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update include headers

pull/1301/head
shenwei41 5 years ago
parent
commit
0f490f37d4
7 changed files with 498 additions and 280 deletions
  1. +4
    -4
      inc/external/acl/acl.h
  2. +261
    -164
      inc/external/acl/ops/acl_dvpp.h
  3. +19
    -0
      third_party/fwkacllib/inc/ops/linalg_ops.h
  4. +183
    -106
      third_party/fwkacllib/inc/ops/nn_detect_ops.h
  5. +25
    -0
      third_party/fwkacllib/inc/ops/nn_norm_ops.h
  6. +4
    -4
      third_party/fwkacllib/inc/ops/nn_pooling_ops.h
  7. +2
    -2
      third_party/fwkacllib/inc/ops/pad_ops.h

+ 4
- 4
inc/external/acl/acl.h View File

@@ -26,9 +26,9 @@ extern "C" {
#endif #endif


// Current version is 1.0.0 // Current version is 1.0.0
#define ACL_MAJOR_VERSION 1
#define ACL_MINOR_VERSION 0
#define ACL_PATCH_VERSION 0
#define ACL_MAJOR_VERSION 1
#define ACL_MINOR_VERSION 0
#define ACL_PATCH_VERSION 0


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -70,4 +70,4 @@ ACL_FUNC_VISIBILITY aclError aclrtGetVersion(int32_t *majorVersion, int32_t *min
} }
#endif #endif


#endif // INC_EXTERNAL_ACL_ACL_H_
#endif // INC_EXTERNAL_ACL_ACL_H_

+ 261
- 164
inc/external/acl/ops/acl_dvpp.h View File

@@ -53,109 +53,123 @@ typedef void (*aclvencCallback)(acldvppPicDesc *input, acldvppStreamDesc *output


// Supported Pixel Format // Supported Pixel Format
enum acldvppPixelFormat { enum acldvppPixelFormat {
PIXEL_FORMAT_YUV_400 = 0, // 0
PIXEL_FORMAT_YUV_SEMIPLANAR_420 = 1, // 1
PIXEL_FORMAT_YVU_SEMIPLANAR_420 = 2, // 2
PIXEL_FORMAT_YUV_SEMIPLANAR_422 = 3, // 3
PIXEL_FORMAT_YVU_SEMIPLANAR_422 = 4, // 4
PIXEL_FORMAT_YUV_SEMIPLANAR_444 = 5, // 5
PIXEL_FORMAT_YVU_SEMIPLANAR_444 = 6, // 6
PIXEL_FORMAT_YUYV_PACKED_422 = 7, // 7
PIXEL_FORMAT_UYVY_PACKED_422 = 8, // 8
PIXEL_FORMAT_YVYU_PACKED_422 = 9, // 9
PIXEL_FORMAT_VYUY_PACKED_422 = 10, // 10
PIXEL_FORMAT_YUV_PACKED_444 = 11, // 11
PIXEL_FORMAT_RGB_888 = 12, // 12
PIXEL_FORMAT_BGR_888 = 13, // 13
PIXEL_FORMAT_ARGB_8888 = 14, // 14
PIXEL_FORMAT_ABGR_8888 = 15, // 15
PIXEL_FORMAT_RGBA_8888 = 16, // 16
PIXEL_FORMAT_BGRA_8888 = 17, // 17
PIXEL_FORMAT_YUV_SEMI_PLANNER_420_10BIT = 18, // 18
PIXEL_FORMAT_YVU_SEMI_PLANNER_420_10BIT = 19, // 19
PIXEL_FORMAT_YVU_PLANAR_420 = 20, // 20
PIXEL_FORMAT_YVU_PLANAR_422,
PIXEL_FORMAT_YVU_PLANAR_444,
PIXEL_FORMAT_RGB_444 = 23,
PIXEL_FORMAT_BGR_444,
PIXEL_FORMAT_ARGB_4444,
PIXEL_FORMAT_ABGR_4444,
PIXEL_FORMAT_RGBA_4444,
PIXEL_FORMAT_BGRA_4444,
PIXEL_FORMAT_RGB_555,
PIXEL_FORMAT_BGR_555,
PIXEL_FORMAT_RGB_565,
PIXEL_FORMAT_BGR_565,
PIXEL_FORMAT_ARGB_1555,
PIXEL_FORMAT_ABGR_1555,
PIXEL_FORMAT_RGBA_1555,
PIXEL_FORMAT_BGRA_1555,
PIXEL_FORMAT_ARGB_8565,
PIXEL_FORMAT_ABGR_8565,
PIXEL_FORMAT_RGBA_8565,
PIXEL_FORMAT_BGRA_8565,
PIXEL_FORMAT_RGB_BAYER_8BPP = 50,
PIXEL_FORMAT_RGB_BAYER_10BPP,
PIXEL_FORMAT_RGB_BAYER_12BPP,
PIXEL_FORMAT_RGB_BAYER_14BPP,
PIXEL_FORMAT_RGB_BAYER_16BPP,
PIXEL_FORMAT_BGR_888_PLANAR = 70,
PIXEL_FORMAT_HSV_888_PACKAGE,
PIXEL_FORMAT_HSV_888_PLANAR,
PIXEL_FORMAT_LAB_888_PACKAGE,
PIXEL_FORMAT_LAB_888_PLANAR,
PIXEL_FORMAT_S8C1,
PIXEL_FORMAT_S8C2_PACKAGE,
PIXEL_FORMAT_S8C2_PLANAR,
PIXEL_FORMAT_S16C1,
PIXEL_FORMAT_U8C1,
PIXEL_FORMAT_U16C1,
PIXEL_FORMAT_S32C1,
PIXEL_FORMAT_U32C1,
PIXEL_FORMAT_U64C1,
PIXEL_FORMAT_S64C1,
PIXEL_FORMAT_YUV_SEMIPLANAR_440 = 1000,
PIXEL_FORMAT_YVU_SEMIPLANAR_440,
PIXEL_FORMAT_FLOAT32,
PIXEL_FORMAT_BUTT,
PIXEL_FORMAT_UNKNOWN = 10000
PIXEL_FORMAT_YUV_400 = 0, // 0
PIXEL_FORMAT_YUV_SEMIPLANAR_420 = 1, // 1
PIXEL_FORMAT_YVU_SEMIPLANAR_420 = 2, // 2
PIXEL_FORMAT_YUV_SEMIPLANAR_422 = 3, // 3
PIXEL_FORMAT_YVU_SEMIPLANAR_422 = 4, // 4
PIXEL_FORMAT_YUV_SEMIPLANAR_444 = 5, // 5
PIXEL_FORMAT_YVU_SEMIPLANAR_444 = 6, // 6
PIXEL_FORMAT_YUYV_PACKED_422 = 7, // 7
PIXEL_FORMAT_UYVY_PACKED_422 = 8, // 8
PIXEL_FORMAT_YVYU_PACKED_422 = 9, // 9
PIXEL_FORMAT_VYUY_PACKED_422 = 10, // 10
PIXEL_FORMAT_YUV_PACKED_444 = 11, // 11
PIXEL_FORMAT_RGB_888 = 12, // 12
PIXEL_FORMAT_BGR_888 = 13, // 13
PIXEL_FORMAT_ARGB_8888 = 14, // 14
PIXEL_FORMAT_ABGR_8888 = 15, // 15
PIXEL_FORMAT_RGBA_8888 = 16, // 16
PIXEL_FORMAT_BGRA_8888 = 17, // 17
PIXEL_FORMAT_YUV_SEMI_PLANNER_420_10BIT = 18, // 18
PIXEL_FORMAT_YVU_SEMI_PLANNER_420_10BIT = 19, // 19
PIXEL_FORMAT_YVU_PLANAR_420 = 20, // 20
PIXEL_FORMAT_YVU_PLANAR_422,
PIXEL_FORMAT_YVU_PLANAR_444,
PIXEL_FORMAT_RGB_444 = 23,
PIXEL_FORMAT_BGR_444,
PIXEL_FORMAT_ARGB_4444,
PIXEL_FORMAT_ABGR_4444,
PIXEL_FORMAT_RGBA_4444,
PIXEL_FORMAT_BGRA_4444,
PIXEL_FORMAT_RGB_555,
PIXEL_FORMAT_BGR_555,
PIXEL_FORMAT_RGB_565,
PIXEL_FORMAT_BGR_565,
PIXEL_FORMAT_ARGB_1555,
PIXEL_FORMAT_ABGR_1555,
PIXEL_FORMAT_RGBA_1555,
PIXEL_FORMAT_BGRA_1555,
PIXEL_FORMAT_ARGB_8565,
PIXEL_FORMAT_ABGR_8565,
PIXEL_FORMAT_RGBA_8565,
PIXEL_FORMAT_BGRA_8565,
PIXEL_FORMAT_RGB_BAYER_8BPP = 50,
PIXEL_FORMAT_RGB_BAYER_10BPP,
PIXEL_FORMAT_RGB_BAYER_12BPP,
PIXEL_FORMAT_RGB_BAYER_14BPP,
PIXEL_FORMAT_RGB_BAYER_16BPP,
PIXEL_FORMAT_BGR_888_PLANAR = 70,
PIXEL_FORMAT_HSV_888_PACKAGE,
PIXEL_FORMAT_HSV_888_PLANAR,
PIXEL_FORMAT_LAB_888_PACKAGE,
PIXEL_FORMAT_LAB_888_PLANAR,
PIXEL_FORMAT_S8C1,
PIXEL_FORMAT_S8C2_PACKAGE,
PIXEL_FORMAT_S8C2_PLANAR,
PIXEL_FORMAT_S16C1,
PIXEL_FORMAT_U8C1,
PIXEL_FORMAT_U16C1,
PIXEL_FORMAT_S32C1,
PIXEL_FORMAT_U32C1,
PIXEL_FORMAT_U64C1,
PIXEL_FORMAT_S64C1,
PIXEL_FORMAT_YUV_SEMIPLANAR_440 = 1000,
PIXEL_FORMAT_YVU_SEMIPLANAR_440,
PIXEL_FORMAT_FLOAT32,
PIXEL_FORMAT_BUTT,
PIXEL_FORMAT_UNKNOWN = 10000
}; };


