from detectron2.data import DatasetMapper, transforms as T 定义数据增强操作: python def build_transforms(): transforms = [ T.RandomFlip(prob=0.5, horizontal=True, vertical=False), T.RandomCrop(crop_type="relative_range", crop_size=(0.8, 0.8)), T.RandomBrightness(intensity_min=0.9, intensi...
因为这里的StrictRandomCrop 的 get_transform 会需要用到 boxes信息,所以还需要改写相关的函数,apply_transform_gens , 在创建 StrictRandomCrop 的时候传入polygon 信息 defapply_transform_gens(transform_gens,img,**kwargs):forgintransform_gens:assertisinstance(g,TransformGen),gcheck_dtype(...
from detectron2.data import transforms as T # 定义一堆数据增强: augs = T.AugmentationList([ T.RandomBrightness(0.9, 1.1), T.RandomFlip(prob=0.5), T.RandomCrop("absolute", (640, 640)) ]) # 定义数据增强的输入(必须输入图片,其它输入可选)。= input = T.AugInput(image, boxes=boxes, sem...
importtorch.utils.dataasdataclassMapDataset(data.Dataset):def__init__(self,dataset,map_func):self._dataset=datasetself._map_func=PicklableWrapper(map_func)# wrap so that a lambda will workdef__len__(self):returnlen(self._dataset)def__getitem__(self,idx):data=self._map_func(self._datas...
结合前面两篇文章的内容可以看到detectron2在构建model,optimizer和data_loader的时候都是在对应的build.py文件里实现的。我们看一下build_detection_train_loader是如何定义的(对应上图中紫色方框内的部分(自下往上的顺序)): def build_detection_train_loader(cfg, mapper=None):""" ...
Returns: ndarray: the transformed image TransformList: contain the transforms that's used. """ 当前图像增强方法有: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 "RandomBrightness", "RandomContrast", "RandomCrop", "RandomExtent", "RandomFlip", "RandomSaturation", "RandomLighting", "Resize...
Applies cropping/geometric transforms to the image and annotations Prepare data and annotations to Tensor and :class:Instances 其源码如下(有删减): class DatasetMapper: def __init__(self, cfg, is_train=True): # 读取cfg的参数 ... def __call__(self, dataset_dict): """ Args: dataset_di...
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bbox=self.anno_data[str(self.root_dir.split("/")[-1])+"/"+ str(frame)][object_id]["000000"] ifpos: z_img=self.z_images[idx] else: z_img=self.z_images[self.random.choice(range(len( self.z_images)))] x_img_cv=x_transforms(Image.open(x_img)) ...
during testing_C.INPUT.MAX_SIZE_TEST=1333# Mode for flipping images used in data augmentation during training# choose one of ["horizontal, "vertical", "none"]_C.INPUT.RANDOM_FLIP="horizontal"# `True` if cropping is used for data augmentation during training_C.INPUT.CROP=CN({"ENABLED":...