def transform(img, boxes, labels): img, boxes = resize(img, boxes, size=(opt.img_size, opt.img_size)) img = transforms.Compose([ transforms.ToTensor(), caffe_normalize ])(img) return img, boxes, labels
def train_(img, boxes, labels): img = random_distort(img) if random.random() < 0.5: img, boxes = random_paste(img, boxes, max_ratio=4, fill=(123, 116, 103)) img, boxes, labels = random_crop(img, boxes, labels) img, boxes = resize(img, boxes, size=(opt.img_size, opt.img_size), random_interpolation=True) img, boxes = random_flip(img, boxes) img = transforms.Compose([transforms.ToTensor(), caffe_normalize])(img) boxes, labels = box_coder.encode(boxes, labels) return img, boxes, labels
def test_(img, boxes, labels): img, boxes = resize(img, boxes, size=(opt.img_size, opt.img_size)) img = transforms.Compose([transforms.ToTensor(), caffe_normalize])(img) boxes, labels = box_coder.encode(boxes, labels) return img, boxes, labels