perf test updates + node serial v1

tools/benchmarking/engine-perf-tests.py

@@ -8,7 +8,7 @@
 
 import os
 # need imports for inference module
-from utils import pre-process, postprocess
+from utils import preprocess, postprocess
 
 def verify_path(trt_engine_path=None):
     pass

tools/benchmarking/model-perf-tests.py

@@ -1,91 +1,91 @@
-import time
-import numpy as np
-import cv2
-import pycuda.driver as cuda
-import pycuda.autoinit
-import tensorrt as trt
-from onnxruntime import InferenceSession
-
-# Helper functions
-def load_ground_truth(file_path):
-    """Load ground truth bounding boxes from text file."""
-    with open(file_path, 'r') as f:
-        bboxes = []
-        for line in f:
-            tokens = line.strip().split()
-            cls, x_center, y_center, width, height = map(float, tokens)
-            bboxes.append((cls, x_center, y_center, width, height))
-    return bboxes
-
-def iou(boxA, boxB):
-    """Compute Intersection Over Union (IoU) between two bounding boxes."""
-    xA = max(boxA[0], boxB[0])
-    yA = max(boxA[1], boxB[1])
-    xB = min(boxA[2], boxB[2])
-    yB = min(boxA[3], boxB[3])
-
-    interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
-    boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
-    boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
-
-    iou = interArea / float(boxAArea + boxBArea - interArea)
-    return iou
-
-def calculate_centroid_offset(pred_box, gt_box):
-    """Calculate percentage centroid offset between two boxes."""
-    pred_center = (pred_box[0] + pred_box[2]) / 2, (pred_box[1] + pred_box[3]) / 2
-    gt_center = (gt_box[0] + gt_box[2]) / 2, (gt_box[1] + gt_box[3]) / 2
-    offset_x = abs(pred_center[0] - gt_center[0]) / (gt_box[2] - gt_box[0])
-    offset_y = abs(pred_center[1] - gt_center[1]) / (gt_box[3] - gt_box[1])
-    return (offset_x + offset_y) / 2 * 100
-
-def preprocess_image(image_path, input_shape):
-    """Preprocess image for inference."""
-    image = cv2.imread(image_path)
-    image_resized = cv2.resize(image, (input_shape[1], input_shape[0]))
-    image = np.asarray(image_resized).astype(np.float32)
-    return np.transpose(image, (2, 0, 1)) / 255.0  # CHW format and normalized
-
-def run_benchmark(trt_model_path, test_images, ground_truth_path):
-    """Run benchmark on the model."""
-    # Load ground truth
-    ground_truth_bboxes = load_ground_truth(ground_truth_path)
-
-    # Initialize TensorRT inference
-    trt_infer = TRTInference(trt_model_path)
-    inputs, outputs, bindings, stream = trt_infer.allocate_buffers()
-
-    inference_times = []
-    iou_scores = []
-    centroid_offsets = []
-
-    for idx, img_path in enumerate(test_images):
-        # Preprocess image
-        image = preprocess_image(img_path, (300, 300))  # Adjust size as needed
-
-        # Perform inference and measure time
-        start_time = time.time()
-        pred_bbox = trt_infer.infer(image, inputs, outputs, bindings, stream)
-        inference_time = time.time() - start_time
-
-        # Compute IoU, centroid offset
-        gt_bbox = ground_truth_bboxes[idx]
-        iou_score = iou(pred_bbox, gt_bbox)
-        offset = calculate_centroid_offset(pred_bbox, gt_bbox)
-
-        # Store results
-        inference_times.append(inference_time)
-        iou_scores.append(iou_score)
-        centroid_offsets.append(offset)
-
-    # Summary of benchmark
-    print(f"Average Inference Time: {np.mean(inference_times):.4f} seconds")
-    print(f"Average IoU: {np.mean(iou_scores) * 100:.2f}%")
-    print(f"Average Centroid Offset: {np.mean(centroid_offsets):.2f}%")
-
-if __name__ == "__main__":
-    trt_model_path = "model.trt"  # Replace with your TensorRT model path
-    test_images = ["test1.jpg", "test2.jpg"]  # Replace with your test images
-    ground_truth_path = "ground_truth.txt"  # Replace with your ground truth file path
-
+import time
+import numpy as np
+import cv2
