multi_gpu multi_scale

utils/datasets.py

@@ -90,7 +90,7 @@ class LoadWebcam:  # for inference
 
 
 class LoadImagesAndLabels:  # for training
-    def __init__(self, path, batch_size=1, img_size=608, multi_scale=False, augment=False):
+    def __init__(self, path, batch_size=1, img_size=608, augment=False):
         with open(path, 'r') as file:
             self.img_files = file.readlines()
             self.img_files = [x.replace('\n', '') for x in self.img_files]
@@ -102,8 +102,7 @@ class LoadImagesAndLabels:  # for training
         self.nF = len(self.img_files)  # number of image files
         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.img_size = img_size
         self.augment = augment
 
         assert self.nF > 0, 'No images found in %s' % path
@@ -121,13 +120,6 @@ class LoadImagesAndLabels:  # for training
         ia = self.count * self.batch_size
         ib = min((self.count + 1) * self.batch_size, self.nF)
 
-        if self.multi_scale:
-            # Multi-Scale YOLO Training
-            height = random.choice(range(10, 20)) * 32  # 320 - 608 pixels
-        else:
-            # Fixed-Scale YOLO Training
-            height = self.height
-
         img_all, labels_all, img_paths, img_shapes = [], [], [], []
         for index, files_index in enumerate(range(ia, ib)):
             img_path = self.img_files[self.shuffled_vector[files_index]]
@@ -159,7 +151,7 @@ class LoadImagesAndLabels:  # for training
                 cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
 
             h, w, _ = img.shape
-            img, ratio, padw, padh = letterbox(img, height=height)
+            img, ratio, padw, padh = letterbox(img, height=self.img_size)
 
             # Load labels
             if os.path.isfile(label_path):
@@ -189,7 +181,7 @@ class LoadImagesAndLabels:  # for training
             nL = len(labels)
             if nL > 0:
                 # convert xyxy to xywh
-                labels[:, 1:5] = xyxy2xywh(labels[:, 1:5].copy()) / height
+                labels[:, 1:5] = xyxy2xywh(labels[:, 1:5].copy()) / self.img_size
 
             if self.augment:
                 # random left-right flip