// Stream Format // Stream Format
enum acldvppStreamFormat { H265_MAIN_LEVEL = 0, H264_BASELINE_LEVEL, H264_MAIN_LEVEL, H264_HIGH_LEVEL };
enum acldvppStreamFormat {
H265_MAIN_LEVEL = 0,
H264_BASELINE_LEVEL,
H264_MAIN_LEVEL,
H264_HIGH_LEVEL
};


// Supported Channel Mode // Supported Channel Mode
enum acldvppChannelMode { DVPP_CHNMODE_VPC = 1, DVPP_CHNMODE_JPEGD = 2, DVPP_CHNMODE_JPEGE = 4 };
enum acldvppChannelMode {
DVPP_CHNMODE_VPC = 1,
DVPP_CHNMODE_JPEGD = 2,
DVPP_CHNMODE_JPEGE = 4
};


// Supported Border Type // Supported Border Type
enum acldvppBorderType { BORDER_CONSTANT = 0, BORDER_REPLICATE, BORDER_REFLECT, BORDER_REFLECT_101 };
enum acldvppBorderType {
BORDER_CONSTANT = 0,
BORDER_REPLICATE,
BORDER_REFLECT,
BORDER_REFLECT_101
};


// Venc parameter type // Venc parameter type
enum aclvencChannelDescParamType { enum aclvencChannelDescParamType {
ACL_VENC_THREAD_ID_UINT64 = 0,
ACL_VENC_CALLBACK_PTR,
ACL_VENC_PIXEL_FORMAT_UINT32,
ACL_VENC_ENCODE_TYPE_UINT32,
ACL_VENC_PIC_WIDTH_UINT32,
ACL_VENC_PIC_HEIGHT_UINT32,
ACL_VENC_KEY_FRAME_INTERVAL_UINT32,
ACL_VENC_BUF_ADDR_PTR,
ACL_VENC_BUF_SIZE_UINT32,
ACL_VENC_RC_MODE_UINT32,
ACL_VENC_SRC_RATE_UINT32,
ACL_VENC_MAX_BITRATE_UINT32,
ACL_VENC_MAX_IP_PROP_UINT32
ACL_VENC_THREAD_ID_UINT64 = 0,
ACL_VENC_CALLBACK_PTR,
ACL_VENC_PIXEL_FORMAT_UINT32,
ACL_VENC_ENCODE_TYPE_UINT32,
ACL_VENC_PIC_WIDTH_UINT32,
ACL_VENC_PIC_HEIGHT_UINT32,
ACL_VENC_KEY_FRAME_INTERVAL_UINT32,
ACL_VENC_BUF_ADDR_PTR,
ACL_VENC_BUF_SIZE_UINT32,
ACL_VENC_RC_MODE_UINT32,
ACL_VENC_SRC_RATE_UINT32,
ACL_VENC_MAX_BITRATE_UINT32,
ACL_VENC_MAX_IP_PROP_UINT32
}; };


// Jpeg picture format // Jpeg picture format
enum acldvppJpegFormat { enum acldvppJpegFormat {
ACL_JPEG_CSS_444 = 0,
ACL_JPEG_CSS_422,
ACL_JPEG_CSS_420,
ACL_JPEG_CSS_GRAY,
ACL_JPEG_CSS_440,
ACL_JPEG_CSS_411,
ACL_JPEG_CSS_UNKNOWN = 1000
ACL_JPEG_CSS_444 = 0,
ACL_JPEG_CSS_422,
ACL_JPEG_CSS_420,
ACL_JPEG_CSS_GRAY,
ACL_JPEG_CSS_440,
ACL_JPEG_CSS_411,
ACL_JPEG_CSS_UNKNOWN = 1000
}; };


/** /**
@@ -509,7 +523,9 @@ ACL_FUNC_VISIBILITY uint32_t acldvppGetPicDescRetCode(const acldvppPicDesc *picD
* @retval null for failed. * @retval null for failed.
* @retval other success * @retval other success
*/ */
ACL_FUNC_VISIBILITY acldvppRoiConfig *acldvppCreateRoiConfig(uint32_t left, uint32_t right, uint32_t top,
ACL_FUNC_VISIBILITY acldvppRoiConfig *acldvppCreateRoiConfig(uint32_t left,
uint32_t right,
uint32_t top,
uint32_t bottom); uint32_t bottom);


/** /**
@@ -588,7 +604,10 @@ ACL_FUNC_VISIBILITY aclError acldvppSetRoiConfigBottom(acldvppRoiConfig *config,
* @retval ACL_SUCCESS The function is successfully executed. * @retval ACL_SUCCESS The function is successfully executed.
* @retval OtherValues Failure * @retval OtherValues Failure
*/ */
ACL_FUNC_VISIBILITY aclError acldvppSetRoiConfig(acldvppRoiConfig *config, uint32_t left, uint32_t right, uint32_t top,
ACL_FUNC_VISIBILITY aclError acldvppSetRoiConfig(acldvppRoiConfig *config,
uint32_t left,
uint32_t right,
uint32_t top,
uint32_t bottom); uint32_t bottom);


/** /**
@@ -1077,8 +1096,7 @@ ACL_FUNC_VISIBILITY aclError aclvencSetChannelDescMaxBitRate(aclvencChannelDesc
* @retval ACL_SUCCESS for success, other for failure * @retval ACL_SUCCESS for success, other for failure
*/ */
ACL_FUNC_VISIBILITY aclError aclvencSetChannelDescParam(aclvencChannelDesc *channelDesc, ACL_FUNC_VISIBILITY aclError aclvencSetChannelDescParam(aclvencChannelDesc *channelDesc,
aclvencChannelDescParamType paramType, size_t length,
const void *param);
aclvencChannelDescParamType paramType, size_t length, const void *param);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -1227,8 +1245,7 @@ ACL_FUNC_VISIBILITY uint32_t aclvencGetChannelDescMaxBitRate(const aclvencChanne
* @retval ACL_SUCCESS for success, other for failure * @retval ACL_SUCCESS for success, other for failure
*/ */
ACL_FUNC_VISIBILITY aclError aclvencGetChannelDescParam(const aclvencChannelDesc *channelDesc, ACL_FUNC_VISIBILITY aclError aclvencGetChannelDescParam(const aclvencChannelDesc *channelDesc,
aclvencChannelDescParamType paramType, size_t length,
size_t *paramRetSize, void *param);
aclvencChannelDescParamType paramType, size_t length, size_t *paramRetSize, void *param);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -1528,7 +1545,10 @@ ACL_FUNC_VISIBILITY aclError aclvdecDestroyFrameConfig(aclvdecFrameConfig *vdecF
* @retval ACL_SUCCESS The function is successfully executed. * @retval ACL_SUCCESS The function is successfully executed.
* @retval OtherValues Failure * @retval OtherValues Failure
*/ */
ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfo(const void *data, uint32_t size, uint32_t *width, uint32_t *height,
ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfo(const void *data,
uint32_t size,
uint32_t *width,
uint32_t *height,
int32_t *components); int32_t *components);


/** /**
@@ -1545,8 +1565,11 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfo(const void *data, uint32_t
* @retval ACL_SUCCESS The function is successfully executed. * @retval ACL_SUCCESS The function is successfully executed.
* @retval OtherValues Failure * @retval OtherValues Failure
*/ */
ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfoV2(const void *data, uint32_t size, uint32_t *width,
uint32_t *height, int32_t *components,
ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfoV2(const void *data,
uint32_t size,
uint32_t *width,
uint32_t *height,
int32_t *components,
acldvppJpegFormat *format); acldvppJpegFormat *format);


