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[Other] add code and docs for ppclas examples (#1312)

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* add code and docs for ppclas examples

* fix doc

* add code for printing results

* add ppcls demo and docs

* modify example according to refined c api

* modify example code and docs for ppcls and ppdet

* modify example code and docs for ppcls and ppdet

* update ppdet demo

* fix demo codes

* fix doc

* release resource when failed

* fix

* fix name

* fix name
This commit is contained in:
chenjian
2023-02-17 15:43:21 +08:00
committed by GitHub
parent 7c4e0d7ba0
commit 859896cd2c
14 changed files with 932 additions and 144 deletions
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120
csharp/fastdeploy/vision/detection/ppdet/model.cs
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@@ -33,7 +33,7 @@ public class PPYOLOE {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_ppyoloe_wrapper = fd_ppyoloe_wrapper =
FD_C_CreatesPPYOLOEWrapper(model_file, params_file, config_file, FD_C_CreatePPYOLOEWrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -84,8 +84,8 @@ public class PPYOLOE {
// below are underlying C api // below are underlying C api
private IntPtr fd_ppyoloe_wrapper; private IntPtr fd_ppyoloe_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesPPYOLOEWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatePPYOLOEWrapper")]
private static extern IntPtr FD_C_CreatesPPYOLOEWrapper( private static extern IntPtr FD_C_CreatePPYOLOEWrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPPYOLOEWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPPYOLOEWrapper")]
@@ -136,7 +136,7 @@ public class PicoDet {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_picodet_wrapper = fd_picodet_wrapper =
FD_C_CreatesPicoDetWrapper(model_file, params_file, config_file, FD_C_CreatePicoDetWrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -187,8 +187,8 @@ public class PicoDet {
// below are underlying C api // below are underlying C api
private IntPtr fd_picodet_wrapper; private IntPtr fd_picodet_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesPicoDetWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatePicoDetWrapper")]
private static extern IntPtr FD_C_CreatesPicoDetWrapper( private static extern IntPtr FD_C_CreatePicoDetWrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPicoDetWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPicoDetWrapper")]
@@ -241,7 +241,7 @@ public class PPYOLO {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_ppyolo_wrapper = fd_ppyolo_wrapper =
FD_C_CreatesPPYOLOWrapper(model_file, params_file, config_file, FD_C_CreatePPYOLOWrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -292,8 +292,8 @@ public class PPYOLO {
// below are underlying C api // below are underlying C api
private IntPtr fd_ppyolo_wrapper; private IntPtr fd_ppyolo_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesPPYOLOWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatePPYOLOWrapper")]
private static extern IntPtr FD_C_CreatesPPYOLOWrapper( private static extern IntPtr FD_C_CreatePPYOLOWrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPPYOLOWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPPYOLOWrapper")]
@@ -345,7 +345,7 @@ public class YOLOv3 {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_yolov3_wrapper = fd_yolov3_wrapper =
FD_C_CreatesYOLOv3Wrapper(model_file, params_file, config_file, FD_C_CreateYOLOv3Wrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -396,8 +396,8 @@ public class YOLOv3 {
// below are underlying C api // below are underlying C api
private IntPtr fd_yolov3_wrapper; private IntPtr fd_yolov3_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesYOLOv3Wrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreateYOLOv3Wrapper")]
private static extern IntPtr FD_C_CreatesYOLOv3Wrapper( private static extern IntPtr FD_C_CreateYOLOv3Wrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyYOLOv3Wrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyYOLOv3Wrapper")]
@@ -449,7 +449,7 @@ public class PaddleYOLOX {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_paddleyolox_wrapper = fd_paddleyolox_wrapper =
FD_C_CreatesPaddleYOLOXWrapper(model_file, params_file, config_file, FD_C_CreatePaddleYOLOXWrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -500,8 +500,8 @@ public class PaddleYOLOX {
// below are underlying C api // below are underlying C api
private IntPtr fd_paddleyolox_wrapper; private IntPtr fd_paddleyolox_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesPaddleYOLOXWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatePaddleYOLOXWrapper")]
private static extern IntPtr FD_C_CreatesPaddleYOLOXWrapper( private static extern IntPtr FD_C_CreatePaddleYOLOXWrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPaddleYOLOXWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPaddleYOLOXWrapper")]
@@ -553,7 +553,7 @@ public class FasterRCNN {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_fasterrcnn_wrapper = fd_fasterrcnn_wrapper =
FD_C_CreatesFasterRCNNWrapper(model_file, params_file, config_file, FD_C_CreateFasterRCNNWrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -604,8 +604,8 @@ public class FasterRCNN {
// below are underlying C api // below are underlying C api
private IntPtr fd_fasterrcnn_wrapper; private IntPtr fd_fasterrcnn_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesFasterRCNNWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreateFasterRCNNWrapper")]
private static extern IntPtr FD_C_CreatesFasterRCNNWrapper( private static extern IntPtr FD_C_CreateFasterRCNNWrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyFasterRCNNWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyFasterRCNNWrapper")]
@@ -657,7 +657,7 @@ public class MaskRCNN {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_maskrcnn_wrapper = fd_maskrcnn_wrapper =
FD_C_CreatesMaskRCNNWrapper(model_file, params_file, config_file, FD_C_CreateMaskRCNNWrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -708,8 +708,8 @@ public class MaskRCNN {
// below are underlying C api // below are underlying C api
private IntPtr fd_maskrcnn_wrapper; private IntPtr fd_maskrcnn_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesMaskRCNNWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreateMaskRCNNWrapper")]
private static extern IntPtr FD_C_CreatesMaskRCNNWrapper( private static extern IntPtr FD_C_CreateMaskRCNNWrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyMaskRCNNWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyMaskRCNNWrapper")]
@@ -761,7 +761,7 @@ public class SSD {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_ssd_wrapper = fd_ssd_wrapper =
FD_C_CreatesSSDWrapper(model_file, params_file, config_file, FD_C_CreateSSDWrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -812,8 +812,8 @@ public class SSD {
// below are underlying C api // below are underlying C api
private IntPtr fd_ssd_wrapper; private IntPtr fd_ssd_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesSSDWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreateSSDWrapper")]
private static extern IntPtr FD_C_CreatesSSDWrapper( private static extern IntPtr FD_C_CreateSSDWrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroySSDWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroySSDWrapper")]
@@ -865,7 +865,7 @@ public class PaddleYOLOv5 {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_paddleyolov5_wrapper = fd_paddleyolov5_wrapper =
FD_C_CreatesPaddleYOLOv5Wrapper(model_file, params_file, config_file, FD_C_CreatePaddleYOLOv5Wrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -916,8 +916,8 @@ public class PaddleYOLOv5 {
// below are underlying C api // below are underlying C api
private IntPtr fd_paddleyolov5_wrapper; private IntPtr fd_paddleyolov5_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesPaddleYOLOv5Wrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatePaddleYOLOv5Wrapper")]
private static extern IntPtr FD_C_CreatesPaddleYOLOv5Wrapper( private static extern IntPtr FD_C_CreatePaddleYOLOv5Wrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPaddleYOLOv5Wrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPaddleYOLOv5Wrapper")]
@@ -969,7 +969,7 @@ public class PaddleYOLOv6 {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_paddleyolov6_wrapper = fd_paddleyolov6_wrapper =
FD_C_CreatesPaddleYOLOv6Wrapper(model_file, params_file, config_file, FD_C_CreatePaddleYOLOv6Wrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -1020,8 +1020,8 @@ public class PaddleYOLOv6 {
// below are underlying C api // below are underlying C api
private IntPtr fd_paddleyolov6_wrapper; private IntPtr fd_paddleyolov6_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesPaddleYOLOv6Wrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatePaddleYOLOv6Wrapper")]
