updates

detect.py

@@ -18,7 +18,7 @@ parser.add_argument('-txt_out', type=bool, default=False)
 
 parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
 parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file')
-parser.add_argument('-conf_thres', type=float, default=0.99, help='object confidence threshold')
+parser.add_argument('-conf_thres', type=float, default=0.9, help='object confidence threshold')
 parser.add_argument('-nms_thres', type=float, default=0.45, help='iou threshold for non-maximum suppression')
 parser.add_argument('-batch_size', type=int, default=1, help='size of the batches')
 parser.add_argument('-img_size', type=int, default=32 * 13, help='size of each image dimension')
@@ -34,7 +34,7 @@ def detect(opt):
     model = Darknet(opt.cfg, opt.img_size)
 
     #weights_path = 'checkpoints/yolov3.weights'
-    weights_path = 'checkpoints/latest.pt'
+    weights_path = 'checkpoints/yolov3.pt'
     if weights_path.endswith('.weights'):  # saved in darknet format
         load_weights(model, weights_path)
     else:  # endswith('.pt'), saved in pytorch format

test.py

@@ -48,18 +48,11 @@ model.to(device).eval()
 # dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu)
 dataloader = load_images_and_labels(test_path, batch_size=opt.batch_size, img_size=opt.img_size)
 
-n_gt = 0
-correct = 0
-
 print('Compute mAP...')
 
-outputs = []
+correct = 0
 targets = None
-mAPs = []
-TP = []
-confidence = []
-pred_class = []
-target_class = []
+outputs, mAPs, TP, confidence, pred_class, target_class = [], [], [], [], [], []
 for batch_i, (imgs, targets) in enumerate(dataloader):
     imgs = imgs.to(device)
 
@@ -67,9 +60,6 @@ for batch_i, (imgs, targets) in enumerate(dataloader):
         output = model(imgs)
         output = non_max_suppression(output, conf_thres=opt.conf_thres, nms_thres=opt.nms_thres)
 
-    # import matplotlib.pyplot as plt
-    # plt.imshow(imgs[1][0])
-
     # Compute average precision for each sample
     for sample_i in range(len(targets)):
         correct = []
@@ -112,7 +102,8 @@ for batch_i, (imgs, targets) in enumerate(dataloader):
                     correct.append(0)
 
         # Compute Average Precision (AP) per class
-        AP = ap_per_class(tp=correct, conf=detections[:, 4], pred_cls=detections[:, 6], target_cls=annotations[:, 0])
+        target_cls = annotations[:, 0] if annotations.size(0) > 1 else annotations[0]
+        AP = ap_per_class(tp=correct, conf=detections[:, 4], pred_cls=detections[:, 6], target_cls=target_cls)
 
         # Compute mean AP for this image
         mAP = AP.mean()
@@ -123,5 +114,4 @@ for batch_i, (imgs, targets) in enumerate(dataloader):
         # Print image mAP and running mean mAP
         print('+ Sample [%d/%d] AP: %.4f (%.4f)' % (len(mAPs), len(dataloader) * opt.batch_size, mAP, np.mean(mAPs)))
 
-
 print('Mean Average Precision: %.4f' % np.mean(mAPs))

utils/gcp.sh

@@ -11,4 +11,4 @@ gsutil cp gs://ultralytics/fresh9_5_e201.pt yolov3/checkpoints
 python3 detect.py
 
 # Test
-python3 test.py -img_size 416 -weights_path checkpoints/latest.pt
+python3 test.py -img_size 416 -weights_path checkpoints/yolov3.weights

utils/utils.py

@@ -105,7 +105,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls):
     ap = []
     for c in unique_classes:
         i = pred_cls == c
-        nGT = sum(target_cls == c)  # Number of ground truth objects
+        n_gt = sum(target_cls == c)  # Number of ground truth objects
 
         if sum(i) == 0:
             ap.append(0)
@@ -115,7 +115,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls):
             tpa = np.cumsum(tp[i])
 
             # Recall
-            recall = tpa / (nGT + 1e-16)
+            recall = tpa / (n_gt + 1e-16)
 
             # Precision
             precision = tpa / (tpa + fpa)