/** /**
@@ -1561,7 +1584,8 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegGetImageInfoV2(const void *data, uint32_
* @retval OtherValues Failure * @retval OtherValues Failure
*/ */
ACL_FUNC_VISIBILITY aclError acldvppJpegPredictEncSize(const acldvppPicDesc *inputDesc, ACL_FUNC_VISIBILITY aclError acldvppJpegPredictEncSize(const acldvppPicDesc *inputDesc,
const acldvppJpegeConfig *config, uint32_t *size);
const acldvppJpegeConfig *config,
uint32_t *size);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -1575,8 +1599,10 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegPredictEncSize(const acldvppPicDesc *inp
* @retval ACL_SUCCESS The function is successfully executed. * @retval ACL_SUCCESS The function is successfully executed.
* @retval OtherValues Failure * @retval OtherValues Failure
*/ */
ACL_FUNC_VISIBILITY aclError acldvppJpegPredictDecSize(const void *data, uint32_t dataSize,
acldvppPixelFormat outputPixelFormat, uint32_t *decSize);
ACL_FUNC_VISIBILITY aclError acldvppJpegPredictDecSize(const void *data,
uint32_t dataSize,
acldvppPixelFormat outputPixelFormat,
uint32_t *decSize);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -1591,8 +1617,11 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegPredictDecSize(const void *data, uint32_
* @retval ACL_SUCCESS The function is successfully executed. * @retval ACL_SUCCESS The function is successfully executed.
* @retval OtherValues Failure * @retval OtherValues Failure
*/ */
ACL_FUNC_VISIBILITY aclError acldvppPngGetImageInfo(const void *data, uint32_t dataSize, uint32_t *width,
uint32_t *height, int32_t *components);
ACL_FUNC_VISIBILITY aclError acldvppPngGetImageInfo(const void *data,
uint32_t dataSize,
uint32_t *width,
uint32_t *height,
int32_t *components);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -1606,8 +1635,10 @@ ACL_FUNC_VISIBILITY aclError acldvppPngGetImageInfo(const void *data, uint32_t d
* @retval ACL_SUCCESS The function is successfully executed. * @retval ACL_SUCCESS The function is successfully executed.
* @retval OtherValues Failure * @retval OtherValues Failure
*/ */
ACL_FUNC_VISIBILITY aclError acldvppPngPredictDecSize(const void *data, uint32_t dataSize,
acldvppPixelFormat outputPixelFormat, uint32_t *decSize);
ACL_FUNC_VISIBILITY aclError acldvppPngPredictDecSize(const void *data,
uint32_t dataSize,
acldvppPixelFormat outputPixelFormat,
uint32_t *decSize);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -1671,8 +1702,10 @@ ACL_FUNC_VISIBILITY aclError acldvppDestroyChannel(acldvppChannelDesc *channelDe
* @see acldvppCreateChannel | acldvppCreatePicDesc * @see acldvppCreateChannel | acldvppCreatePicDesc
* | acldvppCreateResizeConfig * | acldvppCreateResizeConfig
*/ */
ACL_FUNC_VISIBILITY aclError acldvppVpcResizeAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc, acldvppResizeConfig *resizeConfig,
ACL_FUNC_VISIBILITY aclError acldvppVpcResizeAsync(acldvppChannelDesc *channelDesc,
acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc,
acldvppResizeConfig *resizeConfig,
aclrtStream stream); aclrtStream stream);


/** /**
@@ -1708,8 +1741,10 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcResizeAsync(acldvppChannelDesc *channelDe
* @retval ACL_SUCCESS The function is successfully executed. * @retval ACL_SUCCESS The function is successfully executed.
* @retval OtherValues Failure * @retval OtherValues Failure
*/ */
ACL_FUNC_VISIBILITY aclError acldvppVpcCropAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc, acldvppRoiConfig *cropArea,
ACL_FUNC_VISIBILITY aclError acldvppVpcCropAsync(acldvppChannelDesc *channelDesc,
acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc,
acldvppRoiConfig *cropArea,
aclrtStream stream); aclrtStream stream);


/** /**
@@ -1746,9 +1781,13 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCropAsync(acldvppChannelDesc *channelDesc
* @retval ACL_SUCCESS The function is successfully executed. * @retval ACL_SUCCESS The function is successfully executed.
* @retval OtherValues Failure * @retval OtherValues Failure
*/ */
ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizeAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc, acldvppRoiConfig *cropArea,
acldvppResizeConfig *resizeConfig, aclrtStream stream);
ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizeAsync(acldvppChannelDesc *channelDesc,
acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc,
acldvppRoiConfig *cropArea,
acldvppResizeConfig *resizeConfig,
aclrtStream stream);



/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -1772,9 +1811,12 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizeAsync(acldvppChannelDesc *chann
* @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig * @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig
*/ */
ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAsync(acldvppChannelDesc *channelDesc, ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAsync(acldvppChannelDesc *channelDesc,
acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums,
uint32_t size, acldvppBatchPicDesc *dstBatchPicDescs,
acldvppRoiConfig *cropAreas[], aclrtStream stream);
acldvppBatchPicDesc *srcBatchPicDescs,
uint32_t *roiNums,
uint32_t size,
acldvppBatchPicDesc *dstBatchPicDescs,
acldvppRoiConfig *cropAreas[],
aclrtStream stream);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -1799,10 +1841,13 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAsync(acldvppChannelDesc *channe
* @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig | acldvppCreateDvppConfig * @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig | acldvppCreateDvppConfig
*/ */
ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizeAsync(acldvppChannelDesc *channelDesc, ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizeAsync(acldvppChannelDesc *channelDesc,
acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums,
uint32_t size, acldvppBatchPicDesc *dstBatchPicDescs,
acldvppBatchPicDesc *srcBatchPicDescs,
uint32_t *roiNums,
uint32_t size,
acldvppBatchPicDesc *dstBatchPicDescs,
acldvppRoiConfig *cropAreas[], acldvppRoiConfig *cropAreas[],
acldvppResizeConfig *resizeConfig, aclrtStream stream);
acldvppResizeConfig *resizeConfig,
aclrtStream stream);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -1825,9 +1870,12 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizeAsync(acldvppChannelDesc *
* *
* @see acldvppCreateChannel | acldvppCreatePicDesc | acldvppCreateRoiConfig * @see acldvppCreateChannel | acldvppCreatePicDesc | acldvppCreateRoiConfig
*/ */
ACL_FUNC_VISIBILITY aclError acldvppVpcCropAndPasteAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc, acldvppRoiConfig *cropArea,
acldvppRoiConfig *pasteArea, aclrtStream stream);
ACL_FUNC_VISIBILITY aclError acldvppVpcCropAndPasteAsync(acldvppChannelDesc *channelDesc,
acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc,
acldvppRoiConfig *cropArea,
acldvppRoiConfig *pasteArea,
aclrtStream stream);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -1851,10 +1899,13 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCropAndPasteAsync(acldvppChannelDesc *cha
* *
* @see acldvppCreateChannel | acldvppCreatePicDesc | acldvppCreateRoiConfig | acldvppCreateResizeConfig * @see acldvppCreateChannel | acldvppCreatePicDesc | acldvppCreateRoiConfig | acldvppCreateResizeConfig
*/ */
ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizePasteAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc, acldvppRoiConfig *cropArea,
ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizePasteAsync(acldvppChannelDesc *channelDesc,
acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc,
acldvppRoiConfig *cropArea,
acldvppRoiConfig *pasteArea, acldvppRoiConfig *pasteArea,
acldvppResizeConfig *resizeConfig, aclrtStream stream);
acldvppResizeConfig *resizeConfig,
aclrtStream stream);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -1879,11 +1930,14 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCropResizePasteAsync(acldvppChannelDesc *
* *
* @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig * @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig
*/ */
ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAndPasteAsync(acldvppChannelDesc *channelDesc,
acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums,
uint32_t size, acldvppBatchPicDesc *dstBatchPicDescs,
acldvppRoiConfig *cropAreas[],
acldvppRoiConfig *pasteAreas[], aclrtStream stream);
ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAndPasteAsync(acldvppChannelDesc *channelDesc,
acldvppBatchPicDesc *srcBatchPicDescs,
uint32_t *roiNums,
uint32_t size,
acldvppBatchPicDesc *dstBatchPicDescs,
acldvppRoiConfig *cropAreas[],
acldvppRoiConfig *pasteAreas[],
aclrtStream stream);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -1909,10 +1963,16 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropAndPasteAsync(acldvppChannelDesc
* *
* @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig | acldvppCreateResizeConfig * @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig | acldvppCreateResizeConfig
*/ */
ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizePasteAsync(
acldvppChannelDesc *channelDesc, acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums, uint32_t size,
acldvppBatchPicDesc *dstBatchPicDescs, acldvppRoiConfig *cropAreas[], acldvppRoiConfig *pasteAreas[],
acldvppResizeConfig *resizeConfig, aclrtStream stream);
ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizePasteAsync(acldvppChannelDesc *channelDesc,
acldvppBatchPicDesc *srcBatchPicDescs,
uint32_t *roiNums,
uint32_t size,
acldvppBatchPicDesc *dstBatchPicDescs,
acldvppRoiConfig *cropAreas[],
acldvppRoiConfig *pasteAreas[],
acldvppResizeConfig *resizeConfig,
aclrtStream stream);



