学习进度笔记15

观看Tensorflow案例实战视频课程15 使用VGG模型进行测试

import numpy as np
import os
import scipy.misc
import matplotlib.pyplot as plt
import tensorflow as tf

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):
    return image-mean_pixel
def unprocess(image,mean_pixel):
    return image+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)
print("Functions for VGG ready")

def net(data_path,input_image):
    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','pool5',
    )
    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]
            # matconvnet:weights are [width,height,in_channels,out_channels]
            # tensorflow:weights are [height,width,in_channels,out_channels]
            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
print("Network for VGG ready")

cwd=os.getcwd()
VGG_PATH=cwd+"/data/imagenet-vgg-verydeep-19.mat"
IMG_PATH=cwd+"/data/cat.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 all layers
    #layers=('relu2_1','relu3_1','relu4_1')
    for i,layer in enumerate(layers):
        print("[%d/%d] %s"%(i+1,len(layers),layer))
        features=nets[layer].eval(feed_dict={image:input_image_pre})
        
        print("Type of 'features' is ",type(features))
        print("Shape of 'features' is %s"%(features.shape,))
        #Plot response
        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()