CV之IG：基于CNN網(wǎng)絡(luò)架構(gòu)+ResNet網(wǎng)絡(luò)進(jìn)行DIY圖像生成網(wǎng)絡(luò)

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目錄

設(shè)計(jì)思路

實(shí)現(xiàn)代碼

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設(shè)計(jì)思路

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實(shí)現(xiàn)代碼

# 定義圖像生成網(wǎng)絡(luò)：image, training，兩個(gè)參數(shù)# Less border effects when padding a little before passing through ..image = tf.pad(image, [[0, 0], [10, 10], [10, 10], [0, 0]], mode='REFLECT')with tf.variable_scope('conv1'):conv1 = relu(instance_norm(conv2d(image, 3, 32, 9, 1)))with tf.variable_scope('conv2'):conv2 = relu(instance_norm(conv2d(conv1, 32, 64, 3, 2)))with tf.variable_scope('conv3'):conv3 = relu(instance_norm(conv2d(conv2, 64, 128, 3, 2)))with tf.variable_scope('res1'):res1 = residual(conv3, 128, 3, 1)with tf.variable_scope('res2'):res2 = residual(res1, 128, 3, 1)with tf.variable_scope('res3'):res3 = residual(res2, 128, 3, 1)with tf.variable_scope('res4'):res4 = residual(res3, 128, 3, 1)with tf.variable_scope('res5'):res5 = residual(res4, 128, 3, 1)# print(res5.get_shape())with tf.variable_scope('deconv1'):# deconv1 = relu(instance_norm(conv2d_transpose(res5, 128, 64, 3, 2)))deconv1 = relu(instance_norm(resize_conv2d(res5, 128, 64, 3, 2, training)))with tf.variable_scope('deconv2'):# deconv2 = relu(instance_norm(conv2d_transpose(deconv1, 64, 32, 3, 2)))deconv2 = relu(instance_norm(resize_conv2d(deconv1, 64, 32, 3, 2, training)))with tf.variable_scope('deconv3'):# deconv_test = relu(instance_norm(conv2d(deconv2, 32, 32, 2, 1)))deconv3 = tf.nn.tanh(instance_norm(conv2d(deconv2, 32, 3, 9, 1)))y = (deconv3 + 1) * 127.5height = tf.shape(y)[1]width = tf.shape(y)[2]y = tf.slice(y, [0, 10, 10, 0], tf.stack([-1, height - 20, width - 20, -1]))return y

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