LibMTL.utils

get_root_dir()

Return the root path of project.

set_random_seed(seed)

Set the random seed for reproducibility.

Parameters

seed (int, default=0) – The random seed.

set_device(gpu_id)

Set the device where model and data will be allocated.

Parameters

gpu_id (str, default='0') – The id of gpu.

count_parameters(model)

Calculate the number of parameters for a model.

Parameters

model (torch.nn.Module) – A neural network module.

count_improvement(base_result, new_result, weight)

Calculate the improvement between two results as

\[\Delta_{\mathrm{p}}=100\%\times \frac{1}{T}\sum_{t=1}^T \frac{1}{M_t}\sum_{m=1}^{M_t}\frac{(-1)^{w_{t,m}}(B_{t,m}-N_{t,m})}{N_{t,m}}.\]

Parameters

• base_result (dict) – A dictionary of scores of all metrics of all tasks.

• new_result (dict) – The same structure with base_result.

• weight (dict) – The same structure with base_result while each element is binary integer representing whether higher or lower score is better.

Returns

The improvement between new_result and base_result.

Return type

float

Examples:

base_result = {'A': [96, 98], 'B': [0.2]}
new_result = {'A': [93, 99], 'B': [0.5]}
weight = {'A': [1, 0], 'B': [1]}

print(count_improvement(base_result, new_result, weight))