使用pytorch实现可视化中间层的结果

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，

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 cv2
import numpy as np
import torch
from torch.autograd import Variable
from 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 False


def 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_var


class 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()

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pytorch 可视化 中间层