avfilter/vf_scale: store the offset in a local variable before adding it

[ffmpeg] / libavfilter / af_arnndn.c

diff --git a/libavfilter/af_arnndn.c b/libavfilter/af_arnndn.c
index d38dacae8352fe666c049f0627edd87005c8bf83..0c70a32271ffe0d3c06e43e1b4e9a31374811faa 100644 (file)
--- a/libavfilter/af_arnndn.c
+++ b/libavfilter/af_arnndn.c
@@ -185,9 +185,9 @@ static void rnnoise_model_free(RNNModel *model)
     av_free(model);
 }
 
-static RNNModel *rnnoise_model_from_file(FILE *f)
+static int rnnoise_model_from_file(FILE *f, RNNModel **rnn)
 {
-    RNNModel *ret;
+    RNNModel *ret = NULL;
     DenseLayer *input_dense;
     GRULayer *vad_gru;
     GRULayer *noise_gru;
@@ -197,17 +197,17 @@ static RNNModel *rnnoise_model_from_file(FILE *f)
     int in;
 
     if (fscanf(f, "rnnoise-nu model file version %d\n", &in) != 1 || in != 1)
-        return NULL;
+        return AVERROR_INVALIDDATA;
 
     ret = av_calloc(1, sizeof(RNNModel));
     if (!ret)
-        return NULL;
+        return AVERROR(ENOMEM);
 
 #define ALLOC_LAYER(type, name) \
     name = av_calloc(1, sizeof(type)); \
     if (!name) { \
         rnnoise_model_free(ret); \
-        return NULL; \
+        return AVERROR(ENOMEM); \
     } \
     ret->name = name
 
@@ -221,7 +221,7 @@ static RNNModel *rnnoise_model_from_file(FILE *f)
 #define INPUT_VAL(name) do { \
     if (fscanf(f, "%d", &in) != 1 || in < 0 || in > 128) { \
         rnnoise_model_free(ret); \
-        return NULL; \
+        return AVERROR(EINVAL); \
     } \
     name = in; \
     } while (0)
@@ -245,13 +245,13 @@ static RNNModel *rnnoise_model_from_file(FILE *f)
     float *values = av_calloc((len), sizeof(float)); \
     if (!values) { \
         rnnoise_model_free(ret); \
-        return NULL; \
+        return AVERROR(ENOMEM); \
     } \
     name = values; \
     for (int i = 0; i < (len); i++) { \
         if (fscanf(f, "%d", &in) != 1) { \
             rnnoise_model_free(ret); \
-            return NULL; \
+            return AVERROR(EINVAL); \
         } \
         values[i] = in; \
     } \
@@ -261,7 +261,7 @@ static RNNModel *rnnoise_model_from_file(FILE *f)
     float *values = av_calloc(FFALIGN((len0), 4) * FFALIGN((len1), 4) * (len2), sizeof(float)); \
     if (!values) { \
         rnnoise_model_free(ret); \
-        return NULL; \
+        return AVERROR(ENOMEM); \
     } \
     name = values; \
     for (int k = 0; k < (len0); k++) { \
@@ -269,7 +269,7 @@ static RNNModel *rnnoise_model_from_file(FILE *f)
             for (int j = 0; j < (len1); j++) { \
                 if (fscanf(f, "%d", &in) != 1) { \
                     rnnoise_model_free(ret); \
-                    return NULL; \
+                    return AVERROR(EINVAL); \
                 } \
                 values[j * (len2) * FFALIGN((len0), 4) + i * FFALIGN((len0), 4) + k] = in; \
             } \
@@ -277,13 +277,24 @@ static RNNModel *rnnoise_model_from_file(FILE *f)
     } \
     } while (0)
 
+#define NEW_LINE() do { \
+    int c; \
+    while ((c = fgetc(f)) != EOF) { \
+        if (c == '\n') \
+        break; \
+    } \
+    } while (0)
+
 #define INPUT_DENSE(name) do { \
     INPUT_VAL(name->nb_inputs); \
     INPUT_VAL(name->nb_neurons); \
     ret->name ## _size = name->nb_neurons; \
     INPUT_ACTIVATION(name->activation); \
+    NEW_LINE(); \
     INPUT_ARRAY(name->input_weights, name->nb_inputs * name->nb_neurons); \
+    NEW_LINE(); \
     INPUT_ARRAY(name->bias, name->nb_neurons); \
+    NEW_LINE(); \
     } while (0)
 
 #define INPUT_GRU(name) do { \
@@ -291,9 +302,13 @@ static RNNModel *rnnoise_model_from_file(FILE *f)
     INPUT_VAL(name->nb_neurons); \
     ret->name ## _size = name->nb_neurons; \
     INPUT_ACTIVATION(name->activation); \
+    NEW_LINE(); \
     INPUT_ARRAY3(name->input_weights, name->nb_inputs, name->nb_neurons, 3); \
+    NEW_LINE(); \
     INPUT_ARRAY3(name->recurrent_weights, name->nb_neurons, name->nb_neurons, 3); \
+    NEW_LINE(); \
     INPUT_ARRAY(name->bias, name->nb_neurons * 3); \
+    NEW_LINE(); \
     } while (0)
 
     INPUT_DENSE(input_dense);
@@ -305,10 +320,12 @@ static RNNModel *rnnoise_model_from_file(FILE *f)
 
     if (vad_output->nb_neurons != 1) {
         rnnoise_model_free(ret);
-        return NULL;
+        return AVERROR(EINVAL);
     }
 
-    return ret;
+    *rnn = ret;
+
+    return 0;
 }
 
 static int query_formats(AVFilterContext *ctx)
@@ -1468,18 +1485,21 @@ static int activate(AVFilterContext *ctx)
 static int open_model(AVFilterContext *ctx, RNNModel **model)
 {
     AudioRNNContext *s = ctx->priv;
+    int ret;
     FILE *f;
 
     if (!s->model_name)
         return AVERROR(EINVAL);
     f = av_fopen_utf8(s->model_name, "r");
-    if (!f)
+    if (!f) {
+        av_log(ctx, AV_LOG_ERROR, "Failed to open model file: %s\n", s->model_name);
         return AVERROR(EINVAL);
+    }
 
-    *model = rnnoise_model_from_file(f);
+    ret = rnnoise_model_from_file(f, model);
     fclose(f);
-    if (!*model)
-        return AVERROR(EINVAL);
+    if (!*model || ret < 0)
+        return ret;
 
     return 0;
 }
@@ -1599,7 +1619,7 @@ static const AVOption arnndn_options[] = {
 
 AVFILTER_DEFINE_CLASS(arnndn);
 
-AVFilter ff_af_arnndn = {
+const AVFilter ff_af_arnndn = {
     .name          = "arnndn",
     .description   = NULL_IF_CONFIG_SMALL("Reduce noise from speech using Recurrent Neural Networks."),
     .query_formats = query_formats,