* [FFmpeg-devel] [PATCH 2/8] lavfi/dnn: Error Specificity in Native Backend Layers
2022-02-24 8:22 [FFmpeg-devel] [PATCH 1/8] libavfilter: Prepare to handle specific error codes in DNN Filters Shubhanshu Saxena
@ 2022-02-24 8:22 ` Shubhanshu Saxena
2022-02-24 8:22 ` [FFmpeg-devel] [PATCH 3/8] lavfi/dnn_io_proc: Return Specific Error Codes Shubhanshu Saxena
` (5 subsequent siblings)
6 siblings, 0 replies; 9+ messages in thread
From: Shubhanshu Saxena @ 2022-02-24 8:22 UTC (permalink / raw)
To: ffmpeg-devel; +Cc: Shubhanshu Saxena
This commit returns specific error codes from the execution
functions in the Native Backend layers instead of DNN_ERROR.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
---
libavfilter/dnn/dnn_backend_native_layer_avgpool.c | 6 +++---
libavfilter/dnn/dnn_backend_native_layer_avgpool.h | 3 ++-
libavfilter/dnn/dnn_backend_native_layer_conv2d.c | 14 ++++++++------
libavfilter/dnn/dnn_backend_native_layer_conv2d.h | 3 ++-
libavfilter/dnn/dnn_backend_native_layer_dense.c | 6 +++---
libavfilter/dnn/dnn_backend_native_layer_dense.h | 3 ++-
.../dnn/dnn_backend_native_layer_depth2space.c | 6 +++---
.../dnn/dnn_backend_native_layer_depth2space.h | 3 ++-
.../dnn/dnn_backend_native_layer_mathbinary.c | 6 +++---
.../dnn/dnn_backend_native_layer_mathunary.c | 6 +++---
.../dnn/dnn_backend_native_layer_mathunary.h | 3 ++-
libavfilter/dnn/dnn_backend_native_layer_maximum.c | 4 ++--
libavfilter/dnn/dnn_backend_native_layer_pad.c | 4 ++--
libavfilter/dnn_interface.h | 2 ++
14 files changed, 39 insertions(+), 30 deletions(-)
diff --git a/libavfilter/dnn/dnn_backend_native_layer_avgpool.c b/libavfilter/dnn/dnn_backend_native_layer_avgpool.c
index 89f1787523..510a28a8c9 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_avgpool.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_avgpool.c
@@ -109,12 +109,12 @@ int ff_dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t *input_ope
output_operand->length = ff_calculate_operand_data_length(output_operand);
if (output_operand->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
output_operand->data = av_realloc(output_operand->data, output_operand->length);
if (!output_operand->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
output = output_operand->data;
@@ -143,5 +143,5 @@ int ff_dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t *input_ope
}
}
- return 0;
+ return DNN_SUCCESS;
}
diff --git a/libavfilter/dnn/dnn_backend_native_layer_avgpool.h b/libavfilter/dnn/dnn_backend_native_layer_avgpool.h
index d8972487de..118a160090 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_avgpool.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_avgpool.h
@@ -60,7 +60,8 @@ int ff_dnn_load_layer_avg_pool(Layer *layer, AVIOContext *model_file_context, in
* @param parameters average pooling parameters
* @param ctx pointer to Native model context for logging
* @retval 0 if the execution succeeds
- * @retval DNN_ERROR if the execution fails
+ * @retval AVERROR(ENOMEM) if memory allocation fails
+ * @retval AVERROR(EINVAL) for invalid arguments
*/
int ff_dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx);
diff --git a/libavfilter/dnn/dnn_backend_native_layer_conv2d.c b/libavfilter/dnn/dnn_backend_native_layer_conv2d.c
index 7a60aa6a4b..dfa0d1ed36 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_conv2d.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_conv2d.c
@@ -211,12 +211,12 @@ int ff_dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_opera
output_operand->length = ff_calculate_operand_data_length(output_operand);
if (output_operand->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
tmp = av_realloc(output_operand->data, output_operand->length);
if (!tmp) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
output_operand->data = tmp;
thread_common_param.output_data = output_operand->data;
@@ -229,17 +229,19 @@ int ff_dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_opera
#if HAVE_PTHREAD_CANCEL
thread_param = av_malloc_array(thread_num, sizeof(*thread_param));
if (!thread_param)
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
thread_stride = (height - pad_size * 2) / thread_num;
//create threads
for (int i = 0; i < thread_num; i++){
+ int thread_ret = 0;
thread_param[i].thread_common_param = &thread_common_param;
thread_param[i].thread_start = thread_stride * i + pad_size;
thread_param[i].thread_end = (i == thread_num - 1) ? (height - pad_size) : (thread_param[i].thread_start + thread_stride);
- if (pthread_create(&thread_param[i].thread, NULL,
- dnn_execute_layer_conv2d_thread, &thread_param[i])) {
+ thread_ret = pthread_create(&thread_param[i].thread, NULL,
+ dnn_execute_layer_conv2d_thread, &thread_param[i]);
+ if (thread_ret) {
thread_num = i;
- ret = DNN_ERROR;
+ ret = AVERROR(thread_ret);
break;
}
}
diff --git a/libavfilter/dnn/dnn_backend_native_layer_conv2d.h b/libavfilter/dnn/dnn_backend_native_layer_conv2d.h
index 446f48f608..f754a9ba18 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_conv2d.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_conv2d.h
@@ -60,7 +60,8 @@ int ff_dnn_load_layer_conv2d(Layer *layer, AVIOContext *model_file_context, int
* @param parameters convolution parameters
* @param ctx pointer to Native model context for logging
* @retval 0 if the execution succeeds
- * @retval DNN_ERROR if the execution fails
+ * @retval AVERROR(ENOMEM) if memory allocation fails
+ * @retval AVERROR(EINVAL) for invalid arguments
*/
int ff_dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx);
diff --git a/libavfilter/dnn/dnn_backend_native_layer_dense.c b/libavfilter/dnn/dnn_backend_native_layer_dense.c
index 117590d7bb..a22a484464 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_dense.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_dense.c
@@ -104,12 +104,12 @@ int ff_dnn_execute_layer_dense(DnnOperand *operands, const int32_t *input_operan
output_operand->length = ff_calculate_operand_data_length(output_operand);
if (output_operand->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
output_operand->data = av_realloc(output_operand->data, output_operand->length);
if (!output_operand->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
output = output_operand->data;
@@ -147,5 +147,5 @@ int ff_dnn_execute_layer_dense(DnnOperand *operands, const int32_t *input_operan
output += dense_params->output_num;
}
}
- return 0;
+ return DNN_SUCCESS;
}
diff --git a/libavfilter/dnn/dnn_backend_native_layer_dense.h b/libavfilter/dnn/dnn_backend_native_layer_dense.h
index 0488b03cc3..607fc3e684 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_dense.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_dense.h
@@ -57,7 +57,8 @@ int ff_dnn_load_layer_dense(Layer *layer, AVIOContext *model_file_context, int f
* @param parameters dense layer parameters
* @param ctx pointer to Native model context for logging
* @retval 0 if the execution succeeds
- * @retval DNN_ERROR if the execution fails
+ * @retval AVERROR(ENOMEM) if memory allocation fails
+ * @retval AVERROR(EINVAL) for invalid arguments
*/
int ff_dnn_execute_layer_dense(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx);
diff --git a/libavfilter/dnn/dnn_backend_native_layer_depth2space.c b/libavfilter/dnn/dnn_backend_native_layer_depth2space.c
index 30ced43cd5..82b1a52be2 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_depth2space.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_depth2space.c
@@ -76,12 +76,12 @@ int ff_dnn_execute_layer_depth2space(DnnOperand *operands, const int32_t *input_
output_operand->length = ff_calculate_operand_data_length(output_operand);
if (output_operand->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
output_operand->data = av_realloc(output_operand->data, output_operand->length);
if (!output_operand->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
output = output_operand->data;
@@ -98,5 +98,5 @@ int ff_dnn_execute_layer_depth2space(DnnOperand *operands, const int32_t *input_
}
output += output_linesize;
}
- return 0;
+ return DNN_SUCCESS;
}
diff --git a/libavfilter/dnn/dnn_backend_native_layer_depth2space.h b/libavfilter/dnn/dnn_backend_native_layer_depth2space.h
index 2792a33ebe..aaf2df4c13 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_depth2space.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_depth2space.h
@@ -63,7 +63,8 @@ int ff_dnn_load_layer_depth2space(Layer *layer, AVIOContext *model_file_context,
* @param parameters depth to space layer parameters
* @param ctx pointer to Native model context for logging
* @retval 0 if the execution succeeds
- * @retval DNN_ERROR if the execution fails
+ * @retval AVERROR(ENOMEM) if memory allocation fails
+ * @retval AVERROR(EINVAL) for invalid arguments
*/
int ff_dnn_execute_layer_depth2space(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx);
diff --git a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
index 81901c56d9..1a3fa3f132 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
@@ -159,12 +159,12 @@ int ff_dnn_execute_layer_math_binary(DnnOperand *operands, const int32_t *input_
output->length = ff_calculate_operand_data_length(output);
if (output->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
output->data = av_realloc(output->data, output->length);
if (!output->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
switch (params->bin_op) {
@@ -188,6 +188,6 @@ int ff_dnn_execute_layer_math_binary(DnnOperand *operands, const int32_t *input_
return 0;
default:
av_log(ctx, AV_LOG_ERROR, "Unmatch math binary operator\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
}
diff --git a/libavfilter/dnn/dnn_backend_native_layer_mathunary.c b/libavfilter/dnn/dnn_backend_native_layer_mathunary.c
index b8694910d9..e3c5106e5e 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_mathunary.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_mathunary.c
@@ -68,12 +68,12 @@ int ff_dnn_execute_layer_math_unary(DnnOperand *operands, const int32_t *input_o
output->length = ff_calculate_operand_data_length(output);
if (output->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
output->data = av_realloc(output->data, output->length);
if (!output->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
dims_count = ff_calculate_operand_dims_count(output);
@@ -151,6 +151,6 @@ int ff_dnn_execute_layer_math_unary(DnnOperand *operands, const int32_t *input_o
return 0;
default:
av_log(ctx, AV_LOG_ERROR, "Unmatch math unary operator\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
}
diff --git a/libavfilter/dnn/dnn_backend_native_layer_mathunary.h b/libavfilter/dnn/dnn_backend_native_layer_mathunary.h
index ed79947896..806e73b29f 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_mathunary.h
+++ b/libavfilter/dnn/dnn_backend_native_layer_mathunary.h
@@ -83,7 +83,8 @@ int ff_dnn_load_layer_math_unary(Layer *layer, AVIOContext *model_file_context,
* @param parameters unary math layer parameters
* @param ctx pointer to Native model context for logging
* @retval 0 if the execution succeeds
- * @retval DNN_ERROR if the execution fails
+ * @retval AVERROR(ENOMEM) if memory allocation fails
+ * @retval AVERROR(EINVAL) for invalid arguments
*/
int ff_dnn_execute_layer_math_unary(DnnOperand *operands, const int32_t *input_operand_indexes,
int32_t output_operand_index, const void *parameters, NativeContext *ctx);
diff --git a/libavfilter/dnn/dnn_backend_native_layer_maximum.c b/libavfilter/dnn/dnn_backend_native_layer_maximum.c
index a38759eeee..667efaa3b8 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_maximum.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_maximum.c
@@ -65,12 +65,12 @@ int ff_dnn_execute_layer_maximum(DnnOperand *operands, const int32_t *input_oper
output->length = ff_calculate_operand_data_length(output);
if (output->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
output->data = av_realloc(output->data, output->length);
if (!output->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
dims_count = ff_calculate_operand_dims_count(output);
diff --git a/libavfilter/dnn/dnn_backend_native_layer_pad.c b/libavfilter/dnn/dnn_backend_native_layer_pad.c
index a60451a8de..e274fe12c6 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_pad.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_pad.c
@@ -113,12 +113,12 @@ int ff_dnn_execute_layer_pad(DnnOperand *operands, const int32_t *input_operand_
output_operand->length = ff_calculate_operand_data_length(output_operand);
if (output_operand->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The output data length overflow\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
output_operand->data = av_realloc(output_operand->data, output_operand->length);
if (!output_operand->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to reallocate memory for output\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
output = output_operand->data;
diff --git a/libavfilter/dnn_interface.h b/libavfilter/dnn_interface.h
index 37e89d9789..24e0b66661 100644
--- a/libavfilter/dnn_interface.h
+++ b/libavfilter/dnn_interface.h
@@ -30,6 +30,8 @@
#include "libavutil/frame.h"
#include "avfilter.h"
+#define DNN_GENERIC_ERROR FFERRTAG('D','N','N','!')
