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import tensorflow as tf
import numpy as np
def w(shape, stddev=0.01):
"""
@return A weight layer with the given shape and standard deviation. Initialized with a
truncated normal distribution.
"""
return tf.Variable(tf.truncated_normal(shape, stddev=stddev))
def b(shape, const=0.1):
"""
@return A bias layer with the given shape.
"""
return tf.Variable(tf.constant(const, shape=shape))
def conv_out_size(i, p, k, s):
"""
Gets the output size for a 2D convolution. (Assumes square input and kernel).
@param i: The side length of the input.
@param p: The padding type (either 'SAME' or 'VALID').
@param k: The side length of the kernel.
@param s: The stride.
@type i: int
@type p: string
@type k: int
@type s: int
@return The side length of the output.
"""
# convert p to a number
if p == 'SAME':
p = k // 2
elif p == 'VALID':
p = 0
else:
raise ValueError('p must be "SAME" or "VALID".')
return int(((i + (2 * p) - k) / s) + 1)
def log10(t):
"""
Calculates the base-10 log of each element in t.
@param t: The tensor from which to calculate the base-10 log.
@return: A tensor with the base-10 log of each element in t.
"""
numerator = tf.log(t)
denominator = tf.log(tf.constant(10, dtype=numerator.dtype))
return numerator / denominator
def batch_pad_to_bounding_box(images, offset_height, offset_width, target_height, target_width):
"""
Zero-pads a batch of images with the given dimensions.
@param images: 4-D tensor with shape [batch_size, height, width, channels]
@param offset_height: Number of rows of zeros to add on top.
@param offset_width: Number of columns of zeros to add on the left.
@param target_height: Height of output images.
@param target_width: Width of output images.
@return: The batch of images, all zero-padded with the specified dimensions.
"""
batch_size, height, width, channels = tf.Session().run(tf.shape(images))
if not offset_height >= 0:
raise ValueError('offset_height must be >= 0')
if not offset_width >= 0:
raise ValueError('offset_width must be >= 0')
if not target_height >= height + offset_height:
raise ValueError('target_height must be >= height + offset_height')
if not target_width >= width + offset_width:
raise ValueError('target_width must be >= width + offset_width')
num_tpad = offset_height
num_lpad = offset_width
num_bpad = target_height - (height + offset_height)
num_rpad = target_width - (width + offset_width)
tpad = np.zeros([batch_size, num_tpad, width, channels])
bpad = np.zeros([batch_size, num_bpad, width, channels])
lpad = np.zeros([batch_size, target_height, num_lpad, channels])
rpad = np.zeros([batch_size, target_height, num_rpad, channels])
padded = images
if num_tpad > 0 and num_bpad > 0: padded = tf.concat(1, [tpad, padded, bpad])
elif num_tpad > 0: padded = tf.concat(1, [tpad, padded])
elif num_bpad > 0: padded = tf.concat(1, [padded, bpad])
if num_lpad > 0 and num_rpad > 0: padded = tf.concat(2, [lpad, padded, rpad])
elif num_lpad > 0: padded = tf.concat(2, [lpad, padded])
elif num_rpad > 0: padded = tf.concat(2, [padded, rpad])
return padded
def batch_crop_to_bounding_box(images, offset_height, offset_width, target_height, target_width):
"""
Crops a batch of images to the given dimensions.
@param images: 4-D tensor with shape [batch, height, width, channels]
@param offset_height: Vertical coordinate of the top-left corner of the result in the input.
@param offset_width: Horizontal coordinate of the top-left corner of the result in the input.
@param target_height: Height of output images.
@param target_width: Width of output images.
@return: The batch of images, all cropped the specified dimensions.
"""
batch_size, height, width, channels = tf.Session().run(tf.shape(images))
if not offset_height >= 0:
raise ValueError('offset_height must be >= 0')
if not offset_width >= 0:
raise ValueError('offset_width must be >= 0')
if not target_height + offset_height <= height:
raise ValueError('target_height + offset_height must be <= height')
if not target_width <= width - offset_width:
raise ValueError('target_width + offset_width must be <= width')
top = offset_height
bottom = target_height + offset_height
left = offset_width
right = target_width + offset_width
return images[:, top:bottom, left:right, :]
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