/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -1940,8 +2000,11 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizePasteAsync(
* *
* @see acldvppCreateChannel | acldvppCreatePicDesc * @see acldvppCreateChannel | acldvppCreatePicDesc
*/ */
ACL_FUNC_VISIBILITY aclError acldvppJpegDecodeAsync(acldvppChannelDesc *channelDesc, const void *data, uint32_t size,
acldvppPicDesc *outputDesc, aclrtStream stream);
ACL_FUNC_VISIBILITY aclError acldvppJpegDecodeAsync(acldvppChannelDesc *channelDesc,
const void *data,
uint32_t size,
acldvppPicDesc *outputDesc,
aclrtStream stream);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -1959,8 +2022,11 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegDecodeAsync(acldvppChannelDesc *channelD
* *
* @see acldvppCreateChannel | acldvppCreateJpegeConfig * @see acldvppCreateChannel | acldvppCreateJpegeConfig
*/ */
ACL_FUNC_VISIBILITY aclError acldvppJpegEncodeAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
const void *data, uint32_t *size, acldvppJpegeConfig *config,
ACL_FUNC_VISIBILITY aclError acldvppJpegEncodeAsync(acldvppChannelDesc *channelDesc,
acldvppPicDesc *inputDesc,
const void *data,
uint32_t *size,
acldvppJpegeConfig *config,
aclrtStream stream); aclrtStream stream);


/** /**
@@ -1978,8 +2044,11 @@ ACL_FUNC_VISIBILITY aclError acldvppJpegEncodeAsync(acldvppChannelDesc *channelD
* *
* @see acldvppCreateChannel | acldvppCreatePicDesc * @see acldvppCreateChannel | acldvppCreatePicDesc
*/ */
ACL_FUNC_VISIBILITY aclError acldvppPngDecodeAsync(acldvppChannelDesc *channelDesc, const void *data, uint32_t size,
acldvppPicDesc *outputDesc, aclrtStream stream);
ACL_FUNC_VISIBILITY aclError acldvppPngDecodeAsync(acldvppChannelDesc *channelDesc,
const void *data,
uint32_t size,
acldvppPicDesc *outputDesc,
aclrtStream stream);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -2034,8 +2103,11 @@ ACL_FUNC_VISIBILITY aclError aclvdecDestroyChannel(aclvdecChannelDesc *channelDe
* *
* @see aclvdecCreateChannel | acldvppCreateStreamDesc | acldvppCreatePicDesc * @see aclvdecCreateChannel | acldvppCreateStreamDesc | acldvppCreatePicDesc
*/ */
ACL_FUNC_VISIBILITY aclError aclvdecSendFrame(aclvdecChannelDesc *channelDesc, acldvppStreamDesc *input,
acldvppPicDesc *output, aclvdecFrameConfig *config, void *userData);
ACL_FUNC_VISIBILITY aclError aclvdecSendFrame(aclvdecChannelDesc *channelDesc,
acldvppStreamDesc *input,
acldvppPicDesc *output,
aclvdecFrameConfig *config,
void *userData);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -2054,8 +2126,10 @@ ACL_FUNC_VISIBILITY aclError aclvdecSendFrame(aclvdecChannelDesc *channelDesc, a
* *
* @see aclvdecCreateChannel | acldvppCreateStreamDesc | acldvppCreatePicDesc | aclvdecSendFrame * @see aclvdecCreateChannel | acldvppCreateStreamDesc | acldvppCreatePicDesc | aclvdecSendFrame
*/ */
ACL_FUNC_VISIBILITY aclError aclvdecSendSkippedFrame(aclvdecChannelDesc *channelDesc, acldvppStreamDesc *input,
aclvdecFrameConfig *config, void *userData);
ACL_FUNC_VISIBILITY aclError aclvdecSendSkippedFrame(aclvdecChannelDesc *channelDesc,
acldvppStreamDesc *input,
aclvdecFrameConfig *config,
void *userData);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -2076,8 +2150,10 @@ ACL_FUNC_VISIBILITY aclError aclvdecSendSkippedFrame(aclvdecChannelDesc *channel
* *
* @see acldvppCreateChannel | acldvppCreatePicDesc * @see acldvppCreateChannel | acldvppCreatePicDesc
*/ */
ACL_FUNC_VISIBILITY aclError acldvppVpcConvertColorAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc, aclrtStream stream);
ACL_FUNC_VISIBILITY aclError acldvppVpcConvertColorAsync(acldvppChannelDesc *channelDesc,
acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc,
aclrtStream stream);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -2099,8 +2175,11 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcConvertColorAsync(acldvppChannelDesc *cha
* *
* @see acldvppCreateChannel | acldvppCreatePicDesc * @see acldvppCreateChannel | acldvppCreatePicDesc
*/ */
ACL_FUNC_VISIBILITY aclError acldvppVpcPyrDownAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc, void *reserve, aclrtStream stream);
ACL_FUNC_VISIBILITY aclError acldvppVpcPyrDownAsync(acldvppChannelDesc *channelDesc,
acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc,
void *reserve,
aclrtStream stream);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -2112,7 +2191,8 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcPyrDownAsync(acldvppChannelDesc *channelD
* @retval ACL_SUCCESS The function is successfully executed. * @retval ACL_SUCCESS The function is successfully executed.
* @retval OtherValues Failure * @retval OtherValues Failure
*/ */
ACL_FUNC_VISIBILITY aclError acldvppSetChannelDescMode(acldvppChannelDesc *channelDesc, uint32_t mode);
ACL_FUNC_VISIBILITY aclError acldvppSetChannelDescMode(acldvppChannelDesc *channelDesc,
uint32_t mode);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -2147,7 +2227,8 @@ ACL_FUNC_VISIBILITY uint32_t acldvppGetResizeConfigInterpolation(const acldvppRe
* @retval ACL_SUCCESS The function is successfully executed. * @retval ACL_SUCCESS The function is successfully executed.
* @retval OtherValues Failure * @retval OtherValues Failure
*/ */
ACL_FUNC_VISIBILITY aclError aclvdecSetChannelDescOutMode(aclvdecChannelDesc *channelDesc, uint32_t outMode);
ACL_FUNC_VISIBILITY aclError aclvdecSetChannelDescOutMode(aclvdecChannelDesc *channelDesc,
uint32_t outMode);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -2244,7 +2325,9 @@ ACL_FUNC_VISIBILITY uint32_t acldvppGetLutMapDims(const acldvppLutMap *lutMap);
* @retval ACL_SUCCESS The function is successfully executed. * @retval ACL_SUCCESS The function is successfully executed.
* @retval OtherValues Failure * @retval OtherValues Failure
*/ */
ACL_FUNC_VISIBILITY aclError acldvppGetLutMapData(const acldvppLutMap *lutMap, uint32_t dim, uint8_t **data,
ACL_FUNC_VISIBILITY aclError acldvppGetLutMapData(const acldvppLutMap *lutMap,
uint32_t dim,
uint8_t **data,
uint32_t *len); uint32_t *len);
/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -2262,8 +2345,10 @@ ACL_FUNC_VISIBILITY aclError acldvppGetLutMapData(const acldvppLutMap *lutMap, u
* @see acldvppCreateChannel|acldvppCreatePicDesc|acldvppCreateLutMap * @see acldvppCreateChannel|acldvppCreatePicDesc|acldvppCreateLutMap
*/ */
ACL_FUNC_VISIBILITY aclError acldvppVpcEqualizeHistAsync(const acldvppChannelDesc *channelDesc, ACL_FUNC_VISIBILITY aclError acldvppVpcEqualizeHistAsync(const acldvppChannelDesc *channelDesc,
const acldvppPicDesc *inputDesc, acldvppPicDesc *outputDesc,
const acldvppLutMap *lutMap, aclrtStream stream);
const acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc,
const acldvppLutMap *lutMap,
aclrtStream stream);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -2284,7 +2369,8 @@ ACL_FUNC_VISIBILITY acldvppBorderConfig *acldvppCreateBorderConfig();
* *
* @retval ACL_SUCCESS for success, other for failure * @retval ACL_SUCCESS for success, other for failure
*/ */
ACL_FUNC_VISIBILITY aclError acldvppSetBorderConfigValue(acldvppBorderConfig *borderConfig, uint32_t index,
ACL_FUNC_VISIBILITY aclError acldvppSetBorderConfigValue(acldvppBorderConfig *borderConfig,
uint32_t index,
double value); double value);


