CV之IG:基于CNN网络架构+ResNet网络进行DIY图像生成网络
目录
- 定义图像生成网络:image, training,两个参数
-
- 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) * 127.5
-
-
- height = 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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