+
typedef enum {DNN_SUCCESS, DNN_ERROR} DNNReturnType;
typedef enum {DNN_NATIVE, DNN_TF, DNN_OV} DNNBackendType;
--
2.32.0
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^ permalink raw reply [flat|nested] 9+ messages in thread* [FFmpeg-devel] [PATCH 3/8] lavfi/dnn_io_proc: Return Specific Error Codes
2022-02-24 8:22 [FFmpeg-devel] [PATCH 1/8] libavfilter: Prepare to handle specific error codes in DNN Filters Shubhanshu Saxena
2022-02-24 8:22 ` [FFmpeg-devel] [PATCH 2/8] lavfi/dnn: Error Specificity in Native Backend Layers Shubhanshu Saxena
@ 2022-02-24 8:22 ` Shubhanshu Saxena
2022-02-24 8:22 ` [FFmpeg-devel] [PATCH 4/8] lavfi/dnn_backend_openvino: " Shubhanshu Saxena
` (4 subsequent siblings)
6 siblings, 0 replies; 9+ messages in thread
From: Shubhanshu Saxena @ 2022-02-24 8:22 UTC (permalink / raw)
To: ffmpeg-devel; +Cc: Shubhanshu Saxena
This commit returns specific error codes from the functions in the
dnn_io_proc instead of DNN_ERROR.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
---
libavfilter/dnn/dnn_io_proc.c | 48 +++++++++++++++++++----------------
libavfilter/dnn/dnn_io_proc.h | 8 +++---
2 files changed, 30 insertions(+), 26 deletions(-)
diff --git a/libavfilter/dnn/dnn_io_proc.c b/libavfilter/dnn/dnn_io_proc.c
index f55424d97c..36cc051e5e 100644
--- a/libavfilter/dnn/dnn_io_proc.c
+++ b/libavfilter/dnn/dnn_io_proc.c
@@ -24,16 +24,16 @@
#include "libavutil/avassert.h"
#include "libavutil/detection_bbox.h"
-DNNReturnType ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx)
+int ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx)
{
struct SwsContext *sws_ctx;
int bytewidth = av_image_get_linesize(frame->format, frame->width, 0);
if (bytewidth < 0) {
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
if (output->dt != DNN_FLOAT) {
avpriv_report_missing_feature(log_ctx, "data type rather than DNN_FLOAT");
- return DNN_ERROR;
+ return AVERROR(ENOSYS);
}
switch (frame->format) {
@@ -51,7 +51,7 @@ DNNReturnType ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *l
"fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
av_get_pix_fmt_name(AV_PIX_FMT_GRAYF32), frame->width * 3, frame->height,
av_get_pix_fmt_name(AV_PIX_FMT_GRAY8), frame->width * 3, frame->height);
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
sws_scale(sws_ctx, (const uint8_t *[4]){(const uint8_t *)output->data, 0, 0, 0},
(const int[4]){frame->width * 3 * sizeof(float), 0, 0, 0}, 0, frame->height,
@@ -82,7 +82,7 @@ DNNReturnType ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *l
"fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
av_get_pix_fmt_name(AV_PIX_FMT_GRAYF32), frame->width, frame->height,
av_get_pix_fmt_name(AV_PIX_FMT_GRAY8), frame->width, frame->height);
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
sws_scale(sws_ctx, (const uint8_t *[4]){(const uint8_t *)output->data, 0, 0, 0},
(const int[4]){frame->width * sizeof(float), 0, 0, 0}, 0, frame->height,
@@ -91,22 +91,22 @@ DNNReturnType ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *l
return DNN_SUCCESS;
default:
avpriv_report_missing_feature(log_ctx, "%s", av_get_pix_fmt_name(frame->format));
- return DNN_ERROR;
+ return AVERROR(ENOSYS);
}
return DNN_SUCCESS;
}
-DNNReturnType ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *log_ctx)
+int ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *log_ctx)
{
struct SwsContext *sws_ctx;
int bytewidth = av_image_get_linesize(frame->format, frame->width, 0);
if (bytewidth < 0) {
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
if (input->dt != DNN_FLOAT) {
avpriv_report_missing_feature(log_ctx, "data type rather than DNN_FLOAT");
- return DNN_ERROR;
+ return AVERROR(ENOSYS);
}
switch (frame->format) {
@@ -124,7 +124,7 @@ DNNReturnType ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *lo
"fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
av_get_pix_fmt_name(AV_PIX_FMT_GRAY8), frame->width * 3, frame->height,
av_get_pix_fmt_name(AV_PIX_FMT_GRAYF32),frame->width * 3, frame->height);
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
sws_scale(sws_ctx, (const uint8_t **)frame->data,
frame->linesize, 0, frame->height,
@@ -156,7 +156,7 @@ DNNReturnType ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *lo
"fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
av_get_pix_fmt_name(AV_PIX_FMT_GRAY8), frame->width, frame->height,
av_get_pix_fmt_name(AV_PIX_FMT_GRAYF32),frame->width, frame->height);
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
sws_scale(sws_ctx, (const uint8_t **)frame->data,
frame->linesize, 0, frame->height,
@@ -166,7 +166,7 @@ DNNReturnType ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *lo
break;
default:
avpriv_report_missing_feature(log_ctx, "%s", av_get_pix_fmt_name(frame->format));
- return DNN_ERROR;
+ return AVERROR(ENOSYS);
}
return DNN_SUCCESS;
@@ -190,13 +190,14 @@ static enum AVPixelFormat get_pixel_format(DNNData *data)
return AV_PIX_FMT_BGR24;
}
-DNNReturnType ff_frame_to_dnn_classify(AVFrame *frame, DNNData *input, uint32_t bbox_index, void *log_ctx)
+int ff_frame_to_dnn_classify(AVFrame *frame, DNNData *input, uint32_t bbox_index, void *log_ctx)
{
const AVPixFmtDescriptor *desc;
int offsetx[4], offsety[4];
uint8_t *bbox_data[4];
struct SwsContext *sws_ctx;
int linesizes[4];
+ int ret = DNN_SUCCESS;
enum AVPixelFormat fmt;
int left, top, width, height;
const AVDetectionBBoxHeader *header;
@@ -221,13 +222,14 @@ DNNReturnType ff_frame_to_dnn_classify(AVFrame *frame, DNNData *input, uint32_t
"fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
av_get_pix_fmt_name(frame->format), width, height,
av_get_pix_fmt_name(fmt), input->width, input->height);
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
- if (av_image_fill_linesizes(linesizes, fmt, input->width) < 0) {
+ ret = av_image_fill_linesizes(linesizes, fmt, input->width);
+ if (ret < 0) {
av_log(log_ctx, AV_LOG_ERROR, "unable to get linesizes with av_image_fill_linesizes");
sws_freeContext(sws_ctx);
- return DNN_ERROR;
+ return ret;
}
desc = av_pix_fmt_desc_get(frame->format);
@@ -246,13 +248,14 @@ DNNReturnType ff_frame_to_dnn_classify(AVFrame *frame, DNNData *input, uint32_t
sws_freeContext(sws_ctx);
- return DNN_SUCCESS;
+ return ret;
}
-DNNReturnType ff_frame_to_dnn_detect(AVFrame *frame, DNNData *input, void *log_ctx)
+int ff_frame_to_dnn_detect(AVFrame *frame, DNNData *input, void *log_ctx)
{
struct SwsContext *sws_ctx;
int linesizes[4];
+ int ret = DNN_SUCCESS;
enum AVPixelFormat fmt = get_pixel_format(input);
sws_ctx = sws_getContext(frame->width, frame->height, frame->format,
input->width, input->height, fmt,
@@ -262,18 +265,19 @@ DNNReturnType ff_frame_to_dnn_detect(AVFrame *frame, DNNData *input, void *log_c
"fmt:%s s:%dx%d -> fmt:%s s:%dx%d\n",
av_get_pix_fmt_name(frame->format), frame->width, frame->height,
av_get_pix_fmt_name(fmt), input->width, input->height);
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
- if (av_image_fill_linesizes(linesizes, fmt, input->width) < 0) {
+ ret = av_image_fill_linesizes(linesizes, fmt, input->width);
+ if (ret < 0) {
av_log(log_ctx, AV_LOG_ERROR, "unable to get linesizes with av_image_fill_linesizes");
sws_freeContext(sws_ctx);
- return DNN_ERROR;
+ return ret;
}
sws_scale(sws_ctx, (const uint8_t *const *)frame->data, frame->linesize, 0, frame->height,
(uint8_t *const [4]){input->data, 0, 0, 0}, linesizes);
sws_freeContext(sws_ctx);
- return DNN_SUCCESS;
+ return ret;
}
diff --git a/libavfilter/dnn/dnn_io_proc.h b/libavfilter/dnn/dnn_io_proc.h
index daef01aceb..a3dd94675b 100644
--- a/libavfilter/dnn/dnn_io_proc.h
+++ b/libavfilter/dnn/dnn_io_proc.h
@@ -30,9 +30,9 @@
#include "../dnn_interface.h"
#include "libavutil/frame.h"
-DNNReturnType ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *log_ctx);
-DNNReturnType ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx);
-DNNReturnType ff_frame_to_dnn_detect(AVFrame *frame, DNNData *input, void *log_ctx);
-DNNReturnType ff_frame_to_dnn_classify(AVFrame *frame, DNNData *input, uint32_t bbox_index, void *log_ctx);
+int ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *log_ctx);
+int ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx);
+int ff_frame_to_dnn_detect(AVFrame *frame, DNNData *input, void *log_ctx);
+int ff_frame_to_dnn_classify(AVFrame *frame, DNNData *input, uint32_t bbox_index, void *log_ctx);
#endif
--
2.32.0
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^ permalink raw reply [flat|nested] 9+ messages in thread* [FFmpeg-devel] [PATCH 4/8] lavfi/dnn_backend_openvino: Return Specific Error Codes
2022-02-24 8:22 [FFmpeg-devel] [PATCH 1/8] libavfilter: Prepare to handle specific error codes in DNN Filters Shubhanshu Saxena
2022-02-24 8:22 ` [FFmpeg-devel] [PATCH 2/8] lavfi/dnn: Error Specificity in Native Backend Layers Shubhanshu Saxena
2022-02-24 8:22 ` [FFmpeg-devel] [PATCH 3/8] lavfi/dnn_io_proc: Return Specific Error Codes Shubhanshu Saxena
@ 2022-02-24 8:22 ` Shubhanshu Saxena
2022-02-24 8:22 ` [FFmpeg-devel] [PATCH 5/8] lavfi/dnn_backend_tf: " Shubhanshu Saxena
` (3 subsequent siblings)
6 siblings, 0 replies; 9+ messages in thread
From: Shubhanshu Saxena @ 2022-02-24 8:22 UTC (permalink / raw)
To: ffmpeg-devel; +Cc: Shubhanshu Saxena
Switch to returning specific error codes or DNN_GENERIC_ERROR
when an error is encountered. For OpenVINO API errors, currently
DNN_GENERIC_ERROR is returned.
---
libavfilter/dnn/dnn_backend_openvino.c | 138 +++++++++++++++----------
libavfilter/dnn/dnn_backend_openvino.h | 4 +-
libavfilter/dnn_interface.h | 10 +-
3 files changed, 89 insertions(+), 63 deletions(-)
diff --git a/libavfilter/dnn/dnn_backend_openvino.c b/libavfilter/dnn/dnn_backend_openvino.c
index f5b1454d21..2f140e996b 100644
--- a/libavfilter/dnn/dnn_backend_openvino.c
+++ b/libavfilter/dnn/dnn_backend_openvino.c
@@ -112,7 +112,7 @@ static int get_datatype_size(DNNDataType dt)
}
}
-static DNNReturnType fill_model_input_ov(OVModel *ov_model, OVRequestItem *request)
+static int fill_model_input_ov(OVModel *ov_model, OVRequestItem *request)
{
dimensions_t dims;
precision_e precision;
@@ -131,7 +131,7 @@ static DNNReturnType fill_model_input_ov(OVModel *ov_model, OVRequestItem *reque
status = ie_infer_request_get_blob(request->infer_request, task->input_name, &input_blob);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input blob with name %s\n", task->input_name);
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
status |= ie_blob_get_dims(input_blob, &dims);
@@ -139,14 +139,14 @@ static DNNReturnType fill_model_input_ov(OVModel *ov_model, OVRequestItem *reque
if (status != OK) {
ie_blob_free(&input_blob);
av_log(ctx, AV_LOG_ERROR, "Failed to get input blob dims/precision\n");
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
status = ie_blob_get_buffer(input_blob, &blob_buffer);
if (status != OK) {
ie_blob_free(&input_blob);
av_log(ctx, AV_LOG_ERROR, "Failed to get input blob buffer\n");
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
input.height = dims.dims[2];
@@ -301,8 +301,9 @@ static void infer_completion_callback(void *args)
}
}
-static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, const char *output_name)
+static int init_model_ov(OVModel *ov_model, const char *input_name, const char *output_name)
{
+ int ret = DNN_SUCCESS;
OVContext *ctx = &ov_model->ctx;
IEStatusCode status;
ie_available_devices_t a_dev;
@@ -317,14 +318,18 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
if (ctx->options.batch_size > 1) {
input_shapes_t input_shapes;
status = ie_network_get_input_shapes(ov_model->network, &input_shapes);
- if (status != OK)
+ if (status != OK) {
+ ret = DNN_GENERIC_ERROR;
goto err;
+ }
for (int i = 0; i < input_shapes.shape_num; i++)
input_shapes.shapes[i].shape.dims[0] = ctx->options.batch_size;
status = ie_network_reshape(ov_model->network, input_shapes);
ie_network_input_shapes_free(&input_shapes);
- if (status != OK)
+ if (status != OK) {
+ ret = DNN_GENERIC_ERROR;
goto err;
+ }
}
// The order of dims in the openvino is fixed and it is always NCHW for 4-D data.