+import pycuda.driver as cuda
+import pycuda.autoinit
+import tensorrt as trt
+from onnxruntime import InferenceSession
+
+# Helper functions
+def load_ground_truth(file_path):
+    """Load ground truth bounding boxes from text file."""
+    with open(file_path, 'r') as f:
+        bboxes = []
+        for line in f:
+            tokens = line.strip().split()
+            cls, x_center, y_center, width, height = map(float, tokens)
+            bboxes.append((cls, x_center, y_center, width, height))
+    return bboxes
+
+def iou(boxA, boxB):
+    """Compute Intersection Over Union (IoU) between two bounding boxes."""
+    xA = max(boxA[0], boxB[0])
+    yA = max(boxA[1], boxB[1])
+    xB = min(boxA[2], boxB[2])
+    yB = min(boxA[3], boxB[3])
+
+    interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
+    boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
+    boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
+
+    iou = interArea / float(boxAArea + boxBArea - interArea)
+    return iou
+
+def calculate_centroid_offset(pred_box, gt_box):
+    """Calculate percentage centroid offset between two boxes."""
+    pred_center = (pred_box[0] + pred_box[2]) / 2, (pred_box[1] + pred_box[3]) / 2
+    gt_center = (gt_box[0] + gt_box[2]) / 2, (gt_box[1] + gt_box[3]) / 2
+    offset_x = abs(pred_center[0] - gt_center[0]) / (gt_box[2] - gt_box[0])
+    offset_y = abs(pred_center[1] - gt_center[1]) / (gt_box[3] - gt_box[1])
+    return (offset_x + offset_y) / 2 * 100
+
+def preprocess_image(image_path, input_shape):
+    """Preprocess image for inference."""
+    image = cv2.imread(image_path)
+    image_resized = cv2.resize(image, (input_shape[1], input_shape[0]))
+    image = np.asarray(image_resized).astype(np.float32)
+    return np.transpose(image, (2, 0, 1)) / 255.0  # CHW format and normalized
+
+def run_benchmark(trt_model_path, test_images, ground_truth_path):
+    """Run benchmark on the model."""
+    # Load ground truth
+    ground_truth_bboxes = load_ground_truth(ground_truth_path)
+
+    # Initialize TensorRT inference
+    trt_infer = TRTInference(trt_model_path)
+    inputs, outputs, bindings, stream = trt_infer.allocate_buffers()
+
+    inference_times = []
+    iou_scores = []
+    centroid_offsets = []
+
+    for idx, img_path in enumerate(test_images):
+        # Preprocess image
+        image = preprocess_image(img_path, (300, 300))  # Adjust size as needed
+
+        # Perform inference and measure time
+        start_time = time.time()
+        pred_bbox = trt_infer.infer(image, inputs, outputs, bindings, stream)
+        inference_time = time.time() - start_time
+
+        # Compute IoU, centroid offset
+        gt_bbox = ground_truth_bboxes[idx]
+        iou_score = iou(pred_bbox, gt_bbox)
+        offset = calculate_centroid_offset(pred_bbox, gt_bbox)
+
+        # Store results
+        inference_times.append(inference_time)
+        iou_scores.append(iou_score)
+        centroid_offsets.append(offset)
+
+    # Summary of benchmark
+    print(f"Average Inference Time: {np.mean(inference_times):.4f} seconds")
+    print(f"Average IoU: {np.mean(iou_scores) * 100:.2f}%")
+    print(f"Average Centroid Offset: {np.mean(centroid_offsets):.2f}%")
+
+if __name__ == "__main__":
+    trt_model_path = "model.trt"  # Replace with your TensorRT model path
+    test_images = ["test1.jpg", "test2.jpg"]  # Replace with your test images
+    ground_truth_path = "ground_truth.txt"  # Replace with your ground truth file path
+
     run_benchmark(trt_model_path, test_images, ground_truth_path)

tools/calibration/colorspace_calibration.py

@@ -106,7 +106,6 @@ def onTrack7(val):
         frame = cv2.hconcat([myObject, color_square])
 
         cv2.imshow('colorspace calibration', frame)
-
         key = cv2.waitKey(1)
         if key == ord('q'):
             cv2.destroyAllWindows()

tools/testing/engine-test.py

@@ -0,0 +1 @@
+# identical to onnx-test but for engines