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https://github.com/aaron-xichen/pytorch-playground
Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
https://github.com/aaron-xichen/pytorch-playground
pytorch pytorch-tutorial pytorch-tutorials quantization
Last synced: 27 days ago
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Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
- Host: GitHub
- URL: https://github.com/aaron-xichen/pytorch-playground
- Owner: aaron-xichen
- License: mit
- Created: 2017-04-28T06:22:30.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-11-22T04:06:41.000Z (almost 2 years ago)
- Last Synced: 2024-10-01T15:02:35.798Z (about 1 month ago)
- Topics: pytorch, pytorch-tutorial, pytorch-tutorials, quantization
- Language: Python
- Homepage:
- Size: 45.9 KB
- Stars: 2,605
- Watchers: 53
- Forks: 610
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-pytorch - pytorch-playground
- awesome-pytorch - pytorch-playground
README
This is a playground for pytorch beginners, which contains predefined models on popular dataset. Currently we support
- mnist, svhn
- cifar10, cifar100
- stl10
- alexnet
- vgg16, vgg16_bn, vgg19, vgg19_bn
- resnet18, resnet34, resnet50, resnet101, resnet152
- squeezenet_v0, squeezenet_v1
- inception_v3
Here is an example for MNIST dataset. This will download the dataset and pre-trained model automatically.
```
import torch
from torch.autograd import Variable
from utee import selector
model_raw, ds_fetcher, is_imagenet = selector.select('mnist')
ds_val = ds_fetcher(batch_size=10, train=False, val=True)
for idx, (data, target) in enumerate(ds_val):
data = Variable(torch.FloatTensor(data)).cuda()
output = model_raw(data)
```
Also, if want to train the MLP model on mnist, simply run `python mnist/train.py`
# Install
```
python3 setup.py develop --user
```
# ImageNet dataset
We provide precomputed imagenet validation dataset with 224x224x3 size. We first resize the shorter size of image to 256, then we crop 224x224 image in the center. Then we encode the cropped images to jpg string and dump to pickle.
- `cd script`
- Download the `val224_compressed.pkl` ([Tsinghua](http://ml.cs.tsinghua.edu.cn/~chenxi/dataset/val224_compressed.pkl) / [Google Drive](https://drive.google.com/file/d/1U8ir2fOR4Sir3FCj9b7FQRPSVsycTfVc/view?usp=sharing))
- `python convert.py` (needs 48G memory, thanks [@jnorwood](https://github.com/aaron-xichen/pytorch-playground/issues/18) )
# Quantization
We also provide a simple demo to quantize these models to specified bit-width with several methods, including linear method, minmax method and non-linear method.
`quantize --type cifar10 --quant_method linear --param_bits 8 --fwd_bits 8 --bn_bits 8 --ngpu 1`
## Top1 Accuracy
We evaluate the performance of popular dataset and models with linear quantized method. The bit-width of running mean and running variance in BN are 10 bits for all results. (except for 32-float)
|Model|32-float |12-bit |10-bit |8-bit |6-bit |
|:----|:--------:|:------:|:-----:|:-----:|:-----:|
|[MNIST](http://ml.cs.tsinghua.edu.cn/~chenxi/pytorch-models/mnist-b07bb66b.pth)|98.42|98.43|98.44|98.44|98.32|
|[SVHN](http://ml.cs.tsinghua.edu.cn/~chenxi/pytorch-models/svhn-f564f3d8.pth)|96.03|96.03|96.04|96.02|95.46|
|[CIFAR10](http://ml.cs.tsinghua.edu.cn/~chenxi/pytorch-models/cifar10-d875770b.pth)|93.78|93.79|93.80|93.58|90.86|
|[CIFAR100](http://ml.cs.tsinghua.edu.cn/~chenxi/pytorch-models/cifar100-3a55a987.pth)|74.27|74.21|74.19|73.70|66.32|
|[STL10](http://ml.cs.tsinghua.edu.cn/~chenxi/pytorch-models/stl10-866321e9.pth)|77.59|77.65|77.70|77.59|73.40|
|[AlexNet](https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth)|55.70/78.42|55.66/78.41|55.54/78.39|54.17/77.29|18.19/36.25|
|[VGG16](https://download.pytorch.org/models/vgg16-397923af.pth)|70.44/89.43|70.45/89.43|70.44/89.33|69.99/89.17|53.33/76.32|
|[VGG19](https://download.pytorch.org/models/vgg19-dcbb9e9d.pth)|71.36/89.94|71.35/89.93|71.34/89.88|70.88/89.62|56.00/78.62|
|[ResNet18](https://download.pytorch.org/models/resnet18-5c106cde.pth)|68.63/88.31|68.62/88.33|68.49/88.25|66.80/87.20|19.14/36.49|
|[ResNet34](https://download.pytorch.org/models/resnet34-333f7ec4.pth)|72.50/90.86|72.46/90.82|72.45/90.85|71.47/90.00|32.25/55.71|
|[ResNet50](https://download.pytorch.org/models/resnet50-19c8e357.pth)|74.98/92.17|74.94/92.12|74.91/92.09|72.54/90.44|2.43/5.36|
|[ResNet101](https://download.pytorch.org/models/resnet101-5d3b4d8f.pth)|76.69/93.30|76.66/93.25|76.22/92.90|65.69/79.54|1.41/1.18|
|[ResNet152](https://download.pytorch.org/models/resnet152-b121ed2d.pth)|77.55/93.59|77.51/93.62|77.40/93.54|74.95/92.46|9.29/16.75|
|[SqueezeNetV0](https://download.pytorch.org/models/squeezenet1_0-a815701f.pth)|56.73/79.39|56.75/79.40|56.70/79.27|53.93/77.04|14.21/29.74|
|[SqueezeNetV1](https://download.pytorch.org/models/squeezenet1_1-f364aa15.pth)|56.52/79.13|56.52/79.15|56.24/79.03|54.56/77.33|17.10/32.46|
|[InceptionV3](https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth)|76.41/92.78|76.43/92.71|76.44/92.73|73.67/91.34|1.50/4.82|
**Note: ImageNet 32-float models are directly from torchvision**
## Selected Arguments
Here we give an overview of selected arguments of `quantize.py`
|Flag |Default value|Description & Options|
|:-----------------------------|:-----------------------:|:--------------------------------|
|type|cifar10|mnist,svhn,cifar10,cifar100,stl10,alexnet,vgg16,vgg16_bn,vgg19,vgg19_bn,resent18,resent34,resnet50,resnet101,resnet152,squeezenet_v0,squeezenet_v1,inception_v3|
|quant_method|linear|quantization method:linear,minmax,log,tanh|
|param_bits|8|bit-width of weights and bias|
|fwd_bits|8|bit-width of activation|
|bn_bits|32|bit-width of running mean and running vairance|
|overflow_rate|0.0|overflow rate threshold for linear quantization method|
|n_samples|20|number of samples to make statistics for activation|