private static extern IntPtr FD_C_CreatesPaddleYOLOv6Wrapper( private static extern IntPtr FD_C_CreatePaddleYOLOv6Wrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPaddleYOLOv6Wrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPaddleYOLOv6Wrapper")]
@@ -1073,7 +1073,7 @@ public class PaddleYOLOv7 {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_paddleyolov7_wrapper = fd_paddleyolov7_wrapper =
FD_C_CreatesPaddleYOLOv7Wrapper(model_file, params_file, config_file, FD_C_CreatePaddleYOLOv7Wrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -1124,8 +1124,8 @@ public class PaddleYOLOv7 {
// below are underlying C api // below are underlying C api
private IntPtr fd_paddleyolov7_wrapper; private IntPtr fd_paddleyolov7_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesPaddleYOLOv7Wrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatePaddleYOLOv7Wrapper")]
private static extern IntPtr FD_C_CreatesPaddleYOLOv7Wrapper( private static extern IntPtr FD_C_CreatePaddleYOLOv7Wrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPaddleYOLOv7Wrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPaddleYOLOv7Wrapper")]
@@ -1177,7 +1177,7 @@ public class PaddleYOLOv8 {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_paddleyolov8_wrapper = fd_paddleyolov8_wrapper =
FD_C_CreatesPaddleYOLOv8Wrapper(model_file, params_file, config_file, FD_C_CreatePaddleYOLOv8Wrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -1228,8 +1228,8 @@ public class PaddleYOLOv8 {
// below are underlying C api // below are underlying C api
private IntPtr fd_paddleyolov8_wrapper; private IntPtr fd_paddleyolov8_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesPaddleYOLOv8Wrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatePaddleYOLOv8Wrapper")]
private static extern IntPtr FD_C_CreatesPaddleYOLOv8Wrapper( private static extern IntPtr FD_C_CreatePaddleYOLOv8Wrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPaddleYOLOv8Wrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPaddleYOLOv8Wrapper")]
@@ -1281,7 +1281,7 @@ public class RTMDet {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_rtmdet_wrapper = fd_rtmdet_wrapper =
FD_C_CreatesRTMDetWrapper(model_file, params_file, config_file, FD_C_CreateRTMDetWrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -1332,8 +1332,8 @@ public class RTMDet {
// below are underlying C api // below are underlying C api
private IntPtr fd_rtmdet_wrapper; private IntPtr fd_rtmdet_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesRTMDetWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreateRTMDetWrapper")]
private static extern IntPtr FD_C_CreatesRTMDetWrapper( private static extern IntPtr FD_C_CreateRTMDetWrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyRTMDetWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyRTMDetWrapper")]
@@ -1385,7 +1385,7 @@ public class CascadeRCNN {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_cascadercnn_wrapper = fd_cascadercnn_wrapper =
FD_C_CreatesCascadeRCNNWrapper(model_file, params_file, config_file, FD_C_CreateCascadeRCNNWrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -1436,8 +1436,8 @@ public class CascadeRCNN {
// below are underlying C api // below are underlying C api
private IntPtr fd_cascadercnn_wrapper; private IntPtr fd_cascadercnn_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesCascadeRCNNWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreateCascadeRCNNWrapper")]
private static extern IntPtr FD_C_CreatesCascadeRCNNWrapper( private static extern IntPtr FD_C_CreateCascadeRCNNWrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyCascadeRCNNWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyCascadeRCNNWrapper")]
@@ -1489,7 +1489,7 @@ public class PSSDet {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_pssdet_wrapper = fd_pssdet_wrapper =
FD_C_CreatesPSSDetWrapper(model_file, params_file, config_file, FD_C_CreatePSSDetWrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -1540,8 +1540,8 @@ public class PSSDet {
// below are underlying C api // below are underlying C api
private IntPtr fd_pssdet_wrapper; private IntPtr fd_pssdet_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesPSSDetWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatePSSDetWrapper")]
private static extern IntPtr FD_C_CreatesPSSDetWrapper( private static extern IntPtr FD_C_CreatePSSDetWrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPSSDetWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyPSSDetWrapper")]
@@ -1593,7 +1593,7 @@ public class RetinaNet {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_retinanet_wrapper = fd_retinanet_wrapper =
FD_C_CreatesRetinaNetWrapper(model_file, params_file, config_file, FD_C_CreateRetinaNetWrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -1644,8 +1644,8 @@ public class RetinaNet {
// below are underlying C api // below are underlying C api
private IntPtr fd_retinanet_wrapper; private IntPtr fd_retinanet_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesRetinaNetWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreateRetinaNetWrapper")]
private static extern IntPtr FD_C_CreatesRetinaNetWrapper( private static extern IntPtr FD_C_CreateRetinaNetWrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyRetinaNetWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyRetinaNetWrapper")]
@@ -1697,7 +1697,7 @@ public class FCOS {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_fcos_wrapper = fd_fcos_wrapper =
FD_C_CreatesFCOSWrapper(model_file, params_file, config_file, FD_C_CreateFCOSWrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -1748,8 +1748,8 @@ public class FCOS {
// below are underlying C api // below are underlying C api
private IntPtr fd_fcos_wrapper; private IntPtr fd_fcos_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesFCOSWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreateFCOSWrapper")]
private static extern IntPtr FD_C_CreatesFCOSWrapper( private static extern IntPtr FD_C_CreateFCOSWrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyFCOSWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyFCOSWrapper")]
@@ -1801,7 +1801,7 @@ public class TTFNet {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_ttfnet_wrapper = fd_ttfnet_wrapper =
FD_C_CreatesTTFNetWrapper(model_file, params_file, config_file, FD_C_CreateTTFNetWrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -1852,8 +1852,8 @@ public class TTFNet {
// below are underlying C api // below are underlying C api
private IntPtr fd_ttfnet_wrapper; private IntPtr fd_ttfnet_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesTTFNetWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreateTTFNetWrapper")]
private static extern IntPtr FD_C_CreatesTTFNetWrapper( private static extern IntPtr FD_C_CreateTTFNetWrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyTTFNetWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyTTFNetWrapper")]
@@ -1905,7 +1905,7 @@ public class TOOD {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_tood_wrapper = fd_tood_wrapper =
FD_C_CreatesTOODWrapper(model_file, params_file, config_file, FD_C_CreateTOODWrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -1956,8 +1956,8 @@ public class TOOD {
// below are underlying C api // below are underlying C api
private IntPtr fd_tood_wrapper; private IntPtr fd_tood_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesTOODWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreateTOODWrapper")]
private static extern IntPtr FD_C_CreatesTOODWrapper( private static extern IntPtr FD_C_CreateTOODWrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyTOODWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyTOODWrapper")]
@@ -2009,7 +2009,7 @@ public class GFL {
custom_option = new RuntimeOption(); custom_option = new RuntimeOption();
} }
fd_gfl_wrapper = fd_gfl_wrapper =
FD_C_CreatesGFLWrapper(model_file, params_file, config_file, FD_C_CreateGFLWrapper(model_file, params_file, config_file,
custom_option.GetWrapperPtr(), model_format); custom_option.GetWrapperPtr(), model_format);
} }
@@ -2060,8 +2060,8 @@ public class GFL {
// below are underlying C api // below are underlying C api
private IntPtr fd_gfl_wrapper; private IntPtr fd_gfl_wrapper;
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreatesGFLWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_CreateGFLWrapper")]
private static extern IntPtr FD_C_CreatesGFLWrapper( private static extern IntPtr FD_C_CreateGFLWrapper(
string model_file, string params_file, string config_file, string model_file, string params_file, string config_file,
IntPtr fd_runtime_option_wrapper, ModelFormat model_format); IntPtr fd_runtime_option_wrapper, ModelFormat model_format);
[DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyGFLWrapper")] [DllImport("fastdeploy.dll", EntryPoint = "FD_C_DestroyGFLWrapper")]

