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ShapeGuard is a tool to help with handling shapes in Tensorflow.

Project description

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 == 64

Shape 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.


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