/** /**
@@ -2429,8 +2515,10 @@ ACL_FUNC_VISIBILITY aclError acldvppDestroyBorderConfig(acldvppBorderConfig *bor
* @see acldvppCreateChannel|acldvppCreatePicDesc|acldvppCreateBorderConfig * @see acldvppCreateChannel|acldvppCreatePicDesc|acldvppCreateBorderConfig
*/ */
ACL_FUNC_VISIBILITY aclError acldvppVpcMakeBorderAsync(const acldvppChannelDesc *channelDesc, ACL_FUNC_VISIBILITY aclError acldvppVpcMakeBorderAsync(const acldvppChannelDesc *channelDesc,
const acldvppPicDesc *inputDesc, acldvppPicDesc *outputDesc,
const acldvppBorderConfig *borderConfig, aclrtStream stream);
const acldvppPicDesc *inputDesc,
acldvppPicDesc *outputDesc,
const acldvppBorderConfig *borderConfig,
aclrtStream stream);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -2447,8 +2535,11 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcMakeBorderAsync(const acldvppChannelDesc
* *
* @see acldvppCreateChannel | acldvppCreatePicDesc | acldvppCreateHist * @see acldvppCreateChannel | acldvppCreatePicDesc | acldvppCreateHist
*/ */
ACL_FUNC_VISIBILITY aclError acldvppVpcCalcHistAsync(acldvppChannelDesc *channelDesc, acldvppPicDesc *srcPicDesc,
acldvppHist *hist, void *reserve, aclrtStream stream);
ACL_FUNC_VISIBILITY aclError acldvppVpcCalcHistAsync(acldvppChannelDesc *channelDesc,
acldvppPicDesc *srcPicDesc,
acldvppHist *hist,
void *reserve,
aclrtStream stream);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -2457,7 +2548,7 @@ ACL_FUNC_VISIBILITY aclError acldvppVpcCalcHistAsync(acldvppChannelDesc *channel
* @retval null for failed. * @retval null for failed.
* @retval OtherValues success. * @retval OtherValues success.
*/ */
ACL_FUNC_VISIBILITY acldvppHist *acldvppCreateHist();
ACL_FUNC_VISIBILITY acldvppHist* acldvppCreateHist();


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -2514,7 +2605,7 @@ ACL_FUNC_VISIBILITY aclError acldvppGetHistData(acldvppHist *hist, uint32_t dim,
* *
* @see acldvppCreateHist | acldvppVpcCalcHistAsync * @see acldvppCreateHist | acldvppVpcCalcHistAsync
*/ */
ACL_FUNC_VISIBILITY uint32_t acldvppGetHistRetCode(acldvppHist *hist);
ACL_FUNC_VISIBILITY uint32_t acldvppGetHistRetCode(acldvppHist* hist);


/** /**
* @ingroup AscendCL * @ingroup AscendCL
@@ -2533,6 +2624,7 @@ ACL_FUNC_VISIBILITY uint32_t acldvppGetHistRetCode(acldvppHist *hist);
*/ */
ACL_FUNC_VISIBILITY aclError acldvppClearHist(acldvppHist *hist); ACL_FUNC_VISIBILITY aclError acldvppClearHist(acldvppHist *hist);



/** /**
* @ingroup AscendCL * @ingroup AscendCL
* @brief dvpp vpc batch crop, resize config and make border. * @brief dvpp vpc batch crop, resize config and make border.
@@ -2556,13 +2648,18 @@ ACL_FUNC_VISIBILITY aclError acldvppClearHist(acldvppHist *hist);
* *
* @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig | acldvppCreateResizeConfig * @see acldvppCreateChannel | acldvppCreateBatchPicDesc | acldvppCreateRoiConfig | acldvppCreateResizeConfig
*/ */
ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizeMakeBorderAsync(
acldvppChannelDesc *channelDesc, acldvppBatchPicDesc *srcBatchPicDescs, uint32_t *roiNums, uint32_t size,
acldvppBatchPicDesc *dstBatchPicDescs, acldvppRoiConfig *cropAreas[], acldvppBorderConfig *borderCfgs[],
acldvppResizeConfig *resizeConfig, aclrtStream stream);
ACL_FUNC_VISIBILITY aclError acldvppVpcBatchCropResizeMakeBorderAsync(acldvppChannelDesc *channelDesc,
acldvppBatchPicDesc *srcBatchPicDescs,
uint32_t *roiNums,
uint32_t size,
acldvppBatchPicDesc *dstBatchPicDescs,
acldvppRoiConfig *cropAreas[],
acldvppBorderConfig *borderCfgs[],
acldvppResizeConfig *resizeConfig,
aclrtStream stream);


#ifdef __cplusplus #ifdef __cplusplus
} }
#endif #endif


#endif // INC_EXTERNAL_ACL_OPS_ACL_DVPP_H_
#endif // INC_EXTERNAL_ACL_OPS_ACL_DVPP_H_

+ 19
- 0
third_party/fwkacllib/inc/ops/linalg_ops.h View File

@@ -82,6 +82,25 @@ REG_OP(Cholesky)
DT_FLOAT16, DT_COMPLEX64, DT_COMPLEX128})) DT_FLOAT16, DT_COMPLEX64, DT_COMPLEX128}))
.OP_END_FACTORY_REG(Cholesky) .OP_END_FACTORY_REG(Cholesky)


/**
*@brief Computes the outer product of two 1D vectors . \n

*@par Inputs:
*The input x and vec2 has to be a 1D vector.Inputs include:
*@li x:A Tensor. Must be one of the following types: float16, float32.
Shape is [N] . \n
*@li vec2:A Tensor. Must have the same type as x. Shape is [M] . \n

*@par Outputs:
*y:A Tensor. Has the same type as x . \n
*/

REG_OP(Ger)
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(vec2, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
.OP_END_FACTORY_REG(Ger)

/** /**
*@brief Computes the sign and the log of the absolute value of the determinant *@brief Computes the sign and the log of the absolute value of the determinant
of one or more square matrices . \n of one or more square matrices . \n


+ 183
- 106
third_party/fwkacllib/inc/ops/nn_detect_ops.h View File

@@ -254,22 +254,22 @@ is min_size/sqrt(aspect_ratio), the width is min_size*sqrt(aspect_ratio). Defaul
*@par Third-party framework compatibility *@par Third-party framework compatibility
* It is a custom operator. It has no corresponding operator in Caffe. * It is a custom operator. It has no corresponding operator in Caffe.
*/ */
REG_OP(PriorBox)
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(img, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
.REQUIRED_ATTR(min_size, ListFloat)
.REQUIRED_ATTR(max_size, ListFloat)
.REQUIRED_ATTR(aspect_ratio, ListFloat)
.ATTR(img_h, Int, 0)
.ATTR(img_w, Int, 0)
.ATTR(step_h, Float, 0.0)
.ATTR(step_w, Float, 0.0)
.ATTR(flip, Bool, true)
.ATTR(clip, Bool, false)
.ATTR(offset, Float, 0.5)
.ATTR(variance, ListFloat, {0.1})
.OP_END_FACTORY_REG(PriorBox);
REG_OP(PriorBox)
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(img, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
.REQUIRED_ATTR(min_size, ListFloat)
.REQUIRED_ATTR(max_size, ListFloat)
.REQUIRED_ATTR(aspect_ratio, ListFloat)
.ATTR(img_h, Int, 0)
.ATTR(img_w, Int, 0)
.ATTR(step_h, Float, 0.0)
.ATTR(step_w, Float, 0.0)
.ATTR(flip, Bool, true)
.ATTR(clip, Bool, false)
.ATTR(offset, Float, 0.5)
.ATTR(variance, ListFloat, {0.1})
.OP_END_FACTORY_REG(PriorBox);


