text: ########################################################################################
  # 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
  
  
  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(prob[p])
  