Command-Line Arguments

This tool provides a variety of options for configuring the training and testing of the DnCNN model. Below is a table summarizing the available command-line arguments.

Argument	Type	Default	Description
--image_path	str	./data/data.zip	Path to the zip file containing the dataset.
--batch_size	int	16	Batch size for training and testing.
--image_size	int	64	The size to which the images will be resized.
--split_ratio	float	0.2	The ratio of the dataset to be used as the test set.
--epochs	int	100	Number of training epochs.
--lr	float	1e-4	Learning rate for the optimizer.
--device	str	mps	Computing device (cuda, cpu, mps).
--display	bool	True	Whether to display training progress and metrics.
--beta1	float	0.9	Beta1 hyperparameter for Adam optimizer.
--train	flag	N/A	Flag to initiate model training.
--test	flag	N/A	Flag to initiate model testing and visualization.
--adam	bool	True	Whether to use Adam optimizer.
--SGD	bool	False	Whether to use Stochastic Gradient Descent (SGD) optimizer.
--is_l1	bool	False	Whether to use L1 regularization.
--is_l2	bool	False	Whether to use L2 regularization.
--is_huber_loss	bool	False	Whether to use Huber loss as the training criterion.
--is_weight_clip	bool	False	Whether to apply weight clipping.

For running your model on different devices such as CPU, CUDA, and MPS (Metal Performance Shaders for Apple Silicon), it's essential to provide clear instructions tailored to each computing platform. Below is a markdown table that outlines how to specify the device type using the --device argument in your command line interface (CLI).

Specifying Compute Devices

Device Type	Argument	Description
CPU	--device cpu	Run the model on the CPU.
CUDA	--device cuda	Run the model on NVIDIA CUDA-supported GPUs.
MPS	--device mps	Run the model on Apple Silicon GPUs.

Example Usage for Different Devices

To initiate model training or testing on different devices, use the --device argument as shown in the examples below:

Training on CPU:

python cli.py --train --epochs 50 --batch_size 32 --lr 0.0001 --device cpu ..... # Based on your requirements

Training on CUDA GPU:

python cli.py --train --epochs 50 --batch_size 32 --lr 0.0001 --device cuda .....  # Based on your requirements

Training on Apple Silicon GPU (MPS):

python cli.py --train --epochs 50 --batch_size 32 --lr 0.0001 --device mps ......  # Based on your requirements

Testing on CPU:

python cli.py --test --device cpu

Testing on CUDA GPU:

python cli.py --test --device cuda

Testing on Apple Silicon GPU (MPS):

python cli.py --test --device mps