@@ -332,11 +337,13 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
status = ie_network_set_input_layout(ov_model->network, input_name, NHWC);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set layout as NHWC for input %s\n", input_name);
+ ret = DNN_GENERIC_ERROR;
goto err;
}
status = ie_network_set_output_layout(ov_model->network, output_name, NHWC);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set layout as NHWC for output %s\n", output_name);
+ ret = DNN_GENERIC_ERROR;
goto err;
}
@@ -350,6 +357,7 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
status = ie_network_set_input_precision(ov_model->network, input_name, U8);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set input precision as U8 for %s\n", input_name);
+ ret = DNN_GENERIC_ERROR;
goto err;
}
}
@@ -360,6 +368,7 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
status = ie_core_get_available_devices(ov_model->core, &a_dev);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get available devices\n");
+ ret = DNN_GENERIC_ERROR;
goto err;
}
for (int i = 0; i < a_dev.num_devices; i++) {
@@ -367,6 +376,7 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
}
av_log(ctx, AV_LOG_ERROR,"device %s may not be supported, all available devices are: \"%s\"\n",
ctx->options.device_type, all_dev_names);
+ ret = AVERROR(ENODEV);
goto err;
}
@@ -378,12 +388,14 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
ov_model->request_queue = ff_safe_queue_create();
if (!ov_model->request_queue) {
+ ret = AVERROR(ENOMEM);
goto err;
}
for (int i = 0; i < ctx->options.nireq; i++) {
OVRequestItem *item = av_mallocz(sizeof(*item));
if (!item) {
+ ret = AVERROR(ENOMEM);
goto err;
}
@@ -391,16 +403,19 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
item->callback.args = item;
if (ff_safe_queue_push_back(ov_model->request_queue, item) < 0) {
av_freep(&item);
+ ret = AVERROR(ENOMEM);
goto err;
}
status = ie_exec_network_create_infer_request(ov_model->exe_network, &item->infer_request);
if (status != OK) {
+ ret = DNN_GENERIC_ERROR;
goto err;
}
item->lltasks = av_malloc_array(ctx->options.batch_size, sizeof(*item->lltasks));
if (!item->lltasks) {
+ ret = AVERROR(ENOMEM);
goto err;
}
item->lltask_count = 0;
@@ -408,11 +423,13 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
ov_model->task_queue = ff_queue_create();
if (!ov_model->task_queue) {
+ ret = AVERROR(ENOMEM);
goto err;
}
ov_model->lltask_queue = ff_queue_create();
if (!ov_model->lltask_queue) {
+ ret = AVERROR(ENOMEM);
goto err;
}
@@ -420,14 +437,14 @@ static DNNReturnType init_model_ov(OVModel *ov_model, const char *input_name, co
err:
ff_dnn_free_model_ov(&ov_model->model);
- return DNN_ERROR;
+ return ret;
}
-static DNNReturnType execute_model_ov(OVRequestItem *request, Queue *inferenceq)
+static int execute_model_ov(OVRequestItem *request, Queue *inferenceq)
{
IEStatusCode status;
- DNNReturnType ret;
LastLevelTaskItem *lltask;
+ int ret = DNN_SUCCESS;
TaskItem *task;
OVContext *ctx;
OVModel *ov_model;
@@ -451,11 +468,13 @@ static DNNReturnType execute_model_ov(OVRequestItem *request, Queue *inferenceq)
status = ie_infer_set_completion_callback(request->infer_request, &request->callback);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set completion callback for inference\n");
+ ret = DNN_GENERIC_ERROR;
goto err;
}
status = ie_infer_request_infer_async(request->infer_request);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to start async inference\n");
+ ret = DNN_GENERIC_ERROR;
goto err;
}
return DNN_SUCCESS;
@@ -467,20 +486,21 @@ static DNNReturnType execute_model_ov(OVRequestItem *request, Queue *inferenceq)
status = ie_infer_request_infer(request->infer_request);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to start synchronous model inference\n");
+ ret = DNN_GENERIC_ERROR;
goto err;
}
infer_completion_callback(request);
- return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_ERROR;
+ return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_GENERIC_ERROR;
}
err:
if (ff_safe_queue_push_back(ov_model->request_queue, request) < 0) {
ie_infer_request_free(&request->infer_request);
av_freep(&request);
}
- return DNN_ERROR;
+ return ret;
}
-static DNNReturnType get_input_ov(void *model, DNNData *input, const char *input_name)
+static int get_input_ov(void *model, DNNData *input, const char *input_name)
{
OVModel *ov_model = model;
OVContext *ctx = &ov_model->ctx;
@@ -495,14 +515,14 @@ static DNNReturnType get_input_ov(void *model, DNNData *input, const char *input
status = ie_network_get_inputs_number(ov_model->network, &model_input_count);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get input count\n");
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
for (size_t i = 0; i < model_input_count; i++) {
status = ie_network_get_input_name(ov_model->network, i, &model_input_name);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get No.%d input's name\n", (int)i);
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
if (strcmp(model_input_name, input_name) == 0) {
ie_network_name_free(&model_input_name);
@@ -510,7 +530,7 @@ static DNNReturnType get_input_ov(void *model, DNNData *input, const char *input
status |= ie_network_get_input_precision(ov_model->network, input_name, &precision);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to get No.%d input's dims or precision\n", (int)i);
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
input->channels = dims.dims[1];
@@ -527,7 +547,7 @@ static DNNReturnType get_input_ov(void *model, DNNData *input, const char *input
}
av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model, all input(s) are: \"%s\"\n", input_name, all_input_names);
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
static int contain_valid_detection_bbox(AVFrame *frame)
@@ -567,7 +587,7 @@ static int contain_valid_detection_bbox(AVFrame *frame)
return 1;
}
-static DNNReturnType extract_lltask_from_task(DNNFunctionType func_type, TaskItem *task, Queue *lltask_queue, DNNExecBaseParams *exec_params)
+static int extract_lltask_from_task(DNNFunctionType func_type, TaskItem *task, Queue *lltask_queue, DNNExecBaseParams *exec_params)
{
switch (func_type) {
case DFT_PROCESS_FRAME:
@@ -575,14 +595,14 @@ static DNNReturnType extract_lltask_from_task(DNNFunctionType func_type, TaskIte
{
LastLevelTaskItem *lltask = av_malloc(sizeof(*lltask));
if (!lltask) {
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
task->inference_todo = 1;
task->inference_done = 0;
lltask->task = task;
if (ff_queue_push_back(lltask_queue, lltask) < 0) {
av_freep(&lltask);
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
return DNN_SUCCESS;
}
@@ -615,28 +635,28 @@ static DNNReturnType extract_lltask_from_task(DNNFunctionType func_type, TaskIte
lltask = av_malloc(sizeof(*lltask));
if (!lltask) {
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
task->inference_todo++;
lltask->task = task;
lltask->bbox_index = i;
if (ff_queue_push_back(lltask_queue, lltask) < 0) {
av_freep(&lltask);
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
}
return DNN_SUCCESS;
}
default:
av_assert0(!"should not reach here");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
}
-static DNNReturnType get_output_ov(void *model, const char *input_name, int input_width, int input_height,
+static int get_output_ov(void *model, const char *input_name, int input_width, int input_height,
const char *output_name, int *output_width, int *output_height)
{
- DNNReturnType ret;
+ int ret;
OVModel *ov_model = model;
OVContext *ctx = &ov_model->ctx;
TaskItem task;
@@ -653,7 +673,7 @@ static DNNReturnType get_output_ov(void *model, const char *input_name, int inpu
if (ov_model->model->func_type != DFT_PROCESS_FRAME) {
av_log(ctx, AV_LOG_ERROR, "Get output dim only when processing frame.\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
if (ctx->options.input_resizable) {
@@ -664,31 +684,33 @@ static DNNReturnType get_output_ov(void *model, const char *input_name, int inpu
ie_network_input_shapes_free(&input_shapes);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to reshape input size for %s\n", input_name);
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
}
if (!ov_model->exe_network) {
- if (init_model_ov(ov_model, input_name, output_name) != DNN_SUCCESS) {
+ ret = init_model_ov(ov_model, input_name, output_name);
+ if (ret != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
- return DNN_ERROR;
+ return ret;
}
}
- if (ff_dnn_fill_gettingoutput_task(&task, &exec_params, ov_model, input_height, input_width, ctx) != DNN_SUCCESS) {
- return DNN_ERROR;
+ ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, ov_model, input_height, input_width, ctx);
+ if (ret != DNN_SUCCESS) {
+ goto err;
}
- if (extract_lltask_from_task(ov_model->model->func_type, &task, ov_model->lltask_queue, NULL) != DNN_SUCCESS) {
- av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
- ret = DNN_ERROR;
+ ret = extract_lltask_from_task(ov_model->model->func_type, &task, ov_model->lltask_queue, NULL);
+ if (ret != DNN_SUCCESS) {
+ av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
goto err;
}
request = ff_safe_queue_pop_front(ov_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
- ret = DNN_ERROR;
+ ret = AVERROR(EINVAL);
goto err;
}
@@ -758,45 +780,49 @@ err:
return NULL;
}
-DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_params)
+int ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_params)
{
OVModel *ov_model = model->model;
OVContext *ctx = &ov_model->ctx;
OVRequestItem *request;
TaskItem *task;
- DNNReturnType ret;
+ int ret;
- if (ff_check_exec_params(ctx, DNN_OV, model->func_type, exec_params) != 0) {
- return DNN_ERROR;
+ ret = ff_check_exec_params(ctx, DNN_OV, model->func_type, exec_params);
+ if (ret != 0) {
+ return ret;
}
if (!ov_model->exe_network) {
- if (init_model_ov(ov_model, exec_params->input_name, exec_params->output_names[0]) != DNN_SUCCESS) {
+ ret = init_model_ov(ov_model, exec_params->input_name, exec_params->output_names[0]);
+ if (ret != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
- return DNN_ERROR;
+ return ret;
}
}
task = av_malloc(sizeof(*task));
if (!task) {
av_log(ctx, AV_LOG_ERROR, "unable to alloc memory for task item.\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
- if (ff_dnn_fill_task(task, exec_params, ov_model, ctx->options.async, 1) != DNN_SUCCESS) {
+ ret = ff_dnn_fill_task(task, exec_params, ov_model, ctx->options.async, 1);
+ if (ret != DNN_SUCCESS) {
av_freep(&task);
- return DNN_ERROR;
+ return ret;
}
if (ff_queue_push_back(ov_model->task_queue, task) < 0) {
av_freep(&task);
av_log(ctx, AV_LOG_ERROR, "unable to push back task_queue.\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
- if (extract_lltask_from_task(model->func_type, task, ov_model->lltask_queue, exec_params) != DNN_SUCCESS) {
+ ret = extract_lltask_from_task(model->func_type, task, ov_model->lltask_queue, exec_params);
+ if (ret != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
- return DNN_ERROR;
+ return ret;
}
if (ctx->options.async) {
@@ -804,7 +830,7 @@ DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *
request = ff_safe_queue_pop_front(ov_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
ret = execute_model_ov(request, ov_model->lltask_queue);
@@ -820,18 +846,18 @@ DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *
// Classification filter has not been completely
// tested with the sync mode. So, do not support now.
avpriv_report_missing_feature(ctx, "classify for sync execution");
- return DNN_ERROR;
+ return AVERROR(ENOSYS);
}
if (ctx->options.batch_size > 1) {
avpriv_report_missing_feature(ctx, "batch mode for sync execution");
- return DNN_ERROR;
+ return AVERROR(ENOSYS);
}
request = ff_safe_queue_pop_front(ov_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
return execute_model_ov(request, ov_model->lltask_queue);
}
@@ -843,13 +869,13 @@ DNNAsyncStatusType ff_dnn_get_result_ov(const DNNModel *model, AVFrame **in, AVF
return ff_dnn_get_result_common(ov_model->task_queue, in, out);
}
-DNNReturnType ff_dnn_flush_ov(const DNNModel *model)
+int ff_dnn_flush_ov(const DNNModel *model)
{
OVModel *ov_model = model->model;
OVContext *ctx = &ov_model->ctx;
OVRequestItem *request;
IEStatusCode status;
- DNNReturnType ret;
+ int ret;
if (ff_queue_size(ov_model->lltask_queue) == 0) {
// no pending task need to flush
@@ -859,7 +885,7 @@ DNNReturnType ff_dnn_flush_ov(const DNNModel *model)
request = ff_safe_queue_pop_front(ov_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
ret = fill_model_input_ov(ov_model, request);
@@ -870,12 +896,12 @@ DNNReturnType ff_dnn_flush_ov(const DNNModel *model)
status = ie_infer_set_completion_callback(request->infer_request, &request->callback);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to set completion callback for inference\n");
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
status = ie_infer_request_infer_async(request->infer_request);
if (status != OK) {
av_log(ctx, AV_LOG_ERROR, "Failed to start async inference\n");
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
return DNN_SUCCESS;
diff --git a/libavfilter/dnn/dnn_backend_openvino.h b/libavfilter/dnn/dnn_backend_openvino.h
index 0bbca0c057..304bc96b99 100644
--- a/libavfilter/dnn/dnn_backend_openvino.h
+++ b/libavfilter/dnn/dnn_backend_openvino.h
@@ -31,9 +31,9 @@
DNNModel *ff_dnn_load_model_ov(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx);
-DNNReturnType ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_params);
+int ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_params);
DNNAsyncStatusType ff_dnn_get_result_ov(const DNNModel *model, AVFrame **in, AVFrame **out);
-DNNReturnType ff_dnn_flush_ov(const DNNModel *model);
+int ff_dnn_flush_ov(const DNNModel *model);
void ff_dnn_free_model_ov(DNNModel **model);
diff --git a/libavfilter/dnn_interface.h b/libavfilter/dnn_interface.h
index 24e0b66661..06e71f7946 100644
--- a/libavfilter/dnn_interface.h
+++ b/libavfilter/dnn_interface.h
@@ -94,9 +94,9 @@ typedef struct DNNModel{
DNNFunctionType func_type;
// Gets model input information
// Just reuse struct DNNData here, actually the DNNData.data field is not needed.
- DNNReturnType (*get_input)(void *model, DNNData *input, const char *input_name);
+ int (*get_input)(void *model, DNNData *input, const char *input_name);
// Gets model output width/height with given input w/h
- DNNReturnType (*get_output)(void *model, const char *input_name, int input_width, int input_height,
+ int (*get_output)(void *model, const char *input_name, int input_width, int input_height,
const char *output_name, int *output_width, int *output_height);
// set the pre process to transfer data from AVFrame to DNNData
// the default implementation within DNN is used if it is not provided by the filter
@@ -114,12 +114,12 @@ typedef struct DNNModel{
typedef struct DNNModule{
// Loads model and parameters from given file. Returns NULL if it is not possible.
DNNModel *(*load_model)(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx);
- // Executes model with specified input and output. Returns DNN_ERROR otherwise.
- DNNReturnType (*execute_model)(const DNNModel *model, DNNExecBaseParams *exec_params);
+ // Executes model with specified input and output. Returns the error code otherwise.
+ int (*execute_model)(const DNNModel *model, DNNExecBaseParams *exec_params);
// Retrieve inference result.
DNNAsyncStatusType (*get_result)(const DNNModel *model, AVFrame **in, AVFrame **out);
// Flush all the pending tasks.
- DNNReturnType (*flush)(const DNNModel *model);
+ int (*flush)(const DNNModel *model);
// Frees memory allocated for model.
void (*free_model)(DNNModel **model);
} DNNModule;
--
2.32.0
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^ permalink raw reply [flat|nested] 9+ messages in thread* [FFmpeg-devel] [PATCH 5/8] lavfi/dnn_backend_tf: Return Specific Error Codes
2022-02-24 8:22 [FFmpeg-devel] [PATCH 1/8] libavfilter: Prepare to handle specific error codes in DNN Filters Shubhanshu Saxena
` (2 preceding siblings ...)
