GIoU to default
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@ -134,7 +134,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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def __init__(self, path, img_size=416, batch_size=16, augment=False, rect=True, image_weights=False):
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with open(path, 'r') as f:
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img_files = f.read().splitlines()
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self.img_files = list(filter(lambda x: len(x) > 0, img_files))
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self.img_files = [x for x in img_files if os.path.splitext(x)[-1].lower() in img_formats]
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n = len(self.img_files)
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bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index
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@ -149,9 +149,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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self.rect = False if image_weights else rect
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# Define labels
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self.label_files = [x.replace('images', 'labels') for x in self.img_files]
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for f in img_formats:
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self.label_files = [x.replace(f, '.txt') for x in self.label_files]
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self.label_files = [x.replace('images', 'labels').replace(os.path.splitext(x)[-1], '.txt')
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for x in self.img_files]
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# Rectangular Training https://github.com/ultralytics/yolov3/issues/232
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if self.rect:
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