• 【猫狗数据集】可视化resnet18的输出


    数据集下载地址:

    链接:https://pan.baidu.com/s/1l1AnBgkAAEhh0vI5_loWKw
    提取码:2xq4

    创建数据集:https://www.cnblogs.com/xiximayou/p/12398285.html

    读取数据集:https://www.cnblogs.com/xiximayou/p/12422827.html

    进行训练:https://www.cnblogs.com/xiximayou/p/12448300.html

    保存模型并继续进行训练:https://www.cnblogs.com/xiximayou/p/12452624.html

    加载保存的模型并测试:https://www.cnblogs.com/xiximayou/p/12459499.html

    划分验证集并边训练边验证:https://www.cnblogs.com/xiximayou/p/12464738.html

    使用学习率衰减策略并边训练边测试:https://www.cnblogs.com/xiximayou/p/12468010.html

    利用tensorboard可视化训练和测试过程:https://www.cnblogs.com/xiximayou/p/12482573.html

    从命令行接收参数:https://www.cnblogs.com/xiximayou/p/12488662.html

    使用top1和top5准确率来衡量模型:https://www.cnblogs.com/xiximayou/p/12489069.html

    使用预训练的resnet18模型:https://www.cnblogs.com/xiximayou/p/12504579.html

    计算数据集的平均值和方差:https://www.cnblogs.com/xiximayou/p/12507149.html

    读取数据集的第二种方式:https://www.cnblogs.com/xiximayou/p/12516735.html

    对一张张图像进行预测(而不是测试集):https://www.cnblogs.com/xiximayou/p/12522690.html

    epoch、batchsize、step之间的关系:https://www.cnblogs.com/xiximayou/p/12405485.html

    最后读取训练好的模型,可视化特征图,至此猫狗数据集系列就完结了,后面准备着手pyorch-ssd训练自己的数据集(比如是否口罩检测)。

    直接看代码吧:visual.py

    import cv2
    import time
    import os
    import matplotlib.pyplot as plt
    import torch
    from torch import nn
    import torchvision.models as models
    import torchvision.transforms as transforms
    import numpy as np
    import torchvision
    import torch.nn as nn
     
    savepath=r'results'
    if not os.path.exists(savepath):
        os.mkdir(savepath)
     
    def load_model():
      model=torchvision.models.resnet18(pretrained=False)
      model.fc = nn.Linear(model.fc.in_features,2,bias=False)
      save_path="/content/drive/My Drive/colab notebooks/output/resnet18_best.t7" 
      checkpoint = torch.load(save_path,map_location=lambda storage, loc: storage)
      model.load_state_dict(checkpoint['model'])
      return model 
    
    def draw_features(width,height,x,savename):
        tic=time.time()
        fig = plt.figure(figsize=(16, 16))
        fig.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95, wspace=0.05, hspace=0.05)
        for i in range(width*height):
            plt.subplot(height,width, i + 1)
            plt.axis('off')
            img = x[0, i, :, :]
            pmin = np.min(img)
            pmax = np.max(img)
            img = ((img - pmin) / (pmax - pmin + 0.000001))*255  #float在[0,1]之间,转换成0-255
            img=img.astype(np.uint8)  #转成unit8
            img=cv2.applyColorMap(img, cv2.COLORMAP_JET) #生成heat map
            img = img[:, :, ::-1]#注意cv2(BGR)和matplotlib(RGB)通道是相反的
            plt.imshow(img)
            #print("{}/{}".format(i+1,width*height))
        fig.savefig(savename, dpi=100)
        fig.clf()
        plt.close()
        print("time:{}".format(time.time()-tic))
     
     
    class ft_net(nn.Module):
     
        def __init__(self):
            super(ft_net, self).__init__()
            model=load_model()
            self.model = model
     
        def forward(self, x):
            if True: # draw features or not
                x = self.model.conv1(x)
                draw_features(8,8,x.cpu().numpy(),"{}/f1_conv1.png".format(savepath))
     
                x = self.model.bn1(x)
                draw_features(8, 8, x.cpu().numpy(),"{}/f2_bn1.png".format(savepath))
     
                x = self.model.relu(x)
                draw_features(8, 8, x.cpu().numpy(), "{}/f3_relu.png".format(savepath))
     
                x = self.model.maxpool(x)
                draw_features(8, 8, x.cpu().numpy(), "{}/f4_maxpool.png".format(savepath))
     
                x = self.model.layer1(x)
                draw_features(8, 8, x.cpu().numpy(), "{}/f5_layer1.png".format(savepath))
     
