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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()关于使用pytorch怎么实现一个可视化中间层就分享到这里了，希望以上内容可以对大家有一定的帮助，可以学到更多知识。如果觉得文章不错，可以把它分享出去让更多的人看到。
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