使用pytorch实现可视化中间层的结果

Python  /  管理员 发布于 5年前   465

摘要

一直比较想知道图片经过卷积之后中间层的结果，于是使用pytorch写了一个脚本查看，先看效果

这是原图，随便从网上下载的一张大概224*224大小的图片，如下

网络介绍

我们使用的VGG16，包含RULE层总共有30层可以可视化的结果，我们把这30层分别保存在30个文件夹中，每个文件中根据特征的大小保存了64~128张图片

结果如下：

原图大小为224224，经过第一层后大小为64224*224，下面是第一层可视化的结果，总共有64张这样的图片：

下面看看第六层的结果

这层的输出大小是 1128112*112，总共有128张这样的图片

下面是完整的代码

import cv2import numpy as npimport torchfrom torch.autograd import Variablefrom torchvision import models#创建30个文件夹def mkdir(path): # 判断是否存在指定文件夹，不存在则创建  # 引入模块  import os  # 去除首位空格  path = path.strip()  # 去除尾部 \ 符号  path = path.rstrip("\\")  # 判断路径是否存在  # 存在   True  # 不存在  False  isExists = os.path.exists(path)  # 判断结果  if not isExists:    # 如果不存在则创建目录    # 创建目录操作函数    os.makedirs(path)    return True  else:    return Falsedef preprocess_image(cv2im, resize_im=True):  """    Processes image for CNNs  Args:    PIL_img (PIL_img): Image to process    resize_im (bool): Resize to 224 or not  returns:    im_as_var (Pytorch variable): Variable that contains processed float tensor  """  # mean and std list for channels (Imagenet)  mean = [0.485, 0.456, 0.406]  std = [0.229, 0.224, 0.225]  # Resize image  if resize_im:    cv2im = cv2.resize(cv2im, (224, 224))  im_as_arr = np.float32(cv2im)  im_as_arr = np.ascontiguousarray(im_as_arr[..., ::-1])  im_as_arr = im_as_arr.transpose(2, 0, 1) # Convert array to D,W,H  # Normalize the channels  for channel, _ in enumerate(im_as_arr):    im_as_arr[channel] /= 255    im_as_arr[channel] -= mean[channel]    im_as_arr[channel] /= std[channel]  # Convert to float tensor  im_as_ten = torch.from_numpy(im_as_arr).float()  # Add one more channel to the beginning. Tensor shape = 1,3,224,224  im_as_ten.unsqueeze_(0)  # Convert to Pytorch variable  im_as_var = Variable(im_as_ten, requires_grad=True)  return im_as_varclass FeatureVisualization():  def __init__(self,img_path,selected_layer):    self.img_path=img_path    self.selected_layer=selected_layer    self.pretrained_model = models.vgg16(pretrained=True).features    #print( self.pretrained_model)  def process_image(self):    img=cv2.imread(self.img_path)    img=preprocess_image(img)    return img  def get_feature(self):    # input = Variable(torch.randn(1, 3, 224, 224))    input=self.process_image()    print("input shape",input.shape)    x=input    for index,layer in enumerate(self.pretrained_model):      #print(index)      #print(layer)      x=layer(x)      if (index == self.selected_layer):        return x  def get_single_feature(self):    features=self.get_feature()    print("features.shape",features.shape)    feature=features[:,0,:,:]    print(feature.shape)    feature=feature.view(feature.shape[1],feature.shape[2])    print(feature.shape)    return features  def save_feature_to_img(self):    #to numpy    features=self.get_single_feature()    for i in range(features.shape[1]):      feature = features[:, i, :, :]      feature = feature.view(feature.shape[1], feature.shape[2])      feature = feature.data.numpy()      # use sigmod to [0,1]      feature = 1.0 / (1 + np.exp(-1 * feature))      # to [0,255]      feature = np.round(feature * 255)      print(feature[0])      mkdir('./feature/' + str(self.selected_layer))      cv2.imwrite('./feature/'+ str( self.selected_layer)+'/' +str(i)+'.jpg', feature)if __name__=='__main__':  # get class  for k in range(30):    myClass=FeatureVisualization('/home/lqy/examples/TRP.PNG',k)    print (myClass.pretrained_model)    myClass.save_feature_to_img()

以上这篇使用pytorch实现可视化中间层的结果就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。