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PROJECT(infer_demo C)
CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
# 指定下载解压后的fastdeploy库路径
option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
# 添加FastDeploy依赖头文件
include_directories(${FASTDEPLOY_INCS})
add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.c)
target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})

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English | [简体中文](README_CN.md)
# PaddleClas C Deployment Example
This directory provides examples that `infer.c` fast finishes the deployment of PaddleClas models on CPU/GPU.
Before deployment, two steps require confirmation.
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).
- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).
Taking ResNet50_vd inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 1.0.4 or above (x.x.x>=1.0.4) is required to support this model.
```bash
mkdir build
cd build
# Download FastDeploy precompiled library. Users can choose your appropriate version in the`FastDeploy Precompiled Library` mentioned above
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j
# Download ResNet50_vd model file and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
tar -xvf ResNet50_vd_infer.tgz
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
# CPU inference
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0
# GPU inference
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1
```
The above command works for Linux or MacOS. Refer to
- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/cn/faq/use_sdk_on_windows.md) for SDK use-pattern in Windows
## PaddleClas C Interface
### RuntimeOption
```c
FD_C_RuntimeOptionWrapper* FD_C_CreateRuntimeOptionWrapper()
```
> Create a RuntimeOption object, and return a pointer to manipulate it.
>
> **Return**
>
> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): Pointer to manipulate RuntimeOption object.
```c
void FD_C_RuntimeOptionWrapperUseCpu(
FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper)
```
> Enable Cpu inference.
>
> **Params**
>
> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): Pointer to manipulate RuntimeOption object.
```c
void FD_C_RuntimeOptionWrapperUseGpu(
FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
int gpu_id)
```
> 开启GPU推理
>
> **参数**
>
> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): Pointer to manipulate RuntimeOption object.
> * **gpu_id**(int): gpu id
### Model
```c
FD_C_PaddleClasModelWrapper* FD_C_CreatePaddleClasModelWrapper(
const char* model_file, const char* params_file, const char* config_file,
FD_C_RuntimeOptionWrapper* runtime_option,
const FD_C_ModelFormat model_format)
```
> Create a PaddleClas model object, and return a pointer to manipulate it.
>
> **Params**
>
> * **model_file**(const char*): Model file path
> * **params_file**(const char*): Parameter file path
> * **config_file**(const char*): Configuration file path, which is the deployment yaml file exported by PaddleClas.
> * **runtime_option**(FD_C_RuntimeOptionWrapper*): Backend inference configuration. None by default, which is the default configuration
> * **model_format**(FD_C_ModelFormat): Model format. Paddle format by default
>
> **Return**
> * **fd_c_ppclas_wrapper**(FD_C_PaddleClasModelWrapper*): Pointer to manipulate PaddleClas object.
#### Read and write image
```c
FD_C_Mat FD_C_Imread(const char* imgpath)
```
> Read an image, and return a pointer to cv::Mat.
>
> **Params**
>
> * **imgpath**(const char*): image path
>
> **Return**
>
> * **imgmat**(FD_C_Mat): pointer to cv::Mat object which holds the image.
```c
FD_C_Bool FD_C_Imwrite(const char* savepath, FD_C_Mat img);
```
> Write image to a file.
>
> **Params**
>
> * **savepath**(const char*): save path
> * **img**(FD_C_Mat): pointer to cv::Mat object
>
> **Return**
>
> * **result**(FD_C_Bool): bool to indicate success or failure
#### Prediction
```c
FD_C_Bool FD_C_PaddleClasModelWrapperPredict(
__fd_take FD_C_PaddleClasModelWrapper* fd_c_ppclas_wrapper, FD_C_Mat img,
FD_C_ClassifyResult* fd_c_ppclas_result)
```
>
> Predict an image, and generate classification result.
>
> **Params**
> * **fd_c_ppclas_wrapper**(FD_C_PaddleClasModelWrapper*): pointer to manipulate PaddleClas object
> * **img**（FD_C_Mat）: pointer to cv::Mat object, which can be obained by FD_C_Imread interface
> * **fd_c_ppclas_result** (FD_C_ClassifyResult*): The classification result, including label_id, and the corresponding confidence. Refer to [Visual Model Prediction Results](../../../../../docs/api/vision_results/) for the description of ClassifyResult
#### Result
```c
FD_C_ClassifyResultWrapper* FD_C_CreateClassifyResultWrapperFromData(
FD_C_ClassifyResult* fd_c_classify_result)
```
>
> Create a pointer to FD_C_ClassifyResultWrapper structure, which contains `fastdeploy::vision::ClassifyResult` object in C++. You can call methods in C++ ClassifyResult object by C API with this pointer.
>
> **Params**
> * **fd_c_classify_result**(FD_C_ClassifyResult*): pointer to FD_C_ClassifyResult structure
>
> **Return**
> * **fd_c_classify_result_wrapper**(FD_C_ClassifyResultWrapper*): pointer to FD_C_ClassifyResultWrapper structure
```c
char* FD_C_ClassifyResultWrapperStr(
FD_C_ClassifyResultWrapper* fd_c_classify_result_wrapper);
```
>
> Call Str() methods in `fastdeploy::vision::ClassifyResult` object contained in FD_C_ClassifyResultWrapper structure，and return a string to describe information in result.
>
> **Params**
> * **fd_c_classify_result_wrapper**(FD_C_ClassifyResultWrapper*): pointer to FD_C_ClassifyResultWrapper structure
>
> **Return**
> * **str**(char*): a string to describe information in result
- [Model Description](../../)
- [Python Deployment](../python)
- [Visual Model prediction results](../../../../../docs/api/vision_results/)
- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)

