机器视觉 CV

1. 传统视觉

  • 均值滤波

2. 模型

2.1 分类

  • ResNet

    • 解决网络过深带来的梯度消失问题

  • ConvNext

  • ViT

    • Transformer模型的视觉应用

  • 深度可分离卷积

2.2 检测

参考物体检测系统设计

  • 一阶段检测

    • YOLO: 网格负责预测真实的bbox

    • SSD

    • RetinaNet

  • 二阶段检测

    • rcnn, fast-rcnn, faster-rcnn

    • 特征抽取(feature extraction),候选区域提取(Region proposal提取),边框回归(bounding box regression),分类(classification)

  • 多阶段

    • Cascade-rcnn: 不同级采用不同 IoU 阈值来进行重新计算正负样本和采样策略来逐渐提高 bbox 质量

  • anchor_base or anchor_free

  • RPN

  • 旋转目标检测

  • NMS 非极大值抑制

2.3 分割

语义分割

  • Unet

实例分割

2.4 生成

What are Diffusion Models?

图像生成相关:文本生成图像,图像生成图像,文本生成视频,文本生成语音。GAN、扩散模型、图像生成、多模态生成等。

扩散模型 存在一系列高斯噪声(T轮),将输入图片x0变为纯高斯噪声xt。模型则负责将xt复原回图片x0

  • autoencoder (VAE)

  • U-Net

  • text-encoder, CLIP Text Encoder

3. 代码

IOU

import torch

def iou(box1, box2):
    N = box1.size(0)
    M = box2.size(0)

    lt = torch.max(  # 左上角的点
        box1[:, :2].unsqueeze(1).expand(N, M, 2),   # [N,2]->[N,1,2]->[N,M,2]
        box2[:, :2].unsqueeze(0).expand(N, M, 2),   # [M,2]->[1,M,2]->[N,M,2]
    )

    rb = torch.min(
        box1[:, 2:].unsqueeze(1).expand(N, M, 2),
        box2[:, 2:].unsqueeze(0).expand(N, M, 2),
    )

    wh = rb - lt  # [N,M,2]
    wh[wh < 0] = 0   # 两个box没有重叠区域
    inter = wh[:,:,0] * wh[:,:,1]   # [N,M]

    area1 = (box1[:,2]-box1[:,0]) * (box1[:,3]-box1[:,1])  # (N,)
    area2 = (box2[:,2]-box2[:,0]) * (box2[:,3]-box2[:,1])  # (M,)
    area1 = area1.unsqueeze(1).expand(N,M)  # (N,M)
    area2 = area2.unsqueeze(0).expand(N,M)  # (N,M)

    iou = inter / (area1+area2-inter)
    return iou
def compute_iou(boxA, boxB):
    # box:(x1,y1,x2,y2), x1,y1为左上角。原点为左上角,x朝右为正,y朝下为正。
    # 计算相交框的坐标
    xA = max(boxA[0], boxB[0])
    yA = max(boxA[1], boxB[1])
    xB = min(boxA[2], boxB[2])
    yB = min(boxA[3], boxB[3])

    # 计算交区域,并区域,及IOU。要和0比较大小,如果是负数就说明压根不相交
    interArea = max(0, xB - xA) * max(0, yB - yA)

    boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
    boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])

    iou = interArea / (boxAArea + boxBArea - interArea)

    # return the intersection over union value
    return iou

boxA = [1,1,3,3]
boxB = [2,2,4,4]
IOU = compute_iou(boxA, boxB)
  • nms

import numpy as np

def nms(boxes, scores, threshold):
    # boxes: 边界框列表,每个框是一个格式为 [x1, y1, x2, y2] 的列表
    # scores: 每个边界框的得分列表
    # threshold: NMS的IoU阈值

    # 按得分升序排列边界框
    sorted_indices = np.argsort(scores)
    boxes = [boxes[i] for i in sorted_indices]
    scores = [scores[i] for i in sorted_indices]

    keep = []  # 保留的边界框的索引列表

    while boxes:
        # 取得分最高的边界框
        current_box = boxes.pop()
        current_score = scores.pop()

        keep.append(sorted_indices[-1])
        sorted_indices = sorted_indices[:-1]
        discard_indices = []  # 需要丢弃的边界框的索引列表

        for i, box in enumerate(boxes):
            # 计算与当前边界框的IoU
            iou = compute_iou(current_box, box)

            # 如果IoU超过阈值,标记该边界框为需要丢弃
            if iou > threshold:
                discard_indices.append(i)

        # 移除标记为需要丢弃的边界框。从后往前删,不然for循环会出错
        for i in sorted(discard_indices, reverse=True):
            boxes.pop(i)
            scores.pop(i)
            sorted_indices = np.delete(sorted_indices, i) # np与list的方法不同

    return keep
  • DDPM

import torch
import torch.nn as nn

class DenoiseDiffusion:
    def __init__(self, num_time_step=1000, device='cpu'):
        self.num_time_step = num_time_step
        self.device = device
        self.beta = torch.linspace(0.001, 0.02, num_time_step, device=device)
        self.alpha = 1.0 - self.beta
        self.alpha_bar = torch.cumprod(self.alpha, dim=0)

    def q_sample(self, x0, t, noise=None):
        """Adds noise to the clean image x0 at timestep t."""
        if noise is None:
            noise = torch.randn_like(x0)
        alpha_bar_t = self.alpha_bar[t].view(-1, *([1] * (x0.dim() - 1)))  # match shape for broadcasting
        return torch.sqrt(alpha_bar_t) * x0 + torch.sqrt(1 - alpha_bar_t) * noise

    def p_sample(self, model, x_t, t):
        """Performs a denoising step using the model prediction."""
        noise_pred = model(x_t, t)
        alpha_t = self.alpha[t].view(-1, *([1] * (x_t.dim() - 1)))
        alpha_bar_t = self.alpha_bar[t].view(-1, *([1] * (x_t.dim() - 1)))
        beta_t = self.beta[t].view(-1, *([1] * (x_t.dim() - 1)))

        coef = (1 - alpha_t) / torch.sqrt(1 - alpha_bar_t)
        mean = (1 / torch.sqrt(alpha_t)) * (x_t - coef * noise_pred)
        eps = torch.randn_like(x_t)
        return mean + torch.sqrt(beta_t) * eps

    def loss(self, model, x0):
        """Computes training loss."""
        B = x0.shape[0]
        t = torch.randint(0, self.num_time_step, (B,), device=x0.device)
        noise = torch.randn_like(x0)
        x_t = self.q_sample(x0, t, noise)
        noise_pred = model(x_t, t)
        return nn.functional.mse_loss(noise_pred, noise)

4. 问答

  • 感受野

  • 深度可分离卷积

  • 数据增强

  • diffusion model和stable diffusion公司的latent diffusion model特点

  • Diffusion process

  • 为什么diffusion model训练的时候需要1000 time steps,推理时只需要几十步

    • 训练采用的逻辑是基于DDPM的马尔可夫链逻辑,完整执行从t到t+1时刻的扩散过程;推理时采用的是DDIM类似的采样方法,将公式转化为非马尔可夫链的形式,求解任意两个时刻之间的对应公式,因此根据该公式可以在sample过程中跨步。

参考

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