2022-02-24 8:22 ` [FFmpeg-devel] [PATCH 4/8] lavfi/dnn_backend_openvino: " Shubhanshu Saxena
@ 2022-02-24 8:22 ` Shubhanshu Saxena
2022-02-24 8:22 ` [FFmpeg-devel] [PATCH 6/8] lavfi/dnn_backend_native: " Shubhanshu Saxena
` (2 subsequent siblings)
6 siblings, 0 replies; 9+ messages in thread
From: Shubhanshu Saxena @ 2022-02-24 8:22 UTC (permalink / raw)
To: ffmpeg-devel; +Cc: Shubhanshu Saxena
Switch to returning specific error codes or DNN_GENERIC_ERROR
when an error is encountered. For TensorFlow C API errors, currently
DNN_GENERIC_ERROR is returned.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
---
libavfilter/dnn/dnn_backend_tf.c | 147 +++++++++++++++++--------------
libavfilter/dnn/dnn_backend_tf.h | 4 +-
2 files changed, 84 insertions(+), 67 deletions(-)
diff --git a/libavfilter/dnn/dnn_backend_tf.c b/libavfilter/dnn/dnn_backend_tf.c
index 7dd48fb612..d021e3c3bb 100644
--- a/libavfilter/dnn/dnn_backend_tf.c
+++ b/libavfilter/dnn/dnn_backend_tf.c
@@ -90,7 +90,7 @@ static const AVOption dnn_tensorflow_options[] = {
AVFILTER_DEFINE_CLASS(dnn_tensorflow);
-static DNNReturnType execute_model_tf(TFRequestItem *request, Queue *lltask_queue);
+static int execute_model_tf(TFRequestItem *request, Queue *lltask_queue);
static void infer_completion_callback(void *args);
static inline void destroy_request_item(TFRequestItem **arg);
@@ -152,9 +152,10 @@ static TFInferRequest *tf_create_inference_request(void)
*
* @param request pointer to the TFRequestItem for inference
* @retval DNN_SUCCESS if execution is successful
- * @retval DNN_ERROR if execution fails
+ * @retval AVERROR(EINVAL) if request is NULL
+ * @retval DNN_GENERIC_ERROR if execution fails
*/
-static DNNReturnType tf_start_inference(void *args)
+static int tf_start_inference(void *args)
{
TFRequestItem *request = args;
TFInferRequest *infer_request = request->infer_request;
@@ -164,7 +165,7 @@ static DNNReturnType tf_start_inference(void *args)
if (!request) {
av_log(&tf_model->ctx, AV_LOG_ERROR, "TFRequestItem is NULL\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
TF_SessionRun(tf_model->session, NULL,
@@ -178,7 +179,7 @@ static DNNReturnType tf_start_inference(void *args)
if (ff_safe_queue_push_back(tf_model->request_queue, request) < 0) {
destroy_request_item(&request);
}
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
return DNN_SUCCESS;
}
@@ -202,14 +203,14 @@ static inline void destroy_request_item(TFRequestItem **arg) {
av_freep(arg);
}
-static DNNReturnType extract_lltask_from_task(TaskItem *task, Queue *lltask_queue)
+static int extract_lltask_from_task(TaskItem *task, Queue *lltask_queue)
{
TFModel *tf_model = task->model;
TFContext *ctx = &tf_model->ctx;
LastLevelTaskItem *lltask = av_malloc(sizeof(*lltask));
if (!lltask) {
av_log(ctx, AV_LOG_ERROR, "Unable to allocate space for LastLevelTaskItem\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
task->inference_todo = 1;
task->inference_done = 0;
@@ -217,7 +218,7 @@ static DNNReturnType extract_lltask_from_task(TaskItem *task, Queue *lltask_queu
if (ff_queue_push_back(lltask_queue, lltask) < 0) {
av_log(ctx, AV_LOG_ERROR, "Failed to push back lltask_queue.\n");
av_freep(&lltask);
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
return DNN_SUCCESS;
}
@@ -277,7 +278,7 @@ static TF_Tensor *allocate_input_tensor(const DNNData *input)
input_dims[1] * input_dims[2] * input_dims[3] * size);
}
-static DNNReturnType get_input_tf(void *model, DNNData *input, const char *input_name)
+static int get_input_tf(void *model, DNNData *input, const char *input_name)
{
TFModel *tf_model = model;
TFContext *ctx = &tf_model->ctx;
@@ -288,7 +289,7 @@ static DNNReturnType get_input_tf(void *model, DNNData *input, const char *input
tf_output.oper = TF_GraphOperationByName(tf_model->graph, input_name);
if (!tf_output.oper) {
av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", input_name);
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
tf_output.index = 0;
@@ -300,7 +301,7 @@ static DNNReturnType get_input_tf(void *model, DNNData *input, const char *input
if (TF_GetCode(status) != TF_OK){
TF_DeleteStatus(status);
av_log(ctx, AV_LOG_ERROR, "Failed to get input tensor shape: number of dimension incorrect\n");
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
TF_DeleteStatus(status);
@@ -313,10 +314,10 @@ static DNNReturnType get_input_tf(void *model, DNNData *input, const char *input
return DNN_SUCCESS;
}
-static DNNReturnType get_output_tf(void *model, const char *input_name, int input_width, int input_height,
+static int get_output_tf(void *model, const char *input_name, int input_width, int input_height,
const char *output_name, int *output_width, int *output_height)
{
- DNNReturnType ret;
+ int ret;
TFModel *tf_model = model;
TFContext *ctx = &tf_model->ctx;
TaskItem task;
@@ -329,20 +330,21 @@ static DNNReturnType get_output_tf(void *model, const char *input_name, int inpu
.out_frame = NULL,
};
- if (ff_dnn_fill_gettingoutput_task(&task, &exec_params, tf_model, input_height, input_width, ctx) != DNN_SUCCESS) {
+ ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, tf_model, input_height, input_width, ctx);
+ if (ret != DNN_SUCCESS) {
goto err;
}
- if (extract_lltask_from_task(&task, tf_model->lltask_queue) != DNN_SUCCESS) {
+ ret = extract_lltask_from_task(&task, tf_model->lltask_queue);
+ if (ret != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
- ret = DNN_ERROR;
goto err;
}
request = ff_safe_queue_pop_front(tf_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
- ret = DNN_ERROR;
+ ret = AVERROR(EINVAL);
goto err;
}
@@ -386,7 +388,7 @@ static int hex_to_data(uint8_t *data, const char *p)
return len;
}
-static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename)
+static int load_tf_model(TFModel *tf_model, const char *model_filename)
{
TFContext *ctx = &tf_model->ctx;
TF_Buffer *graph_def;
@@ -407,7 +409,7 @@ static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename
*/
if (strncmp(tf_model->ctx.options.sess_config, "0x", 2) != 0) {
av_log(ctx, AV_LOG_ERROR, "sess_config should start with '0x'\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
config = tf_model->ctx.options.sess_config + 2;
sess_config_length = hex_to_data(NULL, config);
@@ -415,11 +417,11 @@ static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename
sess_config = av_mallocz(sess_config_length + AV_INPUT_BUFFER_PADDING_SIZE);
if (!sess_config) {
av_log(ctx, AV_LOG_ERROR, "failed to allocate memory\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
if (hex_to_data(sess_config, config) < 0) {
av_log(ctx, AV_LOG_ERROR, "failed to convert hex to data\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
}
@@ -427,7 +429,7 @@ static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename
if (!graph_def){
av_log(ctx, AV_LOG_ERROR, "Failed to read model \"%s\" graph\n", model_filename);
av_freep(&sess_config);
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
tf_model->graph = TF_NewGraph();
tf_model->status = TF_NewStatus();
@@ -440,7 +442,7 @@ static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename
TF_DeleteStatus(tf_model->status);
av_log(ctx, AV_LOG_ERROR, "Failed to import serialized graph to model graph\n");
av_freep(&sess_config);
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
init_op = TF_GraphOperationByName(tf_model->graph, "init");
@@ -455,7 +457,7 @@ static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename
TF_DeleteSessionOptions(sess_opts);
av_log(ctx, AV_LOG_ERROR, "Failed to set config for sess options with %s\n",
tf_model->ctx.options.sess_config);
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
}
@@ -466,7 +468,7 @@ static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename
TF_DeleteGraph(tf_model->graph);
TF_DeleteStatus(tf_model->status);
av_log(ctx, AV_LOG_ERROR, "Failed to create new session with model graph\n");
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
// Run initialization operation with name "init" if it is present in graph
@@ -481,7 +483,7 @@ static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename
TF_DeleteGraph(tf_model->graph);
TF_DeleteStatus(tf_model->status);
av_log(ctx, AV_LOG_ERROR, "Failed to run session when initializing\n");
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
}
@@ -490,7 +492,7 @@ static DNNReturnType load_tf_model(TFModel *tf_model, const char *model_filename
#define NAME_BUFFER_SIZE 256
-static DNNReturnType add_conv_layer(TFModel *tf_model, TF_Operation *transpose_op, TF_Operation **cur_op,
+static int add_conv_layer(TFModel *tf_model, TF_Operation *transpose_op, TF_Operation **cur_op,
ConvolutionalParams* params, const int layer)
{
TFContext *ctx = &tf_model->ctx;
@@ -594,7 +596,7 @@ static DNNReturnType add_conv_layer(TFModel *tf_model, TF_Operation *transpose_o
break;
default:
avpriv_report_missing_feature(ctx, "convolutional activation function %d", params->activation);
- return DNN_ERROR;
+ return AVERROR(ENOSYS);
}
input.oper = *cur_op;
TF_AddInput(op_desc, input);
@@ -609,10 +611,10 @@ err:
TF_DeleteTensor(kernel_tensor);
TF_DeleteTensor(biases_tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to add conv layer %d\n", layer);
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
-static DNNReturnType add_depth_to_space_layer(TFModel *tf_model, TF_Operation **cur_op,
+static int add_depth_to_space_layer(TFModel *tf_model, TF_Operation **cur_op,
DepthToSpaceParams *params, const int layer)
{
TFContext *ctx = &tf_model->ctx;
@@ -630,13 +632,13 @@ static DNNReturnType add_depth_to_space_layer(TFModel *tf_model, TF_Operation **
*cur_op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
av_log(ctx, AV_LOG_ERROR, "Failed to add depth_to_space to layer %d\n", layer);
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
return DNN_SUCCESS;
}
-static DNNReturnType add_pad_layer(TFModel *tf_model, TF_Operation **cur_op,
+static int add_pad_layer(TFModel *tf_model, TF_Operation **cur_op,
LayerPadParams *params, const int layer)
{
TFContext *ctx = &tf_model->ctx;
@@ -666,13 +668,13 @@ static DNNReturnType add_pad_layer(TFModel *tf_model, TF_Operation **cur_op,
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to set value for pad of layer %d\n", layer);
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to add pad to layer %d\n", layer);
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
op_desc = TF_NewOperation(tf_model->graph, "MirrorPad", "mirror_pad");
@@ -688,13 +690,13 @@ static DNNReturnType add_pad_layer(TFModel *tf_model, TF_Operation **cur_op,
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to add mirror_pad to layer %d\n", layer);
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
return DNN_SUCCESS;
}
-static DNNReturnType add_maximum_layer(TFModel *tf_model, TF_Operation **cur_op,
+static int add_maximum_layer(TFModel *tf_model, TF_Operation **cur_op,
DnnLayerMaximumParams *params, const int layer)
{
TFContext *ctx = &tf_model->ctx;
@@ -716,13 +718,13 @@ static DNNReturnType add_maximum_layer(TFModel *tf_model, TF_Operation **cur_op,
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to set value for maximum/y of layer %d", layer);
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
op = TF_FinishOperation(op_desc, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to add maximum/y to layer %d\n", layer);
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
snprintf(name_buffer, NAME_BUFFER_SIZE, "maximum%d", layer);
@@ -737,13 +739,13 @@ static DNNReturnType add_maximum_layer(TFModel *tf_model, TF_Operation **cur_op,
if (TF_GetCode(tf_model->status) != TF_OK){
TF_DeleteTensor(tensor);
av_log(ctx, AV_LOG_ERROR, "Failed to add maximum to layer %d\n", layer);
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
return DNN_SUCCESS;
}
-static DNNReturnType load_native_model(TFModel *tf_model, const char *model_filename)
+static int load_native_model(TFModel *tf_model, const char *model_filename)
{
TFContext *ctx = &tf_model->ctx;
int32_t layer;
@@ -755,14 +757,14 @@ static DNNReturnType load_native_model(TFModel *tf_model, const char *model_file
int32_t *transpose_perm;
int64_t transpose_perm_shape[] = {4};
int64_t input_shape[] = {1, -1, -1, -1};
- DNNReturnType layer_add_res;
+ int layer_add_res;
DNNModel *model = NULL;
NativeModel *native_model;
model = ff_dnn_load_model_native(model_filename, DFT_PROCESS_FRAME, NULL, NULL);
if (!model){
av_log(ctx, AV_LOG_ERROR, "Failed to load native model\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
native_model = model->model;
@@ -775,7 +777,7 @@ static DNNReturnType load_native_model(TFModel *tf_model, const char *model_file
TF_DeleteGraph(tf_model->graph); \
TF_DeleteStatus(tf_model->status); \
av_log(ctx, AV_LOG_ERROR, "Failed to set value or add operator to layer\n"); \
- return DNN_ERROR; \
+ return DNN_GENERIC_ERROR; \
}
op_desc = TF_NewOperation(tf_model->graph, "Placeholder", "x");
@@ -942,7 +944,7 @@ err:
return NULL;
}
-static DNNReturnType fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
+static int fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
DNNData input;
LastLevelTaskItem *lltask;
TaskItem *task;
@@ -954,7 +956,8 @@ static DNNReturnType fill_model_input_tf(TFModel *tf_model, TFRequestItem *reque
task = lltask->task;
request->lltask = lltask;
- if (get_input_tf(tf_model, &input, task->input_name) != DNN_SUCCESS) {
+ ret = get_input_tf(tf_model, &input, task->input_name);
+ if (ret != DNN_SUCCESS) {
goto err;
}
@@ -965,12 +968,14 @@ static DNNReturnType fill_model_input_tf(TFModel *tf_model, TFRequestItem *reque
infer_request->tf_input = av_malloc(sizeof(TF_Output));
if (!infer_request->tf_input) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input tensor\n");
+ ret = AVERROR(ENOMEM);
goto err;
}
infer_request->tf_input->oper = TF_GraphOperationByName(tf_model->graph, task->input_name);
if (!infer_request->tf_input->oper){
av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", task->input_name);
+ ret = DNN_GENERIC_ERROR;
goto err;
}
infer_request->tf_input->index = 0;
@@ -978,6 +983,7 @@ static DNNReturnType fill_model_input_tf(TFModel *tf_model, TFRequestItem *reque
infer_request->input_tensor = allocate_input_tensor(&input);
if (!infer_request->input_tensor){
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input tensor\n");
+ ret = AVERROR(ENOMEM);
goto err;
}
input.data = (float *)TF_TensorData(infer_request->input_tensor);
@@ -1003,12 +1009,14 @@ static DNNReturnType fill_model_input_tf(TFModel *tf_model, TFRequestItem *reque
infer_request->tf_outputs = av_malloc_array(task->nb_output, sizeof(TF_Output));
if (infer_request->tf_outputs == NULL) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for *tf_outputs\n");
+ ret = AVERROR(ENOMEM);
goto err;
}
infer_request->output_tensors = av_calloc(task->nb_output, sizeof(*infer_request->output_tensors));
if (!infer_request->output_tensors) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output tensor\n");
+ ret = AVERROR(ENOMEM);
goto err;
}
@@ -1017,6 +1025,7 @@ static DNNReturnType fill_model_input_tf(TFModel *tf_model, TFRequestItem *reque
infer_request->tf_outputs[i].oper = TF_GraphOperationByName(tf_model->graph, task->output_names[i]);
if (!infer_request->tf_outputs[i].oper) {
