1 files changed, 133 insertions, 0 deletions
diff --git a/Code/tfutils.py b/Code/tfutils.py
new file mode 100644
index 0000000..22baf95
--- /dev/null
+++ b/Code/tfutils.py
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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, :]