Created
July 12, 2018 14:03
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Coordconv as seen in https://arxiv.org/abs/1807.03247
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import tensorflow as tf | |
def add_coord_channels(inputs, with_r=False): | |
batch_size_tensor = tf.shape(inputs)[0] | |
x_dim, y_dim = inputs.shape[1].value, inputs.shape[2].value | |
x_range, y_range = (tf.linspace(-1., 1., d) for d in (x_dim, y_dim)) | |
x_channel = tf.tile(tf.expand_dims(y_range, 0), [x_dim, 1]) | |
y_channel = tf.tile(tf.expand_dims(x_range, 1), [1, y_dim]) | |
x_channel, y_channel = (tf.expand_dims(tf.expand_dims(x, -1), 0) for x in (x_channel, y_channel)) | |
x_channel, y_channel = (tf.tile(x, [batch_size_tensor, 1, 1, 1]) for x in (x_channel, y_channel)) | |
res = tf.concat([inputs, x_channel, y_channel], axis=-1) | |
if with_r: | |
rr = tf.sqrt(tf.square(x_channel) + tf.square(y_channel)) | |
res = tf.concat([res, rr], axis=-1) | |
return res | |
if __name__ == "__main__": | |
import numpy as np | |
from matplotlib import pyplot as plt | |
img = tf.constant(np.random.normal(size=[2, 32, 32, 1]).astype("float32")) | |
img_coord = add_coord_channels(img, with_r=True) | |
# actual conv can be added using e.g. tf.layers.conv2d(img_coord, ...) | |
sess = tf.Session() | |
img_c = sess.run(img_coord)[0,:] | |
channels = img_c.shape[-1] | |
for i in range(channels): | |
plt.subplot(channels, 1, i+1).imshow(img_c[:,:,i]) | |
plt.show() |
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