av_log(ctx, AV_LOG_ERROR, "Could not find output \"%s\" in model\n", task->output_names[i]);
+ ret = DNN_GENERIC_ERROR;
goto err;
}
infer_request->tf_outputs[i].index = 0;
@@ -1025,7 +1034,7 @@ static DNNReturnType fill_model_input_tf(TFModel *tf_model, TFRequestItem *reque
return DNN_SUCCESS;
err:
tf_free_request(infer_request);
- return DNN_ERROR;
+ return ret;
}
static void infer_completion_callback(void *args) {
@@ -1086,12 +1095,13 @@ err:
}
}
-static DNNReturnType execute_model_tf(TFRequestItem *request, Queue *lltask_queue)
+static int execute_model_tf(TFRequestItem *request, Queue *lltask_queue)
{
TFModel *tf_model;
TFContext *ctx;
LastLevelTaskItem *lltask;
TaskItem *task;
+ int ret = 0;
if (ff_queue_size(lltask_queue) == 0) {
destroy_request_item(&request);
@@ -1103,7 +1113,8 @@ static DNNReturnType execute_model_tf(TFRequestItem *request, Queue *lltask_queu
tf_model = task->model;
ctx = &tf_model->ctx;
- if (fill_model_input_tf(tf_model, request) != DNN_SUCCESS) {
+ ret = fill_model_input_tf(tf_model, request);
+ if (ret != DNN_SUCCESS) {
goto err;
}
@@ -1112,58 +1123,64 @@ static DNNReturnType execute_model_tf(TFRequestItem *request, Queue *lltask_queu
goto err;
}
return DNN_SUCCESS;
- } else {
- if (tf_start_inference(request) != DNN_SUCCESS) {
+ }
+ else {
+ ret = tf_start_inference(request);
+ if (ret != DNN_SUCCESS) {
goto err;
}
infer_completion_callback(request);
- return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_ERROR;
+ return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_GENERIC_ERROR;
}
err:
tf_free_request(request->infer_request);
if (ff_safe_queue_push_back(tf_model->request_queue, request) < 0) {
destroy_request_item(&request);
}
- return DNN_ERROR;
+ return ret;
}
-DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_params)
+int ff_dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_params)
{
TFModel *tf_model = model->model;
TFContext *ctx = &tf_model->ctx;
TaskItem *task;
TFRequestItem *request;
+ int ret = 0;
- if (ff_check_exec_params(ctx, DNN_TF, model->func_type, exec_params) != 0) {
- return DNN_ERROR;
+ ret = ff_check_exec_params(ctx, DNN_TF, model->func_type, exec_params);
+ if (ret != 0) {
+ return ret;
}
task = av_malloc(sizeof(*task));
if (!task) {
av_log(ctx, AV_LOG_ERROR, "unable to alloc memory for task item.\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
- if (ff_dnn_fill_task(task, exec_params, tf_model, ctx->options.async, 1) != DNN_SUCCESS) {
+ ret = ff_dnn_fill_task(task, exec_params, tf_model, ctx->options.async, 1);
+ if (ret != DNN_SUCCESS) {
av_freep(&task);
- return DNN_ERROR;
+ return ret;
}
if (ff_queue_push_back(tf_model->task_queue, task) < 0) {
av_freep(&task);
av_log(ctx, AV_LOG_ERROR, "unable to push back task_queue.\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
- if (extract_lltask_from_task(task, tf_model->lltask_queue) != DNN_SUCCESS) {
+ ret = extract_lltask_from_task(task, tf_model->lltask_queue);
+ if (ret != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
- return DNN_ERROR;
+ return ret;
}
request = ff_safe_queue_pop_front(tf_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
return execute_model_tf(request, tf_model->lltask_queue);
}
@@ -1174,12 +1191,12 @@ DNNAsyncStatusType ff_dnn_get_result_tf(const DNNModel *model, AVFrame **in, AVF
return ff_dnn_get_result_common(tf_model->task_queue, in, out);
}
-DNNReturnType ff_dnn_flush_tf(const DNNModel *model)
+int ff_dnn_flush_tf(const DNNModel *model)
{
TFModel *tf_model = model->model;
TFContext *ctx = &tf_model->ctx;
TFRequestItem *request;
- DNNReturnType ret;
+ int ret;
if (ff_queue_size(tf_model->lltask_queue) == 0) {
// no pending task need to flush
@@ -1189,7 +1206,7 @@ DNNReturnType ff_dnn_flush_tf(const DNNModel *model)
request = ff_safe_queue_pop_front(tf_model->request_queue);
if (!request) {
av_log(ctx, AV_LOG_ERROR, "unable to get infer request.\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
ret = fill_model_input_tf(tf_model, request);
diff --git a/libavfilter/dnn/dnn_backend_tf.h b/libavfilter/dnn/dnn_backend_tf.h
index f14ea8c47a..0b63a4b6d2 100644
--- a/libavfilter/dnn/dnn_backend_tf.h
+++ b/libavfilter/dnn/dnn_backend_tf.h
@@ -31,9 +31,9 @@
DNNModel *ff_dnn_load_model_tf(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx);
-DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_params);
+int ff_dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_params);
DNNAsyncStatusType ff_dnn_get_result_tf(const DNNModel *model, AVFrame **in, AVFrame **out);
-DNNReturnType ff_dnn_flush_tf(const DNNModel *model);
+int ff_dnn_flush_tf(const DNNModel *model);
void ff_dnn_free_model_tf(DNNModel **model);
--
2.32.0
_______________________________________________
ffmpeg-devel mailing list
ffmpeg-devel@ffmpeg.org
https://ffmpeg.org/mailman/listinfo/ffmpeg-devel
To unsubscribe, visit link above, or email
ffmpeg-devel-request@ffmpeg.org with subject "unsubscribe".
^ permalink raw reply [flat|nested] 9+ messages in thread* [FFmpeg-devel] [PATCH 6/8] lavfi/dnn_backend_native: Return Specific Error Codes
2022-02-24 8:22 [FFmpeg-devel] [PATCH 1/8] libavfilter: Prepare to handle specific error codes in DNN Filters Shubhanshu Saxena
` (3 preceding siblings ...)
2022-02-24 8:22 ` [FFmpeg-devel] [PATCH 5/8] lavfi/dnn_backend_tf: " Shubhanshu Saxena
@ 2022-02-24 8:22 ` Shubhanshu Saxena
2022-02-24 8:22 ` [FFmpeg-devel] [PATCH 7/8] lavfi/dnn_backend_common: Return specific error codes Shubhanshu Saxena
2022-02-24 8:22 ` [FFmpeg-devel] [PATCH 8/8] libavfilter: Remove DNNReturnType from DNN Module Shubhanshu Saxena
6 siblings, 0 replies; 9+ messages in thread
From: Shubhanshu Saxena @ 2022-02-24 8:22 UTC (permalink / raw)
To: ffmpeg-devel; +Cc: Shubhanshu Saxena
Switch to returning specific error codes or DNN_GENERIC_ERROR
when an error is encountered.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
---
libavfilter/dnn/dnn_backend_native.c | 82 +++++++++++++++-------------
libavfilter/dnn/dnn_backend_native.h | 4 +-
2 files changed, 45 insertions(+), 41 deletions(-)
diff --git a/libavfilter/dnn/dnn_backend_native.c b/libavfilter/dnn/dnn_backend_native.c
index 13436c0484..f29e0e06bd 100644
--- a/libavfilter/dnn/dnn_backend_native.c
+++ b/libavfilter/dnn/dnn_backend_native.c
@@ -46,9 +46,9 @@ static const AVClass dnn_native_class = {
.category = AV_CLASS_CATEGORY_FILTER,
};
-static DNNReturnType execute_model_native(Queue *lltask_queue);
+static int execute_model_native(Queue *lltask_queue);
-static DNNReturnType extract_lltask_from_task(TaskItem *task, Queue *lltask_queue)
+static int extract_lltask_from_task(TaskItem *task, Queue *lltask_queue)
{
NativeModel *native_model = task->model;
NativeContext *ctx = &native_model->ctx;
@@ -56,7 +56,7 @@ static DNNReturnType extract_lltask_from_task(TaskItem *task, Queue *lltask_queu
if (!lltask) {
av_log(ctx, AV_LOG_ERROR, "Unable to allocate space for LastLevelTaskItem\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
task->inference_todo = 1;
task->inference_done = 0;
@@ -65,12 +65,12 @@ static DNNReturnType extract_lltask_from_task(TaskItem *task, Queue *lltask_queu
if (ff_queue_push_back(lltask_queue, lltask) < 0) {
av_log(ctx, AV_LOG_ERROR, "Failed to push back lltask_queue.\n");
av_freep(&lltask);
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
return DNN_SUCCESS;
}
-static DNNReturnType get_input_native(void *model, DNNData *input, const char *input_name)
+static int get_input_native(void *model, DNNData *input, const char *input_name)
{
NativeModel *native_model = model;
NativeContext *ctx = &native_model->ctx;
@@ -80,7 +80,7 @@ static DNNReturnType get_input_native(void *model, DNNData *input, const char *i
if (strcmp(oprd->name, input_name) == 0) {
if (oprd->type != DOT_INPUT) {
av_log(ctx, AV_LOG_ERROR, "Found \"%s\" in model, but it is not input node\n", input_name);
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
input->dt = oprd->data_type;
av_assert0(oprd->dims[0] == 1);
@@ -93,13 +93,13 @@ static DNNReturnType get_input_native(void *model, DNNData *input, const char *i
// do not find the input operand
av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", input_name);
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
-static DNNReturnType get_output_native(void *model, const char *input_name, int input_width, int input_height,
+static int get_output_native(void *model, const char *input_name, int input_width, int input_height,
const char *output_name, int *output_width, int *output_height)
{
- DNNReturnType ret = 0;
+ int ret = 0;
NativeModel *native_model = model;
NativeContext *ctx = &native_model->ctx;
TaskItem task;
@@ -111,14 +111,14 @@ static DNNReturnType get_output_native(void *model, const char *input_name, int
.out_frame = NULL,
};
- if (ff_dnn_fill_gettingoutput_task(&task, &exec_params, native_model, input_height, input_width, ctx) != DNN_SUCCESS) {
- ret = DNN_ERROR;
+ ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, native_model, input_height, input_width, ctx);
+ if (ret != DNN_SUCCESS) {
goto err;
}
- if (extract_lltask_from_task(&task, native_model->lltask_queue) != DNN_SUCCESS) {
+ ret = extract_lltask_from_task(&task, native_model->lltask_queue);
+ if (ret != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
- ret = DNN_ERROR;
goto err;
}
@@ -297,7 +297,7 @@ fail:
return NULL;
}
-static DNNReturnType execute_model_native(Queue *lltask_queue)
+static int execute_model_native(Queue *lltask_queue)
{
NativeModel *native_model = NULL;
NativeContext *ctx = NULL;
@@ -306,12 +306,12 @@ static DNNReturnType execute_model_native(Queue *lltask_queue)
DnnOperand *oprd = NULL;
LastLevelTaskItem *lltask = NULL;
TaskItem *task = NULL;
- DNNReturnType ret = 0;
+ int ret = 0;
lltask = ff_queue_pop_front(lltask_queue);
if (!lltask) {
av_log(NULL, AV_LOG_ERROR, "Failed to get LastLevelTaskItem\n");
- ret = DNN_ERROR;
+ ret = AVERROR(EINVAL);
goto err;
}
task = lltask->task;
@@ -320,7 +320,7 @@ static DNNReturnType execute_model_native(Queue *lltask_queue)
if (native_model->layers_num <= 0 || native_model->operands_num <= 0) {
av_log(ctx, AV_LOG_ERROR, "No operands or layers in model\n");
- ret = DNN_ERROR;
+ ret = AVERROR(EINVAL);
goto err;
}
@@ -329,7 +329,7 @@ static DNNReturnType execute_model_native(Queue *lltask_queue)
if (strcmp(oprd->name, task->input_name) == 0) {
if (oprd->type != DOT_INPUT) {
av_log(ctx, AV_LOG_ERROR, "Found \"%s\" in model, but it is not input node\n", task->input_name);
- ret = DNN_ERROR;
+ ret = AVERROR(EINVAL);
goto err;
}
break;
@@ -338,7 +338,7 @@ static DNNReturnType execute_model_native(Queue *lltask_queue)
}
if (!oprd) {
av_log(ctx, AV_LOG_ERROR, "Could not find \"%s\" in model\n", task->input_name);
- ret = DNN_ERROR;
+ ret = AVERROR(EINVAL);
goto err;
}
@@ -349,13 +349,13 @@ static DNNReturnType execute_model_native(Queue *lltask_queue)
oprd->length = ff_calculate_operand_data_length(oprd);
if (oprd->length <= 0) {
av_log(ctx, AV_LOG_ERROR, "The input data length overflow\n");
- ret = DNN_ERROR;
+ ret = AVERROR(EINVAL);
goto err;
}
oprd->data = av_malloc(oprd->length);
if (!oprd->data) {
av_log(ctx, AV_LOG_ERROR, "Failed to malloc memory for input data\n");
- ret = DNN_ERROR;
+ ret = AVERROR(ENOMEM);
goto err;
}
@@ -376,19 +376,19 @@ static DNNReturnType execute_model_native(Queue *lltask_queue)
// currently, the filter does not need multiple outputs,
// so we just pending the support until we really need it.
avpriv_report_missing_feature(ctx, "multiple outputs");
- ret = DNN_ERROR;
+ ret = AVERROR(ENOSYS);
goto err;
}
for (layer = 0; layer < native_model->layers_num; ++layer){
DNNLayerType layer_type = native_model->layers[layer].type;
- if (ff_layer_funcs[layer_type].pf_exec(native_model->operands,
- native_model->layers[layer].input_operand_indexes,
- native_model->layers[layer].output_operand_index,
- native_model->layers[layer].params,
- &native_model->ctx) == DNN_ERROR) {
+ ret = ff_layer_funcs[layer_type].pf_exec(native_model->operands,
+ native_model->layers[layer].input_operand_indexes,
+ native_model->layers[layer].output_operand_index,
+ native_model->layers[layer].params,
+ &native_model->ctx);
+ if (ret != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "Failed to execute model\n");
- ret = DNN_ERROR;
goto err;
}
}
@@ -405,7 +405,7 @@ static DNNReturnType execute_model_native(Queue *lltask_queue)
if (oprd == NULL) {
av_log(ctx, AV_LOG_ERROR, "Could not find output in model\n");
- ret = DNN_ERROR;
+ ret = AVERROR(EINVAL);
goto err;
}
@@ -432,42 +432,46 @@ err:
return ret;
}
-DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNExecBaseParams *exec_params)
+int ff_dnn_execute_model_native(const DNNModel *model, DNNExecBaseParams *exec_params)
{
NativeModel *native_model = model->model;
NativeContext *ctx = &native_model->ctx;
TaskItem *task;
+ int ret = 0;
- if (ff_check_exec_params(ctx, DNN_NATIVE, model->func_type, exec_params) != 0) {
- return DNN_ERROR;
+ ret = ff_check_exec_params(ctx, DNN_NATIVE, model->func_type, exec_params);
+ if (ret != 0) {
+ return ret;
}
task = av_malloc(sizeof(*task));
if (!task) {
av_log(ctx, AV_LOG_ERROR, "unable to alloc memory for task item.\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
- if (ff_dnn_fill_task(task, exec_params, native_model, ctx->options.async, 1) != DNN_SUCCESS) {
+ ret = ff_dnn_fill_task(task, exec_params, native_model, ctx->options.async, 1);
+ if (ret != DNN_SUCCESS) {
av_freep(&task);
- return DNN_ERROR;
+ return ret;
}
if (ff_queue_push_back(native_model->task_queue, task) < 0) {
av_freep(&task);
av_log(ctx, AV_LOG_ERROR, "unable to push back task_queue.\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
- if (extract_lltask_from_task(task, native_model->lltask_queue) != DNN_SUCCESS) {
+ ret = extract_lltask_from_task(task, native_model->lltask_queue);
+ if (ret != DNN_SUCCESS) {
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
- return DNN_ERROR;
+ return ret;
}
return execute_model_native(native_model->lltask_queue);
}
-DNNReturnType ff_dnn_flush_native(const DNNModel *model)
+int ff_dnn_flush_native(const DNNModel *model)
{
NativeModel *native_model = model->model;
diff --git a/libavfilter/dnn/dnn_backend_native.h b/libavfilter/dnn/dnn_backend_native.h
index e8017ee4b4..75bd9a44f7 100644
--- a/libavfilter/dnn/dnn_backend_native.h
+++ b/libavfilter/dnn/dnn_backend_native.h
@@ -134,11 +134,11 @@ typedef struct NativeModel{
DNNModel *ff_dnn_load_model_native(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx);
-DNNReturnType ff_dnn_execute_model_native(const DNNModel *model, DNNExecBaseParams *exec_params);
+int ff_dnn_execute_model_native(const DNNModel *model, DNNExecBaseParams *exec_params);
DNNAsyncStatusType ff_dnn_get_result_native(const DNNModel *model, AVFrame **in, AVFrame **out);
-DNNReturnType ff_dnn_flush_native(const DNNModel *model);
+int ff_dnn_flush_native(const DNNModel *model);
void ff_dnn_free_model_native(DNNModel **model);
--
2.32.0
_______________________________________________
ffmpeg-devel mailing list
ffmpeg-devel@ffmpeg.org
https://ffmpeg.org/mailman/listinfo/ffmpeg-devel
To unsubscribe, visit link above, or email
ffmpeg-devel-request@ffmpeg.org with subject "unsubscribe".
