean(coeffs->kernel_l0[n], 64);
+
+ transform(coeffs->kernel_l0[n], 64, m, half);
+ }
+}
+
+static void subtract_mean_predictor(PredictorCoefficients *model)
+{
+ size_t filter_size = model->xdim * model->ydim;
+ int nns = model->nns;
+
+ float softmax_means[256]; // Average of individual softmax filters.
+ float elliott_means[256]; // Average of individual elliott filters.
+ float mean_filter[48 * 6]; // Pointwise average of all softmax filters.
+ float mean_bias;
+
+ // Quality 1.
+ for (int nn = 0; nn < nns; nn++) {
+ softmax_means[nn] = mean(model->softmax_q1 + nn * filter_size, filter_size);
+ elliott_means[nn] = mean(model->elliott_q1 + nn * filter_size, filter_size);
+
+ for (int k = 0; k < filter_size; k++)
+ mean_filter[k] += model->softmax_q1[nn * filter_size + k] - softmax_means[nn];
+ }
+
+ for (int k = 0; k < filter_size; k++)
+ mean_filter[k] /= nns;
+
+ mean_bias = mean(model->softmax_bias_q1, nns);
+
+ for (int nn = 0; nn < nns; nn++) {
+ for (int k = 0; k < filter_size; k++) {
+ model->softmax_q1[nn * filter_size + k] -= softmax_means[nn] + mean_filter[k];
+ model->elliott_q1[nn * filter_size + k] -= elliott_means[nn];
+ }
+ model->softmax_bias_q1[nn] -= mean_bias;
+ }
+
+ // Quality 2.
+ memset(mean_filter, 0, 48 * 6 * sizeof(float));
+
+ for (int nn = 0; nn < nns; nn++) {
+ softmax_means[nn] = mean(model->softmax_q2 + nn * filter_size, filter_size);
+ elliott_means[nn] = mean(model->elliott_q2 + nn * filter_size, filter_size);
+
+ for (int k = 0; k < filter_size; k++) {
+ mean_filter[k] += model->softmax_q2[nn * filter_size + k] - softmax_means[nn];
+ }
+ }
+
+ for (int k = 0; k < filter_size; k++)
+ mean_filter[k] /= nns;
+
+ mean_bias = mean(model->softmax_bias_q2, nns);
+
+ for (unsigned nn = 0; nn < nns; nn++) {
+ for (unsigned k = 0; k < filter_size; k++) {
+ model->softmax_q2[nn * filter_size + k] -= softmax_means[nn] + mean_filter[k];
+ model->elliott_q2[nn * filter_size + k] -= elliott_means[nn];
+ }
+
+ model->softmax_bias_q2[nn] -= mean_bias;
+ }
+}
+
static av_cold int init(AVFilterContext *ctx)
{
NNEDIContext *s = ctx->priv;
FILE *weights_file = NULL;
- int64_t expected_size = 13574928;
int64_t weights_size;
float *bdata;
size_t bytes_read;
- const int xdia_table[NUM_NSIZE] = { 8, 16, 32, 48, 8, 16, 32 };
- const int ydia_table[NUM_NSIZE] = { 6, 6, 6, 6, 4, 4, 4 };
- const int nns_table[NUM_NNS] = { 16, 32, 64, 128, 256 };
- const int dims0 = 49 * 4 + 5 * 4 + 9 * 4;
- const int dims0new = 4 * 65 + 4 * 5;
- const int dims1 = nns_table[s->nnsparam] * 2 * (xdia_table[s->nsize] * ydia_table[s->nsize] + 1);
- int dims1tsize = 0;
- int dims1offset = 0;
- int ret = 0, i, j, k;
+ int ret = 0;
weights_file = av_fopen_utf8(s->weights_file, "rb");
if (!weights_file) {
@@ -940,7 +1050,7 @@ static av_cold int init(AVFilterContext *ctx)
fclose(weights_file);
av_log(ctx, AV_LOG_ERROR, "Couldn't get size of weights file.\n");
return AVERROR(EINVAL);
- } else if (weights_size != expected_size) {
+ } else if (weights_size != NNEDI_WEIGHTS_SIZE) {
fclose(weights_file);
av_log(ctx, AV_LOG_ERROR, "Unexpected weights file size.\n");
return AVERROR(EINVAL);
@@ -952,15 +1062,14 @@ static av_cold int init(AVFilterContext *ctx)
return AVERROR(EINVAL);
}
- bdata = (float *)av_malloc(expected_size);
+ bdata = av_malloc(NNEDI_WEIGHTS_SIZE);
if (!bdata) {
fclose(weights_file);
return AVERROR(ENOMEM);
}
