标签:layers VGG19 image current 深度 input mean pixel 加载
1、加载VGG19获取图片特征图
# coding = utf-8 import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import os import scipy.io import scipy.misc def _conv_layer(input,weights,bias): conv = tf.nn.conv2d(input,tf.constant(weights),strides=(1,1,1,1),padding="SAME") return tf.nn.bias_add(conv,bias) def _pool_layer(input): return tf.nn.max_pool(input,ksize=(1,2,2,1),strides=(1,2,2,1),padding="SAME") def preprocess(image,mean_pixel): '''简单预处理,全部图片减去平均值''' return image - mean_pixel def unprocess(img,mean_pixel): return img + mean_pixel def imread(path): return scipy.misc.imread(path).astype(np.float) def imsave(path,img): img = np.clip(img,0,255).astype(np.uint8) scipy.misc.imsave(path,img) def net(data_path,input_image): """ 读取VGG模型参数,搭建VGG网络 :param data_path: VGG模型文件位置 :param input_image: 输入测试图像 :return: """ layers = ( 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2','pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4','pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4' ) data = scipy.io.loadmat(data_path) mean = data['normalization'][0][0][0] mean_pixel = np.mean(mean,axis=(0,1)) weights = data['layers'][0] net = {} current = input_image for i, name in enumerate(layers): kind =name[:4] if kind == 'conv': kernels,bias = weights[i][0][0][0][0] kernels = np.transpose(kernels,(1,0,2,3)) bias = bias.reshape(-1) current = _conv_layer(current,kernels,bias) elif kind == 'relu': current = tf.nn.relu(current) elif kind == 'pool': current = _pool_layer(current) net[name] = current assert len(net) == len(layers) return net,mean_pixel,layers if __name__ == '__main__': VGG_PATH = "./one/imagenet-vgg-verydeep-19.mat" IMG_PATH = './one/3.jpg' input_image =imread(IMG_PATH) shape = (1, input_image.shape[0], input_image.shape[1], input_image.shape[2]) with tf.Session() as sess: image = tf.placeholder('float', shape=shape) nets, mean_pixel, all_layers= net(VGG_PATH, image) input_image_pre=np.array([preprocess(input_image,mean_pixel)]) layers = all_layers for i , layer in enumerate(layers): print("[%d/%d] %s" % (i+1,len(layers),layers)) features = nets[layer].eval(feed_dict={image:input_image_pre}) print("Type of 'feature' is ",type(features)) print("Shape of 'features' is %s" % (features.shape,)) if 1: plt.figure(i+1,figsize=(10,5)) plt.matshow(features[0,:,:,0],cmap=plt.cm.gray,fignum=i+1) plt.title(""+layer) plt.colorbar() plt.show()
标签:layers,VGG19,image,current,深度,input,mean,pixel,加载 来源: https://www.cnblogs.com/ywjfx/p/11127943.html
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