^ permalink raw reply [flat|nested] 9+ messages in thread* [FFmpeg-devel] [PATCH 7/8] lavfi/dnn_backend_common: Return specific error codes
2022-02-24 8:22 [FFmpeg-devel] [PATCH 1/8] libavfilter: Prepare to handle specific error codes in DNN Filters Shubhanshu Saxena
` (4 preceding siblings ...)
2022-02-24 8:22 ` [FFmpeg-devel] [PATCH 6/8] lavfi/dnn_backend_native: " Shubhanshu Saxena
@ 2022-02-24 8:22 ` Shubhanshu Saxena
2022-02-24 8:22 ` [FFmpeg-devel] [PATCH 8/8] libavfilter: Remove DNNReturnType from DNN Module Shubhanshu Saxena
6 siblings, 0 replies; 9+ messages in thread
From: Shubhanshu Saxena @ 2022-02-24 8:22 UTC (permalink / raw)
To: ffmpeg-devel; +Cc: Shubhanshu Saxena
Switch to returning specific error codes or DNN_GENERIC_ERROR
when an error is encountered in the common DNN backend functions.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
---
libavfilter/dnn/dnn_backend_common.c | 35 ++++++++++++++--------------
libavfilter/dnn/dnn_backend_common.h | 22 +++++++----------
2 files changed, 27 insertions(+), 30 deletions(-)
diff --git a/libavfilter/dnn/dnn_backend_common.c b/libavfilter/dnn/dnn_backend_common.c
index 6a9c4cc87f..64ed441415 100644
--- a/libavfilter/dnn/dnn_backend_common.c
+++ b/libavfilter/dnn/dnn_backend_common.c
@@ -47,19 +47,19 @@ int ff_check_exec_params(void *ctx, DNNBackendType backend, DNNFunctionType func
// currently, the filter does not need multiple outputs,
// so we just pending the support until we really need it.
avpriv_report_missing_feature(ctx, "multiple outputs");
- return AVERROR(EINVAL);
+ return AVERROR(ENOSYS);
}
return 0;
}
-DNNReturnType ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int async, int do_ioproc) {
+int ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int async, int do_ioproc) {
if (task == NULL || exec_params == NULL || backend_model == NULL)
- return DNN_ERROR;
+ return AVERROR(EINVAL);
if (do_ioproc != 0 && do_ioproc != 1)
- return DNN_ERROR;
+ return AVERROR(EINVAL);
if (async != 0 && async != 1)
- return DNN_ERROR;
+ return AVERROR(EINVAL);
task->do_ioproc = do_ioproc;
task->async = async;
@@ -89,17 +89,17 @@ static void *async_thread_routine(void *args)
return DNN_ASYNC_SUCCESS;
}
-DNNReturnType ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module)
+int ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module)
{
void *status = 0;
if (!async_module) {
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
#if HAVE_PTHREAD_CANCEL
pthread_join(async_module->thread_id, &status);
if (status == DNN_ASYNC_FAIL) {
av_log(NULL, AV_LOG_ERROR, "Last Inference Failed.\n");
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
#endif
async_module->start_inference = NULL;
@@ -108,30 +108,31 @@ DNNReturnType ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module)
return DNN_SUCCESS;
}
-DNNReturnType ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module)
+int ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module)
{
int ret;
void *status = 0;
if (!async_module) {
av_log(ctx, AV_LOG_ERROR, "async_module is null when starting async inference.\n");
- return DNN_ERROR;
+ return AVERROR(EINVAL);
}
#if HAVE_PTHREAD_CANCEL
pthread_join(async_module->thread_id, &status);
if (status == DNN_ASYNC_FAIL) {
av_log(ctx, AV_LOG_ERROR, "Unable to start inference as previous inference failed.\n");
- return DNN_ERROR;
+ return DNN_GENERIC_ERROR;
}
ret = pthread_create(&async_module->thread_id, NULL, async_thread_routine, async_module);
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Unable to start async inference.\n");
- return DNN_ERROR;
+ return ret;
}
#else
- if (async_module->start_inference(async_module->args) != DNN_SUCCESS) {
- return DNN_ERROR;
+ ret = async_module->start_inference(async_module->args);
+ if (ret != DNN_SUCCESS) {
+ return ret;
}
async_module->callback(async_module->args);
#endif
@@ -158,7 +159,7 @@ DNNAsyncStatusType ff_dnn_get_result_common(Queue *task_queue, AVFrame **in, AVF
return DAST_SUCCESS;
}
-DNNReturnType ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int input_height, int input_width, void *ctx)
+int ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int input_height, int input_width, void *ctx)
{
AVFrame *in_frame = NULL;
AVFrame *out_frame = NULL;
@@ -166,14 +167,14 @@ DNNReturnType ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *
in_frame = av_frame_alloc();
if (!in_frame) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input frame\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
out_frame = av_frame_alloc();
if (!out_frame) {
av_frame_free(&in_frame);
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output frame\n");
- return DNN_ERROR;
+ return AVERROR(ENOMEM);
}
in_frame->width = input_width;
diff --git a/libavfilter/dnn/dnn_backend_common.h b/libavfilter/dnn/dnn_backend_common.h
index 6b6a5e21ae..fa79caee1f 100644
--- a/libavfilter/dnn/dnn_backend_common.h
+++ b/libavfilter/dnn/dnn_backend_common.h
@@ -60,7 +60,7 @@ typedef struct DNNAsyncExecModule {
* Synchronous inference function for the backend
* with corresponding request item as the argument.
*/
- DNNReturnType (*start_inference)(void *request);
+ int (*start_inference)(void *request);
/**
* Completion Callback for the backend.
@@ -92,20 +92,18 @@ int ff_check_exec_params(void *ctx, DNNBackendType backend, DNNFunctionType func
* @param async flag for async execution. Must be 0 or 1
* @param do_ioproc flag for IO processing. Must be 0 or 1
*
- * @retval DNN_SUCCESS if successful
- * @retval DNN_ERROR if flags are invalid or any parameter is NULL
+ * @returns DNN_SUCCESS if successful or error code otherwise.
*/
-DNNReturnType ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int async, int do_ioproc);
+int ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int async, int do_ioproc);
/**
* Join the Async Execution thread and set module pointers to NULL.
*
* @param async_module pointer to DNNAsyncExecModule module
*
- * @retval DNN_SUCCESS if successful
- * @retval DNN_ERROR if async_module is NULL
+ * @returns DNN_SUCCESS if successful or error code otherwise.
*/
-DNNReturnType ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module);
+int ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module);
/**
* Start asynchronous inference routine for the TensorFlow
@@ -119,10 +117,9 @@ DNNReturnType ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module);
* @param ctx pointer to the backend context
* @param async_module pointer to DNNAsyncExecModule module
*
- * @retval DNN_SUCCESS on the start of async inference.
- * @retval DNN_ERROR in case async inference cannot be started
+ * @returns DNN_SUCCESS on the start of async inference or error code otherwise.
*/
-DNNReturnType ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module);
+int ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module);
/**
* Extract input and output frame from the Task Queue after
@@ -149,9 +146,8 @@ DNNAsyncStatusType ff_dnn_get_result_common(Queue *task_queue, AVFrame **in, AVF
* @param input_width width of input frame
* @param ctx pointer to the backend context
*
- * @retval DNN_SUCCESS if successful
- * @retval DNN_ERROR if allocation fails
+ * @returns DNN_SUCCESS if successful or error code otherwise.
*/
-DNNReturnType ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int input_height, int input_width, void *ctx);
+int ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int input_height, int input_width, void *ctx);
#endif
--
2.32.0
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^ permalink raw reply [flat|nested] 9+ messages in thread* [FFmpeg-devel] [PATCH 8/8] libavfilter: Remove DNNReturnType from DNN Module
2022-02-24 8:22 [FFmpeg-devel] [PATCH 1/8] libavfilter: Prepare to handle specific error codes in DNN Filters Shubhanshu Saxena
` (5 preceding siblings ...)
2022-02-24 8:22 ` [FFmpeg-devel] [PATCH 7/8] lavfi/dnn_backend_common: Return specific error codes Shubhanshu Saxena
@ 2022-02-24 8:22 ` Shubhanshu Saxena
2022-03-02 11:18 ` Guo, Yejun
6 siblings, 1 reply; 9+ messages in thread
From: Shubhanshu Saxena @ 2022-02-24 8:22 UTC (permalink / raw)
To: ffmpeg-devel; +Cc: Shubhanshu Saxena
This patch removes all occurences of DNNReturnType from the DNN module.
This commit replaces DNN_SUCCESS by 0 (essentially the same), so the
functions with DNNReturnType now return 0 in case of success, the negative
values otherwise.
Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
---
libavfilter/dnn/dnn_backend_common.c | 10 ++--
libavfilter/dnn/dnn_backend_common.h | 8 +--
libavfilter/dnn/dnn_backend_native.c | 16 +++---
.../dnn/dnn_backend_native_layer_avgpool.c | 2 +-
.../dnn/dnn_backend_native_layer_conv2d.c | 4 +-
.../dnn/dnn_backend_native_layer_dense.c | 2 +-
.../dnn_backend_native_layer_depth2space.c | 2 +-
libavfilter/dnn/dnn_backend_openvino.c | 48 ++++++++--------
libavfilter/dnn/dnn_backend_tf.c | 56 +++++++++----------
libavfilter/dnn/dnn_io_proc.c | 14 ++---
libavfilter/dnn_interface.h | 2 -
libavfilter/vf_derain.c | 2 +-
libavfilter/vf_dnn_classify.c | 4 +-
libavfilter/vf_dnn_detect.c | 4 +-
libavfilter/vf_dnn_processing.c | 8 +--
libavfilter/vf_sr.c | 4 +-
16 files changed, 92 insertions(+), 94 deletions(-)
diff --git a/libavfilter/dnn/dnn_backend_common.c b/libavfilter/dnn/dnn_backend_common.c
index 64ed441415..91a4a3c4bf 100644
--- a/libavfilter/dnn/dnn_backend_common.c
+++ b/libavfilter/dnn/dnn_backend_common.c
@@ -70,7 +70,7 @@ int ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backe
task->nb_output = exec_params->nb_output;
task->output_names = exec_params->output_names;
- return DNN_SUCCESS;
+ return 0;
}
/**
@@ -82,7 +82,7 @@ static void *async_thread_routine(void *args)
DNNAsyncExecModule *async_module = args;
void *request = async_module->args;
- if (async_module->start_inference(request) != DNN_SUCCESS) {
+ if (async_module->start_inference(request) != 0) {
return DNN_ASYNC_FAIL;
}
async_module->callback(request);
@@ -105,7 +105,7 @@ int ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module)
async_module->start_inference = NULL;
async_module->callback = NULL;
async_module->args = NULL;
- return DNN_SUCCESS;
+ return 0;
}
int ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module)
@@ -131,12 +131,12 @@ int ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module)
}
#else
ret = async_module->start_inference(async_module->args);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
return ret;
}
async_module->callback(async_module->args);
#endif
- return DNN_SUCCESS;
+ return 0;
}
DNNAsyncStatusType ff_dnn_get_result_common(Queue *task_queue, AVFrame **in, AVFrame **out)
diff --git a/libavfilter/dnn/dnn_backend_common.h b/libavfilter/dnn/dnn_backend_common.h
index fa79caee1f..42c67c7040 100644
--- a/libavfilter/dnn/dnn_backend_common.h
+++ b/libavfilter/dnn/dnn_backend_common.h
@@ -92,7 +92,7 @@ int ff_check_exec_params(void *ctx, DNNBackendType backend, DNNFunctionType func
* @param async flag for async execution. Must be 0 or 1
* @param do_ioproc flag for IO processing. Must be 0 or 1
*
- * @returns DNN_SUCCESS if successful or error code otherwise.
+ * @returns 0 if successful or error code otherwise.
*/
int ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int async, int do_ioproc);
@@ -101,7 +101,7 @@ int ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backe
*
* @param async_module pointer to DNNAsyncExecModule module
*
- * @returns DNN_SUCCESS if successful or error code otherwise.
+ * @returns 0 if successful or error code otherwise.
*/
int ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module);
@@ -117,7 +117,7 @@ int ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module);
* @param ctx pointer to the backend context
* @param async_module pointer to DNNAsyncExecModule module
*
- * @returns DNN_SUCCESS on the start of async inference or error code otherwise.
+ * @returns 0 on the start of async inference or error code otherwise.
*/
int ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module);
@@ -146,7 +146,7 @@ DNNAsyncStatusType ff_dnn_get_result_common(Queue *task_queue, AVFrame **in, AVF
* @param input_width width of input frame
* @param ctx pointer to the backend context
*
- * @returns DNN_SUCCESS if successful or error code otherwise.
+ * @returns 0 if successful or error code otherwise.
