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import tensorflow as tf
# Thanks, https://github.com/tensorflow/tensorflow/issues/4079
def LeakyReLU(x, leak=0.1, name="lrelu"):
with tf.variable_scope(name):
f1 = 0.5 * (1.0 + leak)
f2 = 0.5 * (1.0 - leak)
return f1 * x + f2 * abs(x)
def average_endpoint_error(labels, predictions):
"""
Given labels and predictions of size (N, H, W, 2), calculates average endpoint error:
sqrt[sum_across_channels{(X - Y)^2}]
"""
num_samples = predictions.shape.as_list()[0]
with tf.name_scope(None, "average_endpoint_error", (predictions, labels)) as scope:
predictions = tf.to_float(predictions)
labels = tf.to_float(labels)
predictions.get_shape().assert_is_compatible_with(labels.get_shape())
squared_difference = tf.square(tf.subtract(predictions, labels))
# sum across channels: sum[(X - Y)^2] -> N, H, W, 1
loss = tf.reduce_sum(squared_difference, 3, keep_dims=True)
loss = tf.sqrt(loss)
return tf.reduce_sum(loss) / num_samples
def pad(tensor, num=1):
"""
Pads the given tensor along the height and width dimensions with `num` 0s on each side
"""
return tf.pad(tensor, [[0, 0], [num, num], [num, num], [0, 0]], "CONSTANT")
def antipad(tensor, num=1):
"""
Performs a crop. "padding" for a deconvolutional layer (conv2d tranpose) removes
padding from the output rather than adding it to the input.
"""
batch, h, w, c = tensor.get_shape().as_list()
# print(batch, h, w, c)
# print(type(batch), type(h), type(w), type(c))
# return tf.slice(tensor, begin=[0, num, num, 0], size=[batch, h - 2 * num, w - 2 * num, c])
return tensor[:, num: num + h - 2 * num, num: num + w - 2 * num, :]
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