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VGG19模型训练+读取
作者：weixin_40725706 | 2024-03-30 13:35:16
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vgg19模型
- VGG-19的介绍和训练这里不做说明,网上资源很多,而且相对比较简单.
- 本博文主要介绍VGG-19模型调用官方已经训练好的模型,进行测试使用.
友情提示
友情提示不针对第三方，为了给读者更好的体验
VGG-19模型简单介绍
VGG-19模型文件介绍
这里是重难点,VGG-19模型存储的方式有点复杂
- 可以通过作者文档说明去查看
- 可以通过在线调试查看结构,对比模型得出结论
分析模型文件
首先我们通过scipy模块(当然你可以用其它方式入opencv / sklearn等)读取scipy.io.loadmat()文件
data = scipy.io.loadmat(data_path)
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接下来使用Pycharm在线查看data数据结构:
- 总共有很多参数,我们只关心我们需要关注的,W和B在哪里就行了
- 注意这里还有一个mean(平均值),因为VGG使用了图像预处理方式是 input - mean,当然这种处理方式在现在看来不怎么好,但是现在我们用人家的模型,需要遵照人家的意思.
- 这里主要关注"layers"和"normalization"
mean值查看
首先查看normalization的mean值:
- 注意我们使用的是RGB输入,且图像大小为224*224,也就是说图像是[1 * 224 * 224 *3]格式
- 那么我们的mean肯定就是[X,Y,Z]的格式,因为是RGB的平均值
这里使用笨方法,直接带入:
data["normalization"][0][0][0][0][0]
先看大概看一下数据结构,然后一个一个测试,因为在线调试,这样很快,比你精确的去找要快很多.
Weight和Bias查看
这里和mean查看方法不同,W和B得自习查看数据结构,因为太复杂了
- 从下面的图看到存储的43个参数
- 注意里面的
Relu是没有数据的,因为Relu就是一个函数 - 注意
Pool的参数是固定的,因为大小为:[1,2,2,1],步长[1,2,2,1],这里可以自己写,也可以读取参数 WeightBias是存放在ReluPool中间的,而且两个值存在一起的.- 后期代码需要进一步处理,后面会说到.
- 再不明白的,自己画个VGG-19网络就知道了.
读取代码
mean = data["normalization"][0][0][0][0][0]
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data['layers'][0][i][0][0][0][0])
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读取模型
这里默认大家已经会CNN的基本操作了,代码不做详细说明
import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import os import scipy.io import scipy.misc from imagenet_classes import class_names def _conv_layer(input,weight,bias): conv = tf.nn.conv2d(input,weight,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(image,mean_pixel): return image+mean_pixel def imread(path): return scipy.misc.imread(path) def imsave(image,path): img = np.clip(image,0,255).astype(np.int8) scipy.misc.imsave(path,image) def net(data_path,input_image,sess=None): """ 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', 'conv1_2', 'pool1', 'conv2_1', 'conv2_2', 'pool2', 'conv3_1', 'conv3_2', 'conv3_3','conv3_4', 'pool3', 'conv4_1', 'conv4_2', 'conv4_3','conv4_4', 'pool4', 'conv5_1', 'conv5_2', 'conv5_3','conv5_4', 'pool5', 'fc1' , 'fc2' , 'fc3' , 'softmax' ) data = scipy.io.loadmat(data_path) mean = data["normalization"][0][0][0][0][0] input_image = np.array([preprocess(input_image, mean)]).astype(np.float32)#去除平均值 net = {} current = input_image net["src_image"] = tf.constant(current) # 存储数据 count = 0 #计数存储 for i in range(43): if str(data['layers'][0][i][0][0][0][0])[:4] == ("relu"): continue if str(data['layers'][0][i][0][0][0][0])[:4] == ("pool"): current = _pool_layer(current) elif str(data['layers'][0][i][0][0][0][0]) == ("softmax"): current = tf.nn.softmax(current) elif i == (37): shape = int(np.prod(current.get_shape()[1:])) current = tf.reshape(current, [-1, shape]) kernels, bias = data['layers'][0][i][0][0][0][0] kernels = np.reshape(kernels,[-1,4096]) bias = bias.reshape(-1) current = tf.nn.relu(tf.add(tf.matmul(current,kernels),bias)) elif i == (39): kernels, bias = data['layers'][0][i][0][0][0][0] kernels = np.reshape(kernels,[4096,4096]) bias = bias.reshape(-1) current = tf.nn.relu(tf.add(tf.matmul(current,kernels),bias)) elif i == 41: kernels, bias = data['layers'][0][i][0][0][0][0] kernels = np.reshape(kernels, [4096, 1000]) bias = bias.reshape(-1) current = tf.add(tf.matmul(current, kernels), bias) else: kernels,bias = data['layers'][0][i][0][0][0][0] #注意VGG存储方式为[,] #kernels = np.transpose(kernels,[1,0,2,3]) bias = bias.reshape(-1)#降低维度 current = tf.nn.relu(_conv_layer(current,kernels,bias)) net[layers[count]] = current #存储数据 count += 1 return net, mean if __name__ == '__main__': VGG_PATH = os.getcwd()+"/imagenet-vgg-verydeep-19.mat" input_image = scipy.misc.imread("234.jpeg") input_image = scipy.misc.imresize(input_image,[224,224,3]) shape = (1, input_image.shape[0], input_image.shape[1], input_image.shape[2]) #image = tf.placeholder('float', shape=shape) with tf.Session() as sess: nets, mean_pixel, = net(VGG_PATH, input_image, sess=sess) #print(sess.run(nets,feed_dict={image:input_image})) nets = sess.run(nets) ''' for key, values in nets.items(): if len(values.shape)<4: continue plt.figure(key) plt.matshow(values[0, :, :, 0],) plt.title(key) plt.colorbar() plt.show() ''' #打印概率最大的三个数据 net_sort = list(reversed(np.argsort(nets["softmax"]).reshape(-1).tolist())) net_softmax = nets["softmax"].reshape(-1).tolist() for i in range(3): print(class_names[net_sort[i]],": ",net_softmax[net_sort[i]])
