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贫困的蚊子
175 lines
6.1 KiB
Python
175 lines
6.1 KiB
Python
'''
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CVPR 2020 submission, Paper ID 6791
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Source code for 'Learning to Cartoonize Using White-Box Cartoon Representations'
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'''
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import layers
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import tensorflow as tf
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import numpy as np
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import tensorflow.contrib.slim as slim
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from tqdm import tqdm
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def resblock(inputs, out_channel=32, name='resblock'):
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with tf.variable_scope(name):
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x = slim.convolution2d(inputs, out_channel, [3, 3],
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activation_fn=None, scope='conv1')
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x = tf.nn.leaky_relu(x)
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x = slim.convolution2d(x, out_channel, [3, 3],
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activation_fn=None, scope='conv2')
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return x + inputs
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def generator(inputs, channel=32, num_blocks=4, name='generator', reuse=False):
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with tf.variable_scope(name, reuse=reuse):
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x = slim.convolution2d(inputs, channel, [7, 7], activation_fn=None)
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x = tf.nn.leaky_relu(x)
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x = slim.convolution2d(x, channel*2, [3, 3], stride=2, activation_fn=None)
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x = slim.convolution2d(x, channel*2, [3, 3], activation_fn=None)
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x = tf.nn.leaky_relu(x)
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x = slim.convolution2d(x, channel*4, [3, 3], stride=2, activation_fn=None)
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x = slim.convolution2d(x, channel*4, [3, 3], activation_fn=None)
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x = tf.nn.leaky_relu(x)
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for idx in range(num_blocks):
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x = resblock(x, out_channel=channel*4, name='block_{}'.format(idx))
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x = slim.conv2d_transpose(x, channel*2, [3, 3], stride=2, activation_fn=None)
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x = slim.convolution2d(x, channel*2, [3, 3], activation_fn=None)
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x = tf.nn.leaky_relu(x)
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x = slim.conv2d_transpose(x, channel, [3, 3], stride=2, activation_fn=None)
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x = slim.convolution2d(x, channel, [3, 3], activation_fn=None)
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x = tf.nn.leaky_relu(x)
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x = slim.convolution2d(x, 3, [7, 7], activation_fn=None)
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#x = tf.clip_by_value(x, -0.999999, 0.999999)
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return x
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def unet_generator(inputs, channel=32, num_blocks=4, name='generator', reuse=False):
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with tf.variable_scope(name, reuse=reuse):
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x0 = slim.convolution2d(inputs, channel, [7, 7], activation_fn=None)
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x0 = tf.nn.leaky_relu(x0)
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x1 = slim.convolution2d(x0, channel, [3, 3], stride=2, activation_fn=None)
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x1 = tf.nn.leaky_relu(x1)
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x1 = slim.convolution2d(x1, channel*2, [3, 3], activation_fn=None)
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x1 = tf.nn.leaky_relu(x1)
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x2 = slim.convolution2d(x1, channel*2, [3, 3], stride=2, activation_fn=None)
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x2 = tf.nn.leaky_relu(x2)
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x2 = slim.convolution2d(x2, channel*4, [3, 3], activation_fn=None)
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x2 = tf.nn.leaky_relu(x2)
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for idx in range(num_blocks):
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x2 = resblock(x2, out_channel=channel*4, name='block_{}'.format(idx))
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x2 = slim.convolution2d(x2, channel*2, [3, 3], activation_fn=None)
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x2 = tf.nn.leaky_relu(x2)
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h1, w1 = tf.shape(x2)[1], tf.shape(x2)[2]
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x3 = tf.image.resize_bilinear(x2, (h1*2, w1*2))
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x3 = slim.convolution2d(x3+x1, channel*2, [3, 3], activation_fn=None)
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x3 = tf.nn.leaky_relu(x3)
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x3 = slim.convolution2d(x3, channel, [3, 3], activation_fn=None)
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x3 = tf.nn.leaky_relu(x3)
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h2, w2 = tf.shape(x3)[1], tf.shape(x3)[2]
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x4 = tf.image.resize_bilinear(x3, (h2*2, w2*2))
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x4 = slim.convolution2d(x4+x0, channel, [3, 3], activation_fn=None)
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x4 = tf.nn.leaky_relu(x4)
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x4 = slim.convolution2d(x4, 3, [7, 7], activation_fn=None)
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#x4 = tf.clip_by_value(x4, -1, 1)
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return x4
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def disc_bn(x, scale=1, channel=32, is_training=True,
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name='discriminator', patch=True, reuse=False):
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with tf.variable_scope(name, reuse=reuse):
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for idx in range(3):
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x = slim.convolution2d(x, channel*2**idx, [3, 3], stride=2, activation_fn=None)
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x = slim.batch_norm(x, is_training=is_training, center=True, scale=True)
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x = tf.nn.leaky_relu(x)
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x = slim.convolution2d(x, channel*2**idx, [3, 3], activation_fn=None)
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x = slim.batch_norm(x, is_training=is_training, center=True, scale=True)
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x = tf.nn.leaky_relu(x)
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if patch == True:
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x = slim.convolution2d(x, 1, [1, 1], activation_fn=None)
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else:
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x = tf.reduce_mean(x, axis=[1, 2])
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x = slim.fully_connected(x, 1, activation_fn=None)
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return x
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def disc_sn(x, scale=1, channel=32, patch=True, name='discriminator', reuse=False):
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with tf.variable_scope(name, reuse=reuse):
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for idx in range(3):
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x = layers.conv_spectral_norm(x, channel*2**idx, [3, 3],
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stride=2, name='conv{}_1'.format(idx))
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x = tf.nn.leaky_relu(x)
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x = layers.conv_spectral_norm(x, channel*2**idx, [3, 3],
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name='conv{}_2'.format(idx))
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x = tf.nn.leaky_relu(x)
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if patch == True:
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x = layers.conv_spectral_norm(x, 1, [1, 1], name='conv_out'.format(idx))
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else:
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x = tf.reduce_mean(x, axis=[1, 2])
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x = slim.fully_connected(x, 1, activation_fn=None)
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return x
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def disc_ln(x, channel=32, is_training=True, name='discriminator', patch=True, reuse=False):
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with tf.variable_scope(name, reuse=reuse):
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for idx in range(3):
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x = slim.convolution2d(x, channel*2**idx, [3, 3], stride=2, activation_fn=None)
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x = tf.contrib.layers.layer_norm(x)
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x = tf.nn.leaky_relu(x)
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x = slim.convolution2d(x, channel*2**idx, [3, 3], activation_fn=None)
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x = tf.contrib.layers.layer_norm(x)
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x = tf.nn.leaky_relu(x)
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if patch == True:
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x = slim.convolution2d(x, 1, [1, 1], activation_fn=None)
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else:
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x = tf.reduce_mean(x, axis=[1, 2])
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x = slim.fully_connected(x, 1, activation_fn=None)
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return x
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if __name__ == '__main__':
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pass
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