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[English](README.md) | 简体中文
# PaddleClas C 部署示例
本目录下提供`infer_xxx.c`来调用C API快速完成PaddleClas系列模型在CPU/GPU上部署的示例。
在部署前，需确认以下两个步骤
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
以Linux上ResNet50_vd推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.4以上(x.x.x>=1.0.4)
```bash
mkdir build
cd build
# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j
# 下载ResNet50_vd模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
tar -xvf ResNet50_vd_infer.tgz
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
# CPU推理
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0
# GPU推理
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1
```
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
## PaddleClas C API接口
### 配置
```c
FD_C_RuntimeOptionWrapper* FD_C_CreateRuntimeOptionWrapper()
```
> 创建一个RuntimeOption的配置对象，并且返回操作它的指针。
>
> **返回**
>
> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption对象的指针
```c
void FD_C_RuntimeOptionWrapperUseCpu(
FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper)
```
> 开启CPU推理
>
> **参数**
>
> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption对象的指针
```c
void FD_C_RuntimeOptionWrapperUseGpu(
FD_C_RuntimeOptionWrapper* fd_c_runtime_option_wrapper,
int gpu_id)
```
> 开启GPU推理
>
> **参数**
>
> * **fd_c_runtime_option_wrapper**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption对象的指针
> * **gpu_id**(int): 显卡号
### 模型
```c
FD_C_PaddleClasModelWrapper* FD_C_CreatePaddleClasModelWrapper(
const char* model_file, const char* params_file, const char* config_file,
FD_C_RuntimeOptionWrapper* runtime_option,
const FD_C_ModelFormat model_format)
```
> 创建一个PaddleClas的模型，并且返回操作它的指针。
>
> **参数**
>
> * **model_file**(const char*): 模型文件路径
> * **params_file**(const char*): 参数文件路径
> * **config_file**(const char*): 配置文件路径，即PaddleClas导出的部署yaml文件
> * **runtime_option**(FD_C_RuntimeOptionWrapper*): 指向RuntimeOption的指针，表示后端推理配置
> * **model_format**(FD_C_ModelFormat): 模型格式
>
> **返回**
> * **fd_c_ppclas_wrapper**(FD_C_PaddleClasModelWrapper*): 指向PaddleClas模型对象的指针
#### 读写图像
```c
FD_C_Mat FD_C_Imread(const char* imgpath)
```
> 读取一个图像，并且返回cv::Mat的指针。
>
> **参数**
>
> * **imgpath**(const char*): 图像文件路径
>
> **返回**
>
> * **imgmat**(FD_C_Mat): 指向图像数据cv::Mat的指针。
```c
FD_C_Bool FD_C_Imwrite(const char* savepath, FD_C_Mat img);
```
> 将图像写入文件中。
>
> **参数**
>
> * **savepath**(const char*): 保存图像的路径
> * **img**(FD_C_Mat): 指向图像数据的指针
>
> **返回**
>
> * **result**(FD_C_Bool): 表示操作是否成功
#### Predict函数
```c
FD_C_Bool FD_C_PaddleClasModelWrapperPredict(
__fd_take FD_C_PaddleClasModelWrapper* fd_c_ppclas_wrapper, FD_C_Mat img,
FD_C_ClassifyResult* fd_c_ppclas_result)
```
>
> 模型预测接口，输入图像直接并生成分类结果。
>
> **参数**
> * **fd_c_ppclas_wrapper**(FD_C_PaddleClasModelWrapper*): 指向PaddleClas模型的指针
> * **img**（FD_C_Mat）: 输入图像的指针，指向cv::Mat对象，可以调用FD_C_Imread读取图像获取
> * **fd_c_ppclas_result**（FD_C_ClassifyResult*): 分类结果，包括label_id，以及相应的置信度, ClassifyResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
#### Predict结果
```c
FD_C_ClassifyResultWrapper* FD_C_CreateClassifyResultWrapperFromData(
FD_C_ClassifyResult* fd_c_classify_result)
```
>
> 创建一个FD_C_ClassifyResultWrapper对象的指针，FD_C_ClassifyResultWrapper中包含了C++的`fastdeploy::vision::ClassifyResult`对象，通过该指针，使用C API可以访问调用对应C++中的函数。
>
>
> **参数**
> * **fd_c_classify_result**(FD_C_ClassifyResult*): 指向FD_C_ClassifyResult对象的指针
>
> **返回**
> * **fd_c_classify_result_wrapper**(FD_C_ClassifyResultWrapper*): 指向FD_C_ClassifyResultWrapper的指针
```c
char* FD_C_ClassifyResultWrapperStr(
FD_C_ClassifyResultWrapper* fd_c_classify_result_wrapper);
```
>
> 调用FD_C_ClassifyResultWrapper所包含的`fastdeploy::vision::ClassifyResult`对象的Str()方法，返回相关结果内数据信息的字符串。
>
> **参数**
> * **fd_c_classify_result_wrapper**(FD_C_ClassifyResultWrapper*): 指向FD_C_ClassifyResultWrapper对象的指针
>
> **返回**
> * **str**(char*): 表示结果数据信息的字符串
- [模型介绍](../../)
- [Python部署](../python)
- [视觉模型预测结果](../../../../../docs/api/vision_results/)
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)