/** /**
*@brief Performs SSD prior box detection, with four additional matrices and the "aspect_ratio" attribute deleted compared to PriorBox . \n *@brief Performs SSD prior box detection, with four additional matrices and the "aspect_ratio" attribute deleted compared to PriorBox . \n
@@ -306,25 +306,25 @@ is min_size/sqrt(aspect_ratio), the width is min_size*sqrt(aspect_ratio). Defaul
*@par Restrictions: *@par Restrictions:
*Warning: THIS FUNCTION IS DEPRECATED. Please use PriorBox instead. *Warning: THIS FUNCTION IS DEPRECATED. Please use PriorBox instead.
*/ */
REG_OP(PriorBoxD)
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(img, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(data_h, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(data_w, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(box_height, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(box_width, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
.REQUIRED_ATTR(min_size, ListFloat)
.REQUIRED_ATTR(max_size, ListFloat)
.ATTR(img_h, Int, 0)
.ATTR(img_w, Int, 0)
.ATTR(step_h, Float, 0.0)
.ATTR(step_w, Float, 0.0)
.ATTR(flip, Bool, true)
.ATTR(clip, Bool, false)
.ATTR(offset, Float, 0.5)
.ATTR(variance, ListFloat, {0.1})
.OP_END_FACTORY_REG(PriorBoxD);
REG_OP(PriorBoxD)
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(img, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(data_h, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(data_w, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(box_height, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(box_width, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
.REQUIRED_ATTR(min_size, ListFloat)
.REQUIRED_ATTR(max_size, ListFloat)
.ATTR(img_h, Int, 0)
.ATTR(img_w, Int, 0)
.ATTR(step_h, Float, 0.0)
.ATTR(step_w, Float, 0.0)
.ATTR(flip, Bool, true)
.ATTR(clip, Bool, false)
.ATTR(offset, Float, 0.5)
.ATTR(variance, ListFloat, {0.1})
.OP_END_FACTORY_REG(PriorBoxD);


/** /**
*@brief Performs SSD prior box detection, with four additional matrices and the "aspect_ratio" attribute deleted compared to PriorBox . \n *@brief Performs SSD prior box detection, with four additional matrices and the "aspect_ratio" attribute deleted compared to PriorBox . \n
@@ -358,22 +358,22 @@ is min_size/sqrt(aspect_ratio), the width is min_size*sqrt(aspect_ratio). Defaul
*@par Restrictions: *@par Restrictions:
*Warning: THIS FUNCTION IS DEPRECATED. Please use PriorBox instead. *Warning: THIS FUNCTION IS DEPRECATED. Please use PriorBox instead.
*/ */
REG_OP(PriorBoxDV2)
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(img, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(boxes, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
.REQUIRED_ATTR(min_size, ListFloat)
.REQUIRED_ATTR(max_size, ListFloat)
.ATTR(img_h, Int, 0)
.ATTR(img_w, Int, 0)
.ATTR(step_h, Float, 0.0)
.ATTR(step_w, Float, 0.0)
.ATTR(flip, Bool, true)
.ATTR(clip, Bool, false)
.ATTR(offset, Float, 0.5)
.ATTR(variance, ListFloat, {0.1})
.OP_END_FACTORY_REG(PriorBoxDV2);
REG_OP(PriorBoxDV2)
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(img, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(boxes, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
.REQUIRED_ATTR(min_size, ListFloat)
.REQUIRED_ATTR(max_size, ListFloat)
.ATTR(img_h, Int, 0)
.ATTR(img_w, Int, 0)
.ATTR(step_h, Float, 0.0)
.ATTR(step_w, Float, 0.0)
.ATTR(flip, Bool, true)
.ATTR(clip, Bool, false)
.ATTR(offset, Float, 0.5)
.ATTR(variance, ListFloat, {0.1})
.OP_END_FACTORY_REG(PriorBoxDV2);


/** /**
*@brief Performs Position Sensitive ROI Pooling . \n *@brief Performs Position Sensitive ROI Pooling . \n
@@ -531,10 +531,10 @@ as xx...xyy...yww...whh...hbb...bc0c0..c0c1c1...c1......cncn...cn . \n
* It is a custom operator. It has no corresponding operator in Caffe. * It is a custom operator. It has no corresponding operator in Caffe.
*/ */
REG_OP(Yolo) REG_OP(Yolo)
.INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
.OUTPUT(coord_data, TensorType({DT_FLOAT16,DT_FLOAT}))
.OUTPUT(obj_prob, TensorType({DT_FLOAT16,DT_FLOAT}))
.OUTPUT(classes_prob, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(coord_data, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(obj_prob, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(classes_prob, TensorType({DT_FLOAT16, DT_FLOAT}))
.ATTR(boxes, Int, 3) .ATTR(boxes, Int, 3)
.ATTR(coords, Int, 4) .ATTR(coords, Int, 4)
.ATTR(classes, Int, 80) .ATTR(classes, Int, 80)
@@ -584,10 +584,10 @@ REG_OP(Yolo)
* It is a custom operator. It has no corresponding operator in Caffe. * It is a custom operator. It has no corresponding operator in Caffe.
*/ */
REG_OP(YoloV2DetectionOutput) REG_OP(YoloV2DetectionOutput)
.INPUT(coord_data, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(obj_prob, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(classes_prob, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(img_info, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(coord_data, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(obj_prob, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(classes_prob, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(img_info, TensorType({DT_FLOAT16, DT_FLOAT}))
.REQUIRED_ATTR(biases, ListFloat) .REQUIRED_ATTR(biases, ListFloat)
.ATTR(boxes, Int, 5) .ATTR(boxes, Int, 5)
.ATTR(coords, Int, 4) .ATTR(coords, Int, 4)
@@ -598,7 +598,7 @@ REG_OP(YoloV2DetectionOutput)
.ATTR(score_threshold, Float, 0.5) .ATTR(score_threshold, Float, 0.5)
.ATTR(iou_threshold, Float, 0.45) .ATTR(iou_threshold, Float, 0.45)
.ATTR(pre_nms_topn, Int, 512) .ATTR(pre_nms_topn, Int, 512)
.OUTPUT(box_out, TensorType({DT_FLOAT16,DT_FLOAT}))
.OUTPUT(box_out, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(box_out_num, TensorType({DT_INT32})) .OUTPUT(box_out_num, TensorType({DT_INT32}))
.OP_END_FACTORY_REG(YoloV2DetectionOutput) .OP_END_FACTORY_REG(YoloV2DetectionOutput)


@@ -647,12 +647,12 @@ REG_OP(YoloV2DetectionOutput)
*Warning: THIS FUNCTION IS DEPRECATED. Please use YoloV2DetectionOutput instead. *Warning: THIS FUNCTION IS DEPRECATED. Please use YoloV2DetectionOutput instead.
*/ */
REG_OP(YoloV2DetectionOutputD) REG_OP(YoloV2DetectionOutputD)
.INPUT(coord_data, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(obj_prob, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(classes_prob, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(img_info, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(windex, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(hindex, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(coord_data, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(obj_prob, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(classes_prob, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(img_info, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(windex, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(hindex, TensorType({DT_FLOAT16, DT_FLOAT}))
.REQUIRED_ATTR(biases, ListFloat) .REQUIRED_ATTR(biases, ListFloat)
.ATTR(boxes, Int, 5) .ATTR(boxes, Int, 5)
.ATTR(coords, Int, 4) .ATTR(coords, Int, 4)
@@ -663,7 +663,7 @@ REG_OP(YoloV2DetectionOutputD)
.ATTR(score_threshold, Float, 0.5) .ATTR(score_threshold, Float, 0.5)
.ATTR(iou_threshold, Float, 0.45) .ATTR(iou_threshold, Float, 0.45)
.ATTR(pre_nms_topn, Int, 512) .ATTR(pre_nms_topn, Int, 512)
.OUTPUT(box_out, TensorType({DT_FLOAT16,DT_FLOAT}))
.OUTPUT(box_out, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(box_out_num, TensorType({DT_INT32})) .OUTPUT(box_out_num, TensorType({DT_INT32}))
.OP_END_FACTORY_REG(YoloV2DetectionOutputD) .OP_END_FACTORY_REG(YoloV2DetectionOutputD)


@@ -707,16 +707,16 @@ REG_OP(YoloV2DetectionOutputD)
* It is a custom operator. It has no corresponding operator in Caffe. * It is a custom operator. It has no corresponding operator in Caffe.
*/ */
REG_OP(YoloV3DetectionOutput) REG_OP(YoloV3DetectionOutput)
.INPUT(coord_data_low, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(coord_data_mid, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(coord_data_high, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(obj_prob_low, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(obj_prob_mid, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(obj_prob_high, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(classes_prob_low, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(classes_prob_mid, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(classes_prob_high, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(img_info, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(coord_data_low, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(coord_data_mid, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(coord_data_high, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(obj_prob_low, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(obj_prob_mid, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(obj_prob_high, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(classes_prob_low, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(classes_prob_mid, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(classes_prob_high, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(img_info, TensorType({DT_FLOAT16, DT_FLOAT}))
.REQUIRED_ATTR(biases_low, ListFloat) .REQUIRED_ATTR(biases_low, ListFloat)
.REQUIRED_ATTR(biases_mid, ListFloat) .REQUIRED_ATTR(biases_mid, ListFloat)
.REQUIRED_ATTR(biases_high, ListFloat) .REQUIRED_ATTR(biases_high, ListFloat)
@@ -729,7 +729,7 @@ REG_OP(YoloV3DetectionOutput)
.ATTR(score_threshold, Float, 0.5) .ATTR(score_threshold, Float, 0.5)
.ATTR(iou_threshold, Float, 0.45) .ATTR(iou_threshold, Float, 0.45)
.ATTR(pre_nms_topn, Int, 512) .ATTR(pre_nms_topn, Int, 512)
.OUTPUT(box_out, TensorType({DT_FLOAT16,DT_FLOAT}))
.OUTPUT(box_out, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(box_out_num, TensorType({DT_INT32})) .OUTPUT(box_out_num, TensorType({DT_INT32}))
.OP_END_FACTORY_REG(YoloV3DetectionOutput) .OP_END_FACTORY_REG(YoloV3DetectionOutput)


