ELTV

very begining of ELTV

The loop of Deep learning is divided into 4,just summarize the tricks and make a cheatsheet

Extract data

针对pytorch的话，一般要实现一个dataset类，里面重写两个函数就可以了，一个是__len__,另外一个是__getitem__支持从0到len(self)索引所有的整数。实际代码看一下之前对VOC数据集写的dataset类

class YoloDataset(Dataset):
    def __init__(self, annotation_lines, input_shape, num_classes, mosaic, train):
        super(YoloDataset, self).__init__()
        self.annotation_lines   = annotation_lines
        self.input_shape        = input_shape
        self.num_classes        = num_classes
        self.length             = len(self.annotation_lines)
        self.mosaic             = mosaic
        self.train              = train

    def __len__(self):
        return self.length

    def __getitem__(self, index):
        index       = index % self.length
        #---------------------------------------------------#
        #   训练时进行数据的随机增强
        #   验证时不进行数据的随机增强
        #---------------------------------------------------#
        if self.mosaic:
            if self.rand() < 0.5:
                lines = sample(self.annotation_lines, 3)
                lines.append(self.annotation_lines[index])
                shuffle(lines)
                image, box  = self.get_random_data_with_Mosaic(lines, self.input_shape)
            else:
                image, box  = self.get_random_data(self.annotation_lines[index], self.input_shape, random = self.train)
        else:
            image, box      = self.get_random_data(self.annotation_lines[index], self.input_shape, random = self.train)
        image       = np.transpose(preprocess_input(np.array(image, dtype=np.float32)), (2, 0, 1))
        box         = np.array(box, dtype=np.float32)
        if len(box) != 0:
            box[:, [0, 2]] = box[:, [0, 2]] / self.input_shape[1]
            box[:, [1, 3]] = box[:, [1, 3]] / self.input_shape[0]

            box[:, 2:4] = box[:, 2:4] - box[:, 0:2]
            box[:, 0:2] = box[:, 0:2] + box[:, 2:4] / 2
        return image, box

Load the data

load数据的话直接用dataloader就可以了，具体可以看doc

gen = DataLoader(train_dataset, shuffle = True, batch_size = batch_size, num_workers = num_workers, pin_memory=True,drop_last=True, collate_fn=yolo_dataset_collate)

Train the model

常用的训练手法一般是冻结一部分，训练一个头部网络，最后再解冻前面的backbone，最后整个拉着一起SGD

Validation model

具体的评测标准不一样，可能需要看具体怎么做评测