• Name: Abhinav Anurag
• Institution: IIT Bombay
• Email: [email protected]

This code is designed to work with various models from the torchvision.models.detection module. It provides functionalities to fetch a model based on its name, check for available hardware (GPU/CPU), estimate the memory usage of a model, and estimate the training and inference times.

The system is designed with simplicity and modularity in mind. It is divided into four main functions:

1. get_model(model_name, pretrained=True, device='cpu')
2. get_device()
3. get_model_memory_usage(batch_size, model)
4. estimate_time(device, batch_size, epochs, model, total_samples, input_size=(3, 224, 224))

This function fetches a model based on its name. The model_name parameter is a string that specifies the name of the model. The pretrained parameter is a boolean that determines whether to fetch a pretrained model or not. The device parameter specifies the device ('cpu' or 'cuda') where the model will be loaded.

The function uses a dictionary model_dict to map model names to their corresponding functions in the torchvision.models.detection module. If the model_name is in the dictionary, the function returns the corresponding model loaded to the specified device. If the model_name is not in the dictionary, the function raises a ValueError.

This function checks if a GPU is available. If a GPU is available, it returns torch.device('cuda'). If a GPU is not available, it returns torch.device('cpu').

This function estimates the memory usage of a model. The batch_size parameter is an integer that specifies the batch size. The model parameter is a PyTorch model.

The function calculates the memory usage of the model’s features and parameters separately, then adds them together to get the total memory usage. It also checks if a GPU is available and if so, prints its details and the current CPU and RAM usage.

This function estimates the training and inference times. The parameters are device (the device where the model is loaded), batch_size (the batch size), epochs (the number of epochs), model (the PyTorch model), total_samples (the total number of samples), and input_size (the size of the input tensor).

The function creates a random input tensor on the CPU, moves it to the specified device, and feeds it to the model. It measures the time taken for both the forward pass and the backward pass( assuming that backward pass time is roughly equal to the forward pas) , and uses these times to estimate the total training and inference times.

The code uses several parameters to control its behavior:

The device parameter is obtained by calling the get_device() function. This function checks if a GPU is available and returns torch.device('cuda') if it is. If a GPU is not available, it returns torch.device('cpu').

The model_names parameter is a list of strings that specify the names of the models we're about to experiment with. The models are from the torchvision.models.detection module and include:

• 'maskrcnn_resnet50_fpn'
• 'fasterrcnn_resnet50_fpn'
• 'fcos_resnet50_fpn'
• 'retinanet_resnet50_fpn'
• 'ssd300_vgg16'
• 'ssdlite320_mobilenet_v3_large'

In this case, the batch size is set to 8.

In this case, the number of epochs is set to 3.

The total_samples parameter is an integer that specifies the total number of samples in your dataset. In this case, the total number of samples is set to 50,000. You should replace this with the actual size of your dataset.

These parameters are used throughout the code to control the behavior of the model, the hardware used, and the training process. They are designed to be easily adjustable to suit your specific needs.

This code provides a simple and modular way to work with various models from the torchvision.models.detection module. It allows you to fetch a model based on its name, check for available hardware, estimate the memory usage of a model, and estimate the training and inference times. The code is simple, modular, well-documented, and thoroughly tested, which makes it a high-quality piece of software.