1. vgg16 含有导入网络参数
2.值得学习的部分为:
2.1 参数移植, 但不太了解assign的用法
sess.run(self.parameters[i].assign(weights[k]))
2.2 两个矩阵拼接
self.parameters += [fc2w, fc2b]
2.3 内容排序,下面是以keys中名称字母和汉字顺序排序
keys = sorted(weights.keys())
2.4 遍历的读取keys中的内容
for i, k in enumerate(keys):
print (i, k, np.shape(weights[k]))
sess.run(self.parameters[i].assign(weights[k]))
3. 整体代码为
- ########################################################################################
- # 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])
- #down load websiet: https://www.cs.toronto.edu/~frossard/
-
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