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

#?VGG-19?parameters?file
VGG_DOWNLOAD_link?=?“http://www.vlfeat.org/matconvnet/models/imagenet-vgg-verydeep-19.mat“
VGG_FILENAME?=?“imagenet-vgg-verydeep-19.mat“
EXPECTED_BYTES?=?534904783??#?文件大小


class?VGG（object）:
????def?__init__（self?input_img）:
????????#?下載文件
????????utils.download（VGG_DOWNLOAD_link?VGG_FILENAME?EXPECTED_BYTES）
????????#?加載文件
????????self.vgg_layers?=?scipy.io.loadmat（VGG_FILENAME）[“layers“]
????????self.input_img?=?input_img
????????#?VGG在處理圖像時候會將圖片進行mean-center，所以我們首先要計算RGB三個channel上的mean
????????self.mean_pixels?=?np.array（[123.68?116.779?103.939]）.reshape（（1?1?1?3））

????def?_weights（self?layer_idx?expected_layer_name）:
????????“““
????????獲取指定layer層的pre-trained權重

????????:param?layer_idx:?VGG中的layer?id
????????:param?expected_layer_name:?當前layer命名
????????:return:?pre-trained權重W和b
????????“““
????????W?=?self.vgg_layers[0][layer_idx][0][0][2][0][0]
????????b?=?self.vgg_layers[0][layer_idx][0][0][2][0][1]
????????#?當前層的名稱
????????layer_name?=?self.vgg_layers[0][layer_idx][0][0][0][0]
????????assert?layer_name?==?expected_layer_name
????????print（“layer?name?error!“）

????????return?W?b.reshape（b.size）

????def?conv2d_relu（self?prev_layer?layer_idx?layer_name）:
????????“““
????????采用relu作為激活函數的卷積層

????????:param?prev_layer:?前一層網絡
????????:param?layer_idx:?VGG中的layer?id
????????:param?layer_name:?當前layer命名
????????“““
????????with?tf.variable_scope（layer_name）:
????????????#?獲取當前權重（numpy格式）
????????????W?b?=?self._weights（layer_idx?layer_name）
????????????#?將權重轉化為tensor（由于我們不需要重新訓練VGG的權重，因此初始化為常數）
????????????W?=?tf.constant（W?name=“weights“）
????????????b?=?tf.constant（b?name=“bias“）
????????????#?卷積操作
????????????conv2d?=?tf.nn.conv2d（input=prev_layer
??????????????????????????????????filter=W
??????????????????????????????????strides=[1?1?1?1]
??????????????????????????????????padding=“SAME“）
????????????#?激活
????????????out?=?tf.nn.relu（conv2d?+?b）
????????setattr（self?layer_name?out）

????def?avgpool（self?prev_layer?layer_name）:
????????#“““
????????#average?pooling層（這里參考了原論文中提到了avg-pooling比max-pooling效果好，所以采用avg-pooling）

???????#?:param?prev_layer:?前一層網絡（卷積層）
????????#:param?layer_name:?當前layer命名
????????#“““
????????with?tf.variable_scope（layer_name）:
????????????#?average?pooling
????????????out?=?tf.nn.avg_pool（value=prev_layer
?????????????????????????????????ksize=[1?2?2?1]
?????????????????????????????????strides=[1?2?2?1]
?????????????????????????????????padding=“SAME“）

????????setattr（self?layer_name?out）

????def?load（self）:
????????“““
????????加載pre-trained的數據
????????“““
????????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.c

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2019-07-26?21:54??圖像風格遷移?Python?vgg\
?????文件????????4227??2019-07-09?13:08??圖像風格遷移?Python?vgg\load_vgg.py
?????目錄???????????0??2019-07-26?21:53??圖像風格遷移?Python?vgg\style\
?????目錄???????????0??2019-07-26?21:55??圖像風格遷移?Python?vgg\style\.ipynb_checkpoints\
?????文件???????19875??2019-07-09?19:24??圖像風格遷移?Python?vgg\style\resized_fugu.jpg
?????文件???????31984??2019-07-09?15:22??圖像風格遷移?Python?vgg\style\resized_guernica.jpg
?????文件???????33499??2019-07-09?21:17??圖像風格遷移?Python?vgg\style\resized_harlequin.jpg
?????文件???????57785??2019-07-09?15:04??圖像風格遷移?Python?vgg\style\resized_pattern.jpg
?????文件???????34430??2019-07-09?13:26??圖像風格遷移?Python?vgg\style\resized_starry_night.jpg
?????文件???????23973??2019-07-09?17:03??圖像風格遷移?Python?vgg\style\resized_sumiao.jpg
?????文件???????22993??2019-07-09?14:52??圖像風格遷移?Python?vgg\style\resized_white.jpg
?????文件????????8386??2019-07-11?18:56??圖像風格遷移?Python?vgg\styles.py