@@ -776,22 +776,22 @@ s
*Warning: THIS FUNCTION IS DEPRECATED. Please use YoloV3DetectionOutput instead. *Warning: THIS FUNCTION IS DEPRECATED. Please use YoloV3DetectionOutput instead.
*/ */
REG_OP(YoloV3DetectionOutputD) REG_OP(YoloV3DetectionOutputD)
.INPUT(coord_data_low, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(coord_data_mid, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(coord_data_high, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(obj_prob_low, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(obj_prob_mid, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(obj_prob_high, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(classes_prob_low, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(classes_prob_mid, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(classes_prob_high, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(img_info, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(windex1, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(windex2, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(windex3, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(hindex1, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(hindex2, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(hindex3, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(coord_data_low, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(coord_data_mid, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(coord_data_high, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(obj_prob_low, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(obj_prob_mid, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(obj_prob_high, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(classes_prob_low, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(classes_prob_mid, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(classes_prob_high, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(img_info, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(windex1, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(windex2, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(windex3, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(hindex1, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(hindex2, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(hindex3, TensorType({DT_FLOAT16, DT_FLOAT}))
.REQUIRED_ATTR(biases_low, ListFloat) .REQUIRED_ATTR(biases_low, ListFloat)
.REQUIRED_ATTR(biases_mid, ListFloat) .REQUIRED_ATTR(biases_mid, ListFloat)
.REQUIRED_ATTR(biases_high, ListFloat) .REQUIRED_ATTR(biases_high, ListFloat)
@@ -804,7 +804,7 @@ REG_OP(YoloV3DetectionOutputD)
.ATTR(score_threshold, Float, 0.5) .ATTR(score_threshold, Float, 0.5)
.ATTR(iou_threshold, Float, 0.45) .ATTR(iou_threshold, Float, 0.45)
.ATTR(pre_nms_topn, Int, 512) .ATTR(pre_nms_topn, Int, 512)
.OUTPUT(box_out, TensorType({DT_FLOAT16,DT_FLOAT}))
.OUTPUT(box_out, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(box_out_num, TensorType({DT_INT32})) .OUTPUT(box_out_num, TensorType({DT_INT32}))
.OP_END_FACTORY_REG(YoloV3DetectionOutputD) .OP_END_FACTORY_REG(YoloV3DetectionOutputD)


@@ -848,7 +848,7 @@ There are three Yolo operators at Yolov3DetectionOutput's preceding layer on Yol
* It is a custom operator. It has no corresponding operator in Caffe. * It is a custom operator. It has no corresponding operator in Caffe.
*/ */
REG_OP(YoloV3DetectionOutputV2) REG_OP(YoloV3DetectionOutputV2)
.DYNAMIC_INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
.DYNAMIC_INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
.REQUIRED_ATTR(biases, ListFloat) .REQUIRED_ATTR(biases, ListFloat)
.ATTR(boxes, Int, 3) .ATTR(boxes, Int, 3)
.ATTR(coords, Int, 4) .ATTR(coords, Int, 4)
@@ -862,7 +862,7 @@ REG_OP(YoloV3DetectionOutputV2)
.ATTR(N, Int, 10) .ATTR(N, Int, 10)
.ATTR(resize_origin_img_to_net, Bool, false) .ATTR(resize_origin_img_to_net, Bool, false)
.ATTR(out_box_dim, Int, 3) .ATTR(out_box_dim, Int, 3)
.OUTPUT(box_out, TensorType({DT_FLOAT16,DT_FLOAT}))
.OUTPUT(box_out, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(box_out_num, TensorType({DT_INT32})) .OUTPUT(box_out_num, TensorType({DT_INT32}))
.OP_END_FACTORY_REG(YoloV3DetectionOutputV2) .OP_END_FACTORY_REG(YoloV3DetectionOutputV2)


@@ -910,9 +910,9 @@ REG_OP(YoloV3DetectionOutputV2)
* Warning: THIS FUNCTION IS DEPRECATED. Please use YoloV3DetectionOutputV2 instead. * Warning: THIS FUNCTION IS DEPRECATED. Please use YoloV3DetectionOutputV2 instead.
*/ */
REG_OP(YoloV3DetectionOutputV2D) REG_OP(YoloV3DetectionOutputV2D)
.DYNAMIC_INPUT(x, TensorType({DT_FLOAT16,DT_FLOAT}))
.DYNAMIC_INPUT(windex, TensorType({DT_FLOAT16,DT_FLOAT}))
.DYNAMIC_INPUT(hindex, TensorType({DT_FLOAT16,DT_FLOAT}))
.DYNAMIC_INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
.DYNAMIC_INPUT(windex, TensorType({DT_FLOAT16, DT_FLOAT}))
.DYNAMIC_INPUT(hindex, TensorType({DT_FLOAT16, DT_FLOAT}))
.REQUIRED_ATTR(biases, ListFloat) .REQUIRED_ATTR(biases, ListFloat)
.ATTR(boxes, Int, 3) .ATTR(boxes, Int, 3)
.ATTR(coords, Int, 4) .ATTR(coords, Int, 4)
@@ -926,7 +926,7 @@ REG_OP(YoloV3DetectionOutputV2D)
.ATTR(N, Int, 10) .ATTR(N, Int, 10)
.ATTR(resize_origin_img_to_net, Bool, false) .ATTR(resize_origin_img_to_net, Bool, false)
.ATTR(out_box_dim, Int, 3) .ATTR(out_box_dim, Int, 3)
.OUTPUT(box_out, TensorType({DT_FLOAT16,DT_FLOAT}))
.OUTPUT(box_out, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(box_out_num, TensorType({DT_INT32})) .OUTPUT(box_out_num, TensorType({DT_INT32}))
.OP_END_FACTORY_REG(YoloV3DetectionOutputV2D) .OP_END_FACTORY_REG(YoloV3DetectionOutputV2D)


@@ -1466,9 +1466,9 @@ REG_OP(NormalizeBBox)
* y: A Tensor. Must have the same type as box_predictions. * y: A Tensor. Must have the same type as box_predictions.
*/ */
REG_OP(DecodeBboxV2) REG_OP(DecodeBboxV2)
.INPUT(boxes, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(anchors, TensorType({DT_FLOAT16,DT_FLOAT}))
.OUTPUT(y, TensorType({DT_FLOAT16,DT_FLOAT}))
.INPUT(boxes, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(anchors, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
.ATTR(scales, ListFloat, {1.0, 1.0, 1.0, 1.0}) .ATTR(scales, ListFloat, {1.0, 1.0, 1.0, 1.0})
.ATTR(decode_clip, Float, 0.0) .ATTR(decode_clip, Float, 0.0)
.ATTR(reversed_box, Bool, false) .ATTR(reversed_box, Bool, false)
@@ -1592,7 +1592,6 @@ selected indices from the boxes tensor, where M <= max_output_size. \n
*Compatible with onnx NonMaxSuppression operator. *Compatible with onnx NonMaxSuppression operator.
*/ */



REG_OP(NonMaxSuppressionV7) REG_OP(NonMaxSuppressionV7)
.INPUT(boxes, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(boxes, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(scores, TensorType({DT_FLOAT16, DT_FLOAT})) .INPUT(scores, TensorType({DT_FLOAT16, DT_FLOAT}))
@@ -1641,7 +1640,7 @@ REG_OP(RoiExtractor)
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
.ATTR(finest_scale, Int, 56) .ATTR(finest_scale, Int, 56)
.ATTR(roi_scale_factor, Float, 0) .ATTR(roi_scale_factor, Float, 0)
.ATTR(spatial_scale, ListFloat, { 1.f/4, 1.f/8, 1.f/16, 1.f/32 })
.ATTR(spatial_scale, ListFloat, {1.f / 4, 1.f / 8, 1.f / 16, 1.f / 32})
.ATTR(pooled_height, Int, 7) .ATTR(pooled_height, Int, 7)
.ATTR(pooled_width, Int, 7) .ATTR(pooled_width, Int, 7)
.ATTR(sample_num, Int, 0) .ATTR(sample_num, Int, 0)
@@ -1649,6 +1648,84 @@ REG_OP(RoiExtractor)
.ATTR(aligned, Bool, true) .ATTR(aligned, Bool, true)
.OP_END_FACTORY_REG(RoiExtractor) .OP_END_FACTORY_REG(RoiExtractor)