- bytes_read = fread(bdata, 1, expected_size, weights_file);
-
- if (bytes_read != (size_t)expected_size) {
+ bytes_read = fread(bdata, 1, NNEDI_WEIGHTS_SIZE, weights_file);
+ if (bytes_read != NNEDI_WEIGHTS_SIZE) {
fclose(weights_file);
ret = AVERROR_INVALIDDATA;
av_log(ctx, AV_LOG_ERROR, "Couldn't read weights file.\n");
@@ -969,211 +1078,102 @@ static av_cold int init(AVFilterContext *ctx)
fclose(weights_file);
- for (j = 0; j < NUM_NNS; j++) {
- for (i = 0; i < NUM_NSIZE; i++) {
- if (i == s->nsize && j == s->nnsparam)
- dims1offset = dims1tsize;
- dims1tsize += nns_table[j] * 2 * (xdia_table[i] * ydia_table[i] + 1) * 2;
- }
- }
-
- s->weights0 = av_malloc_array(FFMAX(dims0, dims0new), sizeof(float));
- if (!s->weights0) {
+ s->fdsp = avpriv_float_dsp_alloc(0);
+ if (!s->fdsp) {
ret = AVERROR(ENOMEM);
goto fail;
}
- for (i = 0; i < 2; i++) {
- s->weights1[i] = av_malloc_array(dims1, sizeof(float));
- if (!s->weights1[i]) {
- ret = AVERROR(ENOMEM);
- goto fail;
- }
- }
+ ret = read_weights(ctx, bdata);
+ if (ret < 0)
+ goto fail;
- // Adjust prescreener weights
- if (s->pscrn >= 2) {// using new prescreener
- const float *bdw;
- int16_t *ws;
- float *wf;
- double mean[4] = { 0.0, 0.0, 0.0, 0.0 };
- int *offt = av_calloc(4 * 64, sizeof(int));
-
- if (!offt) {
- ret = AVERROR(ENOMEM);
- goto fail;
- }
+fail:
+ av_free(bdata);
+ return ret;
+}
- for (j = 0; j < 4; j++)
- for (k = 0; k < 64; k++)
- offt[j * 64 + k] = ((k >> 3) << 5) + ((j & 3) << 3) + (k & 7);
-
- bdw = bdata + dims0 + dims0new * (s->pscrn - 2);
- ws = (int16_t *)s->weights0;
- wf = (float *)&ws[4 * 64];
- // Calculate mean weight of each first layer neuron
- for (j = 0; j < 4; j++) {
- double cmean = 0.0;
- for (k = 0; k < 64; k++)
- cmean += bdw[offt[j * 64 + k]];
- mean[j] = cmean / 64.0;
- }
- // Factor mean removal and 1.0/127.5 scaling
- // into first layer weights. scale to int16 range
- for (j = 0; j < 4; j++) {
- double scale, mval = 0.0;
-
- for (k = 0; k < 64; k++)
- mval = FFMAX(mval, FFABS((bdw[offt[j * 64 + k]] - mean[j]) / 127.5));
- scale = 32767.0 / mval;
- for (k = 0; k < 64; k++)
- ws[offt[j * 64 + k]] = roundds(((bdw[offt[j * 64 + k]] - mean[j]) / 127.5) * scale);
- wf[j] = (float)(mval / 32767.0);
- }
- memcpy(wf + 4, bdw + 4 * 64, (dims0new - 4 * 64) * sizeof(float));
- av_free(offt);
- } else { // using old prescreener
- double mean[4] = { 0.0, 0.0, 0.0, 0.0 };
- // Calculate mean weight of each first layer neuron
- for (j = 0; j < 4; j++) {
- double cmean = 0.0;
- for (k = 0; k < 48; k++)
- cmean += bdata[j * 48 + k];
- mean[j] = cmean / 48.0;
- }
- if (s->fapprox & 1) {// use int16 dot products in first layer
- int16_t *ws = (int16_t *)s->weights0;
- float *wf = (float *)&ws[4 * 48];
- // Factor mean removal and 1.0/127.5 scaling
- // into first layer weights. scale to int16 range
- for (j = 0; j < 4; j++) {
- double scale, mval = 0.0;
- for (k = 0; k < 48; k++)
- mval = FFMAX(mval, FFABS((bdata[j * 48 + k] - mean[j]) / 127.5));
- scale = 32767.0 / mval;
- for (k = 0; k < 48; k++)
- ws[j * 48 + k] = roundds(((bdata[j * 48 + k] - mean[j]) / 127.5) * scale);
- wf[j] = (float)(mval / 32767.0);
- }
- memcpy(wf + 4, bdata + 4 * 48, (dims0 - 4 * 48) * sizeof(float));
- } else {// use float dot products in first layer
- double half = (1 << 8) - 1;
-
- half /= 2;
-
- // Factor mean removal and 1.0/half scaling
- // into first layer weights.
