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https://github.com/geyang/torch_helpers
A collection of helpers for pyTorch
https://github.com/geyang/torch_helpers
Last synced: about 2 months ago
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A collection of helpers for pyTorch
- Host: GitHub
- URL: https://github.com/geyang/torch_helpers
- Owner: geyang
- Created: 2017-08-15T22:02:07.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-04-19T16:16:36.000Z (over 6 years ago)
- Last Synced: 2024-08-08T15:17:42.729Z (5 months ago)
- Language: Python
- Size: 15.6 KB
- Stars: 1
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Torch Helpers, a set of utilities for PyTorch Machine Learning Workflow
**What does this do?** It does:
1. Automatically setting the `cuda` flag so that you don't have to do that manually.
```python
import torch_helpers as h
h.varify(torch.randn(5, 2)) # <== Automatically setting cuda depending on if torch.cuda.is_availabe()
# And you can override this!
a = h.varify(torch.randn(5, 2), cuda=False)
b = h.varify(torch.randn(5, 2), cuda=True)
# The defaut is "auto"
b = h.varify(torch.randn(5, 2), cuda="auto")
```
## Todo
### Done
- [x] `h.varify`, returns \ requires grad.
- [x] `h.const`, returns \ with `requires_grad=False`
- [x] `h.volatile`, returns \ with `volatile=True`.
- [x] `h.one_hot`, encodes one_hot vector
- [x] `h.sample_probs`, applies a mask to a categorical distribution, and passes gradient back through the distribution.
- [x] `h.cast`, casts variables and tensors to a different type. For example `int -> float`.
## Installation
```bash
pip install torch_helpers
```
## Usage
Here is a snippet from the test:
```python
import torch
import numpy as np
import torch_helpers as h
def test_requires_grad():
assert h.requires_grad(requires_grad=True) is True
assert h.requires_grad(requires_grad=False) is False
assert h.requires_grad(volatile=True) is False
assert h.requires_grad(volatile=False) is False
assert h.requires_grad(requires_grad=True, volatile=True) is False
assert h.requires_grad(requires_grad=False, volatile=True) is False
assert h.requires_grad(requires_grad=False, volatile=False) is False
assert h.requires_grad(requires_grad=True, volatile=False) is True
def test_varify():
# varify takes integer range objects
x = range(0, 3)
v = h.varify(x, 'int') # setting a `Float` tensor results in RunTimeError
# takes float arrays
x = np.arange(0.0, 3.0)
v = h.varify(x)
# takes torch tensors
x = torch.randn(4, 1)
t = h.varify(x)
t = h.varify(x, volatile=True)
t = h.const(x, volatile=True)
assert t.requires_grad is False and t.volatile is True
# You can override the requires_grad flag in constants.
# This is useful when you want to have a constant by default, but
# would like to switch to var when a requires_grad flag is passed in.
t = h.const(x, requires_grad=True)
assert t.requires_grad is True
# h.one_hot gives you one_hot encoding
def test_one_hot():
acts = h.const([1, 2], dtype='int')
n = 3
oh = h.one_hot(acts, n)
h.assert_equal(oh.data, h.tensorify([[0., 1., 0.], [0., 0., 1.]]),
message="one_hot gives incorrect output {}".format(oh))
# For RL tasks, `h.sample_probs` allows you to back-prop through action probability
def test_mask():
probs = h.varify([[0.1, 0.2, 0.7], [0.4, 0.5, 0.1]])
acts = h.const([1, 2], dtype='int')
sampled_probs = h.sample_probs(probs, acts)
sampled_probs.sum().backward()
dp = probs.grad.data.numpy()
assert dp[0, 1] is not None and dp[1, 2] is not None, 'key entries of probs grad should be non-zero'
```
## To Develop
```bash
git clone https://github.com/episodeyang/torch_helpers.git
cd torch_helpers
make dev
```
To test, run
```bash
make test
```
This `make dev` command should build the wheel and install it in your current python environment. Take a look at the [./Makefile](./Makefile) for details.
**To publish**, first update the version number, then do:
```bash
make publish
```