tensorguard 1.0.3

TensorGuard helps to guard against bad Tensor Shapes

TensorGuard

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TensorGuard helps to guard against bad Tensor shapes in any tensor based library (e.g. Numpy, Pytorch, Tensorflow) using an intuitive symbolic-based syntax

Installation

pip install tensorguard

Basic Usage

import numpy as np  # could be tensorflow or torch as well
import tensorguard as tg

# tensorguard = tg.TensorGuard()  #could be done in a OOP fashion
img = np.ones([64, 32, 32, 3])
flat_img = np.ones([64, 1024])
labels = np.ones([64])

# check shape consistency
tg.guard(img, "B, H, W, C")
tg.guard(labels, "B, 1")  # raises error because of rank mismatch
tg.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 = tg.guard(np.mean(img, axis=0), "H, W, C")

# more readable reshapes
flat_img = tg.reshape(img, 'B, H*W*C')

# evaluate templates
assert tg.get_dims('H, W*C+1') == [32, 97]

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])

Original Repo link: https://github.com/Qwlouse/shapeguard