使用pytorch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，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()