TensorFLow 加载VGG19 模型并进行预测

import tensorflow as tf
import scipy.io
import numpy as np
import cv2
 
#download from here http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat
DEFAULT_PATH="./imagenet-vgg-verydeep-19.mat"
VGG19_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',
              'fc6','relu6',
              'fc7','relu7',
              'fc8','softmax',
              )
# input shape: [batch, height, width, channels], only one image, so batch=1.
INPUT_SHAPE=(1,224,224,3)
class VGG19:
    def __init__(self,model_path=None):
        print ("Load Pre-Trained Model.....");
        mat = None;
        if (model_path == None):
            mat = scipy.io.loadmat(DEFAULT_PATH);
        else:
            mat = scipy.io.loadmat(model_path);
 
        assert mat != None
 
        #print (mat)
        #load normalization pixel value to calculate average pixel value
        norm_pic = mat['normalization'][0][0][0];
        
        self.mean_pix = np.mean(norm_pic,axis=(0,1))
        self.layer_params = mat['layers'][0]
 
    def build_VGGnet(self):
        print ("Building VGG19 Net..........")
 
        self.image = tf.placeholder(tf.float32, shape=INPUT_SHAPE,name="input_image")
        self.layers = {}
        last_layer = self.image
        
        assert last_layer != None
 
        for i, name in enumerate(VGG19_LAYERS):
            type = name[:3]
            temp_layer = None
            if type == "con":
                filters,bias = self.layer_params[i][0][0][0][0]
                filters = np.transpose(filters, (1,0,2,3))
                bias = np.reshape(bias,-1)
                conv = tf.nn.conv2d(last_layer, tf.constant(filters), strides=[1,1,1,1], padding='SAME')
                temp_layer = tf.nn.bias_add(conv, tf.constant(bias))
            elif type == 'rel':
                temp_layer = tf.nn.relu(last_layer)
            elif type == 'poo':
                temp_layer = tf.nn.max_pool(last_layer, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
            elif name == 'fc6':
                weights,bias = self.layer_params[i][0][0][0][0]
                bias = np.reshape(bias,-1)
                #flatten the output from pooling layer
                last_layer = tf.reshape(last_layer,[last_layer.shape[0],-1])
                weights = np.reshape(weights,(-1,weights.shape[-1]))
                temp_layer = tf.nn.bias_add(tf.matmul(last_layer,weights),bias)
            elif name == 'fc7':
                weights,bias = self.layer_params[i][0][0][0][0]
                bias = np.reshape(bias,-1)
                weights = np.reshape(weights,(-1,weights.shape[-1]))
                temp_layer = tf.nn.bias_add(tf.matmul(last_layer,weights),bias)
            elif name == 'fc8':
                weights,bias = self.layer_params[i][0][0][0][0]
                bias = np.reshape(bias,-1)
                weights = np.reshape(weights,(-1,weights.shape[-1]))
                temp_layer = tf.nn.bias_add(tf.matmul(last_layer,weights),bias)
            elif name == 'softmax':
                temp_layer = tf.nn.softmax(last_layer)
 
            assert temp_layer != None
            
            self.layers[name]=temp_layer
            last_layer = temp_layer;
    def predict(self,imgPath=None):
        if imgPath is None:
            imgPath="./test_data/test1.jpg"
        #imgPATH="./test_data/cat.jpg"
        # download labels from here https://github.com/sh1r0/caffe-Android-demo/blob/master/app/src/main/assets/synset_words.txt
        labels = [str.strip() for str in open("./synset.txt").readlines()]
        
        # load image and pre process input image
        img = cv2.imread(imgPath)
        assert img is not None
        
        #change BGR -> RGB pattern
        img = img[:,:,::-1]
        img = cv2.resize(img,(224,224),interpolation=cv2.INTER_CUBIC)
        img = img.astype(np.float32)
 
        #extand image into {batch,width,height,channels]
        img = np.expand_dims(img, axis=0)
        #subtract channel mean to adapt vgg19 net input
        img = img - self.mean_pix
 
        with tf.Session() as sess:
            image_feed = {self.image:img}
 
            probs = self.layers['softmax'][0].eval(feed_dict = image_feed)
 
            #probs = np.reshape(probs,-1)
            maxIndex = np.argmax(probs);
            print ("index:",maxIndex)
            print ("prob:",probs[maxIndex])
            print ("label:",labels[maxIndex])
 
if __name__ == "__main__":
    vgg = VGG19();
    vgg.build_VGGnet();
    vgg.predict()