- for (j = 0; j < 4; j++)
- for (k = 0; k < 48; k++)
- s->weights0[j * 48 + k] = (float)((bdata[j * 48 + k] - mean[j]) / half);
- memcpy(s->weights0 + 4 * 48, bdata + 4 * 48, (dims0 - 4 * 48) * sizeof(float));
- }
+static int config_input(AVFilterLink *inlink)
+{
+ AVFilterContext *ctx = inlink->dst;
+ NNEDIContext *s = ctx->priv;
+ const AVPixFmtDescriptor *desc = av_pix_fmt_desc_get(inlink->format);
+ int ret;
+
+ s->depth = desc->comp[0].depth;
+ s->nb_threads = ff_filter_get_nb_threads(ctx);
+ s->nb_planes = av_pix_fmt_count_planes(inlink->format);
+ if ((ret = av_image_fill_linesizes(s->linesize, inlink->format, inlink->w)) < 0)
+ return ret;
+
+ s->planewidth[1] = s->planewidth[2] = AV_CEIL_RSHIFT(inlink->w, desc->log2_chroma_w);
+ s->planewidth[0] = s->planewidth[3] = inlink->w;
+ s->planeheight[1] = s->planeheight[2] = AV_CEIL_RSHIFT(inlink->h, desc->log2_chroma_h);
+ s->planeheight[0] = s->planeheight[3] = inlink->h;
+
+ s->half = ((1 << 8) - 1) / 2.f;
+ s->out_scale = 1 << (s->depth - 8);
+ s->in_scale = 1.f / s->out_scale;
+
+ switch (s->depth) {
+ case 8:
+ s->read = read_bytes;
+ s->write = write_bytes;
+ break;
+ default:
+ s->read = read_words;
+ s->write = write_words;
+ break;
}
- // Adjust prediction weights
- for (i = 0; i < 2; i++) {
- const float *bdataT = bdata + dims0 + dims0new * 3 + dims1tsize * s->etype + dims1offset + i * dims1;
- const int nnst = nns_table[s->nnsparam];
- const int asize = xdia_table[s->nsize] * ydia_table[s->nsize];
- const int boff = nnst * 2 * asize;
- double *mean = (double *)av_calloc(asize + 1 + nnst * 2, sizeof(double));
-
- if (!mean) {
- ret = AVERROR(ENOMEM);
- goto fail;
- }
+ subtract_mean_old(&s->prescreener_old, s->half);
+ subtract_mean_new(&s->prescreener_new[0], s->half);
+ subtract_mean_new(&s->prescreener_new[1], s->half);
+ subtract_mean_new(&s->prescreener_new[2], s->half);
- // Calculate mean weight of each neuron (ignore bias)
- for (j = 0; j < nnst * 2; j++) {
- double cmean = 0.0;
- for (k = 0; k < asize; k++)
- cmean += bdataT[j * asize + k];
- mean[asize + 1 + j] = cmean / (double)asize;
- }
- // Calculate mean softmax neuron
- for (j = 0; j < nnst; j++) {
- for (k = 0; k < asize; k++)
- mean[k] += bdataT[j * asize + k] - mean[asize + 1 + j];
- mean[asize] += bdataT[boff + j];
- }
- for (j = 0; j < asize + 1; j++)
- mean[j] /= (double)(nnst);
-
- if (s->fapprox & 2) { // use int16 dot products
- int16_t *ws = (int16_t *)s->weights1[i];
- float *wf = (float *)&ws[nnst * 2 * asize];
- // Factor mean removal into weights, remove global offset from
- // softmax neurons, and scale weights to int16 range.
