loadVGG.py

# -*- coding: utf-8 -*-
"""
Created on Fri Apr 20 20:59:47 2018

@author: nicemo
"""

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


VGG_FILENAME = 'imagenet-vgg-verydeep-19.mat'

class VGG(object):
    
    def __init__(self, input_img):
        self.Vgg_layers = scipy.io.loadmat(VGG_FILENAME)['layers']
        self.input_img = input_img
        self.mean_pixel = np.array([123.68, 116.779, 103.939]).reshape((1,1,1,3))
        
    def _weight(self, layer_idx, expected_layer_name):
        W = self.Vgg_layers[0][layer_idx][0][0][2][0][0]
        b = self.Vgg_layers[0][layer_idx][0][0][2][0][1]
        layer_name = self.Vgg_layers[0][layer_idx][0][0][0][0]
        assert expected_layer_name == layer_name
        return W, b.reshape(b.size)
    
    def conv2d_relu(self, prev_layer, layer_idx, layer_name):
        with tf.variable_scope(layer_name) as scope:
            W, b = self._weight(layer_idx, layer_name)
            W = tf.constant(W, name='weights')
            b = tf.constant(b, name='bias')
            conv2d = tf.nn.conv2d(prev_layer, filter=W, strides=[1, 1, 1, 1], padding='SAME')
            out = tf.nn.relu(conv2d + b)
        setattr(self, layer_name, out)
        
    def avgpool(self, prev_layer, layer_name):
        with tf.variable_scope(layer_name) as scope:
            out = tf.nn.avg_pool(prev_layer, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
        setattr(self, layer_name, out)
        
    def load(self):
        self.conv2d_relu(self.input_img, 0, 'conv1_1')
        self.conv2d_relu(self.conv1_1, 2, 'conv1_2')
        self.avgpool(self.conv1_2, 'avgpool1')
        self.conv2d_relu(self.avgpool1, 5, 'conv2_1')
        self.conv2d_relu(self.conv2_1, 7, 'conv2_2')
        self.avgpool(self.conv2_2, 'avgpool2')
        self.conv2d_relu(self.avgpool2, 10, 'conv3_1')
        self.conv2d_relu(self.conv3_1, 12, 'conv3_2')
        self.conv2d_relu(self.conv3_2, 14, 'conv3_3')
        self.conv2d_relu(self.conv3_3, 16, 'conv3_4')
        self.avgpool(self.conv3_4, 'avgpool3')
        self.conv2d_relu(self.avgpool3, 19, 'conv4_1')
        self.conv2d_relu(self.conv4_1, 21, 'conv4_2')
        self.conv2d_relu(self.conv4_2, 23, 'conv4_3')
        self.conv2d_relu(self.conv4_3, 25, 'conv4_4')
        self.avgpool(self.conv4_4, 'avgpool4')
        self.conv2d_relu(self.avgpool4, 28, 'conv5_1')
        self.conv2d_relu(self.conv5_1, 30, 'conv5_2')
        self.conv2d_relu(self.conv5_2, 32, 'conv5_3')
        self.conv2d_relu(self.conv5_3, 34, 'conv5_4')
        self.avgpool(self.conv5_4, 'avgpool5')