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输入图片:
结果显示:
百度验证:
完整模型下载地址:
- 网盘老是被封,请留言邮箱发送
训练代码
以下是训练的代码:
这部分随便看看就好,比较简单~~主要看思想和细节!
######################################################################################## # Davi Frossard, 2016 # # VGG16 implementation in TensorFlow # # Details: # # http://www.cs.toronto.edu/~frossard/post/vgg16/ # # # # Model from https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md # # Weights from Caffe converted using https://github.com/ethereon/caffe-tensorflow # ######################################################################################## import tensorflow as tf import numpy as np from scipy.misc import imread, imresize from imagenet_classes import class_names class vgg16: def __init__(self, imgs, weights=None, sess=None): self.imgs = imgs self.convlayers() self.fc_layers() self.probs = tf.nn.softmax(self.fc3l) if weights is not None and sess is not None: self.load_weights(weights, sess) def convlayers(self): self.parameters = [] # zero-mean input with tf.name_scope('preprocess') as scope: mean = tf.constant([123.68, 116.779, 103.939], dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean') images = self.imgs-mean # conv1_1 with tf.name_scope('conv1_1') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 3, 64], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32), trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) self.conv1_1 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases] # conv1_2 with tf.name_scope('conv1_2') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 64], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv1_1, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32), trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) self.conv1_2 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases] # pool1 self.pool1 = tf.nn.max_pool(self.conv1_2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool1') # conv2_1 with tf.name_scope('conv2_1') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 128], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.pool1, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32), trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) self.conv2_1 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases] # conv2_2 with tf.name_scope('conv2_2') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 128], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv2_1, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32), trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) self.conv2_2 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases] # pool2 self.pool2 = tf.nn.max_pool(self.conv2_2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool2') # conv3_1 with tf.name_scope('conv3_1') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 256], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.pool2, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32), trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) self.conv3_1 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases] # conv3_2 with tf.name_scope('conv3_2') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv3_1, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32), trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) self.conv3_2 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases] # conv3_3 with tf.name_scope('conv3_3') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv3_2, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32), trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) self.conv3_3 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases] # pool3 