COCO API Eval

includes/common/logging.h

@@ -26,6 +26,12 @@
 #include <sstream>
 #include <string>
 
+#if NV_TENSORRT_MAJOR >= 8
+#define TRT_NOEXCEPT noexcept
+#else
+#define TRT_NOEXCEPT
+#endif
+
 using Severity = nvinfer1::ILogger::Severity;
 
 class LogStreamConsumerBuffer : public std::stringbuf
@@ -236,7 +242,7 @@ class Logger : public nvinfer1::ILogger
     //! Note samples should not be calling this function directly; it will eventually go away once we eliminate the
     //! inheritance from nvinfer1::ILogger
     //!
-    void log(Severity severity, const char* msg) override
+    void log(Severity severity, const char* msg) TRT_NOEXCEPT override
     {
         LogStreamConsumer(mReportableSeverity, severity) << "[TRT] " << std::string(msg) << std::endl;
     }

yolov7/CMakeLists.txt

@@ -10,6 +10,7 @@ message(STATUS "Find CUDA include at ${CUDA_INCLUDE_DIRS}")
 message(STATUS "Find CUDA libraries: ${CUDA_LIBRARIES}")
 
 # TensorRT
+# set(TENSORRT_ROOT /usr/local/cuda/)
 set(TENSORRT_ROOT /usr/src/tensorrt/)
 find_path(TENSORRT_INCLUDE_DIR NvInfer.h
         HINTS ${TENSORRT_ROOT} PATH_SUFFIXES include/)

yolov7/README.md

@@ -51,3 +51,16 @@ yolov7:
 
 ## 5.Results:
 ![](prediction.jpg)
+
+## COCO AP evaluation
+
+1. Download COCO validation set images (http://images.cocodataset.org/zips/val2017.zip) and annotations (http://images.cocodataset.org/annotations/annotations_trainval2017.zip), unzip them.
+
+2. Compile and run yolov7 with `EXPORT_COCO_JSON` flag enabled
+
+3. Install the COCO python API, then run `coco_eval.py` (make sure the path in the script are correct)
+
+```
+python -m pip install pycocotools
+python coco_eval.py
+```

yolov7/coco_eval.py

@@ -0,0 +1,17 @@
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+
+#initialize COCO ground truth api
+cocoGt = COCO("instances_val2017.json")
+
+#initialize COCO detections api
+cocoDt = cocoGt.loadRes("build/yolov7_coco_eval.json")
+
+imgIds=sorted(cocoGt.getImgIds())
+
+# running evaluation
+cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
+cocoEval.params.imgIds  = imgIds
+cocoEval.evaluate()
+cocoEval.accumulate()
+cocoEval.summarize()