update multi-scale training

train.py

@@ -7,12 +7,11 @@ 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('-img_size', type=int, default=32 * 19, help='size of each image dimension')
 parser.add_argument('-resume', default=False, help='resume training flag')
-parser.add_argument('-multi_scale', default=True, help='train at random img_size 320-608')  # ensure memory for 608 size
 opt = parser.parse_args()
 print(opt)
 
@@ -40,7 +39,7 @@ def main(opt):
         train_path = '../coco/trainvalno5k.part'
 
     # Initialize model
-    model = Darknet(opt.cfg, opt.img_size if opt.multi_scale is False else 608)
+    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)
@@ -100,12 +99,6 @@ def main(opt):
     for epoch in range(opt.epochs):
         epoch += start_epoch
 
-        # Multi-Scale YOLO Training
-        if opt.multi_scale:
-            img_size = random.choice(range(10, 20)) * 32  # 320 - 608 pixels
-            dataloader = load_images_and_labels(train_path, batch_size=opt.batch_size, img_size=img_size, augment=True)
-            print('Running Epoch %g at multi_scale img_size %g' % (epoch, img_size))
-
         # Update scheduler (automatic)
         # scheduler.step()
 

utils/datasets.py

@@ -100,7 +100,13 @@ class load_images_and_labels():  # for training
         ia = self.count * self.batch_size
         ib = min((self.count + 1) * self.batch_size, self.nF)
 
-        height = self.height
+        if self.augment is True:
+            # Multi-Scale YOLO Training
+            height = random.choice(range(10, 20)) * 32  # 320 - 608 pixels
+        else:
+            # Fixed-Scale YOLO Training
+            height = self.height
+        print(height)
 
         img_all = []
         labels_all = []