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// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <stdio.h>
#include <stdlib.h>
#include "fastdeploy_capi/vision.h"
#ifdef WIN32
const char sep = '\\';
#else
const char sep = '/';
#endif
void CpuInfer(const char* model_dir, const char* image_file) {
char model_file[100];
char params_file[100];
char config_file[100];
int max_size = 99;
snprintf(model_file, max_size, "%s%c%s", model_dir, sep, "inference.pdmodel");
snprintf(params_file, max_size, "%s%c%s", model_dir, sep,
"inference.pdiparams");
snprintf(config_file, max_size, "%s%c%s", model_dir, sep,
"inference_cls.yaml");
FD_C_RuntimeOptionWrapper* option = FD_C_CreateRuntimeOptionWrapper();
FD_C_RuntimeOptionWrapperUseCpu(option);
FD_C_PaddleClasModelWrapper* model = FD_C_CreatePaddleClasModelWrapper(
model_file, params_file, config_file, option, PADDLE);
if (!FD_C_PaddleClasModelWrapperInitialized(model)) {
printf("Failed to initialize.\n");
FD_C_DestroyRuntimeOptionWrapper(option);
FD_C_DestroyPaddleClasModelWrapper(model);
return;
}
FD_C_Mat im = FD_C_Imread(image_file);
FD_C_ClassifyResult* result =
(FD_C_ClassifyResult*)malloc(sizeof(FD_C_ClassifyResult));
if (!FD_C_PaddleClasModelWrapperPredict(model, im, result)) {
printf("Failed to predict.\n");
FD_C_DestroyRuntimeOptionWrapper(option);
FD_C_DestroyPaddleClasModelWrapper(model);
FD_C_DestroyMat(im);
free(result);
return;
}
// print res
// You can directly access fields in FD_C_ClassifyResult and print it refer to
// ClassifyResult API Doc Or you can wrap it using
// FD_C_ClassifyResult_Wrapper, which containes C++ structure
// fastdeploy::vision::ClassifyResult, and using C API
// FD_C_ClassifyResultWrapperStr to call
// fastdeploy::vision::ClassifyResult::Str() in it. For convenience, we choose
// this method to print it.
FD_C_ClassifyResultWrapper* result_wrapper =
FD_C_CreateClassifyResultWrapperFromData(result);
printf("%s", FD_C_ClassifyResultWrapperStr(result_wrapper));
FD_C_DestroyRuntimeOptionWrapper(option);
FD_C_DestroyPaddleClasModelWrapper(model);
FD_C_DestroyClassifyResultWrapper(result_wrapper);
FD_C_DestroyClassifyResult(result);
FD_C_DestroyMat(im);
}
void GpuInfer(const char* model_dir, const char* image_file) {
char model_file[100];
char params_file[100];
char config_file[100];
int max_size = 99;
snprintf(model_file, max_size, "%s%c%s", model_dir, sep, "inference.pdmodel");
snprintf(params_file, max_size, "%s%c%s", model_dir, sep,
"inference.pdiparams");
snprintf(config_file, max_size, "%s%c%s", model_dir, sep,
"inference_cls.yaml");
FD_C_RuntimeOptionWrapper* option = FD_C_CreateRuntimeOptionWrapper();
FD_C_RuntimeOptionWrapperUseGpu(option, 0);
FD_C_PaddleClasModelWrapper* model = FD_C_CreatePaddleClasModelWrapper(
model_file, params_file, config_file, option, PADDLE);
if (!FD_C_PaddleClasModelWrapperInitialized(model)) {
printf("Failed to initialize.\n");
FD_C_DestroyRuntimeOptionWrapper(option);
FD_C_DestroyPaddleClasModelWrapper(model);
return;
}
FD_C_Mat im = FD_C_Imread(image_file);
FD_C_ClassifyResult* result =
(FD_C_ClassifyResult*)malloc(sizeof(FD_C_ClassifyResult));
if (!FD_C_PaddleClasModelWrapperPredict(model, im, result)) {
printf("Failed to predict.\n");
FD_C_DestroyRuntimeOptionWrapper(option);
FD_C_DestroyPaddleClasModelWrapper(model);
FD_C_DestroyMat(im);
free(result);
return;
}
// print res
// You can directly access fields in FD_C_ClassifyResult and print it refer to
// ClassifyResult API Doc Or you can wrap it using
// FD_C_ClassifyResult_Wrapper, which containes C++ structure
// fastdeploy::vision::ClassifyResult, and using C API
// FD_C_ClassifyResultWrapperStr to call
// fastdeploy::vision::ClassifyResult::Str() in it. For convenience, we choose
// this method to print it.
FD_C_ClassifyResultWrapper* result_wrapper =
FD_C_CreateClassifyResultWrapperFromData(result);
printf("%s", FD_C_ClassifyResultWrapperStr(result_wrapper));
FD_C_DestroyRuntimeOptionWrapper(option);
FD_C_DestroyPaddleClasModelWrapper(model);
FD_C_DestroyClassifyResultWrapper(result_wrapper);
FD_C_DestroyClassifyResult(result);
FD_C_DestroyMat(im);
}
int main(int argc, char* argv[]) {
if (argc < 4) {
printf(
"Usage: infer_demo path/to/model_dir path/to/image run_option, "
"e.g ./infer_model ./ppyoloe_model_dir ./test.jpeg 0"
"\n");
printf(
"The data type of run_option is int, 0: run with cpu; 1: run with gpu"
"\n");
return -1;
}
if (atoi(argv[3]) == 0) {
CpuInfer(argv[1], argv[2]);
} else if (atoi(argv[3]) == 1) {
GpuInfer(argv[1], argv[2]);
}
return 0;
}

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PROJECT(infer_demo CSharp)
CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
# Set the C# language version (defaults to 3.0 if not set).
set(CMAKE_CSharp_FLAGS "/langversion:10")
set(CMAKE_DOTNET_TARGET_FRAMEWORK "net6.0")
set(CMAKE_DOTNET_SDK "Microsoft.NET.Sdk")
# 指定下载解压后的fastdeploy库路径
option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
include(${FASTDEPLOY_INSTALL_DIR}/FastDeployCSharp.cmake)
add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cs)
set_property(TARGET infer_demo PROPERTY VS_DOTNET_REFERENCES
${FASTDEPLOY_DOTNET_REFERENCES}
)
set_property(TARGET infer_demo
PROPERTY VS_PACKAGE_REFERENCES ${FASTDEPLOY_PACKAGE_REFERENCES})

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English | [简体中文](README_CN.md)
# PaddleClas C# Deployment Example
This directory provides example `infer.cs` to fastly finish the deployment of PaddleClas models on CPU/GPU.
Before deployment, two steps require confirmation
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
Please follow below instructions to compile and test in Windows. FastDeploy version 1.0.4 or above (x.x.x>=1.0.4) is required to support this model.
## 1. Download C# package management tool nuget client
> https://dist.nuget.org/win-x86-commandline/v6.4.0/nuget.exe
Add nuget program into system variable **PATH**
## 2. Download model and image for test
> https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz # (下载后解压缩)
> https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
## 3. Compile example code
Open `x64 Native Tools Command Prompt for VS 2019` command tool on Windows, cd to the demo path of ppyoloe and execute commands
```shell
cd D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\classification\paddleclas\csharp
mkdir build && cd build
cmake .. -G "Visual Studio 16 2019" -A x64 -DFASTDEPLOY_INSTALL_DIR=D:\Download\fastdeploy-win-x64-gpu-x.x.x -DCUDA_DIRECTORY="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2"
nuget restore
msbuild infer_demo.sln /m:4 /p:Configuration=Release /p:Platform=x64
```
For more information about how to use FastDeploy SDK to compile a project with Visual Studio 2019. Please refer to
- [Using the FastDeploy C++ SDK on Windows Platform](../../../../../docs/en/faq/use_sdk_on_windows.md)
## 4. Execute compiled program
fastdeploy.dll and related dynamic libraries are required by the program. FastDeploy provide a script to copy all required dll to your program path.
```shell
cd D:\Download\fastdeploy-win-x64-gpu-x.x.x
fastdeploy_init.bat install %cd% D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\classification\paddleclas\csharp\build\Release
```
Then you can run your program and test the model with image
```shell
cd Release
# CPU inference
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0
# GPU inference
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1
```
## PaddleClas C# Interface
### Model Class
```c#
fastdeploy.vision.classification.PaddleClasModel(
string model_file,
string params_file,
string config_file
fastdeploy.RuntimeOption runtime_option = null,
fastdeploy.ModelFormat model_format = ModelFormat.PADDLE)
```
> PaddleClasModel initilization.
> **Params**
>> * **model_file**(str): Model file path
>> * **params_file**(str): Parameter file path
>> * **config_file**(str): Configuration file path, which is the deployment yaml file exported by PaddleClas
>> * **runtime_option**(RuntimeOption): Backend inference configuration. null by default, which is the default configuration
>> * **model_format**(ModelFormat): Model format. Paddle format by default
#### Predict Function
```c#
fastdeploy.ClassifyResult Predict(OpenCvSharp.Mat im)
```
> Model prediction interface. Input images and output results directly.
>
> **Params**
>
>> * **im**(Mat): Input images in HWC or BGR format
>
> **Return**
>
>> * **result**(ClassifyResult): The classification result, including label_id, and the corresponding confidence. Refer to [Visual Model Prediction Results](../../../../../docs/api/vision_results/) for the description of ClassifyResult
- [Model Description](../../)
- [Python Deployment](../python)
- [Vision Model prediction results](../../../../../docs/api/vision_results/)
- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)

