Shape Guard
ShapeGuard is a tool to help with handling shapes in Tensorflow.
Basic Usage
import tensorflow as tf
from shapeguard import ShapeGuard
sg = ShapeGuard()
img = tf.ones([64, 32, 32, 3])
flat_img = tf.ones([64, 1024])
labels = tf.ones([64])
# check shape consistency
sg.guard(img, "B, H, W, C")
sg.guard(labels, "B, 1") # raises error because of rank mismatch
sg.guard(flat_img, "B, H*W*C") # raises error because 1024 != 32*32*3
# guard also returns the tensor, so it can be inlined
mean_img = sg.guard(tf.reduce_mean(img, axis=0), "H, W, C")
# more readable reshapes
flat_img = sg.reshape(img, 'B, H*W*C')
# evaluate templates
assert sg['H, W*C+1'] == [32, 97]
# attribute access to inferred dimensions
assert sg.B == 64Shape Template Syntax
The shape template mini-DSL supports many different ways of specifying shapes:
- numbers:
"64, 32, 32, 3" - named dimensions:
"B, width, height2, channels" - wildcards:
"B, *, *, *" - ellipsis:
"B, ..., 3" - addition, subtraction, multiplication, division:
"B*N, W/2, H*(C+1)" - dynamic dimensions:
"?, H, W, C"(only matches[None, H, W, C])
DISCLAIMER
This is not an officially supported Google product.