/**
*@brief Performs Position Sensitive PS ROI Pooling . \n

*@par Inputs:
* Two inputs, including:
*@li x: An NC1HWC0 tensor of type float16 or float32, describing the feature
* map, dimension C1 must be equal to
* (int(output_dim+15)/C0))*group_size*group_size.
*@li rois: A tensor of type float16 or float32, with shape
* [batch, 5, rois_num], describing the ROIs, each ROI consists of five
* elements: "batch_id", "x1", "y1", "x2", and "y2", which "batch_id" indicates
* the index of the input feature map, "x1", "y1", "x2", or "y2" must be
* greater than or equal to "0.0" . \n

*@par Attributes:
*@li output_dim: A required int32, specifying the number of output channels,
* must be greater than 0.
*@li group_size: A required int32, specifying the number of groups to encode
* position-sensitive score maps, must be within the range (0, 128).
*@li spatial_scale: A required float32, scaling factor for mapping the input
* coordinates to the ROI coordinates . \n

*@par Outputs:
*y: An NC1HWC0 tensor of type float16 or float32, describing the result
* feature map . \n

*@attention Constraints:
* HC1HWC0: channel must be Group_size squared, rois_num is a multiple of 16
*/
REG_OP(PSROIPoolingV2)
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(rois, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
.REQUIRED_ATTR(spatial_scale, Float)
.REQUIRED_ATTR(output_dim, Int)
.REQUIRED_ATTR(group_size, Int)
.OP_END_FACTORY_REG(PSROIPoolingV2)

/**
*@brief Performs Position Sensitive PS ROI Pooling Grad . \n

*@par Inputs:
* Two inputs, including:
*@li x: An NC1HWC0 tensor of type float16 or float32, describing the result
* feature map . \n
*@li rois: A tensor of type float16 or float32, with shape
* [batch, 5, rois_num], describing the ROIs, each ROI consists of five
* elements: "batch_id", "x1", "y1", "x2", and "y2", which "batch_id" indicates
* the index of the input feature map, "x1", "y1", "x2", or "y2" must be
* greater than or equal to "0.0" . \n

*@par Attributes:
*@li output_dim: A required int32, specifying the number of output channels,
* must be greater than 0.
*@li group_size: A required int32, specifying the number of groups to encode
* position-sensitive score maps, must be within the range (0, 128).
*@li spatial_scale: A required float32, scaling factor for mapping the input
* coordinates to the ROI coordinates . \n
*@li input_size: A required listInt, mapping the gradinput size: (H, W)

*@par Outputs:
*y: An NC1HWC0 tensor of type float16 or float32, describing the feature
* map, dimension C1 must be equal to
* (int(output_dim+15)/C0))*group_size*group_size.

*@attention Constraints:
* HC1HWC0: channel must be Group_size squared, rois_num is a multiple of 16
*/
REG_OP(PSROIPoolingGradV2D)
.INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT}))
.INPUT(rois, TensorType({DT_FLOAT16, DT_FLOAT}))
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT}))
.REQUIRED_ATTR(spatial_scale, Float)
.REQUIRED_ATTR(output_dim, Int)
.REQUIRED_ATTR(group_size, Int)
.REQUIRED_ATTR(input_size, ListInt)
.OP_END_FACTORY_REG(PSROIPoolingGradV2D)

} // namespace ge } // namespace ge


#endif // OPS_BUILT_IN_OP_PROTO_INC_NN_DETECT_OPS_H_ #endif // OPS_BUILT_IN_OP_PROTO_INC_NN_DETECT_OPS_H_

+ 25
- 0
third_party/fwkacllib/inc/ops/nn_norm_ops.h View File

@@ -525,6 +525,31 @@ REG_OP(LayerNorm)
.ATTR(epsilon, Float, 0.0000001) .ATTR(epsilon, Float, 0.0000001)
.OP_END_FACTORY_REG(LayerNorm) .OP_END_FACTORY_REG(LayerNorm)


/**
*@brief Returns a tensor where each sub-tensor of input along dimension
* dim is normalized such that the p-norm of the sub-tensor is lower than the value maxnorm. \n

*@par Inputs:
*One input, including:
* @li x: A Tensor. Must be one of the following types: float16, float32 . \n

*@par Attributes:
* @li p: Specify L_p norm, the type is float.
* @li dim: The processed dim, the type is int.
* @li maxnorm: Threshold for comparison, the type is float. \n

*@par Outputs:
*One outputs, including:
* @li y: shape and dtype of output, should be same shape and type as input.
*/
REG_OP(Renorm)
.INPUT(x, TensorType::BasicType())
.OUTPUT(y, TensorType::BasicType())
.REQUIRED_ATTR(p, Float)
.REQUIRED_ATTR(dim, Int)
.REQUIRED_ATTR(maxnorm, Float)
.OP_END_FACTORY_REG(Renorm)

/** /**
*@brief LayerNormGrad operator interface implementation *@brief LayerNormGrad operator interface implementation
* calculating: dy, x, variance, mean, gamma * calculating: dy, x, variance, mean, gamma


+ 4
- 4
third_party/fwkacllib/inc/ops/nn_pooling_ops.h View File

@@ -397,8 +397,8 @@ No default value.
specifying the stride of the sliding window for each dimension of specifying the stride of the sliding window for each dimension of
the input tensor. No default value. the input tensor. No default value.
*@li padding: A required string type of float16. *@li padding: A required string type of float16.
*@li pads: A list type of int32. Default value {0, 0, 0}.
*@li dilation: A list type of int32. Default value {1, 1, 1}.
*@li pads: A list type of int32. Default value {0,0,0,0,0,0}.
*@li dilation: A list type of int32. Default value {1,1,1,1,1,1}.
*@li ceil_mode: A ceil mode number of int32 . Default value 0. *@li ceil_mode: A ceil mode number of int32 . Default value 0.
*@li data_format: An optional string. Defaults to "NDHWC" . \n *@li data_format: An optional string. Defaults to "NDHWC" . \n


@@ -421,8 +421,8 @@ REG_OP(MaxPool3D)
.REQUIRED_ATTR(ksize, ListInt) .REQUIRED_ATTR(ksize, ListInt)
.REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(strides, ListInt)
.REQUIRED_ATTR(padding, String) .REQUIRED_ATTR(padding, String)
.ATTR(pads, ListInt, {0,0,0})
.ATTR(dilation, ListInt, {1,1,1})
.ATTR(pads, ListInt, {0,0,0,0,0,0})
.ATTR(dilation, ListInt, {1,1,1,1,1,1})
.ATTR(ceil_mode, Int, 0) .ATTR(ceil_mode, Int, 0)
.ATTR(data_format, String, "NDHWC") .ATTR(data_format, String, "NDHWC")
.OP_END_FACTORY_REG(MaxPool3D) .OP_END_FACTORY_REG(MaxPool3D)


+ 2
- 2
third_party/fwkacllib/inc/ops/pad_ops.h View File

@@ -418,7 +418,7 @@ REG_OP(EmbeddingRankId)
*/ */
REG_OP(FillV2) REG_OP(FillV2)
.INPUT(dims, TensorType({DT_INT16, DT_INT32, DT_INT64})) .INPUT(dims, TensorType({DT_INT16, DT_INT32, DT_INT64}))
.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT, DT_DOUBLE, DT_INT8, DT_INT16, DT_INT32, DT_INT64}))
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8, DT_INT16, DT_INT32, DT_INT64}))
.ATTR(value, Float, 0) .ATTR(value, Float, 0)
.OP_END_FACTORY_REG(FillV2) .OP_END_FACTORY_REG(FillV2)


@@ -437,7 +437,7 @@ REG_OP(FillV2)
* Compatible with the ONNX operator ConstantOfShape. * Compatible with the ONNX operator ConstantOfShape.
*/ */
REG_OP(FillV2D) REG_OP(FillV2D)
.OUTPUT(y, TensorType({DT_FLOAT, DT_FLOAT, DT_DOUBLE, DT_INT8, DT_UINT8, DT_INT16, DT_INT32, DT_INT64}))
.OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8, DT_UINT8, DT_INT16, DT_INT32, DT_INT64}))
.ATTR(value, Float, 0) .ATTR(value, Float, 0)
.REQUIRED_ATTR(dims, ListInt) .REQUIRED_ATTR(dims, ListInt)
.OP_END_FACTORY_REG(FillV2D) .OP_END_FACTORY_REG(FillV2D)


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