https://github.com/michedev/tensorguard

TensorGuard helps to guard against bad Tensor shapes in any tensor based library
https://github.com/michedev/tensorguard

deep-learning numpy pytorch tensorflow

Last synced: 13 days ago
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TensorGuard helps to guard against bad Tensor shapes in any tensor based library

• Host: GitHub
• URL: https://github.com/michedev/tensorguard
• Owner: Michedev
• License: apache-2.0
• Created: 2021-03-04T08:45:16.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2021-05-22T15:29:25.000Z (over 3 years ago)
• Last Synced: 2024-10-17T12:41:04.626Z (30 days ago)
• Topics: deep-learning, numpy, pytorch, tensorflow
• Language: Python
• Homepage:
• Size: 81.1 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Tensor Guard

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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

```python

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