Scalable ResNet on CIFAR-10 using PyTorch

Requirements

• Python 3.6+
• PyTorch 1.0+
• TorchVision 0.1+

How to train

# Easily start a new training, run: 
python project1_model.py

# You can manually assign parameters with: 
python project1_model.py --lr 0.01

# To list all configurable parameters use: 
python project1_model.py -h

How to load and view the saved model

# Start your python interactive shell and type these commands: 
import torch
from project1_model import project1_model
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = project1_model().to(device)
model_path = './project1_model.pt' #full directory path to your saved model e.g., './model.pt'
model.load_state_dict(torch.load(model_path, map_location=device), strict=False)
model.eval()

How to re-test your saved model with our built-in function

# Start your python interactive shell and type these commands: 
import project1_model as p
model_path = './project1_model.pt' #full directory path to your saved model e.g., './model.pt'
p.test_model(model_path)

Adjustable parameters

Description	DType	Arguments	Default
Optimizer	string	o	sgd
Learning rate	float	lr	based on optimizer
Momentum	float	m	based on optimizer
Weight decay	float	wd	based on optimizer
Dataset full path	string	path	./CIFAR10/
Saved model full path	string	mp	./project1_model.pt
Number of epochs	int	e	5
Number of data loader workers	int	wk	2
Number of residual layers	int	n	4
Number of residual blocks in each of the residual layers	int	b	2 1 1 1
Number of channels in the first residual layer	int	c	64
Input layer convolutional kernel size	int	f0	3
Residual layer convolutional kernel size	int	f1	3
Skip connection kernel sizes	int	k	1
Input layer convolutional padding size	int	p0	1
Residual layer convolutional padding size	int	p1	1

Parameter setting explanation

Default optimizer's setting

Name	Learning rate	Weight decay	Momentum
SGD	0.1	0.0005	0.9
SGD /w Nesterov	0.1	0.0005	0.9
Adam	0.001	0.0005	None
Adadelta	1.0	0.0005	None
Adagrad	0.01	0.0005	None

References

Liu K., Train CIFAR10 with PyTorch (2017). https://github.com/kuangliu/pytorch-cifar.