提交 2a0aff68 authored 作者: Glenn Jocher's avatar Glenn Jocher

dataset padding mode

上级 eb97b2e4
......@@ -56,8 +56,14 @@ def test(data,
# Dataloader
if dataloader is None:
fast |= conf_thres > 0.001 # enable fast mode
path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images
dataset = LoadImagesAndLabels(path, imgsz, batch_size, rect=True, single_cls=opt.single_cls)
dataset = LoadImagesAndLabels(path,
imgsz,
batch_size,
rect=True, # rectangular inference
single_cls=opt.single_cls, # single class mode
pad=0.0 if fast else 0.5) # padding
batch_size = min(batch_size, len(dataset))
dataloader = DataLoader(dataset,
batch_size=batch_size,
......
......@@ -257,7 +257,7 @@ class LoadStreams: # multiple IP or RTSP cameras
class LoadImagesAndLabels(Dataset): # for training/testing
def __init__(self, path, img_size=416, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
cache_images=False, single_cls=False):
cache_images=False, single_cls=False, pad=0.0):
try:
path = str(Path(path)) # os-agnostic
parent = str(Path(path).parent) + os.sep
......@@ -322,7 +322,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
elif mini > 1:
shapes[i] = [1, 1 / mini]
self.batch_shapes = np.round(np.array(shapes) * img_size / 32. + 1.).astype(np.int) * 32
self.batch_shapes = np.ceil(np.array(shapes) * img_size / 32. + pad).astype(np.int) * 32
# Cache labels
self.imgs = [None] * n
......
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