*/
int ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int input_height, int input_width, void *ctx);
diff --git a/libavfilter/dnn/dnn_backend_native.c b/libavfilter/dnn/dnn_backend_native.c
index f29e0e06bd..b53799f04d 100644
--- a/libavfilter/dnn/dnn_backend_native.c
+++ b/libavfilter/dnn/dnn_backend_native.c
@@ -67,7 +67,7 @@ static int extract_lltask_from_task(TaskItem *task, Queue *lltask_queue)
av_freep(&lltask);
return AVERROR(ENOMEM);
}
- return DNN_SUCCESS;
+ return 0;
}
static int get_input_native(void *model, DNNData *input, const char *input_name)
@@ -87,7 +87,7 @@ static int get_input_native(void *model, DNNData *input, const char *input_name)
input->height = oprd->dims[1];
input->width = oprd->dims[2];
input->channels = oprd->dims[3];
- return DNN_SUCCESS;
+ return 0;
}
}
@@ -112,12 +112,12 @@ static int get_output_native(void *model, const char *input_name, int input_widt
};
ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, native_model, input_height, input_width, ctx);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
goto err;
}
ret = extract_lltask_from_task(&task, native_model->lltask_queue);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
goto err;
}
@@ -387,7 +387,7 @@ static int execute_model_native(Queue *lltask_queue)
native_model->layers[layer].output_operand_index,
native_model->layers[layer].params,
&native_model->ctx);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Failed to execute model\n");
goto err;
}
@@ -451,7 +451,7 @@ int ff_dnn_execute_model_native(const DNNModel *model, DNNExecBaseParams *exec_p
}
ret = ff_dnn_fill_task(task, exec_params, native_model, ctx->options.async, 1);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
av_freep(&task);
return ret;
}
@@ -463,7 +463,7 @@ int ff_dnn_execute_model_native(const DNNModel *model, DNNExecBaseParams *exec_p
}
ret = extract_lltask_from_task(task, native_model->lltask_queue);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
return ret;
}
@@ -477,7 +477,7 @@ int ff_dnn_flush_native(const DNNModel *model)
if (ff_queue_size(native_model->lltask_queue) == 0) {
// no pending task need to flush
- return DNN_SUCCESS;
+ return 0;
}
// for now, use sync node with flush operation
diff --git a/libavfilter/dnn/dnn_backend_native_layer_avgpool.c b/libavfilter/dnn/dnn_backend_native_layer_avgpool.c
index 510a28a8c9..d6fcac8a35 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_avgpool.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_avgpool.c
@@ -143,5 +143,5 @@ int ff_dnn_execute_layer_avg_pool(DnnOperand *operands, const int32_t *input_ope
}
}
- return DNN_SUCCESS;
+ return 0;
}
diff --git a/libavfilter/dnn/dnn_backend_native_layer_conv2d.c b/libavfilter/dnn/dnn_backend_native_layer_conv2d.c
index dfa0d1ed36..2ac37d8855 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_conv2d.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_conv2d.c
@@ -190,7 +190,7 @@ int ff_dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_opera
#if HAVE_PTHREAD_CANCEL
int thread_num = (ctx->options.conv2d_threads <= 0 || ctx->options.conv2d_threads > av_cpu_count())
? (av_cpu_count() + 1) : (ctx->options.conv2d_threads);
- int ret = DNN_SUCCESS, thread_stride;
+ int ret = 0, thread_stride;
ThreadParam *thread_param;
#else
ThreadParam thread_param = { 0 };
@@ -260,6 +260,6 @@ int ff_dnn_execute_layer_conv2d(DnnOperand *operands, const int32_t *input_opera
thread_param.thread_end = height - pad_size;
dnn_execute_layer_conv2d_thread(&thread_param);
- return DNN_SUCCESS;
+ return 0;
#endif
}
diff --git a/libavfilter/dnn/dnn_backend_native_layer_dense.c b/libavfilter/dnn/dnn_backend_native_layer_dense.c
index a22a484464..dff342c1f3 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_dense.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_dense.c
@@ -147,5 +147,5 @@ int ff_dnn_execute_layer_dense(DnnOperand *operands, const int32_t *input_operan
output += dense_params->output_num;
}
}
- return DNN_SUCCESS;
+ return 0;
}
diff --git a/libavfilter/dnn/dnn_backend_native_layer_depth2space.c b/libavfilter/dnn/dnn_backend_native_layer_depth2space.c
index 82b1a52be2..358ac3bcaa 100644
--- a/libavfilter/dnn/dnn_backend_native_layer_depth2space.c
+++ b/libavfilter/dnn/dnn_backend_native_layer_depth2space.c
@@ -98,5 +98,5 @@ int ff_dnn_execute_layer_depth2space(DnnOperand *operands, const int32_t *input_
}
output += output_linesize;
}
- return DNN_SUCCESS;
+ return 0;
}
diff --git a/libavfilter/dnn/dnn_backend_openvino.c b/libavfilter/dnn/dnn_backend_openvino.c
index 2f140e996b..cf012aca4c 100644
--- a/libavfilter/dnn/dnn_backend_openvino.c
+++ b/libavfilter/dnn/dnn_backend_openvino.c
@@ -191,7 +191,7 @@ static int fill_model_input_ov(OVModel *ov_model, OVRequestItem *request)
}
ie_blob_free(&input_blob);
- return DNN_SUCCESS;
+ return 0;
}
static void infer_completion_callback(void *args)
@@ -303,7 +303,7 @@ static void infer_completion_callback(void *args)
static int init_model_ov(OVModel *ov_model, const char *input_name, const char *output_name)
{
- int ret = DNN_SUCCESS;
+ int ret = 0;
OVContext *ctx = &ov_model->ctx;
IEStatusCode status;
ie_available_devices_t a_dev;
@@ -433,7 +433,7 @@ static int init_model_ov(OVModel *ov_model, const char *input_name, const char *
goto err;
}
- return DNN_SUCCESS;
+ return 0;
err:
ff_dnn_free_model_ov(&ov_model->model);
@@ -444,7 +444,7 @@ static int execute_model_ov(OVRequestItem *request, Queue *inferenceq)
{
IEStatusCode status;
LastLevelTaskItem *lltask;
- int ret = DNN_SUCCESS;
+ int ret = 0;
TaskItem *task;
OVContext *ctx;
OVModel *ov_model;
@@ -452,7 +452,7 @@ static int execute_model_ov(OVRequestItem *request, Queue *inferenceq)
if (ff_queue_size(inferenceq) == 0) {
ie_infer_request_free(&request->infer_request);
av_freep(&request);
- return DNN_SUCCESS;
+ return 0;
}
lltask = ff_queue_peek_front(inferenceq);
@@ -462,7 +462,7 @@ static int execute_model_ov(OVRequestItem *request, Queue *inferenceq)
if (task->async) {
ret = fill_model_input_ov(ov_model, request);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
goto err;
}
status = ie_infer_set_completion_callback(request->infer_request, &request->callback);
@@ -477,10 +477,10 @@ static int execute_model_ov(OVRequestItem *request, Queue *inferenceq)
ret = DNN_GENERIC_ERROR;
goto err;
}
- return DNN_SUCCESS;
+ return 0;
} else {
ret = fill_model_input_ov(ov_model, request);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
goto err;
}
status = ie_infer_request_infer(request->infer_request);
@@ -490,7 +490,7 @@ static int execute_model_ov(OVRequestItem *request, Queue *inferenceq)
goto err;
}
infer_completion_callback(request);
- return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_GENERIC_ERROR;
+ return (task->inference_done == task->inference_todo) ? 0 : DNN_GENERIC_ERROR;
}
err:
if (ff_safe_queue_push_back(ov_model->request_queue, request) < 0) {
@@ -537,7 +537,7 @@ static int get_input_ov(void *model, DNNData *input, const char *input_name)
input->height = input_resizable ? -1 : dims.dims[2];
input->width = input_resizable ? -1 : dims.dims[3];
input->dt = precision_to_datatype(precision);
- return DNN_SUCCESS;
+ return 0;
} else {
//incorrect input name
APPEND_STRING(all_input_names, model_input_name)
@@ -604,7 +604,7 @@ static int extract_lltask_from_task(DNNFunctionType func_type, TaskItem *task, Q
av_freep(&lltask);
return AVERROR(ENOMEM);
}
- return DNN_SUCCESS;
+ return 0;
}
case DFT_ANALYTICS_CLASSIFY:
{
@@ -617,7 +617,7 @@ static int extract_lltask_from_task(DNNFunctionType func_type, TaskItem *task, Q
task->inference_done = 0;
if (!contain_valid_detection_bbox(frame)) {
- return DNN_SUCCESS;
+ return 0;
}
sd = av_frame_get_side_data(frame, AV_FRAME_DATA_DETECTION_BBOXES);
@@ -645,7 +645,7 @@ static int extract_lltask_from_task(DNNFunctionType func_type, TaskItem *task, Q
return AVERROR(ENOMEM);
}
}
- return DNN_SUCCESS;
+ return 0;
}
default:
av_assert0(!"should not reach here");
@@ -690,19 +690,19 @@ static int get_output_ov(void *model, const char *input_name, int input_width, i
if (!ov_model->exe_network) {
ret = init_model_ov(ov_model, input_name, output_name);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
return ret;
}
}
ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, ov_model, input_height, input_width, ctx);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
goto err;
}
ret = extract_lltask_from_task(ov_model->model->func_type, &task, ov_model->lltask_queue, NULL);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
goto err;
}
@@ -795,7 +795,7 @@ int ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_param
if (!ov_model->exe_network) {
ret = init_model_ov(ov_model, exec_params->input_name, exec_params->output_names[0]);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Failed init OpenVINO exectuable network or inference request\n");
return ret;
}
@@ -808,7 +808,7 @@ int ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_param
}
ret = ff_dnn_fill_task(task, exec_params, ov_model, ctx->options.async, 1);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
av_freep(&task);
return ret;
}
@@ -820,7 +820,7 @@ int ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_param
}
ret = extract_lltask_from_task(model->func_type, task, ov_model->lltask_queue, exec_params);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
return ret;
}
@@ -834,12 +834,12 @@ int ff_dnn_execute_model_ov(const DNNModel *model, DNNExecBaseParams *exec_param
}
ret = execute_model_ov(request, ov_model->lltask_queue);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
return ret;
}
}
- return DNN_SUCCESS;
+ return 0;
}
else {
if (model->func_type == DFT_ANALYTICS_CLASSIFY) {
@@ -879,7 +879,7 @@ int ff_dnn_flush_ov(const DNNModel *model)
if (ff_queue_size(ov_model->lltask_queue) == 0) {
// no pending task need to flush
- return DNN_SUCCESS;
+ return 0;
}
request = ff_safe_queue_pop_front(ov_model->request_queue);
@@ -889,7 +889,7 @@ int ff_dnn_flush_ov(const DNNModel *model)
}
ret = fill_model_input_ov(ov_model, request);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Failed to fill model input.\n");
return ret;
}
@@ -904,7 +904,7 @@ int ff_dnn_flush_ov(const DNNModel *model)
return DNN_GENERIC_ERROR;
}
- return DNN_SUCCESS;
+ return 0;
}
void ff_dnn_free_model_ov(DNNModel **model)
diff --git a/libavfilter/dnn/dnn_backend_tf.c b/libavfilter/dnn/dnn_backend_tf.c
index d021e3c3bb..94d9533296 100644
--- a/libavfilter/dnn/dnn_backend_tf.c
+++ b/libavfilter/dnn/dnn_backend_tf.c
@@ -151,7 +151,7 @@ static TFInferRequest *tf_create_inference_request(void)
* Start synchronous inference for the TensorFlow model.