- for (j = 0; j < nnst; j++) { // softmax neurons
- double scale, mval = 0.0;
- for (k = 0; k < asize; k++)
- mval = FFMAX(mval, FFABS(bdataT[j * asize + k] - mean[asize + 1 + j] - mean[k]));
- scale = 32767.0 / mval;
- for (k = 0; k < asize; k++)
- ws[j * asize + k] = roundds((bdataT[j * asize + k] - mean[asize + 1 + j] - mean[k]) * scale);
- wf[(j >> 2) * 8 + (j & 3)] = (float)(mval / 32767.0);
- wf[(j >> 2) * 8 + (j & 3) + 4] = (float)(bdataT[boff + j] - mean[asize]);
- }
- for (j = nnst; j < nnst * 2; j++) { // elliott neurons
- double scale, mval = 0.0;
- for (k = 0; k < asize; k++)
- mval = FFMAX(mval, FFABS(bdataT[j * asize + k] - mean[asize + 1 + j]));
- scale = 32767.0 / mval;
- for (k = 0; k < asize; k++)
- ws[j * asize + k] = roundds((bdataT[j * asize + k] - mean[asize + 1 + j]) * scale);
- wf[(j >> 2) * 8 + (j & 3)] = (float)(mval / 32767.0);
- wf[(j >> 2) * 8 + (j & 3) + 4] = bdataT[boff + j];
- }
- } else { // use float dot products
- // Factor mean removal into weights, and remove global
- // offset from softmax neurons.
- for (j = 0; j < nnst * 2; j++) {
- for (k = 0; k < asize; k++) {
- const double q = j < nnst ? mean[k] : 0.0;
- s->weights1[i][j * asize + k] = (float)(bdataT[j * asize + k] - mean[asize + 1 + j] - q);
- }
- s->weights1[i][boff + j] = (float)(bdataT[boff + j] - (j < nnst ? mean[asize] : 0.0));
- }
+ s->prescreen[0] = process_old;
+ s->prescreen[1] = process_new;
+
+ for (int i = 0; i < 2; i++) {
+ for (int j = 0; j < 5; j++) {
+ for (int k = 0; k < 7; k++)
+ subtract_mean_predictor(&s->coeffs[i][j][k]);
}
- av_free(mean);
}
- s->nns = nns_table[s->nnsparam];
- s->xdia = xdia_table[s->nsize];
- s->ydia = ydia_table[s->nsize];
- s->asize = xdia_table[s->nsize] * ydia_table[s->nsize];
-
- s->max_value = 65535 >> 8;
+ s->prescreen_buf = av_calloc(s->nb_threads * s->planewidth[0], sizeof(*s->prescreen_buf));
+ if (!s->prescreen_buf)
+ return AVERROR(ENOMEM);
- select_functions(s);
+ s->input_size = (s->planewidth[0] + 64) * (s->planeheight[0] + 6);
+ s->input_buf = av_calloc(s->nb_threads * s->input_size, sizeof(*s->input_buf));
+ if (!s->input_buf)
+ return AVERROR(ENOMEM);
- s->fdsp = avpriv_float_dsp_alloc(0);
- if (!s->fdsp)
- ret = AVERROR(ENOMEM);
+ s->output_buf = av_calloc(s->nb_threads * s->input_size, sizeof(*s->output_buf));
+ if (!s->output_buf)
+ return AVERROR(ENOMEM);
-fail:
- av_free(bdata);
- return ret;
+ return 0;
}
static av_cold void uninit(AVFilterContext *ctx)
{
NNEDIContext *s = ctx->priv;
- int i;
-
- av_freep(&s->weights0);
- for (i = 0; i < 2; i++)
- av_freep(&s->weights1[i]);
+ av_freep(&s->prescreen_buf);
+ av_freep(&s->input_buf);
+ av_freep(&s->output_buf);
+ av_freep(&s->fdsp);
- for (i = 0; i < s->nb_planes; i++) {
- av_freep(&s->frame_data.paddedp[i]);
- av_freep(&s->frame_data.lcount[i]);
+ for (int i = 0; i < 2; i++) {
+ for (int j = 0; j < 5; j++) {
+ for (int k = 0; k < 7; k++) {
+ av_freep(&s->coeffs[i][j][k].data);
+ }
+ }
}
- av_freep(&s->frame_data.input);
- av_freep(&s->frame_data.temp);
- av_freep(&s->fdsp);
av_frame_free(&s->second);
}
@@ -1207,5 +1207,6 @@ AVFilter ff_vf_nnedi = {
.query_formats = query_formats,
.inputs = inputs,
.outputs = outputs,
- .flags = AVFILTER_FLAG_SUPPORT_TIMELINE_INTERNAL,
+ .flags = AVFILTER_FLAG_SUPPORT_TIMELINE_INTERNAL | AVFILTER_FLAG_SLICE_THREADS,
+ .process_command = ff_filter_process_command,
};
More information about the ffmpeg-cvslog
mailing list