add multi_scale support

models.py

@@ -184,15 +184,14 @@ class YOLOLayer(nn.Module):
                 # plt.hist(self.x)
 
                 # lconf = k * BCEWithLogitsLoss(pred_conf[mask], mask[mask].float())
-                lconf = (k * 64) * BCEWithLogitsLoss(pred_conf, mask.float())
 
                 lcls = (k / 4) * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1))
                 # lcls = (k * 10) * BCEWithLogitsLoss(pred_cls[mask], tcls.float())
             else:
                 lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0])
 
-            # Add confidence loss for background anchors (noobj)
             # lconf += k * BCEWithLogitsLoss(pred_conf[~mask], mask[~mask].float())
+            lconf = (k * 64) * BCEWithLogitsLoss(pred_conf, mask.float())
 
             # Sum loss components
             balance_losses_flag = False

train.py

@@ -8,15 +8,18 @@ from utils.utils import *
 
 parser = argparse.ArgumentParser()
 parser.add_argument('-epochs', type=int, default=100, help='number of epochs')
-parser.add_argument('-batch_size', type=int, default=16, help='size of each image batch')
+parser.add_argument('-batch_size', type=int, default=2, help='size of each image batch')
 parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='data config file path')
 parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
-parser.add_argument('-img_size', type=int, default=32 * 13, help='size of each image dimension')
+parser.add_argument('-multi_scale', default=True, help='random image sizes per batch 320 - 608')
+parser.add_argument('-img_size', type=int, default=32 * 13, help='pixels')
 parser.add_argument('-resume', default=False, help='resume training flag')
 parser.add_argument('-batch_report', default=False, help='report TP, FP, FN, P and R per batch (slower)')
 parser.add_argument('-freeze_darknet53', default=False, help='freeze darknet53.conv.74 layers for first epoch')
 parser.add_argument('-var', type=float, default=0, help='optional test variable')
 opt = parser.parse_args()
+if opt.multi_scale:  # pass maximum multi_scale size
+    opt.img_size = 608
 print(opt)
 
 # Import test.py to get mAP after each epoch
@@ -50,7 +53,8 @@ def main(opt):
     model = Darknet(opt.cfg, opt.img_size)
 
     # Get dataloader
-    dataloader = load_images_and_labels(train_path, batch_size=opt.batch_size, img_size=opt.img_size, augment=True)
+    dataloader = load_images_and_labels(train_path, batch_size=opt.batch_size, img_size=opt.img_size,
+                                        multi_scale=opt.multi_scale, augment=True)
 
     lr0 = 0.001
     if opt.resume:
@@ -217,4 +221,3 @@ def main(opt):
 if __name__ == '__main__':
     torch.cuda.empty_cache()
     main(opt)
-    torch.cuda.empty_cache()

utils/datasets.py

@@ -60,7 +60,7 @@ class load_images():  # for inference
 
 
 class load_images_and_labels():  # for training
-    def __init__(self, path, batch_size=1, img_size=608, augment=False):
+    def __init__(self, path, batch_size=1, img_size=608, multi_scale=False, augment=False):
         self.path = path
         # self.img_files = sorted(glob.glob('%s/*.*' % path))
         with open(path, 'r') as file:
@@ -79,6 +79,7 @@ class load_images_and_labels():  # for training
         self.nB = math.ceil(self.nF / batch_size)  # number of batches
         self.batch_size = batch_size
         self.height = img_size
+        self.multi_scale = multi_scale
         self.augment = augment
 
         assert self.nB > 0, 'No images found in path %s' % path
@@ -100,8 +101,7 @@ class load_images_and_labels():  # for training
         ia = self.count * self.batch_size
         ib = min((self.count + 1) * self.batch_size, self.nF)
 
-        multi_scale = False
-        if multi_scale and self.augment:
+        if self.multi_scale:
             # Multi-Scale YOLO Training
             height = random.choice(range(10, 20)) * 32  # 320 - 608 pixels
         else: