部署架构已固定，如何无痛涨点？

from torchvision import transforms
trans = transforms.Compose([
    transforms.CenterCrop(10),
    transforms.ToTensor(),
])

'''
其他常用的数据增强的方法：
Resize：把给定的图片resize到given size
Normalize：Normalized an tensor image with mean and standard deviation
ToTensor：convert a PIL image to tensor (H*W*C) in range [0,255] to a torch.Tensor(C*H*W) in the range [0.0,1.0]
ToPILImage: convert a tensor to PIL image
Scale：目前已经不用了，推荐用Resize
CenterCrop：在图片的中间区域进行裁剪
RandomCrop：在一个随机的位置进行裁剪
RandomHorizontalFlip：以0.5的概率水平翻转给定的PIL图像
RandomVerticalFlip：以0.5的概率竖直翻转给定的PIL图像
RandomResizedCrop：将PIL图像裁剪成任意大小和纵横比
Grayscale：将图像转换为灰度图像
RandomGrayscale：将图像以一定的概率转换为灰度图像
FiceCrop：把图像裁剪为四个角和一个中心
Pad：填充
ColorJitter：随机改变图像的亮度对比度和饱和度。
'''

def mixup_data(x, y, alpha=1.0, use_cuda=True):

    '''Compute the mixup data. Return mixed inputs, pairs of targets, and lambda'''
    if alpha > 0.:
        lam = np.random.beta(alpha, alpha)
    else:
        lam = 1.
    batch_size = x.size()[0]
    if use_cuda:
        index = torch.randperm(batch_size).cuda()
    else:
        index = torch.randperm(batch_size)

    mixed_x = lam * x + (1 - lam) * x[index,:]
    y_a, y_b = y, y[index]
    return mixed_x, y_a, y_b, lam

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=base_learning_rate, momentum=0.9, weight_decay=args.decay)

def mixup_criterion(y_a, y_b, lam):
    return lambda criterion, pred: lam * criterion(pred, y_a) + (1 - lam) * criterion(pred, y_b)
    
""" 训练 """

def train(epoch):
    print('\nEpoch: %d' % epoch)
    net.train()
    train_loss = 0
    correct = 0
    total = 0
    for batch_idx, (inputs, targets) in enumerate(trainloader):
        if use_cuda:
            inputs, targets = inputs.cuda(), targets.cuda()
        """ generate mixed inputs, two one-hot label vectors and mixing coefficient """
        inputs, targets_a, targets_b, lam = mixup_data(inputs, targets, args.alpha, use_cuda)       
        inputs, targets_a, targets_b = Variable(inputs), Variable(targets_a), Variable(targets_b)
        outputs = net(inputs)
        """ 计算loss """
        loss_func = mixup_criterion(targets_a, targets_b, lam)
        loss = loss_func(criterion, outputs)
        """ 更新梯度 """
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

import numpy as np
"""输入为：样本的size和生成的随机lamda值"""
def rand_bbox(size, lam):
    W = size[2]
    H = size[3]
    """论文里的公式2，求出B的rw,rh"""
    cut_rat = np.sqrt(1. - lam)
    cut_w = np.int(W * cut_rat)
    cut_h = np.int(H * cut_rat)

    # uniform
    """论文里的公式2，求出B的rx,ry（bbox的中心点）"""
    cx = np.random.randint(W)
    cy = np.random.randint(H)
 
 # np.clip限制大小
 """限制B坐标区域不超过样本大小"""
    bbx1 = np.clip(cx - cut_w // 2, 0, W)
    bby1 = np.clip(cy - cut_h // 2, 0, H)
    bbx2 = np.clip(cx + cut_w // 2, 0, W)
    bby2 = np.clip(cy + cut_h // 2, 0, H)

    return bbx1, bby1, bbx2, bby2
    
for i, (input, target) in enumerate(train_loader):
        # measure data loading time
        data_time.update(time.time() - end)

        input = input.cuda()
        target = target.cuda()

        r = np.random.rand(1)
        if args.beta > 0 and r < args.cutmix_prob:
            # generate mixed sample
            """设定lamda的值，服从beta分布"""
            lam = np.random.beta(args.beta, args.beta)
            rand_index = torch.randperm(input.size()[0]).cuda()
            """获取batch里面的两个随机样本 """
            target_a = target
            target_b = target[rand_index]
            """获取裁剪区域bbox坐标位置 """
            bbx1, bby1, bbx2, bby2 = rand_bbox(input.size(), lam)
            """将原有的样本A中的B区域，替换成样本B中的B区域"""
            input[:, :, bbx1:bbx2, bby1:bby2] = input[rand_index, :, bbx1:bbx2, bby1:bby2]
            # adjust lambda to exactly match pixel ratio
            """根据剪裁区域坐标框的值调整lam的值 """ 
            lam = 1 - ((bbx2 - bbx1) * (bby2 - bby1) / (input.size()[-1] * input.size()[-2]))
            # compute output
            """计算模型输出 """
            output = model(input)
            """计算损失 """
            loss = criterion(output, target_a) * lam + criterion(output, target_b) * (1. - lam)
        else:
            # compute output
            output = model(input)
            loss = criterion(output, target)

import os
import numpy as np
import cv2
import random
import math
 
def random_affine(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, border=0):
    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
    # https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4
    # targets = [cls, xyxy]
 
    height = img.shape[0] + border * 2
    width = img.shape[1] + border * 2
 
    # Rotation and Scale
    R = np.eye(3)
    a = random.uniform(-degrees, degrees)
    # a += random.choice([-180, -90, 0, 90])  # add 90deg rotations to small rotations
    s = random.uniform(1 - scale, 1 + scale)
    # s = 2 ** random.uniform(-scale, scale)
    R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s)
 
    # Translation
    T = np.eye(3)
    T[0, 2] = random.uniform(-translate, translate) * img.shape[0] + border  # x translation (pixels)
    T[1, 2] = random.uniform(-translate, translate) * img.shape[1] + border  # y translation (pixels)
 
    # Shear
    S = np.eye(3)
    S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # x shear (deg)
    S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180)  # y shear (deg)
 
    # Combined rotation matrix
    M = S @ T @ R  # ORDER IS IMPORTANT HERE!!
    if (border != 0) or (M != np.eye(3)).any():  # image changed
        img = cv2.warpAffine(img, M[:2], dsize=(width, height), flags=cv2.INTER_LINEAR, borderValue=(114, 114, 114))
 
    # Transform label coordinates
    n = len(targets)
    if n:
        # warp points
        xy = np.ones((n * 4, 3))
        xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2)  # x1y1, x2y2, x1y2, x2y1
        xy = (xy @ M.T)[:, :2].reshape(n, 8)
        # create new boxes
        x = xy[:, [0, 2, 4, 6]]
        y = xy[:, [1, 3, 5, 7]]
        xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
 
        # # apply angle-based reduction of bounding boxes
        # radians = a * math.pi / 180
        # reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
        # x = (xy[:, 2] + xy[:, 0]) / 2
        # y = (xy[:, 3] + xy[:, 1]) / 2
        # w = (xy[:, 2] - xy[:, 0]) * reduction
        # h = (xy[:, 3] - xy[:, 1]) * reduction
        # xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
 
        # reject warped points outside of image
        xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
        xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)
        w = xy[:, 2] - xy[:, 0]
        h = xy[:, 3] - xy[:, 1]
        area = w * h
        area0 = (targets[:, 3] - targets[:, 1]) * (targets[:, 4] - targets[:, 2])
        ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))  # aspect ratio
        i = (w > 4) & (h > 4) & (area / (area0 * s + 1e-16) > 0.2) & (ar < 10)
 
        targets = targets[i]
        targets[:, 1:5] = xy[i]
 
    return img, targets
 
def load_image(img_files, index,img_size=640):
    # loads 1 image from dataset, returns img, original hw, resized hw
    path = img_files[index]
    img = cv2.imread(path)  # BGR
    assert img is not None, 'Image Not Found ' + path
    h0, w0 = img.shape[:2]  # orig hw
    r = img_size / max(h0, w0)  # resize image to img_size
    if r != 1:  # always resize down, only resize up if training with augmentation
        img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=1)
    return img, (h0, w0), img.shape[:2]  # img, hw_original, hw_resized
 
def load_mosaic(img_files,index,img_size,labels):
    # loads images in a mosaic
    labels4 = []
    s = img_size
    xc, yc = [int(random.uniform(s * 0.5, s * 1.5)) for _ in range(2)]  # mosaic center x, y
    indices = [index] + [random.randint(0, len(labels) - 1) for _ in range(3)]  # 3 additional image indices
    for i, index in enumerate(indices):
        # Load image
        img, _, (h, w) = load_image(img_files, index)
        # place img in img4
        if i == 0:  # top left
            img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
            x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
            x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)
        elif i == 1:  # top right
            x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
            x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
        elif i == 2:  # bottom left
            x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
            x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, max(xc, w), min(y2a - y1a, h)
        elif i == 3:  # bottom right
            x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
            x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
        img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
        padw = x1a - x1b
        padh = y1a - y1b
 
        # Labels
        x = labels[index]
        labels_ = x.copy()
        if x.size > 0:  # Normalized xywh to pixel xyxy format
            labels_[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padw
            labels_[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + padh
            labels_[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padw
            labels_[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + padh
        labels4.append(labels_)
 
    # Concat/clip labels
    if len(labels4):
        labels4 = np.concatenate(labels4, 0)
        # np.clip(labels4[:, 1:] - s / 2, 0, s, out=labels4[:, 1:])  # use with center crop
        np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:])  # use with random_affine
 
    # Augment
    # img4 = img4[s // 2: int(s * 1.5), s // 2:int(s * 1.5)]  # center crop (WARNING, requires box pruning)
    img4, labels4 = random_affine(img4, labels4,
                                  degrees=0.0,
                                  translate=0.0,
                                  scale=0.5,
                                  shear=0.0,
                                  border=-s // 2)  # border to remove
    return img4, labels4
 
img_files = []#图片路径列表
labelss = []#[类别，中心点x的归一化,中心点y的归一化,w的归一化,h的归一化]
for dir in os.listdir(r'G:\dirsfirst\data\dataset\labels'):
    name = dir.replace('txt','jpg')
    path = os.path.join(r'G:\dirsfirst\data\dataset\images',name)
    img_files.append(path)
    with open(os.path.join(r'G:\dirsfirst\data\dataset\labels',dir)) as f:
        l = [0]
        data = f.read().strip().split(' ')
        for d in data[1:]:
            l.append(float(d))
        labelss.append(np.array([l]))
for index in range(len(labelss)):
    img, labels =load_mosaic(img_files=img_files,index=index,img_size=640,labels=labelss)
    for label in labels:
        x1 = int(label[1])
        y1 = int(label[2])
        x2 = int(label[3])
        y2 = int(label[4])
        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
    cv2.imshow('',img)
    cv2.waitKey(0)

class Cutout(object):
    """Randomly mask out one or more patches from an image.
    Args:
        n_holes (int): Number of patches to cut out of each image.
        length (int): The length (in pixels) of each square patch.
    """
    def __init__(self, n_holes, length):
        self.n_holes = n_holes
        self.length = length

    def __call__(self, img):
        """
        Args:
            img (Tensor): Tensor image of size (C, H, W).
        Returns:
            Tensor: Image with n_holes of dimension length x length cut out of it.
        """
        h = img.size(1)
        w = img.size(2)

        mask = np.ones((h, w), np.float32)

        for n in range(self.n_holes):
         # (x,y)表示方形补丁的中心位置
            y = np.random.randint(h)
            x = np.random.randint(w)

            y1 = np.clip(y - self.length // 2, 0, h)
            y2 = np.clip(y + self.length // 2, 0, h)
            x1 = np.clip(x - self.length // 2, 0, w)
            x2 = np.clip(x + self.length // 2, 0, w)

            mask[y1: y2, x1: x2] = 0.

        mask = torch.from_numpy(mask)
        mask = mask.expand_as(img)
        img = img * mask

        return img