                x = self.model.layer2(x)
                draw_features(8, 16, x.cpu().numpy(), "{}/f6_layer2.png".format(savepath))
     
                x = self.model.layer3(x)
                draw_features(16, 16, x.cpu().numpy(), "{}/f7_layer3.png".format(savepath))
     
                x = self.model.layer4(x)
                draw_features(16, 32, x.cpu().numpy(), "{}/f8_layer4.png".format(savepath))
                #draw_features(16, 32, x.cpu().numpy()[:, 0:1024, :, :], "{}/f8_layer4_1.png".format(savepath))
                #draw_features(16, 32, x.cpu().numpy()[:, 1024:2048, :, :], "{}/f8_layer4_2.png".format(savepath))
     
                x = self.model.avgpool(x)
                #plt.plot(np.linspace(1, 2048, 2048), x.cpu().numpy()[0, :, 0, 0])
                plt.plot(np.linspace(1, 512, 512), x.cpu().numpy()[0, :, 0, 0])
                plt.savefig("{}/f9_avgpool.png".format(savepath))
                plt.clf()
                plt.close()
     
                x = x.view(x.size(0), -1)
                x = self.model.fc(x)
                #plt.plot(np.linspace(1, 1000, 1000), x.cpu().numpy()[0, :])
                plt.plot(np.linspace(1, 2, 2), x.cpu().numpy()[0, :])
                plt.savefig("{}/f10_fc.png".format(savepath))
                plt.clf()
                plt.close()
            else :
                x = self.model.conv1(x)
                x = self.model.bn1(x)
                x = self.model.relu(x)
                x = self.model.maxpool(x)
                x = self.model.layer1(x)
                x = self.model.layer2(x)
                x = self.model.layer3(x)
                x = self.model.layer4(x)
                x = self.model.avgpool(x)
                x = x.view(x.size(0), -1)
                x = self.model.fc(x)
     
            return x
     
     
    model=ft_net()
     
    # pretrained_dict = resnet50.state_dict()
    # pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
    # model_dict.update(pretrained_dict)
    # net.load_state_dict(model_dict)
    model.eval()
    img=cv2.imread('/content/drive/My Drive/colab notebooks/image/cat7.jpg')
    img=cv2.resize(img,(224,224))
    img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    transform = transforms.Compose(
        [transforms.ToTensor(),
         transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))])
    img=transform(img)
    img=img.unsqueeze(0)
     
    with torch.no_grad():
        start=time.time()
        out=model(img)
        print("total time:{}".format(time.time()-start))
        result=out.cpu().numpy()
        # ind=np.argmax(out.cpu().numpy())
        ind=np.argsort(result,axis=1)
        """
        for i in range(5):
            print("predict:top {} = cls {} : score {}".format(i+1,ind[0,1000-i-1],result[0,1000-i-1]))
        """
        for i in range(2):
            print("predict:top {} = cls {} : score {}".format(i+1,ind[0,2-i-1],result[0,2-i-1]))
        print("done")

    说明:需要注意的地方

    • 在draw_features()中的前两个参数的乘积必须为该层输出的通道数目的大小。
    • 在GPU上训练的模型要转换成CPU模式。
    • 输入的图像转换成测试的格式:图像大小、维度[batchsize,C,H,W]
    • 要注意我们的类别是两类:猫和狗

    运行:

    输出文件夹:

    原始图片:

    查看每一个文件中的图像:只截取部分

    f1_conv1.png 

    f2_bn1.png

    f3_relu.png 

    f4_maxpool.png

    f5_layer1.png

    f6_layer2.png

    f7_layer3.png

    f8_layer4.png

    f9_avgpool.png

    f10_fc.png

    横轴是类别编号,纵轴是评分。最后一个图咋好像不太对劲。。

    参考了:https://blog.csdn.net/weixin_40500230/article/details/93845890

    其实pytorch有一个可视化库很全面,奈何整了半天没整成功,有机会再试试了。 

    https://github.com/utkuozbulak/pytorch-cnn-visualizations/

  • 相关阅读:
    消失的 unittest.makeSuite()
    自定义错误信息在各个浏览器表现不同
    “创建Web应用程序”与“创建Web网站”的区别
    如何将Notepad++改造成Python开发工具
    64位服务器IIS不能识别32位framework版本。IIS没有Asp.net切换界面的解决办法。
    C++文件流,读入数据注意
    Chessboard(二分图完备匹配)
    courses二分图最大匹配
    Linux BASH Shell文件名匹配/输出重定向
    LinuxShell一些很少用到却很有用的指令
  • 原文地址:https://www.cnblogs.com/xiximayou/p/12539378.html
Copyright © 2020-2023  润新知