*
* @param request pointer to the TFRequestItem for inference
- * @retval DNN_SUCCESS if execution is successful
+ * @retval 0 if execution is successful
* @retval AVERROR(EINVAL) if request is NULL
* @retval DNN_GENERIC_ERROR if execution fails
*/
@@ -181,7 +181,7 @@ static int tf_start_inference(void *args)
}
return DNN_GENERIC_ERROR;
}
- return DNN_SUCCESS;
+ return 0;
}
/**
@@ -220,7 +220,7 @@ static int extract_lltask_from_task(TaskItem *task, Queue *lltask_queue)
av_freep(&lltask);
return AVERROR(ENOMEM);
}
- return DNN_SUCCESS;
+ return 0;
}
static TF_Buffer *read_graph(const char *model_filename)
@@ -311,7 +311,7 @@ static int get_input_tf(void *model, DNNData *input, const char *input_name)
input->width = dims[2];
input->channels = dims[3];
- return DNN_SUCCESS;
+ return 0;
}
static int get_output_tf(void *model, const char *input_name, int input_width, int input_height,
@@ -331,12 +331,12 @@ static int get_output_tf(void *model, const char *input_name, int input_width, i
};
ret = ff_dnn_fill_gettingoutput_task(&task, &exec_params, tf_model, input_height, input_width, ctx);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
goto err;
}
ret = extract_lltask_from_task(&task, tf_model->lltask_queue);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract inference from task.\n");
goto err;
}
@@ -487,7 +487,7 @@ static int load_tf_model(TFModel *tf_model, const char *model_filename)
}
}
- return DNN_SUCCESS;
+ return 0;
}
#define NAME_BUFFER_SIZE 256
@@ -606,7 +606,7 @@ static int add_conv_layer(TFModel *tf_model, TF_Operation *transpose_op, TF_Oper
goto err;
}
- return DNN_SUCCESS;
+ return 0;
err:
TF_DeleteTensor(kernel_tensor);
TF_DeleteTensor(biases_tensor);
@@ -635,7 +635,7 @@ static int add_depth_to_space_layer(TFModel *tf_model, TF_Operation **cur_op,
return DNN_GENERIC_ERROR;
}
- return DNN_SUCCESS;
+ return 0;
}
static int add_pad_layer(TFModel *tf_model, TF_Operation **cur_op,
@@ -693,7 +693,7 @@ static int add_pad_layer(TFModel *tf_model, TF_Operation **cur_op,
return DNN_GENERIC_ERROR;
}
- return DNN_SUCCESS;
+ return 0;
}
static int add_maximum_layer(TFModel *tf_model, TF_Operation **cur_op,
@@ -742,7 +742,7 @@ static int add_maximum_layer(TFModel *tf_model, TF_Operation **cur_op,
return DNN_GENERIC_ERROR;
}
- return DNN_SUCCESS;
+ return 0;
}
static int load_native_model(TFModel *tf_model, const char *model_filename)
@@ -808,7 +808,7 @@ static int load_native_model(TFModel *tf_model, const char *model_filename)
for (layer = 0; layer < native_model->layers_num; ++layer){
switch (native_model->layers[layer].type){
case DLT_INPUT:
- layer_add_res = DNN_SUCCESS;
+ layer_add_res = 0;
break;
case DLT_CONV2D:
layer_add_res = add_conv_layer(tf_model, transpose_op, &op,
@@ -830,7 +830,7 @@ static int load_native_model(TFModel *tf_model, const char *model_filename)
CLEANUP_ON_ERROR(tf_model);
}
- if (layer_add_res != DNN_SUCCESS){
+ if (layer_add_res != 0){
CLEANUP_ON_ERROR(tf_model);
}
}
@@ -846,7 +846,7 @@ static int load_native_model(TFModel *tf_model, const char *model_filename)
ff_dnn_free_model_native(&model);
- return DNN_SUCCESS;
+ return 0;
}
DNNModel *ff_dnn_load_model_tf(const char *model_filename, DNNFunctionType func_type, const char *options, AVFilterContext *filter_ctx)
@@ -876,8 +876,8 @@ DNNModel *ff_dnn_load_model_tf(const char *model_filename, DNNFunctionType func_
goto err;
}
- if (load_tf_model(tf_model, model_filename) != DNN_SUCCESS){
- if (load_native_model(tf_model, model_filename) != DNN_SUCCESS){
+ if (load_tf_model(tf_model, model_filename) != 0){
+ if (load_native_model(tf_model, model_filename) != 0){
goto err;
}
}
@@ -957,7 +957,7 @@ static int fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
request->lltask = lltask;
ret = get_input_tf(tf_model, &input, task->input_name);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
goto err;
}
@@ -1031,7 +1031,7 @@ static int fill_model_input_tf(TFModel *tf_model, TFRequestItem *request) {
infer_request->tf_outputs[i].index = 0;
}
- return DNN_SUCCESS;
+ return 0;
err:
tf_free_request(infer_request);
return ret;
@@ -1105,7 +1105,7 @@ static int execute_model_tf(TFRequestItem *request, Queue *lltask_queue)
if (ff_queue_size(lltask_queue) == 0) {
destroy_request_item(&request);
- return DNN_SUCCESS;
+ return 0;
}
lltask = ff_queue_peek_front(lltask_queue);
@@ -1114,23 +1114,23 @@ static int execute_model_tf(TFRequestItem *request, Queue *lltask_queue)
ctx = &tf_model->ctx;
ret = fill_model_input_tf(tf_model, request);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
goto err;
}
if (task->async) {
- if (ff_dnn_start_inference_async(ctx, &request->exec_module) != DNN_SUCCESS) {
+ if (ff_dnn_start_inference_async(ctx, &request->exec_module) != 0) {
goto err;
}
- return DNN_SUCCESS;
+ return 0;
}
else {
ret = tf_start_inference(request);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
goto err;
}
infer_completion_callback(request);
- return (task->inference_done == task->inference_todo) ? DNN_SUCCESS : DNN_GENERIC_ERROR;
+ return (task->inference_done == task->inference_todo) ? 0 : DNN_GENERIC_ERROR;
}
err:
tf_free_request(request->infer_request);
@@ -1160,7 +1160,7 @@ int ff_dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_param
}
ret = ff_dnn_fill_task(task, exec_params, tf_model, ctx->options.async, 1);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
av_freep(&task);
return ret;
}
@@ -1172,7 +1172,7 @@ int ff_dnn_execute_model_tf(const DNNModel *model, DNNExecBaseParams *exec_param
}
ret = extract_lltask_from_task(task, tf_model->lltask_queue);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "unable to extract last level task from task.\n");
return ret;
}
@@ -1200,7 +1200,7 @@ int ff_dnn_flush_tf(const DNNModel *model)
if (ff_queue_size(tf_model->lltask_queue) == 0) {
// no pending task need to flush
- return DNN_SUCCESS;
+ return 0;
}
request = ff_safe_queue_pop_front(tf_model->request_queue);
@@ -1210,7 +1210,7 @@ int ff_dnn_flush_tf(const DNNModel *model)
}
ret = fill_model_input_tf(tf_model, request);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Failed to fill model input.\n");
if (ff_safe_queue_push_back(tf_model->request_queue, request) < 0) {
destroy_request_item(&request);
diff --git a/libavfilter/dnn/dnn_io_proc.c b/libavfilter/dnn/dnn_io_proc.c
index 36cc051e5e..7961bf6b95 100644
--- a/libavfilter/dnn/dnn_io_proc.c
+++ b/libavfilter/dnn/dnn_io_proc.c
@@ -57,12 +57,12 @@ int ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx)
(const int[4]){frame->width * 3 * sizeof(float), 0, 0, 0}, 0, frame->height,
(uint8_t * const*)frame->data, frame->linesize);
sws_freeContext(sws_ctx);
- return DNN_SUCCESS;
+ return 0;
case AV_PIX_FMT_GRAYF32:
av_image_copy_plane(frame->data[0], frame->linesize[0],
output->data, bytewidth,
bytewidth, frame->height);
- return DNN_SUCCESS;
+ return 0;
case AV_PIX_FMT_YUV420P:
case AV_PIX_FMT_YUV422P:
case AV_PIX_FMT_YUV444P:
@@ -88,13 +88,13 @@ int ff_proc_from_dnn_to_frame(AVFrame *frame, DNNData *output, void *log_ctx)
(const int[4]){frame->width * sizeof(float), 0, 0, 0}, 0, frame->height,
(uint8_t * const*)frame->data, frame->linesize);
sws_freeContext(sws_ctx);
- return DNN_SUCCESS;
+ return 0;
default:
avpriv_report_missing_feature(log_ctx, "%s", av_get_pix_fmt_name(frame->format));
return AVERROR(ENOSYS);
}
- return DNN_SUCCESS;
+ return 0;
}
int ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *log_ctx)
@@ -169,7 +169,7 @@ int ff_proc_from_frame_to_dnn(AVFrame *frame, DNNData *input, void *log_ctx)
return AVERROR(ENOSYS);
}
- return DNN_SUCCESS;
+ return 0;
}
static enum AVPixelFormat get_pixel_format(DNNData *data)
@@ -197,7 +197,7 @@ int ff_frame_to_dnn_classify(AVFrame *frame, DNNData *input, uint32_t bbox_index
uint8_t *bbox_data[4];
struct SwsContext *sws_ctx;
int linesizes[4];
- int ret = DNN_SUCCESS;
+ int ret = 0;
enum AVPixelFormat fmt;
int left, top, width, height;
const AVDetectionBBoxHeader *header;
@@ -255,7 +255,7 @@ int ff_frame_to_dnn_detect(AVFrame *frame, DNNData *input, void *log_ctx)
{
struct SwsContext *sws_ctx;
int linesizes[4];
- int ret = DNN_SUCCESS;
+ int ret = 0;
enum AVPixelFormat fmt = get_pixel_format(input);
sws_ctx = sws_getContext(frame->width, frame->height, frame->format,
input->width, input->height, fmt,
diff --git a/libavfilter/dnn_interface.h b/libavfilter/dnn_interface.h
index 06e71f7946..ef8d7ae66f 100644
--- a/libavfilter/dnn_interface.h
+++ b/libavfilter/dnn_interface.h
@@ -32,8 +32,6 @@
#define DNN_GENERIC_ERROR FFERRTAG('D','N','N','!')
-typedef enum {DNN_SUCCESS, DNN_ERROR} DNNReturnType;
-
typedef enum {DNN_NATIVE, DNN_TF, DNN_OV} DNNBackendType;
typedef enum {DNN_FLOAT = 1, DNN_UINT8 = 4} DNNDataType;
diff --git a/libavfilter/vf_derain.c b/libavfilter/vf_derain.c
index 6758cc05d2..86e9eb8752 100644
--- a/libavfilter/vf_derain.c
+++ b/libavfilter/vf_derain.c
@@ -74,7 +74,7 @@ static int filter_frame(AVFilterLink *inlink, AVFrame *in)
av_frame_copy_props(out, in);
dnn_result = ff_dnn_execute_model(&dr_context->dnnctx, in, out);
- if (dnn_result != DNN_SUCCESS){
+ if (dnn_result != 0){
av_log(ctx, AV_LOG_ERROR, "failed to execute model\n");
av_frame_free(&in);
return dnn_result;
diff --git a/libavfilter/vf_dnn_classify.c b/libavfilter/vf_dnn_classify.c
index 5c6942d86a..c612ba8e80 100644
--- a/libavfilter/vf_dnn_classify.c
+++ b/libavfilter/vf_dnn_classify.c
@@ -213,7 +213,7 @@ static int dnn_classify_flush_frame(AVFilterLink *outlink, int64_t pts, int64_t
DNNAsyncStatusType async_state;
ret = ff_dnn_flush(&ctx->dnnctx);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
return -1;
}
@@ -253,7 +253,7 @@ static int dnn_classify_activate(AVFilterContext *filter_ctx)
if (ret < 0)
return ret;
if (ret > 0) {
- if (ff_dnn_execute_model_classification(&ctx->dnnctx, in, NULL, ctx->target) != DNN_SUCCESS) {
+ if (ff_dnn_execute_model_classification(&ctx->dnnctx, in, NULL, ctx->target) != 0) {
return AVERROR(EIO);
}
}
diff --git a/libavfilter/vf_dnn_detect.c b/libavfilter/vf_dnn_detect.c
index 51f8b430df..dd4507250f 100644
--- a/libavfilter/vf_dnn_detect.c
+++ b/libavfilter/vf_dnn_detect.c
@@ -356,7 +356,7 @@ static int dnn_detect_flush_frame(AVFilterLink *outlink, int64_t pts, int64_t *o
DNNAsyncStatusType async_state;
ret = ff_dnn_flush(&ctx->dnnctx);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
return -1;
}
@@ -396,7 +396,7 @@ static int dnn_detect_activate(AVFilterContext *filter_ctx)
if (ret < 0)
return ret;
if (ret > 0) {
- if (ff_dnn_execute_model(&ctx->dnnctx, in, NULL) != DNN_SUCCESS) {
+ if (ff_dnn_execute_model(&ctx->dnnctx, in, NULL) != 0) {
return AVERROR(EIO);
}
}
diff --git a/libavfilter/vf_dnn_processing.c b/libavfilter/vf_dnn_processing.c
index 4a1ff5898f..cac096a19f 100644
--- a/libavfilter/vf_dnn_processing.c
+++ b/libavfilter/vf_dnn_processing.c
@@ -139,7 +139,7 @@ static int config_input(AVFilterLink *inlink)
int check;
result = ff_dnn_get_input(&ctx->dnnctx, &model_input);
- if (result != DNN_SUCCESS) {
+ if (result != 0) {
av_log(ctx, AV_LOG_ERROR, "could not get input from the model\n");
return result;
}
@@ -199,7 +199,7 @@ static int config_output(AVFilterLink *outlink)
// have a try run in case that the dnn model resize the frame
result = ff_dnn_get_output(&ctx->dnnctx, inlink->w, inlink->h, &outlink->w, &outlink->h);
- if (result != DNN_SUCCESS) {
+ if (result != 0) {
av_log(ctx, AV_LOG_ERROR, "could not get output from the model\n");
return result;
}
@@ -247,7 +247,7 @@ static int flush_frame(AVFilterLink *outlink, int64_t pts, int64_t *out_pts)
DNNAsyncStatusType async_state;
ret = ff_dnn_flush(&ctx->dnnctx);
- if (ret != DNN_SUCCESS) {
+ if (ret != 0) {
return -1;
}
@@ -296,7 +296,7 @@ static int activate(AVFilterContext *filter_ctx)
return AVERROR(ENOMEM);
}
av_frame_copy_props(out, in);
- if (ff_dnn_execute_model(&ctx->dnnctx, in, out) != DNN_SUCCESS) {
+ if (ff_dnn_execute_model(&ctx->dnnctx, in, out) != 0) {
return AVERROR(EIO);
}
}
diff --git a/libavfilter/vf_sr.c b/libavfilter/vf_sr.c
index 02d9452681..0890c8ba18 100644
--- a/libavfilter/vf_sr.c
+++ b/libavfilter/vf_sr.c
@@ -82,7 +82,7 @@ static int config_output(AVFilterLink *outlink)
// have a try run in case that the dnn model resize the frame
result = ff_dnn_get_output(&ctx->dnnctx, inlink->w, inlink->h, &out_width, &out_height);
- if (result != DNN_SUCCESS) {
+ if (result != 0) {
av_log(ctx, AV_LOG_ERROR, "could not get output from the model\n");
return result;
}
@@ -139,7 +139,7 @@ static int filter_frame(AVFilterLink *inlink, AVFrame *in)
dnn_result = ff_dnn_execute_model(&ctx->dnnctx, in, out);
}
- if (dnn_result != DNN_SUCCESS){
+ if (dnn_result != 0){
av_log(ctx, AV_LOG_ERROR, "failed to execute loaded model\n");
av_frame_free(&in);
av_frame_free(&out);
--
2.32.0
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^ permalink raw reply [flat|nested] 9+ messages in thread* Re: [FFmpeg-devel] [PATCH 8/8] libavfilter: Remove DNNReturnType from DNN Module
2022-02-24 8:22 ` [FFmpeg-devel] [PATCH 8/8] libavfilter: Remove DNNReturnType from DNN Module Shubhanshu Saxena
@ 2022-03-02 11:18 ` Guo, Yejun
0 siblings, 0 replies; 9+ messages in thread
From: Guo, Yejun @ 2022-03-02 11:18 UTC (permalink / raw)
To: FFmpeg development discussions and patches
> -----Original Message-----
> From: ffmpeg-devel <ffmpeg-devel-bounces@ffmpeg.org> On Behalf Of
> Shubhanshu Saxena
> Sent: Thursday, February 24, 2022 4:23 PM
> To: ffmpeg-devel@ffmpeg.org
> Cc: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
> Subject: [FFmpeg-devel] [PATCH 8/8] libavfilter: Remove DNNReturnType from
> DNN Module
>
> This patch removes all occurences of DNNReturnType from the DNN module.
> This commit replaces DNN_SUCCESS by 0 (essentially the same), so the
> functions with DNNReturnType now return 0 in case of success, the negative
> values otherwise.
>
> Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
> ---
> libavfilter/dnn/dnn_backend_common.c | 10 ++--
> libavfilter/dnn/dnn_backend_common.h | 8 +--
> libavfilter/dnn/dnn_backend_native.c | 16 +++---
> .../dnn/dnn_backend_native_layer_avgpool.c | 2 +-
> .../dnn/dnn_backend_native_layer_conv2d.c | 4 +-
> .../dnn/dnn_backend_native_layer_dense.c | 2 +-
> .../dnn_backend_native_layer_depth2space.c | 2 +-
> libavfilter/dnn/dnn_backend_openvino.c | 48 ++++++++--------
> libavfilter/dnn/dnn_backend_tf.c | 56 +++++++++----------
> libavfilter/dnn/dnn_io_proc.c | 14 ++---
> libavfilter/dnn_interface.h | 2 -
> libavfilter/vf_derain.c | 2 +-
> libavfilter/vf_dnn_classify.c | 4 +-
> libavfilter/vf_dnn_detect.c | 4 +-
> libavfilter/vf_dnn_processing.c | 8 +--
> libavfilter/vf_sr.c | 4 +-
> 16 files changed, 92 insertions(+), 94 deletions(-)
>
LGTM, will push soon, thanks.
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^ permalink raw reply [flat|nested] 9+ messages in thread