updates

test.py

@@ -5,7 +5,7 @@ from utils.datasets import *
 from utils.utils import *
 
 parser = argparse.ArgumentParser(prog='test.py')
-parser.add_argument('-batch_size', type=int, default=32, help='size of each image batch')
+parser.add_argument('-batch_size', type=int, default=64, help='size of each image batch')
 parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='path to model config file')
 parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='path to data config file')
 parser.add_argument('-weights_path', type=str, default='weights/yolov3.pt', help='path to weights file')
@@ -53,41 +53,35 @@ def main(opt):
     outputs, mAPs, mR, mP, TP, confidence, pred_class, target_class = [], [], [], [], [], [], [], []
     AP_accum, AP_accum_count = np.zeros(nC), np.zeros(nC)
     for batch_i, (imgs, targets) in enumerate(dataloader):
-        imgs = imgs.to(device)
 
         with torch.no_grad():
-            output = model(imgs)
+            output = model(imgs.to(device))
             output = non_max_suppression(output, conf_thres=opt.conf_thres, nms_thres=opt.nms_thres)
 
         # Compute average precision for each sample
-        for sample_i in range(len(targets)):
+        for sample_i, (labels, detections) in enumerate(zip(targets, output)):
             correct = []
 
-            # Get labels for sample where width is not zero (dummies)
-            annotations = targets[sample_i]
-            # Extract detections
-            detections = output[sample_i]
-
             if detections is None:
-                # If there are no detections but there are annotations mask as zero AP
-                if annotations.size(0) != 0:
-                    mAPs.append(0)
+                # If there are no detections but there are labels mask as zero AP
+                if labels.size(0) != 0:
+                    mAPs.append(0), mR.append(0), mP.append(0)
                 continue
 
             # Get detections sorted by decreasing confidence scores
+            detections = detections.cpu().numpy()
             detections = detections[np.argsort(-detections[:, 4])]
 
-            # If no annotations add number of detections as incorrect
-            if annotations.size(0) == 0:
+            # If no labels add number of detections as incorrect
+            if labels.size(0) == 0:
                 # correct.extend([0 for _ in range(len(detections))])
-                mAPs.append(0)
+                mAPs.append(0), mR.append(0), mP.append(0)
                 continue
             else:
-                target_cls = annotations[:, 0]
+                target_cls = labels[:, 0]
 
                 # Extract target boxes as (x1, y1, x2, y2)
-                target_boxes = xywh2xyxy(annotations[:, 1:5])
-                target_boxes *= opt.img_size
+                target_boxes = xywh2xyxy(labels[:, 1:5]) * opt.img_size
 
                 detected = []
                 for *pred_bbox, conf, obj_conf, obj_pred in detections:
@@ -98,7 +92,7 @@ def main(opt):
                     # Extract index of largest overlap
                     best_i = np.argmax(iou)
                     # If overlap exceeds threshold and classification is correct mark as correct
-                    if iou[best_i] > opt.iou_thres and obj_pred == annotations[best_i, 0] and best_i not in detected:
+                    if iou[best_i] > opt.iou_thres and obj_pred == labels[best_i, 0] and best_i not in detected:
                         correct.append(1)
                         detected.append(best_i)
                     else:
@@ -123,7 +117,7 @@ def main(opt):
             mean_P = np.mean(mP)
 
             # Print image mAP and running mean mAP
-            print(('%11s%11s' + '%11.3g' * 3) % (len(mAPs), len(dataloader) * opt.batch_size, mean_P, mean_R, mean_mAP))
+            print(('%11s%11s' + '%11.3g' * 3) % (len(mAPs), dataloader.nF, mean_P, mean_R, mean_mAP))
 
     # Print mAP per class
     print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP') + '\n\nmAP Per Class:')