101
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@@ -0,0 +1,101 @@
[English](README.md) | 简体中文
# PaddleClas C#部署示例
本目录下提供`infer.cs`来调用C# API快速完成PaddleClas系列模型在CPU/GPU上部署的示例。
在部署前，需确认以下两个步骤
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
以Linux上ResNet50_vd推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.4以上(x.x.x>=1.0.4)
## 1. 下载C#包管理程序nuget客户端
> https://dist.nuget.org/win-x86-commandline/v6.4.0/nuget.exe
下载完成后将该程序添加到环境变量**PATH**中
## 2. 下载模型文件和测试图片
> https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz # (下载后解压缩)
> https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
## 3. 编译示例代码
本文档编译的示例代码可在解压的库中找到，编译工具依赖VS 2019的安装，**Windows打开x64 Native Tools Command Prompt for VS 2019命令工具**，通过如下命令开始编译
```shell
cd D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\classification\paddleclas\csharp
mkdir build && cd build
cmake .. -G "Visual Studio 16 2019" -A x64 -DFASTDEPLOY_INSTALL_DIR=D:\Download\fastdeploy-win-x64-gpu-x.x.x -DCUDA_DIRECTORY="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2"
nuget restore
msbuild infer_demo.sln /m:4 /p:Configuration=Release /p:Platform=x64
```
关于使用Visual Studio 2019创建sln工程，或者CMake工程等方式编译的更详细信息，可参考如下文档
- [在 Windows 使用 FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
- [FastDeploy C++库在Windows上的多种使用方式](../../../../../docs/cn/faq/use_sdk_on_windows_build.md)
## 4. 运行可执行程序
注意Windows上运行时，需要将FastDeploy依赖的库拷贝至可执行程序所在目录, 或者配置环境变量。FastDeploy提供了工具帮助我们快速将所有依赖库拷贝至可执行程序所在目录,通过如下命令将所有依赖的dll文件拷贝至可执行程序所在的目录(可能生成的可执行文件在Release下还有一层目录，这里假设生成的可执行文件在Release处)
```shell
cd D:\Download\fastdeploy-win-x64-gpu-x.x.x
fastdeploy_init.bat install %cd% D:\Download\fastdeploy-win-x64-gpu-x.x.x\examples\vision\classification\paddleclas\csharp\build\Release
```
将dll拷贝到当前路径后，准备好模型和图片，使用如下命令运行可执行程序即可
```shell
cd Release
# CPU推理
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0
# GPU推理
./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1
```
## PaddleClas C#接口
### 模型
```c#
fastdeploy.vision.classification.PaddleClasModel(
string model_file,
string params_file,
string config_file,
fastdeploy.RuntimeOption runtime_option = null,
fastdeploy.ModelFormat model_format = ModelFormat.PADDLE)
```
> PaddleClasModel模型加载和初始化。
> **参数**
>> * **model_file**(str): 模型文件路径
>> * **params_file**(str): 参数文件路径
>> * **config_file**(str): 配置文件路径，即PaddleClas导出的部署yaml文件
>> * **runtime_option**(RuntimeOption): 后端推理配置，默认为null，即采用默认配置
>> * **model_format**(ModelFormat): 模型格式，默认为PADDLE格式
#### Predict函数
```c#
fastdeploy.ClassifyResult Predict(OpenCvSharp.Mat im)
```
> 模型预测接口，输入图像直接输出检测结果。
>
> **参数**
>
>> * **im**(Mat): 输入图像，注意需为HWC，BGR格式
>>
> **返回值**
>
>> * **result**: 分类结果，包括label_id，以及相应的置信度, ClassifyResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
- [模型介绍](../../)
- [Python部署](../python)
- [视觉模型预测结果](../../../../../docs/api/vision_results/)
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)

58
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@@ -0,0 +1,58 @@
// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
using System;
using System.IO;
using System.Runtime.InteropServices;
using OpenCvSharp;
using fastdeploy;
namespace Test
{
public class TestPaddleClas
{
public static void Main(string[] args)
{
if (args.Length < 3) {
Console.WriteLine(
"Usage: infer_demo path/to/model_dir path/to/image run_option, " +
"e.g ./infer_model ./ppyolo_dirname ./test.jpeg 0"
);
Console.WriteLine( "The data type of run_option is int, 0: run with cpu; 1: run with gpu");
return;
}
string model_dir = args[0];
string image_path = args[1];
string model_file = model_dir + "\\" + "inference.pdmodel";
string params_file = model_dir + "\\" + "inference.pdiparams";
string config_file = model_dir + "\\" + "inference_cls.yaml";
RuntimeOption runtimeoption = new RuntimeOption();
int device_option = Int32.Parse(args[2]);
if(device_option==0){
runtimeoption.UseCpu();
}else{
runtimeoption.UseGpu();
}
fastdeploy.vision.classification.PaddleClasModel model = new fastdeploy.vision.classification.PaddleClasModel(model_file, params_file, config_file, runtimeoption, ModelFormat.PADDLE);
if(!model.Initialized()){
Console.WriteLine("Failed to initialize.\n");
}
Mat image = Cv2.ImRead(image_path);
fastdeploy.vision.ClassifyResult res = model.Predict(image);
Console.WriteLine(res.ToString());
}
}
}

32
examples/vision/detection/paddledetection/c/README.md
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@@ -78,7 +78,7 @@ void FD_C_RuntimeOptionWrapperUseGpu(
```c ```c
FD_C_PPYOLOEWrapper* FD_C_CreatesPPYOLOEWrapper( FD_C_PPYOLOEWrapper* FD_C_CreatePPYOLOEWrapper(
const char* model_file, const char* params_file, const char* config_file, const char* model_file, const char* params_file, const char* config_file,
FD_C_RuntimeOptionWrapper* runtime_option, FD_C_RuntimeOptionWrapper* runtime_option,
const FD_C_ModelFormat model_format) const FD_C_ModelFormat model_format)
@@ -137,7 +137,7 @@ FD_C_Bool FD_C_Imwrite(const char* savepath, FD_C_Mat img);
```c ```c
FD_C_Bool FD_C_PPYOLOEWrapperPredict( FD_C_Bool FD_C_PPYOLOEWrapperPredict(
__fd_take FD_C_PPYOLOEWrapper* fd_c_ppyoloe_wrapper, FD_C_Mat img, __fd_take FD_C_PPYOLOEWrapper* fd_c_ppyoloe_wrapper, FD_C_Mat img,
FD_C_DetectionResultWrapper* fd_c_detection_result_wrapper) FD_C_DetectionResult* fd_c_detection_result)
``` ```
> >
> Predict an image, and generate detection result. > Predict an image, and generate detection result.
@@ -145,37 +145,11 @@ FD_C_Bool FD_C_PPYOLOEWrapperPredict(
> **Params** > **Params**
> * **fd_c_ppyoloe_wrapper**(FD_C_PPYOLOEWrapper*): pointer to manipulate PPYOLOE object > * **fd_c_ppyoloe_wrapper**(FD_C_PPYOLOEWrapper*): pointer to manipulate PPYOLOE object
> * **img**（FD_C_Mat）: pointer to cv::Mat object, which can be obained by FD_C_Imread interface > * **img**（FD_C_Mat）: pointer to cv::Mat object, which can be obained by FD_C_Imread interface
> * **result**（FD_C_DetectionResultWrapper*): Detection result, including detection box and confidence of each box. Refer to [Vision Model Prediction Result](../../../../../docs/api/vision_results/) for DetectionResult > * **fd_c_detection_result**FD_C_DetectionResult*): Detection result, including detection box and confidence of each box. Refer to [Vision Model Prediction Result](../../../../../docs/api/vision_results/) for DetectionResult
#### Result #### Result
```c
FD_C_DetectionResultWrapper* FD_C_CreateDetectionResultWrapper();
```
>
> Create a DetectionResult object to keep the detection result，return a pointer to manipulate it.
>
> **Return**
> * **fd_c_detection_result_wrapper**(FD_C_DetectionResultWrapper*): pointer to manipulate DetectionResult object
```c
FD_C_DetectionResult* FD_C_DetectionResultWrapperGetData(
FD_C_DetectionResultWrapper* fd_c_detection_result_wrapper)
```
>
> Get the C DetectionResult structure from FD_C_DetectionResultWrapper, which can access the fileds directly.
>
> **Params**
> * **fd_c_detection_result_wrapper**(FD_C_DetectionResultWrapper*): pointer to manipulate DetectionResult object
>
> **Return**
> * **fd_c_detection_result**(FD_C_DetectionResult*): pointer to C DetectionResult structure
```c ```c
FD_C_Mat FD_C_VisDetection(FD_C_Mat im, FD_C_DetectionResult* fd_detection_result, FD_C_Mat FD_C_VisDetection(FD_C_Mat im, FD_C_DetectionResult* fd_detection_result,
float score_threshold, int line_size, float font_size); float score_threshold, int line_size, float font_size);