self.pool3 = tf.nn.max_pool(self.conv3_3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool3') # conv4_1 with tf.name_scope('conv4_1') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 512], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.pool3, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32), trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) self.conv4_1 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases] # conv4_2 with tf.name_scope('conv4_2') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv4_1, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32), trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) self.conv4_2 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases] # conv4_3 with tf.name_scope('conv4_3') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv4_2, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32), trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) self.conv4_3 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases] # pool4 self.pool4 = tf.nn.max_pool(self.conv4_3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool4') # conv5_1 with tf.name_scope('conv5_1') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.pool4, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32), trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) self.conv5_1 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases] # conv5_2 with tf.name_scope('conv5_2') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv5_1, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32), trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) self.conv5_2 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases] # conv5_3 with tf.name_scope('conv5_3') as scope: kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32, stddev=1e-1), name='weights') conv = tf.nn.conv2d(self.conv5_2, kernel, [1, 1, 1, 1], padding='SAME') biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32), trainable=True, name='biases') out = tf.nn.bias_add(conv, biases) self.conv5_3 = tf.nn.relu(out, name=scope) self.parameters += [kernel, biases] # pool5 self.pool5 = tf.nn.max_pool(self.conv5_3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name='pool4') def fc_layers(self): # fc1 with tf.name_scope('fc1') as scope: shape = int(np.prod(self.pool5.get_shape()[1:])) fc1w = tf.Variable(tf.truncated_normal([shape, 4096], dtype=tf.float32, stddev=1e-1), name='weights') fc1b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32), trainable=True, name='biases') pool5_flat = tf.reshape(self.pool5, [-1, shape]) fc1l = tf.nn.bias_add(tf.matmul(pool5_flat, fc1w), fc1b) self.fc1 = tf.nn.relu(fc1l) self.parameters += [fc1w, fc1b] # fc2 with tf.name_scope('fc2') as scope: fc2w = tf.Variable(tf.truncated_normal([4096, 4096], dtype=tf.float32, stddev=1e-1), name='weights') fc2b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32), trainable=True, name='biases') fc2l = tf.nn.bias_add(tf.matmul(self.fc1, fc2w), fc2b) self.fc2 = tf.nn.relu(fc2l) self.parameters += [fc2w, fc2b] # fc3 with tf.name_scope('fc3') as scope: fc3w = tf.Variable(tf.truncated_normal([4096, 1000], dtype=tf.float32, stddev=1e-1), name='weights') fc3b = tf.Variable(tf.constant(1.0, shape=[1000], dtype=tf.float32), trainable=True, name='biases') self.fc3l = tf.nn.bias_add(tf.matmul(self.fc2, fc3w), fc3b) self.parameters += [fc3w, fc3b] def load_weights(self, weight_file, sess): weights = np.load(weight_file) keys = sorted(weights.keys()) for i, k in enumerate(keys): print i, k, np.shape(weights[k]) sess.run(self.parameters[i].assign(weights[k])) if __name__ == '__main__': sess = tf.Session() imgs = tf.placeholder(tf.float32, [None, 224, 224, 3]) vgg = vgg16(imgs, 'vgg16_weights.npz', sess) img1 = imread('laska.png', mode='RGB') img1 = imresize(img1, (224, 224)) prob = sess.run(vgg.probs, feed_dict={vgg.imgs: [img1]})[0] preds = (np.argsort(prob)[::-1])[0:5] for p in preds: print class_names[p], prob[p]
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参考资料
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