【猫狗数据集】可视化resnet18的输出

转载

mob604756fca9f3 2020-03-21 14:56:00

文章标签 深度学习猫狗数据集 javascript 数据集读取数据 文章分类 数据分析人工智能

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")