34
examples/vision/detection/paddledetection/c/README_CN.md
View File

@@ -82,7 +82,7 @@ void FD_C_RuntimeOptionWrapperUseGpu(
```c ```c
FD_C_PPYOLOEWrapper* FD_C_CreatesPPYOLOEWrapper( FD_C_PPYOLOEWrapper* FD_C_CreatePPYOLOEWrapper(
const char* model_file, const char* params_file, const char* config_file, const char* model_file, const char* params_file, const char* config_file,
FD_C_RuntimeOptionWrapper* runtime_option, FD_C_RuntimeOptionWrapper* runtime_option,
const FD_C_ModelFormat model_format) const FD_C_ModelFormat model_format)
@@ -141,7 +141,7 @@ FD_C_Bool FD_C_Imwrite(const char* savepath, FD_C_Mat img);
```c ```c
FD_C_Bool FD_C_PPYOLOEWrapperPredict( FD_C_Bool FD_C_PPYOLOEWrapperPredict(
__fd_take FD_C_PPYOLOEWrapper* fd_c_ppyoloe_wrapper, FD_C_Mat img, __fd_take FD_C_PPYOLOEWrapper* fd_c_ppyoloe_wrapper, FD_C_Mat img,
FD_C_DetectionResultWrapper* fd_c_detection_result_wrapper) FD_C_DetectionResult* fd_c_detection_result)
``` ```
> >
> 模型预测接口，输入图像直接并生成检测结果。 > 模型预测接口，输入图像直接并生成检测结果。
@@ -149,37 +149,11 @@ FD_C_Bool FD_C_PPYOLOEWrapperPredict(
> **参数** > **参数**
> * **fd_c_ppyoloe_wrapper**(FD_C_PPYOLOEWrapper*): 指向PPYOLOE模型的指针 > * **fd_c_ppyoloe_wrapper**(FD_C_PPYOLOEWrapper*): 指向PPYOLOE模型的指针
> * **img**（FD_C_Mat）: 输入图像的指针，指向cv::Mat对象，可以调用FD_C_Imread读取图像获取 > * **img**（FD_C_Mat）: 输入图像的指针，指向cv::Mat对象，可以调用FD_C_Imread读取图像获取
> * **result**（FD_C_DetectionResultWrapper*): 指向检测结果的指针，检测结果包括检测框，各个框的置信度, DetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/) > * **fd_c_detection_result**FD_C_DetectionResult*): 指向检测结果的指针，检测结果包括检测框，各个框的置信度, DetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
#### Predict结果 #### Predict结果
```c
FD_C_DetectionResultWrapper* FD_C_CreateDetectionResultWrapper();
```
>
> 创建一个DetectionResult对象，用来保存推理的结果，并返回所创建的DetectionResult对象的指针。
>
> **返回**
> * **fd_c_detection_result_wrapper**(FD_C_DetectionResultWrapper*): 指向DetectionResult对象的指针
```c
FD_C_DetectionResult* FD_C_DetectionResultWrapperGetData(
FD_C_DetectionResultWrapper* fd_c_detection_result_wrapper)
```
>
> 从DetectionResult对象中提取纯C结构的DetectionResult结果，并返回结构指针，通过该指针可直接返回结构中的字段。
>
> **参数**
> * **fd_c_detection_result_wrapper**(FD_C_DetectionResultWrapper*): 指向DetectionResult对象的指针
>
> **返回**
> * **fd_c_detection_result**(FD_C_DetectionResult*): 指向纯C结构的DetectionResult的指针
```c ```c
FD_C_Mat FD_C_VisDetection(FD_C_Mat im, FD_C_DetectionResult* fd_detection_result, FD_C_Mat FD_C_VisDetection(FD_C_Mat im, FD_C_DetectionResult* fd_detection_result,
float score_threshold, int line_size, float font_size); float score_threshold, int line_size, float font_size);
@@ -189,7 +163,7 @@ FD_C_Mat FD_C_VisDetection(FD_C_Mat im, FD_C_DetectionResult* fd_detection_resul
> >
> **参数** > **参数**
> * **im**(FD_C_Mat): 指向输入图像的指针 > * **im**(FD_C_Mat): 指向输入图像的指针
> * **fd_detection_result**(FD_C_DetectionResult*): 指向纯C结构DetectionResult的指针 > * **fd_detection_result**(FD_C_DetectionResult*): 指向FD_C_DetectionResult结构的指针
> * **score_threshold**(float): 检测阈值 > * **score_threshold**(float): 检测阈值
> * **line_size**(int): 检测框线大小 > * **line_size**(int): 检测框线大小
> * **font_size**(float): 检测框字体大小 > * **font_size**(float): 检测框字体大小

