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。