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https://github.com/adgaudio/simplepytorch
My research code to setup and train PyTorch deep nets.
https://github.com/adgaudio/simplepytorch
pytorch
Last synced: 15 days ago
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My research code to setup and train PyTorch deep nets.
- Host: GitHub
- URL: https://github.com/adgaudio/simplepytorch
- Owner: adgaudio
- License: mit
- Created: 2020-02-19T21:31:45.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-09-15T18:01:26.000Z (about 1 year ago)
- Last Synced: 2024-10-03T11:12:04.150Z (about 1 month ago)
- Topics: pytorch
- Language: Python
- Homepage:
- Size: 313 KB
- Stars: 5
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Configure and train PyTorch models with a lot of the
details already or partially implemented.
**DISCLAIMER:** This repo is used for my research.
New versions are not necessarily backwards compatible. The API is
subject to change at a moment's notice. If you happen to use it in your
research or work, make sure in your requirements.txt to pin the version
or reference the specific commit you used so you don't suffer unwanted
surprises.
Install
===
```
pip install --upgrade simplepytorch
```
Try an example
===
Download and extract DRIVE dataset to ./data/DRIVE
```
$ ls data/DRIVE
test test.zip training training.zip
```
Run an experiment, and give it a name.
```
$ python examples/simple_example.py --experiment_id test_experiment_1 --epochs 10
$ ls results/test_experiment_1
checkpoints log perf.csv
```
Look at training curves (note: demo only runs for 2 epochs)
```
simplepytorch_plot test_experiment --mode1-subplots
```
Run demo, with (customizable) guarantees that code completes exactly once and
distributes jobs across GPUs. (note: uses Redis database to temporarily
monitor current jobs).
```
redis-server # must be installed to use the default example
./bin/example_experiments.sh
```
Use hyperparameter optimization with Ray Tune library.
```
pip install -U "simplepytorch[ray]"
python examples/hyperparam_opt.py --experiment_id test_hyperband_1
```
Datasets:
==
The library provides PyTorch Dataset implementations for different datasets.
To use the pre-defined dataset classes, you must download the data and
unzip it yourself. Consult Dataset class docstring for usage details.
```
import simplepytorch.datasets as D
dset = D.RITE(use_train_set=True)
dset[0]
```
For example, some downloaded datasets I use have the following structure:
```
$ ls data/{arsn_qualdr,eyepacs,messidor,IDRiD_segmentation,RITE}
data/IDRiD_segmentation:
'1. Original Images' '2. All Segmentation Groundtruths' CC-BY-4.0.txt LICENSE.txt
data/RITE:
AV_groundTruth.zip introduction.txt read_me.txt test training
data/arsn_qualdr:
README.md annotations annotations.zip imgs1 imgs1.zip imgs2 imgs2.zip
data/eyepacs:
README.md test test.zip.003 test.zip.006 train.zip.001 train.zip.004
sample.zip test.zip.001 test.zip.004 test.zip.007 train.zip.002 train.zip.005
sampleSubmission.csv.zip test.zip.002 test.zip.005 train train.zip.003 trainLabels.csv.zip
data/messidor:
Annotation_Base11.csv Annotation_Base21.csv Annotation_Base31.csv Base11 Base21 Base31
Annotation_Base12.csv Annotation_Base22.csv Annotation_Base32.csv Base12 Base22 Base32
Annotation_Base13.csv Annotation_Base23.csv Annotation_Base33.csv Base13 Base23 Base33
Annotation_Base14.csv Annotation_Base24.csv Annotation_Base34.csv Base14 Base24 Base34
```