56
examples/vision/detection/paddledetection/c/infer_ppyoloe.c
View File

@@ -35,21 +35,30 @@ void CpuInfer(const char* model_dir, const char* image_file) {
FD_C_RuntimeOptionWrapper* option = FD_C_CreateRuntimeOptionWrapper(); FD_C_RuntimeOptionWrapper* option = FD_C_CreateRuntimeOptionWrapper();
FD_C_RuntimeOptionWrapperUseCpu(option); FD_C_RuntimeOptionWrapperUseCpu(option);
FD_C_PPYOLOEWrapper* model = FD_C_CreatesPPYOLOEWrapper( FD_C_PPYOLOEWrapper* model = FD_C_CreatePPYOLOEWrapper(
model_file, params_file, config_file, option, PADDLE); model_file, params_file, config_file, option, PADDLE);
FD_C_Mat im = FD_C_Imread(image_file); if (!FD_C_PPYOLOEWrapperInitialized(model)) {
printf("Failed to initialize.\n");
FD_C_DetectionResultWrapper* result_wrapper = FD_C_DestroyRuntimeOptionWrapper(option);
FD_C_CreateDetectionResultWrapper(); FD_C_DestroyPaddleClasModelWrapper(model);
if (!FD_C_PPYOLOEWrapperPredict(model, im, result_wrapper)) {
printf("Failed to predict.\n");
return; return;
} }
FD_C_Mat im = FD_C_Imread(image_file);
FD_C_DetectionResult* result = FD_C_DetectionResult* result =
FD_C_DetectionResultWrapperGetData(result_wrapper); (FD_C_DetectionResult*)malloc(sizeof(FD_C_DetectionResult));
if (!FD_C_PPYOLOEWrapperPredict(model, im, result)) {
printf("Failed to predict.\n");
FD_C_DestroyRuntimeOptionWrapper(option);
FD_C_DestroyPaddleClasModelWrapper(model);
FD_C_DestroyMat(im);
free(result);
return;
}
FD_C_Mat vis_im = FD_C_VisDetection(im, result, 0.5, 1, 0.5); FD_C_Mat vis_im = FD_C_VisDetection(im, result, 0.5, 1, 0.5);
FD_C_Imwrite("vis_result.jpg", vis_im); FD_C_Imwrite("vis_result.jpg", vis_im);
@@ -57,7 +66,6 @@ void CpuInfer(const char* model_dir, const char* image_file) {
FD_C_DestroyRuntimeOptionWrapper(option); FD_C_DestroyRuntimeOptionWrapper(option);
FD_C_DestroyPPYOLOEWrapper(model); FD_C_DestroyPPYOLOEWrapper(model);
FD_C_DestroyDetectionResultWrapper(result_wrapper);
FD_C_DestroyDetectionResult(result); FD_C_DestroyDetectionResult(result);
FD_C_DestroyMat(im); FD_C_DestroyMat(im);
FD_C_DestroyMat(vis_im); FD_C_DestroyMat(vis_im);
@@ -75,21 +83,30 @@ void GpuInfer(const char* model_dir, const char* image_file) {
FD_C_RuntimeOptionWrapper* option = FD_C_CreateRuntimeOptionWrapper(); FD_C_RuntimeOptionWrapper* option = FD_C_CreateRuntimeOptionWrapper();
FD_C_RuntimeOptionWrapperUseGpu(option, 0); FD_C_RuntimeOptionWrapperUseGpu(option, 0);
FD_C_PPYOLOEWrapper* model = FD_C_CreatesPPYOLOEWrapper( FD_C_PPYOLOEWrapper* model = FD_C_CreatePPYOLOEWrapper(
model_file, params_file, config_file, option, PADDLE); model_file, params_file, config_file, option, PADDLE);
FD_C_Mat im = FD_C_Imread(image_file); if (!FD_C_PPYOLOEWrapperInitialized(model)) {
printf("Failed to initialize.\n");
FD_C_DetectionResultWrapper* result_wrapper = FD_C_DestroyRuntimeOptionWrapper(option);
FD_C_CreateDetectionResultWrapper(); FD_C_DestroyPaddleClasModelWrapper(model);
if (!FD_C_PPYOLOEWrapperPredict(model, im, result_wrapper)) {
printf("Failed to predict.\n");
return; return;
} }
FD_C_Mat im = FD_C_Imread(image_file);
FD_C_DetectionResult* result = FD_C_DetectionResult* result =
FD_C_DetectionResultWrapperGetData(result_wrapper); (FD_C_DetectionResult*)malloc(sizeof(FD_C_DetectionResult));
if (!FD_C_PPYOLOEWrapperPredict(model, im, result)) {
printf("Failed to predict.\n");
FD_C_DestroyRuntimeOptionWrapper(option);
FD_C_DestroyPaddleClasModelWrapper(model);
FD_C_DestroyMat(im);
free(result);
return;
}
FD_C_Mat vis_im = FD_C_VisDetection(im, result, 0.5, 1, 0.5); FD_C_Mat vis_im = FD_C_VisDetection(im, result, 0.5, 1, 0.5);
FD_C_Imwrite("vis_result.jpg", vis_im); FD_C_Imwrite("vis_result.jpg", vis_im);
@@ -97,7 +114,6 @@ void GpuInfer(const char* model_dir, const char* image_file) {
FD_C_DestroyRuntimeOptionWrapper(option); FD_C_DestroyRuntimeOptionWrapper(option);
FD_C_DestroyPPYOLOEWrapper(model); FD_C_DestroyPPYOLOEWrapper(model);
FD_C_DestroyDetectionResultWrapper(result_wrapper);
FD_C_DestroyDetectionResult(result); FD_C_DestroyDetectionResult(result);
FD_C_DestroyMat(im); FD_C_DestroyMat(im);
FD_C_DestroyMat(vis_im); FD_C_DestroyMat(vis_im);

2
examples/vision/detection/paddledetection/csharp/README_CN.md
View File

@@ -39,7 +39,7 @@ msbuild infer_demo.sln /m:4 /p:Configuration=Release /p:Platform=x64
## 4. 运行可执行程序 ## 4. 运行可执行程序
注意Windows上运行时，需要将FastDeploy依赖的库拷贝至可执行程序所在目录, 或者配置环境变量。FastDeploy提供了工具帮助我们快速将所有依赖库拷贝至可执行程序所在目录,通过如下命令将所有依赖的dll文件拷贝至可执行程序所在的目录 注意Windows上运行时，需要将FastDeploy依赖的库拷贝至可执行程序所在目录, 或者配置环境变量。FastDeploy提供了工具帮助我们快速将所有依赖库拷贝至可执行程序所在目录,通过如下命令将所有依赖的dll文件拷贝至可执行程序所在的目录(可能生成的可执行文件在Release下还有一层目录，这里假设生成的可执行文件在Release处)
```shell ```shell
cd D:\Download\fastdeploy-win-x64-gpu-x.x.x cd D:\Download\fastdeploy-win-x64-gpu-x.x.x

11
examples/vision/detection/paddledetection/csharp/infer_ppyoloe.cs
View File

@@ -44,13 +44,16 @@ namespace Test
}else{ }else{
runtimeoption.UseGpu(); runtimeoption.UseGpu();
} }
vision.detection.PPYOLOE model = new vision.detection.PPYOLOE(model_file, params_file, config_file, runtimeoption, ModelFormat.PADDLE); fastdeploy.vision.detection.PPYOLOE model = new fastdeploy.vision.detection.PPYOLOE(model_file, params_file, config_file, runtimeoption, ModelFormat.PADDLE);
if(!model.Initialized()){
Console.WriteLine("Failed to initialize.\n");
}
Mat image = Cv2.ImRead(image_path); Mat image = Cv2.ImRead(image_path);
vision.DetectionResult res = model.Predict(image); fastdeploy.vision.DetectionResult res = model.Predict(image);
Mat res_img = vision.Visualize.VisDetection(image, res, 0, 1, 0.5f); Console.WriteLine(res.ToString());
Mat res_img = fastdeploy.vision.Visualize.VisDetection(image, res, 0, 1, 0.5f);
Cv2.ImShow("result.png", res_img); Cv2.ImShow("result.png", res_img);
Cv2.WaitKey(0); Cv2.WaitKey(0);
} }
} }