https://github.com/eidoslab/pruning-validation
Test suite for pruned models
https://github.com/eidoslab/pruning-validation
Last synced: 3 months ago
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Test suite for pruned models
- Host: GitHub
- URL: https://github.com/eidoslab/pruning-validation
- Owner: EIDOSLAB
- License: bsd-3-clause
- Created: 2021-06-18T09:27:02.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-06-18T09:46:50.000Z (over 4 years ago)
- Last Synced: 2025-07-23T10:08:43.324Z (6 months ago)
- Language: Python
- Size: 14.6 KB
- Stars: 0
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# pruning-validation
Test suite for LOBSTER-pruned models.
## Installation
```bash
git clone https://github.com/EIDOSlab/pruning-validation
cd pruning-validation
pip3 install -r requirements.txt
```
## Usage
Using `test_single.py` is possible to evaluate the chosen model on the
corresponding dataset (e.g. MNIST for LeNet-300).
All the LOBSTER-trained models can be found at:
https://drive.google.com/drive/u/2/folders/1Kv4CMghY3uLMNP81_YktVJhOxgbgX0Zr
### Example
For example, to the LeNet-300 model with lower error (1.65%), on the MNIST dataset present in the folder ./data/MNIST:
```bash
python3 test_single.py \
--model lenet300_mnist_1 \
--data_dir ./data/MNIST \
--batch_size 1000 \
--workers 8 \
--device 0
```
## Arguments
```bash
-h, --help show this help message and exit
--model {lenet300_mnist_1,lenet300_mnist_2,lenet5_mnist_1,lenet5_fashion_1,resnet32_cifar10_1,resnet32_cifar10_2,resnet32_cifar10_3,resnet18_imagenet_1,resnet18_imagenet_2,resnet18_imagenet_3,resnet18_imagenet_4,resnet101_imagenet_1,resnet101_imagenet_2,resnet101_imagenet_3}
Neural network architecture.
--data_dir DATA_DIR Folder containing the dataset. Default = data.
--batch_size BATCH_SIZE
Batch size train. Default = 100.
--workers WORKERS Number of workers. Default = 8.
--device DEVICE Device index (cpu or 0 or 1 etc.). Default = cpu.
```