vgg19.py

# Copyright (c) 2015-2016 Anish Athalye. Released under GPLv3.
# Most code in this file was borrowed from https://github.com/anishathalye/neural-style/blob/master/vgg.py

import tensorflow as tf
import numpy as np
import scipy.io

# download URL : http://www.vlfeat.org/matconvnet/models/imagenet-vgg-verydeep-19.mat
MODEL_FILE_NAME = 'imagenet-vgg-verydeep-19.mat'

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 undo_preprocess(image, mean_pixel):
    return image + mean_pixel

class 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'
    )

    def __init__(self, data_path):
        data = scipy.io.loadmat(data_path)

        self.mean_pixel = np.array([123.68, 116.779, 103.939])

        self.weights = data['layers'][0]

    def preprocess(self, image):
        return image-self.mean_pixel

    def undo_preprocess(self,image):
        return image+self.mean_pixel

    def feed_forward(self, input_image, scope=None):
        net = {}
        current = input_image

        with tf.variable_scope(scope):
            for i, name in enumerate(self.layers):
                kind = name[:4]
                if kind == 'conv':
                    kernels = self.weights[i][0][0][2][0][0]
                    bias = self.weights[i][0][0][2][0][1]

                    # 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(self.layers)
        return net